esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data Transformation Impact on Gray Values Compression
### Dynamic Markov Coding  
Dynamic Markov coding is an adaptive statistical compression procedure in two phases described by [PERSON] and [PERSON] in 1987 [51]. The first phase of this coding process consists of a finite state machine for predicting the probability of the next symbol and the second phase consists of an entropy encoder, usually arithmetic coding that performs the compression. This algorithm was originally developed for binary data, i.e., machine code, images, and sound. This algorithm reads a bit from the input stream, assigns it a certain probability value based on its previous occurrence, and switches it to the next state depending on its value. The algorithm starts with a small finite state machine that is assigned to new states during the encoding process, causing it to be an adaptive process. Thus, a finite state automaton can grow very quickly and fill the entire free memory. This algorithm is divided into two parts:* Probability calculation;
* New states addition.  
The probability calculation is performed by counting 0 and 1 in the input stream. Let it be assumed that the finite machine has been in the state \(S\) several times in the past and has received \(s_{0}\) zeros and \(s_{1}\) ones. The easiest way to assign a probability is this simple expression:  
\[\frac{s_{0}}{s_{0}+s_{1}}\;\;\textit{probability that the input data is 0}.\]  
\[\frac{s_{1}}{s_{0}+s_{1}}\;\;\textit{probability that the input data is 1}.\]
## 3 Materials and Methods  
The database for this study is provided by Shengly oilfield company and comprises 300 m cores, 3D seismic, lithology, wireline, and 1200 petrophysics data from 40 wells from two areas in the northern Dongying subdepression.  
The seismic sections were used to define the stratigraphy units. The lithofacies are determined by analyzing the core data set's rock texture, color, and sedimentary structures. The combination of lithofacies was based on scrutinizing the lithofacies' thicknesses, types, and vertical patterns. RESFORM software was utilized to determine sedimentary facies and their repartition based on lithology data and wireline. A total of 104 thin sections representing all the lithofacies were made with blue resin for pore type and petrography analysis. Fifty thin polished sections were stained with Alizarin (red) and K-ferricyanide solution to discriminate carbonate cement. Forty gold-coated samples covering all the lithofacies were made to analyze authigenic cements and their spatial association. The experiment was carried out with a ZEISS device connected to an energy dispersive X-ray (EDX) spectrometry at a current of 1.0-1.5 nA and 20 kV. Cathodoluminescence pictures were utilized to identify carbonate minerals and mineral growth phases. The cathodoluminescence images were obtained using LEICA-DM4P microscopy running at 10 kV, and 250 mA on twenty thin sections. A total of 400 to 500 points per thin section were counted to quantify the proportion of rock components and pore types. The petrographical analysis was achieved with the LEICA device linked to a camera for picture obtention. Point counting results were used to gauge the COPL (compaction porosity loss), CEPL (porosity loss by cementation), and the Icp (compaction index), utilizing the equations of [PERSON] [23].  
\[\text{COPL}=\text{OP}-((100\times\text{IVG})-(\text{OP}\times\text{IGV}))/(( \text{OP}-\text{IGV})) \tag{1}\]  
\[\text{CEPL}=(\text{OP}-\text{COPL})\times\text{CEM}/\text{IGV} \tag{2}\]  
\(\text{OP}=\text{estimated initial porosity}\).  
\(\text{CEM}=\text{volume of intergranular cement}\).  
\(\text{IGV}=\text{intergranular porosity}+\text{matrix + cement content}\) [24].  
\(\text{Icp}\) is obtained by applying the equation of [PERSON], 1992 [25].  
\[\text{Icp}=\text{COPL}/(\text{COPL}+\text{CEPL}) \tag{3}\]  
where (\(0\leq\text{Icp}\leq 1\)).  
If \(\text{Icp}\geq 0.5\), the mechanical compaction represents the cardinal process in porosity reduction. If \(\text{Icp}\leq 0.5\), cementation represents the key cause in porosity reduction. If \(\text{Icp}=1\), the total porosity destruction is caused by compaction. If \(\text{Icp}=0\), all the porosity reduction is due to cementation.
