esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Dataset
### Description of Deformation Characteristic Factors of the Foundation Pit
#### 2.3.3 Impact of the Stress Monitoring Programs  
The support axial force is generated by the support and restraint of the soil body by the support structure during the foundation pit's construction, reflecting the mechanical characteristics and stability of the soil body around the pit. Earth pressure is one of the essential causes of the deformation of the enclosure structure. When the excavation depth of the pit increases, the constraint of the soil body decreases, and the effect of earth pressure on the pit enclosure structure increases. The size and distribution of earth pressure will directly affect the soil body's stress state and deformation characteristics around the pit,affecting the stability of nearby buildings, pipelines, and roads. Based on the distribution and size of earth pressure, the impact of deformation of the enclosure structure can be effectively reduced by adopting corresponding earth-pressure control measures, such as setting up reasonable support structures, adopting methods of reinforcing the soil body and reducing the groundwater level to ensure the safety of the foundation-pit construction and the stability of the surrounding environment.  
Impact of the Displacement-Monitoring Results  
Structural settlement during subway construction is the vertical downward displacement of a structure's foundation caused by soil excavation, consolidation, and changes in the water table. This settlement affects the surrounding environment, causing problems such as building damage, pipeline rupture and deformation, ground subsidence, and vibration of neighboring buildings [24,25]. The data can be observed and analyzed promptly. Certain connections or associations between pipelines and the ground will also affect the surface somewhat [26]. Column settlement is the phenomenon of sinking of columns or other building foundations. When the soil beneath the ground deforms or settles, the soil on which the columns are located is also affected, causing the columns to sink [26,27]. Column settlement is one of the manifestations of envelope deformation.  
In summary, during the construction of foundation pits, land is excavated and moved, resulting in structure deformation. Some deformation can be obtained directly from the main structure of the foundation pit, such as the crown beam and the column, while others can be indirectly obtained from the surface around the foundation pit, structures, and underground pipelines. There is a link between the deformation phenomena and the enclosure deformation of the foundation pit.
# Analysis of Four Years of Global XCO\({}_{2}\) Anomalies as
## 2 Datasets
### Solar-Induced Chlorophyll Fluorescence (SIF)  
We analyze the solar-induced chlorophyll fluorescence, which is also measured by the OCO-2 instrument [25] at 757 nm and 772 nm. We use the cloud-filtered 757 nm data from the OCO-2 litte files V8r that include quality filtering and bias correction. SIF can serve as a proxy for the vegetation gross primary production (GPP) [26], defined as the synthesis of organic compounds from atmospheric CO\({}_{2}\) and principally occurring through the process of photosynthesis. Therefore, SIF data are expected to provide information on the contribution of the CO\({}_{2}\) uptake by the vegetation on the XCO\({}_{2}\) anomalies.
# A Systematic Review of Intelligent Systems and Analytic Applications in Credit Card Fraud Detection
## 4 Emerging Fraud Detection Techniques
### Agent-Based Modelling in CCFD  
In addition to EBM, ABM approaches have been recently used in several financial areas such as risk management, credit scoring, credit pricing, early warning, and macro economy. There are several ABM studies especially on the effects of credit risk [58; 59]. Also, as stated in [60; 61], there are some studies on how credit transactions affect banks, customers, and financial indicators. All stakeholders on credit network systems are brought together and the macro/micro effects are examined with the ABM [62; 63; 64]. For example, agent-based macroeconomic models have been developed in order to see the financial effects [65; 66]. Due to this capability, it is thought that agent-based approaches can be employed to contribute to the modelling of CCFD studies. With the ABM approach, fraudulent behaviors can be depicted by examining interactions on the credit card fraud data sets. Table 4 shows the studies on ABM in the financial world. The use of ABM modelling approaches in different financial issues is increasing day by day. In the financial world, the use of ABM approaches is increasing in studies on analyzing behavioral information and evaluating impacts. In financial modelling studies, hybrid structures can be developed by combining different sub-branches of the ABM approach with predictive algorithms. The reinforcement learning approach, which is a sub-branch of ABM, can be combined with the deep learning process to produce predictive outputs such as SL. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Title** & **Topic** & **Comment** \\ \hline \multirow{6}{*}{([PERSON], 2014) [58]} & Credit Risk—an Agent & & In the study, an agent model was developed to examine the effect of bankers’ post credit decisions on bank credit losses caused by lending to corporate customers. The model results show that post credit decisions have significant effects on bank loan losses. \\  & Model of Post Credit & Finance–Banking–Credit & bankers’ post credit decisions on bank credit losses caused by lending to corporate customers. The model results show that post credit decisions have significant effects on bank loan losses. \\  & Decision Actions and Credit Losses in Banks & & & In the study, the financing difficulties faced by SMEs were analyzed and an artificial credit market was created with agent-based computational to imitate credit transactions. The model results show that the number of collaterals, the average probability of success of the projects, and the principal interest ratio have an important effect on the average profit of the bank, the bank capital, the total interest ratio, the number of borrowers, the size of the credit, and the degree of the credit score. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Studies on agent-based modelling in the financial world.
