esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Thermal Conductivity, Density and Specific Heat Capacity of the DP/Gypsum Composites  
Figure 11 shows the thermal conductivity of pure gypsum and DP/gypsum composites. The thermal conductivity of pure gypsum was 0.86 W/(m\(\cdot\)K). The thermal conductivity of the DP/gypsum composites decreased as the proportion of DP increased. When 10% and 20% DP were added, the thermal conductivity of G-10% DP and G-20% DP decreased to 0.76 W/(m\(\cdot\)K) and 0.59 W/(m\(\cdot\)K), respectively. This is due to the fact that the thermal conductivity of DP (less than 0.40 W/(m\(\cdot\)K)) [43; 44; 45] is much lower than that of the pure gypsum. However, the addition of 1% CF can increase the thermal conductivity of G-10% DP-1% CF and G-20% DP-1% CF by 10.5% and 20.3%, respectively, compared to the corresponding samples without CF. There are two main reasons for the improvement in thermal conductivity: First, the CF can improve the overall thermal conductivity of the composites. Second, the CF were well dispersed and inter-connected in the gypsum matrix, thus offering more channels for heat transfer [46]. Similar results can be found in the literature. In the research of [PERSON] et al. [47], the thermal conductivity of a beeswaketradecanol/expanded perlite composite increased from 0.443 W/(m\(\cdot\)K) to 1.245 W/(m\(\cdot\)K) because of the addition of 2% CF. Besides this, [PERSON] et al. [48] incorporated 5% CF into an acetyl alcohol/high density polyethylene composite, and the resulting thermal conductivity increased from 0.2125 W/(m\(\cdot\)K) to 0.4719 W/(m\(\cdot\)K). Furthermore, with the increase in DP addition, the density of the gypsum decreased, as shown in Figure 12. When incorporating 20 wt. % DP, the density of gypsum decreased from 1902.3 kg/m\({}^{3}\) to 1626.7 kg/m\({}^{3}\). Therefore, the use of diatomic/paraffin/gypsum composites can also reduce the weight of the whole building. Figure 13 demonstrated that the specific heat capacity increased along with the addition of DP. This is due to the higher specific heat capacity of the incorporated DP in comparison to pure gypsum. Specifically, the specific heat capacities of G-C, G-10% DP, G-10% DP-1% CF, G-20% DP and G-20% DP-1% CF were 0.93 J/(g\(\cdot\)C), 1.10 J/(g\(\cdot\)C), 1.05 J/(g\(\cdot\)C), 1.31 J/(g\(\cdot\)C) and 1.28 J/(g\(\cdot\)C) at 15 \({}^{\circ}\)C, respectively. At 35 \({}^{\circ}\)C, the values were 0.99 J/(g\(\cdot\)C), 1.13 J/(g\(\cdot\)C), 1.12 J/(g\(\cdot\)C), 1.32 J/(g\(\cdot\)C) and 1.31 J/(g\(\cdot\)C), accordingly.  
Figure 10: SEM images of microstructure of the composites: (**a**) the 20% diatomic/paraffin/gypsum; (**b**) gypsum with 1% carbon fiber; and (**c**) gypsum with 20% diatomic/paraffin and 1% carbon fiber.  
Figure 11: The thermal conductivity of diatomic/paraffin/gypsum composites.  
Figure 12: The density of diatomic/paraffin/gypsum composites.
## 3 Results and Discussion  
### Changes in pH, as Well as Lactic Acid, Reducing Sugar, and Uronic Acid Contents, During Pickling  
#### 3.1.1 Changes in pH Values During Pickling  
When talking about pH values changes during fermentation, it is compulsory to relate them to the interdependence between several factors, such as reducing sugar content, lactic bacteria count, and lactic acid production.  
As can be remarked in Figure 2, the pH of raw apples, with an average of 7.95 \(\pm\) 0.005, was significantly (\(p<\) 0.05) decreased by the fermentation process, independent of the type of brine. Thus, similar changes in pH values occurred with NaCl (from 8.053 \(\pm\) 0.003 for the raw sample to 4.036 \(\pm\) 0.003 on the 35 th day of fermentation), KCl (from 8.166 \(\pm\) 0.005 for the raw sample to 4.209 \(\pm\) 0.003 on the 35 th day of fermentation), and MgCl\({}_{2}\) (from 7.658 \(\pm\) 0.02 for the raw sample to 3.381 \(\pm\) 0.003 on the 35 th day of fermentation). The greatest decrease was registered on the 7 th day of fermentation, of almost 31-37%, which is expected due to the intensity of the first stage of the fermentation process, when lactic acid bacteria are producing lactic acid at a rate of 10% of their weight per minute, according to [28]. Thus, a pH value between 5.5 and 6.5 for the first stage of fermentation could prevent both metabolic repression and the loss of bacterial viability as a result of the extremely acidic pH [29].  