## 3 Results  
### Physicochemical Properties  
There were significant differences among the rhizosphere soils of \(E\). _rufipilus_ (Er-R), \(C\). _nepalensis_ (Cn-R), \(P\). _yunanenensis_ (Py-R), and grassland plots (CK) (Table 1) with respect to most physicochemical properties (Table 2). The amounts of SOM, AN, TP, and AP were significantly higher in Py-R as compared with Er-R, Cn-R, and CK. Gathered data demonstrate that rhizosphere soils of \(P\). _yunanenensis_ possess a rich nutrient content  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \begin{tabular}{c} **Soil** \\ **Samples** \\ \end{tabular} & \begin{tabular}{c} **Tem** \\ **(\(\mathbf{{}^{\circ}}\)C)** \\ \end{tabular} & \begin{tabular}{c} **SWC** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **pH** \\ **(g/kg)** \\ \end{tabular} & \begin{tabular}{c} **SOM** \\ **(g/kg)** \\ \end{tabular} & \begin{tabular}{c} **TN** \\ **(mg/kg)** \\ \end{tabular} & \begin{tabular}{c} **AN** \\ **(g/kg)** \\ \end{tabular} & \begin{tabular}{c} **TP** \\ **(mg/kg)** \\ \end{tabular} & \
\begin{tabular}{c} **AP** \\ **(mg/kg)** \\ \end{tabular} &
\begin{tabular}{c} **N/P** \\ **N/P** \\ \end{tabular} \\ \hline \multirow{2}{*}{Er-R} & \(15.45\pm\) & \(30.70\pm\) & \(4.48\pm\) & \(52.45\pm\) & \(2.79\pm\) & \(94.06\pm\) & \(5.13\pm\) & \(117.34\pm\) & \(0.55\pm\) \\  & \(0.87\pm\) & \(5.16\pm\) & \(0.28\pm\) & \(15.68\pm\) & \(0.58\pm\) & \(14.62\pm\) & \(0.79\pm\) & \(\mathbf{0}4.06\pm\) & \(0.14\pm\) \\ \hline \multirow{2}{*}{Er-B} & \(15.61\pm\) & \(29.27\pm\) & \(4.34\pm\) & \(36.83\pm\) & \(2.22\pm\) & \(65.55\pm\) & \(4.57\pm\) & \(72.96\pm\) & \(0.49\pm\) \\  & \(1.49\pm\) & \(2.34\pm\) & \(0.02\pm\) & \(13.75\pm\) & \(0.41\pm\) & \(19.15\pm\) & \(0.65\pm\) & \(30.43\pm\) & \(\mathbf{0}.10\pm\) \\ \hline \multirow{2}{*}{Cn-R} & \(14.61\pm\) & \(28.35\pm\) & \(4.34\pm\) & \(34.21\pm\) & \(2.24\pm\) & \(72.58\pm\) & \(3.93\pm\) & \(64.20\pm\) & \(0.57\pm\) \\  & \(1.16\pm\) & \(2.98\pm\) & \(\mathbf{ab}\) & \(0.04\pm\) & \(5.72\pm\) & \(\mathbf{bc}\) & \(0.11\pm\) & \(\mathbf{ab}\) & \(14.81\pm\) & \(0.22\pm\) & \(24.64\pm\) & \(0.06\pm\) \\ \hline \multirow{2}{*}{Cn-B} & \(15.22\pm\) & \(26.95\pm\) & \(4.32\pm\) & \(27.82\pm\) & \(1.93\pm\) & \(58.43\pm\) & \(4.20\pm\) & \(59.07\pm\) & \(0.46\pm\) \\  & \(1.48\pm\) & \(3.33\pm\) & \(\mathbf{ab}\) & \(0.09\pm\) & \(8.39\pm\) & \(0.40\pm\) & \(\mathbf{b}\) & \(18.33\pm\) & \(\mathbf{c}\) & \(0.22\pm\) & \(20.74\pm\) & \(0.11\pm\) \\ \hline \multirow{2}{*}{Py-R} & \(11.09\pm\) & \(33.47\pm\) & \(4.36\pm\) & \(74.66\pm\) & \(2.99\pm\) & \(166.30\pm\) & \(11.36\pm\) & \(253.10\pm\) & \(0.27\pm\) \\  & \(0.22\pm\) & \(1.03\pm\) & \(\mathbf{a}\) & \(0.12\pm\) & \(\mathbf{a}\) & \(0.43\pm\) & \(\mathbf{a}\) & \(10.24\pm\) & \(1.01\pm\) & \(\mathbf{a}\) & \(30.77\pm\) & \(0.05\pm\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physiological properties (mean \(\pm\) SD) of rhizosphere and bulk soils from three dominant plants and grassland plots.
