esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Characterization of PVA/BC/C-Ag Composite Membranes  
#### 3.1.1 Fourier-Transform Infrared (FT-IR) Spectrometry and X-ray Diffraction Analysis  
FT-IR spectra were used to characterize PVA, PVA/BC, and PVA/BC/C-Ag membranes. The shapes and positions of most peaks in Figure 1a are similar. The pure PVA showed the characteristic PVA peak at 3268 cm\({}^{-1}\) due to the stretching vibration of O-H [20]. Peaks at 1415 cm\({}^{-1}\) and 1085 cm\({}^{-1}\) corresponded to the bending vibrations of C-O-C and C-H in PVA [21]. Comparing the curves of pure PVA and PVA-BC, the stretching vibration peak of the hydroxyl group shifted from 3268 cm\({}^{-1}\) to 3261 cm\({}^{-1}\), probably due to hydrogen bond vibration between the PVA and BC molecules. Comparing the curves of PVA-BC and PVA/BC/C-Ag, the stretching vibration peak of the hydroxyl group shifted from 3261 cm\({}^{-1}\) to 3265 cm\({}^{-1}\), which may be ascribed to the coordination between -OH and Ag. After the introduction of C-Ag, no new peaks appeared in the PVA/BC/C-Ag spectrum compared to the PVA/BC spectrum.  
As shown in Figure 1b, the diffraction peaks of silver appeared at the same positions in the PVA/BC/C-Ag, PVA/C-Ag, and C-Ag composite membranes. The positions of the diffraction peaks at 20 were 38.18\({}^{\circ}\) (111), 44.34\({}^{\circ}\) (200), 64.46\({}^{\circ}\) (220), and 77.47\({}^{\circ}\) (311), consistent with the standard pattern of Ag (JCPDS No 04-0783). Calculated using the [PERSON] formula, the particle size of silver was approximately 50 nm. PVA had a strong diffraction peak at 20 = 20\({}^{\circ}\) [22] and exhibited a weak characteristic peak at 20 = 41\({}^{\circ}\). The reason for the occurrence of diffraction was mainly the influence of the microcrystalline peak of the PVA molecule on the (101) crystal plane. The PVA/BC/C-Ag and PVA/C-Ag composite membranes differed in the occurrence of a weak characteristic peak at 20 = 22\({}^{\circ}\), which may have been a characteristic peak of PVA/BC. A weak characteristic peak near 20 = 12\({}^{\circ}\) may have been a characteristic peak of BC [23].  
Figure 1: Fourier infrared analysis of several membranes (**a**), and X-ray diffraction analysis of different materials (**b**).
# Cryptographic Key Management in Delay Tolerant Networks: A Survey
## 3 Key Management Taxonomy in DTNs
### Key Establishment
#### 3.2.1 Two-Party Communication
More specifically, they use asymmetric authenticated encryption with message recovery to encrypt the parameters of the new key. In their method, they inject protocol messages in the bundle payload as part of the message. In addition, an encryption decision-making workflow diagram of a DTN custodian node is presented. The authors in [47] propose a traditional cryptography based authentication scheme specially designed for Satellite DTN. According to the authors, the proposed scheme does not depend on network administrator's availability during post network authentication communication and facilitates bundle processing by the recipient in the absence of connectivity. More recently, the work in [48] presents an Efficient and Scalable Key Transport Scheme (ESKTS) based on public key cryptography and proxy signatures. This scheme ensures that integrity and authentication is achieved at hop-by-hop as well as end-to-end level. It also achieves end-to-end confidentiality and freshness for end communicating parties. In addition, the authors in [49] propose a secure way of cryptographic key distribution to the DTN nodes. The proposed DTN security architecture offers a way of key distribution and prevention of nodes during possible threats and attacks, while at the same time affords all the security features of BSP. Last but not least, the authors in [50] propose a decentralised distributed scheme that is based on Digital Signature Chains Key Management Scheme (DSC-KM) and Pretty Good Privacy (PGP). According to the authors, the strong point of their scheme is it does not have a single point of failure. This is in contrast to other proposals that follow the centralised model. As with the previous subsections, Table 3 presents a comparison of PKI and PGP-based schemes proposed for DTNs.
