esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Framework for Physital Cultural Heritage Tourism Application Development
### Applying the Framework to the Fort Ontario AR Experience
Iterative Development:
* Conduct usability testing with diverse user groups, including tourists, educators, and older adults, to refine content and interface design. Marketing Strategy:
* Promote the app through social media, local tourism websites, and partnerships with historical organizations. Functional Testing:
* Validate the AR app's functionality in various weather conditions to address feedback about usability challenges in windy environments. * Ensure NFT transactions are seamless and intuitive for non-technical users. User Feedback:
* Collect feedback through on-site surveys and online forms, focusing on engagement, usability, and content appeal. * Address accessibility issues, such as challenges faced by users with arthritis, by refining the interface design. Compliance Check:
* Ensure alignment with cultural heritage preservation standards and accessibility guidelines. Stage 7: Deployment and Support  
Launch Strategy:
* Roll out the updated app with AR enhancements and NFT capabilities, ensuring staff support during the initial launch phase. * Train guides to emphasize the app's unique features during visitor interactions. NFT Placement and Accessibility:
* Offer NFTs representing key historical events or artifacts, available through the app or physical QR codes at the site. * Provide visitors with resources to understand and use NFTs effectively. Compatibility and Maintenance:
* Regularly update the app with new AR content and address technical issues as they arise. Stage 8: Evaluation and Iteration  
Performance Metrics:
* Track user engagement through metrics such as NFT redemptions, AR feature usage, and app session durations. * Assess the app's impact on visitor satisfaction and site revenue. Long-Term Improvements:
* Expand AR content to include more historical narratives and interactive elements. * Explore partnerships with other state parks to replicate the app's success at similar sites. Applying the framework to the Fort Ontario AR Experience enhances its potential as a tool for cultural preservation and visitor engagement. By integrating gamified elements, NFTs, and XR features, the app can offer richer, more personalized interactions with Fort Ontario's history. These enhancements not only attract diverse audiences but also support sustainable tourism and the fort's long-term preservation efforts.
## 3 An Analysis of Average Mutual Coherence \(\mu_{g}\)  
In order to obtain a set of frequency-diverse far-field patterns whose average mutual coherence \(\mu_{g}\) is low, it is essential that different amplitude and phase distributions on one metamaterial aperture are realized at different frequencies from the point of antenna design. However, the strongly resonant elements with frequency-agile responses both on amplitude and phase responses are not realistically achievable. Hence, the phase and amplitude modulations of MAA are performed separately to analyze the average mutual coherence \(\mu_{g}\) of its far-field patterns.  
For the phase modulation of MAA, the phase distribution on the aperture antenna varies when frequency changes while the amplitude distribution on MAA is uniform. Here, 20 \(\times\) 20 resonant elements with varying phase responses at different frequencies are randomly distributed on the metamaterial aperture antenna, which is analyzed by using array synthesis technology in the frequency band from 33 to 37 GHz. The average mutual coherence \(\mu_{g}\) of the far-field patterns of the MAA is calculated using elements with varying \(Q\)-values (or \(\Delta f\)). When the \(\Delta f\) of the elements is changed from 0.005 to 0.5 GHz, the calculated \(\mu_{g}\) varies from 0.02 to 0.13, as shown in Figure 3.  
Figure 2: Flow chart of the \(\mu_{g}\) analysis procedure.  
Similarly, for the amplitude modulation of MAA, the amplitude distribution on the aperture is diverse at different frequencies, while the phase distribution on MAA is constant. And 20 \(\times\) 20 resonant elements with diverse amplitude responses in frequency are also distributed on the aperture randomly. By changing \(\Delta f\), the average mutual coherence \(\mu_{g}\) of the far-field patterns of the MAA is calculated. When the bandwidth \(\Delta f\) of the element is changed from 0.005 to 0.5 GHz, the calculated \(\mu_{g}\) varies from 0.05 to 0.15. The comparison of \(\mu_{g}\) between the phase and amplitude modulation is shown in Figure 3. It can be seen from Figure 3 that the calculated \(\mu_{g}\) of phase modulation is slightly smaller than that of amplitude modulation. When the bandwidth of the element becomes smaller, the average mutual coherence becomes lower. However, the average mutual coherence becomes flat when \(\Delta f\) is less than 0.005 GHz. It is worth mentioning that the average mutual coherence is irrelevant with respect to the specific location of the center frequency, but it is relative to \(\Delta f\). It is common that, at higher operating frequencies, such as the millimeter-wave frequency band, the resonant element often has a larger \(\Delta f\) (usually \(\geq\)0.1 GHz). Hence, it is much more difficult to design a resonant element with \(\Delta f\leq\) 0.1 GHz at the millimeter-wave band, such as 35 GHz.
