esa-sceva/satcom-chunk-collection huggingface dataset:
# I-BIM Applied in Railway Geometric Inspection Activity: Diagnostic and Alert
## 1 Introduction
The rail inspection activity includes the practice of examining rail tracks for faults that could lead to disastrous failures, as track defects can originate accidents on railways [15]. The materials applied on the railway's components, mainly, rails, sleepers, fastenings and ballasts need inspection at frequent intervals [16]. The maintenance of the high quality, adequate efficiency and long-lasting functionality of the railway transport infrastructure requires a well-planned periodic inspection of the railway track components. Therefore, it is essential to establish regular inspections and collect data that constitute the key to analyzing the state of conservation of the railway components. Important ongoing efforts have been made in the sector of railway track modeling of material deterioration to analyze the service life of the infrastructure and avoid failures of the component [17]. In addition, some recent studies involving BIM and Dynamo in a rail context can be mentioned. [PERSON] et al. [18] investigated the use of the unmanned aerial vehicle for the measurement of the track gauge, as a distinct inspection procedure when compared with traditional methods, supported in a pre-built rail BIM model matched with the extracted rail features. [PERSON] et al. [19] studied the mechanical properties, which depend on the service conditions, secondary to estimating with accuracy the mechanical behaviors of the rails that are positioned between the rails and the sleepers, minimizing the transmission of vibrations and noise. [PERSON] et al. [20] presented a proposal to check geometric defects in railway tracks based on the use of a Dynamo script for identifying defects in railway tracks. [PERSON] et al. [21] investigated the detection of rail cracks based on the gathering optimization of features, by verifying the alerts that are identified in the environment of the railway. [PERSON] et al. [22] presented a study concerning the derailment aspect that can result in high death tolls or the loss of property, proposing a system to understand the communication between the train and rail track in real-time. [PERSON] et al. [23] analyzed the BIM implementation in railway infrastructures focused on railway track rehabilitation activity. The efficient management, processing and generation of inspection data require the use of computational tools with the capacity for facilitating decision-making regarding the efforts around the maintenance, rehabilitation or modernization of railways. It is the case of the present study focused on the implementation of BIM through the inspection of diagnostic and the alert activity [9]. In this study, a working research methodology was proposed referring to the related literature, the BIM modeling process and the development of a Dynamo script and its application and evaluation.
## 3 Results
### Research on the Feasibility of Photographic Color Extraction
#### 3.2.2 Comparison between the Colorimeter (CM) and Photography Measurement (PM) Results  
We measured the 19 samples' color data on the back of the facet gem, using a colorimeter and photography, respectively (because some samples are too small to be measured by colorimeter). \(\Delta\)E* could be used to represent the color difference between the CM and PM. 19 samples had an average color difference of 9.7. The point at which the human eye detects a change in hue is \(\Delta\)E* = 3. The human eye detects a color variation when \(\Delta\)E* is between 3-6, yet this difference is tolerable. The color difference is unacceptable when \(\Delta\)E* > 6 [28]. The average color difference between the samples was larger than six, indicating that the two systems' measurements of the colors of the tsavorite samples were significantly different from what the human eye would see. Using simulated color blocks, the color difference between the two approaches was demonstrated in a more perceptible way (Figure 7).  
These differences may be due to the different light environments (the colorimeter uses an integral sphere and photography uses a plane light source) and the shooting angle influence (the colorimeter is vertical and the photography uses a 2 degree angle). If we unify the light environments, photo angles and camera parameters, as well as some other factors, we can establish a new photo color measurement system. As long as the data are measured using the same system, they are comparable. For the general evaluation and rating agencies, a plane light source is enough, but if you want to obtain the real color of the sample, you need an integral sphere environment for accurate testing.  
The 9.7 color difference is easily visible with the unaided eye, and more over 95% of the PM photos are thought to be more like the genuine thing. Therefore, color pickups are a useful instrument for determining the color of colored gemstones.  
