esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation Experiment
### Experiment 2: Clarity of Reconstructed Sound Image for Given Directivity in Proposed Method  
In this experiment, the clarity of the reconstructed sound image for the given directivity in the proposed method was evaluated. The directivities were generated by four different methods; (i) giving only the unit pulse at the target position, (ii) MUSIC, (iii) minimum variance (MV) method [23], and (iv) delay-and-sum (DS) method [24]. These directivities are shown in Figure 13. In this experiment, the directivity patterns were obtained for each position shown in Table 4. Hence, the 32 directivity patterns were used in the experiment. Then, the weight \(w_{i}(k)\) was calculated for each directivity pattern. After that, the 3-D sound image was reconstructed by 22.2 multichannel audio and the emitted sound was recorded by the dummy head microphone. Using the recorded sound obtained at the left and right ears of the dummy head microphone, the inter-aural cross correlation (IACC) [25] was calculated as the evaluation metric. The IACC is obtained by:  
\[\text{IACC}=\max_{-1\leq\tau\leq 1}\text{IACF}(\tau), \tag{31}\] \[\text{IACF}(\tau)=\frac{\sum_{t=0}^{t_{\text{max}}-1}s_{\text{R} }(t)s_{\text{L}}(t+\tau)}{\sqrt{\sum_{t=0}^{t_{\text{max}}-1}s_{\text{R}}^{2 }(t)\sum_{t=0}^{t_{\text{max}}-1}s_{\text{L}}^{2}(t)}}, \tag{32}\]  
where \(s_{\text{L}}(t)\), \(s_{\text{R}}(t)\) are the recorded sounds at the left and right ears, \(t_{\text{max}}\) is the length of the signal, \(\tau\) is the time index related to the ITD, and IACF(\(\tau\)) is the inter-aural cross function (IACF) for \(\tau\). The larger the IACC, the higher the clarity of the reconstructed sound image. In this experiment, we conducted the three trials and the IACC was calculated as the average of three.  
Figure 14 shows the IACCs for each position (a)-(h). In Figure 14, \"Unit pulse\" represents the IACC for the sound image by using the unit pulse at the target position, \"MUSIC,\" \"MV,\" and \"DS\" represent the IACC for the sound images by using MUSIC, the MV method, and DS, respectively. From Figure 14, the IACCs for Unit pulse, MUSIC, MV, and DS are 0.33, 0.37, 0.36, and 0.35, respectively. This result indicates that the directivity with not only the peak but also the sidelobe affects the high clarity of the sound image in the proposed method. In addition, the clarities of the sound image in front of the listening point are higher than those behind the listening point. This is because the many loudspeakers are placed at the front of the 22.2 multichannel audio and it is easy to reproduce the clear sound image to the front side of the 22.2 multichannel audio. From these results, it can be said that the proposed method is effective for reproducing the 3-D sound image in the 22.2 multichannel audio by using the given directivity pattern with the sidelobe.  
Figure 12: Error between the position of the estimated position and that of the target sound image \(D_{\text{err}}\).
## 2 Materials and Methods
### Data Analysis  
Lethal concentrations causing a 10, 25, 50, and 90% effect on the cell viability (LC\({}_{10}\), LC\({}_{25}\), LC\({}_{50}\), and LC\({}_{90}\), respectively) were estimated by fitting the data to a sigmoidal nonlinear regression model with four parameters and are expressed in the text in mg/L of SLE\({}_{n}\)S. A two-way analysis of variance (ANOVA) was conducted to analyze the cytotoxic interaction between the exposure time and SLE\({}_{n}\)S concentrations. The assumptions to conducted the ANOVA were checked with the Shapiro-Wilk test (for normality) and Bartlett's test (for homogeneity of variance). The coefficient of correlations (\(r\)) for linear regressions were determined to identify associations between the values of computed LC\({}_{50}\) (with MTT and RES assays) and the number of EO groups in the SLE\({}_{n}\)S variants. To check for associations between the LC\({}_{50}\) computed using the MTT and the resazurin assays, coefficient correlations were computed after running Pearson correlations. The significance level for the ANOVA, linear regressions, and Pearson correlations was set at \(p<0.05\). All statistical analyses was performed using GraphPad Prism v9.1.0 for Windows (GraphPad Software, San Diego, CA, USA).
