esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Load Transfer Section to Toughness for Steel Fiber-Reinforced Concrete
## 5 Conclusions  
This study aimed to determine the effect of the degree of energy discharge and the variation of the slopes on the overall toughness properties of the flexural behavior as the load is transferred to the fiber after concrete crack occurrence in the load transfer section of steel fiber-reinforced concrete. A flexural behavior test was performed on six specimens with different steel fiber types and fiber contents, and by analyzing the load displacement graph by section, the conclusions shown below were arrived at.  
1. The study verified the increase in the remaining load and toughness based on the increase in the amount of steel fiber mixed with the concrete, and the degree of increase variation differed by fiber type, fiber content, and shape.
2. C-SF was shown to offer superior remaining load and toughness at all the fiber contents, and compared to C-SF, C-SLF offered even more superior remaining load and toughness.
3. From the analysis of the load displacement curve of the steel fiber-reinforced concrete by section, it was verified that the slope of the second section became smooth as with the increase of the fiber content, and that the slope of the third section increased as the section's load increased.
4. The load displacement curve of the steel fiber-reinforced concrete showed a correlation between the fiber content, specifically the area of the second section (the load transfer section) and that of the third section. With the increase of the fiber content, the second section area decreased while the third section ratio increased. Based on these results, the study proposed the LTF of steel fiber.
5. To determine the correlation between the energy transfer capacity of steel fiber and the overall toughness of the load transfer section, the study derived the regression model (\(T_{b}\)) using the LTF.  
This research was supported by a grant (NRF-2016 RIC IB2009489) from the National Research Foundation of Korea. [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.  
Figure 12: Regression model based on the load transfer factor (LTF) and the overall toughness.
# Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems
## 2 Landslide Early Warning Systems
### LEWS in Medellin and Colombia  
In Colombia, a LEWS on national scale already exists by \"IDEAM\"--Instituto de Hidrologia, Meteorologia y Estudios Ambientales [32]. Another system focussing on debris flows exists in Combeima-Tolima, which combines rainfall and geophone data on a regional scale to react once the dynamic thresholds are reached [14; 33; 34]. On the municipal level, LEWS exist in Bogota and Medellin (www:idiger.gov.co) (accessed on 1 March 2021), although most of them are regional systems. There are local LEWS in Colombia that focus on landslides, for example a system in the city of Bolivar, based on an open-source system which measures humidity and temperature to determine the risk of landslides [35]. Yet, site-specific warning systems, especially with community involvement and evacuation alarms, are still rare in urban regions [36].
## 3 Results and Discussion
### Comparison of Results from PAH Analysis and Biological Survival Rates  
The survival rate of test organisms and the concentration of PAHs were determined, after statistical analysis. The degree of correlation was classified as low when the correlation was \(\pm\)0.3 (between 0.3 and \(-\)0.3) while moderate and high correlations were \(\pm\)0.3 to 0.6 \(\pm\)0.6 to 0.9 (between 0.3 and 0.6, \(-\)0.3 to \(-\)0.6). Since naphthalene, acenaphthenene, benzo(b)fluoranthene, dibenz(a,h)anthracene, and acenaphthalene were not detected, the correlation could not be calculated. The biological survival rate showed a low correlation with phenanthrene, anthracene, pyrene, benzo(a) anthracene, chrysene, benzo(b) fluoranthene, and benzo(a) pyrene; medium correlation with fluoranthene and benzo(g,h,i)perylene; and a high correlation with fluorene. Discussion of Trace Metal Concentrations of Sediment Porewater and the Survival Rate of Hyallella zateca  
Table 5 shows heavy metal concentrations in the porewater of sediments of the 35 sites. Test results showed that the concentrations of the trace metals nickel, copper, and lead were mostly below the detection limits. Chromium and zinc were the two most detected metals in sediment porewater. The highest metal concentration for total chromium was detected at sample site LK2; zinc showed the highest concentration at site WR4.
