esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Impact on User Satisfaction  
The impact of waiting times on user satisfaction has been widely documented. In their study of public transport in Istanbul, [PERSON] et al. [26] highlighted that prolonged waiting times were among the main causes of dissatisfaction among bus users. About 33.4% of those surveyed reported waiting times of between 6 and 10 min, while 5.8% noted waits of over 30 min, contributing substantially to dissatisfaction.  
[PERSON] and [PERSON] [27] pointed out that waiting time is one of the most uncomfortable factors for users, significantly influencing their travel decisions. Their proposal for a passenger information system (PIS) based on generalized costs incorporates waiting time as a key parameter in planning optimal routes. This approach is particularly relevant in multimodal contexts in developing countries, where lengthy intermodal transfer times exacerbate the negative perception of the service.  
Waiting times and the transfer-related penalties directly affect user perception. According to [PERSON] et al. [12], these penalties reflect the discomfort and added effort involved in transfers, negatively influencing the user experience--especially in peripheral areas.  
[PERSON] et al. [13] found that long waiting times during peak hours considerably decreased users' willingness to use public transport, negatively affecting the perception of the BRT system in Amman.  
In their analysis of Line 6 of the Santiago Metro, [PERSON] and [PERSON] [28] noted that although users valued travel time savings, waiting times remained a significant source of dissatisfaction due to the lower train frequency compared to other lines.  
[PERSON] and [PERSON] [16] support this discussion by indicating that waiting time is a key factor in user satisfaction, primarily because of its greater influence on the overall travel experience. The discomfort associated with waiting, compared to in-vehicle time, has a notably negative impact on users' willingness to use public transport systems.
## 2 Short-Term Traffic Flow Prediction Model Construction
### Construction of SVR Short-Time Traffic Flow Prediction Model Based on Associated Roads
#### 2.2.3 SVR Short-Term Traffic Flow Prediction Model  
The SVR short-time traffic flow prediction model based on road correlation comprises a data processor, an input vector, a kernel function, and an output vector. The details are shown in Figure 4:  
This figure shows the workflow of the Support Vector Regression (SVR) model, which includes everything from the preprocessing of the road and related data, feature extraction, and mapping through the kernel function, to the model's processing of the support vectors through the hidden layer nodes and outputting the prediction results, and finally comparing the results with the actual target variables to evaluate the model's prediction performance.  
The specific flow chart for SVR model prediction is shown in Figure 5.  
Figure 4: SVR short-term traffic flow prediction model based on road correlation.  
There are several main steps in predicting traffic flow on post-correlation roads using the SVR model:  
1. The dataset is prepared and normalized to set the input vector of the SVR;
2. Selection of a suitable kernel function;
3. Setting the parameters C and \(\varepsilon\), the parameter C is the penalty parameter for the error term, which controls how much the model tolerates error, and \(\varepsilon\) is the maximum error allowed to define the regression model, which determines the shape of the regression curve;
4. Training the SVR model using optimal parameters;
5. Short-term traffic flow prediction using the trained SVR model;
6. By outputting the predicted values, i.e., based on the input feature vectors, the SVR model will output the prediction results corresponding to the target values.