From the 14 th day of fermentation to the 35 th day, only a similar slight decrease was observed for all the tested samples, except the samples fermented in MgCl\({}_{2}\) brine, which registered an almost 25% decrease in pH value from the 14 th to the 21 st day of fermentation, which could be associated with the type of salt used.  
From the first stage until the middle stage of fermentation, lactic acid bacteria, by using the fruit's native fructose and glucose, produce lactate, acetate, and a small amount carbon dioxide, which could be related to the decrease that occurred in pH values, as [PERSON] et al. reported as well [30].  
Moreover, the changes in fermented apples pH values could be attributed to modification of the ratios between populations belonging to different categories of microorganisms present during the spontaneous fermentation process [31].
# An Audio Watermarking Algorithm Based on Adversarial Perturbation +
## 5 Summary  
In this paper, we propose an audio watermarking algorithm, AAW, based on adversarial perturbation. The AAW relies only on a pre-trained differentiable decoder instead of an encoder-decoder framework for watermark embedding and extraction. The watermark embedding is realized by adding tiny perturbations to the host audio. In addition, we adopt an attack simulation layer and a whitening layer to improve the performance of the AAW algorithm. Finally, the experimental results demonstrate that the proposed algorithm is effective and has a performance advantage over existing deep learning-based algorithms.  
However, our work still requires improvement. First, compared to an encoder-decoder deep watermarking framework, the AAW is computationally expensive since it is not a single pass forward. In addition, the AAW algorithm is not yet resistant to WGN and some non-differentiable attacks such as lossy compression. In future works, we hope to be able to propose solutions to improve the above problems.  
Conceptualization, S.W. and Y.H.; methodology, S.W.; Software, S.W.; validation, S.W., Y.H., and H.G.; formal analysis, S.W.; investigation, S.W.; resources, S.W.; data curation, S.W.; writing--original draft preparation, S.W.; writing--review and editing, S.W., Y.H., H.G., and S.Z.; visualization, S.W.; supervision, S.Z. and J.L.; project administration, H.G., S.Z., and J.L.; funding acquisition, S.Z. and J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Program of China (2022 YFF0901900) and the Key R&D Program of Shanxi (202102010101004).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**SWR (dB)**} & \multicolumn{8}{c}{**BDR (\%)**} \\ \cline{3-10}  & & **CLP** & **WGN** & **LPF** & **HPF** & **CRP** & **RVB** & **DEN** & **REC** \\ \hline
**AAW-CN** & 37.64 & 98.62 & 58.27 & 96.29 & 94.78 & 90.97 & 96.11 & 86.02 & 81.43 \\
**AAW-CL** & 37.53 & 98.93 & 58.75 & 97.13 & 95.20 & 93.47 & 98.19 & 89.22 & 83.29 \\
**[PERSON]** [38] & 30.17 & 100 & 0.21 & 0.83 & 1.26 & 2.18 & 1.07 & 0.36 & 0.13 \\
**HiDDeN-A** & 28.24 & 88.73 & 47.93 & 68.49 & 71.46 & 79.78 & 52.48 & 74.51 & 58.22 \\
**[PERSON]** [9] & 26.14 & 98.84 & 86.92 & 94.43 & 95.65 & 60.24 & 63.41 & 74.66 & 91.87 \\
**[PERSON]** [13] & 24.55 & 99.38 & 98.42 & 99.37 & 52.67 & 50.11 & 49.52 & 76.33 & 47.29 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The performance of proposed and other algorithms.  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Acknowledgments. It was also the research achievement of the Beijing Digital Content Engineering Research Center and the Key Laboratory of Digital Rights Services.
