esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Fuzzy Block  
Fuzzy logic has the ability to express the uncertainty of things and make decisions through fuzzy processing and operations, and it can be effectively combined with neural networks to produce good results. Because the Gaussian function has the capacity for non-linearity and fitting functions, the fuzzy block constructed in this paper uses the Gaussian function as the membership function within the neural network, as shown in the following formula:  
\[\mu(x)=\frac{1}{(2\pi)^{\frac{\upsilon}{2}}|\Sigma|^{\frac{1}{2}}}exp\left(- \frac{1}{2}x^{T}\Sigma^{-1}x\right) \tag{1}\]  
The fuzzy block structure is shown in Figure 2. In the fuzzy layer, a feature map is input with dimensions (N, H, W, C), and the number of original image categories is M. In order to calculate the Gaussian function, the two-dimensional pixel value in the feature map needs to be changed to one-dimensional data (H \(\times\) W) through the Gaussian membership function to calculate the degree of certainty that each pixel value belongs to the lesion area. Since the output value of the Gaussian function varies over a wide range, the L2 regularization method is used to process it so that it can be normalized to a value range similar to the distribution of the original input data. After the fuzzy layer outputs the fuzzy logic uncertainty value with dimensions (N, H, W, M), it can affect the distribution of the pixel value information in the feature map. Finally, the uncertainty value and the original input are fused into a feature map with dimensions (N, H, W, C + M) and then output to the neural network for subsequent operations.  
**Fuzzy layer:** If the membership degree of pixel \(i\) is close to 1 or 0, it means that the pixel is likely to be a lesion or background, which implies a high degree of certainty. If the membership degree of pixel \(i\) is close to 0.5, it is difficult to determine whether the pixel  
Figure 1: Pipeline of the proposed segmentation framework.  
belongs to the lesion area or the background. \(\mu(x_{i})\) is the membership degree of pixel \(i\), and Formula (2) is used to calculate its uncertainty and obtain the uncertainty \(o(x_{i})\) of pixel \(i\)  
\[o(x_{i})=\begin{cases}2\times\mu(x_{i})&\text{if }\mu(x_{i})<0.5\\ 2\times(1-\mu(x_{i}))&\text{if }\mu(x_{i})\geq 0.5\end{cases} \tag{2}\]  
**Merge:** By fusing the uncertainty of the pixels with the original input features, the effect of reducing the uncertainty of the blurred boundaries of the original features is achieved, as shown in Formula (3).  
\[p_{i}=(1-o(x_{i}))\cdot x_{i} \tag{3}\]  
where \(o(x_{i})\) is the fuzzy membership degree of pixel \(i\) obtained using Formula (1), and \(x_{i}\) is the original feature of the pixel. This equation shows that if the uncertainty of a pixel is closer to 1, its weight should be decreased. Finally, the obtained result \(p_{i}\) is output to the neural network for subsequent operations.
# mmS-TCP: Scalable TCP for Improving Throughput and Fairness in 5G mmWave Networks
## 4 Evaluations
### Intra-Protocol Fairness  
TCP congestion control must fairly occupy the link capacity, not only in competition between different algorithms, but also in competition between identical algorithms. Following the fairness evaluation for CUBIC TCP, Section 4.6 compares the fairness within the protocol.  
Figure 12: Average throughput of each flow when they compete.  
The intra-protocol fairness experiments assumed a scenario in which the UE moves for 30 s and experiences a total of five NLoS situations at intervals of about 5 s. Here, five TCP flows were tested in a topology through which one UE was connected to five different servers and passed through one mmWave base station.  
Figure 12(a) shows the change in cwnd in the competition between five S-TCP flows, and Figure 12(b) shows the result of mmS-TCP. S-TCP is very dense at the beginning of the connection, but soon shows a higher cwnd increase in certain flows. The flow that is initially dominant overwhelms the others for a long time. The specific S-TCP flow does not increase to the ssthresh after the NLoS situation, and congestion avoidance is performed at a low cwnd. Therefore, it can be seen that S-TCP is somewhat unstable in competition with the same protocol, and an imbalance in throughput occurs.  