## 2 Proposed Method
### Data Normalization through NExT  
The natural excitation technique (NExT) [27] uses auto- and cross-correlation functions for two measurements of a structure to replace an impulse response for modal testing, enabling a structure to be tested in an ambient environment. The correlation between the two measurements is a superposition of decaying oscillations, which are characterized by the natural frequencies and damping ratios of the structure.  
The correlation function \(R_{ij}(T)\) between two measurements at locations \(i\) and \(j\) in terms of time lag \(T\) is expressed as  
\[R_{ij}(T)=\sum_{r=1}^{N}\left[\frac{\phi_{r}^{r}Q_{r}^{r}}{m^{r}\omega_{d}^{ r}}\exp(-\xi^{r}\omega_{n}^{r}T)\sin(\omega_{d}^{r}T+\theta_{r})\right] \tag{1}\]  
where the superscript \(r\) denotes a particular mode from a total of \(N\) modes, \(\phi_{i}^{r}\) is the mode shape at location \(i\) at the \(r\)-th mode, \(m^{r}\) is the modal mass in \(r\)-th mode, \(Q_{i}^{r}\) is constant associated with response at node \(j\), \(\omega_{n}^{r}\) is the \(r\)-th natural frequency, \(\xi^{r}\) is the \(r\)-th modal damping ratio, and \(\theta_{r}\) is the phase angle associated with the \(r\)-th modal response.  
In this study, the correlation functions for all sensor locations were obtained by inverse Fourier transform of cross power spectral density (CPSD) between two measurements at \(i\) and \(j\) as  
\[R_{ij}=\Im^{-1}\{S_{ij}\},\ R=\left[\begin{array}{cccc}R_{11}&R_{12}&\cdots &R_{1m}\\ R_{21}&R_{22}&\cdots&R_{2m}\\ \vdots&\vdots&\ddots&\vdots\\ R_{m1}&R_{m2}&\cdots&R_{mm}\end{array}\right]\in\Re^{m\times m\times\frac{n}{2}} \tag{2}\]  
where \(S_{ij}\) is the CPSD between measurements at \(i\) and \(j\), \(m\) is the number of sensors, and \(n\) is the number of discrete Fourier transforms. The correlation functions in Equation (2) are used as the input data for a 1-D CNN. The proposed data normalization can accept measurements with different data sizes and sampling rates and normalize them to the same data size for flexible deep neural network applications. In the input layer, the normalized correlation functions are reshaped to \(m^{2}\times\frac{n}{2}\) for training the proposed deep  
Figure 1: Proposed damage identification method.  
neural network. For example, if nine accelerations are measured and the number of discrete Fourier transforms is 2048, the size of the normalized cross-correlation is 81 \(\times\) 1024 for nine acceleration measurements on a beam, regardless of the number of samples for the measurement. The normalized input is fed into the 1-D CNN that is introduced in the following section.