###### Abstract  
To understand the individual enological function of different unsaturated fatty acids (UFAs), the separated effects of three different UFAs, linoleic acid (LA), oleic acid (OA), and \(\alpha\)-linolenic acid (ALA), on yeast fermentation and aroma compounds were investigated in the alcoholic fermentation of Cabernet Sauvignon wine. The results showed that, besides concentration, UFAs types could also influence fermentation process and volatiles in final wine. Low concentrations of UFAs (12 and 60 mg/L), especially LA and OA, significantly promoted fermentation activity and most volatiles when compared to the control, however, the effect became the inhibition with increasing concentrations of UFAs (120 and 240 mg/L). It was interesting to find that OA addition (12 and 60 mg/L) could generate more acetate esters (especially isoamyl acetate) in wine, while 12 mg/L LA facilitated more fatty acids formation (octanoic acid and decanoic acid). In comparison, 120 and 240 mg/L ALA produced more amount of C6 alcohols (1-hexanol) and higher alcohols (isobutyl alcohol and 2,3-butanediol). UFAs additions were unfavorable for ethyl esters formation, except for an increment of ethyl hexanoate in 12 mg/L OA wine. As a result, different aromatic profiles of wines were generated by variations of UFAs types and levels, as shown by PCA. The transcriptional data revealed that the expressions of aroma-related genes, such as _BAT1_, _BAT2_, _PDCI_, _PDC5_, _PDC6_, _ACCI_, _FAS1_, _ATF1_, _EEB1_, and _EHT1_ were correlated with aroma compounds productions in different treatments. Our data suggested that the three UFAs have different enological functions and they could generate different aromatic profiles. Thus, besides concentrations, it is essential to consider the types of UFAs when applying the strategy to adjust UFAs contents to modulate the aromatic quality of wines.  
linoleic acid; oleic acid; \(\alpha\)-linolenic acid; _Saccharomyces cerevisiae_; volatile aroma compounds; red wine +
Footnote †: journal: _articles on molecules_
## 4 Simulation and Analysis
### Test Case 1: Search for the CLS  
In this case, according to the search method of the CLS described in Section 2, the IEEE 39-bus New England system is modeled and reduced in dimension. Then, the CLS of the branch removed due to the fault is searched based on the improved shortest path algorithm proposed in this paper. The validity and accuracy of the algorithm is verified.  
There are eleven hanging nodes and two breakpoints in the IEEE 39-bus New England system. Node 16 and node 26 are breakpoints. Three LE regions are obtained according to the breakpoints and hanging nodes, as shown in Figure 6.  
Figure 5: The identification process of LE.  
Assuming branch 5-6 is removed due to a short-circuit fault, and then based on the improved shortest path algorithm, two shortest paths are obtained, constituting the CLS of branch 5-6, as shown in Table 1. The absolute value of the LERPF of all branches on the path is greater than \(d_{0}\), and the shorter the path length, the greater the LERPF, as shown in Table 2. In all branches of the power grid except the branches within the CLS of branch 5-6, the maximum value of the LERPF comes from branch 4-3, and its value is only 0.054. Therefore, these two shortest paths contain all the branches within the CLS of branch 5-6.
## 2 Materials and Methods
### Classification Methods  
Two principal methods were used in this research to classify extracted AE features, namely decision trees (DTs) and discriminant analysis (DA). DTs already include tools to evaluate the informativeness of the extracted features. DA represents a simple yet highly efficient classifier that supports decision boundaries such as linear or quadratic. The DA method was combined with forward feature selection to provide insight into the informativeness of various combinations of selected features. The methods are briefly introduced in the following sections.
# Open Radio Access Networks For Smart IoT Systems: State of Art and Future Directions
## 4 Challenges and Future Research Directions
### Challenges
#### 4.1.3 AI for O-RAN  
AI can support the O-RAN network by enhancing its performance, efficiency, and adaptability. On the other hand, AI deployment in O-RAN is not free from challenges. As for the areas of support, with the intervention of the RIC, i.e., rApps/xApps, intelligence could be deployed in O-RAN to yield an efficient network-slicing method. This will dynamically slice the network to meet the needs of different IoT applications, enhance edge intelligence by offloading computation to the edge of the network, improving the performance and reducing the latency of IoT applications. It will also optimize resource allocation in the network (bandwidth, power, etc.), enhancing IoT application performance and reducing costs. Furthermore, it could orchestrate the deployment and operation of IoT applications across the network, ensuring that applications are deployed most efficiently to facilitate effective communication.  
