esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Electrostatic-Atomic Accelerometer for Future Space Gravity Missions
## 3 Results and Discussions
### Performance Aspects
\begin{table}
\begin{tabular}{c c c c} \hline \hline Parameters & \(k_{eff}\approx 1.6\times 10^{7}\) m\({}^{-1}\) & \(\tau_{R}\) = 50 \(\mu\)s & \(T=1\) s & \(\Omega_{eff}=\pi/(2\tau_{R})\) \\ \hline Uncertainty & \(\sigma_{k_{eff}}/k_{eff}=10^{-9}\) & \(\sigma_{\tau_{R}}=10\) ns & \(\sigma_{T}=10\) ns & \(\sigma_{\Omega_{eff}}/\Omega_{eff}=10^{-2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: CAI parameters for scale factor uncertainty estimations. \(\sigma_{i}\) refers to the uncertainty on the parameter \(i\). * Coupling with Earth's gravity gradient: To evaluate the impact of the gravity gradient on the CAI acceleration measurement stability, we considered the atomic source parameters reported in Table 5 and a rough value of the Earth's gravity gradient \(\Gamma_{xx}=\Gamma_{zz}=3\times 10^{-6}\) s\({}^{-2}\). Two types of terms were considered. The first ones, which have a dominant contribution, depend only on the gravity gradient value and on the position of the atomic cloud. The second ones, which are negligible, are crossed terms depending also on the satellite rotation. Concerning the dominant errors terms, which are reported in Table 4, their impact should be largely compensated thanks to an adequate frequency jump during the second laser Raman pulse [84; 85; 86].
## 2 Materials and Methods
### Measuring Apparatus  
The IMON experimental measuring system is a measuring apparatus designed in the FBME CTU (Faculty of Biomedical Engineering, Czech Technical University in Prague), serving as a universal measuring device, usable for continuous monitoring and recording of up to 6 different pressures at the same time, with conventional or high-frequency lung ventilation. The system is based on an A/D converter USB NIDAQ 6009 (National Instruments, Austin, Texas, TX, USA), to which 26 PC01 pressure sensors (Honeywell, Freeport, IL, USA) with a measurement range of 1 psi (6.9 kPa) are connected. Electronic circuits with INA128 amplifiers (Texas Instruments, Tucson, AZ, USA) provide impedance matching, amplification and basic filtering of the sensed signal (bandwidth 1-500 Hz). This arrangement guarantees full use of the 14-bit range of the A/D converter, with a preset sampling frequency 2 kHz. The flow sensing resolution was 2.14 mL/min, the max. non linearity is \(\pm\)1.75 % span. Lab View Signal Express software (National Instruments, Austin, TX, USA) was used to visualize the course of signals and their saving to a PC.  
Figure 4: Orifice plate as flow-sensor for HFJV: (**a**) Section and dimensions; (**b**) Realized flow sensor— orifice plate from stainless steel.  
Figure 5: Pressure-flow characteristic of the orifice plate
## 3 Results
### Intra-Session Reliability
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Fitted Data**} & \multicolumn{3}{c}{**Dominant Leg**} & \multicolumn{3}{c}{**Non-Dominant Leg**} & \multirow{2}{*}{**\%Diff**} \\ \cline{2-2} \cline{6-9}  & **M (SD)** & **CV** & **SEM** & **ICC** & **M (SD)** & **CV** & **SEM** & **ICC** \\ \hline MVC (_N_) & & & & & & & & \\ Session 1 & 628 (111) & 5.9\% & 37.7 & 0.887 & 568 (93) & 6.2\% & 35.2 & 0.869 & \(-\)10.5\% \\ Session 2 & 646 (117) & 5.6\% & 35.9 & 0.969 & 597 (96) & 4.7\% & 27.9 & 0.975 & \(-\)8.2\% \\ Impulse\({}_{50-50}\) (_N_+_) & & & & & & & & \\ Session 1 & 7.6 (2.9) & 22.6\% & 1.7 & 0.788 & 7.3 (2.6)
