esa-sceva/satcom-chunk-collection huggingface dataset:
# Temperature Sensing with Nd\({}^{3+}\) Doped YAS Laser Microresonators
## 3 Materials and Methods
### Optical Measurements  
The emission spectra of YAS:Nd\({}^{3+}\) microspheres were measured using a modified confocal microluminescence setup, detailed in Reference [10], with a 20\(\times\) microscope objective of NA = 0.4. To obtain the fluorescent emission of the sample, a continuous wave 532 nm DPSSL was used to excite the Nd\({}^{3+}\) ions in the microsphere, and the emission was detected by a CCD spectrograph. The center of the microsphere was chosen as the excitation zone and the surface as the detection zone [10]. The selection of the excitation area was performed by means of the XYZ translational stage with the aid of the television screen images, while the detection region was selected with the mirror.  
The upconversion emission of the sample was also studied by replacing the excitation source with a Spectra-Physics 3900S (manufactured in California, USA) continuous wave titanium-sapphire laser tuned to 808 nm. A minimum excitation power density of 6.4 kW/cm\({}^{2}\) was necessary to observe the upconversion emissions under these experimental conditions.  
Microspheres with a diameter between 30 and 50 \(\upmu\)m were studied. The spectra showing WGM were recorded for increasing values of the laser pumping power to study the microsphere laser emission, WGM peak shift, and upconversion emission.  
Measurement of the temporal evolution of the luminescence were carried out by exciting the bulk glass sample with an EKSPLA NT 342/3/UVE optical parametric oscillator laser (manufactured in Vilnius, Lithuania) that provides 10 ns pulses at a 10 Hz repetition rate. The emission of the sample was focused on the entry slit of a Triax 180 monochromator, and detected with a coupled photomultiplier specific for visible measurements. A TEKTRONIX-2430A digital oscilloscope (manufactured in Japan) was used to register the signal.  
The absorption spectrum of the bulk sample of YAS: Nd\({}^{3+}\) glass in the range from 200 to 1000 nm was measured with the Cary 5000 spectrophotometer (manufactured in Malaysia) to obtain the value of the energy gap between the thermally coupled levels of neodymium, E\({}_{32}\).
# BIM Application in Multi-Criteria Assessment of
## 3 Methodology
### Principal Approach of the Proposed Methodology  
The steps of the suggested research approach to rebuild a masonry brick facade by applying MCDM and BIM methodologies are listed below and detailed in Figure 2:  
Step 1. Defining the criteria of the production stage in terms of cost savings by analyzing three groups of criteria: preparation for production (purchasing), production, and logistics. Each group of criteria consists of six criteria. These criteria evaluate the alternatives in terms of cost savings and promote an economic approach for suspended brickwork facade elements at the production stage.  
Figure 2: Model of operation for the assessment of suspended brickwork facade production methods for a reconstructed building.  
Step 2. Applying an expert survey to estimate the relative weights of the criteria. The experts are asked to arrange the three groups of criteria according to their importance and then to arrange the six criteria in each group according to their relative importance.  
Step 3. Implementation of precise site measurements before the design of the brickwork facade.  
Step 4. Using precise site measurements and the construction data of the brickwork facade design, the BIM construction model is implemented.  
Step 5. Preparing a building information model based on the design of the three alternatives of suspended brickwork elements.  
Step 6. Defining compatibility with heritage conservation requirements as the brickwork facade must be rebuilt, and the architecture and volume of the building must be the same. If compatibility with heritage conservation requirements is not accepted, then it is necessary to return to step 4 and the BIM construction model must be corrected.  
Step 7. Preparing trial production and labor tests. After designing the newly promoted alternative, it is tested in the laboratory for load-bearing capability and deflections (Figure 2).  
Step 8. Ranking the alternatives by applying the multiple-criteria assessment and finding the most rational suspended brickwork facade production alternative by using the WASPAS method.  
Step 9. Preparation for production, production, and logistic phases according to the BIM model.
