esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Meter Gateways: Options for a BSI-Compliant Integration of Energy Management Systems
## 6 Assessment of EMS Integration Options
### Discussion of EMS Integration Options 2a, 2b and 2c  
_Options 2a, 2b_ and 2c_ bypass the meter data collection and processing of the SMGW. In cases where the SMGW is bypassed and both the SMGW and the EMS require access to meter measurements, optocouplers (using a meter's optional infrared interface) are one possible solution for providing meter measurements to the EMS. The data acquisition process in _Option 2b_ and _Option 2c_ is essentially that of a utility sub-metering system. In these cases, the SMGW solely acts as a gateway between the HAN and the WAN in order to benefit from the secure communication channel.  
It is important to note that the mechanisms defined in the BSI technical guideline are intended to provide a secure communication channel and to protect the data of consumers. Therefore, bypassing them is not the originally intended way to communicate measurements from the LMN to the WAN. However, based on the introduced regulations, we do not see any rules preventing an EMS designer from choosing these options that do not require to stick with the meter-specific requirements introduced by the _BSI TR-03109_ or the _PTB-A 50.8_. Since _Option 2a_ also completely bypasses the SMGW, it is entirely independent from the presence of an SMGW.
# Fire Safety Risk Analysis of Conventional Submarines
## 3 Overview of Systems and Risks
### Fire Safety Risks for Conventional Submarines
#### 3.4.3 Fire Risk Considerations  
A more informed, semi-quantitative risk analysis will be undertaken in the later stages of this project, however a high-level assessment of the abovementioned technologies highlights some key fire risk factors to be considered when assessing suitability and effectiveness of different options:  
* Diesel engines:  
The fire risks for diesel engines are well known and there are no new risks that can be foreseen for future submarines. Common causes of fires include hose/pipe leakages, ruptures, and disconnections causing release or spray of flammable fuels and oils onto hot surfaces or other ignition sources.  
* Air Independent Propulsion:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Options** & **Soryu Class (Japan)** & **Barracuda Class—Conventional Variant (France)** & **Type 216 (Germany)** \\ \hline Diesel engines & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline AIP & \(\surd\) (4 Stirling engines) & \(\surd\) (MESMA steam turbine) & \\  & & MESMA-Module Energie Sous-Marin Autonome & \(\surd\) (Hydrogen fuel cell) \\ \hline Lithium ion batteries & \(\surd\) & \(\surd\) * & \(\surd\) \\ \hline \hline \end{tabular}  
* Under consideration for future flights of the submarine program.  
\end{table}
Table 2: Various Powering Options were considered for Australia’s Future Submarine.  
Air Independent Propulsion (AIP) requires the submarine to have its own independent source of air, such as liquefied oxygen, to facilitate the release of energy in order to provide propulsion to the submarine whilst it is submerged. The use of air independent propulsion on submarines dates back to World War II, however newer technologies such as fuel cells and the MESMA steam turbine have only been in use for the last 10-15 years. Further, AIP has had very limited use in large conventional submarines.  
AIP requires high pressure storage of oxygen, hydrogen and other flammable gases in the submarine which provides a readily ignitable fuel source for fires. In addition, the operation of AIP machinery in some cases requires the combustion of fuels and exhaust of hot gases which are unable to be vented outside the submarine when operating at depth. A fire incident is significantly harder to manage during submerged operations which is often when use of AIP is required [39].  
* Lithium-ion batteries:  
With superior energy density, improved charge and discharge dynamics and better overall operational life, lithium-ion batteries can provide significant underwater endurance and range advantages over the lead-acid batteries which are typically used in conventional submarines [39]. However, lithium-ion batteries present a greater fire risk than lead-acid batteries and the implications of their use in conventional submarines over an extended time period is not well understood relative to lead-acid batteries which have been in operation on submarines for around a century.
