esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving GNSS PPP Convergence: The Case of Atmospheric-Constrained, Multi-GNSS PPP-AR
## 5 Dual- and Triple-Frequency Multi-GNSS PPP Atmospheric Constraining with AR
### Dual-Frequency Analysis  
Shown in Figures 6 and 7 are the histogram and time series of dual- frequency GPS(G) + GLONASS(R) + Galileo(E) + BeiDou(C) (GREC) PPP atmospheric-constrained, static horizontal solutions in the first hour of convergence, respectively. Defining convergence period as the time the solutions take to reach a horizontal error of 20 cm, the histogram shows a level of consistency benefiting PPP solutions when they are atmospherically constrained. Percentages of stations reaching the defined convergence threshold are shown. Three different scenarios were analyzed which included (1) unconstrained GREC PPP solutions, (2) Ionospheric (GIM) constrained and (3) GIM + tropospheric constrained GREC PPP solutions. It is interesting to observe that after 20 min, the differences between the solutions for GIM constrained and GIM + ZPD constrained solutions are minimal, indicating that optimal performance with GIM and ZPD constraints was obtained before the first 20 min. Constraining further after the 20 min-mark had very limited effect on improving positioning  
Figure 5: Correlation between estimated slant delays and their sigmas. Correlation coefficient equals 0.19. Each colour marker point represents a particular satellite. Delays are shown for DOY 253, 2018 for GMSD station, located in Japan.  
performance. This characteristic is understandable given that that atmospheric constraints are usually critical and effective only in the first few minutes of PPP convergence and initialization.  
The prominent differences were observed within the first 15 min. The atmospheric constrained solutions saw an average of 6% improvement over the unconstrained, in terms of percentages of stations converging under 20 cm of horizontal error. However, the histograms do not tell the whole story. By observing the time series convergence period, the significance of the constrained solutions becomes even more obvious. By defining a stricter convergence of 10 min under a horizontal positional error of 10 cm, the 95 th percentile of the atmospheric constrained solutions reached convergence in 6 min, as shown in Figure 7. The bump observed in the first few minutes could not be explained and will be investigated further. However, the effectiveness of applying atmospheric constraints is clearly seen in the first few epochs. The quickest initialization was observed for ionospheric and tropospheric constrained solution.  
Figure 6: Histogram showing percentage of 40 stations converging based on 24 hourly solutions in dual-frequency GREC processing mode. Results shown have a 20 cm horizontal error threshold.  
Figure 7: 95 th percentile time series showing horizontal positional error (hourly) based on 24 hourly solutions for 40 stations for dual-frequency GREC processing mode. Blue dotted line represents the 10 cm convergence threshold.
## 2 Experiment Details
### Data Acquisition with Smartphone
#### 2.1.3 Channel Selection  
The video recorded from the subjects was stored onto the phone's memory in MPEG-4 (Moving Picture Experts Group) format. As discussed earlier, acquired smartphone videos were first converted into RGB frames containing information from three color channels (red, green, and blue) with varying wavelengths. Channel selection for obtaining a good quality PPG has been a challenge for researchers and therefore extensively studied in the past [34]. Blood perfusion variations depend on the wavelength of light since radiation of different wavelength penetrates and reaches vascular bed to varying depths in skin layers. Red light, which has a wavelength of 620 nm, reaches deeper (several centimeters) blood vessels in contrast to blue light (432 nm), which penetrates less than 1 mm in deep. The red channel was often a default choice [35; 36]. On the other hand, the authors of [34; 37] claimed that the green color signal provided the best signal amplitude values for smartphones and considered this as more suitable than red and blue. Therefore, the authors of [34; 38] conducted several experiments and compared the PPG signal strength in the three color bands, with and without a flashlight to obtain good quality PPG. [39] [PERSON] reported that the distribution of the pixels in the green channel is not uniform for different smartphone models. [PERSON] [40] suggested different channels for different phones. All these experiments prove that the selection of channel may vary based on several factors that include phone model due to camera characteristics, acquisition techniques (with or without LED), and region of interest.  
Figure 2: (**A**) Normalized Photoplethysmography (PPG) signal acquired using (**a**) Xiaomi Redmi Note 5 Pro, (**b**) Samsung Galaxy Note 8, and (**c**) OnePlus 6T phones. (**B**) Performance evaluation on PPG signal acquisition by the three smartphones.  
