esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Continuous JND Experiments
#### 2.1.2 Simulation and Material  
Based on the information from other studies [30,31], the JND may depend on the characteristics of the motif used to present different sound fields. In addition, [PERSON] [31] reported that the apparent source width (ASW) and loudness in a room are more sensitive to changes in the levels of early reflections among four subjective parameters, since the variation in the physical parameter (1--IACC\({}_{\text{E3}}\)) and G values was 7 motifs, respectively. Notably, they fall into a similar range of directivities sent out by the same loudspeakers. [PERSON] [32] considered that low frequencies from most sources radiated mostly omnidirectionally because the wavelengths were longer compared with the size of the source; the directivity index (DI) was nearly zero. The DI can be interpreted as the difference in dB between the on-axis sound and the total radiated sound power, which, in a room, is related to the difference between the direct sound and the reflected sound field. As the frequency increases, so does the directivity of most sources. As the DI increases, the level of direct sound relative to later arriving reflections becomes higher. In present studies, the W_IACC values of many sound sources, including five instruments' recital tracks and a symphony piece, are calculated, which are the width of the interaural cross-correlation function (IACF) defined by the interval of delay time at a value of 0.1 below the IACC [33]. It indicates that the apparent source width (ASW) and the DI would play the part of W_IACC values of sound sources as they are issued by a loudspeaker. The result of the calculation is illustrated in Figure 3, where the cello and trumpet obviously keep the two lowest W_IACC values of all source samples. It means that a sound with a low W_IACC value can be easily reproduced with a domestic cone loudspeaker in the anechoic chamber.  
A 3 s segment was cut from each piece of music after it began in order to produce an audio sample for simulation purposes. Therefore, each subject had to listen through 91 continuous audio gaps and record their JND on cue. It took 6 min and 5 s (365 s) for each subject to finish listening to an audio piece. They then rested for at least 10 min before listening to the second piece.
## 2 Previous and Related Works
### DTN Architecture and Bundle Protocol  
The DTN Architecture [2] of 2007 describes an architecture for networks with special conditions where traditional protocols (TCP/IP) will not work. This architecture defines a message-oriented layer over the traditional transport layer, the Bundle Layer. The Bundle Layer establishes a store-and-forward kind of network with persistent storage as a measure against network disconnections, and uses Uniform Resource Identifiers (URI) [3] as a naming scheme. Applications can deliver and read Application Data Units (ADUs) to and from the Bundle Layer, which will encapsulate them into Bundles. These Bundles will be stored in persistent storage, and the responsibility of delivery can be transferred with the  
Figure 3: Modified GOLDFISH project. Cluster Nodes (CLN) and Gateway (GW) are depicted.  
Bundle, creating a kind of distributed end-to-end reliability. The DTN Architecture allows unicast, anycast, and multicast communications, and it can also use priorities. Finally, the DTN Architecture can take advantage of continuous, scheduled, predicted, and opportunistic connectivity.  
The Bundle Protocol [4] defines the exchange of messages (Bundles) between DTN nodes. The Bundle Protocol works as an application in the traditional networking model, since it seats on top of the transport layer, communicating DTN applications between DTN nodes. Its inner working looks like a communication between a couple of neighboring nodes for normal protocols, but this communication can include the transfer of custody and responsibility of delivery of a message. DTN nodes, then, can store Bundles until they have the opportunity to send them to their destination or to another node that can deliver them. In normally disconnected or high-delay networks, this storage can lasts for minutes, hours or days. The Bundle Protocol is independent of networking protocols in the layers below its implementation, and applications should be DTN-compliant or at least know what to do when a message arrives long after its request.