Considering R2, Figure 8 shows the optimal regression of PMs and CMs. L*, a*, b*, C*, and h* all have R2 values more than 0.86, indicating a good fit. As a result, the PMs might forecast 86.99% or more of the color values determined by the CMs. So we can study tsavorite colors by taking pictures and picking out the colors.  
Figure 7: Simulated color blocks were used to more clearly illustrate the color discrepancy between photography data and the colorimeter result.
## 4 Test Results of the Active Steering Control
### Steering Angle  
The steering control test was performed according to the test conditions in Section 3.3 to analyze the steering performance according to the steering angle on the curved section. Figure 11 shows the steering angle test results measured by the steering angle sensor to verify that the active steering control test conditions are properly implemented. Table 3 shows the mean values for the steering angles when the steering bogie runs in the circular section of the test section.  
Figure 11: Measured steering angle according to steering control condition.  
Figure 9: Steering angle sensor.  
The steering angle of passive condition with insufficient steering function is 0.029 degrees. This indicates that the measured value is very low at 7.3% of the target value, considering that the target steering angle required to pass the R300 curve is 0.4 degrees according to Equation (1). As the steering angle implementation level increases with active steering control, the steering angle is approximated to the test conditions. That is, in the case of Active (50), it is 0.208 degrees., which is 52% of the target steering angle. In the case of Active (100) which performs 100% steering control, the measured value is 0.393 degrees., which is controlled well as 98.3% of the target steering angle.
# Soft Decision Decoding with Cyclic Information Set and the Decoder Architecture for Cyclic Codes
## 3 Soft Decision Decoding Algorithm Based on Cyclic Information Set  
A low complexity soft decision decoding algorithm based on a series of cyclic information sequences, called an information set, is proposed in this paper. The proposed decoding procedure is decomposed into two steps. First, using the cyclic property of cyclic codes, generate predetermined multiple cyclic information sequences, which form the cyclic information set. Then, a group of \(C\) candidates for each information sequence is tested to find the most possible codeword.
# Bi-Stability in Flexure Beams: Introducing a Setup for Experimental Characterization
## 3 Results and Discussion
### Flexure Beams Deformation
Figure 8 shows the forms of the flexure beams after the cyclic loading experiments. Only c10s maintain their original straight form after the five cycles. While samples c15 and c20 bent slightly at the center, samples c30 and c40 show great deformations, demonstrating that their elastic limit was exceeded during the experiments. Figure 8f visualizes the different degrees of deformation among calibers, where only the c10 flexures remain straight and permanent deformation increases with width. From the experiment results, it can be concluded that c10 flexures (0.25 mm) in this thickness-length ratio are unstable and not suitable for real-life applications. Even though they do not deform after five loads, they present inconsistent results regarding critical forces and snap-through points that are improbable to avoid since they come from small precision errors typical in physical devices. On their part, c30 and c40 samples that are 0.75 mm and 1.00 mm, respectively, require larger input forces and snap at longer distances, which would cause a demand for more energy input and storage. However, what makes c30 and c40 not convenient is their deformation grade during snapping. The cyclic loading experiments prove that flexure beams of 0.75 mm and 1.0 mm exceed their elastic limit fast and even present partial fractures in the stress points. c20 flexures (0.50 mm) demonstrate that they have mechanical properties that make them suitable for real applications: their deformations are slight enough to maintain their functionality. However, the critical force variations within the same thickness are large, and the distance traveled before snapping is long, which would require more energy input time. Finally, the deformation of c15 samples (0.38 mm) is minor, making it suitable for practical uses. The 24.553 mm linear displacement before snapping is also appropriate since it is close to half of the total length of the slider (45 mm), which indicates that the system would only require half the energy input time to achieve bi-stability.
# On the Modeling and Analysis of Brittle Failure in Existing R/C Structures Due to Seismic Loads
## 2 Procedures for Simulating Strength Mechanisms in Substandard R/C Buildings
### Simulation of Shear Failure of the Column Web  
Shear failure of an R/C element web develops when the transverse reinforcement cannot provide adequate supporting shear resistance to displacement reversals beyond flexural yielding. In a third-tier seismic evaluation of existing R/C buildings, simulation of shear failure of the column web is essential, as this mechanism is linked to brittle member failure that can compromise the load-carrying capacity of columns and potentially lead to the collapse of the building.  
