esa-sceva/satcom-chunk-collection huggingface dataset:
# SbMBR Tree--A Spatiotemporal Data Indexing and Compression Algorithm for Data Analysis and Mining
## 4 Experimentation and Results  
### Experimental Setup  
To assess the merit of our proposed SbMBR tree with the Hilbert curve, we implemented and ran experiments on a two-dimensional space. The method was implemented in C++, under Linux. We compared our method against the quadtree [8] and brute R-tree. The brute R-tree method is a method that uses an index structure similar to the SbMBR tree but uses the \(O\big{(}n^{2}\big{)}\) brute method instead of the Hilbert curve in the process of MBR merging. In addition, we also compared our method with the JPEG compression algorithm in machine learning scenarios.  
The hardware specification is listed below:  
* Processor: Intel\(\copyright\) Core\(\copyright\) i7-10875H CPU @ 2.30 GHz
* RAM: 16 GB
* OS: Ubuntu 22.04  
The test data set used in the experiment is WorldClim version 2.1 climate data for 1970-2000 [34]. We mainly chose the data of the average temperature in July and solar radiation in April. Total Table 1 shows the details of the data for the experiment.  
Table 2 explains the details of the params used in all the methods when we experimented.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name of the Data** & **Types of the Data** & **Location of the Data** & **Density of the Data** & **Scale of the Data** \\ \hline Test 1 & Average temperature in July & \(31^{\circ}40^{\prime}\) N–\(48^{\circ}20^{\prime}\) N, & Dense & \(400\times 400\) \\  & & \(96^{\circ}40^{\prime}\) W–\(80^{\circ}00^{\prime}\) W & & \\ Test 2 & Average temperature in July & \(30^{\circ}00^{\prime}\) S–\(6^{\circ}40^{\prime}\) N, & & \\  & & \(180^{\circ}00^{\prime}\)–\(138^{\circ}20^{\prime}\) W & & \\ Test 3 & Solar radiation in April & \(33^{\circ}45^{\prime}\) N–\(50^{\circ}25^{\prime}\) N, & & \\  & & \(100^{\circ}50^{\prime}\) W–\(75^{\circ}50^{\prime}\) W & & \\ Test 4 & Solar radiation in April & \(30^{\circ}00^{\prime}\) S–\(6^{\circ}40^{\prime}\) N, & & \\  & & \(180^{\circ}00^{\prime}\)–\(138^{\circ}20^{\prime}\) W & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of data for the experiment.  
In our implementation, we used the coefficient of variation as _eps_ in our algorithm. The specific definition is as follows:  
\[eps=\frac{\sigma}{\mu} \tag{11}\]  
where \(\sigma\) is the standard deviation and \(\mu\) is the average value. Different _eps_ can also show the advantages and limitations of the methods.
## 2 Materials and Methods
### Monitoring Campaign
#### 2.3.3 Indoor Microclimate Monitoring Campaign of La Specola Museum  
To investigate the thermal and hygrometric conditions inside La Specola museum, a continuous monitoring system was installed in 2011. The details of the monitoring campaign are reported in previous studies [31; 32; 33] and summarized below for the purpose of the comparison with Vasari Corridor. Similarly to the other case study, the monitoring setup consists of a set of data loggers that measure dry-bulb air temperature (T) and relative humidity (RH) [38], which respect the requirement of the reference standards [39; 40].  
In this study, the data monitored in the rooms X and XXII between May 2012 and April 2013 were analyzed. These zones, without HVAC system, were selected as representative of a group of rooms in the museum. The sensor positions in the two zones are reported in Figure 7. The positions of the continuous monitoring system were defined according to the same procedure described for the Vasari Corridor (see Section 2.3.2). The sensors  
Figure 6: (a) Pictures of the dataloggers in positions A (Lungarno) and (b) B (Ponte Vecchio).  
continuously monitored the indoor microclimate parameters (T and RH) in the two zones every 15 min.
# Early-Age Cracking in Concrete: Causes, Consequences, Remedial Measures, and Recommendations
## 1 Introduction  
Early-age cracks are defined as cracks that generally develop within the first seven days after the placement of concrete; however, 60 days can be considered the maximum limit for early-age cracking, accounting for the initiation of cracks at the bottom of the concrete slab that may take more than one week to be visible [1].  
