esa-sceva/satcom-chunk-collection huggingface dataset:
# Sildenafil Citrate Liposomes for Pulmonary Delivery by Ultrasonic Nebulization
## Materials and Methods
### Preparation of Liposomes  
SC-loaded liposomes with bilayers composed of EPC and Ch, with and without Vit E TPGS, were prepared by direct sonication of the components according to a previously described method [39]. Briefly, EPC and Ch were gently mixed with a 1 mg/mL SC solution in water or citrate buffer (pH = 3.2), with or without Vit E TPGS (0.1% \(w/w\)). The mixtures were sonicated for 30 min in a Fisher Scientific FB 15061 ultrasonic bath (50 Hz) at 50 \(\pm\) 2 \({}^{\circ}\)C. The sonicated samples were kept at room temperature for 60 min for liposome stabilization and then stored at 4 \({}^{\circ}\)C until active loading by transmembrane pH gradient.  
For active loading, the liposome suspensions prepared with citrate buffer were adjusted to pH = 7.0 with NaOH 0.1 N. The resulting suspensions were maintained for 20 h at 25 \(\pm\) 2 \({}^{\circ}\)C under mechanical agitation to facilitate the diffusion of the drug across the lipid bilayer and its accumulation in the liposome core (pH = 3.2), according to the pH-dependent solubility profile of sildenafil [40]. The marked influence of pH on sildenafil solubility facilitates the accumulation of the drug in the liposomal acidic core. The adjustment of the external medium to pH = 7.0 maximizes the unionized fraction of the drug, which shows high lipophilicity and permeability. Unionized molecules are able to cross the lipid bilayer to reach the acidic aqueous core, where they are trapped as ionized species. Figure 1 illustrates the mechanism of SC active loading in liposomes by transmembrane pH gradient.  
Figure 1: Schematic illustration of the active loading mechanism by transmembrane pH gradient.
# Real-Time Remote Sensing of the _Lobesia botrana_ Moth Using a Wireless Acoustic Detection Sensor
## 2 Materials and Methods
### Sensor
#### 2.3.1 Microphone  
Microphones are devices that convert sound waves into electric signals. As presented in [36], moths (_Lobesia botrana_ was not included in that work) have the capability of producing and emitting sounds in a broad spectrum including a human audible range (20 Hz-20 kHz), going as high as 120 kHz. For moth detection, it is necessary to have a microphone that has a broad frequency range to include the sound produced by the insect but it is not less important to have a flat frequency response, as in music or voice recording, or at least a known frequency response. The Ultramic 250 k from Dodortonic [43] is based on a MEMS (micro-electro-mechanical systems) sensor and delivers data over a USB connection. It was chosen because it was capable of registering sound from a low frequency to 125 kHz, thus including the frequency range of different moths. This microphone is directly recognized as a sound recording device on most computer platforms including many smartphones, making it ideal for use with different systems as needed (testing, calibrating, filter matching, field use). This microphone also integrates a preamplifier and an A/D converter unit for the received signal.
# Experimental Study of Coaxial Jets Mixing Enhancement Using Synthetic Jets
###### Abstract  
Synthetic jets perpendicular to the mainstream have been used to experimentally study the coaxial jets mixing enhancement in this paper. The parameters of coaxial jets such as vorticity, streamwise velocity, radial velocity, Reynolds shear stress, and turbulence intensity are measured using the particle image velocimetry (PIV) and hot wire anemometers. The distribution characteristics of these parameters with and without synthetic jets were obtained. The mechanism of coaxial jets mixing enhancement using synthetic jets was summarized by analyzing these experimental results, and it was also found that the momentum coefficient was the most critical factor for jets mixing enhancement. The comparative experiments fully verified the mechanism, showing that with an appropriate momentum coefficient, the synthetic jets significantly enhanced coaxial jets mixing.  
synthetic jet; jet mixing enhancement; momentum coefficient; streamwise vortices +
Footnote †: journal: Applied sciences
## 4 Design and Development
### Deployment Configuration and Execution Scripts
#### 4.3.2. Fabric Configuration  
Each peer was configured using a dedicated core.yaml file, and environment variable FABRIC_CFG_PATH was set to enable secure CLI-based interactions.
