esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### Spatial Relationships between Vgreenup and Its Components  
Vgreenup anomalies showed a significantly negative correlation with Dgreenup anomalies and a significantly positive correlation with Agreement anomalies across all sites in the whole study area (Figure 3a,b). This was different from the findings of a previous satellite-based study [19], in which Vgreenup was positively correlated with both Dgreenup and Agreenup at a global scale. Across all sites in this study, the linear spatial correlation between Vgreenup anomalies and Dgreenup anomalies (\(R^{2}\) = 0.61, \(p\) < 0.001) was relatively weaker than that between Vgreenup anomalies and Agreenup anomalies (\(R^{2}\) = 0.83, \(p\) < 0.001), which implies the predominant role of Agreenup in the whole study area. The relative contributions of Dgreenup and Agreenup to the spatial variation of Vgreenup were different between Europe and North America (Figure 3c-f). Dgreenup anomalies exhibited a higher correlation (\(R^{2}\) = 0.67, \(p\) < 0.001) in explaining the  
Figure 2: The relationships between Vgreenup and geographical factors. **(a–c)** The geographical patterns of Vgreenup across all sites in Europe and North America; **(d–f)** the geographical patterns of Vgreenup across all sites in Europe; **(g–i)** the geographical patterns of Vgreenup across all sites in North America.  
spatial variation of Vgreenup anomalies in comparison to Agreenup anomalies (\(R^{2}\) = 0.59, \(p\) < 0.001) in Europe (Figure 3c,d). However, Vgreenup anomalies correlated more strongly with Agreenup anomalies (\(R^{2}\) = 0.95, \(p\) < 0.001) than with Dgreenup anomalies (\(R^{2}\) = 0.58, \(p\) < 0.001) in North America (Figure 3e,f), which indicated that the spatial variation of Vgreenup was more predominated by Agreenup.
## 2 Materials and Methods
### Adaptation Mechanism
As a result, such clocks consume relatively large power. In this paper, we propose a clock generator that is based on NOT logic gates and switches, connected alternately in series. In this approach, the logical values are stored in parasitic capacitances of particular NOT gates that serve as short-time memory cells. In the realized project, the chain of the switches and NOT gates has been supplemented with additional logic gates that allow determining the clock behavior. As a result, we can select the number of clock phases and the position of the first phase in the clock cycle. Additionally the filling ratio (pulse width) may be determined as well. The proposed clock generator is shown in Figure 6. It is composed of a chain of 10 blocks denoted as UCLK (unit clock cell). Selected outputs of the UCLK blocks are fed to an nine-input NOR gate. In the situation, in which the outputs of all UCLK block are equal to \"0\", the NOR gate generates a new \"1\" impulse that is provided to the chain, and then propagated between subsequent UCLK blocks, according to subsequent clk1/clk2 pulses. Each UCLK block provides complementary \"Nclkx\" and \"Pclkx\" clock signals, as switches in the ANN are realized as transmission gates, composed of the NMOS and PMOS transistors, connected in parallel. The structure of the first block in the chain (UCLK_IN) is a bit different than of remaining UCLK blocks, as this circuit plays a different role. It allows providing the generated \"1\" impulse not necessary to the first UCLK block. For this reason, the output of this block is connected to all UCLK blocks in parallel. The UCLK block that receives the \"1\" impulse is determined by the sequence of the Bis9-Bis0 bits. These bits also determine the number of the clock phases. To explain how it works in detail, let us consider an example sequence of Bisi9-Bsi0 bits of 1011111011. The first appearance of the \"0\" signal indicates the first phase, while the second occurrence of the \"0\" signal indicates the last one. Such a programming method was obtained by an appropriate structure of the UCLK blocks, shown in Figure 7. The advantage of this approach is that reprogramming the clock so that it performs a multi-phase cycle with different numbers of phases, requires changing only 2 or 4 bits, regardless of the maximum number of the phases. In this prototype, the circuit allows obtaining 10 phases, however, by extending the length of the chain of the UCLK blocks and by increasing the number of the inputs of the NOR gate, the clock may be very easily adapted to many phases. Regardless of the length of the chain, reprogramming the clock will always require the same amount of steps.
## 3 Proposed System
### PMIPv6/ AAND--Various Multi-Homing Topologies  
PMIPv6/ AAND works powerfully on any single LMA, devoid of the reflection of the position of the MAG. PMIPv6/ AAND consists of three topologies technique.  