## 2 Materials and Methods
### Simulation Platform and Participants  
The experiments utilized a six-degrees-of-freedom (6-dof) vehicle motion simulation platform, complete with UC-win/Road 13.0 software (as shown in Figure 1). This platform includes a vehicle cockpit, steering wheel, brake and accelerator pedals, and automatic transmission, creating a realistic driving experience through visual, auditory, and kinematic feedback. The visual system incorporates three high-definition screens, offering a 130\({}^{\circ}\) horizontal and 40\({}^{\circ}\) vertical field of view. The auditory system simulates road and vehicle exhaust sounds, while the motion system delivers spatial 6-dof movement, allowing drivers to feel acceleration, turning, and sideslip and mimicking the sensation of vehicle vibration and road bumps.  
Participants were selected based on the following criteria:  
1. Good physical health, ensuring their ability to participate fully in the study;
2. Vision of 20/25 or better, corrected or uncorrected, with no eye diseases. This ensures all participants meet the necessary visual acuity requirements for driving;
3. Ownership of a valid Chinese driver's license for a minimum of 3 years, demonstrating a certain level of driving experience and skill.  
Furthermore, to minimize the influence of individual traits such as driver age and experience on the results, this study selected young drivers of similar ages. Following these screening criteria, a total of twenty-five drivers, comprising 14 males and 11 females, participated in the simulation. Their ages ranged from 25 to 33 years old (M = 27.38, SD = 1.78), with driving experience varying from 3 to 7 years (M = 4.97, SD = 1.01). The subjects were neither colorblind nor color-weak, and they had no physiological or psychological diseases. All drivers had not experienced similar simulation experiments and did not know the purpose of the experiment.
## 1 Introduction
In this paper, we present a 3D skeletonization algorithm for 3D mesh models using a partial parallel thinning algorithm and a 3D skeleton correcting algorithm. The proposed algorithm uses pre-defined removing and recovering templates. The removing templates are derived from those proposed in [21]. The partial parallel 3D thinning algorithm separates 62 symmetrical removing templates to two groups based on symmetry. It thins a model with the templates of one group in each thinning procedure. The 3D skeleton correcting algorithm uses six correcting templates to inspect the disconnected voxels in the skeleton and corrects them. The templates are used to inspect 26-adjacent voxels of a current target voxel after it was removed. The correcting algorithm recovers the target voxel based on the recovering templates and an additional criterion that addresses the distances between the 26-adjacent voxels. To obtain a unit-width (one voxel wide) skeleton, we apply a post-process called the valence-driven spatial median (VDSM) algorithm [24] to the extracted skeleton. Several comparisons of the skeletons extracted by different skeletonization algorithms are shown in the experimental results section. The experimental results show that the proposed algorithm can extract the skeleton of each branch of a model and preserve its connectivity. The remainder of this paper is organized as follows. In Section 2, we discuss related work. A brief overview of the basic theory of the adjacencies in a 3D binary array is given in Section 3. The methodology of the partial parallel 3D thinning algorithm is described in Section 4. In Section 5, the 3D skeleton correcting algorithm is introduced. A short review of the VDSM algorithm is given in Section 6. The experimental results of skeletons extracted by the proposed method and other methods are shown in Section 7. Finally, we conclude with the proposed method in Section 8.
## 2 Materials and Methods
### Boundary Conditions  
In order to solve the system of partial differential equations (PDEs), the boundary conditions for fluid dynamics and heat transfer module would be needed. The no-slip boundary condition (\(u=0\), where \(u\) is the velocity field) was assigned to the inner boundary of the device, which indicated the presence of adhesion between fluids and the solid wall. In the heat transfer module, the surrounding wall was assumed as thermally insulated (\(-n\cdot q=0\), where \(q\) is the heat flux). The inlet nozzles for injection of cold plasma had the form \(u_{left}=u_{x+}\) and \(u_{right}=u_{x-}\) for the left and right nozzle as shown in Figure 1a,b, respectively. The values of \(u_{x+}\) and \(u_{x-}\) were set to be 0.1 ms\({}^{-1}\) for the two different cases used to study the mixing phenomena and the level set variable at the inlets was \(\phi=0\); this indicated the presence of plasma. Considering the blood inlet nozzle (\(\phi=1\) at the inlet nozzle), the inlet velocity of the blood \(u_{upward}=u_{y+}\) was set as 0.1 ms\({}^{-1}\), so that mixing chamber filling and steady-state could be fully obtained during the simulation time of 10.0 s. In addition, linear discretization was  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **Density** & **Thermal Conductivity** & **Specific Heat Capacity** & **Dynamic Viscosity** \\ \hline Blood & 1060 kg/m\({}^{3}\)[44] & 0.505 W/m\(\cdot\)K [45] & 4000 J/kg\(\cdot\)K [46] & 4.00 \(\times\) 10\({}^{-3}\) Pa\(\cdot\)s [46] \\ Carrier gas [28] & 0.160 kg/m\({}^{3}\) & 0.200 W/m\(\cdot\)K & 5200 J/kg\(\cdot\)K & 2.10 \(\times\) 10\({}^{-5}\) Pa\(\cdot\)s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of material properties used in the present work.  