# Free Surface Energy and Hansen Solubility Parameter Vector Field. Interface Thickness
## 3 Data and Discussion
### The Hansen Solubility Parameters Vector Field
#### 3.1.2 Interaction Vectors and Free Surface Energy  
As presented in the introduction above, a great deal of research work has been performed to link free surface energy with the Hansen solubility parameters. In all cases, the \(\delta_{D}\), \(\delta_{P}\), and \(\delta_{H}\) parameters have been conceived as three independent magnitudes with no interdependence whatsoever. To name an example, in the Beerbower equation [6], the  
Figure 1: Hansen solubility parameters vector space.  
solubility parameters are affected by the \(a\) and \(b\) parameters, which change depending on the chemical family studied.  
\[\delta_{D}^{2}+a\delta_{P}^{2}+b\delta_{H}^{2}=13.9\bigg{(}\frac{1}{v}\bigg{)}^{1 /3}\sigma \tag{19}\]  
In this paper, \(\delta_{D}\), \(\delta_{P}\), y \(\delta_{H}\) will be conceptualised as components of a vector in a vector space. The three-dimensional basis of this space is defined by a director vector, which has a magnitude of 2 for the dispersion forces. In contrast, the magnitudes of the director vectors for polarity and hydrogen bonding forces are set to 1.  
Taking the above into account, free surface energy is regarded as directly proportional to the square of the magnitude of the interaction vector between the two phases that create the surface:  
\[\sigma_{12}=\tau\bigg{|}\overrightarrow{S}_{12}\bigg{|}^{2} \tag{20}\]  
That is to say:  
\[\sigma_{12}=\tau\Big{[}4\big{(}\delta_{D2}-\delta_{D1}\big{)}^{2}+\big{(} \delta_{P2}-\delta_{P1}\big{)}^{2}+\big{(}\delta_{H2}-\delta_{H1}\big{)}^{2} \Big{]} \tag{21}\]  
If phase 1 is air, and since its cohesion energy is very weak, the solubility parameters can be considered equal to zero. In this case, the surface free energy (surface tension) would be as follows:  
\[\sigma=\tau\Big{[}4\delta_{D}^{2}+\delta_{P}^{2}+\delta_{H}^{2}\Big{]} \tag{22}\]  
The proportionality constant is called \(\tau\), and its units are of length.
# Measurement and Modeling of Narrowband Channels for Ultrasonic Underwater Communications
## 3 Ultrasonic Measurements for UAC  
In this section, a measurement campaign to characterize ultrasonic UAC channels is addressed. The measurement procedure is briefly described, and some of the obtained results are discussed.
# Methodology for the Efficient Progressive Distribution and Visualization of 3D Building Objects
## 2 Related Work
### Multiple Representation and Progressive Transfer  
Multiple representations are important in many 3D applications, such as infrastructure construction application [18] and geology [19] and they play a prominent role in city models. To deal with multi-scale models, it is common to develop multiple representation data structures. The models are not the same as LODs in CityGML [20]. We mainly extend the LOD2 in CityGML to a richer level (CSG-LOD or CLOD) to better fill the gap between LOD3 and LOD1 [21]. The progressive CSG is used as the new LOD which is generated based on the balance between visualization performance and computation costs.  
Multiple representation data structures have been used for the progressive transfer of 2D data [22]. For 3D city models, [PERSON] [23] developed a data model that supports hierarchical LOD. This data model was then implemented in a P-Tree (progressive tree) which stores a coarse representation in the root and a more detailed representation in the leaves. Depending on the distance from the viewer to the building, a different level of the P-tree is traversed. To ensure landmark buildings are represented satisfactorily, they are always stored on a high level of the tree. [PERSON] and [PERSON] [24] proposed a building model with a continuous level of detail (denoted CLOQ buildings). The basic idea is that each part of the building (including facades) is stored as a separate object and that a user can select at which level of detail a building should be visualized. [PERSON] et al. [25] developed a method of the progressive transfer of terrain surfaces and 3D objects based on wavelets.  
Progressive data transfer can also be facilitated by sending a process description of how the geospatial objects should be changed. [PERSON] and [PERSON] [26] developed and demonstrated this  
Figure 1: Examples of elementary geometric features that are used in constructive solid geometry (CSG) modeling.  
strategy for 2D geospatial data. One advantage of this approach is that it (partly) enables continuous zooming [27].
