esa-sceva/satcom-chunk-collection huggingface dataset:
# Nanoencapsulation of Essential Oils as Natural Food Antimicrobial Agents: An Overview
## 5 Main Strategies for Nanoencapsulation of Essential Oils
### Lipid-Based Nanoencapsulation
#### 5.2.2 Nanoliposomes  
Nanoliposomes (Figure 2B) (also known as lipid vesicles) are lipid-based encapsulation carriers commonly used in foods and drugs [113]. Their structure is a phospholipid bilayer. Liposomes have many excellent properties, such as the ability to bring drugs into cells (cell targeting), biological affinity, and low drug toxicity, as well as increasing drug stability and tolerance [114]. Compared to common liposomes, nanoliposomes can simultaneously embed two active substances with different solubilities [115]. Another important advantage of nanoliposomes is targeting, delivering and releasing their load at target sites in vivo and in vitro [116]. In order to encapsulate lipophilic antimicrobials and essential oils into liposomes, these compounds should be pre-dissolved together with the phospholipids. This procedure needs to input energy, allowing the phospholipids to aggregate to form a double-layered capsule and to obtain a thermodynamic equilibrium in the aqueous phase to attain the nanoscale size [117]. Various nanoliposome preparation techniques have been employed, including lyophilization, freezing, spray-drying, and supercritical fluid (SCF) technology [101]. [PERSON] et al. [118] used a membrane dispersion ultrasonic method to prepare liposome-embedded EOs, and found that the liposomes played a better role in the protection of foods, especially meat. [PERSON] et al. [119] used ethanol injection combined with spray-drying technology to prepare liposome-encapsulated coix seed oil, thus overcoming the shortcomings of coix seed oil instability and its poor water solubility, and enhancing its absorption in the intestine. [PERSON] et al. [120] developed a liposome-based carrier agent for the _Zanthoxylum tinguassuiba_ EO in multi- and unilamellar vesicle, and they observed that the carrier system successfully enhanced the thermal stability and bioactivity of EO. The liposome-encapsulated nisin fully inhibited the growth of _E.coli_ O157:H7 at concentrations lower than that reportedly required for unencapsulated nisin.
## 4 Discussion
### Summary of the Discussion and Future Works  
Through the discussion of the experimental results, we can draw the following conclusions: First, the behavior of the ultrasonic wave propagating on the rod is influenced by the bonding strength. The sharp peak appears around 200 kHz, regardless of the bonding strength, and peaks in the lower-frequency region only appear when the bonding is strong, which can be used to detect decreases in bonding. Second, changes in the ultrasonic waveform accompanying the time of the electrolytic corrosion can be used to detect the degree of corrosion of the rebar. Third, the position of the sharp peak around 200 kHz depends on the rod diameter. The peak shift due to the change in the rod diameter can be used to evaluate the degree of deterioration.  
Future work will include an investigation of the relationship between the bonding strength and flexural mode of the ultrasonic guided waves. We also aim to develop countermeasures against radioactive rays, which can disturb the ultrasonic signals. The authors hope that this work will contribute to the field of structural health monitoring technology for aging reinforced concrete structures in locations where access is difficult.  
Figure 11: Comparison of the location of the sharp peak in the frequency domain; (**a**) is the enlarged frequency spectrum of 0 and 60 h from Figure 8 and (**b**) is the frequency spectrum of the naked steel rod with varying diameter.  
**Author Contributions:** Conceptualization, A.F. and A.N.; data curation, A.F., A.N., and Y.T.; methodology, A.F.; software, A.F.; formal analysis, A.F.; investigation, A.F., A.N., and Y.T.; writing-original draft preparation, A.F., writing-review and editing, A.F. and A.N.; supervision, A.N.  
**Acknowledgments:** We would like to express the most profound appreciation to Dr. [PERSON], who is the director of the Tsuruga Comprehensive Research and Developing Center, for his constructive comments and warm encouragement, and we would like to appreciate colleagues of the Innovative technology developing group.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Detecting and Processing Unuspected Sensitive Variables for Robust Machine Learning
## 2 Algorithmic Biases in Machine Learning
### From Determined Bias to Unknown Bias in Image Analysis  
Keeping in mind the potential sources of bias, different situations may also occur in image analysis applications, depending on the availability of the information:  
* _Full information_: images, targets, metadata and sensitive variables, i.e., \((X,Y)\cup\{\text{meta}\}\cup A\). are available. The bias may then come from the meta-observations \(\{\text{meta}\}\), the image itself, the labels or all three.