The challenges include data quality and availability, as AI models and algorithms require large amounts of data to train and learn. This is a challenge in O-RAN, as the data are distributed across multiple networks and devices. The quality of the data that are used as the input to train AI models and algorithms is essential for the accuracy of the results. The IoT data pipeline, especially for intelligent systems such as smart transportation, healthcare, energy, etc., is critical when integrating IoT with O-RAN. This is because the nature of data pipelines determines the quality of AI/ML model output and data analytic tools. However, the data used in O-RAN networks are sometimes noisy and unreliable. Distributed AI algorithms are primarily complex and computationally expensive to train and run. This can be a challenge for O-RAN-based networks, which are mostly resource-constrained.  
Similarly, AI models and algorithms can be challenging to interpret, which makes it much more difficult to understand why they make certain decisions. This can be a challenge for O-RAN operators, who need to be able to trust the decisions made by AI models and algorithms. Finally, AI models and algorithms can be biased, leading to unfair or discriminatory results. These challenges need to be addressed for effective intelligence deployment in O-RAN networks.
## 2 Theoretical Analysis
### Relationship between the Initial Phase and the Phase Sensitivity
The propagation path of the CCW light beam is \(\rm Port\ 1\to Port\ 4\to fiber\) optic ring \(\to\rm Port\ 3\to Port\ 2\), which is coupled twice at the 3 dB coupler, and each of them introduces a \(\pi/2\) phase shift. So, the initial phase difference between the two light beams at the PD is \(\phi_{0}=\pi/2+\pi/2=\pi\). From Equations (3) and (7), the phase sensitivity denoted by \(S_{\phi}\) can be obtained:  
\[S_{\phi}=\frac{\rm d\it{I}_{\rm D}}{\rm d\Delta\phi_{\rm s}}=\frac{\rm d(0.5I_{ 0}\cos(\pi+\Delta\phi_{\rm s}))}{\rm d\Delta\phi_{\rm s}}=0.5I_{0}\sin\Delta \phi_{\rm s} \tag{8}\]  
It can be found that the phase sensitivity of the sensor is very small because \(\Delta\phi_{\rm s}\) produced by AE signals is small in general. Therefore, it is difficult to detect AE signals effectively. If a bias phase \(\Delta\phi\) is introduced into the sensor, then  
\[E_{\text{CW}}=0.5E_{0}\exp\{\text{j}[\omega t+\phi_{\text{CW}}-\phi_{\text{s}} (t-t_{1})]\} \tag{1}\]0  
When \(\Delta\phi=\pi/2\), the initial phase will be modulated to \({\phi_{0}}^{\prime}=\pi+\pi/2=3\pi/2\), and the phase sensitivity can be expressed as  
\[E_{\text{CW}}=0.5E_{0}\exp\{\text{j}[\omega t+\phi_{\text{CW}}-\phi_{\text{s}} (t-t_{1})]\} \tag{1}\]1  
Therefore, the smaller the non-reciprocity phase shift caused by AE signals is, the greater the phase sensitivity will be. This is helpful for detecting weak AE signals.
## 3 Problem Definition and Formulation  
This study focuses on the task scheduling and resource management problem in STIN, specifically on dynamic resource optimization and task offloading based on a collaborative computation framework. In this system, satellite nodes, ground stations, user terminals, and satellite intercommunication links together form the basic architecture of the network. Satellite nodes have varying computational, storage, bandwidth, and task-processing capabilities, all of which are dynamically adjusted in response to changes in network topology, node failures, and fluctuations in task load. Thus, how to minimize system delay and at the same time maximize resource utilization becomes a critical problem.