In the proposed system, we initially extracted a PPG signal from all three channels. In most cases, PPG extracted from the red channel was more prominent and less noisy than that of the green and blue channels, as seen in Figure 3.
## 2 Material and Methods
### Inventory Data  
To address the expected temporal variably of SOC adequately, we restricted the SOC data invoked to inventory and inventory-like data. Our primary source of SOC data was thus the second National Forest Soil Inventory (NFSI) ([PERSON], 2017). An additional NFSI-like soil sampling campaign was conducted at a regional scale to enhance the analysis of topographic effects on SOC. Within the NFSI, field sampling was conducted during 2006 and 2009. At the regional scale, the collection of soil samples was done in the years 2008 and 2009 ([PERSON], 2017). We did not make use of additional legacy profile data to avoid biased estimates on total SOC stocks and to prevent a mix-up of spatial and temporal effects on SOC. In contrast to the often-used legacy profile data, the NFSI comes with six well-defined uniform depth layers (organic layer, 0-5, 5-10, 10-3, 30-60, and 60-90 cm). Hence, the homogenisation or refinement of soil layers was not necessary prior to regionalisation. The volumetric SOC contents (kg m\({}^{-3}\)) were obtained by using Equation (1), where \(\rho_{\text{fe}}\), \(\text{C}_{\text{org}}\), and \(\text{CF}\) denote the bulk density of the fine earth (kg m\({}^{-3}\)), the concentration of organic carbon (kg kg\({}^{-1}\)), and the volume fraction of coarse fragments (%), respectively.  
\[\text{SOC}=\rho_{\text{fe}}\ \text{C}_{\text{org}}(1-\frac{\text{CF}}{100}) \tag{1}\]  
The volume fraction of coarse fragments was estimated in the field and measured by sieve analysis in the laboratory (depending on rock size). Mass fractions obtained from the sieve were transformed into volume fractions by assuming a rock density of 2.65 g cm\({}^{-3}\). The bulk density of the fine earth was determined on soil-core samples. Mass and volume fractions of coarse fragments (>2 mm) contained within the core samples were excluded from the calculation. Organic carbon was determined by dry combustion using a total analyser (LECO CNS-2000). All laboratory analyses were performed in compliance with the NFSI standards, which are documented in detail in GAFA ([PERSON], 2017).
# Influence of Mix Proportions on Rheological Properties, Air Content of Wet Shotcrete--A Case Study
## 1 Introduction
[PERSON] [23] pointed out that according to the test results of BT2 rheometer, the change of rheological properties of fresh concrete with time could be estimated based on the rheological properties of corresponding mortar. Concrete flow in pipes can be summarized as a combination of three flow types, including lubrication layer shear, shear flow and plug flow [24,25,26,27]. [PERSON] et al. [19,28] indicated that the pumping performance of fresh concrete depends on the formation of the lubricating layer and the rheological properties of concrete plug. Mixing various chemical admixtures or mineral additives can have a significant impact on the rheological properties of concrete, thus affecting the pumping performance, at the same time, improving the stability of the mixture, and even changing the thickness of the lubrication layer [28,29]. However, the differences in the composition of mineral additives or chemical admixtures lead to different rheological properties. According to the previous experiments and literature, the pumping performance and sprayability can be estimated by rheological parameters. For example, higher flow resistance (yield shear stress) means better sprayability and poorer pumpability. To study the rheological properties of wet shotcrete with different admixtures and further estimate the pumpability and shotcreting performance before pumping, this paper studies the influence of water-binder ratio (w/b, WC), sand ratio (SR), fiber length (FI), air-entraining agent (AE), water-reducing agent (WR), silica fume (SF) and fly ash (FA) on the rheological properties and air content of wet shotcrete. The influence mechanism of admixtures on the properties of fresh concrete is discussed. By comparing the effect of fly ash, silica fume, and other admixtures, the application of wet shotcrete is improved. It is hoped that this study may provide a useful information for improving the pumping and spraying properties of fresh wet shotcrete and further improving the quality of wet shotcrete. Through this research, the main goal is to provide a simplified guide for shotcrete technologists.
