esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Displacement Measurement Field Experiment on MSE Wall  
A field experiment was conducted on a case site to compare the traditional monitoring method (total station measurement) and digital image photogrammetry using commercial imaging devices. As shown in Figure 1a, the field experiment was conducted at the corners (STA.80 and STA.85) bearing the longitudinal cracks. For measurement and monitoring, traditional monitoring methods and digital image photogrammetry (digital camera) were carried out for 7 months, and digital image photogrammetry (cellphone camera) was used for 4 months. Figure 6 shows the locations of the wall deformation measurements on the MSE wall.  
The field experiment process can be summarized as follows.  
The traditional monitoring method using total stations utilizes a reflection target on a specific surface of the MSE wall corresponding to the measurement location. Then, internal office work on the analysis of position changes is performed in the office, after obtaining 3D coordinates through the measurement of the reflection target by an expert. Digital image photogrammetry uses digital cameras and cellphone cameras, and it acquires two or more MSE wall images of the measurement target from each device. The acquired images are immediately transmitted to the PhotoModeler operator in the office via wireless Internet, and the operator performs image alignment, camera calibration, and 3D position information analyses. Since the use of PhotoModeler requires images, it is only used for digital image photogrammetry. In digital image photogrammetry, the image acquisition location can affect the error in the results of two or more image registrations. Therefore, the digital camera and cellphone camera locations were maintained at \(\pm\)45\({}^{\circ}\) from the front of the measurement location, and they were placed within approximately 10 to 15 m from the measurement location. Figure 7 displays the field experiment.  
Figure 6: Displacement measurement locations on the MSE wall: (**a**) STA. 80; (**b**) STA. 85.  
The field experiment employed a C3 total station manufactured by Trimble Inc., (Westminster, CO, USA). The specifications of Trimble C3 were 800 m and 3.0 mm + 2 ppm for the measurement distance and accuracy without reflective targets, respectively. With the prism, the measurement distance and accuracy were 5000 m and 2.0 mm + 2 ppm, respectively. The digital camera used was D7100, which has a CMOS sensor manufactured by Nikon (Tokyo, Japan). The D7100 has 24,710,000 pixels. The cellphone camera was a Galaxy S21 model from Samsung Electronics (Seoul, Republic of Korea) with 12 million pixels.
## Materials and Methods
### Measurement of \(fPAR_{green}\) and \(APAR_{chl}\)  
The accurate measurement of the fraction of absorbed photosynthetically active radiation absorbed by green leaves (fPAR\({}_{green}\)) is a critical step in the estimation of APAR\({}_{chl}\). An in situ fPAR\({}_{green}\) measurement method for low canopy vegetation based on a digital camera and reference panel was presented by [PERSON] et al. [43] and has proved to be an effective solution for low canopy vegetation. Using this method, fPAR\({}_{green}\) is given by  
\[\text{fPAR}_{green}\ =\ \Big{(}\text{PAR}_{i}-\text{PAR}_{r}-\Big{(}\text{APAR} _{exposed\ background}\ +\ \text{APAR}_{covered\ background}\Big{)}\Big{)}/\text{PAR}_{i} \tag{4}\]  
where PAR\({}_{i}\) and PAR\({}_{r}\) are the incident and reflected (including all exposed components) photosynthetically active radiation (PAR) derived from the DN values of the digital photograph. APAR\({}_{exposed\ background}\) and APAR\({}_{covered\ background}\) are the PAR absorbed by the exposed background (including non-photosynthetic components) and the vegetation-covered background respectively. In green leaves, photosynthesis starts with the absorption of PAR, mainly by chlorophyll [30]. The absorption of APAR by green leaves is mostly due to the chlorophyll content. There is a linear correlation between fPAR\({}_{chl}\) and fPAR\({}_{green}\):  
\[\text{fPAR}_{chl}\ =\ k\times\text{fPAR}_{green} \tag{5}\]  
where k is the ratio of fPAR\({}_{chl}\) to fPAR\({}_{green}\). Then APAR\({}_{chl}\) can be calculated based on Equation (6):  
\[\text{APAR}_{chl}\ =\ \text{PAR}\times\text{fPAR}_{chl}\ =\ \text{PAR} \times\text{fPAR}_{green}\times k \tag{6}\]
# A Preprocessing Strategy for Denoising of Speech Data Based on Speech Segment Detection
## 4 Performance Evaluation
### Results and Discussion  
#### 4.2.1 The Accuracy of the Speech Segment Detection Methods  
We compared the accuracy of the presented five speech segment detection methods: (1) energy-based filtering, (2) entropy-based filtering, (3) fuzzy clustering, (4) energy-based filtering with fuzzy clustering, and (5) entropy-based filtering with fuzzy clustering. To this purpose, we measured the recall, precision, and F1 score of each method based on the clean speech data files. For this experiment, we used all the files of the validation dataset.  