The early-age characteristics of concrete have been a point of research interest for a long time, and researchers have focused on studying different aspects of the early-age characteristics. [PERSON] [2] reviewed the work on the influence of moisture, temperature, and physical/microstructural characteristics on the early-age cracking of concrete. The other examples of research study include cement hydration and microstructure development by [PERSON] [3], and the characteristics of fresh concrete, such as flocculation, rheology, structural buildup, and formwork pressure, which were reviewed by [PERSON] et al. [4]. [PERSON] and [PERSON] [5] focused on the properties of both fresh and hardened concretes, while [PERSON] and [PERSON] [6] studied the mechanisms that affect the properties of concrete and itsperformance at the early age. On the other hand, [PERSON] [7] investigated the assessment methods to quantify the stiffening and hardening behavior of mortar and concrete. [PERSON] and [PERSON] [8] reviewed the mechanisms that cause complex cracking phenomena; they also focused on the methods to control the early-age cracking in concrete.  
This paper provides a unique perspective on early-age concrete by focusing on the types of early-age crack, the causes of early-age cracking, the factors influencing the initiation and growth of early-age cracks, the consequences of early-age cracking, and the modeling and remedial measures for early-age cracking. Most importantly, the authors have made some key recommendations that could be very effective in reducing or eliminating the early-age cracking in concrete. This paper could act as a guideline for construction stakeholders for handling the early-age cracking problem in concrete. On the other hand, it provides a comprehensive discussion on the latest findings and identifies some research gaps in the area of the early-age cracking in concrete. However, this paper does not include any discussion on the occurrence and causes of the early-age cracking in cement paste. In practice, concrete is widely used to construct numerous structures. Therefore, it is highly practical to study the early-age cracking phenomenon of concrete.
## 3 Results and Discussion
### Structural Characterization
Meanwhile, the neighboring fringe spacing of the nanoparticle, dispersed on the surface of rGO, was 0.34 nm. This can be attributed to (110) plane of SnO\({}_{2}\) nanocrystals, indicating that the SnO\({}_{2}\)-rGO nanocomposites were prepared. Figure 4e shows the selected area electron diffraction (SAED) pattern of SnO\({}_{2}\)-rGO; all diffraction rings on this SAED pattern were exactly assigned to the reflections (110), (101), (211), (112) of the rutile SnO\({}_{2}\) phase and (002) of rGO, which agree well with the XRD pattern of SnO\({}_{2}\)-rGO in Figure 3a. The energy dispersive spectroscopy (EDS) spectrum of SnO\({}_{2}\)-rGO indicated that the Sn element is found in the constituent elements of SnO\({}_{2}\)-rGO (C, O and Sn) and the content of the Sn element was calculated as 15.737 wt %, further verifying that SnO\({}_{2}\) was loaded on the surface of rGO. To further investigate the composition and chemical state of the element, X-ray photoelectron spectroscopy (XPS) spectra of the SnO\({}_{2}\)-rGO nanocomposite were carried out and are shown in Figure 5. As depicted in Figure 5a, the peaks of C 1s, O 1s, Sn 4d, Sn 3d and Sn 3p can be observed in survey spectrum. The two peaks with binding energies of 487.5 eV and 496.0 eV corresponded to Sn 3d\({}_{5/2}\) and Sn 3d\({}_{3/2}\) respectively, which indicated the existence of Sn\({}^{2+}\). Figure 5c shows the C 1s  
Figure 4: (**a**) Transmission electron microscopy (TEM) micrograph of rGO (the inset shows the HRTEM (high-resolution TEM) image of rGO); (**b**) Scanning electron microscopy (SEM) image of pristine SnO\({}_{2}\) nanoparticles; (**c**) TEM image; and (**d**) HRTEM of SnO\({}_{2}\)-rGO nanocomposites; (**e**) selected area electron diffraction (SAED) pattern; and (**f**) the EDS (energy dispersive spectroscopy) spectrum of SnO\({}_{2}\)-rGO. spectrum and binding energy of C-C, C-O; C=O and O-C=O are assigned at 284.7 eV, 285.6 eV, 286.8 eV and 288.9 eV, respectively [29]. Figure 5d displays the O 1s spectrum, which has been separated into three peaks at 530.4 eV, 531.5 eV and 533.7 eV. The peak centered at 530.4 eV was attributed to oxidized metal ions (\(O_{L}\)) in the lattice of Sn-O. The binding energy at 531.5 eV is ascribed to oxygen vacancies (\(O_{V}\)) in the surface of SnO\({}_{2}\)-rGO. The high binding energy peak located at 533.7 eV originated from the oxygen atoms (\(O_{S}\)) chemisorbed on the surface of synthesized materials [30]. Based on above results, it is concluded that the as-synthesized nanocomposites are composed of SnO\({}_{2}\) and rGO.
