esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Experimental Setup
In the fusion method proposed in this paper, the threshold for GNSS availability would be set as 90\({}^{\circ}\) equivalently. To be more convincing, the performance of LIO-SAM [42] is also evaluated. * _Method_**2**: This indicates the adaptive GNSS/LIO fusion methods with a different threshold (\(el_{th}\)) in Equation (4) for the GNSS-RTK solution selection proposed in this paper. For urban-1, the threshold is selected as 35\({}^{\circ}\) and 15\({}^{\circ}\). For urban-2 and urban-3, besides these two thresholds, 20\({}^{\circ}\) is additionally selected for a sufficient evaluation. Besides the above-mentioned positioning results of the various integrated pipelines, the positioning accuracy of alone GNSS-RTK measurements and LIO are also shown for exhaustive performance analysis. It is worth mentioning that the LIO here represents the loosely coupled LiDAR-inertial fusion method implemented in our previous work [33] which is different from that in the tightly coupled framework LIO-SAM [42]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Settings** & **Urban-1** & **Urban-2** & **Urban-3** \\ \hline Integer Ambiguity Resolution & Instantaneous & Instantaneous & Instantaneous \\ \hline Min Ratio to Fix Ambiguity & 3.0 & 3.0 & 3.0 \\ \hline Elevation Masking & 15 degree & 15 degree & 15 degree \\ \hline Ephemeris & Hong Kong Land Department & Hong Kong Land Department & Hong Kong Land Department \\ \hline Frequency & L1 + L2/E5b & L1 + L2 + L5 & L1 + L2 + L5 \\ \hline Navigation System & GPS/BeiDou/Glonasss & GPS/Galileo/BDS & GPS/Galileo/BDS \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The settings specified in the RTKLIB when processing the raw GNSS measurements from u-blox to achieve GNSS-RTK solutions. Figure 5: (**a–c**) The ground truth of urban-1, urban-2, and urban-3 is shown by Google Earth from the bird-eye view respectively.
## 2 Literature Study
### Calculation Parameters
#### 2.1.1 Computation Type  
In the computation context, two methods can be distinguished: discrete and moving [10]. A discrete calculation divides the analyzed section into segments of equal length (influence length; Section 2.1.2) and the TQI is calculated separately for each segment. A moving calculation calculates one TQI-value for every cross section. Around the first cross section i, a segment is spanned, extending half the influence length in both directions. All data points within the segment are used to calculate the TQI, which is assigned to point i. Then the segment is shifted one point and the process is repeated for cross section i + 1.  
The differences between the two computation types are illustrated in Figure 1. The plot shows the longitudinal level (left rail) of the test section as well as its standard deviation, calculated in a moving and a discrete manner with an influence length of 100 m. The moving standard deviation depicts the signal characteristics better while the discrete calculation shows directly which 100 m segments require maintenance earlier than others.
## 2 Current Challenges  
The primary limitations of the existing food safety practices are the delays in the identification of unsafe food. By the time a recall decision is made, unsafe food products might have moved along in the distribution process. Also, the implementation of a recall decision is difficult, and it is time-consuming to identify and remove unsafe food from the supply chain.  
Regulatory bodies worldwide have implemented processes to ensure food safety, for example, the Canadian Food Inspection Agency (CFIA) [12], the Food and Drug Administration (FDA) [13], the Food Standards Agency (FSA) [13], Food Standards Australia & NZ (FSANZ) [14], the Danish Agriculture & Food Council (DAFC) [15], etc.  
In general, authorities identify contamination based on periodic sampling like collecting samples from food processors randomly for laboratory testing, end-consumer complaints related to public health, or voluntary declarations by food producers. Regulatory authorities then make health risk assessments before making a recall. Though the current process is foolproof, the existing mechanism of data collection, aggregation, assessment of risk, and eventual decision to recall is extremely effort-intensive, manual, and lacking in speed and, at times, accuracy (when not enough data are available).  
In order to eliminate health hazards or to reduce financial losses, the need of the hour is to identify any food contamination early in the processing lifecycle proactively. Also, a mechanism of instant data aggregation is needed for a decision-making system to review and analyze the data, make timely decisions confidently, and disseminate recall decisions to the intended recipients without any delay in the corrective actions.  