# Analysis and Optimization of Output Low-Pass Filter
## 4 Design Trade-Offs
### Effect of \(Q\) Variation  
The quality factor \(Q\) of the filter varies depending on the level of irradiance, as shown in Figure 20. The THD contours in Figure 20a are quite comparable for \(Q\) values of four and eight. Even though the amplification at the cutoff frequency \(f_{c}\) is higher for \(Q=8\) compared to \(Q=2\) and \(4\), the \(Q\) of \(8\) achieves the lowest THD value due to its better attenuation of higher frequencies. The grid's THD requirements are just slightly exceeded by the \(Q=2\) case.  
Figure 20c shows that \(P_{d}\) in \(R_{D}\) is minimum when \(Q\) is equal to \(8\). However, there is a trade-off between minimising \(P_{d}\) and reducing THD. The decrease in \(P_{d}\) with increasing \(R_{D}\) is due to the decrease in current flowing through \(R_{D}\). This is because \(L_{n}\) remains constant and the current decreases as \(R_{D}\) increases. It is also very important to note that the quality factor \(Q\) is directly proportional to the damping resistance \(R_{D}\). Therefore, when \(Q\) increases, damping resistance is also increases and emphasises the relationship between these two parameters and their impact on power dissipation and system performance.
# Routing Enhancement in MANET Using Particle Swarm Algorithm
###### Abstract  
A Mobile ad hoc Network (MANET) is a collection of wireless mobile nodes that temporarily establish a network without centralized administration or fixed infrastructure. Designing the routing of adequate routing protocols is a major challenge given the constraints of battery, bandwidth, multi-hop, mobility, and enormous network sizes. Recently, Swarm Intelligence (SI) methods have been employed in MANET routing due to similarities between swarm behavior and routing. These methods are applied to obtain ideal solutions that ensure flexibility. This paper implements an enhanced Particle Swarm Optimization (EPSO) algorithm that improves MANET performance by enhancing the routing protocol. The proposed algorithm selects the stable path by considering multiple metrics such as short distance, delay of the path, and energy consumption. The simulation results illustrate that the EPSO outperforms other existing approaches regarding throughput, PDR, and number of valid paths.  
mobile ad hoc networks (MANETs); routing protocol; swarm intelligence; particle swarm optimization (PSO)
## 4 Materials and Methods
### Qualitative and Semi-Quantitative Analysis  
The concentration of each compound was calculated using the following formula after the volatile flavor compounds were automatically identified using the National Institute of Standards Technology Mass Spectral Database 14 (accessed using MassHunter from Agilent Technologies, Inc.) Masshunter workstation. The retention indices (RIs) of volatile flavor compounds were calculated based on the retention time, which was determined by injection of a series of n-alkanes \(C_{9}\)-\(C_{25}\) (from AccuStandard Inc., New Haven, CT, USA) under identical conditions. These RIs were then compared with the RI values reported in the literature ([[http://webbook.nist.gov/chemistry](http://webbook.nist.gov/chemistry)]([http://webbook.nist.gov/chemistry](http://webbook.nist.gov/chemistry)) (accessed on 26 February 2024)). The relative abundances of all identified volatile compounds were compared using their peak areas in total ion chromatography (TIC) and then normalized by the peak areas of the internal standard (ISTD) from the same sample. Finally, the concentration of each compound was calculated using the following formula:  
\[c_{i}=\frac{A_{i}}{A_{s}}\times c_{s} \tag{1}\]  
where \"\(c_{i}\)\" signifies the concentration (mg/L) of each compound, \"\(c_{s}\)\" denotes the concentration (mg/L) of the internal standard (ISTD), \"\(A_{i}\)\" indicates the chromatographic peak area for each compound, and \"\(A_{s}\)\" signifies the chromatographic peak area of the ISTD.
