esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Scenario and Specifications
### Data Transmission Specifications  
This study applied data compression and preprocessing techniques to maximize data transmission efficiency and minimize power consumption in an AIoT scenario. Specifically, techniques such as run-length encoding (RLE) and the k-means clustering algorithm (with \(k=5\)) were used to reduce the size of the transmitted data. Although previous computer-based simulations on sensor data processing (conducted on desktop platforms) demonstrated a modest data reduction of approximately \(2.4\pm 0.7\%\), our approach considers an onboard IoT scenario, where, due to differences in processing conditions and constraints, a more aggressive reduction of up to \(85\%\) is achievable. In other words, only about \(15\%\) of the original data is transmitted. This assumption is based on considerations of a similar dataset and typical IoT hardware performance.  
According to a study conducted by [PERSON] et al [26], the initial daily data transmission volume was set to \(11.52\,\mathrm{MB}\), and after applying an \(85\%\) reduction, the optimized daily transmission target was calculated to be approximately \(1.73\,\mathrm{MB}\). This significant reduction in transmitted data not only decreases transmission delays and conserves energy but also makes the use of LPWAN technology more advantageous in a satellite-based IoT-NTN environment.  
Note that all parameters in both the antenna and data transmission designs are based on theoretical values and simulation experiments. Practical implementation issues, such as measurement uncertainties and environmental variations, are beyond the scope of this study.
## 2 Materials and Methods  
### Chemicals and Materials  
Extra pure graphite, potassium peroxdisulfate (K\({}_{2}\)S\({}_{2}\)O\({}_{8}\)), diphosphorus pentoxide (P\({}_{2}\)O\({}_{5}\)) and sulphuric acid (H\({}_{2}\)SO\({}_{4}\)) (98%) were purchased from Fisher Scientific (Ottawa, ON, Canada). Potassium permanganate (KMnoA), hydrochloric acid (HCI) (37%) and hydrogen peroxide (30%) were purchased from Sigma-Aldrich (Urbana, IL, USA). Cationic dyes rhodamine B (RhB) (C\({}_{28}\)H\({}_{31}\)ClN\({}_{2}\)O\({}_{3}\), Molecular Weight: 479.0 g/mol), molecular size (0.9-1.44 nm) and methylene blue (MB) (C\({}_{16}\)H\({}_{18}\)ClN\({}_{3}\)S, Molecular Weight: 319.9 g/mol), molecular size (0.7 nm) were purchased from Anachemia, a VWR Company (Mississauga, ON, Canada). Granular activated carbon with the commercial name of Norit was purchased from Acros Organics, part of Thermo Fisher Scientific, Branchburg, NJ, USA, with 40 \(\times\) 12 mesh size and produced from bituminous coal. The particle size range was changed to 40 \(\times\) 20 mesh (420-840 \(\upmu\)m) by sieving. Zeolite NaY was purchased from Alfa Aesar (Thermo Fisher Scientific Company, Ottawa, ON, Canada) with a pore range size (0.9-1.2 nm).  
The scanning electron microscope (SEM) model, (Tescan VegaII XMU, Warrendale, PA, USA), was used to examine the surface morphology and structure of GAC, NaY, and GO. The material structure, distance between layers, and presence of functional groups in GAC, NaY, and GO were determined with X-ray diffraction (XRD) from 0 to 80\({}^{\circ}\) of 20 with Cu-K\(\alpha\) radiation with (Rigaku MiniFlex 6G, Woodlands, TX, USA). Determination of surface chemistry, types of functional groups, and their bonding on the surface of GAC, NaY, and GO were investigated with Fourier transform-infrared (FT-IR) (Thermo-Fischer, Nicolet Magna 760, Waltham, MA USA) from 400 to 4000 cm\({}^{-1}\) wavenumber.
# Bearing Fault Diagnosis Based on Improved Convolutional Deep Belief Network
## 3 Experimental Validation
### Diagnosis Results and Comparative Analysis
#### 3.2.1 Comparison with SAE, ANN, and DBN  
The test results of the proposed model are shown in Figure 11b,d. The proposed model can achieve 100% accuracy for most types and degrees of faults. By contrast, misclassifications occur in the case of detecting an IO fault type with a small degree, as a result of the features of the IO fault type are less obvious than those of other types of faults. Operating on the collected experimental data set, the proposed improved CDBN model generally achieved an accuracy of 98.15% with 0 kN load and 96.15% with 1 kN load.  