Table 2 shows the recall, precision, and F1 score for the speech segment detection methods according to different SNR groups. In this experiment, we determined a threshold (or \(\lambda\)) for each speech segment detection method, which was a parameter that affected on the accuracy of each method as presented in Section 3, that showed the highest F1-score under the condition that the recall was greater than the precision so as to reduce filtering of speech segments. However, this criteria could be changed for each target environment. The result indicated that the best method became different according to the group of SNRs. Specifically, the energy-based filtering with fuzzy clustering showed the best accuracy for the low SNR group; the fuzzy clustering method for the medium SNR group; the energy-based filtering for the high SNR group. This implied that our preprocessing strategy could  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Types of** & **Number of** & **Avg. of** & **Avg. of** & **Min\(\sim\)Max** & **Portion of** \\
**Data Sets** & **Data Sets** & **SNR (dB)** & **Signal Length** & **Speech Segments (\%)** \\ \hline Low SNR group & 200 & 0.915 & 2.469 (1.367\(\sim\)9.768) & 46.05 \\ \hline Medium SNR group & 227 & 6.147 & 2.548 (1.320\(\sim\)7.184) & 46.04 \\ \hline High SNR group & 397 & 13.555 & 2.518 (1.237\(\sim\)8.509) & 46.70 \\ \hline Total data set & 824 & 8.446 & 2.514 (1.237\(\sim\)9.768) & 46.34 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The characteristics of the used data set.  
be used to find the best speech segment detection methods for a target SNR. We also noted that we could adjust the threshold (or \(\lambda\)) to control the recall and the precision. According to Figures 7, 11, 13, 15 and 17, we observed the trade-off relationship between the recall and the precision by controlling the threshold (or \(\lambda\)). As a result, we could choose an adequate threshold for a target requirement. For example, for environments where any segments including the speech should not be excluded, we could choose a parameter setting that shows almost 100% of recall.
## 4 Conclusions  
This study systematically evaluated the tolerance and fermentation characteristics of _Pichia kudriavzevii_ GAAS-JG-1, isolated from a naturally fermented apricot system, under various extreme environmental conditions. The results demonstrated that this strain maintained strong growth activity even in an extremely acidic environment (pH 2) and exhibited remarkable tolerance to high ethanol concentrations (16%), high sugar levels (350 g/L), and elevated sulfur dioxide concentrations (500 mg/L). Additionally, _Pichia kudriavzevii_ GAAS-JG-1 displayed exceptional growth performance at 35 \({}^{\circ}\)C, outperforming all control strains. Further co-fementation experiments revealed that _Pichia kudriavzevii_ GAAS-JG-1, when combined with _Saccharomyces cerevisiae_ in sea buckthorn wine fermentation, significantly enhanced the variety and concentration of short-chain esters while moderately regulating the balance of higher alcohols and organic acids. This effectively enhanced the wine's aroma profile by enriching short-chain esters and balancing higher alcohols and organic acids. Based on these findings, _Pichia kudriavzevii_ GAAS-JG-1 demonstrates considerable potential as a fermentation starter for specialty fruit wines and other fermented food products. Future studies should further explore the metabolic pathways and flavor modulation mechanisms of this strain and validate its adaptability and fermentation performance across various fruit wine systems to support its broader application in the food fermentation industry.  
**Supplementary Materials:** The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/microorganisms13071491/s1](https://www.mdpi.com/article/10.3390/microorganisms13071491/s1)]([https://www.mdpi.com/article/10.3390/microorganisms13071491/s1](https://www.mdpi.com/article/10.3390/microorganisms13071491/s1)). Table S1: Changes in volatile flavor compound contents in sea buckthorn wine under different fermentation conditions.  
**Author Contributions:** Y.M.: conceptualization, methodology, formal analysis, investigation, writing--original draft, writing--review and editing; Y.W.: conceptualization, validation; C.Z.: supervision, funding acquisition. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Gansu Provincial Science and Technology Program Project (24 CXNA066).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article.  