## 4 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Functions**} & \multicolumn{4}{c}{**Tools**} \\ \cline{2-5} \multicolumn{2}{c}{} & **BIM** & **IoT** & **BIM + IoT** \\ \hline \multicolumn{2}{c}{Communication} & 426 (W) + 509 (S) & 3920 (W) + 28539 (S) & 42 (W) + 55 (S) \\ \cline{2-5} \multicolumn{2}{c}{} & Finance \& Business & 293 (W) + 468 (S) & 417 (W) + 3413 (S) & 8 (W) + 15 (S) \\ \cline{2-5} \multicolumn{2}{c}{} & Human Factors & 31 (W) + 43 (S) & 362 (W) + 974 (S) & 4 (W) + 4 (S) \\ \cline{2-5} \multicolumn{2}{c}{} & Leadership and Strategy & 32 (W) + 242 (S) & 18 (W) + 500 (S) & 1 (W) + 17 (S) \\ \cline{2-5} \multicolumn{2}{c}{} & O\&M & 557 (W) + 746 (S) & 4374 (W) + 24709 (S) & 66 (W) + 64 (S) \\ \cline{2-5} \multicolumn{2}{c}{} & Project Management & 157 (W) + 965 (S) & 20 (W) + 1875 (S) & 6 (W) + 53 (S) \\ \cline{2-5} \multicolumn{2}{c}{} & Quality & 33 (W) + 200 (S) & 61 (W) + 3767 (S) & 1 (W) + 2 (S) \\ \cline{2-5} \multicolumn{2}{c}{} & Property Management & 57 (W) + 169 (S) & 42 (W) + 1087 (S) & 7 (W) + 19 (S) \\ \cline{2-5} \multicolumn{2}{c}{} & Risk Management & 253 (W) + 267 (S) & 1084 (W) + 5319 (S) & 17 (W) + 75 (S) \\ \cline{2-5} \multicolumn{2}{c}{} & Sustainability & 709 (W) + 794 (S) & 7733 (W) + 33413 (S) & 59(W) + 106 (S) \\ \cline{2-5} \multicolumn{2}{c}{} & Technology & 370 (W) + 1875 (S) & 1219 (W) + 30946 (S) & 34(W) + 104 (S) \\ \hline \multicolumn{2}{c}{} & FM & 237 (W) + 236 (S) & 71 (W) + 341 (S) & 25 (W) + 81 (S) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Journals’ articles on BIM, IoT and their integration for FM and its core functions from WoS (W) and Scopus (S) databases (until February 2021). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **Articles** \\ \hline
2013 & 5 \\
2014 & 9 \\
2015 & 4 \\
2016 & 4 \\
2017 & 12 \\
2018 & 14 \\
2019 & 19 \\
2020 & 24 \\
2021 \({}^{*}\) & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Annual publications of journal articles on BIM–IoT integration for FM (* until February 2021). \begin{table}
\begin{tabular}{c c c} \hline \hline
**FM Core Competencies** & \multicolumn{2}{c}{**BIM + IoT Selected Articles**} \\ \cline{2-3}  & **Web of Science** & **Scopus** \\ \hline Communication & 20 & 24 \\ \hline Finance and Business & 3 & 6 \\ \hline Human Factors & 5 & 4 \\ \hline Leadership \& Strategy & 0 & 4 \\ \hline O\&M & 28 & 28 \\ \hline Project Management & 2 & 14 \\ \hline Quality & 0 & 0 \\ \hline Real Estate and Property Management & 4 & 5 \\ \hline Risk Management & 10 & 23 \\ \hline Sustainability & 28 & 43 \\ \hline Information and Technology Management & 9 & 42 \\ \hline Facility Management & 14 & 37 \\ \hline
**Total** & **68** & **75** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Journals’ articles on BIM–IoT integration, for each FM core competence, from Web of Science and Scopus databases.
## 3 Verification of the Strength of Age-Hardened Maraging Steel Samples Produced with SLM Technology
### Tensile Fatigue Testing  
Fatigue tests were carried out using an Instron 8801 machine (Figure 2) on samples of circular cross section with an as-built surface finish and a 50 \(\upmu\)m layer setup, the shapes and dimensions of which were in accordance with the ASTM E466-15 standard. The build orientation of the specimens was 0 degrees with respect to the sample's longitudinal axis. The tests were carried out at room temperature with a cyclically changing load at a frequency f = 3 Hz. The stresses arising in the sample were defined as one-sided tension with a cycle asymmetry R = 0.05. The results of measured minimum stress \(\sigma_{\text{min}}\) and maximum stress \(\sigma_{\text{max}}\) in the sample for fatigue life (N\({}_{\text{f}}\)) are presented in Table 3. These parameters were used to determine the S-N curve (Figure 3).  
Figure 1: Tensile stress–strain curves.  
Figure 2: The laboratory test rig Instron 8801.  
Comparison of the obtained results with the literature data revealed that the samples made with the additive technologies were damaged at a much lower load. Based on the test results at 300,000 cycles, it can be concluded that the AM samples were approximately 16% less resistant to fatigue compared with the weakest managing steels.
## 5 Sensor Faults and Outlier Detection Models for IoTs
### Comparison of Fault and Outlier Detection Techniques for IoTs
#### 5.6.2 Nearest-Neighbour Techniques  
**Pros:**  
* Very simple to apply to various types of data produced by diverse sensors in an IoT system.  
* Can be left unattended and are primarily required to define a proper distance measure for the given data.
**Cons:**  
* When used on a complex multivariate data produced by IoT, the computation cost increases dramatically.
* Scalability of these types of models is a matter of concern, especially in the IoT context.