# Data Hiding of Multicompressed Images Based on Shamir Threshold Sharing
## 2 Related Works  
### Shamir Threshold Secret Sharing Scheme  
Secret sharing is a cryptographic technology that separates and stores secrets. The purpose is to prevent secrets from being too concentrated, so as to achieve the purpose of dispersing risks and tolerating intrusions. It is an important means in information security and data secrecy. The idea of secret sharing is to split the secret in an appropriate way. Each share is managed by a different participant. A single participant cannot recover the secret information. Only several participants can cooperate to recover the secret message. Moreimportantly, in the event of a problem with any of the corresponding inscope participants, the secret can still be fully recovered.  
The (\(m\), \(n\)) threshold secret sharing scheme, is a famous secret sharing solution proposed by the Turing Award winner, [PERSON], in 1979. The scheme divides the secret message into multiple parts by constructing a polynomial of degree \(m\)-1, and distributes it to multiple participants, thereby ensuring the security of the secret message; m denotes that at least m participants can jointly recover the secret message, any less than or equal to \(m\)-1 participants cannot recover the secret message, and n denotes the number of participants participating in splitting the secret message.  
The (\(m\), \(n\)) threshold scheme works as follows:  
1. Split the secret message \(s\) into n shares; each participant gets one share.
2. When splitting, it is preset that at least \(m\) (\(m\leq n\)) participants gather together to restore s.
3. When \(s\) needs to be restored, and there are at least m participants gathered together, they take out their respective shares and restore \(s\), and when less than m participants gather together, it is impossible to restore \(s\).  
[PERSON] gives a clever way to construct the threshold scheme. The (\(m\), \(n\)) threshold scheme includes two algorithms: a secret segmentation algorithm and a secret reconstruction algorithm.
## 6 Performance Evaluation  
In this section, we evaluate the performance of the TAFLRLR protocol in terms of packet delivery rate (PDR) and total energy consumption (TEC) with the NS3 network simulation platform. The main simulation parameters are listed in Table 5.
## 4 Modelling Approach
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Wind Tunnel Model** & **Whirl Tower Model** \\ \hline Number of nodes & 581,097 & 409,349 \\ Number of elements & 1,782,590 & 1,443,880 \\ Inertial loads & n.a. & Relevant to an angular speed \\  & & of 120 rpm \\ Constraint & \begin{tabular}{c} Clamped—pinned around the \\ span axis at \(\frac{1}{\upomega}\) of the chord \\ \end{tabular} & \begin{tabular}{c} Clamped—free \\ Relevant to a maximum speed \\ of 120 rpm \\ \end{tabular} \\ Aerodynamic loads & \
\begin{tabular}{c} Me 25 m/s \\ Uniform along the span \\ \end{tabular} &
\begin{tabular}{c} Me 10 rpm \\ Quadratic along the span \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Main features of the wind tunnel and whirl tower structural models. The just mentioned model was conceived to simulate the achievable performance and verify the structural integrity of the demonstrators. To this scope, the life phases of the SMA material relevant to the prototype were simulated. In practice, with specific reference to the SMA, the model allows to simulate:  
Figure 7: Simulation step of pre-load, connection, and activation operations of the SMA actuators. * The pre-twist operation necessary to produce within the SMA rods enough martensite phase to be exploited for the activation (step 1);
* The connection of the twisted edges of the SMA rods to the structure, the subsequent release, and the consequent achievement of the elastic equilibrium (step 2);
* The application of the external loads (step 3);
* The activation of the SMA by heating (step 4). At first, a twist of 30 deg is applied to the SMA in a clockwise direction through the yellow rigid element (step 1); this also causes the rotation of the crown element connected to the SMA edge through the red rigid element. Then, the central node of this latter element is linked to the corresponding central node of the blue rigid element already connected to the main structure, and the initial twist is removed (step 2); in this way, the SMA tends to recover the angular deformation, but is hindered by the main structure; at the end of this step, the equilibrium is achieved. Then, the external loads are also applied (step 3). Finally, a temperature load is applied to the SMA, enforcing the transformation of the martensite phase produced during the pre-twist and thus transmitting the twist to the surrounding structure (step 4). Steps 1, 2, and 4 are conceptually common for both the WT and WhT simulations, while step 3 foresees the application of uniform aerodynamic loads along the span for the WT model and of aerodynamics + centrifugal actions on the WhT model. In this latter case, the aerodynamic loads vary linearly and parabolically along the span.