**Conflicts of Interest:** The authors declare no known competing financial interests or personal relationships with other people or organizations that could inappropriately influence this work.
## 2 Governing Equation and Numerical Method
### Governing Equations
\(\tau_{x_{i}}\), \(\tau_{x_{j}}\) are the viscous stresses, \(q_{x_{i}}\) is the heat flux and \(g\) is the acceleration from gravity. \(U\), \(V\), and \(W\) are contravariant velocity components:  
\[U=\xi_{x}u+\xi_{y}v+\xi_{z}w,\ V=\eta_{x}u+\eta_{y}v+\eta_{z}w,\ W=\zeta_{x}u+ \zeta_{y}v+\zeta_{z}w. \tag{6}\]  
Subscripts \(l\), \(v\) and \(ng\) can be assumed to be liquid, vapor and non-condensable gas in this paper. The volume fraction of mixture flow (\(\alpha\)) satisfies the following relationship:  
\[\alpha_{l}+\alpha_{v}+\alpha_{ng}=1. \tag{7}\]  
The viscosity and density of vapor, liquid, and non-condensable gas are assumed to be constant. The viscosity and density for the mixture flows are given by  
\[\mu_{m}=\alpha_{l}\mu_{l}+\alpha_{v}\mu_{v}+\alpha_{ng}\mu_{ng}, \tag{8}\]  
\[\rho_{m}=\alpha_{l}\rho_{l}+\alpha_{v}\rho_{v}+\alpha_{ng}\rho_{ng}. \tag{9}\]  
In the case of the cavitation model based on the homogeneous mixture, the mathematical modeling of the mass transfer between the gas and liquid phase is carried out using \(\dot{m}^{-}\) and \(\dot{m}^{+}\) representing the ratio of evaporation and condensation. In this study, we used a model in which the dependent variable problem was corrected by [PERSON] et al. [16]  
\[\dot{m}^{-}=-k_{v}\frac{\rho_{v}\alpha_{l}}{t_{\infty}}\text{min}\left\{1, \text{max}\left(\frac{(p_{v}-p)}{k_{p}p_{v}},0\right)\right\}, \tag{10}\]  
\[\dot{m}^{+}=-k_{l}\frac{\rho_{v}\alpha_{v}}{t_{\infty}}\text{min}\left\{1, \text{max}\left(\frac{(p-p_{v})}{k_{p}p_{v}},0\right)\right\}, \tag{11}\]  
where \(k_{p}\), \(k_{v}\), and \(k_{l}\) are the scaling constants and t\({}_{\infty}\) is characteristic time (\(t_{\infty}=D/U\)). In this study, \(k_{p}\) is 0.02, \(k_{v}\) is 15.0 and \(k_{l}\) is 10.
## Materials and Methods
### Characterization of Materials  
The fabricated and applied nanomaterials and sensors were analyzed with respect to morphology details and surface elemental composition using scanning electron microscopy (SEM) and an EDS analyzer. An SEM microscope, Phenom ProX (ThermoFisher Scientific, Waltham, MA, USA), working at an accelerating voltage of 10 kV, and Zeiss Supra 35 (Oberkochen, Germany), working at an accelerating voltage of 7-20 kV, were used to examine the morphology of the fabricated materials. Elemental analysis was performed with an energy-dispersive X-ray (EDS) detector (Thermo Scientific(tm) EDX UltraDry, Waltham, MA, USA). The UV-Vis spectrograms of the silver nanowire synthesis products were recorded using a U-2800A spectrometer (Hitachi, Tokyo, Japan) in a 10 mm thick quartz cuvette in the 300-700 nm radiation range. Investigation of the crystalline phase was performed on a Seifert 3003 TT (Agfa-Seifert, Mortsel, Belgium) diffractometer using Cu K\(\alpha\) radiation (\(\lambda\) = 0.1540598 nm) in the _20_ range 30-70\({}^{\circ}\) with a step size of 0.05\({}^{\circ}\).
# Miniaturized MIMO Antenna Array with High Isolation for 5G Metal-Frame Smartphone Application
## 2 Proposed MIMO Antenna Design
### Antenna Element Design
A decoupling structure with a tiny footprint for terminal products with high isolation requirements was introduced to make the adjacent antenna elements (Ant 1, Ant 2 and Ant 3, Ant 4 and Ant 5, Ant 6 and Ant 7, and Ant 8) obtain higher isolation.
