esa-sceva/satcom-chunk-collection huggingface dataset:
# NPP-VIIRS DNB Daily Data in Natural Disaster Assessment: Evidence from Selected Case Studies
## 2 Materials and Methods
### Event Selection
It hit Puerto Rico as a Category 4 hurricane with a maximum sustained wind of 249 km/h, resulting in catastrophic damage and a large scale power outage across the whole island [48]. More than 3 million people were affected by the event [49]. As of 21 September 2017, 100% of the island was without power, and only 20% of these areas were reconnected with power one month after the hurricane [50]. Tropical Cyclone Hudhud was a Category 4 storm with a maximum sustained wind speed of 217 km/h that made landfall at Visakhapatnam [51]. A maximum storm surge of 1.4 m above astronomical tide was reported [52]. The sustained winds and storm surge broke electric poles throughout almost the entire city and destroyed some switching stations, resulting in a lengthy power outage in most parts of Visakhapatnam [53]. Louisiana Flood was caused by upper-level low-pressure weather that brought heavy rainfall of 50 to 80 cm to Southeast Louisiana and Southwest Mississippi from 11 to 13 August 2016. This deluge caused rivers to overflow and resulted in catastrophic flooding and affected large portions of the highly populated East Baton Rouge, Livingston, Ascension, and Tangipahoa Parishes [54]. On the night of the 15 August, about 14,300 Energy (an electric distribution utility company in Louisiana) customers in East Baton Rouge Parish and Livingston Parish were out of power [55]. In addition, 75% of the homes in Livingston Parish were reported as a 'total loss' [56]. For the Yulin Flood, from 6:00 p.m. on 25 July to 6:00 a.m. on 26 July 2017, a heavy rain event hit Yulin City in Shaanxi Province, China. The induced flood inundation of 12 counties included Guide, which was reported as one of the most affected counties. The maximum precipitation in Guide was 103.4 mm [57]. According to the report by the China National Commission for Disaster Reduction, in Guide County alone, the flood had caused 6 deaths, 86 injuries, 194 households to experience house collapse, 61 bridges to collapse, and 213 roads to be damaged [58].
## 5 Measurement Equipment and Testing Setup  
### Four-Dimensional Sensor  
The 4D device [22] is based on a multi-sensor technology with the main purpose of monitoring active earthquake areas. In the sensor panel, there are three built-in acceleration sensors that allow for the measurement of medium or strong earthquakes in the X, Y, and Z directions. A velocity sensor enables the measurement of high-sensitivity vibrations in the Z direction only to obtain micro-seismic data.  
The device is universal and capable of measuring both strong and micro-seismic vibrations. It enables measuring the dynamic behavior of buildings by switching between sensor signals to monitor vibrations with relatively high amplitudes or micro-vibrations in the Z direction only. This is a disadvantage of the device and the reason for using additionally the 3D one.
## 4 Experiments  
### Setup  
#### 4.1.1 Datasets  
We conducted our experiments on five public datasets, namely the MOOCs dataset, Book-Crossing dataset, Movielens-1m dataset, Steam-200k dataset and the Amazon Beauty dataset. The statistics of the five datasets are shown in Table 1.  
The MOOCs dataset [16] is composed of the data of courses and student activities from the real environment of XuetangX. It contains the behavior sequence of users and their click-related knowledge points.  
The Book-Crossing dataset [17] is composed of 278,858 user ratings in the Book-Crossing community. It contains 1,149,780 scores of approximately 271,379 books. The dataset contains three categories: users, books and book-rating. Books include the ISBN of the book and content-based information such as the author, publication year and publisher.  
The MovieLens-1m dataset [18] contains 1 million ratings of 4000 movies by 6000 users. It is divided into three tables: rating, user information and movie information.  
The Steam dataset [19] is a list of user behaviors with a large number of data on games played and purchased by players, as well as game titles, game prices and other information. The value indicates the degree to which the behavior was performed--in the case of 'purchase', the value is always 1; and in the case of 'play', the value represents the number of hours the user has played the game.  
The Amazon Beauty dataset contains a large number of users' ratings of beauty products and timestamps with response scores.