# Clustering Method of Large-Scale Battlefield Airspace Based on Multi A * in Airspace Grid System
## 4 Airspace Clustering Steps Based on Multi-A \({}^{*}\) Algorithm
### Construct Airspace Security Bounding Box
The maximum and minimum values of each column of matrix B are  
Figure 1: Schematic diagram of common polygon bounding box (**a**) spherical bounding box (SBB); (**b**) aligned axis bounding boxes (AABB); (**c**) oriented bounding boxes (OBB). obtained, that is, the maximum and minimum projection coordinates \(x_{\rm max}\), \(x_{\rm min}\), \(y_{\rm max}\), \(y_{\rm min}\) of polygon original vertex coordinates on two axes of OBB are obtained. Step 6: Calculate the length, width, and center coordinates of the OBB bounding box. Assume that the length of OBB bounding box is \(L_{1}\), the width is \(L_{2}\), and the central coordinates are (\(x_{c}\),\(y_{c}\)), then  
\[\left\{ \begin{array}{l}L_{1}=\max(x_{\rm max}-x_{\rm min},y_{\rm max}-y_{\rm min })\\ L_{2}=\min(x_{\rm max}-x_{\rm min},y_{\rm max}-y_{\rm min})\end{array} \right. \tag{1}\]  
\[[x_{c},y_{c}]=[\frac{x_{\rm max}+x_{\rm min}}{2},\frac{y_{\rm max}+y_{\rm min} }{2}]\times\mathbf{N}^{-1} \tag{2}\]  
Through the above steps, a special direction bounding box is built for each mission airspace, and in the later method discussion process, the bounding box and mission airspace fusion is considered as the overall object of the mission airspace.
## 5 Conclusions  
This paper analyzes the principle of QD multi-spot position detection based on OFDM and builds a mathematical model based on its principle to obtain the main influencing factors that affect the accuracy of multi-spot position detection: Gaussian spot radius, the actual position of the spot center of detector, the SNR, and FFT points. Finally, the Monte Carlo simulation method is used to verify the mathematical model using LabView. The results mean that the position detection accuracy value calculated by the proposed mathematical model is consistent with the simulation value. The results show that when the SNR is 31.74 dB, the \(\omega\) is 2 mm, \(N\) is 1024, and the multi-spot is at the center of QD; the accuracy of signal position detection calculated by the equation is 1.433 \(\upmu\)m, and the simulation results are 1.351 \(\upmu\)m, 1.354 \(\upmu\)m, and 1.389 \(\upmu\)m, respectively. Therefore, the method of QD multi-spot position detection based on OFDM proposed in this paper can directly calculate the position detection accuracy. Next, an experimental system will be established to further validate the effectiveness of the method. This method can provide theoretical guidance for engineering applications.  
Conceptualization, J.Y. and X.Y.; methodology, J.Y. and X.Y.; software, J.Y., P.L. and S.C.; validation, P.L. and Z.Z.; formal analysis, J.Y. and S.C.; investigation, J.Y.; resources, S.C. and Z.Z.; data curation, J.Y., X.Y., P.L. and S.C.; writing--original draft preparation, J.Y. and P.L.; writing--review and editing, J.Y., X.Y. and P.L.; visualization, J.Y., X.Y., P.L., S.C. and Z.Z.; supervision, X.Y. and P.L.; project administration, X.Y. and S.T.; funding acquisition, X.Y. and S.T. All authors have read and agreed to the published version of the manuscript. This research was funded by National Key R&D Program of China (2022 YFB3902500, 2022 YFB2903402, 2021 YFA0718804); Education Department of Jilin Province (JKH20220745 KJ).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
# Fast 3D Semantic Mapping in Road Scenes +
## 4 3D Semantic Mapping
### Semi-Dense SLAM  
We explore LSD-SLAM to track camera's trajectory and build consistent, large-scale maps of the environment. LSD-SLAM is a real-time, semi-dense 3D mapping method. It has several advantages: firstly, it is a scale-aware image alignment algorithm to directly estimate the similarity transform between two keyframes against different scale environments, such as office rooms (indoor) and urban roads (outdoor). The second one is that it is a probabilistic approach to incorporate noise on the estimated inverse depth maps into the tracking based on the propagation of uncertainty. Moreover, it could easily integrate with various kinds of sensors like monocular, stereo and panoramic cameras for various applications. Thus, it is able to make a reliable trajectory estimation and map reconstruction even in challenging surroundings.  
LSD-SLAM has three major components: tracking, depth estimation and map optimization. Spatial regularization and outlier removal are incorporated in the depth estimation with small-baseline stereo comparisons. In addition, a direct, scale-drift aware image alignment is carried on these existing keyframes to detect scale-drift and loop closures. Due to the inherent correlation between the depth and the tracking accuracy, depth residual is used to estimate the similarity transform \(\mathfrak{sim}(3)\) constraints between keyframes. Consequently, a 3D point cloud map is built based on a set of keyframes with the estimated inverse depth maps via minimizing the error of image alignment. The map is continuously optimized in the background using a _g2o_ pose-graph optimization. The approach runs at 25 Hz on an Intel i7 CPU. More details like keyframe selection and depth estimation can be referred to the work [21].