* _Partial information_: the sensitive variable is not observed, so we only observe \((X,Y)\cup\{\text{meta}\}\). The sensitive variable may be included in the meta variables \(A\subset\{\text{meta}\}\), or may be estimated using the meta-variables \(\{\text{meta}\}\).
* _Scarce information_: only the images are observed along with their target, i.e., we only observe \((X,Y)\). The sensitive variable \(A\) is, therefore, hidden. The bias it induces is contained inside the images and has to be inferred from the available data \(X\) and used to estimate \(A\).  
For societal applications, the sensitive variable is defined following regulations as presented in Section 2.4. The variable \(A\) is known since it is chosen by the regulator, and, hence, is either directly available in the data or proxies can be found to estimate it. The main difficulty when working with high-dimensional inputs such as images (but also natural language data, time series or graphs) is that the bias may not be explicitly present in a particular input dimension or variable, but is rather hidden in a latent representation of the input data. For instance, an image-based classifier would not naturally have a different rate of positive decisions for males and females because of the intensity of a specific pixel. It would instead detect specific patterns or features in the input images and potentially use this information, leading to unfair decisions with respect to a _gender_-sensitive variable. As discussed in Section 1, neural network classifiers or regressors change by construction of the representation of the input data into a lower-dimensional latent (or feature) space before making their predictions based on this latent information. Illustrations of how a network can project the input information in a latent space can be found in the VGG and ResNet papers [11; 12]. It would then be tempting to believe that the hidden variables explaining the undesirable biases would be found in the latent space, but this is not necessarily the case. This information can still be distilled in different latent variables unless a specific process is used to isolate it [55]. Hence, bias detection is an essential, potentially arduous task when dealing with images.
# Smart Infrastructure and Additive Manufacturing: Synergies, Advantages, and Limitations
## 4 Benefits of Integrating 3D Printing with Smart Infrastructure
### Sustainability and Optimization of Performance and Durability
\begin{table}
\begin{tabular}{c c} \hline
**Benefit** & **Description** \\ \hline
**Customization and** &
\begin{tabular}{c} Enables precise customization of components for specific \\ applications, such as aerodynamic bridges, acoustically optimized \\ structures, and bio-inspired facades. Facilitates generative design \\ and AI-driven optimization for lightweight yet strong structures. \\ \end{tabular} \\ \hline
**Speed and** &
\begin{tabular}{c} Significantly reduces construction time by using automated \\ layer-by-layer fabrication. Capable of rapidly deploying essential \\ infrastructure, such as housing for disaster relief, within hours. \\ Eliminates the need for formworks, scaffolds, and extensive \\ manual labor. \\ \end{tabular} \\ \hline
**Cost Reduction *** &
\begin{tabular}{c} Minimizes material waste by using only the required amount of \\ resources, reducing raw material costs by 30–60\%. Lowers labor \\ expenses due to automation and reduces transportation costs by \\ enabling on-site manufacturing. \\ \end{tabular} \\ \hline
**Sustainability and Environmental Impact** &
\begin{tabular}{c} Uses sustainable and recycled materials (e.g., geopolymer \\ concrete, bio-based polymers, and repurposed industrial waste). \\ Reduces energy consumption by eliminating excessive \\ transportation and energy-intensive manufacturing steps. Lowers \\ carbon emissions by streamlining construction processes. \\ \end{tabular} \\ \hline
**Enhanced Durability and Longevity** &
\begin{tabular}{c} Incorporates self-healing materials, such as microcapsule-filled \\ concrete that repairs cracks, extending infrastructure lifespan. \\ Utilizes adaptive materials like shape-memory alloys and \\ conductive polymers to enhance resilience against environmental \\ stressors. Enables real-time structural monitoring for predictive \\ maintenance, reducing long-term repair costs. \\ \end{tabular} \\ \hline \end{tabular}  
* The costs presented here refer to material and labor savings in 3D printing applications. The capital cost of acquiring and maintaining large-scale 3D printers is not included and may affect the overall financial viability, depending on project scale and frequency of use.  
\end{table} 
Table 1: Key benefits of integrating 3D printing with smart infrastructure.
