esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Study of Indoor Accuracies Between SLAM and Static Scanners
## 2 Materials and Methods
### Processing and Evaluation of the Acquired Data
#### 2.6.6 Noise Evaluation  
To determine the amount of noise, the test profiles were smoothed employing the Smooth using MLS function, using a radius of 0.05 m, a polynomial degree of 2, and a Gaussian quadratic parameter of 0.0025 m. The RMSE was then determined using the Compute stat params function with the Gaussian distribution option.  
Removing the noise yielded a smooth cloud, representing the measured object more clearly. At the same time, however, it also introduced minor edge rounding, which slightly affected the resulting accuracy (had the search radius been smaller, the cloud would not be smoothed sufficiently; had it been larger, the edge rounding would be excessive).  
Figure 7: Position of the profiles (1, 2, 3) for accuracy assessment (highlighted in blue).  
The RMSE of the noise was calculated as the square root of the difference of the RMSE values obtained from individual profiles (and passes) before and after smoothing according to the formula:  
\[RMSE_{noise}=\sqrt{RMSE_{ICP-local}^{2}-RMSE_{ICP-local\ smoothed}^{2}} \tag{2}\]  
To be able to compare the overall performance of the scanners, mean respective RMSEs (e.g., \(\text{RMSE}_{\text{SFHER-\&}}\)) were calculated from RMSEs acquired from the three individual passes using the formula  
\[\text{RMSE}_{\mathcal{O}}=\sqrt{\frac{\left(\text{RMSE}_{1}^{2}+\text{RMSE }_{2}^{2}+\ldots+\text{RMSE}_{n}^{2}\right)}{n}} \tag{3}\]  
where n is the number of values.
## 2 Principle of the Broadcast Ephemeris Correction with the PPP-B2b Service
### Matching Algorithms of PPP-B2b Messages  
There are seven message types in PPP-B2b. These seven message types refer to five types of data, namely the satellite mask, differential code bias (DCB), orbit corrections, clock corrections, and the user ranging accuracy index, as given in Table 1. These PPP-B2b corrections should be matched before being made. The algorithms for PPP-B2b matching and correcting LANV and CNAV ephemerides during the real-time precise orbit determination (RT POD) of LEO satellites are identical to those used in other scenarios, such as traditional PPP, with no distinction. Firstly, the Issue of Data State Space Representation (IOD SSR) should match between various types of messages before the corrections can be used together [10]. Secondly, the Issue of Data of the PRN mask (IODP) included in message types 4, 5, and 6 must be the same as the IODP of message type 1 before the corrections are made. The PPP-B2b Issue of Data, Navigation (IODN) should be consistent with the GNSS IOD. An inconsistency indicates that the GNSS has updated its broadcast ephemeris. The previous broadcast ephemeris should be used for matching the PPP-B2b until the PPP-B2b IODN is updated and matches the GNSS IOD. Again, for the same satellite, the version number of the clock correction message (IOD of the correction, IOD Corr) should be the same as in the orbit correction, such that the clock correction and orbit correction (in message type 2) match each other. Finally, the broadcast time of the broadcast ephemeris must match the broadcast time of its corresponding correction message and must not exceed the validity period of the correction. Thus, the correction message can be used [19]. The validity periods of various message types of PPP-B2b are given in Table 1.
# Study on Thermal Radiation Characteristics and the Multi-Point Source Model of Hydrogen Jet Fire
## 3 Theoretical Basis of Numerical Simulation
### Computational Domain and Boundary Conditions  
In this study, the hydrogen jet fire under leakage pressures of 4 MPa, 8 MPa, and 12 MPa and leakage diameters of 10 mm, 20 mm, and 40 mm were numerically investigated. The simulation conditions are shown in Table 1. We focused on the discussion of the temperature variations along the flame axis and the spatial distribution of the thermal radiation.  
As shown in Figure 3, a 30 m cubic 3D space was set as the computational domain to simulate the space environment in which the jet fire was formed and affected. The three-dimensional computational domain was meshed using the ICEM 2021 R1. Due to the high pressure and velocity gradients present near the hydrogen leakage nozzle, the mesh near the nozzle was encrypted using multiple O-type block mesh divisions. This approach ensured a higher mesh density near the nozzle and a coarser mesh further away, optimizing the computational accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Condition**} & **Leakage** & **Leakage** & **Environmental** & **Description** \\  & **Pressure/Mpa** & **Diameter/mm** & **Temperature/K** & **Description** \\ \hline
0 & 10.5 & 5 & 293 & Verification \\
1 & 4 & 10 & 300 & Different leakage \\
2 & 8 & 10 & 300 & pressures \\
3 & 12 & 10 & 300 & Different leakage \\
4 & 4 & 20 & 300 & diameters \\
5 & 4 & 40 & 300 & diameters \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation conditions under different leakage pressures and diameters.  