## 4 Acceleration by Ultrashort and Tightly Focused Radially Polarized Laser Pulses
### Electron Acceleration Measurements
(**a**) Electron beam profile averaged over \(10^{4}\) shots as observed by the camera (the circular shadow is the projected small aperture of the reflecting mirror); (**b**) Electron signal measurement from the photomultiplier tube while moving gradually from the TE\({}_{01}\) mode (\(0^{\circ}\) and \(180^{\circ}\)) to the TM\({}_{01}\) mode (\(90^{\circ}\)). The normalized longitudinal electric field intensity is also indicated; (**c**)–(**d**) Electron spectrum measurement through the magnetic coil (average of \(10^{4}\) shots); (**c**) Signal with the magnetic coil residual field of 2 mT; (**d**) Signal with a magnetic field of 10 mT. Both measurements confirm the production of 23-keV electrons. Figure adapted from [20]. The electron energy measurement was obtained by deflecting the electron beam with the help of a magnetic coil. The calibrated magnetic field map of the coil allows us to evaluate the electron beam energy to be approximately 23 keV [Figure 9(c)-(d)]. The width of the energy distribution is estimated to be under 10% of the mean value of the energy. Electron energy losses from collisions within the gas chamber is estimated to be 0.02 keV/cm in the 15 to 25 keV range. Therefore, the influence of collisions on the overall structure of the electron energy spectrum is negligible. A Faraday cup was used to measure about \(1.2\times 10^{6}\) electrons per laser shot (\(\sim 0.2\) pC per electron pulse). A linear dependence between the number of charges per shot and the pressure is observed between 50 mTorr and 1 Torr. This suggests that electron pulses with considerably more charges could be produced at higher pressures, or with solid-density targets. However, it should be noted that in these conditions space-charge effects and scattering will also be more important. Finally, it should be stressed that the experimental set-up did not allow to resolve the temporal profile of the electron jets. However, the polarization sensitivity measured in Figure 9(b) indicates that electron emission follows the intensity of the longitudinal field. For a 23-keV electron bunch where \(v_{z}/c\simeq 0.3\), we can thus expect a duration in the 20 fs range, close to the beam waist. We recall that non-relativistic electron pulses will broaden considerably during propagation due to velocity dispersion and space-charge effects.
# Evaluation of Design Parameters on Drying of Kiwi Fruit
## 1 Introduction
There are some works in the literature that examine the effects of parameters during drying of agricultural products such as humidity, air temperature and flow rate. [PERSON] and [PERSON] [14] have examined the effects of drying conditions on the drying behavior of sweet potato in a drying cabinet. In a dryer, the drying behavior of sweet potatoes of 5, 8 and 12 mm thick was investigated for various air temperatures (50-90 \({}^{\circ}\)C) and air velocity (1.5-5.5 m/s). [PERSON] et al. [7], analyzed the evolution of the water content profiles inside cylindrical samples of eggplant during dehydration at 50, 60 and 70 \({}^{\circ}\)C using a mathematical model which takes into account shrinkage effects. [PERSON] and [PERSON] [15] performed drying behaviors of three different kinds of chestnuts under isothermal conditions at 70, 80 and 90 \({}^{\circ}\)C. [PERSON] and [PERSON] [16] developed a different mathematical simulation for the drying of banana by examining the drying characteristics of the product. [PERSON] [17] studied the color change of kiwifruit during the drying of the product with hot air and microwave. [PERSON] et al. [8], evaluated the water transport mechanism and shrinkage in pear during process carried out at 45, 50 and 55 \({}^{\circ}\)C by portable Nuclear Magnetic Resonance (NMR) with the aim of optimizing the process. [PERSON] et al. [18] investigated the effect of the air velocity on the drying of apple in a convective dryer. \begin{table}
\begin{tabular}{c c} \hline \hline
**Indices** & **Kiwi** \\ \hline \(p\) & 2640 \(\pm\) 111 \\ \(K\) & 17.418 \(\pm\) 639 \\ \(Ca\) & 1512 \(\pm\) 15 \\ \(Mg\) & 864 \(\pm\) 41 \\ \(Na\) & 242 \(\pm\) 12 \\ \(Fe\) & 16.1 \(\pm\) 0.8 \\ \(Mn\) & 22.4 \(\pm\) 1.2 \\ \(Cu\) & 1.14 \(\pm\) 0.1 \\ \(Zn\) & 10.8 \(\pm\) 0.3 \\ \(B\) & 6.11 \(\pm\) 0.3 \\ \(S\) & 164 \(\pm\) 5.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mineral content of kiwi fruits organic (mg/kg) [9]. In this paper, the drying characteristics of kiwifruit were presented experimentally for kiwi samples 4 mm and 8 mm thick at air temperature of 45 \({}^{\circ}\)C and air velocity of 0.5, 1.0 and 1.5 m/s in a convective type dryer. For this purpose, an experimental setup was established and required measurements were performed.