# Quantifying Privacy Risks for Continuous Trait Data
## 6 Attack Validation
### Real Data  
We use a real QTL dataset to verify the validity of major factors summarized in the previous Section 6.3.4. The real dataset _CSF pQTL study in the Japanese population_ used in this study consists of proteomics and SNPs publicly available in the gene expression omnibus (GEO) database under references GSE83708, GSE83709 and GSE83710. GSE83708 represents the SNPs of 89 samples with genotyping Q17, GSE83709 represents the SNPs of 44 samples with genotyping Q35, and GSE83710 is the proteomic data of all 133 samples, which were collected between May 2010 and February 2014 at the National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan [48]. The entire dataset contains the relative fluorescence units (RFU) of the cerebrospinal fluid (CSF) levels of 1126 proteins in 133 subjects and 954,703 SNPs.  
Analysis by previous researchers [48] revealed that a total of 421 cis and 25 trans SNP-protein pairs were significantly correlated at a false discovery rate (FDR) of less than 0.01 (\(P_{nominal}<7.66\times 10^{-9}\)); cis-only analysis revealed an additional 580 significant cis SNP-protein pairs with an FDR < 0.01 (\(P_{nominal}<2.13\times 10^{-5}\)). All the data can be downloaded from the National Center for Biotechnology Information (NCBI) database.  
Figure 8: _Sensitivity score_ Sen **with SNP size \(m=50\)**. The sample sizes \(n\) as 50, 100, 200, and 500 are expressed on the abscissa, respectively, while the matching accuracy is expressed on the ordinate. Matching accuracy with different MAFs and their standard deviations are expressed as different colored symbols with error bars, while the orange ellipse, red rectangle, olive diamond and blue triangle represent the MAF value of 0.05, 0.15, 0.30 and 0.45, respectively. **(a,b)** indicate \(z\) of 0.13 and 2.33, respectively. **(a)**\(z=0.13\). **(b)**\(z=2.33\).
## 3 Data  
As reference seismic catalog, we use the global centroid moment tensor (CMT) catalog (22; 23) of shallow seismicity (depth \(\leq\) 50 km) from 1980 to 2019. We do not decluster the catalog, to better exploit the potentiality of our method that does not assume any temporal distribution for earthquakes. As already commented above, this allows using more data and avoiding the introduction of the biases induced by declustering on the \(\beta\) estimation (14; 24). We selected the events in the Atlantic ridge approximately in the latitude range \(-60^{\circ}\):\(60^{\circ}\) (see Figure 5a).  
Regarding the magnitude of completeness, we use \(M_{w}\) 5.5 from 1980 and \(M_{w}\) 5.0 from 2004, the ones suggested by the authors of the catalog ((23), see Figure 6). We then carefully test this choice of completeness: as suggested by [PERSON] et al. (25), if the catalog is complete the magnitudes must follow an exponential distribution, and the exponentiality of the magnitudes can be tested through the Lilliefors (26) test. To apply this test with multiple completeness levels, we can build a vector of variables by subtracting to each magnitude the corresponding magnitude of completeness (\(M-M_{\rm C}\)), and test the exponentiality of this dataset (27). The hypothesis of exponential distribution cannot be rejected at any confidence levels, as we obtain a very large \(p\)-value (0.50). This demonstrates the robustness of the chosen magnitudes of completeness. A further check is also performed in Figure 5b by plotting the \(M-M_{\rm C}\) vs. the sequential number of events: as suggested by [PERSON] et al. (28), a homogeneous pattern near the Y-axis (as it is possible to see in Figure 1b) suggests the correct selection of completeness values for the catalog. The final catalog contains 1168 events.  
Figure 4: Contour plot of the bivariate log-likelihood function for the parameters of the Tapered Gutenberg-Richter distribution; different colors represent the different log-likelihood values, according to the color bar on the left; the black curve represents the 95% confidence region, and the black dot represents the maximum likelihood estimation MLE.
