esa-sceva/satcom-chunk-collection huggingface dataset:
# Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis
## 5 Discussion  
### Prediction Performance Evaluation  
We provide strong evidence that automatic detection of COVID-19 is a promising future endeavour and competitive compared to CT and CXR based models, with a sensitivity of 90% and a specificity of 96% for COVID-19, achieved on our dataset of 202 lung US videos. In comparison, sensitivity up to 98% and specificity up to 92% was reported for CT [2,79]. We verified our results with independent test data, compared to other architectures and models of related work [57], studied model uncertainty and concluded a significant ability of our model to recognize low-confidence situations. If implemented and validated as a decision support tool, it might bring value to the diagnostic process of symptomatic patients, but could also pose risks as we expand on in Appendix E.  
Certainly, there are many approaches yet to be explored in order to improve on the results presented here, including further work on video classification, improving prediction robustness [49], incorporating a LUS probe quality assessment module [53] or a pulmonary  
Figure 6: Pathological pattern highlighting. Patterns highlighted by CAMs compared to patterns visible in the video (colors correspond to success ratio in detection).  
symptom classifier [54] which could be coupled with our disease classifier. Furthermore, differentiating COVID-19 from other viral pneumonias remains a key challenge for diagnostic imaging [67] and is beyond the scope of this work. Further work could explore the possibility to use interpretable methods to determine differences of COVID-19 to other viral pneumonia which could be exploited in (automatic) differential diagnosis. Here, we investigated the value of interpretable methods in a quantitative manner with the implementation and validation of class activation mapping in a study involving medical experts. While the analysis provides excellent evidence for the successful detection of pathological patterns like consolidations, A-lines and effusion, it reveals problems in the model's \"focal point\" (e.g., missing B-lines and sometimes highlighting muscles instead of the lung) which should be further addressed using ultrasound segmentation techniques [42].
# Customer Churn Prediction in B2B Non-Contractual Business Settings Using Invoice Data
## 1 Introduction
### Approach  
We cast churn prediction as a binary classification problem and use three established methods to devise predictive models (logistic regression, random forests, and support vector machines). To derive features, we use a multi-slicing approach proposed in [18],which we augment by introducing two explicit groups of attributes that span different lengths of historical data (in effect acting as constraints for calculating the recency, frequency, and monetary feature values that we mainly rely on). We then compare the performance of the resulting models with respect to the width of the windows used. In addition, we experiment with different churn definitions (variable number of consecutive months without purchase used to define churn) and evaluate predictive models with respect to the definitions. Finally, we assess the performance of models devised by using multi-slicing and single-slicing approaches. All this is done while relying on the minimum subset of input data that is expected to be present in virtually any company for deriving features, thus making the proposed approach potentially feasible in other non-contractual business settings.
The resulting extracts were evaluated against reference extracts obtained without PEF. The evaluation of the extraction efficiency was performed via determination of the content in total polyphenols (Folin-Ciocalteu method), individual phenolics (high-performance liquid chromatography, HPLC), volatile compounds (headspace solid-phase microextraction coupled to gas chromatography-mass spectrometry, HS-SPME/GC-MS) as well as resistance to oxidation (differential scanning calorimetry, DSC). The novelty of this work lies upon the use of a non-thermal and eco-friendly technology, PEF, as the primary standalone extraction method for the extraction of _Salvia officinalis_ L. BACs (including the thermolabile compounds) in green solvents (pure water, pure ethanol, and their mixtures), using fresh plant material instead of dried. The contribution to the scientific area of interest lies in the lack of similar experimental research on _Salvia officinalis_ L.