## 2 Materials and Methods
### Methods
#### 2.2.3 Multi-Layer Perceptron  
A multi-layer perceptron (MLP) was used to estimate LUE, based on 15 variables including PFT, T\({}_{a}\), accumulated precipitation value of 8 days (P), R\({}_{g}\), R\({}_{L}\), VPD, P\({}_{a}\), WS, C\({}_{a}\), NDVI, EVI, NDWI, GVMI, NDDI, and NIRV. All input variables except PFT were normalized to zero mean and one unit. For PFT, there are 10 possible types, i.e., C3 crops (CRO-C3), C4 crops (CRO-C4), DBF, EBF, ENF, GRA, MF, SAV, SHR, and WET, and we used dummy variables, which use the values 0 and 1 to represent a variable, as inputs to express them. We treated the LUE produced by EnKF as the target for model training. The MLP used here consists of a five-layer network (excluding the input layer): (1) an input layer directly connected to the input data, (2) four hidden layers with 30 neurons each, and (3) an output layer consisting of one neuron. The input layer serves to receive input data and transmit them to the next layer, the hidden layers are responsible for constructing the relationships between the input and output, and the output layer produces target estimates. We utilized the rectified linear unit (ReLU) as the activation function in our implementation of the MLP and employed early stopping as a means of preventing the overfitting of the model.  
We first selected 12 flux sites that covered all 10 types of PFTs as a test dataset. Then, we shuffled the dataset of the remaining 168 flux sites randomly across time and sites and split it into two subsets: a training dataset and a validation dataset, accounting for 70% and 30% of the total number of flux sites, respectively. We repeated the training for several MLPs with an increasing number of hidden layers from 1 to 5 and an increasing number of neurons, e.g., 10, 20, 30, 40, and 50, to avoid overfitting. As a result, the MLP with four hidden layers and 30 neurons in each hidden layer is the best performing model in terms of root mean square error (RMSE).
## 3 Experimental Study
### Experimental Setup
#### 3.1.3 Autoencoder-Based Feature Extraction  
In this work, data from 14 selected sensors were processed to extract a single signal via a three-layer autoencoder. Hyperparameters of the autoencoder were evaluated based on the ranges in Table 4, and the parameters that performed best were chosen for optimal performance.  
The number of epochs was set to 30, and the batch size was set to 128. The number of neurons in the input layer and the output layer was equal to the number of selected features and was 14. The number of the nodes in the hidden layers, which is shown in Figure 3 as \"Encoder\" and \"Decoder\", was set to 7 and the bottleneck size, which was also the desired reduction size of the input, was 1. The MSE was utilized as the loss function, and adaptive moment estimation (Adam) was used to minimize the loss function. A total of 80% of the training dataset was dedicated to the training process, with the remaining 20% used for validation purposes. The conversion process was applied to both the training and test datasets. The sensor signals after the feature extraction process are shown in Figure 7. Signals in different colors represent each engine in the dataset.
# Optimization of Nonspherical Gold Nanoparticles for Photothermal Therapy
###### Abstract  
Previous investigations devoted to the optimization of nonspherical gold nanoparticles for photothermal therapy (PTT) encountered two issues, namely, the appropriate selection of objective functions and the processing of particle random orientations. In this study, these issues were resolved, and accurate optimization results were obtained for the three typical nonspherical gold nanoparticles (nanospheroid, nanocylinder, and nanorod) by using the T-matrix method. The dependence of the optimization results on the excitation wavelength and the refractive index of tissue was investigated. Regardless of the excitation wavelength and tissue type, gold nanospheroids were found to be the most effective therapeutic agents for PTT. The light absorption ability of optimized nanoparticles could be enhanced by using a laser with a longer wavelength. Finally, the design tolerance for the different sizes of nanoparticles was provided.  
gold nanoparticles; light absorption; photothermal therapy; T-matrix method; optimization +
Footnote †: journal: Applied Physics Letters
## 4 End to End Security Analysis on the Proposed IIoT Security Architecture
### Device Layer  
The device layer consists of a large scale of \"things\" distributed across the IIoT infrastructure landscape. These heterogeneous \"things\" need to be identified uniquely before being interconnected and collaborate to capture, transfer, exchange and process data. Generally, these identification schemes and technologies must be unique, consistent, persistent and able to support the identity management and scalability issues of \"things\" [57; 58]. Recently, several naming and address schemes have been used to define the unique identifier for both the physical \"things\" and virtual \"things\", either within a local or global scope. These schemes are electronic product code (EPC), ubiquitous code (ucode), media access control (MAC) and Internet protocol (IP) addresses, and other higher layer identifiers and naming schemes such as uniform resource name (URN), object identifier (OID), digital object identifier (DOI), network basic input/output system (NetBIOS), etc.