Tables 5 and 6 illustrate the comparison results of the proposed improved CDBN model with SAE, ANN, and DBN. The length of the input signal was 1024. A single hidden layer with 100 hidden neurons was used in SAE; ANN also had the same structure as SAE; DBN had two hidden layers, whose first and second hidden layers each have 100 neurons. The listed results indicate that the proposed model had an obvious advantage over the other models. When the detection performance of the other models was considerably decreased, the proposed model was still able to maintain a high detection accuracy.  
Figure 11: (**a**) Testing result of dataset1 using the standard CDBN. (**b**) Testing result of dataset1 using the improved CDBN. (**c**) Testing result of dataset2 using the standard CDBN. (**d**) Testing result of dataset2 using the improved CDBN.
# Revealing Accessibility of Cryptic Protein Binding Sites within the Functional Collagen Fibril
## 4 Computational Studies of the Collagen Fibril
### Building the Atomic Model of the Collagen Fibril  
We have constructed an atomic model of a collagen fibril unit cell based on the low-resolution fiber diffraction structure (PDB entry 3 HR2 [53], see Figure 1 for fibril architecture). The fiber diffraction provided structural information about the C\(\alpha\) atoms, giving the overall shape and orientation of the collagen monomers. From this structure, we built a complete all-atom monomer model by adding side-chain and backbone atoms, plus water molecules to fill the repeating unit cell, following work by the [PERSON] group [99; 100]. This was equilibrated by MD simulations with loose tethering of the C\(\alpha\) atoms to their positions in the 3 HR2 structure [53]. Five collagen triple helix monomers from the equilibrated model were duplicated and packed by D-period to create a microfibril and then cut along the z-axis to the length of one D-period to generate one SRU. A finite, non-periodic fibril SRU model was then built with one complete D-period along the \(z\)-axis, and three SRUs along the \(x\)- (depth) and \(y\)- (height) axes (Figure 2A). Unlike periodic models, this fibril model has a surface on either side, surface layer A (with D5 and D4 on the surface) and B (with D1 on the surface), where proteins or other effectors may bind, and an inner layer that is completely shielded from a fibril surface.
# Feasibility Analysis of Online Uncertainty Metrics for PMU-Based State Estimation
## 6 Conclusions  
In this paper, we investigated the feasibility of using online performance metrics in PMU-based state estimation. For this analysis, we considered a metric that does not suffer from definitional uncertainty and is characterized by reduced computational requirements. From a metrological point of view, we evaluated its significance and variation range in standard and real-world test conditions, and we compared its confidence interval with other accuracy metrics, whose evaluation is possible only in controlled laboratory conditions.  
By means of extensive numerical simulations, we have verified the feasibility of this approach with two well-known state estimators, namely WLS and DKF. The obtained results showed a limited performance improvement, due to the still non-compensated contribution of the other uncertainty components of the measurement chain. Nevertheless, the proposed approach represents a promising first effort to associate to each measurement value reliability indices that depend not on fixed a priori information, but on the actual operating condition of the system and of the instrument.
## 2 Performance Assessment
### Signal-to-Noise Ratio Simulations
#### 2.2.1 Atmospheric Model  
As explained previously, the lidar system sounding of the atmosphere will be horizontal and so a homogenous optical atmospheric model is considered in the SNR simulations. Clear-air oversimplified atmospheric conditions are assumed in this model (15 km visibility at 550 nm) and the total extinction and backscatter coefficients, \(\alpha_{tot}\) and \(\beta_{tot}\), respectively, are obtained as the sum of the opto-atmospheric components due to aerosols (\(\alpha^{aer}\), \(\beta^{aer}\)) and molecules (\(\alpha^{mol}\), \(\beta^{mol}\)). The calculation of each of these components is given below.  
The molecular (Rayleigh) extinction coefficients at different wavelengths are obtained by applying the following expression:  
\[\alpha^{mol}\left(\lambda\right)=\left(\frac{550}{\lambda\left[nm\right]} \right)^{4}\alpha^{mol}_{zzzz} \tag{1}\]  
where \(\alpha^{mol}_{zzzz}=0.0116\) km\({}^{-1}\) is the molecular extinction at 550 nm [18].  