## 3 Experimental Results
### Discussion  
#### 3.4.1 The Effectiveness of Combined Region, Super-Pixel, and Pixel Information  
The compensated information combined multi-level information from the pixel level, super-pixel level, and shadow region level. The combination makes the compensated results find the pixel information change on different levels and adapt to this change. Therefore, the strategy provides a more balanced way to compensate for shadow information self-adaptively. The results of image #1 in Figure 5a compensated for by the global shadow region, the super-pixel information, and multi-level information are shown in Figure 10a-c, respectively. The detailed shadow compensated results are shown in Figure 10d,e. The multi-level information combined with the super-pixel local information can provide information from the local to the global regions. It can be seen that the imbalance of the internal brightness distribution is obvious while only compensating for the global shadow area.Uneven compensation can be balanced by using the statistics of super-pixel units. However, a contrast over-compensation problem may occur, leading to partial compensation distortion, as shown in Figure 10e.  
The statistical results of the brightness histogram in the shadow area are shown in Figure 11. Figure 11a is the histogram of Figure 10a, which was compensated for only using the region information by setting \(\mu=1,v=0\). The adjacent brightness  
Figure 10: Comparison of shadow compensation results of image #1 in Figure 5a using shadow information of different regions. (**a**) results using shadow region information. (**b**) results using super-pixel information. (**c**) results combining multi-level information. (**d**) the detailed compensated results of (**a**). (**e**) the detailed compensated results of (**b**). (**f**) the detailed compensated results of (**c**). The yellow frame in (**a–c**) is an area with different compensation results. The yellow circle and the red frame in (**d–f**) are the zoomed region with obvious difference.  
values have great differences, and the variance is larger. Figure 11b is the histogram of Figure 10b, which was compensated for only using the super-pixel information by setting \(\mu=0,v=1\). The histogram is much smoother. However, the total brightness is not improved enough, as shown in Figure 11a. Figure 11c is the histogram of Figure 10c, which was compensated for using the region information and super-pixel information together by setting \(\mu=0.5,v=0.5\). The histogram is closer to a normal image without cloud shadows, which is smooth and conforms to a normal distribution. Thus, using the balanced compensation model, integrated with the statistical information of a shadow area and the super-pixel unit, balances the uneven compensation effectively and does not lead to compensation distortion problems.
## 6 Optimization of TMDI and Equivalent TMD
### Effect of Mass Ratio and Inertance Ratios on the Performance of TMDI
However, when the mass ratio increases by 2% (2% to 4%), the extra reduction in the response is only 4%. This observation suggests that a significant impact on the mass ratio can be observed when the mass of TMD is smaller. A curve through points A, D, and E represents the typical tuned inerter damper (TID), where the attached mass is almost zero. From point A to D, the STD displacement at the top floor decreases sharply, and the reduction rate decreases as we go from point D to E. This pattern in TID is similar to that observed in TMD, where the inertance ratio of TID is analogous to the mass ratio of TMD. If we consider any point between points A and C and move along \(\beta\) axis, the response increases for smaller values of the inertance ratio. Here, the inerter negatively impacts the energy-dissipating capacity of the TMD. As defined by Equation (5), the inertance ratio increases the mass of equivalent TMD. Even if the equivalent mass is not considered, STD displacement reduced by TMD at a 4% mass ratio is more significant than TMDI with the same mass ratio and an inertance ratio of 0.9. Thus, the curve representing linear TMD (curve through points A, B, and C) performs better than any other parallel lines. This finding strengthens the argument that linear TMD with optimum parameters can serve better than TMDI. Figure 9: Effect of the inertance ratio and the mass ratio on the structural responses under filtered white-noise excitations: (**a**) RMS displacement at the top floor, (**b**) RMS acceleration at the top floor, (**c**) peak displacement at the top floor, and (**d**) peak acceleration at the top floor.