The best approach to simulate the brittle response of R/C columns to shear failure of the web is to use a non-linear, zero-length element (the two end element nodes are distinct from each other but placed at the same location) at mid-height of the column's clear (deformable) length, \(H_{cl}\) (see Figure 1). Element response in each section direction is defined by the multilinear Moment-Rotation (\(M\)-\(\theta\)) relationship illustrated in Figure 3, where \(M_{v}=V_{v}\cdot(H_{cl}/2)\) is the bending moment associated with the shear strength \(V_{v}\), of the column web in the examined direction. This \(V_{v}\) is calculated according to the RSA system from Equation (A4) in the Appendix A. Coefficient \(r_{v}\) in Figure 3 is the resistance ratio to shear failure of the column web, \(r_{v}=V_{v}/V_{flex}\), where \(V_{flex}\) is calculated according to Equation (A3) in the Appendix A. Note that the column normalized axial load appearing in the expressions given in the Appendix A (denoted as \(v_{d}\)) is obtained from static analysis of the examined R/C building to gravity loading and for the vertical component of the seismic load combination \(G_{k}+\psi_{E}\cdot Q_{k}\), as perEN1998-1 [2].  
Figure 2: Simulation of the inelastic flexural response of R/C columns and beams using fiber discretization of cross-sections.
## 4 Mechanical Response of Pipeline Under Mining Influence
### Characteristics of Pipeline Deformation in the Site of the Mining Area  
When soil settlement occurred, the deformation curve was estimated in terms of a pipeline reconstruction algorithm based on a distributed fiber optic analyzer [31; 32]. As shown in Figure 9, the settlement curve of the pipeline was derived.  
From Figures 9 and 10, it was observed that the deformation of the pipeline and the surrounding soil was not completely synchronized. An examination of the data revealed that the settlement values of the pipe at various points within the mining zone were slightly less than those of the surrounding soil. In the initial stage, the settlement of the pipeline paralleled that of the surrounding soil, with maximum subsidence occurring at the middle of the pipe. When the mining ratio reached 50%, the pipe's maximum settlement  
Figure 8: Variation trend of axial strain in the various section of the pipeline: (**a**) top of the pipeline and (**b**) bottom of the pipeline.  
was measured at 2.22 mm, whereas the soil's maximum settlement was slightly higher at 3.43 mm. This discrepancy indicated a separation between the pipe and soil, with the pipe exhibiting a suspended state over a limited area. As the mining ratio increased, the degree of pipe-soil separation gradually escalated, accelerating markedly after surpassing the 50% mining ratio threshold. The separation peaked at an 86% mining ratio, where the maximum relative displacement between the pipe and soil reached 5.93 mm, before decreasing rapidly to 4.27 mm as mining continued. This phenomenon emerged during the overlying strata transport process instigated by mining activities. Due to the significantly greater stiffness of the pipe compared to the surrounding soil, the soil directly above the pipe settled less, influenced by the mechanical properties of the stiff pipe. The pipe exerted a supportive effect on the overburden soil, resulting in lesser settlement at the pipe's sides and its base, thereby creating a soil-arching effect around the pipe. In the initial stage, this effect was not pronounced due to the minimal impact of mining on the overlying strata movement. However, as mining progressed into its middle stage, this phenomenon became more apparent and rapidly influenced the mechanical properties of the pipe, leading to its apparent suspended state.  
Figure 10: Maximum relative displacement between the pipeline and soil.  
Figure 9: Settlement distribution of the pipeline. (P corresponds to the pipeline displacement curve, and L corresponds to the soil around the pipeline displacement).