# Soil Moisture Retrieval from Multi-GNSS Reflectometry on FY-3E GNOS-II by Land Cover Classification
## 3 Methods
### Soil Moisture Retrieval  
The algorithm in this study assumes that, given a certain location, the only variables that change over time are soil moisture and biomass, whereas the soil surface roughness (topography) and soil texture remain constant. In this case, the reflectivity of GNSS-II could be linearly related to the SMAP reference soil moisture at each ease grid cell [13].  
\[sm=a\cdot\Gamma_{soil}+b \tag{10}\]  
where \(sm\) represents the reference data of soil moisture; parameters a and b are polynomial fitting coefficients, \(\Gamma_{soil}\) is the observed reflectivity with vegetation correction, which could be derived from Equations (4)-(6).  
Figure 7: The estimated topography attenuation at an ease grid of 36 km.  
The coefficients \(a\) and \(b\) were fitted for every ease grid of 36 km, and the SMAP soil moisture at a resolution of 9 km was used as the reference value for sm. One-year observations (July 2021-June 2022) were used for the model development. There are about 100 specular points in average in each 36 km grid in the training. No separation by GNSS systems was made in this training step. The incidence angle will also affect the coherent reflectivity. This effect only becomes substantial when the incidence angle exceeds 50 degrees. GNOS-II has a maximum incidence angle of 53 degrees, with most observations concentrated in the range of (15, 45) degrees as shown in Figure 8. Thus, the incidence influence was ignored in this study.
# Radiation Qualification by Means of the System-Level Testing: Opportunities and Limitations
## 6 Conclusions  
Based on the experimental results from several radiation test campaigns, the overview of opportunities and limitations of system qualification using the system-level radiation testing was presented. Impact on the SiP product development was discussed. Limitations for the SEE qualification are substantially stronger than for the TID qualification, however, there are still important question marks regarding also TID qualification: how to define the worst case conditions, how important is testing in these conditions, how many system units should be tested to have a high confidence level of results? Future works are planned to answer some of these questions, as well as to investigate the limited qualification approach for system qualification.  
Conceptualization, T.R., F.S. and P.-X.W.; methodology, T.R., F.S. and P.-X.W.; software, T.R.; validation, T.R., F.S. and P.-X.W.; formal analysis, T.R. and F.S.; investigation, T.R., F.S. and P.-X.W.; resources, T.R., F.S. and P.-X.W.; data curation, T.R.; writing--original draft preparation, T.R. and F.S.; writing--review and editing, T.R., F.S. and P.-X.W.; visualization, T.R.; supervision, F.S. and P.-X.W.; project administration, F.S. and P.-X.W.; funding acquisition, F.S. and P.-X.W. All authors have read and agreed to the published version of the manuscript.  
Laser: X-rays and Co60 tests have been performed using the PRESERVE Platform which has been funded by the Occitanie Region and the EU via ERDF funds. This work has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement number 721624.  
Not applicable.  
Most of data used for this study might be found at [6; 22; 23; 24; 25].  
This work was realized within the RADSAGA project. The RADSAGA Innovative Training Network project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie-Sklodowska-Curie grant agreement number 721624. Authors wish to thank colleagues from CERN for their support in organizing and performing tests at the CHARM facility: [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON].  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Semantic Service Description and Context-Based Orchestration
### Use Cases
To this end, a network discovery mechanism is needed as well as a way to uniquely identify the device. 2. To be able to perform a selection of appropriate field device services, the device must know about its capabilities and provided functions, which should be modeled on a semantic level in order to make an automatic discovery possible. 3. Additional information is needed to improve the matching and selection process. For our use cases this includes information about functional parameters, consumed resources and field device types. 4. In order to react to unforeseen events (e.g. device failures), contextual information must be gathered, interpreted and used in the discovery and selection process of device services. 5. Ways to represent abstract descriptions of manufacturing processes are needed, which can be transformed into concrete, directly executable process representations in an easy manner.
