esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review and Theoretical Foundation
### Antecedents to Information Disclosure
#### 2.3.1 Elaboration Likelihood Model  
The Elaboration Likelihood Model (ELM) comes from the psychology literature and helps explain how humans process information cognitively and are persuaded when presented with different stimuli [28]. The main idea of this model is that people process information with two routes (or paths), the central and peripheral paths. The central route represents \"the processes involved when elaboration likelihood is high\", whereas the peripheral route is the \"processes operative when elaboration likelihood is low\" [28] (p. 674). When elaboration likelihood is high, issue-relevant thinking, such as careful consideration of the true benefits of the information presented, will predict the recipient's response to the stimuli [85]. When elaboration likelihood is low, factors other than reasoning come into play, and cues (e.g., credibility and attractiveness of the stimuli, quality of the message) tend to be the more important determinant of persuasion [85].  
A common example to explain the ELM is the purchase of a car. Some consumers might base their choice based on the fuel efficiency of the car, its reliability, and price information given by their car dealership, while others might be convinced to opt for the sporty car that comes in a flashy red color and will impress their friends. In this case, the former is known to use the central, more rational, route to information processing, while the former uses the peripheral route by basing their choice on fewer informational and more emotional cues about the car.  
In sum, ELM helps explain how people are persuaded. Persuasion occurs when a persuader is successful in influencing a person in a certain way. Based on ELM theory, we leverage cues in user-chatbot interactions to inform users that they are about to disclose certain types of information. These cues could consequently influence users' information sharing behaviors with the chatbot. The _Nudge Theory_[31] and [PERSON]'s [30]_Persuasion Theory_ presented below provide the theoretical foundation to inform the design of chatbot design elements that we call _information disclosure nudges_ for this research.
## 1 Introduction
### Motivation  
A plethora of research efforts have examined privacy concerns in federated learning. These studies encompassed various aspects and topics including foundational concepts [15; 16; 17], identification of threats and corresponding solutions [18; 19; 20; 21], exploration of privacy techniques [22; 23], and applications within healthcare [24; 25], as well as communications and mobile networks [26; 27]. The highlights and the key concepts included in these studies are listed in Table 1. While these works offer comprehensive surveys of techniques, they often do not delve into the detailed application of these techniques as evidenced in the literature. Furthermore, with the exception of [22], these studies have largely overlooked the hybrid application of privacy methods where multiple techniques are employed in concert.  
The authors of [22] did acknowledge different combinations of techniques in their work, but they did not closely examine the specific methods by which these techniques are  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ref**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Attacks**} & \multicolumn{4}{c}{**Defenses**} \\  & & & **DP** & **HE** & **Hybrid (DP + HE)** & **Detailed Methods and Strategies** \\ \hline
[17] & 2019 & & & ✓ & ✓ & \\
[18] & 2020 & ✓ & & & & \\
[23] & 2020 & & ✓ & ✓ & & \\
[24] & 2020 & ✓ & ✓ & ✓ & & \\
[26] & 2020 & ✓ & ✓ & ✓ & & \\
[21] & 2020 & ✓ & ✓ & ✓ & & \\
[20] & 2021 & ✓ & ✓ & ✓ & & \\
[16] & 2021 & ✓ & ✓ & ✓ & & \\
[22] & 2022 & ✓ & ✓ & ✓ & ✓ & \\
[19] & 2023 & ✓ & ✓ & & & \\
[25] & 2023 & & ✓ & ✓ & ✓ & \\ Ours & 2023 & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with related surveys.  
employed. In contrast, our paper focuses on the intersection of homomorphic encryption (HE) and differential privacy (DP) within the framework of federated learning. The factors and parameters we take into account while comparing our work with previous studies are the specific attacks and defenses they discuss. Another aspect we consider is how thoroughly they explain their methods and strategies. We delve into the advantages and drawbacks of combining these techniques, as we think that this combination could lead to better privacy-preserving federated learning systems. This would allow us to leverage the unique strengths inherent to each individual technique.