On the other hand, for the mmS-TCP of Figure 12(b), some flows dominate at the beginning of the connection, but eventually the cwnd values of all flows closely converge. mmS-TCP has smaller cwnd fluctuations between flows than does S-TCP, and it competes with others within a relatively narrow range. These results are due to the redesign of the cwnd growth mechanism, which increases rapidly in the initial stage and slowly increases in the last stage. This makes convergence easier to reach during competition between the same protocols.  
For an additional intra-protocol fairness comparison, Figure 14 shows the throughput of each flow for the six TCP CCAs evaluated in Section 4.4. mmS-TCP and HS-TCP show relatively similar throughput between flows, but the remaining CCAs show a large difference in throughput between the five flows. Table 3 shows the [PERSON]'s fairness index values between the five flows according to CCAs, the throughput of each flow, and the average total throughput. mmS-TCP simultaneously improves average total throughput and intra-protocol fairness through the redesign of the cwnd increase/decrease mechanism.  
Figure 13: Cwnd changes in competition between five flows: (**a**) S-TCP; (**b**) mmS-TCP.
# Open-Pit Bench Blasting Fragmentation Prediction Based on Stacking Integrated Strategy
## 4 Results and Discussion
### Model Construction and Prediction Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **True Value** & **Predicted Value** \\ \hline  & 0.44 & 0.4154 \\  & 0.1 & 0.1259 \\ Random Forest & 0.35 & 0.4042 \\  & 0.25 & 0.2578 \\  & 0.74 & 0.6014 \\ \hline  & 0.44 & 0.2504 \\  & 0.1 & 0.1826 \\ Support Vector Machine & 0.35 & 0.2503 \\  & 0.25 & 0.3287 \\  & 0.74 & 0.4342 \\ \hline  & 0.44 & 0.3273 \\  & 0.1 & 0.2011 \\ XGBoost & 0.35 & 0.2740 \\  & 0.25 & 0.2437 \\  & 0.74 & 0.7453 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Partial prediction results of the testing sets. When constructing the stacking integrated model, considering the size of the dataset, the first attempt was to use cross-validation to increase the generalization ability of the model, but the cross-validated model had a poor prediction effect on the test set, and the stacking fusion model itself could effectively improve the residuals, thus canceling the cross-validation in order to improve the prediction effect. At the same time, the XGBoost algorithm has the phenomenon of overfitting, so when building the meta-learner, linear regression, a relatively simple model, was chosen to avoid overfitting caused by excessive complexity. The meta-model was trained, and the prediction results of the stacking integrated model were obtained, as shown in Figure 7d. For the training set, the prediction effect of the stacking integrated model is relatively close to that of the random forest, but the accuracy is not as good as that of XGBoost; for the testing set, the prediction effect of the stacking integrated model is the best, with the sample points closer to the diagonal. In summary, the stacking integrated model demonstrates better prediction capability in the training and testing sets, particularly in the testing set, indicating that the integrated model is more stable and can avoid overfitting.
## 6 Results
#### Linear Regression Model  
In order to obtain a detailed statistical analysis and validation of our system that allowed for the measurement of the service quality and the influence of environmental factors on the signal quality,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Distance (m)** & **Avg (RSSI)** \\ \hline
10 & –60.4 \\
50 & –82.3 \\
100 & –93.0 \\
150 & –99.8 \\
200 & –100.6 \\
250 & –104.2 \\
300 & –104.8 \\
350 & –113.6 \\
400 & –119.1 \\
450 & –121.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of distances from gateway and average Received Signal Strength Indication (RSSI) value.  
Figure 12: Graphical representation of average RSSI data and respective distances.  
we applied a linear regression model to the data obtained, with the dependent variable being power of RSSI (pRSSI) and the independent variable being the distance to the gateway. The results in Table 4 allow us to observe that, with a correlation factor, Multiple R is equal to 0.9016, showing that there is a strong correlation between the two variables. However, the value obtained for R Square, 0.813, shows us that only 81.3% of the observed cases fit the obtained model, when the desirable value would be 95%. Several factors may have contributed to this result, namely environmental factors such as obstacles, trees, walls, and the geography of the terrain.  
From the analysis, a \(p\)-value much lower than 0.05 (tends to zero) was obtained, so it is clear that there is a strong dependence of the variable pRSSI on distance, as expected.  