# Satellite Measured Ionospheric Magnetic Field Variations over Natural Hazards Sites
## 3 Lithospheric Atmospheric Ionospheric Coupling  
Important scientific objectives are the couplings and energy carriers from solid earth sources up to ionospheric heights, the so-called lithospheric atmospheric ionospheric coupling (LAIC). This is in particular relevant if physically based short-term earthquake forecasting in the context of precursory signals of natural hazards shall be achieved, see [11] and references therein. Joint collaborative efforts in a multi-parameter approach shall facilitate early risk mitigation of this most of the time cascade of events and protect society and infrastructure. [PERSON] et al. (2012) [12] gave a summary of these couplings in terms of electromagnetic waves, refs. [13; 14; 15] investigated precursory phenomena, the latter with _Swarm_ data in respect of, e.g., precursor time versus main shock magnitude [16]. [PERSON] et al. (2020) [17] conducted co-seismic investigations with _Swarm_ magnetic field data, ref. [18] used magnetic field and electron density measurements in the analysis of large earthquakes.
## 2 Materials and Methods
### Polarization in Quantum Interference
The coincidence rate is:  
\[P_{c}\propto\int d\omega^{\prime}|f(\omega^{\prime})|^{2}[R^{2}+T^{2}-2 RT \langle s,r|r,s\rangle cos(2\omega^{\prime}(\delta l/c-\alpha d))] \tag{7}\]  
The second term responsible for the group velocity dispersion, the term containing \(\beta\), is effectively canceled. The interference dip is centered around \(\delta l/c=\alpha d\). The interference profile is defined by the coherence times of the photons emitted from the crystal \(|f(\omega^{\prime})|^{2}\) and the polarization projection of signal and reference photons. The above expression is identical to Hong-Ou-Mandel dip [2] in a dispersionless medium, where \(\langle s,r|r,s\rangle=1\) all signal photons are of the same polarization as reference photons. In the case of strong birefringence where signal and reference photons are of orthogonal polarization \(\langle s,r|r,s\rangle=0\) third term in Equation (7) is canceled, and the probability of detecting coincidence is uniform regardless of the scanning position and \(\delta l\) the optical path length difference, Figure 2.  
The ratio between two images, first at the interference dip where \(\delta l/c=\alpha d\) and second far outside the interference \(\delta l/c>\alpha d\), where the third term in Equation (7) is effectively zero, normalizes and highlights the effects of the birefringence profile, while simultaneously minimizes classical noise effects, such as scattering and absorption; for a balanced beam splitter, the ratio is simplified to  
\[ \begin{gathered} N_{c}\propto\int d\omega^{\prime}\frac{|f( \omega^{\prime})|^{2}[R^{2}+T^{2}-2 RT\langle s,r|r,s\rangle]}{|f(\omega^{ \prime})|^{2}[R^{2}+T^{2}]}\\ N_{c}\propto\int d\omega^{\prime}\frac{[0.5-0.5(s,r|r,s)]}{0.5}\\ N_{c}\propto 1-\langle s,r|r,s\rangle\end{gathered}  \tag{8}\]  
In a homogeneous and dispersionless medium, the sample and reference photons are of the same polarization, and the normalized coefficient is zero, while in the highly birefringent sample, the polarization of the entangled photons is orthogonal, and the normalized coefficient is one. This is the central result of our paper. In the next sections, we scanned biological tissue samples and imaged the birefringence and normalized coefficient.
# A Bayesian Modeling Approach to Situated Design of Personalized Soundscaping Algorithms
## 3 Experimental Validation
### Data Selection  
Two data sets have been used for experimental validation:  
* The LibriSpeech data set (the LibriSpeech data set is available at [[https://www.openslr.org/12](https://www.openslr.org/12)]([https://www.openslr.org/12](https://www.openslr.org/12)), accessed on 31 March 2021) [51], which is a corpus of approximately 1000 h of 16 kHz read English speech.