## 3 Results
### Shishaldin Volcano  
Shishaldin volcano is one of over 40 active volcanoes of Alaska (USA). This stratovolcano, which is located in the Unimak Island (54.7554\({}^{\circ}\) N-163.9711\({}^{\circ}\) W), has a nearly symmetrical cone (\(\sim\)16 km of diameter at the base) and reaches 2857 m above sea level (the highest altitude in the Aleutian Islands). About 40 historical eruptions have occurred at Shishaldin volcano (a summary of those events can be found in [40]). After 2000, a number of eruptive episodes occurred; the main eruption occurring during 2000-2017 was that recorded on 17 January 2014. It was characterized by the lava emission within the crater, low-level steam plumes, sporadic dustings of ash and ballistics on volcano flanks [41]. Starting from 30 January 2014, satellite observations revealed an increase of the surface temperature [42]. The presence of lava inside the crater was then reported on 25 March, 13 May, 1 October and in late January 2015 [40,42]. Afterwards, Shishaldin showed a low-level activity, continuing throughout 2015 and until the first months of 2016. A new lava emission inside the crater occurred on 23 October 2019. In the following weeks, lava flowed down the N and NE flanks; lava flows were revealed by independent satellite observations during November-December 2019 [43]. In January 2020, a new lava effusion  
Figure 4: Thermal anomalies detected by RASTer at Mt. Etna during the period January–May 2020, associated with the Strombolian activity recorded at the Voragine-Boca Nuova complex.  
occurred; lava affected the northeast (traveling down for 2 km) and north flanks, producing meltwater lahars [43].  
Figure 5 displays the time series of RASTer and AVA detections at Shishaldin for the period January 2014-2016, when both systems were operational. We considered an area of 8000 m of radius around the summit crater to perform the comparison. The plot shows that RASTer detected a higher number of thermal anomalies than AVA and better emphasized a number of eruptive episodes of the investigated volcano (in terms of daily number of hotspots), indicating that the most intense Shishaldin activity occurred on 24 January 2015, in presence of lava inside the crater.  
Additionally, to assess the RASTer capacity in providing information about thermal anomalies at Shishaldin in recent years, Figure 6a shows an example of thermal anomalies detected on ASTER TIR data on 20 October 2019, in correspondence with a minor thermal activity that occurred at the summit crater. Figure 6b displays the lava flow on 8 January 2020, separating in two branches moving in the NW and NE direction, respectively. Those maps confirm the RASTer efficiency in mapping lava flows (providing unique information in periods when AVA products were not available), preserving a high sensitivity level even in areas located at the high latitudes, characterized by a cold background, where traditional fixed thresholds methods generally do not perform well (e.g., [44]).  
Figure 5: Time series of daily number of hotspots flagged by RASTer and AVA (January 2014-2016) at Shishaldin volcano.
## 3 Results and Discussion
### Effect of Bubbles on Displacement of Elastic Cylinder  
As discussed in the previous section on bubble morphology and elastic cylinder displacement, there exists a complex relationship between bubble shape and the displacement of the elastic cylinder, which extends beyond the influence of the dimensionless distance. It is not necessarily the case that the closer or farther the primary bubble is to the elastic cylinder boundary, the larger the displacement at the lowest or highest point of the elastic cylinder boundary. Moreover, under the same conditions, it does not imply that both the highest and lowest point displacements occur simultaneously. This is an important point to note.  