# Imaging Liquid Water in a Polymer Electrolyte Fuel Cell with High-Energy X-ray Compton Scattering
## 5 Conclusions  
This paper reports the first experiment of Compton scattering imaging with synchrotron-based high-energy X-rays on an operating polymer electrolyte fuel cell. Water generation and accumulation is observed in the vicinity of the cathode catalyst layer. The amount of generated water increases at current densities from 100 to 500 mA/cm\({}^{2}\), while it remains constant or slightly decreases from 500 to 900 mA/cm\({}^{2}\). In both the gas diffusion layer and the channel, liquid water is locally observed above 500 mA/cm\({}^{2}\), indicating the formation of a water flow path. Simultaneous measurements of Pt-K\(\alpha\) fluorescent X-rays show that the  
Figure 9: (**a**) Images of Pt-Ka fluorescent X-ray intensity; (**b,c**) correlation maps between generated liquid water and Pt catalysts at 100 and 500 mA/cm\({}^{2}\).  
local amount of generated liquid water is proportional to that of Pt catalyst nanoparticles at high current densities, while generated liquid water is dispersed in the catalyst layer without any correlation with the local amount of Pt catalysts at low current densities. This study demonstrates that Compton scattering imaging is one of the unique techniques to observe liquid water and to characterize its behaviors in operating polymer electrolyte fuel cells.  
Conceptualization, Y.U., H.I. and Y.S.; methodology, N.T. and Y.S.; software, T.M. and N.T.; validation, Y.S., Y.U. and H.I.; formal analysis, T.M.; investigation, T.M., D.F., Y.M. and T.K.; resources, Y.S. and H.I.; data curation, T.M.; writing--original draft preparation, T.M.; writing--review and editing, Y.S.; visualization, T.M. and Y.S.; supervision, H.I.; project administration, Y.S.; funding acquisition, H.I. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the New Energy and Industrial Technology Development Organization (NEDO), grant number 20001309-0.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The Compton scattering imaging experiment was performed with the approval of JASRI (Proposal Nos. 2021B2078, 2022A1017, 2022B1007, 2023A1010).  
The authors declare no conflict of interest.
# Optimization Design for Sparse Planar Array in Satellite Communications
## 2 Sparse Planar Array Optimization Design Model
### Optimization Model Based on Minimum Peak Sidelobe Level  
Sidelobes, also known as secondary lobes, are lobes in the antenna pattern in addition to the main lobes. An essential performance indicator of the antenna pattern is the peak sidelobe level (PSLL). The lower the peak sidelobe level is, the better the anti-interference performance of satellite communication will be. The peak sidelobe level is a common objective in sparse planar array optimization designs.  
When thinning the plane array, under the condition of determining the array aperture, the number of elements can be reduced, the amplitude-phase excitation can be adjusted, and the peak sidelobe level can be reduced by optimizing the array element layout. In  
Figure 4: Uniform and sparse array simulation models: (**a**) uniform arrays; (**b**) sparse arrays.  
Figure 5: The comparison of the two-dimensional patterns of the uniform array and sparse array, both the results of HFSS full-wave simulation: (**a**) \(\varphi=0^{\circ}\) azimuth plane pattern; (**b**) \(\varphi=90^{\circ}\) azimuth plane pattern.  
sparse planar array optimization design, the peak sidelobe level can generally be utilized as an optimization goal and is defined as  
\[PSLL=\max_{(\theta,\varphi)\in S}\bigg{\{}20\log_{10}\frac{|AF(\theta,\varphi)|}{| AF_{\max}|}\bigg{\}}, \tag{8}\]  
where \(S\) represents the sidelobe range of the antenna pattern, and \(AF_{\max}\) is the maximum value of the array factor. The basic optimization model based on the minimum peak sidelobe level array design is expressed as  
\[\underset{w_{mn}}{\text{min}}PSLL, \tag{9}\]  
where \(w_{mn}\) represents the complex excitation of the \((m,n)\) element, where \(m=0,1,\ldots,M-1\) and \(n=0,1,\ldots,N-1\). The objective of the optimization model in Equation (9) is to obtain a sparse planar array with the lowest \(PSLL\) in the visible area. However, since the decrease in the peak sidelobe level often leads to the broadening of the main lobe and the reduction in gain of the main lobe, many studies added control constraints to the main lobe to achieve a reduction in the peak sidelobe level.