## 2 Methodology
### Expected Risk  
Given the stochastic nature of measuring the expected value of indicators directly and indirectly related to the reliability of the system, it is necessary to assume a form of risk, model it, and then imbed it in the KPI. To achieve the latter, a Weibull distribution is assumed to model the degradation of components with editable parameters of shape and scale, which is the usual purpose of such models. However, any distribution can be used as long as it can be coded in Python; the developed script enables the user to adapt it into any programable distribution function. Moreover, just as the grouping strategy, in this article, we assume perfect maintenance to model the degradation of components. Hence, the failure rate for the \(j^{th}\) execution of activity \(i\) at time \(t\) is as follows:  
\[\lambda_{i,j}(t)=\left\{\begin{aligned} &\frac{\beta_{i}}{\eta_{i}}\left( \frac{t-\hat{f}\hat{f}_{i,j-1}-\gamma_{i,j}}{\eta_{i}}\right)^{(\beta_{i}-1)},\ \forall(i,j)\in\big{\{}i,j\in I\times J_{i}:\ t\in\big{[}\hat{f}\hat{f}_{i,j-1}, \ ts_{i,j}\big{]}\wedge j\
eq 1\big{\}}\\ &\frac{\beta_{i}}{\eta_{i}}\left(\frac{t-\gamma_{i,j}}{\eta_{i}} \right)^{(\beta_{i}-1)},\ \forall(i,j)\in\big{\{}i,j\in I\times J_{i}:\ t\in\big{[}0, \ ts_{i,j}\big{]}\wedge j=1\big{\}}\\ & 0\,\ \text{otherwise}\end{aligned}\right. \tag{5}\]  
where \(\beta_{i}\) is the shape parameter for all executions of activity \(i\) of the Weibull distribution, \(\gamma_{i,j}\) is the location parameter for the _jth_ execution of activity \(i\), \(\eta_{i}\) is the scale parameter for all executions of activity \(i\), and \(\hat{tf}_{i,j}\) is the finishing time of the previous single or grouped stoppage for the _jth_ execution of activity \(i\).  
Equation (5) describes the failure rate for any execution considering that the first execution (\(j=1\)) for any activity is only subject to its start time.  
Moreover, the cumulated failure probability for the _jth_ execution of activity \(i\) at time \(t\) is:  
\[F_{i,j}(t)=\left\{\begin{aligned} & 1-e^{-(\frac{t-\hat{f}_{i,j-1}- \gamma_{i,j}}{\eta_{i}})},\ \forall i,j:\{i\in I\ \wedge\ j\in\{I_{i}\backslash\{1\}\}\},\ \forall t\in\big{[}\hat{f}_{i,j-1},\ ts_{i,j}\big{]}\\ & 1-e^{-(\frac{t-\gamma_{i,j}}{\eta_{i}})},\ \text{otherwise}\end{aligned}\right. \tag{6}\]Therefore, the expected downtime due to failure for each time interval between interventions for the same activity at time \(t\) can be described as:  
\[T_{i,j}^{c}(t)=cmt_{i}\times\int_{0}^{(t-\widehat{t}f_{i,j-1})}\lambda_{i,j}(x) \ dx \tag{7}\]  
where \(cmt_{i}\) is the mean duration of a corrective intervention regarding activity \(i\).