* Often produces a high false-negative rate for sensor faults and outlier detection.
## 4 Detecting Anthropogenic Disturbance Regimes
Recent advancements in automated classification of aerial photographs may open the possibility of interpreting the historical caches of aerial photographs and make change detection at finer scales practical for some regions [106]. Reliable detection of ecosystem structure around access corridors will permit sound characterization of ecological response to anthropogenic disturbance. Light detection and ranging (LiDAR) has emerged as a means to directly sense--rather than infer from optical wavelengths--forest structure. Discrete return LiDAR systems (small footprint LiDAR) operate at a spatial scale of approximately 0.2 to 0.9 m and full waveform systems (large footprint LiDAR) generally detect structures above 8 m [107]. Discrete return systems are useful for detecting structural information at the scale of an individual tree while full waveform systems can detect the variability of structure height at a plot scale. Airborne scanning LiDAR has been applied to measure a variety of structural attributes of forests including canopy height, mean stem diameter, stem number, and stem volume [108]. LiDAR may be particularly robust for detecting structural forest change, especially for regions that are disturbed with finer-scale access corridors such as roads, cut lines, pipe lines, and transmission lines. Full waveform systems have the potential to detect and characterize anthropogenic edge features that often dominate anthropogenic landscapes. Detection of anthropogenic disturbance regimes will require the use of multi-scalar Earth-observations. Combinations of spectral, contextual and structural information will lead to new breakthroughs in processing, analyzing and characterizing remotely sensed data. There remain many possibilities for detecting anthropogenic disturbance regimes. For example, night-time light emittance by human settlements and other activities is a fascinating and underexplored field of investigation [109], especially for detecting the spatial influence of anthropogenic disturbance regimes (Figure 4). Night-time light emittance was recently used to estimate global natural gas flaring [110], which could be particularly relevant to some North American forests where non-renewable resource development is expanding. Ultimately, Earth-observation and characterization of anthropogenic disturbance regimes should aim to detect energy exchange via a range of spectral, spatial, and temporal domains. The importance of direct observation, such as _in situ_ long-term monitoring should not be understated. Ground surveys and studies are essential to supplement, train, and verify remote sensing techniques. Developing research should focus on relating ground data to spectral signals that can be applied across broad scales for monitoring purposes. The future development of a framework for relating remotely-sensed indicators to ecological and social responses will be a capstone in the progress toward monitoring the Anthropocene.
## 2 Numerical Simulation Method
### Validation of Numerical Simulation  
Figure 4 shows the test bench of full tubular pump device. The operating flow rate and pump head were measured by an LDG-500 electromagnetic flowmeter (Shanghai Zhanghua Instrument Co., Ltd) and JC-E110A-EMS4A-92 DA differential pressure transmitter (Xi'an Instrument Factory), respectively. The rotation speed and torque of shaft were measured by JN338 torque meter (Beijing Sanjing Technology Group). The measurement uncertainty of flow rate, head, rotation speed and torque were 0.24%, 0.1%, 0.1%, and 0.1%, respectively. Figure 5 shows the comparison of pump performance between experimental data and calculated results under \(R_{\text{b}}\) = 2 mm. According to the engineering requirements, the design flow rate was determined as 0.2726 m\({}^{3}\)/s, and external characteristic parameters at five characteristic flow points (0.8\(Q_{\text{des}}\), 0.9\(Q_{\text{des}}\), 1.0\(Q_{\text{des}}\), 1.1\(Q_{\text{des}}\), 1.2\(Q_{\text{des}}\)) were obtained by transient calculation. As shown in the figure, the external characteristic curve of calculated results is similar to that of the experiment. The calculated head and efficiency are higher than the experimental results under large flow rate but lower under small flow rate. At the design flow rate, the relative error between the calculated head and experimental head is 3.19%, and the relative error between the calculated efficiency and the experimental efficiency is 1.14%, which proves that the calculated results is reliable.  
Figure 4: The test bench of full tubular pump device.  
Figure 5: Comparison of experiment data and calculated results (\(R_{\text{b}}\) = 2 mm).