# Performance Evaluation of MPTCP on Simultaneous Use of 5G and 4G Networks
## 4 Performance Evaluation
### Performance Evaluations of MPTCP Schedulers When the CCA Is OLIA  
The received data by the considered MPTCP schedulers while the MPTCP CCA is OLIA are shown in Figure 6. Following the results of LIA, the schedulers show a similar trend in performance. However, the total outcomes of each of them are a bit lower than the LIA case. We believe that OLIA could not adapt to the changing network scenarios like LIA. This led to slight overall performance degradation by all the schedulers.  
Figure 7 shows the real-time throughout and \(\Delta\)_PDU_ obtained by the schedulers during the simulation time. The SINR was the same, as shown in Figure 3. Again, a similar trend in the result is observed as that of LIA. Like LIA, OLIA also increases the CWND of the 5G SF at 8 s. The SRTT, Round-robin, and ECF schedulers schedule the packets for the 5G SF considering the availability of CWND and lowest RTT, which are lost due to a drop in  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Flow Completion Time (FCT)** \\ \cline{2-3}
**Scheduler** & **ms** \\ \hline Default (SRTT) & 11.476 \\ Round-robin & 11.331 \\ Redundant & 7.999 \\ ECF & 11.655 \\ BLEST & 7.219 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of the tested MPTCP schedulers in terms of FCT for LIA as a CCA.  
Figure 6: Performance of the tested MPTCP schedulers in terms of total received data for OLIA as a CCA.  
SINR and result in HoL blocking at the receiver. However, we notice a different event here, i.e., ECF stops sending through the 5G SF after 17 s. We believe that as the SINR remains low for almost 8 s by that 17-second time, ECF does not receive enough improvements in the CWND by OLIA. It leads ECF to believe that the 5G SF is no longer providing any benefit for data transmission through it.  
Figure 7: Real-time performance of the tested MPTCP schedulers for OLIA as a CCA in terms of throughput and \(\Delta\)_PDU_ for: (**a,b**) SRTT, (**c,d**) Round-robin, (**e,f**) Redundant, (**g,h**) ECF, and (**i,j**) BLEST, respectively.  
Table 3 summarizes the FCT for this experiment. Again, it produces a similar trend with a slight increase in overall FCT, which can be mainly attributed to OLIA's excessive fairer behavior towards SPTCP flows.
# Golgi Metal Ion Homeostasis in Human Health and Diseases
## 2 Ca\({}^{2+}\)/Mn\({}^{2+}\) Transporters and Channels
### Ca\({}^{2+}\)-Release Channels in the Golgi  
The inositol 1,4,5-trisphosphate (IP3) receptors (IP3 Rs) and ryanodine receptors (RyRs) are two major types of Ca\({}^{2+}\)-release channels in the Golgi. They both open at low cytosolic Ca\({}^{2+}\) and close at high cytosolic Ca\({}^{2+}\) concentrations. IP3R receptors are encoded by 3 genes (_ITPR1, 2 and 3_) in human, and are found in Golgi membranes [28; 47]. These proteins are better characterized as ER-resident Ca\({}^{2+}\) channels that release Ca\({}^{2+}\) from the ER lumen into the cytosol in response to the stimulation of IP3 [9]. The RyR family members encoded by 3 genes (_RYR1, 2 and 3_) in humans are the other major types of Ca\({}^{2+}\) channels that mediate the release of Ca\({}^{2+}\) into the cytoplasm [48]. The IP3 Rs were once considered to be the only Golgi Ca\({}^{2+}\) channels, since IP3 but not RyR agonists significantly induced Ca\({}^{2+}\) release from Golgi membrane fractions in the presence of thapsigargin [49]. However, other experiments in rat sympathetic neurons and neonatal cardiac myocytes argued that functional RyRs are found in the _trans_-Golgi and probably take part in Golgi Ca\({}^{2+}\) release [50; 51]. Emetine, an alkaloid, induces Ca\({}^{2+}\) release from _trans_-Golgi without inducing Ca\({}^{2+}\) release from the ER. It is unlikely that emetine induces Ca\({}^{2+}\) release via activation of RyR2 or inhibition of SPCA, and the underlying molecular mechanism requires further investigation [52].