## 3 Results and Discussion
### Quantitative Identification of 11 Hypersensitive Strains
Compared to drop test, where cells are spotted onto plates containing a compound or during stationary culture conditions in the minimum bactericidal concentration (MBC) and the minimum inhibitory concentration (MIC) tests, cells grow more vigorously and tolerate higher concentrations of antifungals in shake cultures [30]. Figure 3 shows a representative plot chosen from three replicates. The growth of BY4742 wildtype and the mutants in controls was also similar, presenting no variation in the timing of the start of exponential growth, the rate of growth or end point cell titer reached after 18 h. Compound **1** inhibited the growth of wt and mutants and there was greater difference in growth between wt and mutants. Compound **2** at concentration of 15 \(\upmu\)M completely inhibited the growth of mutants. Compound **2** also exhibited a greater decrease in the growth of wt as compared to compound **1**. The greater inhibition of \(\Delta yap1\), deficient in oxidative stress tolerance proteins, compared to wt in the presence of test compound, identifies the role of oxidative stress in yeast cells inhibition and/or death after treatment with selected selenocyanates. Similarly, the inhibition of \(\Delta ytr1\) and \(\Delta trx2\) deficient in GSH confirms the role of glutathione in cells protection against the test compounds. There is also growth inhibition of mutants \(\Delta zwf1\) and \(\Delta grad1\) lacking proteins producing NADPH required for GSH production. Thus, these findings unravel the central role of GSH and protein thiol oxidation as mode of action of selenocyanates. To summarize the results of the chemogenetic profiling, the observation that the mutants were generally more sensitive to selenocyanates than the wt suggests that the selenocyanates are targeting the cellular GSH pool and GSH metabolism, as well as resulting in protein thiol oxidation [41,42]. The significance of the Zwf1p and Gnd1p effects are clear, because these enzymes are the chief source of NADPH required for Glr1p for the reduction of GSSG to GSH and Trx2p activity via thioredoxin reductases, utilizing NADPH [43]. Allicin (diallylithiosulfinate), for instance, exhibits similar mechanism of action and was shown to oxidize critical cysteines in Yap1p [38]. The mutants \(\Delta yap1\) and \(\Delta gltr1\) also demonstrated hypersensitivity towards allicin and thiosulfinate analogues of allicin [30]. Dipyridyl disulfide (DPS) is highly reactive thiol oxidant and very toxic to yeast, as it also targets the glutathione pool and metabolism [44]. Thus, selenocyanates exhibit antimicrobial activities by a similar mode of action as allicin and DPS. Figure 2: Effects of various concentrations of two selenocyanates on the growth in shake culture of wt BY4742. The experiment was carried out in triplicates and repeated twice with similar results. **C1**—Compound **1** and **C2**—Compound **2**.
## 3 Geometric Model, Computational Mesh, and Physical Parameter
### Geometric Model
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mesh Direction** & **Mesh Zones (m)** & **Number of Cells** & **Size of Cells (m)** \\ \hline \multirow{3}{*}{X} & \(-\)20–100 & 150 & 3.25 \\ \cline{2-4}  & 100–166 & 165 & 0.4 \\ \cline{2-4}  & 166–246 & 80 & 1 \\ \hline \multirow{3}{*}{Y} & 0–17 & 17 & 1 \\ \cline{2-4}  & 17–83 & 165 & 0.4 \\ \cline{2-4}  & 83–100 & 17 & 1 \\ \hline \multirow{3}{*}{Z} & 50–90 & 40 & 1 \\ \cline{2-4}  & 90–130 & 100 & 0.4 \\ \cline{1-1} \cline{2-4}  & 130–140 & 10 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Setting of grid size in the wave model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & \(H\) **(m)** & \(d\) **(m)** & \(T\) **(s)** & **Wave Type** \\ \hline
1 & 3.0 & 50.0 & 15.0 & Third-Order Stokes \\ \hline
2 & 5.0 & 50.0 & 15.0 & Third-Order Stokes \\ \hline
3 & 7.0 & 50.0 & 15.0 & Third-Order Stokes \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters of the ocean waves in hydrodynamics modeling. Figure 2: Generated mesh in the wave model around the jack-up offshore platform.
## 2 The Equivalent Stress Intensity Factor \(K_{eq}\)  
Several approaches are available to define the equivalent stress intensity factor \(K_{eq}\) for a mixed-mode-I-II situation. For a given situation characterized by the pair \(K_{I}\), \(K_{II}\), the proposed equations for the calculation of \(K_{eq}\) provide different values; the question of how different \(K_{eq}\) values compare may be relevant in engineering practice and is not readily apparent from a quick perusal of the relevant equations.  
