esa-sceva/satcom-chunk-collection huggingface dataset:
# Correlating Disorder Microstructure and Magnetotransport of Carbon Nanowalls
## 3 Results and Discussion
### Magnetoresistance
#### 3.2.1 Sample I, Low Disorder  
The interference effect between the charges' carriers is most pronounced in low-dimensional systems (such as graphene) because there are higher probabilities to intersect their own path, leading to the weak localization (WL). The [PERSON]'s model [46] is used to confirm the presence of weak localization and antilocalization. It points out that the phase of interference correction to the magnetoresistance in graphene can be expressed as follows:  
\[\begin{array}{rl}\Delta R=-R^{2}\frac{\sigma^{2}}{\pi h}&\left[F\Big{(}\frac {2\tau_{\varphi}}{\tau_{\text{B}}}\Big{)}-F\bigg{(}\frac{2}{\tau_{\text{B}} \left(\tau_{\varphi}-1+2\tau_{i}-1\right)}\Big{)}\right.\\ &\left.-2F\bigg{(}\frac{2}{\tau_{\text{B}}\left(\tau_{\varphi}-1+\tau_{i}-1+ \tau_{i}-1\right)}\Big{)}\right)\end{array} \tag{4}\]  
where \(\Delta R=R(B)-R(0)\), \(F(z)=lnZ+\psi\Big{(}\frac{1}{2}+\frac{1}{Z}\Big{)}\) and \(\psi\) is the Digamma Function. Moreover, \(\tau_{\text{B}}=\frac{h}{\Sigma DBB}\), \(\tau_{\text{s}}^{-1}=\tau_{\text{s}}^{-1}+\tau_{\text{w}}^{-1}\), \(D\) is the diffusion coefficient and \(e\) de charge of electron. The \(\tau_{w}{}^{-1}\) and \(\tau_{\text{s}}{}^{-1}\) are the trigonal warping and elastic scattering rate, respectively. The intervalley scattering \(\tau_{i}{}^{-1}\) is an elastic process wherein a charge is scattered from the edges or defects with the size of the order of the lattice space (adatoms and vacancies) [47], while the phase coherence \(\tau_{\varphi}{}^{-1}\) is originated via electron-phonon scattering. The quantum correction to the conductivity occurs when \(\tau_{\varphi}{}^{-1}<\tau_{i}{}^{-1}\). The first term in Equation (4) corresponds to WL, while both the second and third terms correspond to WAL.  
Figure 6a shows the \(\Delta R\) as a function of the magnetic field for Sample I. The good fit with the [PERSON]'s model (represented in black lines) confirms the apparition of the WL and WAL effect. The transition from WL to WAL occurs around 0.7 T, at low temperature (below 100 K), with a displacement along the x-axis for higher temperatures (above 100 K). This last one corresponds to the reduction of the \(\tau_{\varphi}{}^{-1}/\tau_{i}{}^{-1}\) relation (3.5 to 2.7 for T = 100 K and T = 250 K, respectively); see Figure 6b, the green line. Above 100 K, the \(\tau_{i}{}^{-1}\) increases with more rate than \(\tau_{\varphi}{}^{-1}\), probably by an addition of electron-like carriers from interfaces and impurities (interstitial, excess atoms or ions). In Figure 6b (red line), the temperature causes an increment of the number of phonons, which could be released from the substrate, thus improving the phase coherence \(\tau_{\varphi}{}^{-1}\).  
A general view of the WL-to-WAL transition can be seen in Figure 7a, which represents the 3D magnetoresistance as a function of the magnetic field and temperature. It denotes an evident crossover around 100 K, where, above this temperature, the negative values of MR (blue region) achieve a higher magnetic field.
## 4 Discussion  
The present study aims to computationally evaluate the anti-inflammatory potential of six naturally occurring terpenes by investigating their binding interactions with four enzymes involved in AA metabolism. Arachidonic acid, as the natural substrate for these enzymes and a central precursor to pro-inflammatory mediators, serves as a reference ligand.  