## 3 System Model
### Energy Expenditure Model  
The power consumption of a sensor node consists of five parts: sensing and generating data, idling, listening, receiving, and transmitting. The the power for generating one bit of data, from an idle state consumed by a node, is denoted by \(e_{g}\) and \(e_{idle}\), respectively. They are assumed to be the same for all nodes and independent of traffic. \(e_{l}\) denotes the power expenditure for a sensor listening channel at each time point before sending a packet. For power consumption in receiving and transmitting, the first order radio model is adopted in [64]. Specifically, a node needs \(e_{elc}=\) 50 nJ for running the circuitry and \(\epsilon_{amp}=\) 100 pJ/bit/m\({}^{2}\) for the transmitting amplifier. Therefore, the power consumption for receiving one bit of data is given by \(e_{r}=\epsilon_{elc}\). The power consumption for transmitting one bit of data to a neighbor node \(j\) is given by \(e_{t}=\epsilon_{elc}+\epsilon_{amp}\times d_{i,j}^{n}\), where \(n\) is the path loss exponent, which typically ranges between 2 and 4 for free-space and short-to-medium-range radio communication. \(d_{i,j}\) denotes the Euclidean distance of the node \(i\) and \(j\). If the coordinate of node \(i\) and \(j\) are \((x_{i},y_{i})\) and \((x_{j},y_{j})\), respectively, the \(d_{i,j}\) can be formulated as:  
\[d_{i,j}=\sqrt{(x_{j}-x_{i})^{2}+(y_{j}-y_{i})^{2}} \tag{1}\]  
We assume the the data traffic from node \(i\) to \(j\) per unit time is \(f_{i,j}\). The per unit time can be set to one second or one minute or one times data transmission in the work, which is decided by the requirement of experiment. The different units selected will not affect the performance and evaluation of the system. The energy consumption for node \(i\) in receiving and transmitting data flow \(f_{i,j}\) are \(e_{t}(i,j)\) and \(e_{r}(i,j)\), respectively, which can be expressed as:  
\[e_{t}(i,j)=e_{t}\times\sum_{i,j\in V,j\in S_{i}}f_{i,j} \tag{2}\]  
\[e_{r}(i,j)=e_{r}\times\sum_{i,j\in V,j\in S_{i}}f_{i,j} \tag{3}\]Let \(w_{i}\) denote the total fraction of power consumption for node \(i\) per unit time. Considering the energy deplete for five parts, \(w_{i}\) can be formulated as:  
\[w_{i}=e_{g}\times g_{i}+e_{iidle}+e_{l}+e_{t}(i,j)+e_{r}(i,j) \tag{4}\]
# Microencapsulation of Polyphenols and Their Application in Food Technology
## 3 Polyphenols: Properties and Significance in Food
### Application of Polyphenols in Food Technology
#### 3.3.1 Preservation and Shelf-Life Extension  
Polyphenols function as natural preservatives, effectively inhibiting the growth of bacteria, moulds, and yeasts. Their antibacterial, antifungal, and antioxidant properties provide a clean-label solution for extending the shelf life of food products, reducing reliance on synthetic preservatives [95; 96]. The preservative effects of polyphenols stem from their ability to disrupt microbial growth and prevent oxidative degradation. By scavenging free radicals, polyphenols mitigate the oxidative processes that lead to the deterioration of fats, proteins, and other sensitive components in food [97; 98]. Polyphenol-rich extracts have been successfully incorporated into a variety of food systems to prolong freshness and maintain quality. For instance, rosmary and thyme extracts are widely used in baked goods such as cookies, where they prevent lipid oxidation and stabilize flavour profiles over time. Similarly, polyphenols derived from olive leaves and olive mill wastewater have demonstrated efficacy in preserving edible oils, such as sunflower and olive oils. These compounds protect against taste changes and nutrient loss, ensuring product stability during storage. In meat products, polyphenols contribute to both microbial safety and sensory attributes. By reducing lipid oxidation, they help maintain the colour and flavour of processed meats while extending shelf life [99]. Additionally, polyphenols have been incorporated into dairy and bakery products, where they improve the structural integrity and prolong freshness. Innovative approaches, such as combining polyphenols with encapsulation techniques, have further enhanced their stability and efficacy. Encapsulation in liposomes or biodegradable polymers protects polyphenols from environmental factors, enabling their controlled release and prolonged activity in food systems. The increasing demand for natural preservatives aligns with the multifunctional benefits of polyphenols, making them indispensable in modern food technology. Future research is anticipated to focus on optimising their extraction, bioavailability, and application in diverse food matrices [100].
