esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### In Vivo Antibacterial Activity  
In order to study the effect of the obtained CNC and LNP on plant-pathogen interactions, the concentrations that showed, in the in vitro assay, the most promising antibacterial  
Figure 10: In vitro Xanthomonasomas arboricola pv. corylina growth inhibition calculated at 24 (**a**) and 48 (**b**) hours through a microdilution method. Results are expressed as percentage (%). Data are represented as the mean and SD, and different letters show significantly different values after one-way ANOVA (_p_ < 0.01) followed by [PERSON]’s LSD post hoc test.  
activity and the most relevant TPC were selected to perform an in vivo test. Hazelnut plants were treated with a 1% w/v suspension of HS CNC and HP CNC and with a 1% _w/v_ suspension of HP LNP 100 and HS LNP F. After 24 h, plants were spray-inoculated with a Xac suspension. Symptoms started to appear on leaves 20 days after foliar inoculation. At 21 dpi, the disease indices (incidence and severity) were calculated as previously described. Results showed a high statistical significance (_p_ < 0.01) in terms of DI. Each of the tested substances was able to diminish the appearance of symptoms in a way comparable to copper oxychloride when used at the field dose. Regarding the DS, even if the recorded values were in almost all cases lower than the one recorded in water-treated plants, a statistical difference (_p_ < 0.05) was noted only in copper-treated plant (Table 2). This behavior could be explained assuming that CNC and LNP antimicrobial effects could have affected the initial adhesion and epiphytical survival of the bacterial cells on the leaves, or prevent them by entering through the stomata, thus reducing the first symptoms display. While copper and its derivatives are well known bactericidals, this study may help advancing the knowledge on the antimicrobial properties of CNC and LNP [15; 94; 95; 96]. The proposed nanomaterials, as also inferred from the in vitro assay results, had prevalent inhibition activity. Presumably, once the inhibition effect is over, bacterial cells may recover the ability to reproduce and infect plant tissues. Further studies are scheduled in order to better clarify the span of the inhibition effect on leaves since the phyllosphere is a very complex environment where organic substances interact with a large number of biological and climatic variables, which could influence even their composition during time [97; 98].
## 1 Introduction  
Location-based social networks (LBSNs) [1], such as Foursquare, Gowalla, and Yelp, have experienced rapid growth in recent years due to the continuous improvement in mobile-device technologies [2; 3; 4]. These platforms are popular for users to share their experiences and explore their surroundings [5; 6]. They have accumulated a large amount of user check-in data that reflect users' activity trajectories and preferences. These data provide an important source for the next-point-of-interest (POI) recommendation service [7; 8].  
It is important to note that users' behaviors are not only influenced by their historical preferences but also by their real-time preferences and time constraints. For example, Figure 1 illustrates that the user left home at 7:00 a.m., went shopping at 9:00 a.m., and then visited a cafe. When recommending the next point of interest (POI) to the user between 1:00 p.m. and 2:00 p.m., it is important to consider their real-time needs and preferences. Therefore, the user's trajectory should be used to mine their preferences at a specific time period for real-time POI recommendations. However, applying these trajectories to real-time POI recommendations is not simple due to two issues. Firstly, the time series is unstructured in terms of users' trajectories. Users have irregular time intervals between visits to different POIs, or they visit different POIs for inconsistent lengths of time in different time periods. This situation may be caused by the user's personal habits, practical needs, or environment, which makes it difficult for structured models to extract relevant temporal features. Additionally, the harvest data from LBSNs often contain information about the regions and categories to which the POIs belong [9]. The utilization of these features is insufficient in most commonly used POI recommendation engines, thereby limiting further improvements in the performances of these models.  
To address the above issues, this paper proposes a new real-time POI recommendation model, namely, the Attention-Based Spatial-Temporal Point-of-Interest-Group (AST-PG) model. The proposed model mines the transfer patterns of check-in behaviors in the sequence of user check-in records by a temporal module and a spatial module and then couples them together by a multi-head attention mechanism [10]. The temporal module converts the unstructured temporal trajectory information into a fixed-length vector.  