# Pigment Dyeing of Atmospheric Pressure Plasma-Treated Cotton Fabric
## 2 Experimental
### Atmospheric Pressure Plasma Treatment of Cotton Fabric  
The atmospheric pressure plasma treatment set-up consisted of a (i) gas supply; (ii) plasma generator; (iii) plasma head; and (iv) sample stage [5] (Figure 1). The carrier gas used for plasma treatment was helium (99.995% purity), and oxygen (99.7% purity) was used as the reactive gas. The flow rate of helium was kept at 30 L/min, while that of the reactive gas was varied, according to Table 1. The plasma is generated at a radio frequency of 13.56 MHz in the plasma generator (Atomflo 400, Surfx Technologies LLC, Redondo Beach, CA, USA). A plasma head (AH-500L, Surfx Technologies LLC, Redondo Beach, CA, USA) was connected to the plasma generator vertically above the cotton fabric, such that the active plasma species could interact with the cotton fabric. The cotton fabric was stationed in a sample stage, and the plasma head was moved at varying speeds (Table 1). Three plasma operational parameters: discharge power, the flow rate of oxygen, and the head moving speed were varied in different combinations (Table 1). The head-to-substrate distance was kept at 3 mm. After atmospheric pressure plasma treatment, the cotton fabric was stored in standard condition (20 \(\pm\) 2 \({}^{\circ}\)C and 65 \(\pm\) 2% relative humidity) for 24 h before pigment dyeing.  
Figure 1: Atmospheric pressure plasma treatment set-up.
# Analysis of Load-Settlement Curve Based on Load Transfer at Pile-Soil Interface
## 4 Analysis of Pile Settlement Mechanism
### Analysis of Basic Physical Parameters
#### 4.1.3 Analysis of Soil Compression Coefficients  
As shown in Figure 7, the compression coefficient of the soil samples steadily declines with increasing depth, and its fluctuation pattern stays consistent, tending to moderate with increasing pressure. The compression coefficient drops significantly with extremely big variations when the soil sample is subjected to pressures ranging from 100-200 kPa. Soil samples at a depth of 0-15 m belong to extremely compressible soils, soil samples at a depth of 15-25 m belong to medium compressible soils, and soil samples at a depth of 25-30 m belong to low compressible soils.  
Figure 6: The depth of soil sample deformation varies with pressure.  
Figure 7: Compression coefficient of a soil sample at various depths with a pressure change curve.
## 2 Model and Methods  
### Satellite Signal Model  
We use the same satellite signal model presented in previous studies on rain field [19] and cloud field retrieval [34]. The model consists of a LEO satellite passing directly over the area of interest and multiple ground receivers equipped with electronically steerable antennas. It should be noted that the receivers designed for satellite broadband will have a minimum elevation angle requirement in order to satisfy the minimum signal quality requirement for communication. When the minimum elevation angle is met, the satellite-to-ground link becomes operational and the SNRs at the ground receivers are estimated for the purpose of measuring the path-integrated attenuation. For one overpass, suppose that \(k\) denotes the elevation angle in degrees, and \(\rho(k)\) is the SNR at the receivers in the form of [19; 20]  
\[\rho(k)=C(k)-F_{n}(k)-A_{I}(k)-A_{F}(k), \tag{2}\]  
where \(A_{I}(k)\) and \(A_{F}(k)\) are the path-integrated rain attenuation and the free space path loss, in decibels, respectively. Parameter \(C(k)\) is an unknown baseline value for a receiver, which is mainly determined by the power of the transmitted signal, the antenna gain and the noise temperature of the receiving system. Noise figure \(F_{n}(k)\) is a function of the sky temperature and the noise temperature of the receiving system. We assume that the noise temperature of the receiving system is known so \(F_{n}(k)\) is a function of the unknown sky noise. A detailed description of \(C(k)\), \(A_{F}(k)\) and \(F_{n}(k)\) can be found in [19] (pp. 5437-5438).  
It is assumed that each receiver is operating on a dual-polarization mode, i.e., both vertical and horizontal polarizations are used at the same time. Hence, the system offers two estimated SNRs, namely \(\hat{\rho}_{V}(k)\) and \(\hat{\rho}_{H}(k)\) at every receiver, for measuring path-integrated attenuations.