With the support of Matlab scripting, a systematic comparison of the various approaches for calculating \(K_{eq}\) was performed and led to graphical presentations of \(K_{eq}/K_{I}\) vs. \(K_{I}/K_{II}\) or \(K_{II}/K_{I}\). The literature sources for definitions of \(K_{eq}\) include research involving monotonic and fatigue loading. In the context of monotonic loading and unstable crack propagation, \(K_{eq}=\mathbf{f}(K_{I},\,K_{II})=K_{Ic}\). In the context of FCG, the stress intensity factor range \(\Delta K_{eq}\) is used. In both cases (unstable crack propagation or fatigue crack propagation), the concept of \(K_{eq}\) plays the same role of substituting one single value (\(K_{eq}\)) for the original two (\(K_{I}\) and \(K_{II}\)).
# Study on Elastoplastic Damage Coupling of Reservoir Mudstone Considering Permeability Change
## 3 Elastoplastic Damage Coupling Mechanical Model
### Realization of the Mechanical Model  
When considering plasticity and damage, the strain increment is:  
\[\dot{\mathbf{\sigma}}=\mathbf{D}(\omega):\left(\dot{\mathbf{\epsilon}}-\dot{\mathbf{\epsilon}} ^{p}\right)+\mathbf{D}\prime(\omega):\epsilon^{\epsilon}\;\dot{\omega} \tag{29}\]  
In the loading process, the mudstone presents two stages of elastic damage coupling and elastoplastic damage coupling:  
The elastic damage stage is \(\dot{\mathbf{\epsilon}}^{p}=0,\;\mathrm{d}\omega>0\). The damage increment can be obtained using Formula (24):  
\[\dot{\omega}=\frac{\partial f^{\omega}}{\partial Y^{\epsilon}}\frac{\partial Y ^{\epsilon}}{\partial\epsilon^{\epsilon}}:\dot{\mathbf{\epsilon}}=-\epsilon^{ \epsilon}:\mathcal{C}\prime(\omega):\dot{\mathbf{\epsilon}} \tag{30}\]  
It can be solved using the simultaneous Formulas (29) and (30):  
\[\begin{array}{ll}\dot{\mathbf{\sigma}}&=\left[\mathbf{D}(\omega)-\left(\mathbf{D}^{ \prime(\omega)}:\epsilon^{\epsilon}\right)\otimes\left(\epsilon^{\epsilon}: \mathbf{D}^{\prime(\omega)}\right)\right]:\dot{\mathbf{\epsilon}}\\ &=\mathbf{D}^{\prime q}(\omega):\dot{\mathbf{\epsilon}}\end{array} \tag{31}\]  
\(\mathbf{D}^{\mathrm{eq}}(\omega)\) is the fourth-order equivalent stiffness tensor.  
The elastoplastic damage stage \(\dot{\mathbf{\epsilon}}^{p}\
eq 0,\;\dot{\omega}>0\), plastic internal variables and damage internal variables need to be solved using the simultaneous Formulas (24) and (27):  
\[\frac{\partial f^{p}}{\partial\mathbf{\sigma}}:\mathbf{D}(\omega):\dot{\mathbf{\epsilon}} +\left(\frac{\partial f^{p}}{\partial\mathbf{\sigma}}:\mathbf{D}^{\prime}(\omega): \mathbf{\epsilon}^{\epsilon}+\frac{\partial f^{p}}{\partial\omega}\right)\dot{ \omega}-H\dot{\lambda}=0 \tag{32}\]  
\[-\epsilon^{\epsilon}:\mathbf{D}^{\prime}(\omega):\dot{\mathbf{\epsilon}}+\left[\mathbf{D} ^{\prime}(\omega):\epsilon^{\epsilon}:\frac{\partial g^{p}}{\partial\mathbf{ \sigma}}+\frac{\partial f^{\omega}}{\partial\gamma_{p}}\right]\dot{\lambda}- m\dot{\omega}=0 \tag{33}\]  
\[H=\frac{\partial f^{p}}{\partial\mathbf{\sigma}}:\mathbf{D}(\omega):\frac{\partial g^ {p}}{\partial\mathbf{\sigma}}-\frac{\partial f^{p}}{\partial\gamma_{p}} \tag{34}\]  
Thus, the damage evolution and stress-strain relationship of mudstone under elastoplastic damage coupling conditions are obtained.