## 2 Simulation Model of ICE-ORC System
### ORC Modeling
#### 2.2.1 Evaporator  
The waste heat of the exhaust gas was transferred to the working fluid inside the evaporator. A shell-and-tube heat exchanger was employed in this study. The main parameters are listed in Table 2. The heat transfer process inside the evaporator can be divided into three zones: The liquid zone, the two-phase zone, and the gaseous zone. For the single-phase zone, the convective heat transfer of the working fluid was determined by the Dittus-Boelter equation:  
\[h=0.023 Re^{0.8}Pr^{0.4}\left(\frac{k}{D}\right) \tag{1}\]  
where \(h\) is the convective heat transfer coefficient (W/m\({}^{2}\cdot\)K), \(Re\) is the Reynolds number, \(Pr\) is the Prandtl number, \(K\) is the thermal conductivity (W/m\(\cdot\)K), and \(D\) is the characteristic length.  
For the two-phase zone, Shah-Thome equation was used to calculate the convective heat transfer coefficient:  
\[Nu_{l}=0.023 Re_{l}^{0.8}Pr_{l}^{0.4} \tag{2}\]  
\[h=Nu_{l}(1-x)^{0.8}\left(\frac{3.8x^{0.76}\left(1-x\right)^{0.04}}{P_{rd}^{0. 38}}\right)\left(\frac{k}{D}\right) \tag{3}\]  
\[P_{rd}=\frac{p}{P_{cr}} \tag{4}\]  
where \(x\) is the quality of the two-phase working fluid, \(P\) is the operation pressure, and \(P_{cr}\) is the critical pressure.
# Artificial Intelligence in Virtual Telemedicine Triage:
## 4 Results
### Performance of the Triage Measurement Device
#### 4.2.2 SpO2 (Blood Oxygen Saturation) Measurement  
In order to establish the reliability of the measurement of blood oxygen saturation, the device was compared with a commercially available device. The sample used was 20 people who showed a variation of 2.25% in SpO2 with respect to a commercially available pulse oximeter. The average measurement error recorded was 2.45%, resulting in a reliability of 97.55%. This result is within the guidelines of the physician with whom we consulted since it allows the patient to know if there is an anomaly in the amount of oxygenated hemoglobins in their body.  
As shown in Figure 12, the behavior of the SpO2 measurements can be visualized, where there is a tendency for greater precision when the saturation level range is between 90% and 93%. When the real value is higher than 97%, the device begins to have a slight increase in measurement error. It should be noted that the accuracy of the measurement will depend on how the user positions their finger on the device, since a wrong positioning of the finger on the sensor will cause an erroneous measurement.
# Zero-Day Threat Mitigation via Deep Learning in Cloud Environments
## 4 Methodology
### Training and Validation  
The MVT model was trained using a supervised learning approach within a Docker-based containerized environment to simulate realistic deployment scenarios. Training was conducted over 60 epochs, with a batch size of 32 and an initial learning rate of 0.01. A momentum of 0.8 and the Gradient Descent optimizer were chosen due to their proven stability in training deep networks with vision transformers.  
Hyperparameters were selected through empirical tuning and validation experiments, considering computational constraints and model generalization. These values were informed by recent literature on hyperparameter optimization in deep learning systems [37, 38], favoring configurations that improved convergence speed and avoided overfitting.  
To improve training efficiency, a learning rate scheduler, StepLR, was implemented to reduce the learning rate by a factor of 0.1 every 10 epochs, ensuring gradual refinement during late stages of training.  
Early stopping was applied with a patience of 5 epochs, terminating training when validation accuracy failed to improve over five consecutive epochs. This strategy, supported by guidelines for overfitting prevention [39], helps ensure optimal generalization without unnecessary training.  
The model was evaluated on three data splits (training, validation, and testing) using standard metrics (accuracy, recall, and F1-Score). This setup allowed for a comprehensive assessment of both training stability and robustness under various configurations. Future improvements may explore more advanced hyperparameter optimization methods such as random search, Hyperband, or Bayesian optimization [38, 40].