Figure 12(a) presents a boxplot of the data obtained, with some deviations that help to explain the bias observed in R Square. In Figure 12(b), we present a scatter plot with a trend line, which shows the adjustment of the line to the point cloud obtained from the pRSSI readings.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Regression Statistics**} \\ \hline Multiple R & 0.901694435 \\ R Square & 0.813052854 \\ Adjusted R Square & 0.81114523 \\ Standard Error & 7.784507372 \\ Observations & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Linear regression summary output.  
Figure 13: _Cont._  
To carry out accurate measurements with an error of less than one meter, we used the Google Maps(c) tool, specifically the \"Measure distance\" option. Figure 14 shows the process of obtaining the sites for measuring RSSI power.  
We are convinced that the use of strategically located gateways that comply with the LoRa and LoRaWAN specifications, namely in terms of the power and gain of the external antennas, will have a considerable impact on the coverage and power of the received signal.
## 2 Introduction to the Research Area  
### Natural Geographical Conditions  
This study takes the Shibei District of Qingdao as an example. The basic conditions for developing urban agriculture in this area are as follows: Qingdao City is located in the southeast of Shandong Peninsula, including seven districts, such as Shinn, Shibei, and Licang, and three cities, such as Pingdu, Jiaozhou, and Laixi. The first three districts are the central jurisdiction of Qingdao, while Shibei District is basically located in the center of the jurisdiction, far from suburban agriculture, making the development of urban agriculture significant. Shibei District covers an area of 66 square kilometers, including the old urban area and some new areas. There are relatively few vacant plots, and it is advisable to develop urban agriculture through rooftop agriculture. By the end of 2022, statistical data showed that the permanent resident population of Qingdao was 10.3421 million. Among them, the permanent population of the districts and urban areas was 7.41 million, the population of the three districts in the city was 2.55 million, and the population of Shibei District was the largest, which was 1.09 million. The overall urbanization rate of Qingdao has reached 77.32%, higher than the national average level.  
Qingdao is located in hilly terrain, and the urban terrain has a certain undulating quality. The Shibei District is mainly characterized by hilly terrain, with high terrain in the east and low terrain in the west. The urban area of Qingdao is directly regulated by the marine environment, with significant marine climate characteristics, such as humid air and abundant rainfall. The annual average temperature is 12.7 \({}^{\circ}\)C, with moderate temperature and four distinct seasons. Qingdao has abundant wind energy resources and good solar energy resources, with a total annual solar radiation of 120 kcal/cm\({}^{2}\).  
From the above, it can be seen that the climate in Shibei District of Qingdao is suitable, with abundant rainfall, for agricultural cultivation, and the urban agricultural development conditions are good. Light and wind energy resources are abundant and can provide energy supply for agricultural cultivation.
## 4 Calculation of Various Reliability Indices
### Mean Time to Failure (MTTF) Analysis  
This is a measure of elapsed time between inherent systems during regular system operation. If \(R(t)\) is the reliability function as obtained in Equation (20), then the mean time to failure (MTTF) for the considered system is given as Equation (22).  