The selection of the six terpen is guided by both pharmacological relevance and chemical diversity. These compounds are among the most widely distributed monoterpenes in nature, occurring in a broad range of essential oils derived from medicinal and aromatic plants [61]. They have been historically used in traditional medicine for the treatment of inflammatory conditions, and several preclinical studies have reported anti-inflammatory, analgesic, or antioxidant properties for these molecules [62; 63; 64; 65]. From a structural perspective, the selected terpen represent a diverse subset of the monoter  
Figure 8: The 2D visualization of ligand–enzyme interactions between the selected terpenes and the PLA\({}_{2}\) active site. Interaction diagrams of the six investigated terpenes—(**a**) limonene, (**b**) camphor, (**c**) linalool, (**d**) menthol, (**e**) \(\alpha\)-pinene, and (**f**) \(\beta\)-pinene—and the reference ligand arachidonic acid (**g**) with the active site of PLA\({}_{2}\). The diagrams depict the key non-covalent interactions, including conventional hydrogen bonds (green), unfavorable donor–donor bonds (red), \(\pi\)-sigma (purple), and alkyl or \(\pi\)–alkyl hydrophobic contacts (pink). Amino acid residues involved in the interaction are labeled and positioned according to their spatial orientation within the active site.  
pene class, encompassing acyclic (linalool), monocyclic (limonene), and bicyclic (\(\alpha\)- and \(\beta\)-pinene, camphor) skeletons, as well as different functional groups, including alcohols (menthol, linalool) and ketones (camphor). The focused nature of this compound set allows for a detailed comparative analysis of binding behavior while maintaining a manageable scope for computational evaluation. While broader screening libraries could provide a wider range of chemical scaffolds, our aim here is to explore the binding potential of well-characterized, biologically active terpenes with established use in natural medicine, as a proof-of-concept framework for further expansion in future work.
## 3 VLC for Underwater Communication
### Optical Signal Transmission Losses  
Underwater light transmission is primarily affected by absorption and scattering [22; 23]. Absorption converts light energy into heat or other forms, reducing intensity, while scattering redirects light upon interaction with water molecules or particles, weakening theoptical signal [23]. Both phenomena contribute to signal attenuation in water, as illustrated in Figure 2[24].  
The geometric model formula for power transmission can be expressed as mentioned below.  
\[P_{i}(\lambda)=P_{a}(\lambda)+P_{s}(\lambda,\phi)+P_{t}(\lambda,\phi)\]  
where \(P_{i}\) denotes incident light power, and \(P_{a}\) and \(P_{s}\) denote absorbed and scattered power, respectively. \(P_{t}\) is the remaining power. The volume of water is \(\Delta V\) and the thickness is \(\Delta r\).  
The total attenuation in underwater optical communication is determined by the beam extinction coefficient, \(c(\lambda)\), which is the sum of the absorption coefficient \(a(\lambda)\) and the scattering coefficient \(b(\lambda)\)[22; 23]:  
\[c(\lambda)=a(\lambda)+b(\lambda)\]  
where \(a(\lambda)\), \(b(\lambda)\), and \(c(\lambda)\) are in units of m\({}^{-1}\). These coefficients heavily depend on water types and depths [24].  
Pure seawater consists of water molecules and dissolved salts contributing to the absorption coefficient. The scattering coefficient of pure seawater is negligible [24]. Clear ocean water has a higher concentration of dissolved particles that affect scattering. Coastal ocean water consists of a higher concentration of planktonic matter, detritus, and mineral components that affect absorption and scattering. Turbid harbor water has a very high concentration of dissolved and in-suspension matters [25].  
Beer-Lambert's Law is widely used to simulate optical signal attenuation in underwater environments. It models attenuation caused by suspended particles and relates it to signal strength, distance, and the extinction coefficient [26]:  
\[P_{r}(\lambda,d)=P_{T}e^{-c(\lambda)}\cdot d\]  
where \(P_{r}\) and \(P_{T}\) denote received and transmitted optical signal strength at the receiver and transmitter ends. The distance between the transmitter and the receiver is denoted by \(d\).  
The visible spectrum extends from 400 nm to 700 nm, and the light penetration in water varies with wavelength. The range from 450 nm to 550 nm is identified as the lowest attenuation and corresponds to the ranges of blue and green light [25; 27].
## 3 Materials and Methods
### Multi-Band Visual Feature Extraction and Fusion
#### 3.3.2 Orientation Invariant Features  
In traditional ML-based strategies, image responses to Gabor wavelets with varying kernel parameters are typically stacked into a Gabor jet [40]. Their statistical descriptors were then extracted to form feature vectors. Alternatively, real-value Gabor jets were usedin the first layer of a CNN [41]. These approaches were either applied to objects with fixed poses, neglecting scale and orientation variabilities, or relied on highly complex neurons to implicitly learn such variations.  