## 5 Conclusions  
To mitigate heading drift, we introduced a hybrid framework that includes a foot-state classifier that triggers the ZUPT algorithm, ZARU algorithm, and state lock, an HMC-MED-aided AFM to provide complementary heading error measurements, and an ESKF that estimates the optimal systematic errors. The rich experimental results demonstrate that the proposed framework outperforms other frameworks that utilize inertial-data-based or magnetic-data-based heading error correction methods. All TTDEs of the proposed framework were less than 1.3% on indoor and outdoor loop datasets. Based on sufficient experiments, ZUPT-AFM is recommended for mitigation of heading drift in PNSs based on foot-mounted IMUs.  
In our future work, we plan to investigate a data-driven robust state detection module that outputs foot states, human motions, and magnetic field qualities to improve the fixed-parameter-based FSC, HMC, and MED proposed in this framework. Additionally, a heterogeneous vision-based loop closure method could be introduced to further mitigate heading drift.  
Conceptualization, L.Z.; Methodology, L.Z.; Software, L.Z.; Validation, L.Z.; Resource, Y.L.; Writing--original draft, L.Z.; Writing--review and editing, Y.L. and J.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant Number: 61771338) and Tianjin Research Funding (Grant Number: 18 ZXRHSY00190).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Demonstration of the Model's Potential for Application as a Decision Support Tool
#### 2.2.3 Weather Data  
Local historical weather datasets (air temperature, wind speed and direction, precipitation, solar radiation, soil temperature and water content) for Naoussa Valley were obtained from the EU Copernicus Era5-Land instrument (European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom), covering the period from 1 January to 31 December 2023. The spatial and temporal resolution of the obtained data was about 9 \(\times\) 9 km and 1 h, respectively.
# Detection of Defective Features in Cerasus Humilis Fruit Based on Hyperspectral Imaging Technology
## 3 Results and Discussion
### Effective Wavelength Selection
#### 3.3.2 Successive Projections Algorithm  
In this study, the SPA algorithm was used to extract the characteristic wavelength. The minimum and maximum values of the wavelength variable were 2 and 30, respectively. Figure 5a shows the RMSE change curve under different variables. When the 17 characteristic wavelengths were selected (marked as an open blue square), the RMSE reached its optimal value (RMSE = 0.6548). Figure 5b shows the distribution of the selected 17 characteristic wavelengths in the full spectrum band. The seventeen characteristic wave lengths were 1135, 1122, 1342, 1380, 1596, 1545, 1662, 1495, 1148, 957, 1317, 953, 1628, 1675, 1408, 1463, and 1189 nm, and the importance of the wavelength decreases in turn.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pretreatment** & & \multicolumn{2}{c}{**Calibration Set**} & \multicolumn{2}{c}{**Prediction Set**} \\ \cline{3-6}
**Methods** & **Lvs** & **Rc** & **RMSEC** & **Rp** & **RMSEP** \\ \hline Original spectra & 12 & 0.7857 & 0.2638 & 0.8496 & 0.3073 \\ S-G & 10 & 0.7681 & 0.2731 & 0.8343 & 0.3247 \\ SNV & 9 & 0.6902 & 0.3086 & 0.8069 & 0.3986 \\ MSC & 9 & 0.6863 & 0.3101 & 0.8045 & 0.4011 \\ BC & 9 & 0.7209 & 0.2955 & 0.8065 & 0.3692 \\ De-T & 9 & 0.8047 & 0.2545 & 0.8571 & 0.2964 \\ \hline \hline \end{tabular}  
Lvs: latent variables. Rc: correlation coefficient of calibration. Rp: correlation coefficient of prediction. RMSEC: root mean square error of calibration. RMSEP: root mean square error of prediction.  
\end{table}
Table 2: Results of PLS models based on different spectral pretreatment methods.  
Figure 4: Characteristic wavelength selection results of RC.