Incorporating endemic macrophytes into wetland systems for wastewater treatment represents a novel advancement in this emerging technology [5; 7]. However, selecting plant species for CW requires careful consideration for each specific case. Macrophytes play a vital role in CW systems by enhancing wastewater treatment through nutrient uptake, oxygen transfer, biofilm support, sedimentation, toxin transformation, and hydraulic flow regulation. Effective selection and management of macrophytes are essential for maximizing the efficiency and sustainability of these systems. It is recommended to use native plants or those well adapted to the local climate and environmental conditions. Endemic macrophytes may provide superior performance compared to exotic macrophytes, which are traditionally employed in these systems [5; 7]. In rural and semi-arid areas, the lack of access to advanced wastewater treatment technologies poses significant challenges, as conventional centralized systems are often impractical due to economic and infrastructural limitations. These regions typically suffer from water scarcity, further intensifying the need for greywater reuse and effective, low-cost treatment solutions. CW systems, particularly those using vertical subsurface flow, offer a promising alternative due to their low operational costs and ability to treat wastewater effectively in decentralized settings. Despite their potential, there is a noticeable gap in research concerning the use of such systems, specifically for greywater treatment in these areas, where sustainable and affordable solutions are crucial. Brazil, with its favorable climatic conditions and a significant deficit in wastewater treatment infrastructure in some areas, is particularly well suited for broader application of CW technology [5; 7]. However, CW remain relatively underexplored and underutilized in the country, primarily confined to scientific research and small-scale wastewater treatment projects [5]. Addressing this gap, the present study evaluates the feasibility of a vertical subsurface flow CW system using alternative substrates, such as seashells and ceramic bricks, which are both cost-effective and sustainable. Additionally, the study explores the role of native macrophytes, specifically _Echinodorus subalatus_, in enhancing treatment efficiency. This investigation holds particular relevance for rural and semi-arid regions, where low-cost, decentralized wastewater treatment solutions are urgently needed. Therefore, the objective of this study was to assess the feasibility of treating greywater using a CW system with vertical subsurface flow on a bench scale. This system employed a mixed filter bed of seashells and ceramic bricks, which was evaluated for its treatment efficiency both with and without the presence of the native macrophyte species _Echinodorus subalatus_.
###### Abstract
In other countries such as China, Russia, and the USA, the AFM1 ML allowed in milk is higher, equal to 0.5 \(\mathrm{\SIUnitSymbolMicro g}\)/kg [28]. To prevent AFM1 contamination and its occurrence in dairy products, the most effective approach is to reduce AFB1 contamination in animal feed, i.e., through the application of a field biological control, i.e., by using non-toxic strains of _A. flavus_ and appropriate storage of crops [29]. Another valuable approach to prevent contamination may be the use of detoxification techniques on animal feed products, i.e., physical, chemical, and biological processes whose acceptability criteria are defined in the EU Regulation 2015/786 [30]. Recent studies focused on AFM1 decontamination of milk based on the proposed use of microbial or mineral adsorbents [31; 32]. Several analytical procedures for the determination of AFM1 in milk have been developed. Enzyme linked immunosorbent assay (ELISA)-based techniques have been used for screening purposes and became very popular due to their relatively low cost, easy application, rapidity, and high sensitivity [33], with the disadvantage of pseudo-positive results and quite low accuracy. Therefore, the additional confirmatory analysis is needed [34]. High performance liquid chromatography (HPLC) combined with different detection principles has become the most important technique for the determination of most important mycotoxins in food and feed. HPLC with fluorescence detection (HPLC/FLD) and chromatography tandem mass-spectrometry (HPLC-MS/MS) represent the most used approaches for confirmatory analysis of mycotoxins in food and feed comprising AFM1 [35; 36]. These methods of analysis, as used for food control purposes (screening and confirmation), shall comply with the performance criteria requirements described in Annex II of the Regulation (EC) No 401/2006 [37]. The aim of this study was the monitoring of AFM\({}_{1}\) occurrence in raw and processed bovine milk samples, collected from two Italian regions (Puglia and Basilicata) and analyzed during the last twelve years (2012-2023), within the official control activities in charge of the Istituto Zooprofilattico Sperimentale della Puglia e della Basilicata (Foggia, Italy). A total of 1017 milk samples, 843 raw milk and 174 processed milk, were submitted for official control analysis. Screening analyses were conducted using the ELISA method, followed by confirmatory analysis by HPLC/FLD, only for samples with AFM\({}_{1}\) concentration above 0.042 \(\upmu\)g/kg. After full validation, both methods have been accredited by ACCREDIA, the Italian Organism of accreditation for laboratories. All data have been then elaborated in order to provide a final contribution to risk assessment.