Relevant experiments on large public datasets show that the model developed in this study has a state-of-the-art predictive performance. Moreover, the ablation experiments show that both the temporal and spatial modules developed in this study are effective.
## 2 Materials and Methods
### Landslide Conditioning Factors
The structural stability of the surrounding area is reduced by induced regional perturbations in the fracturing occurrence and enhanced weathering in the rocks [46]. The distance to lineament factor and landslide shows direct correlation with the highest weightage (0.93) is within 0-250 m proximity. Slope which has closer proximity to roads especially near cut and fill slope could affect the stability by increase of stress in its base and accumulation of water from nearby slope [47; 48]. The highest weightage of 0.18 produced from the 25-50 m class interval representing 25% of total landslides while the majority of landslides (47%) occurs within 0-25 m distance from a roadway yields a low weightage of 0.02 due to larger class area. Surface roughness generally correlates to excavation, surface erosion and the density of vegetation cover and could signify the type of land cover of that particular area and has been classified into high, moderate and low values [46]. Half of the landslides (53%) are categorised as having moderate surface roughness with a weightage of 0.07, however the highest weightage of 0.29 represented by slope with high roughness due to smaller areal class. TPI gives an indication to where the landslide point is located with reference to the topographical position or sometimes expressed as landscape position [49; 50]. The highest percentage and weightage of landslides is within the middle slope with a value of 61.17% and 1.13, respectively, with the upper slope class having a weightage of \(-0.01\) with 15.08% of total landslides. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Landslide** & **Definition** & **Source** \\  & **Conditioning Factors** & **Relative slope position or landform** & \\
1 & **Topographic Position** & **category based on relative** & **DTM** \\  & **Index (TPI)** & **relief values** & \\ \hline
2 & **Slope Gradient** & **Degree of inclination of the slope** & **DTM** \\ \hline
3 & **Distance to Linearment** & **Distance to fault and lineament** & **DTM, air photo** \\ \hline
4 & **Distance to Road** & **Distance to major roads in metres** & **DBKL road map** \\ \hline
5 & **Surface Geology** & **Recent material deposit** & **Topographic map,** **geological map, air photo and borehole** \\ \hline
6 & **Elevation** & **Height above sea level** & **DTM** \\ \hline
7 & **Surface Roughness** & **Degree of variation of surface elevation** & **DTM** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Landslide conditioning factors and their definition as used in the bivariate statistical analysis. Figure 2: Seven landslide conditioning factors were selected according to the steps highlighted in the flow chart (**a**). The factors are surface geology (**b**), slope gradient (**c**), and elevation (**d**).
## 2 Navigation System
### Further Fundamental Components (Compass and Barometers)  
The navigation of a UAS is carried out not only with GNSS and inertial navigation but also with a barometer and compass [52].  
The barometer sensor recognises the pressure difference between the point where the drone is flying and a reference point and is, therefore, able to estimate the altitude of the UAV. For example, the new generation of barometers for UAVs, such as the MS5611-01 BA03, developed by the TE Connectivity company, allows an altitude resolution of 10 cm.  
The compass provides accurate readings of magnetic north. Knowing the orientation of the drone is essential, particularly when the drone is distant, and it is difficult or impossible with sight to tell which direction it is facing. It is important to check that the compass provides consistent results before each flight. A test to check the correct alignment with the direction of the north consists of positioning the pilot behind the drone so that they are on the line between the bow and the stern. Once in position, it is necessary to check on the UAV's management app that the compass is aligned consistently: if the two alignments deviate by more than a few degrees, the initialisation has failed, and consequently, this operation must be repeated until correct alignment is achieved.