The molecular backscatter coefficient is a constant multiple of the coefficient of extinction and is given by the relationship:  
\[\beta^{mol}\left(\lambda\right)=\frac{3}{8\pi}\alpha^{mol}\left(\lambda\right) \tag{2}\]  
With known atmospheric visibility \(V_{M}\) [km], the particulate (Mie) extinction coefficient \(\alpha^{aer}_{zzzz}\) [km\({}^{-1}\)] at 550 nm is calculated by applying [PERSON]'s relationship [19]:  
\[\alpha^{aer}_{zzzz}=\frac{3.91}{V_{M}\left[km\right]} \tag{3}\]  
At different wavelengths, extinction due to aerosols is given by:  
\[\alpha^{aer}=\alpha^{aer}_{zzzz}\left(\frac{550}{\lambda\left[nm\right]}\right) ^{1.3} \tag{4}\]  
For link-budget studies, as is the case here, a typical Mie backscatter coefficient \(\beta^{aer}_{zzzz}\) [km\({}^{-1}\)-sr\({}^{-1}\)] at 550 nm is well assumed for different atmospheric visibility conditions [18]. At other wavelengths, the backscatter coefficient is calculated by applying:  
\[\beta^{aer}=\beta^{aer}_{zzzz}\frac{550}{\lambda\left[nm\right]} \tag{5}\]Table 1 gives, for each of the wavelengths under consideration, the total coefficients of atmospheric extinction \(\alpha_{tot}\) and backscatter \(\beta_{tot}\), calculated through Equations (1)-(5). The values of the diffuse component of solar radiation \(L_{b}\)[20] are also shown.  
In the SNR simulations to be presented next, the presence of a pesticide cloud located at 500 m is considered. Extinction or backscatter coefficient values for spray drift clouds have not been found in the literature. For this reason, standard values for low-water clouds (\(\alpha_{cloud}\) = 10 km\({}^{-1}\), \(\beta_{cloud}\) = 0.5 km\({}^{-1}\cdot\)sr\({}^{-1}\), [18]) were used in this study, this being a conservative approximation.
## 2 GPS Attitude Baseline Determination
### Cycle Slip Detection  
A loss of a satellite signal lock leads to a reinitialization of the corresponding integer counter in the GPS receiver. As a result, the previously known ambiguity parameter of the affected satellite is not valid any longer. These cycle slips have to be detected, if the ambiguities are held fixed. The cycle slip detection can be done using a Kalman filter [37, 38], where an exceptional change in the carrier phases between two measurement epochs will be conspicuous, when a good prediction of the baseline parameters is available. These requirements are met here, since the motion behavior of the baseline parameters between two epochs is well known from the sensor integration.  
The transfer of the information from the sensor integration to the cycle slip detection is realized using the attitude change, which can be converted into the first deviation of the baseline parameters \(\Delta\mathbf{\tilde{x}}_{b}^{e}\). Finally, this information serves as control input, to check the consistency of the ambiguity parameters in a second GPS Kalman filter, which is decoupled from the float solution.  
The state vector \(\mathbf{x}_{C}\) of this simple filter includes the baseline and the ambiguity parameters. The system dynamics model is a random walk model:  
\[\mathbf{x}_{C,k}^{-} =\mathbf{\Psi}_{k}\mathbf{x}_{C,k-1}^{+}+\mathbf{B}_{k}\mathbf{u }_{k} \tag{16}\] \[=\begin{bmatrix}\mathbf{I}_{3\times 3}&\mathbf{0}_{3\times m}\\ \mathbf{0}_{m\times 3}&\mathbf{I}_{m\times m}\end{bmatrix}\begin{bmatrix}\Delta \mathbf{x}_{b}^{e}\\ \mathbf{N}_{AB}^{j}\end{bmatrix}+\begin{bmatrix}\Delta t&\mathbf{I}_{3\times 3} \\ \mathbf{0}_{m\times 3}\end{bmatrix}\Delta\mathbf{\tilde{x}}_{b}^{e}\]  
The observation vector includes the carrier phases and the pseudoranges:  
\[\mathbf{z}_{k}=\begin{bmatrix}\phi_{AB}^{1k}&\ldots&\phi_{AB}^{mk}&P_{AB}^{1k }&\ldots&P_{AB}^{mk}\end{bmatrix}^{T} \tag{17}\]  
and the measurement matrix represents the nonlinear functional model for a single baseline:  
\[\mathbf{H}_{k}=\begin{bmatrix}\mathbf{A}_{n\times 3}&\lambda\mathbf{D}_{n \times m}\\ \mathbf{A}_{n\times 3}&\mathbf{0}_{n\times m}\end{bmatrix} \tag{18}\]  
Cycle slips can now be detected considering the innovation:  
\[\mathbf{d}_{k}=\mathbf{z}_{k}-\mathbf{H}_{k}\mathbf{x}_{C,k}^{-} \tag{19}\]  
In case no cycle slips exist, the innovation of the carrier phases remains close to zero. When the innovation of one DD carrier phase exceeds a threshold \(d>0.1\) m, a cycle slip is probably the reason, and the corresponding ambiguity parameter is re-initialized. If at least three DD carrierphases show inaccuracies, the complete ambiguity set is rejected, and the ambiguity resolution process is re-initialized.