# A Program Model of Fuzzy Interpreted Petri Net to Control Discrete Event Systems
## 3 The Formal Basis and the Conception of FIPN
While in the system based on FSC a binary sensor to monitor the level of liquid is used (\(LLS_{1}\)), the system based on FIPN allows the application of an analogue sensor (the same name \(LLS_{1}\) is used to facilitate a comparison of both systems). First, the filling process of \(TK_{1}\) begins, as shown in Figure 3a. Then, different levels of liquid in \(TK_{1}\) during filling process are presented in Figure 3b. Finally, \(TK_{1}\) is completely filled (see Figure 3d). The control system based on SFC enables only the display of two states: if the tank \(TK_{1}\) is being filled (Figure 3a-c) or is completely filled (Figure 3d), whereas the system based on FIPN shows the actual level of liquid all the time. In addition, FIPN allows actions to be performed based on the actual level of liquid (the current marking of places), e.g., while a mixer is being filled with two liquids stored in tanks, and one of the tanks is being emptied too fast/slow compared to the other one, the mixing time can be increased. Hence, FIPN offers a more precise visualization and control of DES than classic Petri nets. Figure 3: Different levels of liquid in the tank \(TK_{1}\) and corresponding to the states of control systems based on Sequential Function Chart (SFC) and fuzzy interpreted PN (FIPN). Figure 2: The change of the marking for the input and output places for \(\Delta\theta=0.2\). to which the condition corresponding to the transition is satisfied, and the increment of the degree \(tet_{k}\). The set of variables \(V^{P}\) from Definition 4 can be considered as the program implementation of Definition 1. Functions which operate on places and transitions from Definition 1 are replaced by appropriate variables from Definition 4.
## 4 A Pilot Study
### Discussion on the Annotated Data
An analysis of sentiment polarity cross-referenced to targets is also provided from the annotated data in Table 3. Table 3 shows that around 35% (\(\pm\)4.3%) of the posts with a 95% confidence level contained neutral sentiments and the rest were either positive (58.73% \(\pm\) 4.43%) or negative (5.89% \(\pm\) 2.12%). Table 3 further indicates that the attraction target presented the highest share of posts across the three sentiment polarity categories. The posts with Accommodation, Food & Beverage, and Weather, as the other three targets, were also posted and shared by people in the data considered for this study. Figure 5 also indicates that posts with positive sentiment were more than the posts with negative sentiment. When interpreting the results, it is important to consider the nature and impacts of the low number of tweets in some findings and discussion around them. Particularly, findings and discussion about negative tweets in our dataset should be interpreted with precautions, as negative tweets were small in number and the negative sentiment about the \"Holiday in general\" target, for example, was computed based on three tweets. Figure 6 presents the sentiment scores of annotators for targets by emotions. Overall, posts were most positive about attraction holiday in general and the \"other\" category. The \"other\" category includes targets, such as sports and events, which generally include a higher level of personal involvement, resulting in a high sentiment score. Transport, food and beverage, and shopping and gift indicated less positive sentiment, suggesting that these are the least performing aspects of the destination. Our annotation process further revealed that posts were more positive when they did not contain any emotional words. This may sound odd, but positive words (e.g., \"good\", \"right\") often do not convey emotions. There were also a small number of tweets containing words/phrases, such as \"Surfers Paradise\" (name of the most popular tourist beach) and \"Dreamworld\" (name of a popular theme park). Emotion-wise, posts were most positive in relation to emotions of excitement, love, and happiness. Sentiment polarities were most positive when posts expressed excitement about holidays in general, people and culture and attraction, as well as happiness about attraction. On the contrary, sadness, anger, and fear generally imply negative sentiments. Considering the data annotated in this study, posts were mainly negative when sadness and anger were shown in the posts.