\[\begin{split}\text{MTTF}&=\int_{0}^{\infty}t\!f(t )dt=\int_{0}^{\infty}R(t)dt\\ \text{MTTF}&=\frac{1}{\lambda_{m}+\lambda_{su}+ \lambda_{ts}+\lambda_{p}}+\frac{\lambda_{m}}{\left(\lambda_{m}+\lambda_{su}+ \lambda_{ts}+\lambda_{p}\right)^{2}}\\ &\quad+\frac{\lambda_{p}}{\left(\lambda_{sp}+\lambda_{su}+ \lambda_{ts}+\lambda_{m}\right)\left(\lambda_{m}+\lambda_{su}+\lambda_{ts}+ \lambda_{p}\right)}\\ &\quad+\frac{\lambda_{p}\lambda_{m}}{\lambda_{m}+\lambda_{su}+ \lambda_{ts}+\lambda_{p}}+\frac{\lambda_{p}\lambda_{m}}{\lambda_{sp}+\lambda_ {su}+\lambda_{ts}+\lambda_{m}}\\ \end{split} \tag{22}\]  
Now, substituting different values for the parameters, i.e., \(\lambda_{su}=0.22\), \(\lambda_{ts}=0.15\), \(\lambda_{m}=0.35\), \(\lambda_{p}=0.03\), and \(\lambda_{sp}=0.02\), and varying each failure rate from 0.01 to 0.09 one by one while keeping other failure rates fixed, one can obtain a graphical representation and estimation of MTTF, as shown in Table 3 and Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Failure Rate** & \(\lambda_{su}\) & \(\lambda_{ts}\) & \(\lambda_{m}\) & \(\lambda_{p}\) & \(\lambda_{sp}\) \\ \hline
0.01 & 3.29411 & 2.75676 & 2.69047 & 2.06279 & 2.06219 \\
0.02 & 3.20597 & 2.69303 & 2.68534 & 2.06158 & 2.0604 \\
0.03 & 3.12205 & 2.63197 & 2.6774 & 2.0604 & 2.05867 \\
0.04 & 3
## 3 Results
### Baseline vs. CCP Results  
To test the statistical significance of the differences in the classifier performances, a McNemar-Bowker test was performed on the classifiers' prediction outcome [49]. Indeed, whereas [PERSON]'s test requires that there are only two possible categories for each classification outcome to be tested, in the McNemar-Bowker test the outcome analyzed can be classified in more than two classes. No statistical difference was found between the classifiers' performance in the two experimental procedures (i.e., baseline vs. CCP). In detail, both comparisons between the DCNN and the 3D-DCNN performance and between  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**DCNN**} & \multicolumn{2}{c}{**SVC**} \\ \hline
**Classes** & **Sensitivity** & **Specificity** & **Sensitivity** & **Specificity** \\ \hline HC & 0.84 & 0.91 & 0.84 & 0.83 \\ SSc & 0.75 & 0.87 & 0.67 & 0.90 \\ PRP & 0.90 & 0.96 & 0.81 & 0.92 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sensitivity and specificity of DCNN and SVC models on HC, SSc, and PRP participants’ classification.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**3D-DCNN**} & \multicolumn{2}{c}{**SVC**} \\ \hline
**Classes** & **Sensitivity** & **Specificity** & **Sensitivity** & **Specificity** \\ \hline HC & 0.92 & 0.91 & 1 & 0.95 \\ SSc & 0.83 & 0.92 & 0.58 & 0.91 \\ PRP & 0.90 & 1 & 0.81 & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sensitivity and specificity of 3D-DCNN and SVC models on HC, SSc, and PRP participants’ classification.  
Figure 9: 3D-DCNN average (and related standard error) cross-validated accuracy as a function of the training epoch for the training and the testing set, respectively.  
the baseline SVC and the CCP SVC performance were found not significant (B = 2, df = 3, p = n.s. and B = 0.8, df = 3, p = n.s, respectively).  
Within the procedures, the difference between the performances of the machine learning approaches employed were also tested and no statistically significant difference was reported. In detail, both comparisons between the DCNN and the SVC performance in the baseline condition (B = 0.6, df = 3, p = n.s.) and between the 3D-DCNN and the SVC in CCP (B = 2.7, df = 3, p = n.s.) were not significant. The overall performance metrics of both classifiers in both procedures are shown in Table 4.
###### Abstract  
Insufficient acidity in grape berries from warm climate regions has been exacerbated due to global warming, thereby becoming a major concern for winemaking. The wine lactic acid bacterium _Lactiplantibacillus plantarum_ has potential to ameliorate wine acidity by producing lactic acid from hexose metabolism, but its impact on wine compositions and sensory outcomes is not well studied. Here, we evaluated acidification and fermentation performance of indigenous _L. plantarum_ in two inoculation regimes (i.e., reverse inoculation and co-inoculation) by conducting pilot-scale viniification using Cabernet Sauvignon with low acidity. Important parameters of the bio-acidified wines, including fermentation kinetics, basic enological parameters, volatile and sensory profile were compared to those in wines produced by single _Saccharomyces cerevisiae_ with/without chemical acidification. Total titutable acidity in _L. plantarum_ wines were either comparable or significantly higher compared to the chemical acidification control. Chemical profiling reviewed remarkable differences in certain organic acids and major volatile compounds, especially an up to a five-fold, six-fold, and nine-fold increase in lactic acid, ethyl lactate and isoamyl lactate, respectively. Changes in chemical compositions of the bio-acidified wines resulted in differentiated sensory perception compared to the control wines. Except having higher scores for \"wine acidity\", the flavour profile of the bio-acidified wines was shifted towards \"jammy fruit\" and \"butter\" aromas. Together, these findings highlighted the applicability of using _L. plantarum_ to induce biological acidification along with modulation of wine flavour.  