## 5 Results and Discussion
### RBC Volume Fraction (VF)  
The two-phase mixture theory model of RBCs and plasma was primarily simulated with 45% hematocrit concentration. The volumetric fraction counters of RBC for the rigid (top) and FSI (bottom) models during the systolic and diastolic phase of the cardiac cycle is shown in Figure 10 on planes 1-12 revealed in Figure 3. The cross-section plans across the normal, pre, mid, and post stenosis for 70%, 80%, and 90% of the area of stenosis were plotted. It was determined that the volume fraction of RBC increases across the stenosis region for the peak diastolic as compared to the systolic phase of the cardiac cycle. The volume fraction of RBCs reduces as the velocity was found to increase at the pre stenosis region compared to the mid and post stenosis region. The wall shear stress across the stenosis region was increased significantly with increases in the RBCs volume fraction which can be observed in Figure 10 for both the rigid and FSI models.  
Figure 10: RBC volume fraction across the stenosis with degrees: 70%, 80% and 90% in left coronary artery (LCA) during systolic and diastolic phase of cardiac cycle within a rigid model (top) and FSI model (bottom) on planes 1–12.
## 5 Conclusions  
In this letter, the idea of interference spreading and its effect on BER performance is studied in detail. The interference spreading occurs because of a random subcarrier allocation method in a CRN. To evaluate the BER performance, theoretical expressions for SINR (with subcarrier collision and no-collisions), BER and mean BER under different modulation orders were subsequently derived and analyzed. The performance of CRN with uniform random allocation of subcarriers resulted in the SUs interference being uniformly distributed among the all the different PUs, thereby not degrading any PU completely. This type of interference will result in a better performance in a CRN overall, and does not require any energy-draining sensing mechanism in CRN.  
Methodology, analysis, investigation, writing--original draft preparation: A.K., A.P., S.E.; writing--review and editing: M.I., A.I.; supervision: S.E. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Science Foundation under Grants 1923295 and 1923669.  
We would like to thank the anonymous reviewers for their feedback in improving the quality of the manuscript, and also like to thank the Editor for handling the manuscript.  
The authors declare no conflict of interest.  
Figure 8: Mean BER of M-QAM with \(M=4,16,64\) and \(256\) and \(q=17\) dB.  
Figure 7: Comparison of average BER for 16-QAM with different values of interference temperature (\(q=10,20,30\) dB).
## 2 Materials and Methods
### Fatty Acid Analysis  
Intramuscular fatty acid methyl esters (FAMEs) were analysed following the method proposed by [PERSON] et al. [15]. A sample of approximately 1 g of LTL was thawed and saponified in 6 mL of 5M KOH in methanol:water (50:50 \(v/v\)) with hydroxyquinone (1 g L\({}^{-1}\)) at 60 \({}^{\circ}\)C for 1 h, after flushing with nitrogen, after which the mixture was diluted with 12 mL of 0.5% NaCl and 5 mL of petroleum ether and the non-saponifiable fraction was removed. To neutralise the KOH, 3 mL of glacial acetic acid was added. Double petroleum ether clearance was used to extract the FAMEs, with nitrogen used to evaporate the solvent. The extracted FAMEs were then methylated using 200 \(\upmu\)L of TMS-DM in methanol:toluene (2:1, \(v/v\)) at 40 \({}^{\circ}\)C for 10 min, dried under nitrogen and dissolved in 1 mL of n-hexane containing 50 ppm of butylated hydroxy toluene. The samples were centrifuged at 15,000 rpm for 5 min and the supernatant was transferred for analysis. The separation of FAMEs was carried out using a gas chromatograph Agilent 6890N Network GS System (Agilent, Inc., Santa Clara, CA, USA) equipped with a flame ionisation detector (FID) and fitted with a HP-88 capillary column (100 m, 0.25 mm i.d., 0.2 \(\upmu\)m film thickness, Agilent Technologies Spain, S.L., Madrid, Spain). The samples were automatically injected using an HP 7683 injector. The chromatographic conditions were as follows: initial column temperature 100 \({}^{\circ}\)C, programmed to increase at a rate of 30 \({}^{\circ}\)C/min to 158 \({}^{\circ}\)C and then at 1.5 \({}^{\circ}\)C/min to 190 \({}^{\circ}\)C, maintaining this temperature for 15 min, then at 2 \({}^{\circ}\)C/min to 200 \({}^{\circ}\)C and then increasing again at 10 \({}^{\circ}\)C/min to a final temperature of 240 \({}^{\circ}\)C for 10 min. The injector and detector were kept at 300 and 320 \({}^{\circ}\)C, respectively. Hydrogen was used as the carrier gas at a flow rate of 2.7 mL/min. The split ratio was 17.7:1, and 1 \(\upmu\)L of solution was injected. Nonadecanoic acid methyl ester (C19:0 ME) at 10 mg/mL was used as an internal standard. Individual FAMEs were identified by comparing their retention times with those of authenticated standards from Sigma (Sigma Chemical Co., Ltd., Poole, UK).  