## 4 Method
### Data Generation for Pulse Type  
As shown in Figure 1c, due to the short pulse length of the pulse type data itself and the large jump degree between the data, it cannot be classified as the above oscillation and step types. This paper adopts the step-by-step generation method. Firstly, the positive sample generated by the generative adversarial network is used as the basic trend of the negative sample, and then the location of the pulse is calculated through Formula (7) for the original data, and the pulse length is calculated:  
\[y_{3}=\left\{\begin{array}{l}1,\left|x_{t-1}-x_{t}\right|\
eq 0\\ 0,\left|x_{t-1}-x_{t}\right|=0\end{array}\right. \tag{7}\]  
When \(y_{3}\) is 1 for the first time, it is the start position of the pulse; when the second time \(y_{3}\) is 1, it is the end position of the pulse. Between two times \(y_{3}\) is 1, the pulse length can be obtained.  
The value of pulse position \(t\) and pulse length \(\Delta t\) is converted to percentage. Then, read the pulse position value and generate new pulses. The sample distribution is calculated by the probability formula, and the positions of the high and low binary values in the pulse are arranged according to the sample distribution to generate a new pulse. Finally, according to the location and length of the original data, the pulse is inserted into the basic trend to form a new attack sample. Figure 3 shows the overall flow chart of the algorithm.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Number** & **Events** \\ \hline
1 & Initialize learning rate, attenuation rate, update times, cumulative gradient and cumulative gradient square. \\
2 & Receive current network parameters. If it is the first update, save the network parameters of each layer of the model. \\
3 & Update training times and calculate gradient. \\
4 & Calculate the exponential moving average of gradient value and the exponential moving average of square gradient value. \\
5 & Correct deviation. \\
6 & Update parameters with Equation (6). \\  & Repeat the above steps 2 to 6 until the training is completed. If the judgment result of the turning point is 1, the network parameters of each layer of the current model are saved after Step 2, and replaced by the previous record. \\ \hline \hline \end{tabular}
\end{table}
Table 2: The main steps in the optimizer.  
Figure 3: The overall flow chart of the algorithm.
## 4 Conclusions  
We have demonstrated that when using pump pulses in the transient regime, extra peaks occur in the multi-frequency Raman generated spectra. These peaks occur only on the red side of the Raman orders when pumped with linearly chirped pump pulses. The frequency shift of the red-detuned peaks does not follow the instantaneous frequency separation of the pump pulses indicating that non-resonant four-wave mixing is not responsible for the red-shifted peaks. On the other hand, the amount of shift is intensity dependent, which implies that the extra peaks are a result of Stark shifting of the Raman levels. We noted that pumping with chirped pulse duration of just 1 ps in the transient regime gives very similar results to the adiabatic regime where the molecular states are expected to evolve into the coherent states. In particular, more Raman orders are generated when the instantaneous frequency separation of the pump beams are slightly red-shifted from the Raman transition, but blue shifting does not change the spectrum from the on-resonant pumping. Linear Raman scattering from a two-photon Rabi frequency shifted coherent states can explain the generation of the red-shifted shoulders that appear in the MRG spectra. In comparison with chirped laser pumping, unchirped pulse pumping with detuned peak frequencies displays very different behavior. In particular, not only are the orders red-shifted for red-detuned pumps, but the orders are blue shifted when the pumps are blue shifted, indicating that non-resonant frequency mixing becomes a dominant process when the pulses are unchirped. Although a two-photon Stark shift can explain the red-shifted shoulders and also why blue shifting of the pumps allows a non-resonant process to dominate, it remains to be determined why a constant blue shift for all orders is observed with blue detuned peak pump frequencies.
## 2 Materials and Methods  
To understand the use of the isotopic and nuclear techniques analyzed, a traditional literature review was carried out to determine both the \"state of the art and the state of the matter\", that is, the conception of their application and use, analyzing whether they are combined or integrated in their use or if they are developed in isolation in search of particular results on a single type of degradation (soil or water).  