Contaminated food often manifests as a major public health hazard [16], and a delayed decision to recall contaminated food invariably results in financial losses to the food industry. Also, delayed recall decisions invariably result in food wastage [17], which adversely impacts the environment [18]; every country is thus making substantial investments into mechanisms that can assess risks early in the food processing/distribution cycles to address food safety challenges.
## 4 Implementation and Experiment Results  
### Datasets and Data Collection  
To demonstrate the feasibility and efficiency of the proposed CS item recommendation system, a real-world dataset created by the Netflix Prize is adopted. The dataset contains 100,498,277 ratings contributed from 480,189 anonymous users for 17,770 movies. The  
Figure 3: The structure of the NNCF predictor.  
density of the dataset is 1.1778%. Each record in the dataset includes movie IDs, user IDs, users' ratings for the movie, and rating date. The average number of ratings per user is 209. The textural descriptions (movie plots) for the movies are scratched from OMDb (Open Movie Database). Since not every movie in the Netflix dataset can be found in OMDb, the movies with missing textural descriptions are removed. Besides, the users who have rated less than 100 times are also deleted. Finally, the dataset of 49,771,100 ratings provided by 87,121 users for 12,747 movies is used in the following experiments.
# Productivity Analysis of Documentation Based on 3D Model in Plant Facility Construction Project
## 2 Literature Review  
### Productivity in Off-Site Construction  
In traditional on-site construction, engineered to order (ETO) components (e.g., structural steel frames, structural precast concrete pieces, facade panels, etc.) are prefabricated at the factory by the supplier. Depending on the level of system integration and engineering, emerging ETO components include fully integrated building modules (e.g., modular construction), fully integrated sub-assemblies (e.g., prefabricated units), efficient structural systems (e.g., structural steel, total precast buildings), integrated architectural multi trade systems (e.g., slabs, walls fitted with services), integrated MEP multi trade systems (e.g., MEP racks, machines with services), prefabricated components (e.g., structural ETO, architectural ETO, MEP ETO), etc. [2].  
Since off-site construction involves the manufacturing process, it is important to secure the quality of the design, which is a preliminary process [14]. Due to the large impact of design quality on manufacturing and construction, project delivery methods (e.g., IPD, CM at Risk, Precon) of early contractor involvement (ECI) type have been tried [15, 16, 17]. The goal of this procurement approach is to develop a complete plan that ensures the quality of the design drawings.  
Productivity can be described as output versus input. From an economic point of view, productivity is defined as carrying out more achievement per unit resource [18]. [PERSON] and [PERSON] (2014) [19] analyzed resource development, worker involvement, process improvement and task recognition as having a significant impact on offsite construction productivity. [PERSON] et al. (2012) [20] focused on improving the process among the factors affecting offsite manufacturing. [PERSON] et al. (2015) [21] identified obstacles to off-site construction, including traditional design practices [22], lack of standardized manufacturing components [23,24], and lack of skilled technicians [25].  
Methods for measuring and improving productivity at construction sites include measuring and interpreting work and crew effectiveness (e.g., field rating, work sampling, five-minute rating, etc.), field surveys (e.g., foreman delay survey, craftsman questionnaire, etc.), the method productivity delay model, charting techniques, simulation modeling, and analysis [8]. While many researchers have been working to measure and improve productivity, productivity analysis of the entire process is a very difficult research topic because the construction process is very complex and many engineering professionals are involved [26]. In particular, it is very difficult to collect research data on the productivity of real-world construction projects depending on the application of BIM [7]. In this regard, research is needed to measure and improve productivity, focusing on micro tasks that are influential in the construction process [27]. The purpose of this study is to analyze the productivity of documentation tasks that directly affect all tasks throughout the construction lifecycle.
## 3 Results
### Accuracy Correction  
For Areas 44,333 and 44,600, a correction technique is presented that can reduce the offset component of the relative error to less than 0.5%. The essence of the method is that in practice, although the use of pseudo measurements is a good and efficient alternative for better LV network monitoring, it is worth integrating real measurements into the DSSE method. A total of 1933 iterations were run in connection with the estimations. These made it possible to identify those buses in the networks that give the closest estimation error to the offset error by test estimations on a separated dataset. So, if measurement devices are placed on these buses, even a moderate number of them is enough to significantly improve the accuracy of DSSE.  