## 6 Implementation of Structural Reliability Assessment on the OWT Support Structure
### Constraints and Design Criteria
#### 6.1.1 Stress Constraint  
The ULS (ultimate limit state) defines the ability of the structure to resist yielding. In terms of ultimate limit state, the maximum stress in the support structure \(\sigma_{VM,\ max}\) should not exceed the allowable stress limits \(\sigma_{VM,\ allow}\). The limit state function for the von Mises criterion can be expressed as follows:  
\[g_{u}(x)=\ \sigma_{VM,\ allow}-\sigma_{VM,\ max}. \tag{21}\]  
The allowable stress \(\sigma_{VM,\ allow}\) can be expressed as follows:  
\[\sigma_{VM,\ allow}=\frac{\sigma_{y}}{\gamma_{m}}, \tag{22}\]  
where \(\sigma_{y}\) is the yield strength, with a value of 355 MPa for steel S355, and \(\gamma_{m}\) is the material safety factor, with a value of 1.1 suggested by DNV-OS-J101 standard [71]. Thus, the allowable stress \(\sigma_{VM,\ allow}\) is 323 MPa.
## 5 Multi-Energy Microgrid Double-Layer Planning Model
### Constraints  
1. Maximum load constraint: The maximum output capacity of the energy supply equipment configuration must meet the maximum power demand of the microgrid system in power, heating, and cooling loads: \[\sum_{m}P_{\text{output,max}}^{m}(t)\geq P_{\text{Load,max}}^{[\cdot]}(t)\] (44) where \(P_{\text{output,max}}^{m}(t)\) is the maximum output power of \(m\)th energy supply equipment unit at time interval \(t\); and \(P_{\text{Load,max}}^{[\cdot]}(t)\) is the maximum load demand for some kind of energy at time interval \(t\).
2. Power balancing constraint: If the energy storage function is ensured, a real-time balance of power/heating/cooling energy supply and demand in the multi-energy microgrid should be achieved: \[\sum_{m}P_{\text{output}}^{m}(t)+\sum_{m}P_{\text{D}}^{m}(t)=P_{\text{Load}}^{[ \cdot]}(t)+\sum_{m}P_{\text{C}}^{m}(t)\] (45) where \(P_{\text{output}}^{[\cdot]}(t)\) is the output power of \(m\)th energy supply equipment unit at time interval \(t\); \(P_{\text{[C/D]}}^{m}(t)\) is the charge or discharge power of \(m\)th energy storage equipment unit at time interval \(t\); and \(P_{\text{Load}}^{[\cdot]}(t)\) is the load demand at time interval \(t\).
3. Self-sufficiency probability constraint: The multi-energy microgrid works in electric-isolated mode, so it is crucial in regard to meeting the load demands and operation stability of the system. The self-sufficiency probability of meeting the system load demand in the planning period can be employed to direct the planning and configuration of the microgrid system: \[\Pr\left(\sum_{m}P_{\text{output}}^{m}(t)+\sum_{m}P_{\text{D}}^{m}(t)\geq P_{ \text{Load}}^{[\cdot]}(t)\right)\geq P_{\text{st}}^{[\cdot]}\] (46)where \(P_{\rm{sk}}^{[\cdot]}\) is the probability of self-sufficiency of a certain type of energy load.
4. Operation constraint of power supply equipment: The energy supply equipment of the multi-energy microgrid must meet the rated power and climbing constraints of the equipment during operation. \[P_{\rm{output,min}}^{m}\leq P_{\rm{output}}^{m}(t)\leq P_{\rm{output,max}}^{m}\] (47) \[-\Delta P_{\rm{down}}^{m}\leq P_{\rm{output}}^{m}(t)-P_{\rm{output}}^{m}(t-1 )\leq\Delta P_{\rm{up}}^{m}\] (48) where \(-\Delta P_{\rm{down}}^{m}\) and \(\Delta P_{\rm{up}}^{m}\) are the climbing power of the \(m\)th energy supply component to reduce or increase output power.
5. Operation constraint of power storage equipment: The energy storage equipment of the multi-energy microgrid must meet the energy charge/discharge power, and capacity constraints of the equipment during operation: \[M_{\rm{min}}^{m}(t)\leq M^{m}(t)\leq M_{\rm{max}}^{m}(t)\] (49) \[P_{[\rm{C/D]}}^{m}(t)\leq P_{[\rm{C/D],max}}^{m}(t)\] (50) where \(M_{\rm{max}}^{m}(t)\) and \(M_{\rm{min}}^{m}(t)\) are the maximum or minimum capacity of the \(m\)th energy storage equipment unit.