# A Validated Thermal Computational Fluid Dynamics Model of Wine Warming in a Glass
## 2 Problem Configuration and Experiments
### Experimental Setup and Procedure  
The experimental study investigated the warming of red wine, white wine, and tap water at \(T_{\text{R}}=23\,^{\circ}\text{C}\). Two experiments were performed according to the configuration described above to obtain comprehensive reference data for validation. In the first experiment, the average temperature of wine and water was measured after predefined periods. The second one determined the temperature increase over time of water at specified locations inside the glass. The experiments included tap water to simplify the comparability to the simulation for detailed validation and to investigate the liquid composition's influence.  
Figure 1a shows the experimental setup. A Greisinger resistance thermometer with a platinum sensor (Pt1000, accuracy \(\pm 0.1\,^{\circ}\text{C}\) for \(T\in[-20\,,\,150]\,^{\circ}\text{C}\)) was used as in similar experiments related to food [18; 19] and wine [20]. A mounting device fixed the thermometer sensor in a vertical direction above the glass. The sensor tip was positioned inside the liquid (wine or water) and could be moved in a vertical direction. The diameter of the cylindrical sensor was 1.5 mm. The mass of the sensor was relatively small compared to the mass of the wines, which allowed the assumption of negligible heat transfer from the thermometer to the liquid. The mounting device was designed to influence the physical  
Figure 1: Experimental setup and CAD model of a wine glass with 150 mL of wine. (**a**) Experimental setup with a Greisinger thermometer (Regenstauf, Germany), the PT 1000 sensor is positioned in the center of the wine glass. (**b**) CAD model (dimensions in mm).  
processes as little as possible. The glass was placed in the middle of a spruce wooden board, with the dimensions \(800\times 400\times 18\) mm. The examined red wine was from Primitivo grapes containing 13.5 % of volume alcohol, 10.7 g L\({}^{-1}\) (grams per liter) residual sugar, and 6.8 g L\({}^{-1}\) acidity. The white wine was a Cuveee with 11.5% of volume alcohol, 11.8 g L\({}^{-1}\) residual sugar, and 6.5 g L\({}^{-1}\) acidity, as communicated by the producers.  
Before starting the experiments, the wine and tap water were kept cool in a refrigerator at 6 \({}^{\circ}\)C. 150 mL of the corresponding liquid were poured into a calibrated measuring cylinder, with an accuracy of \(\pm 2\) mL. The liquid was slowly heated until it nearly reached the required initial temperature to account for the temperature increase during the pouring. The small temperature change was caused by interaction with the air and was about 0.2 \({}^{\circ}\)C for \(T_{0}=12\,^{\circ}\)C. The liquid was poured into the glass, where the thermometer sensor was placed to verify the correct initial temperature immediately after finishing the pouring process. The timing started as soon as the liquid had been poured into the glass.
## 2 Methodology
### End-of-Life Modeling  
Three practical end-of-life (EoL) routes [landfill baseline (BAS), open-loop recycling (OLR) and closed-loop recycling (CLR)] were evaluated for PV modules and Li-ion batteries.  
(i) BAS--disposal to an engineered Class-II landfill.  
(ii) OLR--mechanical/thermal delamination with recovery of glass and aluminum.  
(iii) CLR--hydrometallurgical recycling that also recovers silver paste (PV) and Li, Ni, Co salts (battery).  
Open loop glass and aluminum recovery is already practiced at the RenewSys PV glass facility in Gujarat (5000 t/yr, commissioned in 2023) [27]. Closed-loop hydrometallurgical recycling of Li-ion cells is technically feasible as Attero CleanTech's Karnataka plant reached 15,000 t/yr capacity in 2024 [28], and India's draft E-Waste Rules [29] target a 30% panel-collection rate by 2026.
## 4 Experiments and Results  
This section presents the experiments and results obtained by optimizing the dynamic balance of a six-bar Watt linkage modeled using _FCC_. It is organized into three subsections to facilitate the analysis and interpretation of the performance of different optimization algorithms and the sensitivity of key features.  
Section 4.1 details the physical properties of the six-bar Watt linkage in its uncompensated state (i.e., without counterweights). Section 4.2 analyzes the performance of various optimization techniques, encompassing single-objective and multi-objective algorithms. Finally, Section 4.3 presents a sensitivity analysis of the solutions identified by the most effective algorithm.
# Realistic Simulation for Body Area and Body-To-Body Networks
## 3 Proposed Simulator  
The WSNet simulator has been adapted to BAN and BBN contexts in order to fulfill the upper layers' requirements (_i.e._, MAC, network and application layers) as shown in Figure 1. Importantly WSNet is adapted with respect to the multi-scale dynamics of inter-BAN multi-link channels.  