The place of the first measurement device is the bus that best approximates the average error to eliminate offset. To find this bus, a separate pseudo-measurement dataset was used. From this dataset with multiple DSSE run, a set of relative error data was procured. By grouping this data by bus, it was possible to identify the bus with error closest to the average relative error. The place of the second measurement device is one of the endpoints of the network, where the estimation accuracy is expected to be the lowest. Experience has shown that it is worth correcting the estimations of the buses closer to the transformer with the estimations created for the first bus and that the correction between this point and the endpoint must be made according to the 'lever rule'. In line with this, the correction given by both points must be used in inverse proportion to the 'test point distance' from them. This 'distance' basically equals the impedance between the bus and the transformer, which can be replaced by the difference in the bus indices of the given circuit if the variance of the differences between the pylon distances is not extraordinarily large.  
The calculated correction and the applicable approximation are described in Equations (8) and (9). The essence of the correction is that buses close to each other have similar relative errors. Thus, if real measurements are available on bus 'a' and bus 'c' bus, the estimation for bus 'b' can also be corrected. The correction is proportionally more affected by the difference between the real measurement and the estimation of the closer bus and less affected by the difference between the real measurement and the estimation of the further bus. This method is widely used in other sciences, such as materials science [33].  
\[\Delta U_{n}=\frac{\Delta U_{v^{\cdot}}(Z_{n}-Z_{b})+\Delta U_{b}\cdot(Z_{v}-Z _{n})}{(Z_{v}-Z_{b})} \tag{8}\]
## 3 Analysis of Factors Affecting Flexural Performance of Reinforced Beams
### Effect of FRP Grid Material Type on Flexural Performance of Reinforced Beams  
To investigate the effect of FRP grid material type on the flexural performance of the reinforced beams, the FRP grid material was changed to CFRP, BFRP, and GFRP, respectively, using BG-L-PCM as the reference beam with the rest of the parameters unchanged.  
In this subsection, a total of three reinforced beams with different FRP grids are subjected to finite element analysis, and the load-midspan deflection curves of the reinforced beams are obtained, as shown in Figure 8. The characteristic loads, deflection values, and  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Beam ID**} & \multicolumn{2}{c}{**Cross-Sectional**} & \multirow{2}{*}{**Yield Load (kN)**} & \multirow{2}{*}{**Ultimate Load (kN)**} & \multirow{2}{*}{**Ultimate Load (kN)**} & \multirow{2}{*}{**Deflection**} & \multirow{2}{*}{**FRP Grid**} & \multirow{2}{*}{**Failure Mode**} \\  & & & & & & & \\ \hline B3O50-50 & 135 & 164.4 & 211.8 & 51.5 & 21.14 & 0.0163 & D + FR \\ B4O50-50 & 180 & 165.5 & 213.3 & 52.6 & 15.90 & 0.0114 & D + CC \\ B5O50-50 & 225 & 166.3 & 217.1 & 55.3 & 14.63 & 0.0098 & D + CC \\ B3T50-50 & 270 & 173.8 & 218.5 & 56.3 & 13.40 & 0.0120 & D \\ B4T50-50 & 360 & 175.3 & 216.1 & 54.6 & 11.01 & 0.0084 & D \\ B5T50-50 & 450 & 183.9 & 215.5 & 54.1 & 9.79 & 0.0066 & D \\ \hline \hline \end{tabular} Note: D = debonding, denoting composite layer stripping damage; CC = concrete crush, denoting concrete crushing damage; FR = fibers rupture, denoting FRP grid fracture, the same below; FRP grid strain is the ultimate load corresponding to the grid strain in the span, the same below; the same as below.  
\end{table}
Table 4: Effect of FRP grid reinforcement amount on reinforced beams.  
