esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Infrared Study
Small variations in both C-H peaks (1917 cm\({}^{-1}\) and 2848 cm\({}^{-1}\)) and Si-O (1012 cm\({}^{-1}\)) peaks are due to the alteration of the conformation of the surfactant chains as water molecules are affected by the MW. Therefore, the shifts in the peaks are apparent in the MW irradiated sample. Figure 2: Effect of heating on morphology of micelle–clay granules. 1. Non-spherical granules—drying for 90 min in an ordinary oven. 2. Non-spherical granules—drying for 4 min in a MW oven. 3. Spherical granules—drying for 90 min in an ordinary oven. 4. Spherical granules—drying for 4 min in a MW oven. Complementary information can be obtained from a study by FT-IR on the adsorption of ODTMA as monomers and micelles by the clay-mineral Na-montmorillonite [21]. As in the case of ODTMA-clay, no change was observed in the structural water peak observed at 3628 cm\({}^{-1}\) in the BDMHDA-clay sample (Figure 4). The broad peak observed at 3351 cm\({}^{-1}\) arises from H-bonded water molecules. This broad band (H bound water), which is generally seen in a high concentration of surfactant, shifts to a low wave number with increasing concentration [22]. This peak  
Figure 4: FT-IR spectra of BDMHDA–clay sample under: (**a**) MW free; (**b**) MW heated. Figure 3: FT-IR spectra of ODTMA–clay sample under: (**a**) MW free; (**b**) MW heated. shifted to 3345 cm\({}^{-1}\) after microwave heating. The decrease in band intensity is an indication of low levels of water molecules, which indicates low water content [22]. In this study, the increase in the intensity of this peak in the MW heated sample indicates the change of H-bonded water (Figure 4). This may be due to the increase in pore sites and diameters with the removal of existing water by MW heating. Thus, a greater amount of water settles on the surface with increasing pore diameters. The peaks observed at 2921 cm\({}^{-1}\) and 2951 cm\({}^{-1}\) are related to the antisymmetric and symmetric CH\({}_{2}\) stretching modes of amine, respectively (Figure 4). The narrow peaks and the increase in the intensity represent the increased density of the amine chain and the more regular amine chains [22]. The intensity of these peaks increased and the band width narrowed. The increase in peak intensity is high in the MW heated sample. The peak observed at 1638 cm\({}^{-1}\) is related to adsorbed water. This peak shifted to a higher wave number by MW heating, (Figure 4). The shift of adsorbed water peak to higher wave numbers represents high surfactant concentration and thus lower adsorbed water content [22].
# Online Detection and Fuzzy Clustering of Anomalies in Non-Stationary Time Series +
## 4 Detection Results
### Real System Implementation  
We also illustrate the performance of NARX-SCAPA on the original (i.e., not simulated) time series. Again, the model shows success at one-step ahead prediction in non-anomalous regions, and anomaly detection based on residuals. The NARX neural network was capable of handling the non-stationary time series and was adaptive to fluctuations within the normal range. Moreover, the model remained robust to the impact of severe anomalies, e.g., the regions around 7300, 11,000, and 13,000 min, where the observations were scattered, interfered, and split, respectively. The structures and the intensities of these anomalous regions were retained in the detrended residual series. However, it can also be seen in Figure 5 that the prediction of the NARX-SCAPA-based model was distorted between 13,250 to 13,400 min, and this was due to the limitation of the NARX model and the long duration of strong anomalies. Yet, such distortions of the model prediction in the anomaly region were not able to compromise the detection of anomalies, as seen in Figure 5. We see success in detecting point and collective anomalies, including those low-intensity anomalies which are hard to observe by eye alone.  
\begin{table}
\begin{tabular}{r r r r r r r r r r r} \hline \hline  & & \multicolumn{4}{c}{Split} & \multicolumn{4}{c}{Interference} \\ \hline Type & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 \\ \hline Actual points & 1000 & 1500 & 2000 & 1000 & 1500 & 2000 & 750 & 1500 & 2000 & 2500 \\ Identified points & 974 & 1490 & 1953 & 982 & 1499 & 1856 & 743 & 1491 & 1354 & 2089 \\ Accuracy (\%) & 97.4 & 99.3 & 97.7 & 98.2 & 99.9 & 92.8 & 99.1 & 99.4 & 67.7 & 83.6 \\ \hline \multicolumn{10}{c}{Shift} & \multicolumn{4}{c}{Point} \\ \hline Type & 11 & 12 & 13 & 14 & 15 & 16 & 17 & 18 & 19 & 20 \\ \hline Actual points & 750 & 1500 & 2000 & 2500 & 750 & 1500 & 10 & 10 & 10 \\ Identified Points & 320 & 778 & 1128 & 1666 & 748 & 1498 & 6 & 10 & 7 & 10 \\ Accuracy (\%) & 42.7 & 51.9 & 68.5 & 66.6 & 99.7 & 99.9 & 60.0 & 100 & 70.0 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: NARX-SCAPA anomaly detection on the simulated BT network traffic data (see Table 1 in Appendix B for details of anomaly types).  