# Performance of Strengthened Accelerated Oscillator Damper for Vibration Control of Bridges
###### Abstract  
Vibration control has emerged as a significant concern in civil engineering, aiming to minimize the displacement and stress exerted on structures during seismic events. The accelerated oscillator damper (AOD), which is a damping device that depends on acceleration, has been demonstrated to be highly effective. However, in the case of traditional bridges, it is difficult to accurately place the secondary mass, spring, and damping components at the piers. Additionally, it has been found that as a general single-degree-of-freedom (SDOF) damping device, a significant limitation of the AOD system is its insufficient damping effect in the near-resonance region. This study presents a strengthened AOD with a liner spring (SAOD-LS), in which the secondary spring and damper are linked to the primary structure rather than being attached to the piers. This design not only provides enough space for the secondary system but also has a higher amplification factor of secondary spring and damping components compared with the original layout. In addition, we suggest a nonlinear spring device (NSD) that includes connecting rods and inclined linear springs arranged in a diamond configuration. This innovative design is intended to introduce nonlinear stiffness characteristics into the equivalent stiffness, thereby improving the device's performance and providing effective anti-resonance features in the near-resonance region. We have confirmed the motion equations for the SAOD-LS and used finite element (FE) analysis to validate the formulation of the equivalent external force and deformation of the NSD. We have thoroughly investigated both the SAOD-LS and the strengthened AOD equipped with NSD as the secondary spring (SAOD-NSD) for their potential implementation in a bridge project. These damping systems demonstrate exceptional performance and robustness, making them highly suitable for enhancing structural resistance to seismic activity.  
s +
Footnote †: journal: Applied sciences  
Article Performance of Strengthened Accelerated Oscillator Damper for Vibration Control of Bridges
# Potato Leaf Disease Segmentation Method Based on Improved UNet
## 2 Materials and Methods
### Methods
#### 2.2.2 Residual Network  
The performance of the shallow layers of the UNet network may degrade. To address this issue, the concept of residual networks was introduced, which consists of identity mapping and residual blocks. In this context, the residual block used comprised three consecutive convolutional layers (1 \(\times\) 1, 3 \(\times\) 3, 1 \(\times\) 1 convolutions), as illustrated in Figure 5. Unlike traditional sequential network structures, residual units introduce skip connections that add the input and output, compensating for feature information loss during convolution [21; 22]. The introduction of residual units makes the feature mapping more sensitive to output changes, facilitating easier gradient training. Even when the residual is zero, indicating that the stacked layers only achieve identity mapping, the network performance does not degrade.  
Figure 4: The UNet network model.  
Figure 5: ResNet50 Residual Unit.  
In the deep ResNet50 + UNet network, a deeper ResNet50 network was chosen as the front-end feature-extraction component [23]. A 1 \(\times\) 1 convolutional layer was added for dimensionality reduction before computation, followed by 3 \(\times\) 3 convolution, and then 1 \(\times\) 1 convolution for restoration, reducing computational complexity. This network structure effectively utilizes feature information obtained through downsampling and introduces residual networks to enhance feature extraction from potato leaf disease spot images. This combination has two main advantages: first, the introduction of residual units simplifies the entire network training process; second, the skip connections within residual units and between high and low layers promote information propagation within the network, further improving the performance of neural networks with fewer parameters in semantic segmentation tasks [24].