## 1 Introduction
Improved weighting methods and novel damage models enhance evaluation accuracy, while emerging technologies such as machine learning increase the efficiency of damage identification [19; 20; 21; 22]. The damage analysis of liquefied petroleum gas wharfs using the modal strain energy method was investigated. The research results indicate the method's effectiveness in accurately assessing damage location and severity [23]. [PERSON] et al. [24] employed the absolute value of the residual force vector as an indicator for the analysis of high-pile wharf foundation pile damage. The research results demonstrate that this method can accurately and swiftly identify damage by utilizing the low-order scenarios of the structure, thus offering a novel approach for the diagnosis of damage in high-pile wharf. Figure 1: Structural collapse caused by ship collision with the wharf. shrard foundation piles. [PERSON] et al. [25] demonstrated that the modal curvature method accurately locates cracks but is dependent on the distribution of measurement points and data quantity. The modal flexibility method is suitable for preliminary detection but is not sensitive to small cracks. A combination of both methods is necessary to optimize the results. A damage analysis method for pile foundations in high-piled wharfs based on axial static strain distribution was introduced. This approach utilizes two symmetrically arranged strain sensor arrays to precisely locate the damage location and assess its severity [26]. A method for damage identification based on the variability of modal strain energy residuals in high-piled wharfs was proposed by [PERSON] et al. [27]. This method utilizes sensor placement and the modal shape characteristics of piles, achieving a simple, accurate, and practical approach for early damage identification. [PERSON] et al. [28] improved the accuracy of modal flexibility identification by developing a novel mode selection method using a two-stage extraction strategy and a frequency domain similarity index. This approach effectively addresses noise issues in the Damage-Sensitive Index algorithm, enhancing the accuracy of bridge load-bearing capacity assessments. Under the impact of ship collisions, high-piled wharf structures may experience damage that not only disrupts their normal operation but also results in significant economic losses and safety risks. Furthermore, structural damage can lead to environmental issues, such as increased material corrosion and pollution emissions, which can harm marine ecosystems. This study aims to assess the extent of damage following impacts through numerical simulations and a dynamic damage identification model, providing a scientific basis for timely repairs. This approach seeks to mitigate economic losses, reduce safety risks, and minimize potential environmental impacts.
## 1 Introduction  
The development of new technologies in total joint arthroplasty (TJA) has gained increasing interest in the last two decades. Since the introduction of minimally invasive surgery (MIS), computer-assisted orthopaedic surgery (CAOS), and robotic surgery, computer navigation has become a powerful tool for the correct positioning of the implant components [1, 2, 3]. Conventionally, surgical imaging can be classified as off-line, taken before and after surgery (X-rays, computed tomography (CT), magnetic resonance imaging (MRI)), and on-line (intraoperative X-ray fluoroscopy, ultrasound (US)) [4, 5]. On-line2-dimensional (2D) intraoperative imaging can be supportive to enhance accuracy in TJA; however, despite the advantages, it is associated with several major problems including the necessity for video monitors, the interpretation and conversion of the 2D images into 3-dimensional (3D) images, the potential overlapping of anatomic structures, and increased radiation exposure [6].  
The concept of augmented reality (AR) was introduced by [PERSON] et al. [7] as a variation of virtual reality (VR). The difference is that AR allows the user to see the real world with virtual objects superimposed upon it, supplementing the reality with virtual computer-generated sensory impression, enhancing the user's ability to visualize a patient's anatomy.  
Several studies have evaluated the application of AR in orthopaedic surgery [8; 9; 10]; however, its use in TJA is still limited and mostly explored at preclinical levels. Therefore, there remains no consensus on the overall performance of these systems in the different settings. We performed a systematic review of the literature in order to examine (1) the applications of AR systems in TJA surgery, (2) their preclinical and clinical results in terms of accuracy and precision, and (3) the different systems currently available and their main characteristics. Moreover, to better understand the information provided in this study, the aim of the authors was to give a simple and thorough explanation of the AR systems used in orthopaedic surgery.
## 4 Performance Comparison of Different Models
### Multi-Step Ahead Forecasting  
The emergency management hopes the forecasting can be as far ahead of time as possible. Hence, multi-step ahead forecasting is also appealing for tropical storms, although the error increases as the number of forecasting steps increases [49]. The difference between one-step and multi-step ahead forecasting is that more outputs are required in the model building of a multi-step case. Figure 10 shows a comparison of multi-step forecasted wind speeds by the ARIMA model with comparison with the measured data. The results support the fact that the deviations between forecasted and measured wind speeds become more and more prominent as the time horizon expands, as the correlation between the wind speeds in the more distant future and the historical data becomes weaker.  
