esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The European DEMOnstration Power Plant (EU-DEMO) is foreseen to consume more than 100 kg of tritium (T, or \({}^{3}_{1}\)H) per full-power-year of operation [1]. Given its scarcity from natural and anthropogenic sources [2], tritium self-sufficiency is considered a strong requirement for future Fusion Power Plants (FPPs) [3]. That is, tritium must be produced in situ by Breeding Blanket (BB) systems, using a combination of Neutron Multiplier Material (NMM) and lithium compound (breeder). Liquid lead-lithium in eutectic proportions (herein referred to as Pb-Li) can act as both breeder and NMM, so it has been chosen for some BB concepts, including one of the current designs under Research and Development (R&D) focus, the Water-Cooled Liquid-Lithium (WCLL) concept [1].  
Design of BB concepts is not only driven by tritium economy requirements, but also safety concerns. Design studies of both aspects must assess crucial mitigation strategies for permeation losses, since FPPs must comply with hard requirements on environmental releases and plant inventory due to retention [4, 5]. Hydrogen permeation through metals is largely governed by its characteristic diffusivity and Sieverts' constant (solubility),but literature reports a rather large scattering of measurements for these values for Pb-Li, especially for the latter: over two and four orders of magnitude, respectively. The difference between higher and lower values can be decisive in the feasibility of FPPs, which warrants a reduction of this uncertainty [6].  
Possible reasons for this uncertainty include experimental methodology and associated measurement techniques. In particular, static experiments attempt to infer the Sieverts' constant by measuring: (a) how much of a fixed initial amount of hydrogen is absorbed by a Pb-Li sample; or (b) how much hydrogen desorbs from a Pb-Li sample previously exposed to a hydrogen atmosphere [6; 7]. In both cases, however, the eventual passage of hydrogen through the experimental container walls is non-negligible; walls are usually made of steel, which presents solubility, for the great majority of literature measurements, at least one order of magnitude higher than Pb-Li. This can interfere with the accuracy of measurements and lead to an overestimation of absorption or underestimation of desorption. Thus, it is crucial for any experiment of this type to distinguish the dissolution of gas into samples from the loss of gas through the container walls.  
To tackle this, a physical model for gas dissolution into metals was developed (Sections 2 and 3). Subsequently, it was applied to part of an experimental setup, designed to study the Vacuum Sieve Tray (VST) concept for hydrogen extraction from Pb-Li, in the Tritium Laboratory Karlsruhe (TLK) [8]. The derived numerical model was used to estimate the diffusivity and Sieverts' constant for 316L steel in a control experiment without Pb-Li, and comparison with literature allowed for model validation (Section 4). The model was then applied to a simulated experiment with Pb-Li, to provide predictions and guidelines for future measurements with the setup (Section 5).
# Information-Centric Network-Based Vehicular Communications: Overview and Research Opportunities
## 6 Lessons Learned  
The ICN-based vehicular network is a part of human-centric communications that use private and public-scale participating information gathering. This type of data network is driven by the production of high-performance nodes. Currently, these nodes contain billions of smartphones and now growing into smart meters, GPS in vehicles, and activity- supervising sportswear accessible to subscribers [57]. Drivers and vehicles can use the information, such as weather, road, traffic, and health conditions, on the road. Vehicular ICN has several applications [57], for example, vehicles can distribute the collected data via the network though pull-based or push-based mechanisms, they can interact with neighboring vehicles in a wireless fashion so as to send or receive road or traffic information.  
ICN is a name-based communications paradigm that allows caching contents at local in-network nodes. With technological advancements, Internet is evolving in every field, which introduces the concept of Internet of Things (IoT). Things ranging from a small body-fit gadget to large vehicles. In addition, as the Internet shifts to name-based model (i.e., ICN) from the existing nature of IP-based communication, the combination of VANETs with the ICN may face various issues such as intermittent connectivity due to rapid mobility of vehicles. In this study, we investigated the ICN-based VANET challenges in terms of mobility, security, routing, naming, caching, and 5G communications.  
Furthermore, due to several other communications enigmas such as security, DoS attacks, and exponentially increasing number of Internet users, the integration of ICN-based VANETs with other architectures, i.e., SDN, Cloud, and Edge, is the need of the day. We presented the idea of these architectures with the ICN-based VANET in line with their glitches that may probably be faced when deploy together.