## 2 Materials and Methods
### Scanning Electron Microscopy Coupled with Energy Dispersive X-ray Spectroscopy (SEM-EDX)  
Small slabs of the stone samples and the thin sections were analyzed by SEM FEI Quanta 200 scanning electron microscope (SEM) combined with an EDX Ametek Octane Pro system. The stone slabs were placed on a sulfur-free carbon adhesive glued onto an aluminum stub. SEM images of the examined surfaces were obtained in low vacuum mode, without the need of a conductive coating, with a secondary electron LFD detector for morphological analysis of the stone slabs or a dual backscattered electrons (BSE) detector for analysis of the thin sections, at 20 kV voltage, an average working distance of 10 mm and a chamber pressure between 70 and 90 Pa. EDX analyses were performed to gain chemical information about areas of interest both as punctual measurement and elemental maps. For punctual measurements, the acquisition time was set at 50 s.
## 2 The Structure of Typical DC Microgrids and Traditional Control Strategies
### Power Allocation between Different Energy Storage Systems of HESS  
Power fluctuations of DC microgrids are always eliminated by HESS. Batteries are utilized to absorb or release low-frequency powers, while the supercapacitor is used to re-strain high-frequency powers. Consequently, it is necessary to allocate powers at high and low frequencies, and the low-pass filter becomes a preferred option due to its simplicity and ease of implementation.  
The topology of the first-order low-pass filter is shown in Figure 1. \(U_{in}\) is the input, \(U_{out}\) is the output, and \(R\) and \(C\) are the filter resistor and capacitor, respectively.  
According to Figure 2, the differential equation is obtained and shown as  
\[U_{in}=U_{out}+RC\frac{dU_{out}}{dt} \tag{1}\]  
In (1), define \(T=RC\) be the filtering time constant, and the transfer function of the low-pass filter is obtained by (1) and shown as  
\[H(s)=\frac{1}{Ts+1} \tag{2}\]  
As the time constant \(T\) decreases, the passband of the filter becomes wide, and more low-frequency inputs pass through while fewer high-frequency inputs pass through.  
As shown in Figure 3, \(P_{load}\) is the load power, \(P_{\text{DG}}\) is the distributed supply power, and \(P_{batter}\) is the reference battery power, and \(P_{scrf}\) is the reference supercapacitor power.  
Figure 1: Schemes of typical islanded DC microgrids.  
Figure 2: First-order low-pass filter.  
Based on Figure 3, the relationship between powers is obtained and shown as  
\[P_{\text{DG}}-P_{\text{load}}=P_{\text{batterf}}+P_{\text{scref}} \tag{3}\]  
According to (1) and Equation (2), the reference powers of batteries are obtained as follows:  
\[P_{\text{batterf}}=\frac{1}{Ts+1}(P_{\text{DG}}-P_{\text{load}})=\frac{1}{Ts+1} P_{\text{HESS}} \tag{4}\]  
Similarly, the reference power of the supercapacitor is  
\[P_{\text{scref}}=\frac{Ts}{Ts+1}(P_{\text{DG}}-P_{\text{load}})=\frac{Ts}{Ts+1 }P_{\text{HESS}} \tag{5}\]  
The transformation of (5) is  
\[P_{\text{scref}}(t)+T\frac{\text{d}P_{scref}(t)}{\text{d}t}=T\frac{\text{d}P_ {\text{HESS}}(t)}{\text{d}t} \tag{6}\]  
The discretization of (6) leads to  
\[P_{scref}(k+1)=\frac{T}{T+T_{p}}(P_{scref}(k)+P_{HESS}(k+1)-P_{HESS}(k)) \tag{7}\]  
In (7), \(T_{p}\) is the calculation period. Define \(P_{\text{HESS}}(k+1)-P_{\text{HESS}}(k)=\Delta P_{\text{HESS}}\), and (7) is transformed as  
\[P_{scref}(k+1)=\frac{T}{T+Tp}(P_{scref}(k)+\Delta P_{HESS}) \tag{8}\]  
Based on (8), it is obvious that the reference power \(P_{scref}(k+1)\) of the supercapacitor is related to the power \(P_{scref}(k)\) of the previous moment, the low-pass filtering time constant \(T\), and the power variation \(\Delta P_{HESS}\) of HESS. Adjusting the filtering time constant \(T\) could change power distributions between batteries and the supercapacitor.
## 2 Materials and Methods
### Functional Properties Evaluation  
#### 2.4.1 Electrical Conductivity  
To evaluate the electrical conductivity, it was necessary to measure the electrical resistance of the material using an electrical source, which was connected to the material by conductive electrodes (2-wire method). Using this method, the electrical resistance was obtained through the _I-V_ curves (electric current intensity-voltage applied) [29].  