1. CN-MAG1-MN. (Without handover)
2. MN-MAG1; CN-MAG2; (Hand over between MN-MAG2 i.e., MAG1-MAG2)
3. MN-MAG1; CN-MAG2 (Hand over between MN-MAG3 from MAG1 to MAG3 through MAG2, i.e., MAG1-MAG-MAG3  
In these different multi-homing situations, it is assumed that the localized routing route between MN and CN is already provided via the MAG address which is present in the same local mobility domain (LMD).
# Automatic Detection of Online Recruitment Frauds: Characteristics, Methods, and a Public Dataset
## 6 Analysis and Experimentation
### Empirical Analysis
#### 6.2.1 Geography  
As illustrated in Figure 2a, the dataset indicates the vast majority of scammers (86.7%) were published in USA and Australia in contrast to European countries where employment scam is less frequent. It is important to point out though, that EMSCAD is location biased as it only contains entries from English spoken countries.  
Moreover, a good indicator of a fraudulent posting is whether it advertises a telecommuting (work from home) position. As observed from Figure 2b, about 10% of the fraudulent postings in the chosen sample contain this characteristic. The predictive power of this feature is stronger if one takes into account that the amount malicious postings that contain the characteristic is over two times greater than the corresponding benign ones.
# Process-Aware Enactment of Clinical Guidelines through Multimodal Interfaces
## 5 User Evaluation
### Evaluation Setting and Results of the First User Study
The responsiveness of the GUI is another aspect that was investigated in the range of the first working prototype. In this direction, further tests were performed to calculate the time required by doctors to perform a single step of the survey associated with the CG deployed into the system (see also Section 2.3). Specifically, we assumed that a doctor completed a single step in the survey when s/he passed from one scene (in TESTMED, as discussed in Section 4, MT4j is exploited to build the GUI frames of the system, referred to as _scenes_, which enable to handle (multi)touch input events) of the GUI to the next one by answering the corresponding question of the survey. We monitored the time required by doctors to complete any scene associated with the CG's survey, until its completion. We ran this test twice: firstly, using the touch interface, and secondly using the vocal interface. A summary of the collected results is shown in Figure 9, where each scene transition is represented on the \(x\)-axis, and the corresponding time required for generating the new scene and displaying it on the screen is on the \(y\)-axis. In our case study, which was focused on the chest pain CG, we needed three scene transitions before generating the final chest pain score. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline  & **Q1** & **Q2** & **Q3** & **Q4** & **Q5** & **Q6** & **Q7** & **Q8** & **Q9** & **Q10** & **Q11** \\ \hline
**User1** & 4 & 3 & 4 & 3 & 2 & 3 & 4 & 4 & 4 & 3 & 3 \\
**User2** & 4 & 3 & 4 & 2 & 4 & 2 & 2 & 3 & 2 & 3 & 3 \\
**User3** & 5 & 3 & 4 & 3 & 5 & 2 & 5 & 4 & 5 & 4 & 4 \\
**User4** & 4 & 4 & 4 & 3 & 3 & 4 & 4 & 4 & 3 & 3 & 4 \\
**User5** & 3 & 3 & 4 & 2 & 3 & 4 & 4 & 4 & 4 & 3 & 4 \\
**User6** & 3 & 4 & 5 & 3 & 3 & 5 & 5 & 4 & 4 & 4 & 4 \\
**User7** & 3 & 4 & 4 & 3 & 4 & 4 & 5 & 4 & 4 & 4 & 4 \\ \hline
**Avg** & 3.7 & 3.43 & 4.14 & 2.7 & 3.43 & 3.43 & 4.14 & 3.86 & 3.71 & 3.43 & 3.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of the first user study. Figure 9: The vocal/touch user interface responsiveness tests. The tests were performed using an ACER Iconia Tab W500 (ACER Inc., Xizhi, Taiwan) with a 1 Ghz AMD CPU and 2 GHz of RAM, which was running Windows 7 OS (Microsoft Redmond, Redmond, Washington, USA). With the exclusive use of the touch interface, the analysis shows an average time of 400 ms for completing a scene transition, compared to 6-700 ms required when just the vocal interface is used. The reason of the delay caused by using the vocal interface is due to the (extra) time needed by the system to contact the ASR engine (usually around 200-250 ms). Nonetheless, from the user point of view, this delay has a low impact on the overall responsiveness of the system, since timing that does not exceed 700 ms. to perform a scene transition is usually considered acceptable by the users [38].