Figure 3: Hydrogen jet fire simulation grid.  
The boundaries of the cubic computational domain were specified as pressure outlets, while the ground was defined as an isothermal wall boundary. The material chosen from the Fluent material library was CaCOO3, and the wall temperature was set to 300 K, in line with the ambient temperature.  
A round nozzle was used to simulate the hydrogen leakage hole, which was defined as a velocity outlet. The diameter and velocity of the leakage nozzle were determined using the virtual nozzle model. The governing equations were solved using the finite volume method, employing the coupled algorithm to manage the pressure-velocity coupling. For the high-pressure hydrogen leakage condition, since the flow velocity near the nozzle was substantial, a second-order upwind scheme was used for the discretization of both the convective and diffusive terms.
## 2 Methods
### Package Assessment and Feature Comparison
#### 2.2.1 Data Quality Framework
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Dimension** & **Domain** & **Indicator** \\ \hline
**Integrity** & & \\  & Structural dataset error & \\  & & 1001: Unexpected data elements \\  & & 1002: Unexpected data records \\  & & 1003: Duplicates \\  & Dataset combination error & \\  & & 1004: Data record mismatch \\  & & 1005: Data element mismatch \\  & Value format error & \\  & & 1006: Data type mismatch \\  & & 1007: Inhomogeneous value formats \\  & & 1008: Uncertain missingness status \\
**Completeness** & & \\  & Crude missingness & \\  & & 2001: Missing values \\  & Qualified missingness & \\  & & 2002: Non-response rate \\  & & 2003: Refusal rate \\  & & 2004: Drop-out rate \\  & & 2005: Missing due to specified reason \\
**Consistency** & & \\  & Range and value violations & \\  & & 3001: Inadmissible numerical values (hard limits) \\  & & 3002: Inadmissible time-date values \\  & & 3003: Inadmissible categorical values \\  & & 3004: Inadmissible standardized vocabulary \\  & & 3005: Inadmissible precision \\  & & 3006: Uncertain numerical values (soft limits) \\  & & 3007: Uncertain time-date values \\ Contradictions & & \\  & & 3008: Logical contradictions \\  & & 3009: Empirical contradictions \\
**Accuracy** & & \\  & Unexpected distribution & \\  & & 4001: Univariate outliers \\  & & 4002: Multivariate outliers \\  & & 4003: Unexpected locations \\  & & 4004: Unexpected shape \\  & & 4005: Unexpected scale \\  & & 4006: Unexpected proportions \\ Unexpected association & & 4007: Unexpected association strength \\  & & 4008: Unexpected association direction \\  & & 4009: Unexpected association form \\ Disagreement of repeated measurements & & \\  & & 4011: Inter-Class reliability \\  & & 4012: Intra-Class reliability \\  & & 4013: Disagreement with gold standard \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Reference framework for harmonized data quality assessments [6; 29] used to compare the selected R packages.
# A Human Health Toxicity Assessment of Biogas Engines Regulated and Unregulated Emissions
## 2 Literature Review
### Regulated and Unregulated Emissions  
In the literature, the definition of both regulated and unregulated emissions is clearly stated.  
Regulated emissions are substances that have regulatory limits for their maximum levels, while unregulated emissions are compounds without fixed regulatory limits.  
However, an uncertain aspect of these definitions concerns the substances that must be included in the \"regulated\" or \"unregulated\" emissions groups.  
This is important because, due to the absence of a common and widely adopted emissions standard, the regulatory body of each country independently defines the type of substances that need to be tracked and their maximum levels.  
Substances commonly considered as \"regulated emissions\" for ICEs are NO\({}_{\rm x}\), SO\({}_{\rm x}\), HCl, volatile organic compounds (VOCs), CO and particulate matter (PM), while aldehydes, polycyclic aromatic hydrocarbons (PAHs), dioxins and furans are considered \"unregulated emissions\".  
The Italian standard (Legislative Decree No. 118 of May 19 th, 2016 [36]) distinguishes the limits based on fuel type and, in some cases, based also on the engine nameplate thermal power. For biogas ICEs, NO\({}_{\rm x}\), CO, HCl and VOCs are considered regulated emissions, while, for natural gas, tracked substances are NO\({}_{\rm x}\), SO\({}_{\rm x}\) and particulate.  
A detailed description of the Italian legislation is given in [33; 34; 35] while a comparison between German and Italian regulatory frameworks is presented in Appendix A.1.  
Note that the German standard includes formaldehyde among regulated emissions. So, it is possible to claim that Germany is one step ahead of other EU countries, because it is working to limit toxic and volatile compounds which are known to be human carcinogens. An aspect which confirms that measuring the so called \"unregulated emissions\" which comprise formaldehyde is a crucial point in view of the prevention of disease and the standards update.  