# Emotion Recognition from Speech in a Subject-Independent Approach
## 6 Results and Discussion
### Feature Analysis and Their Impact on Classification Performance
SHAP assigns a quantitative contribution to each feature in the classifier's final  
Figure 5: Visualization of the IEmax and RMSmax features for the emotions angry and neutral. Figure 6: Projection of features onto the space of the first two principal components for the classes angry and neutral. decision [74]. It treats each feature as a player in a decision-making game and computes its average contribution across all possible combinations of co-occurring features. By satisfying key properties such as efficiency, symmetry, additivity, and the null effect for irrelevant features, SHAP provides a consistent and reliable interpretation of model behavior. In the context of this study, the SHAP method enabled the identification of features with the greatest impact on emotion classification. The SHAP value analysis, carried out separately for the RAVDESS and EMO-BAJKA dataset, revealed significant differences in the structure of key features for classification. Figure 7 presents the mean absolute Shapley values for the predictors in the RAVDESS. In this analysis, a support vector machine classifier with a cubic kernel was used. According to the SHAP method for the EMO-BAJKA dataset, the highest importance was assigned to the following features: mc02 sr, mc10 sr, ddcsr, mc01 sr, mc07 sr, ddcvar, ddcmax, mc03 sr, mc06 sr, and mc09 var. In contrast, for the RAVDESS, the most significant features were energy-related: Evar, Emax, RMSvar, Esr, Emin, RMSsr, RMSmax, RMS, RMSmin, and GP0 min. These differences can be attributed to linguistic, age-related, and acoustic variations. The recordings in both datasets come from different groups of actors speaking different languages, which affects accent, prosody, and the distribution of energy and frequency. The Polish language is characterized by greater phonetic variability, which may increase the importance of features related to cepstral dynamics, such as ddcsr or ddcmax. Additionally, the EMO-BAJKA dataset contains recordings from both children and adults, which favors the increased significance of higher-order MFCC nuances. In contrast, the RAVDESS is based on professional recordings of adult English-speaking actors, where strong energy-related signals predominate. Figure 7: Mean of absolute Shapley values for the RAVDESS.
## 3 Thermo-Mechanical Coupling Mathematical Model
### Coupling of Stress Field to Temperature Field
\begin{array}{c}\varepsilon_{11}=\frac{1-\mu^{2}}{E}\Big{[}\sigma_{ 11}-\frac{\mu}{1-\mu}\sigma_{22}\Big{]}+\big{[}\eta+\frac{\mu}{2}(1-\eta) \big{]}\varepsilon_{f}^{T}\\ \varepsilon_{22}=\frac{1-\mu^{2}}{E}\Big{[}\sigma_{22}-\frac{\mu}{1-\mu} \sigma_{11}\Big{]}+\frac{1}{2}(1-\eta)(1+\eta)\varepsilon_{f}^{T}\\ \varepsilon_{12}=\frac{2(1+\mu)}{E}\tau_{12}\end{array} \right. \tag{24}\]  
From the above equation, the transiently thermal strain component is expressed as  
\[\left\{ \begin{array}{c}\varepsilon_{11}^{T}=\big{[}\eta+\frac{\mu}{2}(1-\eta )\big{]}\varepsilon_{f}^{T}\\ \varepsilon_{22}^{T}=\frac{1}{2}(1-\eta)(1+\eta)\varepsilon_{f}^{T}\\ \varepsilon_{12}^{T}=0\end{array} \right. \tag{25}\]  
As shown in Figure 2, where \(\theta\) represents the included angle between the coordinate axes (\(1\) and \(x\)), and the transient thermal strain component is  
\[\left\{ \begin{array}{c}\varepsilon_{x}^{T}\\ \varepsilon_{y}^{T}\\ \varepsilon_{xy}^{T}\end{array} \right\}=\left[ \begin{array}{ccc}\cos^{2} \theta&\sin^{2}\theta&-2\sin\theta\cos\theta\\ \sin^{2}\theta&\cos^{2}\theta&2\sin\theta\cos\theta\\ \sin\theta\cos\theta&-\sin\theta\cos\theta&\cos^{2}\theta-\sin^{2}\theta\end{array}  \right]\left\{ \begin{array}{c}\varepsilon_{11}^{T}\\ \varepsilon_{22}^{T}\\ \varepsilon_{12}^{T}\end{array} \right\} \tag{26}\]  
Substituting Equation (25) into (26), we can get  
\[\left\{ \begin{array}{c}\varepsilon_{x}^{T}=\Big{\{}\cos^{2}\theta\big{[}\eta+ \frac{\mu}{2}(1-\eta)\big{]}+\frac{\sin^{2}\theta}{2}(1-\mu)(1+\mu)\Big{\}} \varepsilon_{f}^{T}\\ \varepsilon_{y}^{T}=\Big{\{}\sin^{2}\theta\big{[}\eta+\frac{\mu}{2}(1-\eta) \big{]}+\frac{\cos^{2}\theta}{2}(1-\mu)(1+\mu)\Big{\}}\varepsilon_{f}^{T}\\ \varepsilon_{xy}^{T}=\frac{\sin\theta\cos\theta}{2}(3\eta-1)\varepsilon_{f}^{T} \end{array} \right. \tag{27}\]  
From this, the calculation formula of instantaneous temperature strain component considering the orthotropic deformation characteristics of freeze-thaw soil is obtained.