# Effects of Outlet Shrinkage on Hydraulics in Hyper-Concentrated Sediment-Laden Flow
## 4 Discussion
### Frequency Domain Characteristics  
The maximum fluctuating pressure on the bottom of the plunge pool was a key feature when studying the frequency domain characteristics. The power spectral density function usually represented the frequency characteristics of fluctuating pressure and reflected the distribution characteristics of the fluctuating energy of water flow in the frequency domain.  
Figure 12 depicts the relationship between the power spectrum and frequency of different overflow shapes at 2.20 m upstream water head with different sediment concentrations, where the ordinate is the normalized power spectrum of the fluctuating pressure at a typical measuring point and the abscissa is the frequency of pressure. The results showed that the turbulent energy was most concentrated at a frequency band of 0-0.2 Hz. The power spectral density distribution curve decreased with the shape change. As the outlet became narrower, the power spectral density decreased more slowly. The variation in power spectral density was closely related to the vortex in the plunge pool. The dominant frequency of the large-scale vortex was low. The fluctuating energy was mainly concentrated in the low-frequency range and the frequency band of fluctuating energy widened as the sediment concentration increased. When the outlet became narrower, the vortex scale was wide and the spectral density increased due to the influence of air entrainment.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sediment**} & \multicolumn{3}{c}{\(C_{S}\)} & \multicolumn{3}{c}{\(C_{E}\)} \\ \cline{2-9}  & **0** & **50** & **150** & **250** & **0** & **50** & **150** & **250** \\ \hline Shape 1 & 0.5756 & 0.7667 & 0.6073 & 0.2048 & 3.4021 & 3.6736 & 2.9954 & 2.8885 \\ Shape 2 & 0.3059 & 0.1312 & 0.0717 & 0.2021 & 2.9309 & 3.5226 & 3.2167 & 2.6378 \\ Shape 3 & 0.9112 & 0.8501 & 0.8359 & 0.3711 & 3.482 & 3.6932 & 3.4116 & 2.8181 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Skewness coefficient \(C_{S}\) and kurtosis coefficient \(C_{E}\) at 2.20 m water head.  
Figure 12: Relationship between power spectral density and frequency at 2.20 m upstream with different sediment concentrations.  
The frequency decreased as the outlet shrinkage increased, and the turbulent energy was transmitted to the vortex at a low frequency and large scale. With the increase in sediment concentration, the influence of air entrainment became smaller, while the influence of viscosity increased, and the large-scale vortex of various body shapes was more concentrated. These results are similar to those of [PERSON] and [PERSON] [16], who conducted experiments using plastic granules to study turbulent structures flowing in open channels.
## 1 Introduction
In addition, analyses of the load distributions in riveted structures have primarily focused on the uniformity of the rivet transfer loads; in addition, the established mechanical models are all one-dimensional models, which cannot effectively explain the influences of various factors on the strengths of riveted lap joints. In this paper, the effects of the number of rivets, rivet rows, rivet arrangement, row spacing, edge distance, and rivet spacing on the lap joint strength were explored through experiments and simulations. The mechanical properties of the lap joint, such as the load capacity and stress distribution under different rivet arrangements, were analyzed. The optimum rivet arrangement was obtained. Finally, the spring-mass model can be established to accurately calculate the force-displacement curve of the lap structure during the stretching process. The research results have important practical applications for the design and manufacture of high-strength and high-reliability lap joint structures.
# Analysis of Water Quality Improvement Effect of Controlled River by Multi-Functional Weir Operation
## 2 Research Background  
### Pulsed Flow  
Pulsed flow refers to a gate operation method that temporarily discharges a large amount of water to improve downstream water quality and river habitats [11]. Pulsed flow is performed in the Nakdong River to improve water quality when an algal bloom occurs. The operation affects the water quality of the Nakdong River where the surface water is used as a water supply source for the neighboring regions. As multiple agricultural intake stations are distributed from the Gangjeong-Goryeong Weir to the Dalseong Weir of the Nakdong River, it is necessary to verify the operation methods of the weirs and the effect of pulsed flow on the improvement of water quality. Observation is required in order to improve the water quality using pulsed flow, but on-site verification is difficult when a large amount of flow is discharged at once. Therefore, in this study, the water quality improvement effect of pulsed flow was verified according to the operation methods of the weirs, through numerical experiments that used the Environmental Fluid Dynamics Code (EFDC).