Figure 8: Effects of reinforcement amount on load–midspan deflection curve.  
damage modes are shown in Table 5. From Figure 9, it can be seen that the simulated beams show a clear three-stage variation, where the curves of the three beams match well during the first two stages, and in the third stage, it is found that due to the higher elastic modulus of CFRP grid, beam C3O50-50 shows the highest improvement in bending stiffness, followed by beam B3O50-50. As seen in Table 5, the ultimate loads of beam G3O50-50, beam C3O50-50, and beam B3O50-50 increased by 55.2%, 53.7%, and 51.5%, respectively; the deflections of beam G3O50-50, beam C3O50-50, and beam B3O50-50 were 18.57 mm, 11.65 mm, and 18.57 mm, respectively. A total of two damage modes were experienced by the simulated beams: beam B3O50-50 and beam G3O50-50 were damaged by composite layer peeling and then the FRP grid was pulled off; beam C3O50-50 was damaged by composite layer peeling. In summary, although the CFRP grid can improve the flexural stiffness of the reinforced beams, it is not ideal to improve the load carrying capacity; the modulus of elasticity of the BFRP grid is small, but it is ideal to improve the load carrying capacity of the reinforced beams as well as the deflection.
## 4 Discussion
### Limitations and Uncertainty  
We estimated the carbon sequestration potential of forest ecosystems in Yunnan Province using the individual tree-based model FORCCHN. The uncertainty of the results mainly consisted of two parts: the model and the driving data. On the one hand, it was found through validation that there was a certain underestimation of GPP and ER and a slight overestimation of NEP, mainly because the model underestimation of GPP was smaller than the model underestimation of ER, thus leading to a slight overestimation of NEP [29]. The model considers only the input of litter matter in the soil carbon pool model due to the lack of localized parameters [31; 56] and does not consider the input of microbial action and other carbon sources. On the other hand, there was uncertainty in the assessment of climate change scenarios by the GCM [89], which may lead to uncertainty in the driving data. In addition, this study did not consider the area of new afforestation, mainly because existing studies show that existing forests account for more than 90% of total carbon sequestration and that there are uncertainties in the distribution of afforestation [76]. In the future, we will further improve the FORCCHN model, especially by incorporating soil microorganisms to reduce its uncertainty, because soil carbon pools are very large and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Management Measures** & **Carbon Fixation Rate** & **Technology Maturity** & **Environmental Adaptability** & **Public Acceptability** \\ \hline
\begin{tabular}{c} Afforestation and \\ Reforestation \\ Returning farmland to \\ forest \\ Natural forest restoration \\ Forest Nurture \\ Thinning \\ \end{tabular} & **...** & **...** & **...** \\ \hline \hline \end{tabular}  
* The more asterisks, the stronger the carbon fixation rate or the more suitability for the promotion of the management measures.  
\end{table}
Table 6: Qualitative evaluation of the effect of anthropogenic management measures on carbon sequestration [88].  
have an important role in estimating forest carbon sequestration [90]. Considering the increasing frequency of extreme climate events, we will develop a plant response module to extreme drought in the model to improve the accuracy of the simulation. In addition, the limitations of GCM data will be reduced by averaging multiple model ensembles [91].
# Weighting of Multi-GNSS Observations in Real-Time Precise Point Positioning
## 2 Data and Methods
### Development of Stochastic Model
#### 2.3.3 Observation Noise  
Another factor that influences the quality of GNSS observables is their noise, which caused by characteristics of individual signals and related propagation errors. Significant differences in signal noise between systems and satellite blocks are reported, but the differences between the frequencies are relatively small [42]. Table 4 presents the generalized results, in which the noise standard deviations for observables at each frequency are replaced by a frequency mean value of the noise. Because the results do not contain information on some of the satellite types, namely GLONASS K and Galileo FOC, we assume that they have the same system noise as other satellites of the same system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**System**} & \multirow{2}{*}{**Block**} & \multicolumn{3}{c}{**Noise StdDev**} \\ \cline{3-4}  & & **Carrier Phase [mm]** & **Pseudorange [m]** \\ \hline GPS & IIR & 1.100 & 0.155 \\  & IIR-M & 1.100 & 0.150 \\  & IIF & 1.033 & 0.150 \\ GLONASS & M & 1.250 & 0.235 \\ Galileo & IOV & 1.400 & 0.068 \\ BeiDou & GEO & 1.467 & 0.130 \\  & IGSO & 1.400 & 0.143 \\  & MEO & 1.133 & 0.117 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Standard deviation of carrier phase and code receiver noises.  