The selection of research works took into account the timing of their completion. The search for scientific articles was conducted by consulting several databases, such as the Web of Science, Scopus, Scientific Electronic Library Online (SciELO), ResearchGate, and Google Scholar. The search for current studies covered the period 2017-2024. Publications were reviewed to establish research trends, referring to how researchers approach soil and water source degradation assessments by applying three isotopic and nuclear techniques (FRN, FP, and IH). Based on these criteria, it was established as a hypothesis that \"It is possible to integrate, sequentially, using the synergistic convergence of isotopic and nuclear techniques, e.g., FRN, FP, and IH, and take advantage of the results of erosion and sedimentation obtained by radionuclide concentrations (as primary data from the analysis of the tracer fingerprint used) and the results of both contributions (as input data to the analysis of flow dynamics) to evaluate processes that degrade soil and water resources, reducing their impacts on terrestrial and aquatic ecosystems\".  
Use of the Universal Soil Loss Equation and Isotopic and Nuclear Techniques to Assess Soil and Water Resource Degradation  
Erosion must be critically studied as a process relevant to agricultural lands and water resources. The integrated study of its cycle, transport, and sediment accumulation facilitates understanding its landscape dynamics [31].  
The universal soil loss equation (USLE) and its family of later models, the revised equation (RUSLE), and the modified equation (MUSLE) have been used to determine rainfall erosivity and soil erosion susceptibility factors. The use of these equations, such as RUSLE, is now commonplace.  
Isotopic and nuclear techniques related to FRN and FP using several tracers like chemical elements, compound-specific stable isotopic (CSSI), etc., and IH are valuable tools to evaluate the degradation of soil and water resources. These techniques play an important and unique role in providing essential information for developing strategies to improve agricultural land and water use efficiency, providing solutions to mitigate degradation and increase water scarcity [32]. Modeling watershed management practices is vital in reducing soil erosion, land degradation, and sediment production. Environmental protection authorities, decision-makers, and the scientific community must undertake intervention techniques for sub-basins with severe points of soil erosion.
## 3 DBSCAN Algorithm  
### Definition of the DBSCAN Algorithm  
Density-based spatial clustering of applications with noise (DBSCAN) is a density model-based clustering algorithm [30]. As one of the unsupervised learning techniques in machine learning, it uses data location information. K-means, which is often used for clustering, clusters on the basis of the distance between clusters, and the hierarchical and fuzzy clustering method is also based on the distance between points [30; 31]. The grid-based method turns the object space into a finite number of spaces composing a grid structure, and all clustering processes are performed in the grid structure. The model-based method is about hypothesizing about each cluster and finding what works best for each one. It is based on the assumption that it is produced by a mixture of probability distributions.  
However, DBSCAN clusters high-density elements, even though the dots are densely clustered using a density-based clustering method [32]. Furthermore, geometric shapes can also be clustered. This method does not determine the number of clusters in advance, and it is possible to analyze very large databases. In this study, the dataset distributed according to the vessel AIS reception interval was analyzed over a time series. AIS data will be received at a high density when the vessel decreases speed and changes course. Furthermore, if the ship is moving at a high speed, the data will be distributed at a lower density. Therefore, the DBSCAN algorithm, one of the density-based methods, is appropriate for the clustering method of the dataset used in this study.  
In order to utilize DBSCAN, two parameters must be set: epsilon (\(\varepsilon\)) and Minimum Points (minPts) [33]. \(\varepsilon\) is the minimum distance for determining whether each data point neighbors the others, and minPts is the minimum amount of data required in \(\varepsilon\) to be recognized as a cluster [34]. As is shown in Figure 6, if there is more than minPts in \(\varepsilon\), the DBSCAN creates a cluster and expands this by performing the same check around  
Figure 5: Pre-processed AIS ship trajectory data: (**a**) all data; (**b**) arrival data; (**c**) departure data.  
neighboring data. When defining a dataset as \(D\) and a point in it as \(p\), the \(\varepsilon\) neighborhood (\(N\)) can be defined in terms of the following equation:  
\[N(p)\ =\ \{q\in D|dist(p,q)\ \leq\ \varepsilon\} \tag{2}\]  
When \(p\) satisfies the following equation, it constitutes the core point.  
\[|N(p)|\ \geq\ MinPts \tag{3}\]  
The border points are those near the core point, but with fewer than minPts within the \(\varepsilon\) neighborhood, and noise is defined as the points other than the core and border points.