# Effect of Organic and Conventional Production on the Quality of Lemon \"Fino 49\"
## 3 Results and Discussion
### Sensory Analysis  
Of the total number of consumers (100), 44% were male and 56% were female, of which 39% were between 18 and 24 years old, 39% were between 25 and 35 years old and 22% were older than 36 years. The color, bitterness and aftertaste of the samples did not have significant differences between the samples. On the other hand, consumers liked the lemon odor (6.9), lemon flavor (6.5) and sourness (5.5) more in the organically grown samples (Table 7). These data are correlated with those found in the results of volatile compounds, since the organic samples obtained the highest total concentration of these compounds. On the other hand, these results are not directly related to the values found in the analyses of the content of sugars and organic acids. Lemons from conventional cultivation had a higher concentration of sugars and organic acids than those from organic cultivation. However, consumers found the organically grown samples to be sourer. This may be due to the fact that, despite having a lower concentration of organic acids (the organic samples compared to the conventional ones), they also had a lower concentration of sugars, so it is possible that the total acid/sugar ratio is higher, and the acidity stands out. Likewise, consumers also preferred organic samples (6.2) for overall liking (global), the attribute that defines the final opinion of consumers about the overall quality of a sample. [PERSON], et al. [54] showed that small changes in sensory sourness can cause cognitive associations that lead consumers to relate this change with greater intensities of fruity and citrus flavors. Sourness is the sensory characteristic most appreciated by consumers in lemons, so it is justified that a higher perception of acidity is related to a higher degree of satisfaction. When consumers were forced to choose which was their favorite sample, organic lemons were the most liked ones (78%) and, in the same way, organic lemons had 50% of consumers willing to pay, in contrast to only 33% presented by conventional lemons (Figure 1). Consumers mentioned that the main reasons for selecting the preferred sample (the most liked one) were: (i) lemon flavor (\(\sim\)67%), (ii) sourness (\(\sim\)44%) and (iii) lemon odor (\(\sim\)28%).
# Real-Time Monitoring of Passenger's Psychological Stress
## 2 Results
### Test Phase
#### 2.2.2 Estimation Performances  
For the last three (non-stressful) conditions described above, the RMSE from participant's self-rated stress level was computed, along with the proportion of correctly classified 60 s time windows. The results are shown in Table 2 below. The second column gives the number of instances per period. Stress was not estimated when the heart rate data was not available enough for feature extraction (see Section 4): this explains why the number of time windows does not sum up to the total duration of each condition. The third column shows RMSEs of raw model outputs for each period. The fourth column shows RMSEs for the data displayed in Figure 1, i.e., clipped between 0 and 1. The last column displays the classification rate (actually a true negative rate) for each condition, and its expected error rate. The last line displays the mean value and error for each of these columns.  
One may notice that classification performance for the _relaxed_ condition (i.e., \"Train II\") reaches the maximum value. This period also shows the greatest performances in RMSE. Classification rates remain high in all three conditions, the greatest false positive rate (7.4%) being reached in the most active condition (\"Train I\").
###### Abstract  
Active noise control (ANC) is a re-emerging technique to mitigate noise pollution. To reduce the noise power in large spaces, multiple channels are usually required, which complicates the implementation of ANC systems. In this paper, we separate the multichannel ANC problem into two subproblems, where the subproblem of computing the control filter is usually an underdetermined problem. Therefore, we could leverage the underdetermined system to simplify the ANC system without degrading the noise reduction performance. For a single incidence, we compare the conventional fully-coupled (pseudoinverse) multichannel control with the colocated (diagonal) control method and find that they can achieve equivalent performance, but the colocated control method is less computationally intensive. Furthermore, the underdetermined system presents an opportunity to control noise from multiple incidences with one common fixed filter. Both the full-rank and the overdetermined optimal control filters are realized. The performance of these control methods was analyzed numerically with the Finite Element Method (FEM) and the results validate the feasibility of the full-rank and overdetermined optimal control methods, where the latter could even offer more robust performance in more complex noise scenarios.  
Active noise control (ANC); multichannel control; open window; underdetermined system; finite element method (FEM).