# Uncertainty Propagation through a Point Model for Steady-State Two-Phase Pipe Flow
## 3 Results
### Pressure drop  
As for holdup, we summarize the pressure drop results in terms of sensitivity and prediction. The averaged sensitivities are given in Figure 7. The weighted average is similar to the plain average. The uncertainty in the diameter measurement is responsible for 90 percent of the uncertainty in pressure drop predictions. The liquid viscosity also contributes, and in some low-variance cases, the pipe inclination. Figure 8 shows the sensitivities by regime. The distributions of sensitivities are similar for stratified flow and slug flow.  
Pressure drop predictions are compared to measurements in Figure 9. Only converged cases are included. The prediction uncertainty is much larger than the measurement uncertainty and increases with the pressure drop. The model under-predicts the pressure drop for small values and over-predicts for large values. The pressure drop predictions are much less accurate than the holdup predictions. The 90% intervals are off in 126 (64.9%) out of 194 cases, while the 95% intervals are off in 62 (59.0%) out of 105 cases.  
Figure 5: Frequency histograms of the holdup sensitivity estimates across 233 experiments. The sensitivity distribution for one input variable is given in each panel. The experiments are categorized as slug (blue) or stratified (red) and we compare estimates from Monte Carlo (solid lines) and PC (dotted).  
Figure 6: **(Left)** Predicted versus measured holdup for 194 experiments. The diagonal line is where the coordinates are equal, and cases with a discrepancy between prediction and observation are highlighted. The first axis gives measured value with two standard errors either way. The second axis is predicted mean, lower quantile and upper quantile. Slug experiments are blue and stratified ones are red. **(Right)** The relative difference in PC predictions compared to MC predictions with respect to measured holdup. Upper quantiles (dashed), means (solid) and lower quantiles (dash-dotted).
# Advances in the Monitoring of Algal Blooms by Remote Sensing: A Bibliometric Analysis
## 2 Materials and Methods  
### Data Source  
The data were retrieved from the Science Citation Index (SCI), the Clarivate Analytics' Web of Science. It is one the most used data source for bibliometric studies [29; 30; 31]. The search was restricted to the core collection. The retrieval strategy was TS = (bloom * AND (remote sens * OR satellite *)). TS herein means topic. The used of the asterisk in this search ensures the inclusion of all the possible variants such as bloom, blooms, blooming, remote sensing, remote sensed, remote sensors, satellite, and satellites to avoid missing results. The search was restricted to articles published in English. This search returned 2173 peer-reviewed articles.  
Data cleaning was performed removing irrelevant publications through initial screening of the title, abstract, and keywords. Due to the difficulties in identifying the needed information, in some cases, it was necessary to read the methodology section. Finally, we collected 1292 peer-reviewed articles.
# Evaluation of Shear Effect on Deflection of RC Beams
## 3 Experimental Results  
### Load vs. Deflection Relationships  
Figure 4 shows the load-deflection relationships of the specimens measured in this test. The experimental results of the load and deflection at the principal states are shown in Table 3. As shown in Figure 4, all specimens exhibited a flexural failure mode in which the final fracture occurred owing to the crushing of the compressed concrete after flexural yielding. In addition, because the amount of shear reinforcement in all specimens was higher than that recommended in the shear provisions of the ACI building code, the shear steel bars did not yield until the end of the experiment.  