## 2 Experimental Section
### Transmission Electron Microscopy (TEM) Imaging  
_B. subtilis_ cells were treated with the hybrid dots at 0.1 mg/mL for 2 h under light illumination. After treatment, 600 \(\upmu\)L of both the treated and control samples were fixed with the fixative chemical (4% glutaraldehydehyde in 0.1 M sodium cacodylate buffer, pH 7.4). The contents were mixed by gently inverting the tube back and forth. Centrifugation was done at 1000\(\times\)_g_ for 10 min. The samples were stored overnight at 4 \({}^{\circ}\)C prior to the next procedure. The pellet was rinsed in 0.1 M sodium cacodylate buffer several times, and fixed in 1% buffered osmium tetraxide for one hour at room temperature. Following dehydration with graded series of ethanol (30%, 50%, 75%, 100%, 100%) and two changes of propylene oxide, the cells were infiltrated and embedded in PolyBed 812 epoxy resin (Polysciences, Inc., Warrington, PA, USA). Ultrathin sections (70 nm) were cut and mounted on copper grids followed by staining with 4% uranyl acetate and 0.4% lead acetate. Sections were imaged under a JEOL JEM-1230 transmission electron microscope (JEOL USA, Inc., Peabody, MA,USA) operating at 80 kV, with digital images acquired with a Gatan Orius SC1000 CCD Digital Camera and Gatan Microscopy Suite v3.0 software (Gatan, Inc., Pleasanton, CA, USA).
# Graph-Level Label-Only Membership Inference Attack Against Graph Neural Networks
## 4 Graph-Level Label-Only Membership Inference Attack
### Attack Methodology  
#### 4.2.1 Attack Intuition  
In the label-only setting, the adversary can obtain nothing more than the label-only output from the target model, making it impossible to rely on prediction probability vectors for the attack. [PERSON] et al. [24] evaluated the robustness of the model's prediction labels under perturbations of the input to infer membership, and their proposed MIAs could be applied in the image, audio, and natural language domains. Inspired by their work, we infer membership of the target graph by calculating the robustness score, which is defined as follows:  
\[score_{t}=\begin{cases}\frac{\sum_{g_{t}\in G_{t}}\mathbb{I}(f_{t}(g_{t}^{ \prime}),g_{t})}{|G_{t}|},&y_{t}=y\\ 0,&\text{otherwise}\end{cases} \tag{8}\]where \(\mathit{score}_{t}\) denotes the robustness score of a given target graph \(g_{t}\), the target graph corresponds to the ground-truth label \(y\), \(y_{t}\) is the label of \(g_{t}\) predicted by the target model \(f_{t}\), \(G_{t}\) is a set of perturbed graphs of \(g_{t}\), and \(g_{t}^{\prime}\in G_{t}\) is one of the perturbed graphs. \(|G_{t}|\) represents the number of elements in \(G_{t}\). The function \(\mathbb{I}(\cdot)\) is the indicator function: if \(f_{t}(g_{t}^{\prime})=y_{t}\), it is recorded as 1; otherwise, it is 0. Graphs with relatively high robustness scores are considered members.
## 3 Strain Gauges and Distributed Optic Fiber Strain Measurements
### Berea Sandstone--Increasing Confining Pressure
#### 3.1.3. Optic Fiber Measurement--Brillouin Frequency Shift  
Figure 7 presents the corresponding Brillouin frequency measurements over at the sandstone core (sensor region A). In contrast to the Rayleigh frequency, the Brillouin frequency is reduced (from \(\sim\)10.9 GHz) with increasing confining pressure. Furthermore, the measured Brillouin frequency displays low-magnitude spatial fluctuations which are absent from the Rayleigh frequency measurement. Figure 8 presents the frequency shifts measured at the 11 monitoring points (which are plotted separately in Figure S3) as the confining pressure is increased from 0.5 MPa to 12 MPa, and their mean value at each pressure step compared with the baseline value (sensor region B). As with the Brillouin frequency shift, the mean frequency shift value also increases in a bi-linear manner (though the transition occurs at a higher confining pressure of 8 MPa) with increasing confining pressure. The frequency shift for the free-standing optic fiber in the pressure vessel is about 1/6 th of that measured from the rock.