The fatty acid profile of the intramuscular fat was expressed as a percentage of the total fatty acid detected.
## 1 Introduction  
SACLA, SPring-8 Angstrom Compact free electron LASer, is an X-ray Free Electron Laser (XFEL) facility at SPring-8, Japan. It was inaugurated in March 2012 [1], becoming the second XFEL facility in the world, following the 2009 inauguration of LCLS, the Linac Coherent Light Source, at SLAC in the US [2]. The LCLS and the European XFEL [3] at DESY in Germany, which were first proposed in the 1990s, utilize high-energy linacs with beam energies around 15 GeV and lengths of a few kilometers to produce short wavelength XFEL radiation. In contrast, SACLA was designed as the first compact XFEL facility to produce brilliant and stable XFEL radiation with substantially lower costs for construction and operations. To enable this, we employed unique accelerator technologies: a low emittance injector with a thermionic electron gun (e-gun) and a velocity bunching system; a high-gradient normal-conducting C-band linac; and a short-period in-vacuum undulator [4]. Combining these devices with state-of-the-art X-ray optics [5; 6], we have steadily generated XFEL light for users more than 4000 h in FY2016, allowing researchers to produce a number of important scientific results in the fields of biology [7; 8; 9; 10; 11; 12], chemistry [13; 14; 15; 16], materials science [17; 18; 19; 20], high-energy density science [21], and non-linear X-ray optics [22; 23; 24; 25; 26]. The success of SACLA has promoted the development of similar compact XFEL facilities, such as SwissFEL at Paul Scherrer Institut in Switzerland [27].  
In parallel to conducting user operations, we have continued to upgrade the facility. One of the most critical demands from users is to increase beam time, which recently led us to construct new beamlines and to develop an innovative scheme to switch XFEL over multiple beamlines in a pulse-by-pulse manner [28]. We have also developed various new beamline/end-station instruments and data acquisition/analysis systems to advance experimental capabilities [29; 30; 31; 32; 33; 34; 35; 36; 37].  
Over the years, we have published several articles about the SACLA facility. In [1], we reported the concept, design, and initial performance of SACLA. [PERSON] et al. also reported the initial beamline design and performance [5]. In [6], we provided updated information on beam performance, beamline instruments, and early scientific highlights. In this article, we report the current status of SACLA, including recent scientific achievements and new capabilities enabled in early 2017. In Section 2, we summarize the typical performance of a hard X-ray FEL beamline, BL3, with the updated design. Section 3 describes recent scientific highlights and the advanced technologies that enable these achievements. In Section 4, we review the construction and operation of two new beamlines, BL2 and BL1. Finally, Section 5 presents our plans for the future.
# Development of a Two-Dimensional Hybrid Sediment-Transport Model
## 3 Numerical Method  
The governing equations of the flow (Equations (1)-(3) and sediment transport (Equations (7) and (11)) were discretized using the finite volume method (FVM) on a collocated unstructured hybrid-mesh system with mixed-triangle and quadrilateral cells. Compared to the structured-mesh system, the unstructured mesh is more suitable for geometrically complex domains with higher adaptivity.