## 3 Results and Discussion
### Characterization of the Produced Biofloculant  
The flocculation characteristics of bioflocculants are contingent upon the presence of functional groups [25]. Diverse microorganisms can create bioflocculants that exhibit distinct compositions and chemical structures [26]. FTIR was used to analyze the functional groups of the biofloculant (D2). The resultant spectrum shown in Figure 1 had a broad polymeric stretch of the hydroxyl (O-H) group at 3296.94 cm\({}^{-1}\). A peak identified at 1631.88 cm\({}^{-1}\) is related to the carbonyl group from amides, which represents the presence of proteins [27]. This was followed by a weak stretch at 1406.04 cm\({}^{-1}\), indicating the presence of the carboxylate group. Hydroxyl and carboxyl groups are favorable for flocculation, as they are known to participate in hydrogen bonding with the particles or pollutants [28]. The peaks at 1083.65 and 990.76 cm\({}^{-1}\) depict ethers, typically sugar derivatives. Overall, it was evident from the FTIR analysis that biofloculant D2 is made up of polysaccharides and proteins. This agrees with the literature reviewed that proteins and polysaccharides make up the highest composition of EPS, and they indirectly affect flocculation activity [4].
## 2 Engineering Overview and Finite Element Model Establishment
### Bridge Finite Element Modeling
#### 2.2.1 Simulation of Bearing  
The bearing adopts a plate rubber bearing. The test results of a large number of plate rubber bearings show that the hysteresis curve of plate rubber bearings is narrow and long, which can be processed approximately linearly [32; 33]. Therefore, this study uses a linear spring element to simulate the restoring force model of the rubber plate bearing. The plane size of the plate rubber bearing is 300 mm \(\times\) 400 mm, and the total thickness is 47 mm. The calculation method of shear stiffness and vertical stiffness of the bearing is as follows.  
Figure 4: Fiber model to simulate the pier section.  
Figure 5: Constitutive Mander model of concrete.  
Figure 6: Giuffre–Menegotto constitutive model of steel bar.  
Horizontal rigidity of the bearing:  
\[k=\frac{G_{d}A_{r}}{\sum t} \tag{1}\]  
Vertical stiffness of bearing:  
\[k_{\rm v}=\frac{EA_{r}}{\sum t} \tag{2}\]  
where \(G_{d}\) is the dynamic shear modulus of the plate rubber bearing, which is generally taken as 1200 \(\rm{kN}/\rm{m}^{2}\); \(A_{r}\) is the shear area of the rubber bearing; \(\sum t\) is the total thickness of the rubber layer. \(E\) is the vertical compressive elastic modulus of the plate rubber bearing.
# Enhancing Visual Data Security: A Novel FSM-Based Image Encryption and Decryption Methodology
## 4 Results and Discussion
### Comparison of Results
Lower diagonal correlation is indicative of robust encryption that effectively masks the diagonal patterns, which are critical for maintaining image structure. Compared to advanced methods like those using hyper-chaotic systems or spatial bit-level permutation, the FSM-based method not only holds its own but often surpasses these in terms of reducing correlation, demonstrating its robustness and reliability.
# A Survey of Graphene-to-Phoneme Conversion Methods
## 2 Grapheme-to-Phoneme Methods
### Methods Based on Deep Learning  
With the development of artificial intelligence, more researchers are using deep learning models to tackle the G2P conversion problem. Their goal is to outperform earlier approaches in terms of accuracy and make it simple to include in speech recognition and end-to-end text-to-speech systems. Compared with the traditional statistical machine learning approaches, neural network-based G2P conversion models are usually more robust with better performance. However, they have to face common problems in deep learning such as overfitting, vanishing and exploding gradients, and high computational resource requirements.