This study analyzed histopathological images, digitized from glass slides at a known magnification. Stained tissue samples with similar textural structures but differing in poses can be represented by totally different responses. Instead of extracting individual wavelets, this study used orientation invariant Gabor responses, i.e., max and mean Gabors, as defined in Equations (2a) and (2b), respectively.  
\[r_{max}(\mathbf{p};\sigma)=\underset{\theta}{max}(G_{\sigma,\theta}*I)( \mathbf{p}) \tag{2a}\] \[r_{mean}(\mathbf{p};\sigma)=\underset{\theta}{mean}(G_{\sigma,\theta}*I)( \mathbf{p}) \tag{2b}\]  
An image patch (\(I\)) was split into RGB planes. Each plane was processed using Gabor kernels at a specified scale (\(\sigma\)), with orientations set at 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\). The maximum response was derived from impulse responses, whose intensity \(I\) at a coinciding coordinate \(\mathbf{p}=[x\ y]^{\mathrm{T}}\) represented the highest value across the entire spectra. Similarly, the mean response was obtained by averaging intensities across this range. The resulting planes (\(r_{\mathrm{R}}\), \(r_{\mathrm{G}}\), \(r_{\mathrm{B}}\)) were then recomposed (\(R\)) with respect to the corresponding response type.  
Figure 1A illustrates the responses of a single channel to filters with four distinct orientations, while Figure 1B illustrates the true-color responses to maximum and mean orientation invariant filters.
# Theoretical Study on Prestress Loss in Cross-Tensioned Concrete Pavement with BFRP Tendons
## 4 Comparison of the Prestress Loss with Steel Strands  
### Calculation Process of the Prestress Loss for Steel Strands  
#### 4.1.1 Prestress Loss Due to Anchorage Deformation and Prestress Retraction  
For the prestress loss caused by anchorage deformation and prestress retraction, the calculation process is the same as that for BFRP tendons, as shown in Equation (1).
## 4 Implementation and Optimization
### Compression Module  
**High reliability and high compatibility:** To ensure the high reliability of QZRAM, the compression module introduces many reliable mechanisms and strategies for data compression. In the compression ratio detector, to prevent the memory overflow from directly causing runtime errors, which can crash the system, the compression module conducts a detailed boundary check for each physical page I/O operation and ensures its atomicity during data operation. In the management of the compression algorithm, if the hardware-assisted compression algorithm does not work, the compression module uses the corresponding algorithm implemented in pure software as a replacement to ensure that compression can be successfully completed. This mechanism makes QZRAM independent of the underlying hardware platform. Even if the QAT accelerator is damaged or there is no device, it can still complete the compression work, which is a highly reliable solution to meet the data compression scenario. In addition, the compression module is transparent to the upper-level module and has high compatibility with different compression algorithms.
# Recent Advances in Muconic Acid Extraction Process
## 4 Muconic Acid Extraction
### Reactive Extraction Systems Used for MA Separation
#### 4.2.1 Reactive Extraction Mechanism
(2015) [52] analyzed the interfacial compound structure in systems with two types of extractants (organophosphorus--TBP, TOPO and amines--TOA and DOA), obtaining the results presented in Figure 4. Because MA and the extractant form the complex via an H-bond (Figure 4), aqueous-phase pH values below pKa are necessary to ensure efficient extraction. The key mechanisms that provide a framework for the translocation of the generated complex from the interface to the extract phase are solubilization and diffusion [49; 52]. Complexes containing only one molecule of MA and extractant were proved for TBP, TOPO, and TOA. For di-octylamine, the complex formed contains two or more bound MA molecules to a single amine [52], a fact proved by the low extractant concentration required for MA high yield extraction at optimum aqueous phase pH of 2.6. The central nitrogen atom of the amine forms the H-bond with the acid's hydroxyl group (Figure 5). The formation of this bond is facilitated by the central nitrogen atom's higher negative polarization [50; 51; 52; 53; 54; 55], affected by both the number of bond carbon chains (greatest impact) and the length of the carbon chains. For the di-octylamine, negative polarization and reduced steric hindrance toward the central nitrogen atom enable the formation of complexes involving two MA molecules with high yields at low amine concentrations.
# CoSiWiNeT: A Clock Synchronization Algorithm for Wide Area IIoT Network
## 5 CoSiWiNeT Evaluation
### Predicted Clock Offset
#### 5.1.1 Network Trace 1  
The clock offset output of algorithms, KF, LS, CoSiWiNeT, along with the raw input clock offset for trace 1 are shown in Figure 5. The outliers in raw clock offset represented by spikes are minimized by all these algorithms, more evidently by KF and CoSiWiNeT. However, LS shows additional spikes in its clock offset output.  