# Two-Stage Single-Channel Speech Enhancement with Multi-Frame Filtering
## 4 Experiment
### Baselines and Experimental Settings  
The proposed model is compared with several popular baseline models, including LSTM, Conv-TasNet, and DPRNN. Figure 3 shows the diagrams of these baseline models, and detailed descriptions of all the models are provided below.  
LSTM: Three-layers of bi-directional LSTM, where each layer has 512 hidden units, followed by a linear layer with 257 output units and tanh activation function. A Dropout layer with dropout probability equal to 0.4 is introduced on the outputs of each LSTM layer except the last layer.  
Conv-TasNet: The encoder and decoder are symmetric 1D convolution layers. The mask estimator comprises a layer normalization and a 1D convolution layer with a kernel size of 1 (1 \(\times\) 1-conv block) and 256 output channels. It is then followed by 8 convolutional blocks with a kernel size of 3, output channels of 512, and dilation factors ranging from 1 to 2\({}^{7}\), which are repeated 4 times. Ultimately, a 1 \(\times\) 1-conv block with a ReLU activation function is employed to estimate the mask.  
DPRNN: The encoder and decoder are symmetric 1D convolution layers. Six DPRNN blocks described in [21] are used to predict the mask. The DPRNN blocks utilize intra-block and inter-block RNNs, both of which adopt residual connections. These RNNs consist of bidirectional LSTMs with 128 hidden units, linear layers with 64 output units, and layer normalization. A dropout layer with a 0.1 dropout rate is inserted between the LSTM and linear layer.  
All utterances are resampled to 8 kHz. For T-F domain models used in the experiments, STFT with frame size of 64 ms, frame shift of 16 ms, and a 64 ms Hanning window is employed to extract the features. For Conv-TasNet the encoder is a 1D convolution layer with kernel size 40, stride 20, and 256 output channels. For DPRNN, the kernel size and stride for the convolutional encoder are 2 and 1, respectively, and the number of output channels is 64.  
The LSTM takes the magnitude of the speech as input and estimates the magnitude mask. The batch size is set to 16. The Conv-TasNet and DPRNN accept raw waves as input and directly output enhanced waves. The batch size for Conv-TasNet and DPRNN are 8 and 4, respectively. The input for the proposed model is the stacked RI components of the noisy spectrum and the output is the estimated RI components. The batch size is set to 16.  
All the models are optimized using Adam [31] with a learning rate of \(1.0\times 10^{-3}\). The negative Source-to-Noise Ratio (SNR) is used as the loss function. The maximum number of training epoch is 100. For T-F domain models, the training will stop if the loss is not decreasing on the development set for 10 consecutive epochs, and for time domain models, this number is 4. We set the forgetting factors \(\lambda_{y}\) and \(\lambda_{n}\) to 0.6 in this study.
## 2 Methods
### Analysis of Data  
#### 2.4.1 Reasons for Abnormal Elevation Data  
Over the past few decades, with the scale expansion of water diversion projects, increasing numbers of hydraulic facilities involved, and the increasingly complexity of the terrain along the pipeline, elevation data of the pipe centerline have become increasingly complicated. There are several reasons for the abnormal elevation data of the pipe centerline, such as environmental factors, monitoring equipment failure, data transmission errors, and manual recording errors.  
Figure 2 shows the collected data for the elevation of pipeline. From the red circle, it can be seen that there are abnormal data in the original data that do not conform to the trend, showing a \"needle tip\" in the figure. Such data should be identified and restored.
## 3 Results and Discussion
### UV/H\({}_{2}\)O\({}_{2}\)-SSHFCW System
Both systems were stable before 40 days of operation when the maximum variation is \(\pm 10\%\), acceptable for secondary wastewater treatment systems [18]. TOC removal efficiency achieved in both systems was \(80\pm 6.9\%\), which is higher than the value reported for biological systems treating pesticides [48] and similar to that reported for AOP operated with high irradiation times of 150-300 min [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49]. The similar results obtained for systems operated with 2 and 3d HRT during the second stage, was probably due to the recalcitrant nature of some pesticide byproducts. Therefore, microorganisms can no longer degrade or mineralize the compounds even after a long time contact, thus TOC concentration does not decrease with higher HRT. Some studies have suggested the degradation of malathion to phosphorylated thiol groups before mineralization [11; 12], but these compounds are barely biodegradable due to their bactericidal effect [50]. On the other hand, authors have highlighted that the importance of vegetation in CW resides in the rhizosphere, especially in rhizospheric bacterial strains [51; 52]. The vegetation planted in the CWs was collected from agricultural drains where organophosphate pesticides and traces of organochlorines have been reported [22]. This suggests that rhizosphere bacteria could be tolerant to pesticides and even assimilate them, as it has been demonstrated for in vitro studies [53] and bioreactors [54] that the adapted bacteria promoted higher removal rates and, therefore, lower HRT requirements. Nonetheless, this fact requires evaluation in future research.