## 3 Results and Discussion
### Model Fitting Results
\begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **Hyper-Parameters** \\ \hline Feed forward neural network (FFNN) & 3 layers (input, hidden, and output), tan sigmoid activation function, resilient backpropagation training algorithm, and 3 nodes in the hidden layer. \\ Bagged decision tree & Employed number of trees = 1000; learning rate = 0.001 \\ Boosted decision tree & Employed number of trees = 5000; learning rate = 0.001 \\ Gradient boosted decision tree & Employed number of learning cycles and bins = 100 and 50. \\  & boosting method = least square. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Machine learning model hyper-parameters. Figure 6: DT model output comparison: (**a**) Scatter plot for the training dataset; (**b**) scatter plot for the testing dataset; and (**c**) estimated vs. DT models predicted GHG emissions. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Stage** & **RMSE (GtCO2e)** & **MAPE (GtCO2e)** & **RSR** & **WIA** & **R2** \\ \hline \multirow{3}{*}{Bagged decision tree} & Training & 0.87 & 0.31 & 0.454 & 0.95 & 0.898 \\  & Testing & 0.84 & 0.29 & 0.438 & 0.95 & 0.901 \\ \cline{1-1}  & Training & 0.98 & 0.33 & 0.508 & 0.94 & 0.873 \\ \cline{1-1}  & Testing & 0.94 & 0.33 & 0.489 & 0.94 & 0.878 \\ \cline{1-1}  & Training & 1.01 & 0.34 & 0.527 & 0.93 & 0.864 \\ \cline{1-1}  & Testing & 1.00 & 0.35 & 0.523 & 0.93 & 0.862 \\ \cline{1-1}  & Training & 1.18 & 0.43 & 0.612 & 0.91 & 0.878 \\ \cline{1-1}  & Testing & 1.13 & 0.39 & 0.588 & 0.91 & 0.879 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical performance measures for employed decision tree models. Similarly, [PERSON] et al. [64] used a gradient-boosting regression tree to assess the effects of spatial life cycle on climate change and eutrophication. They used the production of corn in the Midwest as an example. The findings indicated that the gradient-boosting regression tree model that was created with monthly meteorological data provided the highest level of accuracy in terms of forecasting the life-cycle impact of global warming as well as life-cycle eutrophication. In a similar vein, [PERSON] et al. [65] employed a gradient-boosting tree prediction model optimized using the enhanced whale algorithm to successfully forecast China's carbon emission levels from 2020 to 2035.
# Research on Cavitation Characteristics of Two-Throat Nozzle Submerged Jet
## 4 Conclusions  
In order to investigate the cavitation characteristics of the two-throat nozzle submerged jet, high-speed photographic visualization experiments and erosion experiments were conducted. The FDM was used to analyze the cavitation cloud in a single period under different inlet pressures. Moreover, macroscopic and microscopic evaluation of cavitation characteristics of the two-throat nozzle submerged cavitation jet were carried out under different target distances and impact times. According to the results of this study, there are the following key points:  
1. The two-throat nozzle submerged jet cavitation cloud has obvious periodicity. With the increase in inlet pressure, the length, width, and area of the cavitation cloud increased, and the period of the cavitation cloud was slightly prolonged.
2. With the increase in target distance, the cavitation intensity was enhanced and then weakened, and the effect of the high-speed water jet continued to decrease. In addition,  
Figure 12: Variation in RMS roughness of the sample surface with target distance.  
Figure 13: Variation in skewness with target distance.  
the cavitation cloud fully developed and collapsed near the sample surface to achieve the maximum cavitation effect when the target distance was 30 mm.
* As the impact time increased, the mass loss of the sample gradually increased. The mass loss rate first increased, then decreased, and finally stabilized.
* From a microscopic point of view, the large cavitation pits were highly overlapping regions of cavitation collapse. The magnitude of the absolute values of the RMS surface roughness and surface skewness increased with the increase in cavitation intensity.  
Conceptualization, writing, review and editing, project administration, funding acquisition, J.D.; investigation, validation, formal analysis, S.L. and R.M.; methodology, X.Z.; data analysis, S.L.; project administration, funding acquisition, X.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 51979022, and Fundamental Research Funds for the Central Universities, grant number 3132019330.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### 2D CNN for EEG Classification  
To classify the dataset (original and synthetic data), we adopted the methodology from previous work [31], which demonstrated success in classifying EEG data. The 2D-CNN model employed in this study was specifically designed to classify mental states. The raw EEG data are preprocessed via their segmentation into overlapping windows of one second to extract relevant features. Each window is transformed into a 2D matrix using either a continuous wavelet transform (CWT) or a discrete wavelet transform (DWT), depending on the specific configuration. From the five signals extracted (\(\alpha,\beta,\theta,\delta,\gamma\)), statistical features such as the mean, skewness, kurtosis, maximum, and minimum values are extracted. Additionally, frequency components are derived through fast Fourier transform (FFT) analysis. These extracted features serve as inputs to the CNN model. The CNN architecture consists of two convolutional layers with 32 and 64 filters, respectively, followed by max-pooling and dropout layers for regularization. The pooled feature maps are flattened and passed through dense layers with ReLU activation. The final layer employs a SoftMax activation function to classify the input into three distinct categories. This architecture effectively captures the temporal and spectral characteristics of EEG signals, enabling accurate mental state classification. It is important to note that the final 2D-CNN architecture consists of five CNNs, as aforementioned, one for each signal, followed by its feature extraction, and then the five CNNs are merged in the flatten layer for the final classification.  