To formulate the variability of responses, a box-plot representation of input and output offset values for various algorithms is shown in Figure 6.  
The interquartile range (IQR), the measure of variability, is represented by a height of the box, and it indicates the spread of data in a data set. The IQR of KF, LS, and CoSiWiNeT is 1.4804, 37.8196, and 1.077, respectively, compared to 1.3908 of a raw offset, which indicates that CoSiWiNeT performs better than KF and LS.  
The output of the NTP algorithm, i.e., the corrected offset is shown in Figure 7.  
Figure 5: Trace 1—Comparison of predicted clock offset.  
Figure 6: Trace 1—Statistical distribution of predicted clock offset.  
The corrected offset values ranges from 0.058 ms to 7.531 ms. The clock frequency varies from 381.23 ppm to 498.95 ppm.
# Adaptive Jamming Mitigation for Clustered Energy-Efficient LoRa-BLE Hybrid Wireless Sensor Networks
## 3 Materials and Methods
### Energy Model
Specifically, the jammer monitored the ongoing transmissions in the network and emitted disruptive signals only when it detected communication, effectively degrading the packet delivery success rate and increasing retransmissions. The interference patterns varied in terms of timing and duration to evaluate the adaptability of the mitigation algorithm. The jammer was placed at varying distances from the sensor nodes to examine its impact on received signal strength, transmission reliability, and energy consumption. To ensure a comprehensive evaluation, network parameters such as retransmission rates, energy depletion trends, and protocol switching events were continuously logged. These experimental conditions were designed to ensure the reproducibility of our results and to provide valuable insightsinto the effectiveness of the adaptive protocol switching mechanism under real-world jamming scenarios. ```
Metrics:(retransmissions,routing tables, resilience, energy, valid routes) JanningZone:(Affected nodes) CoordinatorNode:Network coordinator Network stability and minimized energy impact.
## 2 Background
### Unmanned Aerial Equipment (UAV) or Drones  
One of the most versatile IoT devices are the Unmanned Aerial Equipment (UAV). This unmanned vehicle is usually composed of a ground controller and a communication system. Usually, its function in agriculture is related to aerial surveillance in precision agriculture for monitoring weeds, vegetation growth, irrigation, crops, etc. [15]. To enhance system efficiency, other technologies can be aggregated, such as artificial intelligence (machine learning or deep learning), mobile edge computing, and software-defined networks [16]. Associated with specialized sensors, UAVs are becoming powerful sensing systems that complement IoT-based techniques [15].
## 2 Materials and Methods
### Surface Analysis  
Specific surface area, according to Brunauer, Emmett and Teller (BET), was determined with a SA 3100 (Beckman Coulter, Brea, CA, USA) by nitrogen gas adsorption. Samples were degassed at ~313 K for 240 min under vacuum and then measured at a temperature of ~77 K in liquid nitrogen.  
Dispersive surface energy measurements were made by inverse gas chromatography (iGC) using a SMS iGC (Surface Measurement Systems Ltd., London, UK). Special iGC columns of 3 mm in diameter were packed with ~0.120 g of sample for supercritical fluid extracts and 0.090 g for organic solvent extract. Different alkanes (hexane, heptane, octane, nonane and decane) were injected at infinite dilution (0.03 p/p\({}^{\circ}\)) and their peaks as well as the appropriate retention times were detected with a flame ionization detector (FID). Methane was used to determine the dead volume. IGC Advanced Analysis Macro V1.41 was applied for evaluation using Microsoft Excel 2010 and 2013 (Surface Measurement Systems Ltd., London, UK/Microsoft Corporation, Albuquerque, NM, USA).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time (min)** & **Mode** & **Ratio Acetonitrile/Water** & **Flow Rate (mL/min)** \\ \hline
0–13 & isocratic & 75/25 & 1.2 \\
14–18 & gradient & up to 90/10 & 1.2 \\
19–35 & isocratic & 90/10 & 1.2 \\
36–40 & gradient & down to 75/25 & 1.2 \\
41–45 & isocratic & 75/25 & 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time program for High Performance Liquid Chromatography (HPLC) analysis of triterpenoids contained in birch bark.