# Accuracy of Extraoral Digital Impressions with Multi-Unit Implants
## 3 Results  
Overall best-fit measurements were performed between the 80 virtual models and the master model. The RMS values were presented in Table 2 and Figure 6. For the impression groups, the RMS values ranged between 69.90 and 87.43 \(\upmu\)m, and for the stone-cast groups, they ranged between 81.74 and 110.62 \(\upmu\)m.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scanner** & **Material** & **Group** & **RMS \(\pm\) 95\% CI (\(\upmu\)m)** & **MEDIAN** & **IQR** & **N** \\ \hline \multirow{4}{*}{Medit} & Coltene silicone & MCI & 82.26 [53.18; 111.34] & 70.9 & 13.98 & 10 \\ \cline{2-6}  & Zhermack silicone & MZI & 87.43 [72.00; 102.86] & 78.9 & 28 & 10 \\ \cline{2-6}  & Coltene stone model & MCM & 81.74 [72.97; 90.51] & 80.8 & 20.58 & 10 \\ \cline{2-6}  & [PERSON] stone model & MZM & 110.62 [94.57; 126.67] & 104.65 & 29.38 & 10 \\ \hline \multirow{4}{*}{Zirkonzhan} & Coltene silicone & ZCI & 69.90 [62.37; 77.43] & 69.25 & 17.38 & 10 \\ \cline{2-6}  & Zhermack silicone & ZZI & 80.67 [65.77; 95.57] & 75.35 & 9.58 & 10 \\ \cline{1-1} \cline{2-6}  & Coltene stone model & ZCM & 84.25 [71.23; 97.27] & 82.5 & 30.55 & 10 \\ \cline{1-1} \cline{2-6}  & [PERSON] stone model & ZZM & 105.04 [88.72; 121.36] & 95.95 & 27.53 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy (RMS \(\pm\) 95% CI) in \(\upmu\)m between each group and the master model.  
Figure 5: Virtual position of each implant in the master cast.  
The Mann-Whitney U test did not show statistically significant differences between the STLs achieved with the Identica T500 Medit(r) and S600 ARTI Zirkonzhan scanners for the comparison between the silicones (groups MZI vs. ZZI, \(p\) = 0.384 and groups MCI vs. ZCI, \(p\) = 0.705) and between the stone casts (groups MZM vs. ZZM, \(p\) = 0.406 and groups MCM vs. ZCM, \(p\) = 0.821).  
No statistically significant differences were observed for the Medit scanner (Mann-Whitney U, \(p\) = 0.112) and the Zirkonzhan scanner (Mann-Whitney U, \(p\) = 0.162) when comparing the two silicones studied (groups MCI vs. MZI and ZCI vs. ZCI). The stone casts (groups MCM vs. MZM and ZCM vs. ZZM) presented statistically significant differences for both the Medit scanner (Mann-Whitney U, \(p\) = 0.005) and the Zirkonzhan scanner (Mann-Whitney U, \(p\) = 0.041), with the best results found for the stone casts from the Coltene silicone.  
Mann-Whitney U tests were used to compare the silicone impressions and the corresponding stone casts without statistically significant differences between the Coltene(r) silicone impressions and the corresponding digitized stone casts acquired with the Medit(r) (groups MCI vs. MCM, \(p\) = 0.096) and Zirkonzhan(r) scanners (gruppos ZCI vs. ZCM, \(p\) = 0.059). The accuracy of the impressions made with Zhermack(r) silicone was higher than the corresponding stone casts for both scanners (groups MZI vs. MZM, \(p\) = 0.019 and groups ZZI vs. ZZM, \(p\) = 0.002).