# Deep Cross-Network Alignment with Anchor Node Pair Diverse Local Structure
## 4 Experiment
### Datasets  
We validate the effectiveness of DCSNA on both real-world datasets and the synthetic datasets.  
* Real-world dataset. There are three datasets in it, ACM-DBLP(Dataset.1) [(43)], arXiv dataset(Dataset.2) [(44)], and Twitter-Foursquare(Dataset.3) [(3)].
* The synthetic dataset. It is constructed based on Facebook [(45)]. We remove nodes with degrees of less than 10 and sample two networks in accordance with the methodology described in [(12)], resulting in 38,344 nodes and 1,183,080 edges. It uses a distribution \(p\sim U(0,1)\) to determine which network each edge belongs to. Whenever \(p\leq 1-2a_{s}+a_{s}a_{c}\), the edge is thrown away; if the edge is only preserved in the first network, \(1-2a_{s}+a_{s}a_{c}<p\leq 1-a_{s}\); if \(1-a_{s}<p\leq 1-a_{s}a_{c}\), only the other network retains the edge; otherwise, both networks maintain the edge. We set \(a_{s}=a_{c}=0.5\) and denote the synthetic dataset as Dataset.4.  
The statistics of datasets are summarized in Table 1 (The datasets can be found at [[https://github.com/tjuwangyinghui/DCSNA](https://github.com/tjuwangyinghui/DCSNA)]([https://github.com/tjuwangyinghui/DCSNA](https://github.com/tjuwangyinghui/DCSNA)) (accessed on 26 April 2023)). As shown in Figure 2, we use the anchor node neighbors to their neighbors \(r\) as the analysis index. Specifically, if \(r\) in each network less than 0.5 means that about half of the local structures between anchor pairs are inconsistent. \(p_{r}\) is the proportion of anchor node pairs in each dataset whose \(r\) is less than 0.5 in both networks.
## 2 Study Area  
The study area, Lingang tidal flat (LGTF), is located on the south coast of the Shanghai Lingang New Area, which is situated at the intersection of the Changjiang River Estuary and Hangzhou Bay. Its exact location is shown in Figure 3. As of 21 November 2023, the tidal flat area measures approximately 2.54 km\({}^{2}\), with an alongshore extent of around 4 km and a maximum offshore extent of approximately 1 km. The maximum extent of the tidal flat area was \(\sim\)4 km\({}^{2}\) based on the satellite image from 2018 [13]. The tide pattern follows a regular semi-diurnal cycle, featuring a tidal range of \(\sim\)4.5 m during spring tide and \(\sim\)2.8 m during neap tide. The Changjiang River's maximum flood discharge is 92,600 m\({}^{3}\)/s, and the minimum low-flow discharge is 4620 m\({}^{3}\)/s. The multi-year average discharge is approximately 29,000 m\({}^{3}\)/s, with an annual runoff volume of 9 \(\times\) 10\({}^{11}\) m\({}^{3}\)[39]. The river runoff exhibits significant seasonal variations, with May to October considered the flood season, accounting for 71.7% of the annual runoff, and November to April of the following year being the dry season, contributing 28.3% of the annual runoff. The annual suspended sediment concentration averages 0.468 kg/m\({}^{3}\), with a maximum sediment transport of 678 million tons (in 1964) and a minimum of 239 million tons (in 1994), averaging 433 million tons annually. Due to the large amount of suspended sediment from the Yangtze Riverand strong variable hydrodynamic conditions, a dynamic tidal flat and a supratidal zone with dense vegetation have been formed. The LGTF consists of a small portion of sandy beach (which might be submerged under extremely high tidal levels) and a large area of mudflats, with noticeable tidal channels between them. Both the intertidal topography and the positions of the tidal channel have been dynamically changing in recent years [13]. The existence and depth of tidal channels, along with the topographical distribution of the LGTF, have a substantial impact on the risk assessment.  
Shanghai is not typically renowned for coastal tourism [40]. However, with the reclamation of nearshore areas and the development of new zones along the adjacent coast,  
Figure 3: Location of the study area. The yellow box in the bottom panel marks the main area in which the UAVs were used and the in situ topographies were monitored.  
the mud-dominated LGTF has attracted numerous visitors in recent years. During the holiday period, there can be tens of thousands of people on the tidal flat. At the same time, safety assessment indicators and public service systems are severely lacking and lagging behind. Therefore, convenient, frequent, accurate, and secure monitoring of the LGFT's topography is needed.