In this paper, we describe two approaches computing the received signal strength (RSS) in which we focus on joint mobility and propagation models _i.e._, deterministic bio-mechanical modeling (blue approach) and semi-deterministic stochastic mobility modeling (green approach). Once the RSS is computed according to the considered propagation models and parameters, the propagation modules return the evaluated RSS to the WSNet simulation core, which, according to the current concurrent transmissions, will evaluate the corresponding SINR (signal-to-interference plus noise ratio), BER (bit error rate) and PER (packet error rate). The BER between the transmit node \(i\) and receive node \(j\) is computed based on the current SINR level at time \(t\) (_i.e._, \(SINR_{ij}^{t}\)), and the considered physical layer characteristics (e.g., data rates and modulation schema) are as follows:  
Figure 1: Adaptation of the WSNet simulator to BAN/body-to-body networks (BBN) networks.  
\[BER_{ij}^{t}=\begin{cases}0.5\times e^{-Eb/No}&\text{DBPSK}\\ Q(\sqrt{4^{Eb/No}}\times sin(\sqrt{2}\times\pi/4))&\text{DQPSK}\end{cases} \tag{1}\]  
where \(Eb/No\) is the energy per bit-to-noise power spectral density ratio in dBm which is computed based on the current SINR level as:  
\[Eb/No[dB]=SINR_{ij}^{t}[dB]+10\times log_{10}(BW/R) \tag{2}\]  
where \(BW\) is the bandwidth in Hz and \(R\) is the data rate in bps.  
Finally, the PER between the transmit node \(i\) and receive node \(j\) can be evaluated as:  
\[PER_{ij}=1-(1-BER_{ij}^{t})^{n} \tag{3}\]  
where \(n\) is the packet length in bits and \(BER_{ij}^{t}\) is the corresponding BER.
## 3 Results and Discussion
### EAE as a Pretreatment Method of CEM
EAE has several advantages regarding the extraction of bioactive compounds; however, it encounters potential commercial and technical shortcomings. The cost of enzymes could be comparatively expensive if processing with a high ratio of enzyme to substrate in large scale or industrial-scale production. Moreover, EAE can be challenging to scale up in the industry because enzymes behave differently under different environmental conditions (such as temperature and pH), and temperature and pH could vary inside an industrial-scale extractor [34; 35]. On the other hand, as it is shown by the results of the present work, enzyme pretreatment of rosmenary leaves can offer extracts of better quality regarding phenolic content and antioxidant activity; thus, the cost of the enzyme can be compensated to a certain extent if an industrial application is considered.
# Implementation of an Attribute-Based Encryption Scheme Based on SM9
## 3 Implementation and Evaluation of SM9 Identity-Based Encryption
### ABE Scheme Based on SM9 IBBE
#### 4.2.1 Setup (\(1^{k}\))  
Given a security parameter \(k\), this algorithm calls the Setup algorithm of SM9 scheme and it sets the ABE scheme's master public key \(M_{pk}\) and the master secret key \(M_{sk}\).  
\[\left(M_{pk},M_{sk}\right)\gets SM9.\textbf{Setup}\Big{(}1^{k}\Big{)} \tag{5}\]
## 4 Results and Discussions
### Impact of Operational Parameters on the Performance of the Air Compressor  
#### 4.2.1 Total Power Consumption  
The influence of operational parameters on the total power consumption of the two-stage centrifugal air compressor is depicted in Figure 7. The research findings indicate that the total power consumption increases with a higher rotational speed and intake air temperature. It is noteworthy that with an increase in the pressure ratio, the total power consumption initially rises before subsequently decreasing. It is noteworthy that the total power consumption increases first and then decreases with the increase in the pressure ratio. This means that a specific pressure ratio exists at which the total power consumption of the air compressor reaches its maximum, which is undesirable for achieving low-energy operation of the compressor and should be avoided as much as possible. In this experiment, the pressure ratio corresponding to the maximum energy consumption is 2.3. Considering the sensitivity analysis of operational parameters on total power consumption, the primary step would be to decrease the rotational speed to reduce the energy utilized by the air compressor.