# Study on the Impact of the Coastline Changes on Hydrodynamics in Xiangshan Bay
## 2 Materials and Methods
### Hydrodynamic Analysis
#### 3.3.4 Experimental D  
The distributions of surface residual currents in the surrounding before and after the change in the Area 4 coastline are shown in Figure 12a,b, respectively. The main change occurred in the Tie inlet, where the direction of residual currents changed significantly. Previously, residual currents in the northern part of the Tie inlet exhibited a relatively regular clockwise rotation. However, after the change, residual currents could only rotate within a smaller range. Meanwhile, the velocity of the current in the Tie inlet also decreased noticeably. Especially at the narrow entrance of the Tie inlet, there used to be a strong northeast-southwest residual current. However, this residual current experienced a significant decrease in velocity.  
Figure 12: Current changes in Area 4. (**a**) Residual currents distribution before the change in coastline; (**b**) Residual currents distribution after the change in coastline; (**c**) Changes in the velocity of the peak flood tidal currents; (**d**) Changes in the velocity of the peak ebb tidal currents.  
The variations in the flood and ebb surface current velocities in the Tie inlet before and after the change in the Area 4 coastline are shown in Figure 12c,d. The flood and ebb current velocities in Tie inlet generally decreased, with only a slight increase in the velocity of the flood current on the west side. Therefore, this study took the location of station D (29.51\({}^{\circ}\) N, 121.50\({}^{\circ}\) E) in the deceleration zone to compare the changes in flood and ebb current velocities before and after the coastline change. Before the coastline change, the maximum current velocities for flood tide and ebb tide at Station D were 0.25 m/s and 0.23 m/s, with the average velocities being 0.15 m/s and 0.13 m/s, respectively. After the coastline change, the maximum current velocities for flood tide and ebb tide were 0.11 m/s and 0.10 m/s, with the average velocities being 0.08 m/s and 0.07 m/s, respectively. Comparing currents before the coastline change, the maximum flood and ebb current velocities at this location decreased by 0.14 m/s and 0.13 m/s, whereas the average flood and ebb current velocities decreased by 0.07 m/s and 0.05 m/s, respectively. Overall, the current velocities at station D were small after the coastline changed, but the flood and ebb current velocities decreased after the change.
## 4 Establishment of a New Prediction Model for Blasting Vibration Attenuation  
### Conventional Model for Predicting Blasting Vibration Attenuation  
To further establish the attenuation pattern of blasting vibration in interlayered sandstone-mudstone rock slopes, the Sadowsky formula (Equation (1)) was applied to fit  
Figure 6: Vibration velocity of V3 measuring point.  
the results of each of the three blasting tests separately. The fitting results are presented in Table 3. Figure 7 shows an illustration of the fitting formula.  
\[v=k\bigg{(}\frac{\sqrt[3]{Q}}{R}\bigg{)}^{\alpha} \tag{1}\]  
In the formula, \(v\) represents the blasting vibration velocity; \(Q\) is the explosive charge weight. \(R\) is the distance from the blast center, i.e., the distance from the measuring point to the center of the explosive charge; \(k\) and \(\alpha\) are coefficients related to the blasting site conditions and geological conditions.  