* The FSDnoisy18k data set (the FSDnoisy18k data set is available at [[https://www.eduardofonseca.net/FSDnoisy18k](https://www.eduardofonseca.net/FSDnoisy18k)]([https://www.eduardofonseca.net/FSDnoisy18k](https://www.eduardofonseca.net/FSDnoisy18k)), accessed on 13 April 2021) [52], is an audio data set, which has been collected with the aim of fostering the investigation of label noise in sound event classification. It contains 42.5 h of audio samples across 20 sound classes, including a small amount of manually-labeled data and a larger quantity of real-world noise data.  
From the LibriSpeech data set the first 1000 audio excerpts, consisting of approximately 200 min of speech, are used to train the speech source model. Besides this a random speech excerpt has been selected, outside of the first 1000 excerpts, to perform source separation and soundscaping. From the FSDnoisy18k data set also a random noise excerpt has been selected, in this case representing a clapping audience. The first 3 s of this signal were used to train the noise model and the consecutive 10 s were used for source separation and soundscaping.
# Preparation of Magnetically Driven Nickel Phosphide Nanowires and Their Electrochemical Properties
## 2 Experimental Procedure  
### Preparation of Nickel Phosphide Nanowires  
To prepare nickel phosphide nanowires, electrolyte solutions were prepared by mixing two aqueous solutions, A and B, at room temperature. Aqueous solution A was prepared by mixing various concentrations of sodium hypophosphite (a reducing agent, Sigma-Aldrich, St. Louis, MI, USA, anhydrous, 98-101%) in a mixed aqueous solution containing nickel sulfate (0.1 M, Junsei, Tokyo, Japan, 98.0%), ammonium chloride (0.34 M, Junsei, Tokyo, Japan, 98.5%), and trisodium citrate dihydrate (27 mM, a surfactant, Junsei, Japan, 99%). The pH was adjusted using sodium hydroxide (Junsei, Tokyo, Japan, 97%). Aqueous solution A was classified into two types as follows (Table 1): (1) solutions in which the pH and reducing agent concentration are fixed to investigate the effect of the magnetic field strength (samples 0L, 2L, and 18L in Table 1) and (2) solutions in which the magnetic field strength is fixed to investigate the effect of pH and the reducing agent (samples 18L, 18M, and 18H in Table 1). Aqueous solution B contained palladium chloride (1.7 mM, Sigma-Aldrich, St. Louis, MI, USA, 99%) as a nucleating agent, and the solution pH was adjusted to 2 using hydrochloric acid (Junsei, Tokyo, Japan, 99.5%).  
After mixing 50 mL of aqueous solution A and 3 mL of aqueous solution B, the resulting solution was heated for approximately 1 h at 70 \({}^{\circ}\)C to form nickel phosphide nuclei (the color of the electrolyte changed from green to black). Subsequently, a magnetic field was applied for approximately 4 min to grow them into nanowires. The magnetic field strength was controlled by changing the distance between two neodymium magnets located at both ends of the beaker containing the mixed aqueous solution and was measured using a Gauss meter (TM-801, Kanetec, Tokoyo, Japan). When the distances between the two magnets were 5 cm and 15 cm, magnetic fields of 18 mT and 2 mT, respectively, were incident on the center of the beaker under the cell configuration used in this study. After the experiment, the nickel phosphide nanowires that formed in the center of the beaker were  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample Name**} & **Concentration of Sodium** & \multirow{2}{*}{**pH**} & \multirow{2}{*}{**Magnetic Field Strength (mT)**} \\  & **Hypophosphite (M)** & & \\ \hline
0L & 0.15 & 11 & 0 \\
2L & 0.15 & 11 & 2 \\
18L & 0.15 & 11 & 18 \\
18M & 0.5 & 9 & 18 \\
18H & 1 & 7 & 18 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Preparation conditions of nickel phosphide nanowires.  
collected and washed with ethanol. The composition, shape, and crystal structure of the synthesized nickel phosphides were confirmed by energy-dispersive X-ray spectroscopy (EDS, 51-XMZ1004, Oxford instruments, Oxford, UK), field-emission scanning electron microscopy (FE-SEM, MIRA3, TESCAN, Brno, Czech Republic), and X-ray diffraction (XRD, Ultima-IV Rigaku, Tokoyo, Japan), respectively.