## 4 Evaluation and Discussion
### Experimental Settings  
The experiments are designed using the configurations tabulated in Table 1. The simulation parameters can be divided into two parts: One is the link budget parameters. The parameters used include the channel bandwidth, effective isotropic radiated power (EIRP), and receiving antenna gain per the SpaceX system's characteristics [30]. SpaceX's system uses the Ku-band for communications: specifically, the 12 GHz band used for our simulation system. The other is the satellite and UAV parameters. We follow 3 GPP's example and focus on the LEO constellations at 600 km altitude, for which a satellite moves at a velocity of 7.56 km/s relative to Earth [31]. For the reason that the distance of the UAV from the ground is much smaller than the orbital height, the height of the UAVs is negligible. The deployment of UAVs is different according to different situations. For example, for monitoring highways or high-voltage lines, UAVs are evenly distributed in a line; during an earthquake, UAVs are generally randomly distributed within a radius of several kilometers to tens of kilometers. Thus, we design two experimental scenarios that are Case 1: UAVs are evenly distributed in a line, and Case 2: UAVs are randomly distributed under the footprints of satellites.  
Figure 3: The optimized adjustable satellite association process.  
Besides the proposed algorithm, two reference algorithms are adopted to compare the performance. One reference algorithm is based on the maximum capacity; the other is based on the maximum service time. These two are commonly used for solving the LEO satellite association problem [32]. For the three different algorithms, we repeated the random association process 1000 times and took the average.
## 3 Results and Discussion
### Setting Time
#### 3.2.2 Effect of Composite Component Retarder on Setting Time of HBCSA Paste  
When \(\beta\)-CD and CA are mixed together, the time interval between the initial setting and the final setting is large, which is difficult to control. The time interval between the initial setting and final setting of the mortar after the mixing of the two was significantly shortened, and the setting time was longer than that of the single mixing (Table 3). When the content of \(\beta\)-CD was 0.5% and the content of CA was 0.5%, the coagulation time reached 73 min and 80 min, respectively. It can be seen that after compounding in a certain proportion to the single mixing conditions, the slow setting effect has been improved, the setting time is controlled in about 1 h, and the initial and final setting time interval can also be controlled within the time. However, the retardation effect of the composite components \(\beta\)-CD and B is also more obvious, and the setting time is improved compared with the single-mixed setting time, and the initial setting time is extended to about 60 min or even 90 min. When the content of \(\beta\)-CD is constant, the setting time will also increase with the increase in the content of B. When the content of \(\beta\)-CD reaches 0.9%, the retardation effect is the best, which further indicates that the smaller the B content is, the less obvious the retardation effect is.  
The results from the two composite components show that the retardation effect is more obvious than that of the single retarder, which indicates that the two have a certain synergistic effect. This is also because both \(\beta\)-CD and CA contain hydroxyl groups, which can effectively synergize the adsorption of water molecules in the cement slurry after compounding, so that the cement hydration is slower, thereby effectively prolonging the setting time. When \(\beta\)-CD is added alone, the effect of inhibiting the cement hydration is remarkable. Therefore, after adding B, Ca2+ dissolved in water cannot participate in the reaction and will react with B4O72- ionized by B to generate more calcium borate and form a protective film that covers the cement particles, further preventing cement hydration.
# Collision Frequency and Energy Transfer Rate in e-He Scattering
## 3 Results and Discussion  
### Interaction Potentials  
The graphs of the potentials described above are shown in Figure 1. The main contributions to the optical potential are from the polarization potential \(V_{P}(r)\) and the Dirac-Hartree-Fock-Slater potential \(V_{DHFS}(r)\). As shown in Figure 1, the behavior of the optical potential tends asymptotically to \(-\alpha_{d}/(2r^{4})\) for distances greater than the cutoff parameter \(r_{c}\) in the polarization potential (see Equation (9)). The optical potential has a minimum, which is due to the superposition of the different potentials.  
The polarization potential (\(V_{P}(r)\)) accounts for the induced dipole moment resulting from the interaction between the electron and the helium atom. The exchange potential (\(V_{\text{ex}}(r)\)) accounts for the exchange interaction where the incoming electron and one of the atomic electrons exchange places, which becomes significant for low-energy scattering.  
The figure illustrates that the optical potential has a well-defined minimum due to the combined effects of the static and polarization components, providing an attractive interaction at certain distances that influence scattering properties.