Figure 3: Test setup of a typical specimen.  
Figure 2: Details of specimens: (**a**) B3-2.5a; (**b**) B3-2.5b; (**c**) B3-2.5c; (**d**) B6-2.5a; (**e**) B6-2.5b; (**f**) B6-2.5c; (**g**) B6-4.0a; (**h**) B6-4.0b; (**i**) B6-4.0c (unit: mm).  
As shown in Table 3, the load and deflection at the first flexural yielding of the B3-2.5 series specimens with the tension reinforcement ratio of \(0.3\rho_{b}(=\rho-\rho^{\prime})\) were lower than those of the B6-2.5 series specimens with the ratio of \(0.6\rho_{b}\), while the deflection at peak load was larger. The B6-2.5 and B6-4.0 series specimens were identical except for the shear span-to-depth ratio. As shown in Figure 4, as the shear span-to-depth ratio increased from 2.5 to 4.0, the flexural strength was significantly reduced while the ductile behavior was remarkable. However, the shear capacity ratio had little effect on the load-deflection relationships.
## 6 Evaluation
### Bundle Delivery Rate  
The simulation result for delivery rate is shown in Figure 9. By \"delivery rate including bundles kept\" in this figure is meant that \"delivered bundles\" assumed in this delivery rate include those bundles that carrier terminals received from victim terminals but did not yet deliver to shelters before the simulation ended. This is conceived because we can assume that, even when carrier terminals are not able to deliver these bundles to shelters before the end of the simulation execution time of one hour, they eventually will.  
The figure shows that the proposed method records a higher delivery rate than other existing methods. The reason is as follows. If battery consumption is not taken into consideration, the existing methods yield a higher delivery rate than the proposed method but when battery consumption is taken into consideration, batteries of victim terminals often run out and become unable to send safety confirmation information. In contrast, in our proposed method, the batteries of victim terminals continue to have enough charge to send bundles until the end of the one-hour simulation.
# Throughput Prediction of 5G Network Based on Trace Similarity for Adaptive Video
## 5 Conclusions  
Large datasets can enhance ANN training but negatively impact the prediction accuracy of global models due to statistical diversity. On the other hand, large datasets are statistically more diverse, which negatively impacts the prediction accuracy of a global model trained on the whole trace. Hence, we clustered the network traces and grouped them according to similar statistical features measured by the mean and variance. For comparison purposes, we applied three clustering techniques, namely KMeans, OPTICS, and AutoClass. For our analysis, we used four traces--two generated in an experiment, and two from a public repository. These traces were collected from fixed and mobile UE devices. To compare the groupings, we used three clustering techniques, namely KMeans, OPTICS, and AutoClass, to assess the robustness of our framework. The experiment's results showed that prior traces clustering into more statically homogeneous improved the prediction accuracy. The local models--LSTM with different cluster variants--performed better than the global baseline LSTM model, especially in the case of a mobile scenario. All the ANN-based models, whether local or global, achieved significantly better scores than the benchmark model MA(1), where the predictor was the time series' previous value. In the case of traces produced by stationary UE devices, the local models yielded comparable outcomes. However, for the mobile scenario, the more advanced clustering techniques like AutoClass and OPTICS showed a lower prediction error. A possible explanation for these results may be that the traces generated in the mobile environment were more susceptible to clustering and generated more specific groups, which were easier to capture by more elaborate clustering algorithms. Overall, the results showed that the multiple local models working on clustered data effectively exploited the similarities of the network traces and consequently outperformed the single model approach. The proposed algorithm can be used primarily in adaptive video streaming to improve the selection of a video's bitrate. The predictive models can be integrated into the client's bitrate selection logic, replacing the default decision mechanism based on the moving average of historical bit rates.  
This research received no external funding.  
Not applicable.