Consequently, the results of the experimental campaign presented in this work can complete the picture of biogas engines emissions by investigating both regulated and unregulated emissions. Additionally, it can also allow us to identify the substances with the highest impact on human health, to compute the health damage and to provide an answer to people's concerns about polluting content of biogas engine waste gases. Finally, the outcomes of the investigation can be used to update the in-force emission standards in view of improving the public acceptance of biogas ICEs.  
These aspects are a point of novelty because the vast majority of published investigations are focused on biogas ICEs' performance and regulated emissions, while only a few have analysed the unregulated ones.
## 4 Experimental Framework
### Data Preprocessing  
#### 4.2.1 Feature Selection Using RFE Algorithm  
Feature selection is used to select small and informative feature subsets [47]. On-board QAR data encompass various types of feature parameter data, exhibiting a mixed data type structure that includes both continuous numerical and discrete numerical variables. Before applying the CatBoost classifier to the QAR data, feature selection needs to be performed. Feature selection, as a data preprocessing strategy, has been proven to be effective and efficient in preparing data (especially high-dimensional data) for various data mining and machine learning problems [48].  
Among the numerous features in QAR data, some contribute significantly to classification, while others may be redundant. If redundant features are present in the dataset and used for model training, it can lead to longer computation times and reduce classification accuracy. Currently, feature selection primarily employs three methods: wrapper methods, embedded methods, and filter methods. Wrapper methods, particularly the RFE algorithm, demonstrate excellent performance in feature selection [49; 50], when compared to the other two methods.  
RFE is an advanced method that obtains the weights of each feature and progressively eliminates features with lower contributions through multiple iterations, thereby completing the feature selection process. After standardizing the QAR data, the specific implementation steps of using RFE to find the best subset of features for the CatBoost model were as follows:  
1. Input all features into the CatBoost model and obtain the model's performance evaluation metric (accuracy).
2. Based on the weights or importance of features, selectively remove the feature with the lowest performance evaluation metric ranking (the feature with the minimum weight) from the feature set, resulting in a new feature set.
3. Retrain the model and compute the model's performance evaluation metric.
4. Repeat steps 2 and 3, removing one feature at a time, until the number of features reaches the predetermined value, or it is no longer possible to remove features.  
During this process, we can record the performance evaluation metric after each feature selection iteration to choose the optimal feature set. In the end, we selected a feature subset containing 23 features, as depicted in Figure 7, which illustrates the pie-chart proportion of feature weights. Based on the findings from the pie chart, certain identification feature parameters unrelated to turbulence, such as departure and destination, were excluded as they did not contribute significantly to the relevance of our identification results.  
Figure 7: Proportion of each feature.
## 2 Materials and Methods
### Process of Collecting Scenarios
#### 2.1.1 Control Centers  
To date, many real-world labs have tested automated shuttles with on-board operators in order to expand public transport services. AVs in real-world labs are usually operated with the assistance of a steward on board the shuttle who, among other things, monitors the traffic situation and the technical vehicle status and, if necessary, intervenes and initiates appropriate measures depending on the situation.  
Remotely operated shuttles _without_ onboard operators, however, will differ fundamentally regarding their interactions. Instead of interacting with the AV's on-board operators, the control center will interact directly with the AV while the tasks of the onboard operator will be shifted to the control center. However, only crude concepts and isolated prototypes for control centers to monitor, supervise, and, if necessary, remote-control AVs without on-board operators exist at the moment. Thus, in a first step, the roles and activities of today's control centers in public transport were analyzed by means of observation and interviewing. Participatory observation and expert interviews with control center staff in Hamburg and Braunschweig, Germany, helped examine the working equipment, tasks, roles, and collaborations in a control center for teleoperation in public transport in general. More importantly, these methods yielded scenarios with potential relevance for remote operation.  
First, _observation_ was used as a tool to collect essential data. It includes the description of behavioral and temporal patterns, the consequences for control center staff and their environment, as well as the spatial relationship of the control center employee with other people. Observations were characterized by the following attributes:  
* _Time sampling_: Control center staff's behaviors were observed during a fixed time interval by researchers.
* _Unstructured observation_: Observations were conducted in a holistic way. The researcher entered the field with some general ideas of what might be salient, but not of what specifically will be observed, i.e., without using any pre-determined objectives, schedules, or variables (cf. [24]).
* _Naturalistic setting_: The process involved observing and studying the spontaneous behavior of the control center employees in their natural environment.  
Second, based on these observations, several expert workshops yielded a set of categories which were subsequently used for two card-sorting studies. In a first study, interdisciplinary traffic researchers clustered the categories, assigned concrete task sets to them, and finalized them. In a second study, expert interviews were conducted using _the card-sorting approach_[20] to identify tasks and roles in future control centers.