Figure 5: NARX-SCAPA anomaly detection on the original BT network traffic data.
# Topology of the World Tourism Web
## 1 Introduction  
Complex systems, such as tourism, may be observed from the social network analysis (SNA) perspective. The importance of SNA use in the analysis of economic and social networks arose during the last crisis [1, 2]. Accordingly, most of the research applying SNA to tourism dates back a decade or less.  
The motivation for this research is to fill in the research gap regarding the use of SNA in tourism. While SNA has been used to determine the relations and interplay of the actors at the local, country, or regional level, the comprehensive endeavors to examine the tourist network at the global level, which have been done for trade [3, 4, 5], are scarce, except for a few efforts in the field. [PERSON] and [PERSON] [6] studied inbound and outbound tourism based on international tourist arrivals for the year 2004. In addition, [PERSON] and [PERSON] [7] examined the top outbound vs. inbound countries in the tourism network on the global scale for the years 2013 and 2014. [PERSON] et al. [8] examined the international tourism flow for the period from 2002 to 2014 based on UN World Tourism Organization (UNWTO) data for 218 listed countries. In addition to the complexity of the network, tourism is also a dynamic phenomenon, indicating network growth and development over time. Thus, findings from the previous years may not be valid over a longer period of time.  
The goal of this research is to examine the topology of the world tourism network and to discuss the meaning of its characteristics in light of the current situation. We build on the previous research [6, 7, 8], aiming for a more extensive and exhaustive world tourism network overview with the most recent data.  
The following section offers a theoretical basis for the research, with an overview of similar research and research gaps, followed by the definitions of used data, data collection descriptions, and data-related difficulties, ending with a description of our analysis. The Results and Discussion section reveals calculated network properties and discusses those properties in the tourism, economic, and pandemic contexts. The Results and Discussion section is divided into sub-sections to enable reading and navigating by topic, which include Network Topology and Structure (with the sub-sections BasicNetwork Measures, Countries' In- and Out-Degrees, Centrality Measures and Clustering, Communities, and Patterns), examination Whether the Network Behaves as a Social or Technological/Biological Network (with the sub-sections Small World and Free-Scale Networks, Pareto Rule, Assortativity, Patterns between the Core and the Periphery, High Clustering Coefficients, Homophily, Weak Ties, and Structural Holes), and Network Diffusion Properties.  
While the article is primarily quantitatively oriented and focused on the topology of the tourism network, our goal is also to keep interested even non-quantitatively oriented readers by providing a sufficient topic background and the interpretation of the findings in relation to realistic settings.
# Ten Years of TerraSAR-X Operations
## 5 Radar Experiments with TerraSAR-X
### BiDiSAR  
The newly developed bidirectional synthetic aperture radar (BiDi) imaging mode [27] was demonstrated with TerraSAR-X. Bi-directional means the simultaneous imaging of two directions by one antenna into one receiving channel. The BiDi imaging is based on an azimuth pattern with two steered lobes that are pointing into different directions, an increased PRF, and a separation of the simultaneously received images in the Doppler spectral domain. Figure 8 shows the BiDi acquisition geometry for simultaneous forward and backward acquisitions in the directions \(\psi_{\text{fore}}\) and \(\psi_{\text{aft}}\), respectively. The slant range vector in the forward direction is oriented by \(90^{\circ}-\psi_{\text{fore}}\) w.r.t. azimuth. In the backward direction, the slant range vector is oriented at \(90^{\circ}+\psi_{\text{aft}}\). The same target area is acquired twice as the sensor flies by with an along-track separation in the range of sections in-between the two illuminations.  
Figure 9 shows an excerpt of a larger BiDi acquisition in the form of a color composite. The fore image is in red and the aft image in green color. At positions where the backscatter is high and is equal in both images, the colors combine to yellow. There are a number of nonmoving manmade targets that already show a considerable difference in the backscatter due to the acquisition azimuth angle difference of \(4.4^{\circ}\). The motion of some ships is clearly visible as they appear partially in red and green, as the ship moved between the two illuminations.  