The electrical resistivity \(\rho\) (\(\Omega\cdot\)m) was calculated by the Equation (3):  
\[\rho=\ R\ \times\ \frac{A}{L} \tag{3}\]  
where \(R\) is the electrical resistance, \(A\) is the cross-sectional area of the electrode (mm\({}^{2}\)), and \(L\) is the distance between the electrodes (mm). The electrical conductivity is given by the inverse of the electrical resistivity [29].  
Electrical resistivity is defined as the capacity of a given material to resist the flow of electric current intensity. In general, the electrical resistivity is inversely proportional to the electrical conductivity, as represented in Equation (4) [30]:  
\[\sigma=\ \frac{1}{\rho^{\prime}} \tag{4}\]  
where \(\sigma\) represents the electrical conductivity (S/m) and \(\rho\) the electrical resistivity (\(\Omega\cdot\)m).  
For this work, electrical resistance measurements were performed using a Keithley 6487 Pico-ammeter/Voltage source by applying a potential difference in a range between \(-1\) to 1 V, with a step of 0.1 V. All measurements were performed in direct current (DC) mode at room temperature. In order to determine the electrical resistance through the current intensity measurements, an electrode system with a cross-sectional area of 5 \(\times\) 1 mm\({}^{2}\) and a distance between electrodes of 3 mm was developed. Equation (3) was utilized to obtain the electrical resistivity, as the electrical resistance was determined by the slope of the _I-V_ curves. Then, Equation (4) was applied in order to obtain the electrical conductivity values. The electrical resistance was measured in 5 different places of the sample, and the average value was calculated.
# A Robust Cover Song Identification System with Two-Level Similarity Fusion and Post-Processing
## 2 Proposed Model
### Post-Processing
#### 2.4.2. Hubness Reduction  
To reduce the negative influence caused by the \"hubness\" phenomenon existing in the track community, we adopt MP algorithm [15] to transform the obtained arbitrary similarity scores to probability-based similarity scores. MP is a global scaling method, and its general idea is to reinterpret the original distance space so that two objects sharing similar nearest neighbors are more closely tied to each other. Under the assumption that all distances in a data set follow a certain distribution, any similarity \(s_{x,y}\) can now be reinterpreted as the probability of \(v_{y}\) being the nearest neighbor of \(v_{x}\), \(P(X)\) is defined by the similarities of \(v_{x}\) to all other objects in the collection, and the probability of an element \(v_{y}\) being a nearest neighbor of \(v_{x}\) is:  
\[P(X<s_{x,y})=\mathcal{F}_{x}(s_{x,y}). \tag{11}\]  
\(\mathcal{F}_{x}\) denotes the cumulative distribution function (CDF), which is assumed for the distribution of similarity scores \(s_{x,i=1..n}\). Then, the MP-based similarity between \(\mathbf{v}_{x}\) and \(\mathbf{v}_{y}\), denoted as \(MP(x,y)\), is defined as the probability that \(\mathbf{v}_{y}\) is the nearest neighbor of \(\mathbf{v}_{x}\) given \(P(X)\) and \(\mathbf{v}_{x}\) is the nearest neighbor of \(\mathbf{v}_{y}\) given \(P(Y)\) as follows:  
\[MP(x,y)=P(X<s_{x,y}\cap Y<s_{y,x}). \tag{12}\]  
Figure 3: Illustration of the effectiveness of the diffusion process in classification. Pentagrams represent two queries from different groups. Each element is assigned to one of the two queries according to its distances with the query samples: (**a**) without diffusion (**b**) with diffusion.  
By visualizing the joint similarity score distribution of X and Y, computing MP for a given similarity score \(s_{x,y}\) in a collection of N objects can be boiled down to simply counting the number of objects j having a smaller similarity score to \(\mathbf{v}_{x}\) and \(\mathbf{v}_{y}\) than \(s_{x,y}\):  
\[MP(x,y)=\frac{|j:s_{x,j}<s_{x,y}\cap j:s_{y,j}<s_{y,x}|}{N}. \tag{13}\]  
Figure 4 shows the probability distribution of the diffused similarities on Covers40 before and after applying the MP algorithm to them. It can be seen that the MP algorithm helps to enlarge the difference between inter tracks (unrelated tracks), which helps to reduce the false positive rate.