## 4 Results and Discussion
### Analysis of Random Field Results
#### 4.4.2 Effect of Different \(l_{x}\)  
Because of the long-term depositional process and load history, the spatial correlation structure of geomaterials should be considered as a basic property that cannot be ignored. In previous studies, the magnitude of the vertical correlation length \(l_{y}\) which is from 1 to 2 m, is always lower than the horizontal correlation length \(l_{x}\), which is usually from 30 to 60 m [6]. In this paper, the \(l_{y}\) is assumed to be 1 m as a fixed value, and the  
Figure 12: The contours of shear dissipation energy based on \(l_{x}=50\) m, WL = 5 m: (**a**) COV = 0.1; (**b**) COV = 0.2; (**c**) COV = 0.3; (**d**) COV = 0.4; (**e**) COV = 0.5.  
Figure 13: FOSs vs. COV of shear strength for different WLs (\(l_{x}=50\) m) (**(a)** The Mean of FOS for six different WLs; (**b**) The Std. of FOS for six different WLs).  
influence on the FOS of five different \(l_{x}\) is mainly studied. Figure 14 shows five different random fields of cohesion generated by the K-L series expansion method. The obvious spatial correlation structure can be observed by the contours.  
Figure 15 shows the variation of mean FOSs with \(l_{x}\) for different COVs at the 5 m high water level. The mean FOSs slightly decrease with the increase in the \(l_{x}\) for the range 1-50 m, and this is in good agreement with [PERSON] et al. [28]. The mean FOSs reach a minimum when the \(l_{x}\) ranges from 30 to 50 m, because the width of the foundation of this test embankment in this paper is 36 m. The stability of this test model will almost not be influenced when the \(l_{x}\) exceeds 50 m. Hence, the end part of the curve in Figure 11 tends to be horizontal. This is also consistent with real engineering, and the spatial correlation length is constant for a specific case, and the maximum length is no more than the outline. However, the standard deviation of FOS increases with the increase in \(l_{x}\). The increasing trend is obvious when the COV is high.
## 3 Results and Discussion
### Optimization of the SPME Procedure
Static desorption improved the peak symmetry but resulted in carry-over, particularly for the PEG and PDA fibers, as shown in Table 3, which summarizes the S\({}_{\text{W0,05h}}\) values detected for each analyte. Dynamic desorption was selected for the subsequent experiments due to its quantitative recoveries. The SPME-HPLC method was validated for the PA, PEG, and PDA fibers using seven standard solutions (0.1 ng/mL-1 \(\upmu\)g/mL). Linear calibration curves with high correlation coefficients and satisfactory reproducibility were obtained (Table 4). The PDA fiber performed comparably to PA and PEG for R but had higher LODs for Q. The intra-day precision (% RSD) ranged from 3.4% to 9.8% at 0.06 \(\upmu\)g/mL. The inter-day precision was not assessed due to Q instability in aqueous solutions. Figure 4: Comparison of fibers under the same extraction conditions (15 mL volume, 6.6% ethanol, 10% NaCl, pH 2.3, 60 min at room temperature). Q (dark grey) and R (light grey) concentration: 1 \(\upmu\)g/mL. Dynamic desorption mode. Figure 3: Comparison of the normalized response of PDA fiber (dark grey) and PA fiber (light grey) at different times for Q (**A**) and R (**B**).
# Method for Estimating the Optimal Coefficient of L1C/B1C Signal Correlator Joint Receiving
## 4 Experiment and Analysis
### Joint Positioning Accuracy  
To quantify the positioning errors of the five positioning schemes in this paper, Tables 4 and 5 test the positioning errors of B1C/L1C in the Earth-Centered, Earth-Fixed (ECEF) coordinates, the Universal Transverse Mercator Grid System (UTM) coordinates and the geodetic coordinates.  
The std of the combined positioning error for the B1C signal is much smaller than the combined positioning error std of the L1C signal. The reason for this is that the space position accuracy (PDOP) of the BDS satellites participating in the positioning of this experiment, as shown in Figure 10, is far lower than that of the QZSS system. Most importantly, in the ECEF coordinate system, the UTM coordinate system and the GEO coordinate system, the std of the \"amplitude ratio combined positioning\" error of the B1C (L1C) signal is the smallest, followed by the \"power ratio combined positioning\" and the \"1:1 combination\" position\".  