As shown in Table 3, the fitting accuracy of the Sadowsky formula varies across directions for the three blasting tests. The determination coefficients for the primary vibration direction range between 0.7 and 0.8, indicating moderate fitting accuracy, while in non-primary vibration directions, most determination coefficients exceed 0.9, showing a higher fitting accuracy. The significant difference in fitting accuracy may be due to the lower vibration velocity in non-primary directions, which leads to smaller fitting errors, whereas the higher vibration velocity in the primary direction results in larger fitting errors. These findings also reflect the instability of the predictive accuracy of Equation (1) across different  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test** & **Direction** & \multicolumn{2}{c}{**The Fitting Results of the**} & \multicolumn{2}{c}{**Coefficient of**} \\
**Sequence** & **Direction** & \multicolumn{2}{c}{**Sadowsky Formula**} & \multicolumn{2}{c}{**Determination (\(r^{2}\))**} \\ \hline \multirow{4}{*}{I} & X-direction & \(v=56.86\Big{(}\frac{\sqrt[3]{Q}}{R}\Big{)}^{0.818}\) & 0.720 \\  & Y-direction & \(v=43.14\Big{(}\frac{\sqrt[3]{Q}}{R}\Big{)}^{0.9973}\) & 0.965 \\  & Z-direction & \(v=48.96\Big{(}\frac{\sqrt[3]{Q}}{R}\Big{)}^{0.619}\) & 0.722 \\ \hline \multirow{4}{*}{II} & X-direction & \(v=36.46\Big{(}\frac{\sqrt[3]{Q}}{R}\Big{)}^{1.307}\) & 0.975 \\  & Y-direction & \(v=78.02\Big{(}\frac{\sqrt[3]{Q}}{R}\Big{)}^{1.577}\) & 0.967 \\  & Z-direction & \(v=81.86\Big{(}\frac{\sqrt[3]{Q}}{R}\Big{)}^{1.037}\) & 0.719 \\ \hline \multirow{4}{*}{III} & X-direction & \(v=36.14\Big{(}\frac{\sqrt[3]{Q}}{R}\Big{)}^{1.004}\) & 0.929 \\  & Y-direction & \(v=43.48\Big{(}\frac{\sqrt[3]{Q}}{R}\Big{)}^{0.9973}\) & 0.817 \\ \cline{1-1}  & Z-direction & \(v=83.67\Big{(}\frac{\sqrt[3]{Q}}{R}\Big{)}^{1.055}\) & 0.772 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The fitting results of the Sadowsky formula.  
Figure 7: Illustration of the fitting results using the Sadowsky formula. (**a**) Test I; (**b**) Test II; (**c**) Test III.  
directions. Therefore, it is necessary to explore a more accurate and stable predictive model for blasting vibration attenuation.
###### Abstract  
Scarce water resources are available in the arid and semi-arid areas of Northwest China, where significant water-related challenges will be faced in the coming decades. Quantitative evaluations of the spatio-temporal dynamics in ecosystem water use efficiency (WUE), as well as the underlying environmental controls, are crucial for predicting future climate change impacts on ecosystem carbon-water interactions and agricultural production. However, these questions remain poorly understood in this typical region. By means of continuous eddy covariance (EC) measurements and time-series MODIS data, this study revealed the distinct seasonal cycles in gross primary productivity (GPP), evapotranspiration (ET), and WUE for both grassland and cropland ecosystems, and the dominant climate factors performed jointly by temperature and precipitation. The MODIS WUE estimates from GPP and ET products can capture the broad trend in WUE variability of grassland, but with large biases for maize cropland, which was mainly ascribed to large uncertainties resulting from both GPP and ET algorithms. Given the excellent biophysical performance of the MODIS-derived enhanced vegetation index (EVI), a new greenness model (GR) was proposed to track the eight-day changes in ecosystem WUE. Seasonal variations and the scatterplots between EC-based WUE and the estimates from time-series EVI data (\(\text{WUE}_{\text{GR}}\)) also certified its prediction accuracy with \(R^{2}\) and RMSE of both grassland and cropland ecosystems over 0.90 and less than 0.30 g kg\({}^{-1}\), respectively. The application of the GR model to regional scales in the near future will provide accurate WUE information to support water resource management in dry regions around the world.  
water use efficiency; eddy covariance technique; MODIS; grassland; cropland +
Footnote †: journal: Remote Sensing  
1
## 5 Proposed MOEA/D-AMSF Algorithm
### Hybrid Generation Method for Initial Population
#### 5.3.1 Uniform Design Method  
Each gene is composed of two layers of routing and core. Since routing is independent of each other in the selection between different connection requests, we used the uniform design method [21] to generate the initial value of routing genes. This method can make the test points evenly distributed in the test domain, which is conducive to the uniform distribution of connection requests bearing on the network links. The value of each VCGC is randomly generated so that the VCG component values for the same gene on two different chromosomes are different even if the routing gene values are the same, which is beneficial to improving population diversity. Algorithm 1 presents the specific initialization process. For each gene, the VCGCs length (hops, line 3 of the algorithm) is obtained after the initialization of the routing gene, and then the value of each VCG component is randomly generated. The traffic_routing_matrix in the table is used to store the routing results, which has a total of three columns, corresponding to each of all connection requests, the nodes that pass through the selected KSP, and the hops of such a KSP.  