# LPBERT: A Protein-Protein Interaction Prediction Method Based on a Pre-Trained Language Model
## 3 Results
### Cross-Species Generalization Analysis  
The above work evaluated the performance of LPBERT when trained and tested within the same species. To fully explore the abilities of LPBERT, we trained it on the BioGRID _H. sapiens_ and then tested it on datasets from other species. The testing scope included five datasets: BioGRID _S. cerevisiae_, _S. cerevisiae_ core (DeepFE), Guo, Multi-species, and Human-virus. In all the datasets, the ratio of positive to negative samples was approximately 1:1. By isolating the data by species, we evaluated the predictive ability of the model beyond the training data. This validation method provided a reliable reference for analyzing the generalization capability of the model in cross-species prediction, and it helped assess the inference ability of the model on unknown data by existing data. We selected comprehensive metrics, namely, accuracy, precision, and F1-score, as the evaluation metrics. Table 11 presents the specific test results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Similarity**} & **Training–Validation** & **Test** & **Accuracy** & **Precision** & **F1-Score** \\  & **Samples** & **Samples** & **Accuracy** & **Precision** & **F1-Score** \\ \hline Any & 12,354 & 2968 & 93.19 & 98.48 & 93.40 \\ \(\leq\)90\% & 12,308 & 2833 & 94.25 & 98.50 & 94.65 \\ \(\leq\)80\% & 13,216 & 2178 & 94.67 & 97.96 & 91.40 \\ \(\leq\)70\% & 13,224 & 2373 & 95.41 & 98.49 & 95.31 \\ \(\leq\)60\% & 12,918 & 2360 & 94.66 & 98.21 & 94.58 \\ \(\leq\)50\% & 12,414 & 2440 & 94.84 & 98.24 & 94.66 \\ \hline \hline \end{tabular} Any means it was not processed by CD-HIT.  
\end{table}
Table 10: The sequence similarity analysis of LPBERT (%).  
The experimental results demonstrate that LPBERT exhibited outstanding performance on both the BioGRID _S. cerevisiae_ and Human-virus datasets, where all metrics achieved high levels. On the BioGRID _S. cerevisiae_ dataset in particular, although there was a slight decline compared with the results obtained from the same-species training and testing, all the metrics remained above 90%. However, on the other three datasets, LPBERT's performance was less satisfactory, with the highest accuracy reaching only 51.82%. These results indicate that LPBERT encountered difficulties in the cross-species data prediction, as the learned feature patterns failed to support accurate inferences on unknown data. Overall, LPBERT's cross-species generalization ability appeared to be limited, suggesting room for improvement.
## 3 Information-Theoretic Results  
Having established the theoretical foundations of perceptual measures, we now turn to their implications in information theory. Understanding how these measures interact with rate and distortion constraints requires a rigorous information-theoretic analysis of the RDP trade-off. This section summarizes recent information-theoretic results for the RDP problem. Typically, a set of distortion constraints **(D)** and a set of perceptual constraints **(P)** are enforced to ensure that the distortion and perceptual fidelities are bounded by given constants \(D\) and \(P\), respectively.  
As in classical information theory, we consider a memoryless source \(\{X_{i}\}_{i=1}^{\infty}\) drawn from finite alphabets \(\mathcal{X}\). For continuous sources, Assumption 2 in [26] can be invoked to ensure that distortion and perceptual measures are not overly sensitive to discretizations.
## 3 Predict and Forward Scheme Based on Node Profile
### Predict Phase: Studying the State of the Network  
During the Predict phase, Prelearn messages route from a node to another to study their attribute characteristics in the network, but the data packets are not forwarded to the nodes. Meanwhile, Prelearn message constantly builds enough and accurate statistics about the attribute characteristics of nodes. Whenever a new node is routed by Prelearn message, the statistics carried by Prelearn message are updated once in the Predict phase. Certainly, there are several different ways for a Prelearn message to build and update the statistics about the network. Figure 2 gives an example for the routing process in Predict phase. During this phase, the source \(node_{A}\) sends a Prelearn message to study the state of the network. The Prelearn message routes from \(node_{A}\) to \(node_{I}\) in the network, finds the nodes belonging to the destination profile, and collects and updates the historical encounter information between nodes. Moreover, each node in the network adopts the information carried by Prelearn message to predict the probability of future encounters with other nodes.