## 3 Results and Discussion
### Trends of NDVI
#### 3.2.2 Spatial Pattern of NDVI Trends  
The trend of NDVI showed strong spatial heterogeneity across China (Figure 6). From 1982 to 2011, the growing season NDVI in 75.6% of the research area increased (30.5% of the total increased  
Figure 5: Interannual variation of NDVI in (**a**) growing season; (**b**) spring; (**c**) summer and (**d**) autumn over the period of 1982–2011 in China. Solid lines are trend lines over the past 30 years and dash lines are trend lines before and after abrupt change point (TP).  
significantly), especially in grass covered Loess Plateau and northern Xinjiang as well as forested northeastern and southern China (Figure 6a). Merely a small portion of research area such as grassland in northeast of Inner Mongolia showed sharp decreasing trends. In spring (Figure 6b), NDVI increased at high rates in 72.7% of the area, mainly evergreen broadleaf forest, evergreen needle forest and deciduous broadleaf forest in central and southern China, while NDVI in grasses and shrubs in northern China and west Tibetan only changed slightly. However in summer (Figure 6c), decreasing trends were found in a large portion of area (41.7%), despite grasslands in Loess Plateau and northern Xinjiang showed increasing trends. The pattern of NDVI trends in autumn (Figure 6d) was similar to that in the growing season, with increasing trends in 71.8% of the area. Despite the strong spatial heterogeneity in the growing season NDVI trends, the NDVI trends within each land cover type did not vary much in terms of both median and variation range (Figure 7). The medians of trends in ENF, EBF, DBF, MF, shrubs and grasses were \(8.7\times 10^{-4},10.9\times 10^{-4},8.0\times 10^{-4},8.9\times 10^{-4},9.0 \times 10^{-4},3.9\times 10^{-4}\) respectively. Despite the relatively higher trends in EBF and lower trends in grasses due to increased temperature and decreased precipitation respectively (see Section 3.3.2 for details), the differences among different land cover types were relatively small compared with the variation range within each type, suggesting that land cover type itself may not make a big difference in NDVI trends, yet climatic drivers could have led to the large heterogeneity within each type.  
Figure 6: Spatial patterns of NDVI trends in (**a**) growing season; (**b**) spring; (**c**) summer and (**d**) autumn over the period of 1982–2011.
## 2 Related Works
### State-of-the-Art Object Detection Algorithms  
There are two distinct forms of deep learning object detectors: one- and two-stage detectors. The two-stage object detection method thus requires two stages to conclude. The RoI (Region of Interest) pooling layer can partition two-stage detectors into two stages [49]. The R-CNN series has built and implemented representative networks based on possible regions [50]. R-CNN [51], Fast R-CNN [52], Faster R-CNN [53], and Mask R-CNN [54] have a two-stage detection process. For example, the most popular Faster R-CNN begins with a stage known as RPN, which stands for Region Proposal Network, which is a stage used to identify potential bounding boxes. In the second stage, for the classification and bounding box regression tasks, features are retrieved from each bounding box using an RoI pooling approach. As a preliminary phase, proposals and regional classification are made [55]. Fast/Faster Recurrent Convolutional Neural Network (RCNN two-stage detector) is preferable for immediate information access since the bounding box and object class estimation processes are carried out in tandem [56].  
Due to the processing cost of region proposal approaches and their incompatibility with smartphones and other wearable sensors, numerous researchers have pioneered the development of single-stage detection pathways [7]. This is because single-stage detectors are faster in detecting speed and produce more reliable results in terms of accuracy in real-world scenarios. As a result, a single-stage detector is optimal for vast volumes of data, such as those seen in clothing fields. OverFeat [57], Single Shot Detector (SSD) [58], RetinaNet [59], Cornernet [60], and YOLO [61], which are all one-stage detectors, have attracted recognition for their efficiency of processing. Certain parameters, including localization, inference speed, and accuracy, must be carefully examined while designing and selecting one of these detectors.