## 10 Discussion and Future Developments
### Object Recognition  
First and foremost, the project met the basic requirement of conducting a census of the assets in the buildings. This recognition, which is currently performed directly by technicians, may see the implementation of machine learning and artificial intelligence algorithms. The prospects for the use of such technologies are obviously very interesting. Several tests are under development to apply AI technology in this application field. There are numerous artificial intelligence applications aimed at object recognition, which should be studied in the future for easier and more immediate implementation in asset recognition directly from images in an automated manner. Meanwhile, these photo-based technologies are becoming increasingly widespread [103; 104]. Numerous studies are being developed in object recognition through the combined analysis of images and three-dimensional geometries [105; 106; 107; 108].
## 4 Multi-Objective Area Coverage Optimization Problem  
To improve the coverage quality of DSN, we should improve coverage ratio and reduce redundancy ratio by sensing direction rotation, and it is a multi-objective optimization problem. It is important to derive a multi-objective area coverage optimization model after coverage situation verification.
# Application of Molecularly Imprinted Polymers in the Analysis of Waters and Wastewaters
## 2 Molecularly Imprinted Polymers (MIPs)  
Molecularly imprinted polymers (MIPs) are synthetic materials produced from different types of polymerization reactions between a cross-linking agent with a complex formed between a template and functional monomer(s) having functional groups specifically interacting with the template through covalent or noncovalent approach. Then, when removing the template, using a highly polar solvent, a specific cavity for the target template is rendered to be ready for analysis and determination in a different matrix [3; 4; 5].  
The history of molecularly imprinted polymers (MIPs) goes back to 1972, when [PERSON] et al. were able to form MIPs from organic polymers using covalent bonds, knowing that the first molecular imprinting was in 1949 when [PERSON] imprinted silica gels [1]. A noncovalent approach was adopted in 1981 by [PERSON] and [PERSON] [6], which combined template and in situ monomer with noncovalent interactions, such as electrostatic forces, hydrogen bonding, van der Waals forces, etc. The weak interactions make this approach relatively simple, especially with the availability of so many functional monomers that can interact with different types of templates [3]. The development is continuous until the first MIP-based sensor was produced by [PERSON] (1992), and is still under way to use polymers in different areas for various applications [7].
# An Alternative Procedure for the Description of Seismic Intensity Parameter-Based Damage Potential
## 2 Methods
### Damage Indices
The last DI is the Maximum Softening (\(\delta_{\text{M}}\)) [8; 48], which uses the first-mode natural period of the structure to produce a lumped approximation of the accumulated damage sustained during an earthquake:  
\[\delta_{\text{M}}=1-\frac{\text{T}_{0}}{\text{T}_{\text{max}}} \tag{4}\]  
where \(\text{T}_{0}\) is the first-mode natural period of the system at its unharmed state, and \(\text{T}_{\text{max}}\) is the maximum value of the first-mode period observed during the analysis. A moving average smoothing procedure, described in [48], must be employed to compute the Maximum Softening in a non-linear analysis using the instantaneous natural period. This procedure attenuates the influence of all the sudden, momentary increases in the natural period during yield zone formations that would otherwise result in a significant overestimation of the actual sustained damage. The examined DIs are well-known and extensively studied in numerical and experimental research. Many researchers have verified the modified Park-Ang DI experimentally, specifically for R/C frames [7; 49; 50]. The Maximum Softening DI has a sound theoretical background and agrees well with the Park-Ang DI in estimating the observed structural damage [8]. The MISDR can be found in most building codes as the defining criterion for damage limitation of structural and non-structural members [1; 51]. In addition, the MISDR can be evaluated numerically and experimentally without enormous effort. Utilizing more than one DI allows for cross-checking the validity of one's result to another's. Thus, the results' validity increases when all DIs provide similar damage grades for a specific seismic event. A classification of the different damage grades based on these Damage Indices is shown in Table 3, as described in [16]. The minor category corresponds to a healthy structure or one that has sustained minor damage, usually requiring no further actions. Moderate refers to repairable damage and Severe refers to either irreparable damage or collapse of the structure, partially or totally. Since an irreparable damage grade leads to the demolition of said structure, the distinction between demolition and collapse is deemed insignificant and the two can be merged into one category.
# Design and Experiment of Magnetic Antenna Vibration and Noise Reduction System
## 1 Introduction
In this paper, the vibration reduction requirements of magnetic antenna are identified, and the principle of vibration absorber system of the magnetic antenna model is established. A magnetic antenna damping system, mainly focused on low frequency, is proposed by using a silicon rubber elastic element. The prototype of the damping system was then verified by dynamic simulation and function comparison experiments. The experiment results prove that the damping method of magnetic antenna meets the design requirements.