```
19:Convolutional Layer 2:
20:for each filter \(f\in\text{Conv}2\)do
21:for each pixel \((i,j)\) in the output from Conv1 do
22:\(Z_{i}^{(2)}=\text{ReLU}\Big{(}\sum_{m=1}^{3}\sum_{n=1}^{3}W_{mn}^{(2)}Z_{(i+m)(j+ n)}^{(1)}+b^{(2)}\Big{)}\)
23:endfor
24:endfor
25:Max-Pooling Layer:
26:for each pooling region \((i,j)\)do
27:\(P_{ij}=\max_{p,q\in[0,1]}\Big{(}Z_{(i+p)(j+q)}^{(2)}\Big{)}\)
28:endfor
29:Dropout Layer (0.25): Apply dropout with a 25% probability during training
30:Flatten Layer: Flatten the pooled output \(P_{ij}\) into a 1D vector
31:endfor
32:Step 3: Concatenation Layer Concatenate the flattened outputs from all 5 CNNs:
33:\(C=[F_{a},F_{\beta},F_{\theta},F_{\delta},F_{\gamma}]\)
34:Step 4: Fully Connected (Dense) Layer
35:for each unit \(u\) in the Dense Layer (512 units) do
36:\(Y_{u}^{(l)}=\text{ReLU}(W_{u}^{(l)}\cdot C+b_{u}^{(l)})\)
37:endfor
38:Step 5: Dropout Layer (0.5) Apply dropout with a 50% probability
39:Step 6: Output Layer (Dense + SoftMax)
40:for each class \(i\) (concentrated, neutral, relaxed) do
41: Compute the SoftMax probability: \(\hat{y}_{i}=\frac{\exp(z_{i})}{\sum_{j=1}^{3}\exp(z_{j})}\)
42:endfor
43:Step 7: Classification
44:return: The class with the highest probability = \(\arg\max(\hat{y}_{i})\)
```  
**Algorithm 2**_Cont._
## 5 Energy Absorption and Dissipation of the SCP and Soil
### Total Strain Energy (ALLSE) of the Pile  
Internal strain energy dissipation within SCP is a crucial mechanism for resisting seismic energy-induced damage in the foundation. Figure 12 shows the total strain energy of a single pile. When \(\theta_{1}=45^{\circ}\) and \(\theta_{3}=90^{\circ}\), the strain energy of the pile only undergoes the \(\Delta e_{1}\) energy change process. This leads to the maximum strain energy near the top of the pile. The fixed connection between the pile top and the immersed tunnel structure results in a top-heavy structural mass distribution, intensifying the seismic forces on the top of the pile. Conversely, at \(\theta_{2}=60^{\circ}\), there are both strain energy increase (\(\Delta e_{1}\)) and decrease (\(\Delta e_{2}\)) processes. This distribution of mass, with more mass concentrated at the top, causes reduced strain energy both at the top of the pile and at the end of the pile. The presence of the triangular prism damping units impacts this variation. It partially cancels the seismic wavefront, reducing the energy transmitted to the top. The larger embedment depth of the pile at the bottom increases the surrounding soil pressure. This also reduces the deformations of the pile. As a result, there is a lower strain energy than that in the middle section.  
Figure 12: Cont.  
To distinguish the ability of a pile to absorb seismic energy, we evaluate the difference in strain energy transition amplitudes. Refer to the energy change lines (denoted as \(\lambda\)) in Figure 12a-c, where \(\lambda\)1 = 3.75 m, \(\lambda\)2 = 4.1 m, \(\lambda\)3 = 8.3 m, and \(\lambda\)4 = 5 m. This correlates with the plastic regions discussed in Section 3.2.