# Absence of Weak Localization Effects in Strontium Ferromolybdate
## 1 Introduction
The electron-electron interaction term (\(B^{\prime}T^{1/2}\)) was also applied to La\({}_{0.7-x}\)Y\({}_{x}\)Sr\({}_{0.3}\)MnO\({}_{3}\) (0 \(\leq\) x \(\leq\) 0.2) ceramics with a thickness of 1 mm [29], La\({}_{0.7}\)A\({}_{0.3}\)MnO\({}_{3}\) (A = Ca, Sr, Ba) ceramics [30] and La\({}_{0.6}\)Re\({}_{0.1}\)Ca\({}_{0.3}\)MnO\({}_{3}\) (Re = Pr, Sm, Gd, Dy) ceramics with a thickness of 1 mm--all three of which were prepared by conventional solid-state reaction [31]--as well as to SrRuO\({}_{3}\) thin films with a thickness of 6 to 8 nm deposited by pulsed laser deposition onto (100)SrTiO\({}_{3}\) substrates [24], ultrathin La\({}_{0.7}\)Sr\({}_{0.3}\)MnO\({}_{3}\) films (3.5 to 40 nm) deposited by molecular beam epitaxy [25] and metallic SrRuO\({}_{3}\) and LaNiO\({}_{3}\) thin films deposited by pulsed laser deposition with a thickness of 6 and 240 nm, respectively [32]. In the case of La\({}_{0.7}\)Sr\({}_{0.3}\)MnO\({}_{3}\) ultrathin film, crossover from \(T^{1/2}\) to ln\(T\) behavior in the low-temperature resistivity dependence with decreasing thicknesses was attributed to a change in the dimensionality of the system, from 3D for samples thicker than 20 nm to 2D in the limit of ultrathin samples. The origin of this effect is that in the case of a film thickness greater than the Landau orbit length (\(L_{H}=(\hbar/2 eB)^{1/2}\)), the system behaves essentially as 3D, while the opposite electron confinement results in 2D behavior of the system. In the case of 3D metallic and ferromagnetic SrRuO\({}_{3}\) and metallic and paramagnetic LaNiO\({}_{3}\) epitaxial thin films, the term \(B^{\prime}T^{1/2}\) was split into two terms; in the 3D case, the first term (\(b_{1}T^{p/2}\)) accounts for the weak localization, and the \(b_{2}T^{1/2}\) term stands for the renormalized electron-electron interaction quantum corrections [33]. In the 2D case, both these quantum corrections to conductivity have a similar temperature dependence. Here, only an ln\(T\) term remains. The quantum correction of conductivity in metallic LaNi\({}_{1-x}\)Co\({}_{x}\)O\({}_{3}\) (0 \(\leq\) x \(\leq\) 0.75) below 2 K follows a power law (\(BT^{m}\)), where, away from the metal-insulator transition (\(x\leq\) 0.4), \(m\) takes a value of \(m=0.3\dots\) 0.4. Such power-law conductivities are also observed on the metallic side of the metal-insulator transition for other ABO\({}_{3}\) oxides [34]. In this work, we attributed the temperature dependence of the SFMO ceramic resistivity in the absence of a magnetic field to the fluctuation-induced tunneling model and the decrease in resistivity in the temperature range of 409 K to 590 K and not to localization effects but to adiabatic small polaron hopping. Neither fluctuation-induced tunneling nor adiabatic small polaron hopping favors quantum interference. We also demonstrate that the resistivity upturn behavior of SFMO cannot be explained by the weak localization effect.
## 4 Carrier Phase Accuracy Tests  
Based on the characteristics of strong earthquake signal, a motion case with sinusoidal acceleration with a frequency of 2 Hz, and a magnitude of 2 g, was set on the simulator, and a shake table was used to simulate a strong earthquake. The carrier phase measurement accuracy of the stand-alone second-order PLL, stand-alone third-order PLL, and MEMS accelerometer-aided second-order PLL were compared based on the simulator and shake table, respectively.
# Genetic Algorithm-Based Intelligent Selection Method of Universal Shield Segment Assembly Points
## 4 Genetic Algorithm-Based Weights Optimization
### Genetic Algorithm Design
#### 4.2.4 Operation Process of the Genetic Algorithm  
The genetic algorithm is used to optimize the weights in each interval. The flow of the genetic algorithm is shown in Figure 11.  
Figure 10: Structure of chromosome.  
The steps of the proposed algorithm are as follows:  
Step 1: Working condition partition.  
Divide the working conditions into 81 intervals, and we refer to each interval as \(\Omega_{j}\)(j = 1,2,... 81). Correspondingly, the example dataset is divided into 81 intervals, too.  