Lactipantibacillus plantarum; Saccharomyces cerevisiae; wine acidity; lactic acid; volatile compounds; sensory analysis +
Footnote †: journal: _Journal of Foods_
# Digital Twin Technology-Based Networking Solution in Low Earth Orbit Satellite Constellations
## 1 Introduction
We propose a network topology simulation method that combines pre-generated ephemeris and orbital information, enabling the dynamic analysis of the satellite interconnection structure in low Earth orbit (LEO) constellations. 2. For LEO constellation networks, we develop a digital twin algorithm based on network virtualization, cloud platform management, and software-defined networking. The proposed algorithm effectively meets the twin validation requirements of large-scale, high-fidelity, and low-cost simulations. 3. Based on the twin validation needs at different stages of satellite development, we construct an LEO constellation twin validation platform that comprehensively enhances the virtual-to-physical mapping, virtual-to-physical interconnection, and virtual-to-physical optimization. Such a validation platform facilitates twin validation work for network protocols at different stages. The remaining sections of this paper are organized as follows. Section 2 presents related work. Section 3 describes the different stages of digital twin system design. Following this, Section 4 describes the system model and Section 5 elaborates the networking protocol based on digital twins. In Section 6, the simulations and analysis are provided, followed by the conclusions in Section 7.
# The Ground-Based Absolute Radiometric Calibration of the Landsat 9 Operational Land Imager
## 2 Methodology
### Automated Measurements: The Radiometric Calibration Test Site (RadCaTS)
#### 2.2.5 Atmospheric Measurements at the RadCaTS  
The atmospheric measurements at the RadCaTS are performed using a Cimel sun photometer that follows the AERONET measurment collection protocol [29; 30; 82; 83; 84]. Throughout the day, direct solar irradiance and sky radiance measurements are made automatically, and the data are uploaded hourly through a satellite uplink to the NASA Goddard Space Flight Center (GSFC) for further processing, where they are subsequently made available for public access on the AERONET web portal. In the case of the RadCaTS, the atmospheric quantities required as the input to the radiative transfer code include the following:  
* Aerosol optical depth (AOD);
* Angstrom exponent;
* Columnar water vapor;
* Columnar ozone;
* Carbon dioxide.  
The AOD, Angstrom exponent, and columnar water vapor data are obtained from AERONET, while the columnar ozone data are obtained from either the Ozone Monitoring Instrument (OMI) or the Ozone Mapping and Profiler Suite (OMPS) data portals. The daily carbon dioxide amount is downloaded from the NOAA Global Monitoring Laboratory (GML) web portal (gml.noaa.gov, accessed on 1 February 2024).  
Once these data have been acquired, they are used as input into the radiative transfer code to determine the spectral transmission of the direct solar beam, as well as the diffuse sky irradiance. The MODTRAN mid-latitude summer atmospheric profile and  
Figure 4: A Landsat 9 OLI image of Railroad Valley (10 July 2022, 18:21 UTC) created using OLI bands 4, 3, and 2. The yellow box denotes the 1 km \(\times\) 1 km RadCaTS ROI (WRS-2 path 40, row 33).  
corresponding standard atmosphere vertical profile column amounts are used for the RadCaTS. The exoatmospheric solar irradiance model used for Landsat work is Chance-Kurucz (ChKur) [85]. The aerosol optical properties are determined using an internal database of Mie-generated aerosol phase functions in MODTRAN. The aerosol optical properties at the RadCaTS are assumed to follow a power-law size distribution that has a given Angstrom exponent and complex refractive index. The use of such an assumption at desert sites such as Railroad Valley has been justified because of the typical low amount of aerosol loading coupled with a relatively high surface BRF. The MODTRAN output of interest for the RadCaTS is the atmospheric spectral transmittance from 350 nm to 2500 nm as well as the diffuse sky irradiance, \(E_{\text{sky}}\), which is used in the determination of the surface BRF, described in the following section.