Figure 1: Mixing chambers (**a**) with and (**b**) without obstacles, with inlets and outlets shown.  
employed using an average element quality of \(\sim\)2 \(\times\) 10\({}^{-4}\) m. Numerical convergence was verified by considering simulations with different grid resolutions and no inconsistencies were observed in numerical convergence and the final results. The mesh figures for the two configurations of the mixing chamber are shown in Figure 2.
## 4 Test Bed and Materials
### Materials  
As described in the previous section, the channel emulator implements 5 different TDL models, which are inspired by the TDL models defined in [20] for 3 GPP but which have been modified using the Nagakami-m fading model rather than Rayleigh or Rice model as in the initial definition of [20] (i.e., 3 GPP-like models). With the addition of the No Fading case, 6 different classes of wireless signals are used for channel identification. They are called in the subsequent parts of this paper (1) No fading, (2) TDL-A, (3) TDL-B, (4) TDL-C, (5) TDL-D, and (6) TLD-E.  
To support a generalization of the results, the signals were processed with different values of m in the Nagakami-m fading model for each TDL class. The values of m = 0.5, 1, 2, 3, 4 were used.  
A set of 1680 weather radar signals were generated for each value of m and for each of the 6 classes (e.g., an equal number of radar pulses was generated for the No fading case for consistency) for a total of 1680 \(\times\) 5 \(\times\) 6 = 50,400 signals samples on which the TFTs and the CNN algorithm was applied.  
In addition, we evaluated the impact of different SNR levels expressed in dB, as previously mentioned. Starting from the baseline data set at SNR = 30 dB, different data sets were created with decreasing values of SNR in dB: \(-\)15 dB, \(-\)10 dB, \(-\)5 dB, 0 dB, 5 dB, 10 dB, 15 dB, 20 dB, and 25 dB. This range of values was chosen to evaluate the impact of noise with a good level of granularity (5 dB steps) and on the basis of the empirical observation that at \(-\)15 dB, every algorithm proposed in this paper reaches almost random choice classification, and it is pointless to evaluate even lower levels of SNR in dB. In the rest of this paper, this data set is called the weather radar data set.  
Due to the similarity of the TDL fading models, the classification of this data set is particularly challenging (on purpose) as we do not limit the scope of the study to LOS/NLOS binary classification, but the objective is to distinguish different fading models as well.  
The visualization of one sample of each of the 6 different fading conditions and the related CWT transform is shown in Figure 3, where it can be seen that some representations are similar (e.g., TDL-E, TDL-F) from a visual point of view (this will also be assessed in the Section 7).
## 6 Results
### Completeness  
The completeness of the diagnostic predictions was assessed using a scale from 0 to 100, where 0 indicates incomplete, and 100 is fully comprehensive. We measured the completeness of the predictions made by the three LLMs with low and high-temperature levels. The results are presented in Figures 6 and 7, respectively.  
Figure 5: The recall was obtained by each Diagnostics Model with high temperature (2) and zero-shot, few-shot, and RAG prompt.  
Figure 6: Completeness score for each Diagnostics Model with a temperature of 0 and diverse prompt techniques.  
Figure 7: Completeness score for each Diagnostics Model with a temperature of 2 and zero-shot, few-shot, and RAG prompts.  