## 3 Chitosan-Containing Materials and Composites as Adsorbents
### Magnetically Separable Chitosan Composites
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**CS-Containing** & **Crosslinking** & **Shaping** & **Contaminant** & **Absorption** & **Absorption** & **Ref.** \\
**adsorbent** & **Agent** & & & **Conditions** & **Capacity** & **Ref.** \\ \hline CS/MAH–DVB-3 & DVB & No & MB & 298 K, pH 10 & 503 mg/g & [97] \\ \hline Pyridine-modified CS & No & No & Cu(II) & 25 \({}^{\circ}\)C, pH 5.5 & 1.84 mmol/g & [98] \\  & & & Cr(VI) & 25 \({}^{\circ}\)C, pH 3.6 & 3.86 mmol/g & [98] \\ \hline CS–DEO & DEO & Beads & RR2 & Room temperature, pH 2 & 788.6 mg/g & [99] \\ \hline CS–DEO–SP & DEO & Beads & Cr(VI) & 25 \({}^{\circ}\)C, pH 2 & 352 mg/g & [100] \\ \hline CS & GA & Beads & MG & 30–40 \({}^{\circ}\)C, pH 6 & 714.29 mg/g & [101] \\  & & & IC & 30–40 \({}^{\circ}\)C, pH 6 & 11.02 mg/g & [101] \\ \hline CS & GA & Beads & Diesel oil solution & Ambient & 1.820 mg/g & [102] \\  & & & in hexane & Ambient & 1.642 mg/g & [102] \\ \hline CS–EGDE & EGDE & Film & RR120 & 303 K, pH 5 & 165.3 mg/g & [103] \\  & & & MO & 303 K, pH 4 & 131.2 mg/g & [103] \\ \hline EPCS@FEI & ECH & Beads & DS & 308 K, pH 4.2 & 253.32 mg/g & [104] \\ \hline PEI–ECH–FA–CS & ECH & Micro & Cr(VI) & 298 K, pH 3 & 331.32 mg/g & [106] \\  & & spheres & Pb(II) & 298 K, pH 5 & 302.56 mg/g & [106] \\ \hline CS & TPP and ECH & Beads & MO & Room temperature, pH 3 & 79.55 mg/g & [107] \\ \hline AgCl@CS & GA & Microspheres & Iodide anion & 25 \({}^{\circ}\)C, pH 2-10 & 1.5267 mmol/g & [110] \\ \hline CS–GNPs & GA & Spheres & MO & 298 K, pH 3 & 230.91 mg/g & [116] \\  & & & AR 1 & 298 K, pH 4 & 132.94 mg/g & [116] \\ \hline CS–GA/TNC-25 & GA & No & RR 120 & 303 K, pH 3 & 103.1 mg/g & [117] \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of adsorption performance of some of the recent CS-based materials.
# Dosimetric Optimization of a Laser-Driven Irradiation Facility Using the G4-ELIMED Application
## 3 Results
### Transmission Efficiency  
Another important aspect to be discussed is the capability of the beamline to efficiently transport protons around a given energy. The transmission efficiency was defined as the percentage of transmitted protons within \(\pm\)10% of the selected kinetic energy (e.g, in the 20 MeV case, we would consider protons with kinetic energy between 18 and 22 MeV). The transmission efficiencies at diverse positions along the beamline are compiled in Table 5.  
Despite the relatively low transmission efficiency for the given source term [30], it is important to stress that the low transmission values reported in Table 5 are still acceptable thanks to the relatively large proton number at the source. Ultimately, considering the available laser repetition rate, we focus our study on the time required to perform a sample irradiation experiment, i.e., the time needed to deliver the required dose at the irradiation point.  
In radiobiology experiments, doses of the order of 1-2 Gy are typically required. Hence, the number of shots necessary to reach a 2 Gy dose level was calculated considering 10\({}^{10}\) initial protons in the full energy spectrum (i.e., between 0 and 33 MeV) and the transmission efficiency of every single energy. A total number of 2812 shots were obtained. Considering that the L3 HAPLS laser system at ELI Beamlines [36] can deliver PW-class laser pulses at 10 Hz, approximately 280 s will be needed to reach the required dose on the user sample. However, this does not consider the time required to change the position of the PMQs, which was calculated considering a speed of 1 mm/s, thus returning about 110 s. Ultimately, the whole irradiation time is estimated to be approximately 6 min for the given source term.