# Automatic Breast Tumor Screening of Mammographic Images with Optimal Convolutional Neural Network
To reduce the error rates, an adaptive moment estimation method (ADAM) can compute the adaptive learning rates for updating network parameters by storing an exponentially decaying average of past squared gradients [31; 32], which combines two stochastic gradient descent approaches, including adaptive gradients and root mean square propagation. Its optimization algorithm uses randomly selected training data subsets to compute the gradient, instead of using the entire dataset. The momentum term can speed up the gradient descent by converging faster. The ADAM algorithm has a simple implementation, computation efficiency, and fewer memory requirements, and is appropriate for operations with large datasets and parameters for training the multilayer CNN models. A total of 78 subjects is selected from the MIAS (Mammographic Image Analysis Society) Digital Mammogram Database (United Kingdom National Breast Screening Program) for experimental analysis. The clinical information was confirmed and agreed upon by expert radiologists for biomarkers, such as image size, image category, background tissue, class of abnormality, and severity of abnormality [33; 34]; the image database included a total of 156 mammography images (including right and left images), including 94 normality cases and 62 abnormalities involving benign and malignant cases. The ROIs were extracted by a 100 \(\times\) 100 bounding box, and then the 932 feature patterns were extracted by using the proposed convolutional-pooling processes including 564 abnormalities and 368 tumor-free patterns. By making use of cross-validation, the dataset was randomly divided into two halves: 50% of the dataset was used for training the classifier, and 50% of the dataset was used for evaluating the classifier's performance. Thus, tenfold cross-validation is used to verify the performances of the proposed multilayer deep-learning-based CNN with the proposed convolutional-pooling layers in terms of recall (%), precision (%), accuracy (%), F1 score, and Youden's index [35; 36]. Therefore, the optimal architecture of multilayer CNN can be determined, and may potentially be applied to establish a classifier for automatic breast tumor screening in clinical applications. The remainder of this study is organized as follows: Section 2 describes the methodology, including the design of the multilayer deep-learning-based CNN, the adaptive moment estimation method, the classifier's performance evaluations, and the computer assistive system. Sections 3 and 4 present the experimental setup, testing of different multilayer CNN models and determination of the suitable CNN architecture, testing of the first convolutional layer for image enhancement, determination of the mask types, feasibility tests, and experiment results for clinical applications, and the conclusions, respectively. Figure 1: Architecture of the proposed multilayer CNN for automatic breast tumor screening.
# Study on Reinforcement Mechanism and Reinforcement Effect of Saturated Soil with a Weak Layer by DC
## 1 Introduction
### Dynamic Compaction
In the alluvial plain of the lower Yellow River, China, the ground is typically layered (e.g., layers of silty clay, silt, silty sand, and soft clay sometimes) along with long-term alluvialation and sedimentation, and the strength, deformation, and permeability properties may vary significantly in layers. Meanwhile, the groundwater level is normally high. DC is one of the most commonly used ground improvement methods in this area [24; 25]. Some pioneering studies [26; 27; 28] have attempted to investigate the performance of DC in this typical layered silty ground, and they have shown that the existence of soft soil interlayer obviously hinders the reinforcement effect and restrains further development of the effective reinforcement depth. However, the influences of the position and the thickness of weak interlayers on the compaction mechanism and improvement effects have rarely been studied. Furthermore, unlike the uniform saturated soil improvement, the pore pressure of the soil with a weak interlayer dissipates slowly, and there is a cushioning effect, which has a damping action on the transfer of impact energy.
## 3 Motion Vehicle Target Extraction
### ViBe Algorithm  
The ViBe background modeling algorithm initializes a background sample library for each pixel position using the first frame image and uses a distance metric to classify each pixel as either a foreground or background point. If a pixel is classified as a background point, the background sample library is updated accordingly. To accurately detect moving vehicles in subsequent frames, a conservative update strategy should be adopted for the sample library. Specifically, if a pixel is classified as a foreground point for multiple consecutive frames, it is forced to be updated as a background point. Additionally, there is a probability of \(1/\psi\) of updating the sample library of itself or its neighbors. When a pixel is classified as a background point, there is also a probability of \(1/\psi\) of updating the sample library. The initial value of \(\psi\) when the algorithm was first proposed is 16.  