To compare the improvement effect of the combined positioning scheme on the positioning accuracy in more detail, Figure 16 shows the relative improvement ratio of the positioning accuracy in each coordinate axis. The black curves represent the improvement ratio of the three combinations method relative to the data component positioning, and the red curves represent the improvement ratio of the three combinations method relative to the pilot component positioning. The positioning accuracy was improved in any coordinate axis direction, and the \"amplitude ratio combined positioning\" scheme showed the best improvement effect.
## 1 Introduction
### Related Work
#### 1.2.1 General Purpose Software (Spreadsheet, etc.)  
Environmental data management has traditionally involved the use of some general-purpose software tools. For example, spreadsheets (mostly MS Excel) are used for data visualization and some time as data storage. Various statistical tools (such as Statistica or R Statistics) are used for statistical analysis and some advanced data visualization. General purpose spatial data software (such as QGIS [9] or ESRI ArcView) is used to draw maps of the stations. The above programs have a wide range of applications and meet some data management requirements, but also have some major drawbacks:  
* Processing a subset of data is time-consuming. Example: If the user has a large Excel spreadsheet with data from all stations, it is time consuming to extract just one station or data from a group of stations;  
Figure 1: Schematic view of traditional “one direction” data flow.  
Returning the analysis results to the main dataset is time consuming or non-existent. Example: the analysis of data from one station shows some errors in the values. It is time consuming to find the exact rows in the original dataset containing all stations and update the values;
* Visualization of data only (chart) or map only (stations). One tool is used to draw charts (usually Excel) and another is used for maps (e.g., QGIS). In the end, the user has to extract both and put them together in a word processor;
* Poor efficiency of the whole process. The process described is repetitive and time consuming.
# Monitoring of Liquid Viscosity for Viscous Dampers through a Wireless Impedance Measurement System
## 6 Conclusions  
In this paper, a wireless impedance measurement system was used to monitor and quantitatively evaluate the viscosity of damping fluid in a viscous damper. PZT sensors were installed inside the cylinder specimen and the impedance characteristics of these sensors were measured for damping fluids of different viscosities. The test results show that the wireless impedance measurement system can detect changes in the viscosity of the fluid inside the damper. The change of viscosity will cause the impedance signal of PZT sensor to change. The change in the real part of the impedance signal can provide a qualitative measure of viscosity change. Changes in the real part can further be quantified through the proposed VI, which is based on the RMSD of the impedance signal. The VI can eliminate the effects of variation between PZT sensors and help evaluate the viscosity of damper fluids in a more accurate way. This study can serve as a guiding reference for the application of a wireless impedance measurement systems in SHM and liquid viscosity monitoring. The results can help improve and expand the application of wireless impedance measurement systems in practical engineering.  
Methodology, M.L. and S.J.; validation, M.L. and S.J.; formal analysis, M.L. and S.J.; investigation, S.J.; resources, M.L.; data curation, S.J.; writing--original draft preparation, S.J.; writing--review and editing, M.L.; visualization, S.J.; supervision, M.L.; project administration, M.L.; funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No.51978079) and the Hubei Provincial Outstanding Young and middle-aged Science and Technology Innovation Team Project (No. T2020007).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflict of interest.
# Pulmonary Fissure Detection in 3D CT Images Using a Multiple Section Model
## 5 Discussion  
In this study, a novel method was presented for pulmonary fissure segmentation in CT images. It has a variety of characteristics and merits. First, multi-section vector information was used for pulmonary fissure enhancement, whereas many existing methods [12; 13; 22] use only the magnitude information to enhance pulmonary fissures, which cannot efficiently distinguish between fissures and clutters. Second, an improved orientation partition scheme was presented to suppress clutters. This approach had the capability to highlight fissure representation and simultaneously reduce the loss of fissures. Third, a specific multiple section model was introduced to separate pulmonary fissures. Generally, the proposed method resulted in a salient improvement in both fissure segmentation and interference suppression. Compared with many conventional methods [7; 13; 14], the presented method appeared to be more efficient at pulmonary fissure segmentation, obtaining the highest F1-score and the lowest FDR and FNR indices in the validating experiments. The reason for this is that our method is able to identify weaker fissure objects and perform better on noise suppression due to the adoption of a new multiple section model. This model is responsible for the low missing detection ratio of FDR and low false detection of FNR thus resulting in a higher accuracy.  