Figure 11 shows the prediction errors of different models with respect to the forecasting steps. The step varies from one to six, which means the maximum reaction time can be expanded to 6 min. It is found in Figure 11 that the forecasting errors of the SVR model are generally the largest for each forecasting step. Although a relatively large error is observed in the GRU model when the forecasting step is three, the differences in the errors of the LSTM, BPNN, and GRU models in other steps are not large. The ARIMA model forecasts the wind speeds with the least error even when the forecasting step is six. The error increment of the ARIMA model is relatively small. For example, from one to six steps ahead, the MAE, MRPE, and RMSE of the ARIMA model increase by 0.06, 0.27%, and 0.05, respectively, while the R\({}^{2}\) value decreases from 0.68 to 0.67. Hence, the ARIMA model provides a stable forecasting at varying forecasting steps, and the errors have slight changes.  
Given the discussions above, it can be concluded that the performances of all the BPNN, LSTM, GRU, and SVR models are not satisfied as they cannot well consider the non-stationary and non-Gaussian properties. However, the ARIMA model can provide better forecasting, especially in the multi-step cases, as the involved differencing step can make the predicted variable to be stationary and Gaussian.  
Figure 10: Multi-step ahead forecasted wind speeds by the ARIMA model.  
Figure 9: Density heatmap of forecasting errors.
# Measurement-Device-Independent Two-Party Cryptography with Error Estimation
## 4 1-2 Random Oblivious Transfer
### Protocol  
We now give the specific 1-2 ROT protocol using error estimation as follows:  
1. Preparation: [PERSON] chooses \(x^{n}\in_{R}\{0,1\}^{n}\) and \(\theta_{A}^{n}\in_{R}\{+,\times\}^{n}\), and [PERSON] chooses \(y^{n}\in_{R}\{0,1\}^{n}\) and \(\theta_{B}^{n}\in_{R}\{+,\times\}^{n}\). Both parties send the encoding quantum state \(|x\rangle_{\theta_{A}}^{n}\) or \(|y\rangle_{\theta_{B}}^{n}\) to third party [PERSON];
2. Measurement: [PERSON] measures \(|x\rangle_{\theta_{A}}^{n}\) and \(|y\rangle_{\theta_{B}}^{n}\) with [PERSON] measurement, and announces the outcome;
3. Error estimation: [PERSON] chooses \(I_{check}\in_{R}2^{[n]}\) and \(|I_{check}|=m\), and sends \(I_{check}\) to [PERSON]. [PERSON] sends \(y_{check}\), and \(\theta_{B_{check}}=\{y_{i},\theta_{B_{i}}|i\in I_{check}\}\) to [PERSON]. Then [PERSON] calculates the error rate: \[Q_{u}=\frac{\mathrm{num}(x_{i}\
eq y_{i}|\theta_{B_{i}}=\theta_{A_{i}})}{ \mathrm{num}(\theta_{B_{i}}=\theta_{A_{i}})}.\] If \(Q_{u}>e_{r}\), they stop communication, otherwise they continue where \(e_{r}\) is the error correction upper bound;
4. Key division: Both parties discard the data that used in error estimation. [PERSON] sends \(\theta_{A}^{n-m}\) to [PERSON], [PERSON] divides the key according to \(\theta_{A}^{n-m}\), \(\theta_{B}^{n-m}\), where \(I_{c}=\{i|\theta_{A_{i}}=\theta_{B_{i}}\}\) and \(I_{1-c}=\{i|\theta_{A_{i}}\
eq\theta_{B_{i}}\}\). [PERSON] sends \(I_{0},I_{1}\) to [PERSON];
5. Post processing: [PERSON] chooses two hash function \(f_{0},f_{1}\in_{R}\mathcal{F}_{h}\), and calculates \(\mathit{syn}(X|I_{0}),\mathit{syn}(X|I_{1})\). [PERSON] passes \(f_{0}\), \(f_{1}\), \(\mathit{syn}(X|I_{0})\), and \(\mathit{syn}(X|I_{1})\) to [PERSON]. [PERSON] corrects the errors and outputs \(S_{c}=f_{c}(Y|I_{c})\). [PERSON] outputs \(S_{0}=f_{0}(X|I_{0})\) and \(S_{1}=f_{1}(X|I_{1})\).