```
Input:\(N\): population size, geneLen: gene length, traffic_routing_matrix: routing result, \(|R|\): number of requests, \(K\): number of routing candidates Output:\(P\): population
01 for\(i=1\) to \(|R|\)do
02\(P(i,1)=\text{mod}(i\ \times\ 2,\ K)+1\);
03\(\text{hop}=\text{traffic\_routing\_matrix}\)[1](P(\(i,1\)));
04\(\text{P}(i,2\):1 + hop) = \(\text{randi}(|C|,1\),hop);
05 for\(j=2\) to \(N\)do
06\(P(i,1+(j-1)\ \times\ geneLen)=\text{mod}(\text{P}(i,(j-2)\ \times\ geneLen)\ \times\ 2,\ K)+1\);
07\(\text{hop}=\text{traffic\_routing\_matrix}\)[1](P(\(i,1+(j-1)\ \times\ geneLen));
08\(P(i,2+(j-1)\ \times\ geneLen\): \(1+(j-1)\ \times\ geneLen+\text{hop})=\text{randi}(|C|,1\),hop);
09 endfor
10 endfor
```  
**Algorithm 1** population initialization with uniform design
## 2 System Model
### HetNet System Model  
This section describes the system model of this paper. Figure 1 shows the proposed HetNet system. This system consists of one macro cell and three pico cells. All pico cells are located in the macro cell. The base station in the macro cell has two transmit/receive antennas, the base station in the pico cell and mobile have one transmit/receive antenna. This system can form a virtual multiple input single output (MISO) system using other base station antenna in the adjacent cells.  
In Figure 1, the LBS and SBS \(i\) (\(i=1,\,2,\,3\)) represent the base stations in the macro and pico cells respectively. The \(H_{LB,1}\) and \(H_{LB,2}\) refer to the channel coefficients between the base station of the macro cell and mobile. The \(H_{SB,1}\), \(H_{SB,2}\) and \(H_{SB,3}\) refer to the channel coefficients between the base  
Figure 1: The system model for the proposed heterogeneous network.  
station of the pico cells and mobile. The mobile transmits the channel state information (CSI) to the base stations. The CSI includes the position of the mobile and the phase information of the channel coefficient. The base stations modify the phase of the channel coefficient by applying the pre-coding scheme using the phase information of the channel coefficient included in the received CSI. Specifically, this paper uses the SPC scheme, which is one of the pre-coding techniques [17].
# Mechanical Analysis of DS in Horizontal Directional Drilling
###### Abstract  
In recent years, trenchless horizontal directional drilling (HDD) technology has developed rapidly towards the direction of large pipe diameter and long span. During construction, the buckling deformation and large drag torque of the drillstring (DS) are the key problems restricting the development of the HDD technology. In order to understand the buckling deformation law of the DS in horizontal directional drilling engineering, as well as the transfer and the distribution of the drag torque, and the relationship between the buckling deformation and drag torque, the dynamic model of the DS system in drilling engineering is applied to HDD engineering. The dynamics model of HDD at a long distance (5200 m) is established and calculated. The research indicates (1) when the buckling deformation of the DS is small, priority should be given to reducing the friction and torsion at the bending position; (2) excessive inlet thrust is the main reason for the buckling deformation of the DS, and the smaller the hole diameter, the more serious the buckling deformation; (3) severe buckling deformation of the DS will lead to an increase in the frictional resistance at the deformed part, and even local self-locking, resulting in the inability to continue to increase the weight on bit (WOB). The research results preliminarily reveal the relationship between DS buckling deformation and drag torque in ultra-long distance HDD engineering and point out the phenomenon law of drag torque and buckling deformation, which has reference value for the development of HDD technology.  
horizontal directional drilling; drillstring; buckling deformation; drag and torque +
Footnote †: journal: Applied  
doi  
doi  
10.3390/app12063145