Figure 7: Terrain Observation with Progressive Scans SAR (TOPSAR) (**a**) and ScanSAR (**b**) comparison, both without scalloping correction. One sub-swath is shown. The measured scalloping in the ScanSAR image is around 1.2 dB, as compared to 0.3 dB in the TOPSAR image. Thirty-three ScanSAR bursts were necessary in contrast to only nine required in the TOPSAR mode.
# Antimicrobial and Antibiofilm Properties of Bioceramic Materials in Endodontics
## 3 Bioceramic Root Repair Material
### Other MTA-Based Cements  
Numerous MTA-based cements have been developed, aiming to retain the excellent physico-chemical and biological properties of MTA and overcome the limitations of traditional MTA, e.g., long setting time, handling difficulty, and staining.  
One example is NeoMTA Plus (NuSmile, Houston, TX, USA), which is a calcium silicate cement developed based on the MTA formulation, containing tantalum oxide as a radiopaque element. MTA Angelus (Angelus, Londrina, PR, Brazil) [37] and MTA Repair HP (Angelus, Londrina, PR, Brazil) [38] are MTA-based cements from Brazil. MTA Angelus was reported to have similar antimicrobial activity against _E. faecalis_, _S. mutans_, _Microoccus luteus_, _S. aureus_, _P. aeruginosa_, and _C. albicans_ compared to regular MTA [11]. MTA Repair HP was reported to have a short setting time and antimicrobial activity against planktonic  
Figure 3: SEM image of cross-section of **(a)** MTA and **(b)** EndoSequence root repair material putty exposed to butyric acid at pH 7.4 after 7 days of setting.  
_E. faecalis_[39]. A new bioceramic cement White-MTAFlow (Ultradent Products Inc., South Jordan, UT, USA) showed similar antimicrobial properties to ProRoot MTA [30].  
[PERSON] et al. [14] showed that MTA Angelus and NeoMTA Plus were not effective against multi-species biofilm using an intraoral-infected dentin biofilm model. A more recent study also showed MTA Angelus and NeoMTA Plus did not disrupt the multilayer structure formed by dual-species biofilm (_E. faecalis_ and _C. albicans_) [19]. The possible reasons for their weak antibiofilm activity could be due to the methods used in these studies. The endodontic cements used in this study were set before applying on the biofilm. [PERSON] et al. [40] indicated that the antibacterial behavior of MTA could only be detected when freshly mixed using a direct contact test. Therefore, it is possible that the antibiofilm activity of MTA-based materials could be diminished after setting.  
In addition, dentin block models were used in these recent studies, which mimics the clinical reality by direct contact of the material with multi-species biofilm in human dentin [25]. Multi-species biofilms grown in clinical-simulated conditions were more difficult to kill than planktonic bacteria [41]. Moreover, the use of confocal laser scanning microscopy and viability staining allows a three-dimensional evaluation of the viability of the biofilm adhered to dentin [14; 25].
# A Novel Predictable Rock Breaker Using Intelligent Hydraulic Control with ICT Convergences
## 3 Performance Evaluation and Field Test
\begin{table}
\begin{tabular}{c c c} \hline
**Nomenclature** & **Description** & **Unit** \\ \hline \(t_{i}\) & operation cycle time & s \\ \(E_{i}\) & impact energy & J \\ \(CF\) & calibration factor & \(\frac{\text{F}_{\text{cal}}}{\epsilon_{\text{meas}}}\) \\ \(\varepsilon_{\text{meas}}\) & deformation while calibration & \\ \(A_{t}\) & cross-section area of test chisel & mm\({}^{2}\) \\ \(E_{t}\) & module of direct elasticity & N/mm\({}^{2}\) \\ \(p_{t}\) & density of test chisel & kg/m\({}^{3}\) \\ \(e_{i}\) & measured deformation & \\ \(t_{i}\) & initial impact time & s \\ \(t_{n}\) & terminate impact time & s \\ \(k\) & gauge factor & \\ \(U\) & output voltage & V \\ \(U_{B}\) & input voltage & V \\ \(K_{\alpha}\) & amplifying factor & \\ \(\eta_{\text{tot}}\) & total efficiency of hydraulic breaker & \% \\ \(P_{s}\) & operating pressure & MPa \\ \(P_{r}\) & output pressure & MPa \\ \(Q\) & flow rate of hydraulic breaker & \(l\)/min \\ \hline \end{tabular}
\end{table} 
Table 4: Description of variables. Figure 9: Control system and site for the experiment; the blue circle indicates the strain gauge. \begin{table}
\begin{tabular}{c c c} \hline
**Specification** & **Unit** & **B500** \\ \hline Operating weight & kg & 3720 \\ \hline Tool(chisel) outer diameter & mm & 175 \\ \hline Operating pressure & MPa & 13.7-17.6 \\ \hline Oil flow rate & \(l\)/min & 200–280 \\ \hline Suitable excavator & ton & 40–50 \\ \hline \end{tabular}
\end{table} 
Table 3: Specifications of products used in the experiment. Figure 11: _Cont._  