# Nucleobase-Containing Polymers: Structure, Synthesis, and Applications
## 1 Introduction  
Nucleic acids in nature (DNA/RNA) represent the most powerful biomolecules. They serve as genetic material by hybridizing to a corresponding complementary strand to perform gene transcription and translation. Specifically, DNA carries genetic information from one generation to the next in living systems, and RNA guides the accurate assembly of amino acids into proteins by copying genetic information from DNA. Furthermore, in the materials science field, nucleic acids are also exploited for their selective hybridization capacities, unique geometric structures, ability to form nanoscale building blocks, directed self-assembly, and flexible programmability [1; 2; 3; 4]. For example, DNA functionalized particles assemble into unique structures simply through the hybridization of functionalized DNA strands. This simple interaction also enables DNA nanostructures to fold into controlled shapes [5; 6], nanoactuators [7; 8], nanosensors [9; 10; 11; 12], nanotweezers [13; 14], and other nano-devices [15; 16; 17; 18]. Additionally, DNA has been used as a therapeutic measure for various other biological purposes [15; 16]. Despite the fact that DNA has so far enabled a handful of revolutionary advances for proof-of-concept nanomaterials, its broader impact has been limited due to restrictions imposed by the cost, scale, and scope of DNA. Thus, various attempts have been made by polymer chemists to move towards a DNA-mimicking nucleobase containing polymers [17; 18; 19]. In this review, we will highlight the recent advances in synthetic nucleobase-containing polymers, from their synthetic methods to their representative applications in materials science.
# An Improved PSO Algorithm and Its Application in GNSS Ambiguity Resolution
## 3 Experiments and Result Analysis
### Performance Analysis of IPSO-AR
#### 3.2.1 Scheme #1 Experiments  
In Scheme #1, the selected DD ambiguity float resolutions are obtained from the smoothed results of observation data with 10 epochs. The corresponding ADOP value is less than 0.1, indicating relatively high precision. In IPSO-AR or SPSO-AR, initial particles are generated randomly. We independently conduct the experiment 10,000 times to obtain statistical information (i.e., average efficiency and correct rate).  
Figure 5 shows the average number of iterations and search time with various DD ambiguity dimensions for IPSO/SPSO-AR. Under low dimensions (3-5), IPSO-AR and SPSO-AR perform efficiently (approximately 5 ms). However, as the number of dimensions increases, the efficiency of IPSO-AR decreases sharply, but not that of SPSO-AR. When the number of dimensions is 12 under the P3 parameter setting, the average search time of IPSO-AR reaches 1 s, because IPSO-AR constructs a Gauss mutation in the particle evolution of the optimal population (\(S_{1}\)). This condition indicates that some particle evolution paths are disrupted. This disruption, in turn, causes particle evolution to increase and to converge to one optimal position. In addition, the efficiency of IPSO-AR is related to parameter setting. Thus, efficiency is low when total population size (\(n\)) is large.  
Figure 6 shows the correct rate of IPSO/SPSO-AR under various DD ambiguity dimensions. The correct rate of SPSO-AR decreases sharply under the same parameter setting and increasing dimensions, especially under high dimensions. Meanwhile, the correct rate of IPSO-AR shows the opposite behavior under the same conditions. The correct rate of IPSO-AR/SPSO-AR will increase under a certain dimension of DD ambiguity (Figure 6) and as the parameter \(n\) increases. However, the increment in the the correct rate of IPSO-AR is negligible. The correct rate of IPSO-AR is higher than that of SPSO-AR under any condition but is particularly higher under high-dimensional conditions. IPSO-AR can maintain a high correct rate (more than 99.9%) under the following conditions: when the precision of the DD ambiguity float resolutions is relatively high under low-dimensional conditions (3-5), when \(n\) is 30 under median-dimensional conditions (6-9), when \(n\) is 60 under high-dimensional conditions (10-12), and when \(n\) is 90.  
Figure 5: Average number of iterations and search time with various DD ambiguity dimensions for IPSO/standard PSO algorithm (SPSO)–AR under Scheme #1. GPS: Global Positioning System; BDS: BeiDou Navigation Satellite System.
## 4 Application of Operating Model to the Simulator
### Pre-Setting Values for Simulation Testing  
Before initiating the simulation, the ground conditions and TBM specifications are pre-determined. For the example simulation, the ground was specified as comprising sand and clay, and the maximum vertical earth pressure was set to 0.2 MPa. The TBM specifications for the example simulation are as listed in Table 3, except for the total number of shield jacks. Activation of the TBM generates continuous mechanical vibration. This can be simulated by the values of the mechanical responses also oscillating in real-time within specific ranges, despite constant operational actions. The simulator handles this through a deviation control device on the right side of the pre-setting panel (Figure 11a). This simulation has all deviations set to zero. The coefficients of variables in each multiple regression equation must be pre-determined (Figure 11b). For the example simulation,  
Figure 9: (**a**) Configuration and (**b**) warning alarm panels in the TBM simulator.  
these coefficients take the values in Table 4. These settings can be changed depending on the various user environments.  