# Code Obfuscation: A Comprehensive Approach to Detection, Classification, and Ethical Challenges
## 5 Practices and Tools for Code Obfuscation  
There are numerous tools available for different programming languages that facilitate code obfuscation. Before diving into the specifics of the obfuscation tools we utilized, it is important to mention that control flow obfuscation is subtly integrated into the obfuscators we explored, including Jiaive, Oxyry, PyObfuscate, Pyarmor, and Py-obfuscator. While this technique may not always be explicitly documented or visible in the code, it plays a crucial role in complicating the program's execution path. Each tool likely employs control flow obfuscation, which makes it harder for someone to trace the program's logic, even though the exact implementation details might be concealed.  
The tables below (Table 2) provide an overview and comparison of the selected code obfuscation tools. They highlight the key features, benefits, and limitations of each tool, offering a clear perspective on their suitability for various security needs and implementation contexts. Each tool presents a range of functionalities tailored to different levels of ease of use, obfuscation strength, platform support, and available documentation and support. This comparison aims to assist developers in choosing the most appropriate tool for their specific requirements.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tool** & **Description** & **Lexical Obfuscation** & **Data Obfuscation** \\ \hline \multirow{8}{*}{Jlaive [25]} & An open-source obfuscation tool & \multirow{8}{*}{Uses complex string manipulation and variable assignments to hide within scripts, helping obfuscated batch files may not offer the same level of obfuscation strength as some commercial tools, potentially limiting its effectiveness in highly sensitive or complex scenarios.} \\ \cline{1-1}  & A straightforward obfuscation service for Python code that employs basic techniques to enhance code obscurity. It focuses on renaming variables and functions and removing comments to make the code less readable. & Renames symbol names (variables, functions, classes, arguments) and avoids direct 1:1 mapping. Removes documentation strings and comments to obscure code functionality and purpose. \\ \cline{1-1}  & A Python-specific tool that employs a combination of obfuscation techniques to make code harder to read and understand. It is designed for easy integration into existing projects, offering a balance between security and performance. PyObfuscate is particularly valuable for developers seeking a straightforward obfuscation solution that does not require extensive configuration, providing an effective way to enhance code protection with minimal setup.
## 2 General Considerations
### Heat-Transfer Equations  
The general heat-exchange equation can describe precisely the energy accumulated in the mass electrode. This provides a relationship between heat flux and the temperature of the system.  
\[\mathrm{mc}\frac{\mathrm{dT}}{\mathrm{dt}}=\dot{\mathrm{Q}}+\dot{\mathrm{Q}}_{ \mathrm{cor}}+\mathrm{A}\sigma\varepsilon\left(\mathrm{T}^{4}-\mathrm{T}_{ \mathrm{atm}}^{4}\right)-\mathrm{Ah}(\mathrm{T}-\mathrm{T}_{\mathrm{atm}}) \tag{5}\]  
where m is the mass of the mass electrode (kg), \(\mathrm{c}\) is the specific heat capacity of the metal (J-kg-1.K-1), T is the temperature of the mass electrode (K), \(\dot{\mathrm{Q}}\) is the heat flux of the heat source (W), A is the surface of the mass electrode (m\({}^{2}\)), \(\mathrm{\sigma}\) is the Stefan-Boltzmann constant (\(5.67\times 10^{-8}\) W-m-2.K-4), \(\mathrm{\varepsilon}\) is the emissivity of the metal, \(\mathrm{T}_{\mathrm{atm}}\) is ambient temperature (K), and h is heat transfer coefficient (W-m-2.K-1).  
Assuming the radiation term is negligible, considering steady state, and hypothesizing that \(\dot{\mathrm{Q}}_{\mathrm{cor}}\) is practically the power consumption of the discharge, heat equation can be rewritten as follows [35]:  
\[\mathrm{T}-\mathrm{T}_{\mathrm{atm}}=\mathrm{R}_{\mathrm{th}}\left(\dot{ \mathrm{Q}}+\dot{\mathrm{Q}}_{\mathrm{cor}}\right) \tag{6}\]  
where \(\dot{\mathrm{Q}}_{\mathrm{cor}}\) is the heat flux induced by corona discharge (W), and \(\mathrm{R}_{\mathrm{th}}\)= A \({}^{-1}\cdot\mathrm{h}^{-1}\) is the thermal resistance of the system. The term \(\dot{\mathrm{Q}}_{\mathrm{cor}}\) is a function of temperature and power consumed by the corona discharge, and can be approximated in the first order with the equation:  
\[\dot{\mathrm{Q}}_{\mathrm{cor}}=\mathrm{Ah}(\mathrm{T}_{\mathrm{cor}}-\mathrm{ T}_{\mathrm{atm}})=\mathrm{P}_{0,\mathrm{cor}}+\mathrm{\xi}(\mathrm{T}-\mathrm{T}_{ \mathrm{atm}}) \tag{7}\]  
where \(\mathrm{\xi}\), is the variation of corona power with temperature parameter, a constant determined experimentally, and \(\mathrm{P}_{0,\mathrm{cor}}\) is power consumption (W) at ambient temperature. Finally, we can model the system behaviour like in the form:  
\[\dot{\mathrm{Q}}+\mathrm{P}_{0,\mathrm{cor}}=(\mathrm{Ah}-\mathrm{\xi})\cdot (\mathrm{T}-\mathrm{T}_{\mathrm{atm}}) \tag{8}\]  
This equation shows an inherent residual heat flux and thermal resistance derived from corona discharge. Accordingly, power consumption and thermal resistance would define the global performances in each geometry. Power is imposed by voltage supply and geometry (gap, barriers, among others) and thermal resistance can be determined experimentally. The higher the heat transfer coefficient h, the lower the thermal resistance, so a higher heat flux could be dissipated for the same temperature. Therefore, geometry optimization will focus in decrease thermal resistance, keeping constants mass and volume.