In this section, the maximum displacement of the elastic cylinder is non-dimensionalized as \(\gamma_{S}\), where \(\gamma_{S}=S/R_{max}\). Figure 14 illustrates the relationship between the maximum displacement and dimensionless distance during the expansion and collapse of the bubble in several experiments. It can be observed that, on the \(\gamma-\gamma_{S}\) graph, the maximum value of the maximum displacement differs between the collapse and expansion processes as the dimensionless distance changes, with the collapse process requiring a larger dimensionless distance. This section will specifically examine whether there are conditions for the maximum displacement and summarize the pattern of elastic boundary displacement, which will have positive implications for both the maritime and medical fields.
## 3 Case Study
### Characteristics of Rock Slope Anchoring System  
Based on the investigation data of the left bank water cushion pond rock mass slope, the rock mass of the slope and above is mainly composed of first-class columnar-jointed basalt, massive basalt, and agglomerate lava. The structural surfaces are well developed within the slope body, the unloading of the rock mass is relatively deep, and the rock mass is primarily classified as type III2 or IV. The dip angle of the gently inclined structural surfaces is smaller than that of the slope, which leads to poor slope stability. Locally, there are blocks formed by NWW-direction faults, near-SN-direction unloading joints, and NNE-direction faults along with gently inclined shear zones. The potential failure mode of the slope stability is block sliding failure along the gently inclined structural surface as the sliding surface. There are two main local failure modes: one is collapse, and the other is block sliding failure. To ensure the long-term stability of the slope, after excavation of the left bank water cushion pond slope, anchor cables are used for reinforcement. The engineering geological slope map and anchor cable layout of the left bank water cushion pond slope are shown in Figure 2.  
Figure 2: Cross-sectional Diagram of the Left Bank Water Cushion Pond Slope. (**a**) Engineering Geological Profile (**b**) Layout of Anchor Cables (Rods) and Monitoring Setup for Slope Surface Displacement.
# Improved Remote Photoplethysmography Using Machine Learning-Based Filter Bank
## 2 Materials and Methods
### Machine Learning-Based Filter Bank (MLFB) Algorithm  
Figure 1 shows the overall scheme of the MLFB algorithm proposed in this study. First, the pulse signal of rPPGPOS(t) was processed from the RGB component using a POS algorithm that eliminated intensity variations by projecting the RGB components onto a plane perpendicular to a specified normalized skin-tone vector [13]. Subsequently, the MLFB algorithm operates in four major steps: (1) decomposing using 16 sub-band pass  
Figure 1: Schematic of the proposed machine learning-based filter bank (MLFB) algorithm. The pulse signal of remote photoplethysmography (rPPGPOS) is derived using the plane-to-orthogonal-skin (POS) algorithm, using the red [R(t)], green [G(t)], and blue [B(t)] components of the video signals. The signal is then processed through a 16-sub-band filter bank, and the feature matrix (F) is computed. A support vector machine (SVM) is employed to generate a dominant sub-band temporal matrix (DSTM), which is used to output the refined pulse signal of rPPGMLFB.  
filters, (2) feature extraction, (3) computation of the most dominant spectral sub-band over time, and (4) generation of the pulse signal. First, the pulse signal was decomposed to 16 Y\({}_{i}\) by 16 narrow band-pass filters, each with a bandwidth of 0.4 Hz and an overlap of 0.2 Hz, covering the range from 0.8 to 4.2 Hz. The filter bank was specifically designed to cover the range of human heart-beating activity from 48 to 252 bpm [22].  
Eleven features were extracted from each filtered signal, as listed in Table 1, and denoted by F\({}_{i,j}\), where \(i\) represents a sub-band (i.e., one of the bands produced by the filter bank) numbered from 1 to 16, and \(j\) denotes a feature class ranging from 1 to 11. The support vector machine (SVM) was trained using 10-fold cross-validation to identify the sub-band where the pulse signal is most dominant at each time point. The model produced a binary output, with 0 indicating a less-dominant sub-band and 1 indicating a dominant sub-band. The series of binary outputs was named the dominant sub-band temporal matrix (DSTM). Finally, the refined pulse signal of rPPG\({}_{\text{MLFB}}\) was computed by multiplying the filtered signal of Y\({}_{i}\) with DSTM.