# Short Text Event Coreference Resolution Based on Context Prediction
## 3 Model
### BERT-Based Encoder Layer  
The BERT-based encoder layer is generally in a unified manner. The BERT input vector is used as the input sequence  
\[X=\{x_{1},x_{2},\dots,x_{n}\} \tag{1}\]  
the embedding representation of each word incorporating semantic information, location information, and sentence information is obtained by Formula (1),  
\[E=\{e_{1},e_{2},\dots,e_{n}\}. \tag{2}\]  
The encoding process goes through two main parts of BERT: the Transformer encoder module and the pooling module (total 12 layers). Specifically, each encoder layer in the Transformer encoder module contains a self-attention mechanism and a feedforward neural network. Among them, the self-attention mechanism calculates an attention weight matrix \(A_{i}\). Based on the embedding representation \(e_{i}\) of each word in the input sequence, we interact the embedding representation of each word with the embedding representations of other words. Then, the feedforward neural network will perform a non-linear transformation on the interacted embedding representation to obtain the encoded embedding representation \(H_{i}\),  
\[H=TransformerEncoder(E). \tag{3}\]  
Within the final layer of BERT, the pooling module is applied to transform the encoded embedded representation into a fixed-dimension vector representation \(T_{i}\). Two specific pooling techniques, namely average pooling and max pooling, can be employed. Subsequently, the results obtained from these pooling operations are concatenated to yield the ultimate vector representation \(C\) (i.e., BERT output vector),  
\[C=Concatenate([AvgPooling(H),MaxPooling(H)]) \tag{4}\]  
In the given equations, the term TransformerEncoder represents the Transformer encoder, while AvgPooling and MaxPooling correspond to average pooling and max pooling operations, respectively. Concatenate denotes the vector concatenation operation. The ultimate textual vector representation \(C\) obtained from this process can be effectively employed in a range of downstream natural language processing tasks.
## 3 Results
### Prevailing Visibility--Case Studies  
Considerable differences in the perception of the prevailing visibility between the standard AWOS and human observers appear predominantly in situations when spatially inhomogeneous fog covers the target area. Two of such situations were selected as illustrations.  
The first example presents fog in patches at LZTT (Figure 15, Table 11). It occurred on 10 October 2018 when the overall synoptic situation was governed by an anticyclone over the Central Europe. Consequently, the sky was clear, and due to the absence of clouds overnight, radiation fog formed over the area of LZTT. The light wind (up to 1-2 m/s) during the night did not disrupt the formation of the radiation fog.  
At the time of measurement (04:50 UTC), the standard AWOS was covered by some fog patches; therefore, it indicated medium range visibility of 4200 m (Table 11). On the contrary, the remote observer, with the help of camera images, was able to see the entire horizon, and consequently, was able identify different visibility conditions in different directions, with minimum visibility towards the east (Table 11). The same was observed by the LO. Fog with patches (BCFG) was observed between 02:00 and 5:30 UTC, then it changed to VCFG, i.e., fog in the vicinity, and it disappeared at about 08:00 UTC.  
The second use case demonstrates a meteorological situation with fog of spatially variable density (Figure 16, Table 12). On 28 October 2018, the region of the Central Europe was influenced by a low with a center over the Ligurian Sea, and by an associated warm front, progressing from south to north across Slovakia. The fog at LZTT occurred in this synoptic situation, approximately between 04:00 and 07:00 UTC. Due to the uneven density  
\begin{table}
\begin{tabular}{c c c c} \hline  & **AWOS** & **RO** & **LO** \\ \hline Visibility [m] & 4200 & 9999 & 9999 \\ Phenomenon & non & BCFG & BCFG \\ \hline \end{tabular}
\end{table}
Table 11: Estimation of visibility using three different approaches at LZTT, on 10 October 2018, at 04:50 UTC. BCFG—Fog in patches.  
Figure 15: Fog in patches at LZTT, on 10 October 2018, at 04:50 UTC.  
of the fog, the standard AWOS estimated the visibility as of 3000 m, and consequently, the fog was categorized as mist. On the other hand, both the local and the remote observers reported worse visibility conditions than the AWOS: both human observers agreed that the prevailing visibility was 700 m with fog as the dominant meteorological phenomenon (Table 12).