# NDVI or PPI: A (Quick) Comparison for Vegetation Dynamics Monitoring in Mountainous Area
## 3 Results
### Time-Series: By Vegetation Class  
In comparing the time-series of vegetation indices (VIs), we observe that the PPI exhibits a higher amplitude than the NDVI across all vegetation classes (Figure 6). This difference is less pronounced for coniferous trees. Additionally, PPI peaks are more distinct for deciduous trees, grasslands, and shrublands. The annual shape of the indices also differs, with PPI showing a tighter and more elongated pattern. For coniferous trees, PPI and NDVI display different scaled values.  
A Shapiro test [57] revealed that the time-series data are not normally distributed. Consequently, we performed a Spearman correlation test [58] for each pair of vegetation classes [28; 38]. The Spearman correlation coefficients (rho) are as follows: 0.91 for deciduous trees, 0.87 for coniferous trees, 0.93 for grasslands, and 0.96 for shrublands, each with a p-value less than 0.05.
## 5 Conclusions and Future Work  
In order to improve the reliability and accuracy of data transmission, especially in environments that require high signal integrity such as 5G and 6G communications, we propose a novel error correction scheme based on CRC codes and Polar Codes by using a new sequence interleaving technique and dynamic error pattern analysis. This approach enables more efficient identification and correction of errors without the need to re-transmit data when errors cannot be corrected. The proposed method significantly reduces the BER compared to conventional decoding techniques. These improvements not only increase the reliability of communication but also reduce delay and computational burden, making the scheme more suitable for modern communication systems that require high efficiency and low latency. Experimental results demonstrate that the proposed scheme outperforms existing error correction methods, particularly in high-signal-to-noise ratio environments.  
_Future Work_  
Despite the demonstrated advantages, the proposed error correction scheme has several limitations that require further exploration. First, the effectiveness of the sequence interleaving technique may vary with different types of communication channels and error distributions. Future work could focus on adaptive interleaving strategies that dynamically adjust to the channel conditions. Second, while the dynamic error pattern analysis enhances error correction performance, it introduces additional computational complexity. Research into optimization techniques, such as hardware acceleration or efficient algorithms, could help mitigate this issue. Additionally, the current scheme is primarily evaluated in scenarios with high SNRs; extending the analysis to low-SNR environments would provide a more comprehensive understanding of its performance. Finally, integrating the proposed scheme with emerging technologies, such as quantum communication and machine learning-based decoding, offers promising directions to further enhance error correction capabilities and meet the demands of next-generation communication systems.  
Methodology, F.A., J.Y. and Z.Y.; validation, F.A. and J.Y.; writing--original draft preparation, Z.Y.; writing--review and editing, F.A. and J.Y.; supervision, J.Y.; project administration, F.A. and J.Y. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (No. 62162020), the Haikou Science and Technology Special Fund (2420016000142), and the Science Project of Hainan University (KYQD(ZR)20021).  
Figure 7: Error correction complexity experiment.  
Data Availability Statement: The data involved in our research are all randomly generated binary bits. Since our research has nothing to do with the selection of data, we do not need to disclose the data here.  
Conflicts of Interest: The authors declare no conflicts of interest.