The ViBe algorithm [23] is a highly efficient algorithm for detecting moving targets, but it has some drawbacks. Firstly, if there is a vehicle target in the first frame, it can contaminate the background sample library, causing ghosting and making it difficult to eliminate. Secondly, it cannot effectively eliminate the shadow parts of the vehicle, which leads to a lower accuracy of the algorithm.
# Exploiting the Capture Effect to Enhance RACH Performance in Cellular-Based M2M Communications
## 5 Simulation Results
### Coexistence with Other Schemes  
Because the Msg3 PR scheme only controls the Msg3 transmission power, it can easily coexist with other RA-related schemes. In this sub-section, we discuss the possibility of coexistence of the Msg3 PR scheme with other typical RA-related schemes through simulation results.  
The simulations assume bursts of M2M traffic. In [20], the M2M traffic arrival is modeled as a beta-distribution to express the burstiness of M2M traffic. Let \(g(\tau)\) denote the probability that each device is activated at time \(\tau\). Then,  
\[g(\tau)=\frac{\tau^{a-1}{(T_{A}-\tau)}^{b-1}}{{T_{A}}^{a+b-2}\beta(a,b)},\quad 0 \leq\tau\leq T_{A}, \tag{31}\]  
where \(\beta(a,b)\) is the beta-function, \(T_{A}\) is the activation time which denotes the period during which all devices are activated, and \(a=3,b=4\) for bursty-M2M traffic. In this simulation, 10,000 devices are uniformly distributed in a cell, and each device generates traffic only once. That is, when a device experiences connection failure/success, it does not participate in the RA again. The activation time, \(T_{A}\), is 10 s. Optimal ACB is assumed in this simulation, and the barring time and back-off window size are 200 ms and 20 ms, respectively.  
Figure 11 illustrates the cumulative distribution function (CDF) of the RA results over time when the optimal ACB is used. The ACB scheme controls the number of participants, so that the efficiency of the RACH could be maximized. Therefore, if severe congestion is predicted, it protects the RACH efficiency by blocking accesses of devices, but it does not improve RA throughput itself. As seen in Figure 11, the number of connection failed-devices is increased due to bursty arrivals if only the ACB is used. However, when using the ACB with the Msg3 PR, the number of devices experiencing connection failure is much smaller than in the case of using the ACB only. Because the Msg3 PR scheme improves the RA throughput, approximately 20% of devices (almost 2000 devices) are more successful in acquiring connections by using the Msg3 PR together. Furthermore, this performance is the result when the ACB is performed without considering the RA efficiency improvement due to the Msg3 PR scheme. Therefore, although it is not the scope of this study, a larger performance improvement can be expected if ACB optimization is performed considering Msg3 PR.
# XAI in the Context of Predictive Process Monitoring: An Empirical Analysis Framework
## 5 Results and Observations
### Global Methods Comparability
Aggregation encoding tends to increase the number of ordinal categorical attributes and preserve the existence of numerical attributes. Therefore, feature ranks of a single run tend to be less affected by collinearity. These observations are valid regardless of the underlying predictive model, which enables neutralising the effect of characteristics of the used predictive model. **SHapley Additive exPlanations (SHAP). ** SHAP is an explanation method belonging to the class of feature additive attribution methods [5]. These methods use a linear explanation model to compute the contribution of each feature to a change in the prediction outcome with respect to a baseline prediction. Afterwards, a summation of the contributions of all features approximates the prediction of the original model. To maintain comparability of the global XAI methods used in our experiments, we constructed a SHAP explainer model on training event logs independently of another SHAP explainer model constructed on relevant testing event logs. The concluded observations made in this section are drawn based on the training SHAP explainer model, whereas the observations based on the testing SHAP explainer model are discussed in Section 5.1.1 along with other observations concerning local XAI methods. _Observation 3: While comparing the two execution runs, results did not depend on the preprocessing technique used, but differed depending on the predictive model being explained._  
When explaining predictions of the LR model, performing two executions of the SHAP method did neither result in different feature sets nor different ranks based on SHAP values, regardless the used preprocessing combination. In turn, explaining predictions of XGBoost model reveals the first most contributing feature as being the same across both execution runs, while the rest of the feature set is the same, but differs with respect to features' ranks. An exception is present in the feature set of the three _Sepsis_ event logs, where feature ranks are the same across both runs.