## 1 Introduction
Since the electromagnetic field on a macro scale follows Maxwell's equations, it is logical to approximate it using layered bianisotropic media to study the macroscopic parameters. This implies that these parameters can be measured using existing electromagnetic monitoring systems that usually help in identifying natural disasters such as earthquakes. Research on electrical parameters and related phenomena in geodynamically active zones is being conducted worldwide [24; 25; 26; 27], including seismically hazardous regions such as Altai [28; 29; 30], Tien Shan [31; 32; 33; 34], Kamchatka [35; 36], and Kola [21; 37]. The results of these studies indicate a correlation between changes in electrical resistivity and geo-dynamic processes occurring in deep layers of the Earth's crust. In certain cases, it has been observed that the apparent electrical resistivity is particularly sensitive to seismic events at specific azimuthal rotations, as demonstrated by detailed hourly variations in the relative resistivity value \(\Delta\rho_{k}\)[33]. Various works, such as [16; 38; 39; 40; 41], provide evidence of geoelectric anisotropy. Summary outline:  
* The novelty of this research lies in the advancement of geophysical monitoring methods applying effective media approximation (EMA) averaging theory to real geological media as layered bianisotropic (fractured) media, subsequently employing them for simplified retrieval of effective parameters. Additionally, the correlation between effective parameters and geodynamic processes is under attention. * We try to prove this approach by showing experimental data obtained during monitoring studies in a geodynamically active region. For this reason, in the next section, we present the results of monitoring data from stress-sensitive points in Kyrgyz Tien Shan over time. These results prove that the measured electromagnetic parameters in the same place are not stable and show variability over time, which could be influenced by changing telluric currents or due to external factors, either internal structural and textural rock reorganization (e.g., fluid flow convection), leading to temporal geoelectric anisotropy. * Section 3 presents the mathematical formulation of the electromagnetic theory for bianisotropic media. It explains that simple parametrization of Maxwell's equation may contain effective parameters, which could even be complex or a function of capillarity. * The Results and Discussion and Conclusions sections summarize achievements and practical geological model applications. Appendix A is additional, helping readers understand the averaging procedures used (homogenization).
# Development of Post Processing for Wave Propagation Problem: Response Filtering Method
## 5 Conclusions  
In numerical integration, the transient solutions of [PERSON]'s second law obtained through either the implicit or explicit method inevitably exhibit undershooting or overshooting phenomena. Although both the stability and accuracy of the numerical methods are important, it is rare to investigate the accuracy of the time integration scheme compared with the stability issue. Thus, these numerical discrepancies are often neglected through the safety factor in mechanical or civil engineering. As a remedy for these discrepancies, this study develops an RFM for averaging the mechanical stress values in the time domain that improves the accuracy of the numerical solution. The RFM is a simple post-process averaging method to filter out the oscillations observed in the numerical solution and makes piecewise constant solutions of wave propagation problems. The performance of the present RFM was tested in both space and time with both the explicit and the implicit integration method. It was empirically found that a filtering ratio, FR, around 2% can improve the accuracy of numerical solutions. In addition, the frequency response functions for stress values before and after RFM application show that higher-frequency signals are added after filtering by generating piecewise constant responses. These higher frequency signals are essential to represent ramp phenomena in time domain. Though some benchmark problems, these higher-frequency components were investigated. One of the limitations of the present study is that it cannot provide the optimum filtering length for generalized wave propagation problems by a rigorous mathematical verification. In the same way, if the FR is too small it would not get rid of the oscillations, while if too large it would give big chunks of the solution into constant. By adopting a proper filtering ratio (around 2%) for the averaging operator in the RFM, it was observed that the averaged stress values converge toward the analytical stress values. For future research, it is necessary to conduct a theoretical study to reveal a reasonable filtering ratio as well as a reasonable filtering length.  
H.S.K., carries out the research and makes the computational codes; J.W.L., contributes to make the initial research codes and examples; K.K., provides the theorem; G.H.Y., provides key idea and conducts the studies. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No.2018R1A5A7025522) and was supported by the research program of Dongguk University, 2020.  
The authors declare no conflict of interest