Figure 10: Experiment result graph of the impact frequency (upper: long/lower: short). In the long stroke mode, the energy was observed to reach about 6583 J, while it reached 3102 J in the short stroke mode. The result shows that the impact efficiency improved 7%, as shown in Table 5. Furthermore, the amount of impact energy in the short stroke mode is suitable for breaking up soft rock with a strength of 70-100 MPa. The ICT convergence system was adapted and verified to transmit between the controller and the hydraulic breaker, due to the harsh conditions of the operation field such as high levels of vibration and impact. An additional test applying wireless communication was certified by Korea Conformity Laboratories (KCL). All of the results qualified for a condition up to 85 \({}^{\circ}\)C temperature and 50-150 \({}^{\circ}\)C in the thermal shock test, up to 30G in the triaxial shock test, and distances of less than 30 m for the salt spray tests.
## 2 Detection Algorithms
### Cell-Averaging CFAR (CA-CFAR)  
CA-CFAR is an adaptive thresholding method that estimates local noise power using training cells around the cell under test (CUT), while excluding adjacent guard cells to prevent target signal contamination. The detection threshold is  
\[\gamma_{k}=\alpha\cdot\frac{1}{2T}\sum_{i\in\mathcal{T}_{k}}|Y[i]|. \tag{5}\]  
Detection occurs if  
\[|Y[k]|>\gamma_{k}, \tag{6}\]  
where \(T\) is the number of training cells on each side, \(\mathcal{T}_{k}\) is the set of training indices, and \(\alpha\) is a scaling factor that controls the false alarm rate.
###### Abstract
About 50% of Anxin County, in Xiong'an New District, is covered by Baiyangdian Lake, the largest lake in the North China Plain and a feature that led to dramatic changes in transportation modes in the past. Before the 1960s, Baiyangdian Lake was the transportation hub of the Daqing River system. After the 1960s, due to the construction of upstream and downstream dams, all external waterways were cut off and only internal waterways were retained [19]. During the lengthy drought of 1983-1988, the government had to use \"water diversions\" to secure the water supply of Baiyangdian Lake. Both the loss of external waterways and the water shortage led to a decline in water transportation. At the same time, land transportation developed rapidly. By 2008, a comprehensive road network was established. In general, the transportation mode in Anxin County shifted from water-based to land-based transportation from the 1960s to the early 2000s, until land-based transportation became the mainstay and internal water transportation secondary. Today, the construction of an efficient land transportation system is the main objective of the Xiong'an New District plan [20] and regulations have been introduced to shift the center of gravity of the population in the area further inland [21]. There is an urgent need for a new land transportation-oriented development strategy to guide the upcoming modern transport system transformation, residential mobility, and land consolidation in Anxin County. A land transportation-oriented spatial organization model is thus needed, but this has been lacking in previous studies. Therefore, we take the waterside villages in Anxin County as an empirical case to explore the following questions: (1) How has the land transportation accessibility and potential energy of waterside villages changed under the transportation mode shifting? (2) In what ways can villages respond to this shift? To answer these questions, we focus on two attributes related to land transportation. The first is _accessibility_, which refers to the ability of a normal village to be reached in the road network. The second is potential energy, simply called _potential_, which is defined as the capability of a normal village to develop into an important node in the road network. _Potential_ can be considered as a comprehensive evaluation of accessibility and socio-economic condition. Although the detailed relationship between the _accessibility_ of villages and the villages' socio-economic conditions is worth being considered, correlation and regression analysis in our pre-experiment indicated that correlation was not significant before modern land transportation system establishment and only weakly significant afterward. The analytical result also implicates that the reason for villages' development was much more complicated than a single reason in land transport location. Therefore, this study only provides a quantitative evaluation of the two attributes, _accessibility_ and _potential_. We further proposed a village structure to adapt to the land transport system based on its potential.
# Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning
## 2 Materials and Methods
### Inference  
To detect ships with our model we first cut the target image into patches of the same size as in training-64 \(\times\) 64 pixels-using a regular grid. However, for inference we take these patches with an overlap of 32 pixels. It is well known that U-Net architectures, and CNNs in general have lower performance on the borders of the image than in the center, due to the influence of padding in the backbone. Since our backbone is trained with a contrastive objective task during SSL, this type of padding was the most straightforward approach. When transferred to the U-Net setting, 0-padding introduces artifacts on image borders. We chose to simply cut out 16 pixel wide borders of the patches.  
Finally, the full image binary mask is produced by stitching the individual patch masks together. We apply the connected components algorithm with 4-connectivity to extract blobs in this mask. Each blob is considered as a detected ship, without additional filtering.
## 4 Performance
### Performance in a Laboratory Environment  
Performance tests were conducted in a tank lined with anechoic material [27]. To simulate range, a source level of 156 dB re \(\upmu\)Pa @ 1 m was used because it was close to the typical source level of existing JSATS transmitters [12]. The minimum source level of JSATS transmitters is 153 dB re \(\upmu\)Pa @ 1 m.  
Figure 7: Phase angle around the most likely message, which is cycles 270 to 580 (310 total): **(a)** actual; **(b)** bipolar.  
The encoded waveforms were transmitted at signal levels of 116, 106, 101, 96, 91, and 86 dB re 1 \(\upmu\)Pa _@_ 1 m (measured at the center frequency of 416.7 KHz), resulting in six levels of signal attenuation at \(-\)40, \(-\)50, \(-\)55, \(-\)60, \(-\)65, and \(-\)70 dB, respectively. Assuming the transmission loss is due only to spherical spreading, the six signal attenuation levels are equivalent to 100, 316, 562, 1,000, 1,778, and 3,162 m, respectively, in an ideal environment. A broadband spherical hydrophone (Model TC 4034, RESON, Slangerup, Denmark) was used to transmit the encoded signal. The cabled receiver was placed, hydrophone tip down, in the tank. Twenty-eight tag codes were used to represent the 14 different phase shift counts of all possible tag code combinations, with two tag codes randomly selected from each phase shift count category [27]. Decoding efficiency was calculated as the number of correctly decoded detections divided by the number of transmissions. A typical JSATS cabled Model 2008 hydrophone has a decoding efficiency of 59% at \(-\)55 dB; _i.e._, a detection range of 562 m (decoding efficiency \(>\) 50%) in an ideal environment (Figure 8).
###### Abstract  
Meadows are valuable areas that play an important role in the carbon cycle. Depending on several factors, these areas can be carbon sinks or net emitters of carbon dioxide (CO\({}_{2}\)) into the atmosphere. In the present study, the use of an anintranspirant (AT) with silicon and the groundwater level in a subirrigation system in a three-cut meadow were evaluated on the carbon dioxide exchange balance and the yield of aboveground biomass. The study was carried out in four experimental plots: with high groundwater level (HWL), with a high water level with AT application (HWL_Si), with a lower groundwater level (LWL), and with a lower groundwater level and AT application (LWL_Si). Flux measurements were made using the closed dynamic chamber method. In the drier and colder 2021, the meadow was a net CO\({}_{2}\) emitter (mean annual net ecosystem exchange (NEE) of all plots: +247.4 gCO\({}_{2}\)-C\(\cdot\)m\({}^{-2}\)y\({}^{-1}\)), whereas in the more wet and warmer 2022, assimilation outweighed emissions (mean annual NEE of all plots: -187.4 gCO\({}_{2}\)-C\(\cdot\)m\({}^{-2}\)y\({}^{-1}\)). A positive effect of the silicon antitranspirant application was observed on the reduction of carbon dioxide emissions and the increase of gross primary production (GPP) from the plots with higher groundwater levels. For the area with lower water levels, the positive impact of AT occurred only in the second year of the experiment. The yield of aboveground biomass was higher by 5.4% (in 2021) up to 11.7% (in 2022) at the plot with the higher groundwater level. However, the application of AT with silicon contributed to yield reduction in each cut, regardless of the groundwater level. On an annual basis, AT application with silicon reduced the yield by 11.1-17.8%.  
grassland; net ecosystem exchange; CO\({}_{2}\) emission; subirrigation; groundwater level; yields; silicon; antitranspirant +
Footnote †: journal: water