# Development of Energy Storage Systems for High Penetration of Renewable Energy Grids
## 3 Frequency Regulation Technology Model
### Static Regulation Control Model  
The static regulation control model needs to consider frequency precision, triggering conditions, deadband range, and response speed. Setting these control parameters accurately is essential for ensuring the effective operation of the static regulation control system. Therefore, for the static regulation control model based on the WECC second-generation generic model, and the corresponding control parameters must be designed. When the static regulation strategy is not active, the upper threshold of the static regulation control model is 59.88 Hz. The symbol of the upper threshold is \(f_{dtd2}\), and the calculation equation can be written as follows:  
\[f_{dtd2\_sReg}=\frac{-59.88+60}{60}=0.002\;\text{pu} \tag{1}\]  
Figure 5: The block diagram of the REEC_C model [29].  
When the static regulation strategy is active, the upper threshold of the static regulation control model is 59.98 Hz. The symbol of the upper threshold is \(f_{dbd2^{\prime}\_sReg}\), and the calculation equation can be written as follows:  
\[f_{dbd2^{\prime}\_sReg}=\frac{-59.98+60}{60}=0.00033333\;\text{pu} \tag{2}\]  
Here, we assume that the flexible adjustment range of the static regulation control model is not considered. The lower threshold of the static regulation control model is 60 Hz, and the symbol of the lower threshold is \(f_{dbd1}\), and its value is designed to be 0. The frequency detection precision of the battery storage system is \(\pm\)0.001 Hz. When the grid frequency touches the upper threshold, \(f_{dbd2}\), and the grid frequency is \(\leq\)59.879 Hz, the battery energy storage system outputs an active power command, which is 1 pu. Therefore, the function of the droop gain, \(D_{up\_sReg}\), can be written as follows:  
\[D_{up\_sReg}\geq\frac{P_{start}-P_{0}}{-f_{start}+f_{ref}-f_{dbd2}} \tag{3}\]  
Equations (1)-(3) are defined as follows:  
\(D_{up\_sReg}\), under frequency droop gain of static regulation control model (pu);  
\(P_{start}\), starting output power value (pu);  
\(P_{0}\), initial output power value (pu);  
\(f_{start}\), starting output frequency value (pu);  
\(f_{ref}\), grid frequency reference value (pu);  
\(f_{dbd2\_sReg}\), upper frequency threshold of static regulation control model (pu);  
\(f_{dbd2^{\prime}\_sReg}\), upper frequency threshold of static regulation control model under strategy operation (pu).  
According to the proposed static regulation control model, the frequency detection accuracy and upper threshold are considered. The droop gain of the static regulation control model is designed to be 60,000.
# An Inspection of IFC Models from Practice
## 2 Industry Foundation Classes
### Disciplinary Models and Federated Model  
BIMs are usually split into several models, each of which describe the information related to a design discipline: architectural, structural, installations [33]. They are combined together in a federated model.  
For using the BIM, and in particular the IFC files representing each model, which are separately exported, it is important to know what to expect within each model: what elements are included and which ones are not. Note that these are potentially sharing the same space.  
The correct merger of multiple models into one is important for many use cases. For example, the computation of routes within the building should consider all the walls, whether they are load-bearing or not, and the calculation of volumes and heights have to count on all walls and slabs. Additionally, with respect to integration with geoinformation, it is important to know which surrounding elements from the context where the building is designed are stored in the BIM. Another example is the computation of the maximum envelope of the building, such as for building permit regulation checks [30], where all the elements that extend towards the outside of the building should be included (e.g., chimneys and balconies).  
Unfortunately for interoperability, a unique subdivision of elements within such models is neither provided by standardization nor by shared practice. Instead, requirements and specifications are defined in each call for tenders according to the specific requirements of a particular use case. Although suboptimal, it is currently the most reasonable choice in order for users to obtain exactly the representation(s) they need. However, it means that: first, the elements can be stored in any of the models composing the federated one;and second, an element (such as a slab or a wall or an installation part) can be stored redundantly (and possibly inconsistently) within several discipline models. Arguably, in these circumstances, a validity checker should not detect the duplication of the same elements as a clash. Due to the way references between elements are stored within IFC models, in most cases, references can only be made between elements stored in the same file. This can cause issues when one depends on relationships (such as space boundaries) when the elements are contained in distinct aspect models. [PERSON] et al. [34] provides options to later infer such relationships.  
The developers of tools to automatically process IFC models should consider this practice and possibly propose guidelines and prescriptions about the objects to be included in the representation for each case, or about how to document the modelling choices as machine-readable metadata.