## 1 Introduction
### Hydrolysis Kinetics  
Hydrolysis is often assumed to be a first-order reaction [12; 13] and its rate can be determined using batch reactor tests. Biomethane potential (BMP) and hydrolysis rate constant (K\({}_{\text{h}}\)) are obtained by performing data fitting from batch reactor data. First-order kinetics is the simplest and most common hydrolysis rate expression.  
\[\frac{\text{d}\text{X}}{\text{dt}}=\text{K}_{\text{h}}\,\text{X} \tag{1a}\] \[\frac{\text{d}\text{X}_{\text{ch}}}{\text{dt}}=\text{K}_{\text{h},\text{ch}} \,\text{X}_{\text{ch}}\] (1b) \[\frac{\text{d}\text{X}_{\text{pr}}}{\text{dt}}=\text{K}_{\text{h},\text{pr}} \,\text{X}_{\text{pr}}\] (1c) \[\frac{\text{d}\text{X}_{\text{li}}}{\text{dt}}=\text{K}_{\text{h},\text{li}} \,\text{X}_{\text{li}} \tag{1d}\]  
where dX/dt is hydrolysis rate in kg CODm\({}^{-3}\)d\({}^{-1}\), K\({}_{\text{h}}\) is hydrolysis rate constant in d\({}^{-1}\), X is the particulate component in kg CODm\({}^{-3}\) and subscripts ch, pr, and li denote carbohydrate, protein and lipid, respectively. [PERSON] et al. [14] proposed a protocol for the determination of K\({}_{\text{h}}\) from batch tests using an integrated form of the generalized hydrolysis rate expression (Equation (1a)).  
\[\ln\frac{\text{X}_{\infty}-\text{X}}{\text{X}_{\infty}}=\ -\text{K}_{\text{h}}\, \text{t} \tag{2}\]  
\[\text{X}=\ \text{X}_{\infty}\Big{(}1-\text{e}^{-\text{K}_{\text{h}}\,\text{t}} \Big{)} \tag{3}\]  
where, X\({}_{\infty}\) is the value of ultimate methane production and X is the amount of methane produced at a given time, t. After batch test data are collected, a graph is plotted where K\({}_{\text{h}}\) is determined as a slope of \(\ln\frac{\text{X}_{\infty}-\text{X}}{\text{X}_{\infty}}\) and t. The last day of the batch test should be when the difference between biogas productions at day n and day n + 1 is less than or equal to 1% of the cumulative biogas production. This is in accordance with the German Guideline VDI 4630 for BMP estimation [15]. The value of K\({}_{\text{h}}\) is important because it is a unique characteristic of a substrate and it can be used to assess the suitability of a given substrate for anaerobic digestion. It tells us how much time it takes to reach a certain percentage of the ultimate methane production [16]. There are also other methods to determine K\({}_{\text{h}}\) experimentally such as the one suggested by [PERSON] and [PERSON] [17].
# SambotII: A New Self-Assembly Modular Robot Platform Based on Sambot
## 5 Self-Assembly of SambottII
### Experimental Verification of the Angle Measurement Method  
When the angle between two docking surfaces is too large, the adjacent LED lights in the same horizontal surface will become too close to be identified in the image. Thus, four group of data are measured at the angles of 15\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), and 60\({}^{\circ}\). When the distance between two docking surfaces are too small, the camera cannot capture all of the LED lights. When the distance is too far, the measurement accuracy will reduce dramatically because of the limited resolution of the camera. Therefore, the distance is restricted between 15-50 cm in the angle measurement experiments and its variation step is prescribed as 5 cm. Based on our previous work [18] and the additional supplementary experiment, the measurement results of the angle \(\theta\) are shown in Figure 14.  
From Figure 14, it is seen that the maximum angle error approaches 6\({}^{\circ}\). Except for few data, the overall measurement results are smaller than the actual angle. The error mainly comes from two aspects:  
1. The error of Formula (21) and (25).
2. The error caused by the LED identification algorithm, because the point that is found by the algorithm may not be the center of the LEDs.  
Because the mechanical structure of SambotII allows a range of measurement error in the docking process and the maximum error of angle measurement is in that range, the angle information that was obtained by the present LED algorithm can be used in the docking process.  
Figure 14: Experimental results of angle measurement. L is the distance, the \(\theta\) axis is the angle in degree. Four types of lines with different color represent the measurement results under different test conditions.