To balance the earth pressure during excavation, upper and lower limits for the control pressure must be determined. The TBM operator must control the estimated earth pressure to be between these limits. The upper limit was set to be equal to the maximum vertical pressure, and the lower limit was set to be half the upper limit considering the static coefficient of earth pressure is assumed to be 0.5, based on prior works [18,19]. As a result, the limits here were 0.2 and 0.1 MPa, respectively.  
The pre-excavation settings had the belt conveyor in normal mode and all 24 shield jacks and all seven cutterhead motors selected for operation at full capacity. The cutterhead rotation direction was set as clockwise. Its rotation speed and the shield jack speed were set to 66% (0.66 rev/min) and 67% (40 mm/min), respectively, based on the estimated values from Table 2 derived for ground composed of sand and clay and the TBM specifications in Table 3. Once the shield jack operation mode was set to extension, the screw gate opening ratio was set to 100% to prevent failure of the screw gate during excavation. In the last stage for excavation preparation, the screw conveyor rotation speed was set to 100% (14.2 rev/min), and the rotation mode was set to normal (i.e., clockwise) (Figure 8b).
# Enhanced LDM for Next-Generation Digital Broadcasting Transmission
## 3 Proposed En-LDM Scheme
### System Model
#### 3.1.1 En-LDM Transmitter  
The block diagram of the transmitter of the proposed En-LDM scheme is shown in Figure 2a. Each service can flexibly choose the appropriate FEC coding, BICM scheme according to the requirements, and the selection of the used BICM scheme for the puncturing part of the data can also be agile. The data stream of mobile service uses a BICM encoder to generate quadrature phase-shift keying (QPSK) or non-uniform quadrature amplitude modulation (NU-QAM) symbols, \(X_{M}(k)\). The fixed service data stream can be divided into two parts through bit puncturing after FEC encoding; i.e., the punctured bits (Punctured bits) are called EL1, and the removed bits (Removed bits) are called EL2. The symbols of EL1 and EL2 after bit interleaving and mapping are \(X_{E1}(k)\) and \(X_{E2}(k)\), respectively. The symbols \(X_{C}(k)\) generated by the multiplexing of \(X_{M}(k)\) and \(X_{E2}(k)\) are allocated to CL of LDM in the frequency domain. The symbols \(X_{C}(k)\) of CL can be expressed as follows:  
\[X_{C}(k)=\left\{\begin{array}{l}X_{M}(k),k\leq L-1\\ X_{E2}(k-L),k>L-1\end{array}\right. \tag{1}\]  
Figure 2: Block diagram of the enhanced LDM (En-LDM) scheme transceiver: (**a**) transmitter: En-LDM scheme that punctures LDPC coded bits before symbol modulation; (**b**) receiver.  
where \(k\) represents the subcarrier index, and \(L\) denotes the number of subcarriers occupied by the mobile service symbol \(X_{M}(k)\) in the LDM-CL.  
While \(X_{E1}(k)\) is assigned to LDM's EL, the transmitted superimposed symbol \(X_{LDM}(k)\) modulated by LDM is expressed as follows:  
\[X_{LDM}(k)=\sqrt{\frac{1}{1+\alpha}}X_{\mathrm{C}}(k)+\sqrt{\frac{\alpha}{1+ \alpha}}X_{E1}(k) \tag{2}\]  
\[\Delta=10\cdot\log_{10}(\frac{1}{\alpha}) \tag{3}\]  
where \(k\) represents the subcarrier index, \(\alpha\) expresses the LDM average power ratio, and \(\Delta\) stands for the power injection level, respectively. \(X_{C}(k)\) and \(X_{E1}(k)\) respectively represent the transmission symbols used for CL and EL on the \(k\)-th subcarrier, with the average powers of \(X_{\mathrm{C}}(k)\) and \(X_{E1}(k)\) both normalized.  
After the LDM symbols are generated, subcarrier mapping and pilot insertion are performed to generate an OFDM frame, which is then transformed to the time domain through an inverse fast Fourier transform (IFFT) module with a cyclic prefix (CP) added. CP can deal with inter-symbol interference (ISI) as a result of the multipath fading.