## 3 Materials and Methods
### Data Acquisition  
The proposed DDSM requires supply and demand data, which refer to road network, public transit, and LBS demand data.  
Road network data is a collection of information about roads, typically used in the applications such as geographic information system, transportation planning, traffic simulation, and navigation systems, and can be obtained through open data platforms like Google Maps API and OSM. To acquire precise road network data, this study drew upon comprehensive data resources from AMap and OpenStreetMap. The information in road network data can be categorized into road segment information and key road node/intersection information. Attributes of road segment information include link name (or link ID), length,  
Figure 1: Flowchart of the dual-driven simulation model.  
width, speed limit, level, type, and number of lanes. Key road node/intersection information includes intersection ID, intersection coordinates (latitude and longitude), as well as the latitude (lat) and longitude (lon) of key nodes for road alignment between intersections. Aside from the foundational road network data mentioned above, a multi-modal road network also requires public transportation information, including bus and subway operational information. The pertinent details pertaining to public transportation routes and schedules were gathered from AMap, along with the official online platforms of local public transportation operators. These data primarily encompass public transit schedules (departure times and estimated arrival times), route planning, station waiting times, the coordinates of stations (latitude and longitude), and public transit vehicle information.  
In addition to multi-modal road network data, LBS data are needed for modeling OD and mode-level travel demand in the network. LBS data collection typically begins with users using devices or applications equipped with location-aware capabilities (e.g., social media, navigation apps, and weather apps), which acquire the geographical coordinates of the device/user along with timestamp information. The LBS data obtained in this paper were derived from the navigation data of AMap in Hangzhou City, Zhejiang Province. The data recorded the navigation trajectory information generated by users when using AMap from 23 to 29 May 2022. Taking into account the protection of user privacy, the data anonymized identifiable information such as user IDs, while preserving the user's spatial-temporal trajectory points, travel modes, and the travel duration of the entire trip. The more densely detailed LBS data that record the traveler's frequent travel trajectory points make it possible to infer the traveler's travel path (e.g., information about the sequence of road segments entered and entry times). However, the accrual path information of some trajectories is missing. It is challenging to infer due to the large number of alternative routes connecting the OD pair. The data with missing trajectories are the target for LBS data path completion.
## 3 Results and Discussion
### Analysis of Vegetable Drinks
#### 3.2.1 Presence of Mycotoxins  
From the analysis performed, the presence of BEA (16%), ENA (3%), ENA1 (11%), ENB (22%), ENB1 (19%), ZEA (7%), HT-2 (5%), T-2 (3%), AOH (3%) and TENT (4%) has been observed.  
Figure 5: Recoveries obtained from the repeatability study (n = 3) at the L1 level for the four matrices.  
Figure 6: Distribution of the mycotoxins analyzed in the four types of drinks analyzed and in the total samples analyzed (n = 56).  
This presence according to the type of matrix was different, observing a higher presence of ENB in soy beverages (43%) and oat beverages (20%) than ENB1 (19%) in almonds. On the other hand, in rice, the most detected was ZEA (29%) (Figure 6).  
As shown in Table 4, the detected values were between 0.1 ng/mL (AFG\({}_{2}\)) and 108.9 ng/mL (ENB) not exceeding either the maximum or recommended limits for leg-islated mycotoxins and, therefore, being safe and compliant according to the legislation. Regarding the highest level that was detected, we found a sample of almond drink that contained 108.9 ng/mL of ENB. These concentrations are below the levels detected in other studies carried out on the cereals with which these drinks are made, thus indicating that it could have a very important role in reducing mycotoxin levels in the production and elaboration processes of beverages.  