# Before and After: A Multiscale Remote Sensing Assessment of the Sinop Dam, Mato Grosso, Brazil
## 3 Results
### Flooding of Small Stream Tributaries  
The daily revisit period of the Dove PS constellation allows for a dense time sequence view of the filling of the reservoir and the impact the filling had on the small Creek tributaries. As an example, we illustrate the sequence for the confluence of the Rio Roquete Creek and the Teles Pires river over the December 2018-February 2020 period (Figure 12). In December 2018, the original course of the narrow stream can be seen draining into the Teles Pires river. The forest has been cut along the banks of the stream in the area planned to be flooded by the HPPS reservoir. The same can be seen from the image acquired 16 January 2019, prior to the filling of the reservoir. From the image acquired 1 February 2019 one day after the filling began, the incursion of water from the silt laden Teles Pires (high water) into the Creek can be seen, along with the stream having overflowed its usual banks. In March, not only has the flooded stream surface area increased considerably, covering nearly the entire expected range, but the main branch of the Teles Pires can also be seen flooding standing forest on the banks. In April 2019, the flooding of the Rio Roquete Creek is complete, and the main branch of the Teles Pires has also widened to include flooding of forest and agricultural areas. There is a marked decrease in turbidity over the course of the low water season (June-August). The turbidity of the Teles Pires increases again in the high-water season (December 2019-February 2020). From February 2019 onwards, silt from construction, modification of the riverbanks to prevent flooding, and permanent inundation from the reservoir have permanently changed the habitats in the small stream tributaries.
# SNA: Sensitivity-Aware Neural Architecture Adaptation for Uniform Quantization
## 3 Methodology
### Motivation
\[C_{\text{in}}\times C_{\text{out}}\times b_{t}=[C_{\text{in}}\times r]\times [C_{\text{out}}\times r]\times b \tag{3}\]  
Based on the number of parameters in the weight matrices, we can choose the appropriate multiplier \(r\). In Formula (3), \(b_{t}\) represents the target bitwidth for the base model and \(b\) represents the scaled bitwidth for the expanded model. Usually, \(b_{t}\) is determined by the target memory usage and \(b\) is determined by the network itself, which will be further discussed later. With \(b_{t}\) and \(b\), we can determine the multiplier \(r\):  
\[r=\sqrt{\frac{b_{t}}{b}} \tag{4}\]  
For instance, if we set the target bitwidth at 8 bit and the scaled bitwidth at 4 bit, then the multiplier \(r\) is \(\sqrt{\frac{8}{4}}=\sqrt{2}\). In this paragraph, we will explain how \(b\) is determined by the network. Though networks with more channels are generally less sensitive to quantization, the benefit of channel splitting is different for different networks. To further examine the hypothesis, we also investigated the relationship between the network size and sensitivity to quantization by conducting empirical experiments on redundant models and compact models respectively. The results are shown in Section 4. To save training time, we also formulated four extra channel-allocation strategies, which will be discussed in the following section. These strategies allow the expanded model to inherit a significant portion of the pre-trained weights from the base model, eliminating the need to train from scratch and quickening the fine-tuning process. Figure 1: Illustration of the SANA pipeline. First, decide on the target quantized model size. Then, choose the bitwidth and multiplier based on the trade-off between quantization and model sensitivity. Next, perform channel splitting using the initialization techniques. Afterwards, fine-tune the model. Finally, perform uniform quantization.
## Impact Sources, Methods, and Performance Rating Standards in Republic of Korea
### Floor Impact Sound Performance Rating of Weight Impact Sound  
The ratings for heavy-weight impact sound in the floor structure of domestic multi-family housing are outlined in Table 3. Under the pre-certification system, the minimum range for the inverse A-weighted floor impact sound is between 48 dB and 50 dB or lower. A range from 47 dB to 44 dB corresponds to a performance rating of Level 3. Additionally, a range from 43 dB to 41 dB is classified as Level 2. The performance rating of Level 1 is given for a floor impact sound of 40 dB or lower. The smaller the value, the better the floor's impact sound insulation performance.  