## 5 Multiobjective Optimization of Main Beam Structure Based on RSM
### Multiobjective Optimization of Main Beam Structure
#### 5.2.5 Response Surface Analysis  
The response surface graph can directly reflect the influence of design variables on the objective function. According to the response surface graph, the influence of the design variables on the objective function can be determined, and the response surface graph can be obtained through the multiobjective optimization module of ANSYS Workbench. Taking the design variables P3 and P4 of the main beam as an example, the response surface of P3 and P4 to the mass, maximum deformation, maximum stress, first-order natural frequency, and fatigue life of the main beam were obtained, as shown in Figures 28-32, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fit Goodness Index** & **D(x)** & **S(x)** & **L** & **F(\(\alpha\))** & **M(x)** \\ \hline \(R^{2}\) & 0.999 & 0.946 & 0.945 & 0.999 & 0.999 \\ \(R_{a}^{2}\) & 0.999 & 0.935 & 0.934 & 0.999 & 0.999 \\ \(NRMSD\) & \(8.58\times 10^{-4}\) & \(8.63\times 10^{-4}\) & \(3.00\times 10^{-6}\) & \(7.58\times 10^{-3}\) & \(2.37\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 12: Response surface model significance analysis parameters.  
Figure 27: Fitting degree of test factors to objective function.  
Figure 28: Quality response surface.  
Figure 29: Deformation response surface.  
Figure 31: First order natural frequency response surface.  
Figure 30: Equivalent stress response surface.  
It can be seen from the above response surface diagram that with the increase of P3 and P4, the weight of the main beam increased, the maximum deformation decreased, the maximum stress decreased, the first natural frequency increased, and the fatigue life increased. The influence of the above design variables on the objective function provided a basis for the subsequent correction of the design variables and determination of the optimal design scheme.
## 3 Experimental Results and Analysis  
In this section, we outline and present the validation of our proposed framework (in the scope of this paper, we focus on a framework to mitigate the heading drift; therefore, the positioning errors reported in this paper are 2D errors, and the height constraint method will be introduced in our future work). First, the specifications of the sensor applied in the experiment and the processing platform are introduced in Section 3.1. Second, in Section 3.2, the performance metrics are introduced to evaluate the ZUPT-ZARU-ESKF-aided PNS (abbreviated as ZUPT), the ZUPT-ZARU-AFM-ESKF-aided PNS (abbreviated as ZUPT-AFM), the ZUPT-ZARU-EC-ESKF-aided PNS (abbreviated as ZUPT-EC), the ZUPT-ZARU-HDR-ESKF-aided PNS (abbreviated as ZUPT-HDR), and the ZUPT-ZARU-MED-EC-ESKF-aided PNS (abbreviated as ZUPT-MED-EC). In the following sections, these frameworks are examined on the Vicon dataset and are further evaluated on several real-world long-term indoor and outdoor moving datasets. As shown in Table 2, different heading drift constraint method based frameworks can be generated using the proposed framework by setting different states of the EC and HDR algorithms, which indicates that our proposed framework is flexible and modular. Except for the heading drift correction module, the remaining parts of all frameworks are the same. All frameworks are explained as follows:  
* ZUPT: Only ZUPT and ZARU are used.
* ZUPT-MED-EC: ZUPT and ZARU are used. Magnetic fields filtered by the MED are used to enhance the heading estimation (i.e., if \(EC=2\), \(\Delta\psi_{k}=\psi_{k}^{m}\); otherwise, \(\Delta\psi_{k}=0\)).
* ZUPT-AFM: ZUPT and ZARU are used. The adaptive fusion module is used to mitigate the heading drift (i.e., \(\Delta\psi_{k}=a_{m}\Delta\psi_{k}^{m}+a_{R}\Delta\psi_{k}^{R}\)).
* ZUPT-HDR: ZUPT and ZARU are used. Only the HDR algorithm is performed (i.e., if \(HDR=1\), \(\Delta\psi_{k}=\Delta\psi_{k}^{R}\); otherwise, \(\Delta\psi_{k}=0\)).
* ZUPT-EC: ZUPT and ZARU are used. Raw magnetic fields are used to enhance the heading estimation (i.e., \(\Delta\psi_{k}=\Delta\psi_{k}^{m}\)).