## 3 Proposed Method  
### Cluster Formation  
Each vehicle periodically broadcasts beacon message that includes its Id, speed, and the direction of movement, color, and cluster Id (\(C_{id}\)). Upon receiving the beacon messages each vehicle \"\(u\)\", updates its neighbourhood table (\(N_{u}\)). Initially, the vehicle's colour is white, and \(C_{id}\) is \(-1\). Once it identifies a  
Figure 2: The vertex subsets [1, 6, 8], [1, 4, 9], [3, 8], [3, 5, 9], and [3, 7, 10] are few dominating sets of \(G\) where as [3, 8] is a minimal dominating set. Hence \(\gamma(G)=2\). The vertex sets [3, 4, 5, 8], [3, 4, 7, 8], and [3, 6, 7, 8] are few minimum connected dominating sets Hence \(\gamma_{c}(G)=4\).  
cluster head in its neighbourhood, its colour becomes blue, and its \(C_{id}\) is set to the Id of its cluster head. If it identifies more than one cluster head within its neighborhood, its color becomes grey, and it's \(C_{id}\) includes the Ids' of those cluster heads. If it cannot identify any cluster head in its transmission range within a limited period and has white/yellow neighbors, its color becomes yellow, and the yellow nodes participate in the cluster head selection process.
# Multi-Source Satellite Imagery and Machine Learning
## Appendix A  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Geological Data Types**} & \multicolumn{2}{c}{**DPCAP**} & \multicolumn{3}{c}{**DGP**} & \multicolumn{3}{c}{**DOLIP**} \\ \hline  & **GBC (\%)** & **RF (\%)** & **XGB (\%)** & **GBC (\%)** & **RF (\%)** & **XGB (\%)** & **GBC (\%)** & **RF (\%)** & **XGB (\%)** \\ \hline Alluviums & 67 & 74 & 69 & 88 & 90 & 86 & 70 & 71 & 70 \\ Basalt & 63 & 62 & 59 & 72 & 67 & 68 & 64 & 64 & 64 \\ Gn + Pyro & 59 & 62 & 55 & 65 & 66 & 58 & 66 & 59 & 62 \\ Gneiss & 65 & 66 & 65 & 66 & 66 & 66 & 69 & 68 & 69 \\ Granites & 56 & 58 & 54 & 59 & 62 & 59 & 57 & 61 & 61 \\ Pyroclastics & 53 & 54 & 50 & 68 & 63 & 65 & 51 & 52 & 51 \\ Traclytes & 69 & 71 & 68 & 73 & 70 & 70 & 71 & 69 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation of algorithm performance for each type of geological data with the best parameters.
# Advances in Emerging Memory Technologies: From Data Storage to Artificial Intelligence
## 2 Market and Trends of Memory Technologies
### Technology Trends of Nonvolatile Memories
#### 2.1.2 Evolution of Standalone Nonvolatile Memory Technologies  
Today, the Flash memory market is driven by high capacity standalone memories for mass storage applications. High memory capacity is required by applications such as digital still and video cameras, mp3 players, solid state drives (flash), mobile phones, and all manner of flash card products. For standalone applications, where density is doubling every 12 months [14], cell area is the key factor. Indeed, standalone memories need aggressive cell dimensions to achieve high memory densities with low cost per bit, required in the applications mentioned above.  
For over 40 years, the evolution of nonvolatile memories was mostly based on the floating gate MOS transistor [1]. Scaling of the NAND technology was pursued below the 20-nm node [15]. Then, critical limits led to the appearance of 3D memory [16] that entered the market in \(\sim\)2015 and changed the conservative memory world. In 2020, vertical NAND stacked up to 96 layers, used QLC (quad level cells, four bits per cells) and reached a density of about 10 Gb/mm\({}^{2}\), with typical memory capacities of one terabyte [17]. In 21 years, multilevel cell flash memory density has increased 10,000 times.  
Flash NAND has been adopted in solid state drives (SSD) over the past 10 years and now shares the data storage market with hard disk drives (HDD) thanks to its constant decreasing bit cost. Three-dimensional NAND continues to improve and is expected to continue as a leading technology for data storage [1]. In the case of 3D NAND, scaling is mainly achieved by stacking, making it possible to retain the same cell dimensions in some successive nodes to maintain cell characteristics. Despite many challenges, Flash continues with constant technology, performance, architecture, and design improvements. In particular, in order to pursue scaling, stacking of two arrays (to relax the aspect ratio), integrating the CMOS under the memory array (to increase density), and adopting a more aggressive multilevel (with optimized programming algorithms) [18] with 96 stacked levels. A state-of-the-art multistacked memory hole process was presented by Toshiba in 2018 [19].