## 2 Materials and Methods
### Methods
#### 2.2.2 Theory of PSO-MLP Model  
PSO is a global optimization algorithm that simulates the foraging behavior of birds in groups [61]. The position and velocity of each particle can be updated based on the globally optimal solution and the current optimal solution; as a result, all the particles move in the direction guided by the objective function. Then, the final global optimal solution can be calculated. The PSO algorithm has better global optimization capability and higher calculation performance than other optimization algorithms (e.g., the genetic algorithm and the ant colony algorithm) [62].  
The mini-batch gradient descent algorithm is mainly used to determine the appropriate connection weights between the neurons of MLP. However, several structural parameters, such as the learning rate, learning delay rate, and the learning momentum, are needed for appropriate determination [39]. The learning rate indicates the change range of the weight values between the neurons for each training iteration, and the learning momentum is applied to ensure that the change direction of the weight value is stable. In addition, the number of neurons in the hidden layer has an important effect on  
Figure 4: Perceptron network.  
the prediction performance of MLP. Thus, it is necessary to select a proper number of neurons in the hidden layer [41]. In this study, the PSO was proposed to determine these four structural parameters of MLP.  
The calculation processes of the proposed PSO-MLP are shown in Figure 5. Firstly, the initial parameters of PSO itself were selected, including the number of particles, maximum iterations, and so forth. Secondly, the MLP model with the mini-batch gradient descent algorithm was trained and tested based on landslide and non-landslide samples. Then, the prediction accuracy index of the area under the receiver operating characteristic (ROC) curve (AUC) was selected as the fitness function of the MLP, and this fitness function was also the objective function of PSO. Fourthly, by comparing the AUC value of each particle to the global and local best AUC values, the position and velocity of each particle were updated gradually. Finally, the update process continued until the end of the setting maximum iteration of PSO was reached.
## 4 Results
### Analysis of Wire Break Events and Structural Attenuation
While sensor 8 is generally closer to the crossbeam than sensor 2 and could provide a more accurate attenuation assessment, the measuring range is constrained to 134 dB due to the hardware configuration, precluding quantitative analysis. Consequently, using sensor 2 tends to overestimate the attenuation. The average value of the attenuation is approximately 14.3 dB. In the frequency domain, additional component-specific characteristics become apparent. At the tendon, spectral centroids are remarkably low, likely due to the excitation of body vibrations in addition to elastic waves during a wire break [43]. The frequency increases at the steel components adjacent to the tendon (anchor plate of the novel anchoring construction and steel plate of the crossbeam), indicating significant attenuation of lower frequencies during the transmission through the contact zones between those components. The spectral centroid naturally moves towards the sensor-specific response spectrum or the frequency range's mean as a signal is attenuated. Consequently, the spectral centroid at the foundation crossbeam is approximately 25 kHz and shows a low scattering across all sensors. The sensor has a local resonance frequency at this frequency, which appears predominant in strongly noisy signals [35]. For the investigated signal features, varying scatter was observed across different components and sensor positions. At the tendon and anchor, the features exhibit lower scatter than the transition piece crossbeam. This increased scattering is likely due to signal transmission through the tensioning anchor, the excitation, and the dimension of \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Structural Component** & **Sensors Evaluated** & \(\mu\) & \(\sigma\) & \(Q_{0.9}\) \\ \hline Tendon (longitudinal) & 2 to 5 & \(-1.62\) & 4.16 & \(-2.19\) \\ Foundation & 13 to 16 & \(-5.48\) & 5.39 & \(-6.30\) \\ (transversal) & 21 to 24 & \(-1.49\) & 7.40 & \(-2.56\) \\ Novel anchorage & 1 and 16 & \(-43.62\) & 5.37 & \(-45.95\) \\ Tendon to transition & 2 and 24 & \(-14.30\) & 5.40 & \(-17.10\) \\ piece & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Attenuation coefficients and statistical parameters for the amplitude of wire break signals, considering the tendon in the longitudinal direction, crossbeams in the transversal direction (foundation and transition piece), and respective component transitions. the steel plate. To account for such scattering effects, quantiles were derived from the feature distributions at specific measurement positions for use in the PoD model. These quantiles were determined for both the magnitude and the attenuation coefficients and are presented in Tables 2 and 3. This approach ensures that new wire break events are captured reliably. The mean value and standard deviation are also documented for completeness and informational purposes.