## 1 Introduction  
Over the past few decades, chalcogenide phase change materials have received increased attention for next-generation non-volatile memory [1; 2; 3] and high density optical recording [4; 5; 6]. Typically, a chalcogenide material has two or more discrete states at which it exhibits distinguishable material properties. The change in the state is driven by thermal excitation, usually via an electrical or optical pulse. The significant difference between these states in electrical and optical properties upon the reversible switching allows storing the rewritable digital bit information. The most ubiquitous phase change material, GeSbTe (germanium-antimony-tellurium or GST), is a ternary compound consisting of germanium, antimony, and tellurium that is capable of reversibly switching at high speeds between its amorphous and crystalline states in response to thermal excitation. The crystallization temperature of the alloy is between 100 \({}^{\circ}\)C and 150 \({}^{\circ}\)C and the melting point is about 600 \({}^{\circ}\)C (873 K). Due to the non-volatility and high stability of both states, chalcogenide phase change materials have been used in rewritable optical recording media for years [7; 8; 9]. In the optical recording media application, a laser with controllable intensity and pulse duration is used to interact with the material, namely, heat a small volume to switch the material between crystalline and amorphous states. The information is then stored in the reflectivity of the phase change material layer. For electronic memories, even though Flash memory is the currently leading technology for non-volatile memory devices, the next generation of memory requires even higher speeds for write and erase processes, while maintaining high endurance, good scalability, low cost, and high power efficiency. With developments in lithography and discoveries in chalcogenide compounds, recently GST emerged as an important candidate for the electronic nonvolatile memory devices [10; 11; 12; 13]. Within longer-term prospects, new proposals and early demonstrations have emerged for exploiting the optical properties of GST for dynamic light modulation applications. By utilizing the difference in optical properties between different phases, researchers have developed GST-based reconfigurable light modulators [14], optical limiters [15], optical switches [16], and polarizing reflectors [17].  
In this review paper, we start with a description of basic GeSbTe alloys and their phase switching properties, material properties including the electrical, structural and optical (Section 2). Non-volatile phase change memory devices are reviewed in Section 3.1. Light modulators that utilize the contrast of the optical constants are discussed in Section 3.2. Even though GST phase change material shows promising in both non-volatile memory and optical modulators, it is necessary to modify the electrical property for power efficiency, high-speed operation, and stability. Therefore, in Section 4, doping mechanisms, and their effects on the material response are reviewed. Finally, fabrication methods of GST and doped-GST are discussed in Section 5.
# You Only Attack Once: Single-Step DeepFool Algorithm
## 5 Discussion and Future Work
### Discussion on Experiments  
We carefully designed a series of experiments to evaluate the effectiveness of the YOAO (You Only Attack Once) algorithm. Initially, we conducted a thorough comparison between YOAO and other advanced algorithms, assessing their performance in terms of attack success rates, attack times, and L2 norm perturbation sizes. The experiments confirmed that the YOAO algorithm performs better than other algorithms, achieving not only a higher success rate in attacks, but also quicker attack times.  
Subsequently, we conducted a detailed comparison between the YOAO algorithm and DeepFool algorithms across different datasets and classifiers, validating the reasons behind YOAO's superior attack efficiency. This further emphasizes the importance of optimizing the search for the maximum gradient differences, while also confirming that classes with the greatest gradient differences from the original sample are more likely to impact the classifier's decisions.  
Lastly, we visualized and compared the adversarial samples created by YOAO algorithms and DeepFool algorithms. While YOAO algorithms introduced more changes to the pixels in the generated adversarial samples, we noticed that the perturbation size remained within a small range, making it undetectable to the human eye. This observation underscores the strong attack capabilities of YOAO algorithms, as the perturbations, although slightly higher than those of DeepFool algorithms, are still subtle enough to avoid visual detection.
# A Study of 5G Edge-Central Core Network Split Options
## 2 Related Work
The core network functions are depicted twice at edge and at central to illustrate that they can be placed at either location. A specific placement model was proposed by ETSI Mobile Edge Computing (MEC) standardization group [6]. It presumes that a single UPF is placed at the edge of the network to enable a localized data path. Albeit this model provides significant advantages for some of the use cases, other alternatives should be considered as we propose in the next section. Another option to be considered is inter-operator roaming, where one visited operator offers the access and mobility services based on the home operator authorization and expected session management rules [7]. This can be applied for the edge-central split model considering that the edge network is a separate administrative domain with its own functioning policies.