However, as the presented method originated from the DoS and ODoS filters, it inevitably inherited the drawbacks of these line detection methods. While the fissure lines take a point-cloud like distribution, the large intensity variance along the objects might mean that some remain undetected. In the future, we will attempt to merge stronger or more global prior constraints into the model to improve its noise immunity and ability to resist interference. Additionally, the machine learning, group component analysis and other feature extraction algorithms could be potential research directions. Furthermore, we will seek collaboration with medical experts and researchers to apply our method in clinical diagnosis and surgical navigation, for example in COPD quantitative assessment and assisting lung nodule detection.
# A Survey on Diabetic Retinopathy Lesion Detection and Segmentation
## 2 Related Works
### Diabetic Retinopathy Retinal Blood Vessel/Optic Disc Segmentation
Convolutional long short term memory was merged into the encoder to improve accuracy. [PERSON] et al. [90] used a mathematical morphology operation before extracting the retinal blood vessels from fundus images. OD segmentation was done with the help of watershed transform. Finally, classification was done using radial basis function neural network (RBFNN). [PERSON] et al. [91] used a preprocessing technique for OD segmentation called local differential filter (LDF). Good prediction results were obtained when LDF was used for U-Net model training data to differentiate between OD and background regions. Retinal blood vessel segmentation, as well as extraction of the optic disc as well as fovea centers, were performed by the authors in [92] using an end-to-end encoder-decoder network known as DRNet. [PERSON] et al. [93] introduced a deep CNN model named Synergic DL model for retinopathy grading. They also performed histogram-based OD segmentation prior to this. Another set of studies in this category focused on ROI segmentation. An algorithm named \"adaptive-learning-rate-based\" enhanced GMM was used for ROI segmentation in fundus images by [PERSON] [94]. After this, they used connected component analysis (CCA) to process the input to AlexNet architecture. This was followed by feature selection and grading using a polynomial kernel-based SVM classifier. Active DL was used in a seven-layer CNN architecture (ADL-CNN) by [PERSON] et al. [95] to automatically grade the images into the five stages of DR after retinal blood vessel segmentation as well as ROI localization. Table 2 summarizes the studies that were presented in this section.
# Torsional Fatigue Performance of a Spot-Welded Structure: An XFEM Analysis
## 3 Results and Discussion
### Influence of \(R\)  
One of the crucial factors that could affect a box's torsional fatigue performance is the load ratio. In this section, its effect was analyzed, where three different load ratios \(R\) were considered, namely \(-\)1.0, 0.0, and 0.5. Table 3 shows the obtained \(N_{f}\) values. It was observed that there were 42,375, 48,058, and 93,900 cycles for \(R\) equaled to \(-\)1.0, 0.0, and 0.50, respectively. It was noticed that the change was not significant when \(R\) changed from \(-\)1.0 to 0.0, but this change was noticeable when \(R\) became 1.0. As \(R\) is the ratio between \(T_{\text{min}}\) and \(T_{\text{min}}\) and their difference characterizes the fatigue loading, for \(R\) equaled to 0.5, the stress levels during the cyclic loading were not as significant as \(R\) equaled to 0.0 or 1.0, and the life span of the structure increased around two times. Parallel to this, its average crack propagation rate (5.85 \(\times\) 10\({}^{-5}\) mm/cycle) was almost half of the others. Figure 7 presents the cracks around spot welds for each case when \(N_{f}\) was reached. For all of them, the identical spot weld twins experienced the fatigue failure. That concludes that the load ratio does not affect the failed region of the box when subjected to torsional loading.  
Figure 5: The depiction of the cracks that spread around the spot weld at the bottom adherends’ overlapping surface for different numbers of spot weld twins at \(N=N_{f}\).