# Research on Learning Resource Recommendation Based on Knowledge Graph and Collaborative Filtering
## 3 Combining Knowledge Graph and Collaborative Filtering Recommendation Algorithm
### Knowledge Graph Similarity Calculation
#### 3.1.2 Knowledge Graph Representation Learning  
In this paper, the training of the knowledge graph utilizes the TransD model. The TransD model employs a margin-based ranking loss function to compute the distance between real relationships and vector representations. It employs the backpropagation algorithm to iteratively update the vector representation values. This iterative process aims to attain optimal vector representations. The loss function for this model is expressed as follows:  
\[L=\sum_{\epsilon\in\Delta}\sum_{\epsilon^{\prime}\in\Delta^{\prime}}\bigl{[} \gamma+f_{r}\bigl{(}\epsilon^{\prime}\bigr{)}-f_{r}(\epsilon)\bigr{]}_{+} \tag{5}\]  
\(\epsilon\) represents positive triplets and \(\epsilon^{\prime}\) represents the corresponding negative triplets. \(\Delta\) is the set of positive triplets and \(\Delta^{\prime}\) is the set of negative triplets. \(\gamma\) is the margin value that separates positive and negative triplets. \([\gamma+f_{r}(\epsilon^{\prime})-f_{r}(\epsilon)]_{+}\) represents setting elements less than 0 to zero while keeping the values of elements greater than 0 unchanged.  
Figure 3: Structure concept map of knowledge graph.  
Based on the pre-constructed knowledge graph triplets, such as \(<\)Chapter A, Precedes, Chapter B\(>\) and \(<\)Chapter B, Contains, Knowledge Point C\(>\), the entities in the knowledge graph are embedded into a high-dimensional vector space. For each entity and relationship, two vectors are initialized, one in the entity space representing the entity's position and another in the relationship space representing the relationship's position. A loss function is defined, and the stochastic gradient descent algorithm is used to minimize the loss function. Through batch training, vector representations for each chapter entity are obtained. Next, the entity vectors are used to calculate the similarity between entities. Different similarity metrics can be used for similarity calculation. In this paper, cosine similarity is chosen as the similarity calculation method. It measures the similarity by calculating the cosine value of the angle between two vectors. The calculation formula is as follows (Equation (6)):  
\[cos(x,y)=\frac{x\cdot y}{\|x\|\times\|y\|} \tag{6}\]  
After obtaining the similarity between vectors, the semantic vectors obtained from the knowledge graph can be transformed into a similarity matrix. The chapter set is defined as \(Y=\{Y_{1},Y_{2},Y_{3},\ldots,Y_{\triangledown}\}\).  
Indeed, when the cosine similarity approaches 1, it signifies that the directions of the two vectors are nearly identical, and the angle between them is close to 0 degrees. This implies that they can be regarded as highly similar. The notation \(simA(x,y)\) represents the similarity between chapters achieved through the knowledge graph method.
## 4 Simulation and Comparison of FMCW Laser Target Characteristics
### Simulation Process of Single Laser Based on 3D Particle System
Considering a large particle size distribution in a smoke environment, the influence of particle size on detection performance is of negligible practical significance [25]. The particle size of the smoke particles during the simulation can be set to the same value. Under this condition, by setting the laser wavelength to be less than 1 \(\upmu\)m, the size of \begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Value** \\ \hline Laser wavelength & 0.808 \(\upmu\)m \\ Sweep bandwidth & 150 MHz \\ Modulation period & 0.5 ms \\ Emission divergence angle & 5 mrad \\ Receiving radius of lens & 6 mm \\ Receiving field of view & 45\({}^{\circ}\) \\ Target distance & 3 m \\ Target reflectivity & 0.3 \\ Smoke visibility & 8 m, 15 m \\ Smoke particle count reduction range & \([0,0.45n_{smoke}]\) \\ Smoke particle size enlargement range & \([0,0.9r_{s}]\) \\ Smoke particle position downshift range & \([0,0.9r_{s}]\) \\ \hline \end{tabular}
\end{table} 
Table 1: Main parameters of the laser simulation in smoke. photon particles is consistently smaller than that of smoke particles. The particle size ratio (PSR) is expressed as  
\[PSR=\frac{LaserWavelength}{SmokeParticleSize} \tag{24}\]  
In the virtual environment of Unity3D, a low-visibility smoke scene was represented by setting up a large number of equal size and non-overlapping rigid-body particles. For the particle emission process under a single optical channel, the particle system simulation environments and processes corresponding to the three cases in Figure 7 are shown in Figure 9. To improve the simulation efficiency, it was necessary to increase the possibility of particle collision. In this case, the size of the smoke particles was taken as 1 \(\upmu\)m. Based on the laser wavelengths in Table 1, the PSR was approximated as 0.8. Under this condition, the target was set as an extended target and its area was assumed to be infinite. Figure 8: Simulation process of FMCW laser beat signal based on 3D particle collision types.