# Microbial Application to Improve Olive Mill Wastewater Phenolic Extracts
## 4 Materials and Methods
### HPLC Analysis  
HPLC analyses of phenol fraction of OMW and extracts were obtained by directly injecting the filtered samples (0.45 \(\upmu\)m Millipore filters) in the chromatographic HPLC system. The system consisted of a liquid chromatography Waters Alliance 2695 HPLC equipped with a Waters 996 photodiode array detector (PDA) set at 280 nm and with Waters Empower software (Waters Corporation, MA, USA). The column was a Luna C18 (250 mm \(\times\) 4.6 mm i.d., 5 \(\upmu\)m, 100 A; Phenomenex, Torrence, CA, USA) maintained in an oven at 40 \({}^{\circ}\)C. Chromatographic separation was achieved by elution gradient using an initial composition of 95% of A solution (water acidified with 2% acetic acid) and 5% of B solution (methanol). Solvents were HPLC grade (Merck KGaA, Darmstadt, Germany). The B solution increased to 30% in 15 min and to 70% in 25 min and then, after 2 min in isocratic, the mobile phase was set at the initial conditions for 8 min. A flow of 1 mL/min was used [48]. The internal standard (I.S.) of 5 mM pure gallic acid (Fluka, Switzerland) was used to quantify the phenolic compounds. The identification of phenolic compounds was obtained by comparing retention time with pure tyrosol, oleuropein, and hydroxytyrosol standards (Extrasynthese, Genay, France). All the analyses were carried out in triplicate for each sample.
## 5 Results
### Hypothesis Test  
Figure 3 presents the path analysis results. H1 argues that the effect of perceived interactivity on users' attitudes toward data journalism will be mediated through enjoyment. From the path coefficients in the figure, it is clear that perceived interactivity positively predicts users' enjoyment level (b = 0.475, \(p\) < 0.001). The user's enjoyment level positively influences their attitude toward the news (b = 0.39, \(p\) < 0.001). According to H2, user engagement will mediate the effect of perceived interactivity on user attitudes. The results show that perceived interactivity positively predicts user engagement (b = 0.26, \(p\) < 0.01). The user's engagement level positively influences their attitude toward the news (b = 0.31, \(p\) < 0.01). In addition, H3 suggested that perceived interactivity may influence user engagement through enjoyment, which, in turn, affects user attitudes toward the news. The results showed that perceived interactivity positively predicted users' enjoyment (b = 0.475, \(p\) < 0.001), users' enjoyment level positively influenced users' engagement (b = 0.604, \(p\) < 0.001), and users' engagement level positively influenced their attitude toward the news (b = 0.312, \(p\) < 0.01).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Construct Indicators** & **Indicator’s Loadings** & **Composite Reliability** & **Average Variance Extracted** \\ \hline Perceived Interactivity\_1 & 0.735 & & 0.708 & 0.549 \\ Perceived Interactivity\_2 & 0.746 & & & \\ Enjoyment\_1 & 0.770 & & & \\ Enjoyment\_2 & 0.879 & 0.860 & 0.673 \\ Enjoyment\_3 & 0.809 & & & \\ Engagement\_1 & 0.673 & & & \\ Engagement\_2 & 0.738 & 0.838 & 0.565 \\ Engagement\_3 & 0.841 & & & \\ Engagement\_4 & 0.745 & & & \\ Attitude\_1 & 0.964 & & & \\ Attitude\_2 & 0.894 & 0.909 & 0.771 \\ Attitude\_3 & 0.764 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of the construct assessment.  
Figure 3: Results of path analysis.  
A mediation analysis was conducted using [PERSON] process (Model 6) [61] with 5000 replicate samples and 95% confidence intervals. The results of the mediation analysis are presented in Table 4. The indirect effect value of perceived interactivity influencing user attitudes through enjoyment was 0.221 with a 95% confidence interval [0.072-0.394], and the indirect effect value of perceived interactivity influencing user attitudes through engagement was 0.097 with a 95% confidence interval [0.002-0.243], both excluding 0. In summary, hypotheses H1a, H1b, H2a, and H2b of this study were accepted. According to the results of the analysis, the third mediated path had a mediated effect value of 0.105, with a 95% confidence interval of [0.010-0.223], excluding 0. H3 was accepted.