# Maintaining Secure Level on Symmetric Encryption under Quantum Attack
## 2 Literature Review
### Parallel Computing  
Parallel computing refers to the use of multiple computing resources to improve the efficiency of data processing, and parallel computing can be broadly divided into spatial and temporal parallelism. Time parallelism refers to pipelining, which is splitting instructions into multiple steps so that they can be processed in parallel to speed up instruction execution; spatial parallelism involves the use of multiple computing resources at the same time to improve computation speed. In this case, we programmed the AES C++ code into a hardware description language [14] in a pipelined manner so that it can be executed on FPGAs to achieve hardware acceleration.
## 3 Results and Discussion
### In Vitro ROS Induced by KKD-ZnO-NPs  
Intracellular ROS level was determined by the DCFH-DA reagent with cisplatin, Kakadu plum extract, and KKD-ZnO-NPs on A549 cells. Zinc oxide nanoparticles (ZnO-NPs) are important reputed materials for anticancer activity due to reactive oxygen species (ROS) generation [55]. However, KKD-ZnO nanoparticles have not been reported on anticancer activity. In our study, we focused on the preparation of KKD-ZnO-NPs and their application in human lung carcinoma (A549). For evaluation of intracellular ROS production, cells were medicated with cisplatin, Kakadu plum extract, and KKD-ZnO-NPs for 24 h with different concentrations. The DCFH-DA reagent was used to test the expansion of ROS levels. As displayed in Figure 8, KKD-ZnO-NPs created a higher ROS level compared to Kakadu extract at 10 \(\upmu\)g/mL. Additionally, ZnO nanoparticles initiate oxidative stress in cancer cells, one of the top mechanisms that have the ideal capability on cytotoxicity examination [56; 57; 58]. Generally, the cell cytotoxicity was observable due to insoluble criteria of ZnO nanoparticles to free Zn\({}^{2+}\) (higher concentration). The properties of ZnO-NPs can induce ROS at the body surface, resulting in cell death when the ROS ratio exceeds the antioxidant level of the cell [59]. These findings suggest that KKD-ZnO-NPs might be a potential anticancer drug treating lung cancer.
# Automatic False Alarm Detection Based on XAI and Reliability Analysis
## 6 Conclusions  
The proposed method efficiently analyzes large-scale threats using XAI and can identify features that contribute significantly to the training of AI models so that even humans can easily interpret and understand the basis for AI predictions. When we carry out analyses based on the SSSF, which indicates the bias of the label, it is possible to preferentially select the data that the AI judges in disagreement to what the expert expects. This means that it is possible to examine what AI judges incorrectly from a human point of view. When we carry out analyses using the reliability score, this indicator contains sophisticated differences that may affect AI judgment. From the point of view of AI, it is possible to inspect the parts that AI can get wrong by adding indicators that can be intuitively interpreted by humans.  
We conducted experiments on the IDS dataset and the malware dataset to verify the performance of the proposed method. As a result of the experiment, the analysis with SSSF and the reliability score was able to detect a false alarm two to three times more efficiently than an analysis using AI models alone. The performance improved from 30% to 80% compared to the FOS used in previous studies [24]. Excellent results were obtained not only on the IDS dataset but also on the malware dataset. Through this, it was confirmed that the proposed method is generally applicable and is not constrained by the dataset. The proposed method can efficiently detect errors in the existing AI model. The proposed method can measure the reliability of the prediction of the AI model. This enables more the efficient and accurate identification of valuable alerts, enabling more efficient workflow for human analysts. Improved identification can also enhance system performance through analysis by promoting AI model approaches in real-world environments.  
The reliability indicators generated in this paper go beyond simply measuring reliability. They contain meaningful information that experts can use to analyze data. In addition to reliability, analysts can provide an environment in which efficient analysis can be performed through XAI-based analysis.  
Conceptualization, E.L., Y.L. and T.L.; methodology, E.L. and T.L.; software, E.L. and Y.L.; formal analysis, E.L.; investigation, E.L. and Y.L.; resources, Y.L.; writing--original draft preparation, E.L. and T.L.; writing--review and editing, E.L., Y.L. and T.L.; visualization, E.L.; supervision, T.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Institute for Information & communication Technology Planning & evaluation (IITP) grant funded by the Korean Government (MSIT) (No. 2019-0-00026, ICT infrastructure protection against intelligent malware threats).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.