According to the type of drink, it was found that oat beverages and soy beverages were the most contaminated, finding levels of mycotoxins in all oat beverages samples and in 93% of soy beverages (Figure 6). This is a worrying aspect since these types of drink are usually the most chosen by consumers of vegetable drinks. On the other hand, in rice and almond drinks, 86% presented mycotoxin levels.  
In oat beverages, a greater presence of emerging mycotoxins was detected, mainly ENB and ENB1, a result that coincides with previous studies carried out on this type of cereal. For example, [PERSON] et al. [24] detected ENB in 43% of oats marketed in Italy and, in 2014, in 21% of baby food samples that combined between 2 and 8 different cereals including oats [25]. For this matrix, there are limits established for some of the mycotoxins, but no oat beverages exceeded these values.  
Regarding soy beverages, the major mycotoxin also present was ENB (43%), followed by ENB1 (18%), ZEA and BEA (11%). No limits have been established for this food, so regardless of the results, they would be compliant samples; however, it should be noted that depending on the increase in consumption, the possible presence of the same mycotoxin and the fact that more than one mycotoxin may be found in a same sample should be considered, and more extensive studies and a collection of analytical data to make a full assessment should be performed.  
The analyzed rice drink showed a higher presence of ZEA, being detected in 23% of the samples, but the values were lower than the legislated ones. On the other hand, for almond drinks, the most detected was ENB1 (19%), followed by the presence of ENB, ENA, ENA1 and ZEA in 9% of the samples (Figure 6).
# Resistance of \(L\). _monocytogenes_ and \(S\). Typhimurium
## 3 Results and Discussion
In general, three different modes of biofilm dispersal have been observed in literature, i.e., (i) the continuous release of single cells or small clusters of cells (erosion), (ii) a sudden release of a large portion of the biofilm (sloughing), and (iii) the rapid release of a large number of single cells from hollow cavities that have been formed inside the biofilm colony (seeding) [31; 32; 33; 34; 35]. Quorum sensing can play an important role in this matter. Upon replication, cell-to-cell signaling molecules are produced and once a certain threshold is exceeded, the expression of certain genes is induced, e.g., hydrolytic enzymes can be produced and excreted by the bacteria. Consequently, components of the EPS matrix get degraded and parts of the biofilm (or single cells) can detach and become planktonic again as the biofilm ages [36; 30; 37]. Based on the visual observations during the crystal violet staining of the seven and 10 days old _L. monocytogenes_ biofilms, it could indeed be  
Figure 3: Cell density (log(CFU/cm\({}^{2}\))) determined on general and selective medium for the untreated (**a**) _L. monocytogenes_ and (**b**) \(S\). Typhimurium biofilms following 1, 2, 3, 7, and 10 day(s) of incubation. Bars bearing different letters (no capital or small letters in common for the general and the selective medium, respectively) are significantly different (\(p\leq 0.05\)). Figure 2: Total biofilm mass, expressed as optical density (-), for the untreated (**a**) _L. monocytogenes_ and (**b**) \(S\). Typhimurium biofilms following 1, 2, 3, 7, and 10 day(s) of incubation. For each species, bars bearing different lowercase letters are significantly different (\(p\leq 0.05\)). concluded that some parts of the biofilm are detached and rinsed off prior to biofilm quantification. Nevertheless, since the biomass of the seven and 10 days old \(L\). _monocytogenes_ biofilms is still much higher than the biomass of the one-day old (reference) biofilm, the lower cell density at seven and 10 days of incubation is most likely the result of cells that have died due to starvation or waste accumulation [38], possibly in combination with (a low number of) single cells that became planktonic again. For the \(S\). Typhimurium biofilms, the hypothesis of biofilm dispersal can, however, not be confirmed based on the (visual) observations, i.e., (i) there was no detachment of the biofilms during the crystal violet staining and (ii) the total biofilm mass of the seven days old biofilms is not significantly lower than the biofilm mass of the one-day old (reference) biofilms. Consequently, the lower cell density of the seven days old \(S\). Typhimurium biofilms should be the result of cells that died due to nutrient depletion and/or waste accumulation [38]. As for \(L\). _monocytogenes_, it might also be possible that (a low amount of) the \(S\). Typhimurium cells became planktonic again.