Step 2: Population initialization.  
For the interval \(\Omega_{j}\)(j = 1,2,... 81), initialize the genetic algorithm population, and \(M\) chromosomes are obtained.  
Step 3: Fitness value calculation.  
The weights of the chromosome representation are brought into the objective function to select the assembly points of the example data, and the assembly point selection correct rate is used as the fitness value of the chromosome.  
Step 4: Genetic operations.  
* Select \(M-1\) chromosomes based on the fitness value using Roulette Wheel Selection.
* Randomly select two parent chromosomes to perform one-point crossover with probability \(P_{c}\).
* Randomly select a point to perform a one-point mutation with probability \(P_{m}\).
* Keep the best chromosome (the elite) of the previous population to the next, and a new population containing \(M\) chromosomes is obtained.  
Step 5: Iteration termination judgment.  
Repeat steps 3-4 until the number of iterations reaches _MaxIter_, and the optimization of the weights of the interval \(\Omega_{j}\) is completed.  
Step 6: Complete optimization of all intervals.  
Figure 11: Flowchart of the genetic algorithm.  
Go back to Step 2 until the optimization of the weights of all intervals is completed, and then generate the optimal weights \(\left\{\left(\lambda_{hj}^{*},\lambda_{oj^{\prime}}^{*},\xi_{hj}^{*},\xi_{egj}^{* }\right),j=1,2,\ldots 81\right\}\) and the total correct rate of selected assembly points \(P^{*}\). The total correct rate \(P^{*}\) is calculated from Equation (27)  
\[P^{*}=\frac{\sum\limits_{j=1}^{81}p_{j}\cdot nd_{j}}{N_{D}} \tag{27}\]  
where \(p_{j}\) is the correct rate in \(\Omega_{j}\), \(nd_{j}\) denotes the number of examples in \(\Omega_{j}\) and \(N_{D}\) denotes the total number of examples.  
The parameters used in the genetic algorithm are described in Table 4.
## 4 Experimental Results and Discussion
### Normal Travel Analysis  
The management department analyzes the travel of normal passengers to get the travel rules of normal passengers, and can better serve normal passengers in the public transportation system.  
Figure 11: Passenger 2D travel trajectory view (**a**) 2D travel trajectory with different travel, (**b**) 2D travel trajectory similar to travel, (**c**) 2D travel trajectory of normal card travel, (**d**) 2D travel trajectory of abnormal card travel.  
More importantly, analyzing passengers who may be associated can determine if they have an association and deduce their relationship.  
Most passengers travel with a certain regularity, and there are fixed patterns in travel time distribution and travel area distribution, and also have certain preferences for the choice of travel vehicles. These characteristics can be seen when using our method to analyze normal passengers. By analyzing the travel of normal passengers, it is possible to summarize the travel rules of passengers and find out the association between different passengers. We can deduce their relationship based on the association between passengers.  
We explored the distribution of passenger travel near Xierqi and found that card numbers 23185887 and 88792712 have the characteristics of infrequent travel, but have the most frequent travel routes. We infer that they may be normal passengers and use our method to explore the association between them to deduce their relationship.  
The result is shown in Figure 12. It shows the travel of two normal cards with card numbers 23185887 and 88792712. It can be visually observed that there are overlaps on the 3D trajectory; their 2D trajectory and the chord view of the entry and exit stations are similar. Their travel time is shown in the brown part of the picture, almost completely overlapping, going out from 7:00 to 8:30 in the morning and returning from 15:00 to 17:00 in the afternoon. It seems that they are not office workers. The initial inference is that they are the elderly who exercised in the morning and afternoon.  
From Figure 13, the bubble chart view shows that the card numbers 23185887 and 88907211 have similar preferences in the choice of travel vehicles. They usually travel by bus 384, bus 543, bus 560 and bus 996. Due to the similarity of their travel time, it can be inferred that they are likely to be passengers traveling together every day, maybe friends living in the same community, or maybe a couple. Further analysis of the distribution of entry station and exit station of the two cards, as shown in Figure 14, found that their travel stations are almost the same, the most frequent route taken is bus 384 from Xiaoniufang to Forest Park and from Forest Park back to Xiaoniufang. Therefore, we can be sure that they live near Xiaoniufang and exercise in the forest park every day. It has been verified that the area of the Xiaoniufang does belong to the residential area. This case can verify that our method is very suitable for analyzing the association of normal passengers.  
Figure 12: Normal passenger analysis.