Figure 3: Signal-in-space ranging errors (SISRE) values for GNSS satellites calculated individually (horizontal bars) and one general value for a particular system (horizontal black lines).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**System**} & \multirow{2}{*}{**Block**} & \multicolumn{3}{c}{**Noise StdDev**} \\ \cline{3-5}  & & **Carrier Phase [mm]** & **Pseudorange [m]** \\ \hline GPS & IIR & 1.100 & 0.155 \\  & IIR-M & 1.100 & 0.150 \\  & IIF & 1.033 & 0.150 \\ GLONASS & M & 1.250 & 0.235 \\ Galileo & IOV & 1.400 & 0.068 \\ BeiDou & GEO & 1.467 & 0.130 \\  & IGSO & 1.400 & 0.143 \\  & MEO & 1.133 & 0.117 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Standard deviation of carrier phase and code receiver noises.  
The application of frequency mean value is a simplified approach, in which the noise difference among frequencies and codes are ignored. As an alternative, one can additionally differentiate raw carrier phase noise by assuming the accuracy of 0.01 cycle [43]. However, it is hard to discriminate pseudoranges because of the lack of knowledge regarding the actual performance of the individual pseudo-range measurements depending on modulation, receiver, correlator type, and so on [44].
# Designing Advanced Multistatic Imaging Systems with Optimal 2D Sparse Arrays
## 4 Simulation
 \tag{10}\]  
Simplify Equations (6) and (10), since the distance between elements is much larger than the wavelength:  
\[\vec{H}_{outn}=\frac{j}{2\lambda}\frac{e^{-jkR_{outn}}}{R_{outn}}\left(\vec{ \mu}_{out}\times\hat{R}_{outn}\right)I_{outn} \tag{11}\]  
\[\vec{E}_{outn}=\eta\vec{H}_{outn}\times\hat{R}_{outn} \tag{12}\]  
This entire process is iterated for all \(\text{tr}_{m}\text{-tr}_{n}\) combinations and across all \(f_{l}\). Consequently, at the end of this procedure, a matrix (\(S_{12}\)) is generated, representing the received electromagnetic fields at each frequency, as observed by the receivers for each specific \(\text{tr}_{m}\text{-tr}_{n}\) pair. This simulation was carried out so that the process was the same as that by which the measurement system was configured, with which the same (\(S_{12}\)) parameters are obtained. In the context of ISAR (inverse synthetic aperture radar) processing on the dataset [42; 43], the initial step involves defining the imaging plane of interest, which usually, but not necessarily, includes the OUT. For every \(\text{tr}_{m}\text{-tr}_{n}\) combination, two crucial distances are computed, the distance from \(\text{tr}_{m}\) to every point in the imaging plane (\(\text{R}_{inc_{m}}\)) and the distance from every point in the imaging plane to \(\text{tr}_{n}\) (\(R_{scat_{n}}\)). Once these distances are established, they are used to calculate the value of the reflected signal (\(I_{(m,n)}\)) as follows:  
\[I_{(m,n)}=\sum_{l}(R_{inc_{(m)}}\cdot R_{scat_{(n)}}\cdot e^{i\kappa_{l}(R_{ inc_{(m)}}+R_{scat_{(n)}})}\cdot S_{12_{(m,n)}_{(l)}}) \tag{13}\]  
The global image (\(I_{r}\)) is generated by coherently adding up the individual reflected signals obtained from all \(\text{tr}_{m}\text{-tr}_{n}\) combinations. Mathematically, this summation process is represented as follows:  
\[I_{r}=\sum_{m,n}I_{(m,n)} \tag{14}\]  
\(I_{r}\) is a composite image that consolidates the information from all the \(\text{tr}_{m}\text{-tr}_{n}\) pairs and provides a comprehensive view of the object's scattering characteristics within the imaging plane. This global image is a valuable output of the ISAR processing, aiding in the detailed analysis and interpretation of the object's characteristics and behavior in response to electromagnetic waves.