At a low temperature (0), Gemini Pro and Mixral 8x22b obtain similar completeness results, with median values close to 80. However, Mixral 8x22b with few-shot prompting exhibits more diversity and lower results in its completeness scores. Medichat Llama3 generates the least complete diagnoses, particularly with few-shot prompting, where the average score is 20. Conversely, Medichat Llama3 achieves a mean completeness score of approximately 70 with the zero-shot and RAG techniques.  
At a higher temperature (2), Figure 7 shows greater diversity. Gemini Pro consistently achieves the highest completeness scores, maintaining a median value of around 80 across all prompt techniques. Medichat Llama3 exhibits increased diversity in its results, with significantly better completeness scores for the few-shot technique, reaching a mean score of around 40. Mixral 8x22b also shows more variability with the zero-shot and few-shot techniques, with scores mostly between 60 and 80, while it achieves a completeness score of 80 with the RAG technique, similar to Gemini Pro.  
Overall, Gemini Pro and Mixral 8x22b generally obtain high completeness across different temperatures and techniques, while Medichat Llama3 shows more variability, particularly with the few-shot prompting technique.
# Random Access for Underwater Acoustic Cellular Systems
## 1 Introduction
Thus, in underwater acoustic systems, it will be beneficial to allocate a high frequency band to the nearby UE whereas a low frequency band is allocated to far UE. The capacity of the system can be maximized using a channel allocation scheme that utilizes the location of the UE. In this paper, a design technique of RAP for underwater acoustic cellular systems is proposed. First, it is shown that the conventional RAP used in long term evolution (LTE) systems is not appropriate for underwater acoustic cellular systems because acoustic waves propagate in water at a very low speed, which is five orders of magnitude smaller than the propagation speed of radio waves in the air. Subsequently, two different types of RAPs (RAP 1 and RAP 2) are proposed to detect the identity of UE and estimate the time delay between the UBS and UE at the physical layer. RAP 1 is generated using a Zadoff-Chu (ZC) sequence where the identity of the UE is mapped to its root index, whereas RAP 2 is generated using a linear frequency modulation (LFM) waveform where the identity of the UE is mapped to its frequency sweeping parameter and frequency shifting parameter. Ambiguity functions (AFs) of RAP 1 and RAP 2 are analyzed to investigate their correlation properties under the effect of time delay and Doppler shift. Subsequently, the definition of AF is extended to a cross-ambiguity function (CAF) to analyze the cross-correlation properties of the proposed RAPs with different IDs of UE under the effect of time delay and Doppler shift. The performance of RAP detection is investigated by analyzing the detection probabilities and false alarm probabilities of the proposed RAPs in a Doppler environment. By evaluating the performances of the proposed RAPs in various situations, it is concluded that RAP 2 is more suitable for underwater acoustic cellular systems. The AF and CAF analytically obtained in this paper are shown to be similar to those obtained using experimental data. The rest of this paper is organized as follows: in Section 2, we provide an overview of an underwater acoustic cellular system including underwater frequency allocation map and downlink frame structure. In Section 3, RAP 1 and RAP 2 are proposed for underwater acoustic cellular systems after reviewing the conventional technique used in LTE cellular systems. In Section 4, we analyze the detection probability and false alarm probability of the proposed RAPs in a Doppler environment. In Section 5, the experimental results are compared with the analytical results obtained in Section 3. The conclusions drawn are presented in Section 6.
## 2 Approach Description
### Hardware Design
Figure 6 shows the hardware connection diagram of the measurement system. A separate direct current (DC) power supply is required for the laser transmitter and the motor. The MCU, the capacitive grating displacement sensor, and the CCD are directly connected to the host computer, and are electrically driven by the host computer. The laser emitted by the laser transmitter is reflected by the wall of the holes and is received by the CCD. After the optical signal received by the CCD is processed into image information, this image signal will be finally output to the host computer. Under the control of the MCU, the motor gradually pushes the probe into the hole to be tested. When the laser beam has completely scanned the entire hole, the motor drives the probe to retract and return to the original position. The capacitive grating displacement sensor transmits the displacement data to the host computer in real time. The port for clearing the displacement data of capacitive grating displacement sensor is connected with the MCU. When this port is at a low level, the displacement data transmitted by the capacitive grating displacement sensor is always zero; when the port is at a high level, the displacement data can be recorded normally.