With the revision to the floor impact sound post-certification performance verification system, the heavy-weight impact sound performance ratings for floor structures are shown in Table 4. The minimum standard for heavy-weight impact sound is now between 46 dB and 49 dB or lower. The Level 3 range is from 45 dB to 41 dB, while Level 2 is between 41 dB and 37 dB. Level 1 corresponds to a floor impact sound of 37 dB or lower.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Frequency** & **50 Hz** & **63 Hz** & **80 Hz** & **100 Hz** & **125 Hz** & **160 Hz** & **200 Hz** & **250 Hz** & **315 Hz** & **400 Hz** & **500 Hz** & **630 Hz** \\ \hline One-third & \multirow{2}{*}{\(A\) (dB)} & \multirow{2}{*}{\(-\)30.3} & \multirow{2}{*}{\(-\)26.2} & \multirow{2}{*}{\(-\)22.4} & \multirow{2}{*}{\(-\)19.1} & \multirow{2}{*}{\(-\)16.2} & \multirow{2}{*}{\(-\)13.2} & \multirow{2}{*}{\(-\)10.8} & \multirow{2}{*}{\(-\)8.7} & \multirow{2}{*}{\(-\)6.6} & \multirow{2}{*}{\(-\)4.8} & \multirow{2}{*}{\(-\)3.2} & \multirow{2}{*}{\(-\)1.9} \\ octave bands & & & & & & & & & & & & \\ \hline Octave bands & \(A\) (dB) & \multicolumn{3}{c}{\(-\)26.2} & \multicolumn{3}{c}{\(-\)16.2} & \multicolumn{3}{c}{\(-\)8.7} & \multicolumn{3}{c}{\(-\)3.2} \\ \hline \hline \end{tabular}
\end{table}
Table 2: A-weighting correction.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Level** & **Inverse A-Weighted Floor Impact Sound Level (dB)** \\ \hline
1 & \(L_{i,Fmax,AW}\leq 40\) \\ \hline
2 & \(40\leq L_{i,Fmax,AW}\leq 43\) \\ \hline
3 & \(43\leq L_{i,Fmax,AW}\leq 47\) \\ \hline
4 & \(47\leq L_{i,Fmax,AW}\leq 50\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Criteria for the classification of heavy-weight impact sound insulation performance (Pre-certification).  
Figure 2: An example for calculating \(L_{i,Fmax,AW}\) = 49 dB.
# A Low Sampling Rate Receiver Design for Multi-Antenna Multi-User OFDM Systems
## 2 OFDM Systems
### Signal Model
\right), \tag{7}\]  
\(\tilde{\mathbf{H}}_{k,m}\in\mathbb{C}^{N}\) is the channel coefficient vector:  
\[\tilde{\mathbf{H}}_{k,m}=(\tilde{H}_{k,m}^{0}  
The ADC output \(\mathbf{y}_{k}\) is then transformed by FFT, which yields:  
\[\mathbf{Y}_{k}=\mathbf{F}\mathbf{y}_{k}=\sum\limits_{n=1}^{M}\text{diag}(\tilde{\mathbf{H}}_{k,m })\mathbf{x}_{m}+\mathbf{Z}_{k}, \tag{9}\]  
where \(\mathbf{Z}_{k}=\mathbf{F}\mathbf{z}_{k}\).
## 3 Model Descriptions
### Quantitative Description of Grain Shape  
For an irregular grain, the shape can be described from two aspects [14]. First is the overall contour, which can be simply described as circular, square, plate, or column. The second aspect is the surface morphology, which refines the grain outline based on the first aspect. It focuses on the  
Figure 3: The construction of three particle shapes using the CLUMP method.  
irregularity of the grain surface or the fluctuation of the grain boundary. Flatness was adopted in this study to quantify the overall contour of the grain, and the surface morphology of the grain was described by roughness [28; 29]. As shown in Equation (1), flatness is the ratio of the maximum Feret's diameter of the grain to the minimum Feret's diameter. Feret's diameter means the vertical distance between two parallel lines along the grain boundaries, as shown in Figure 4. Flatness characterizes the elongation property of the grain with a minimum value of 1. The grain approaches are spherical or circular when this value is closer to 1. On the contrary, the larger the flatness value, the flatter and narrower the grain. As shown in Equation (2), roughness is defined as the square of the ratio of two perimeters, which are the perimeter of the grain itself and the perimeter of the smallest circumscribing polygon of the grain (Figure 4). Roughness characterizes the fluctuation of the grain boundary curve, and greater roughness means a more irregular surface morphology of the grain.  
\[e=L/B; \tag{1}\]  
\[r=(P/PC)^{2}; \tag{2}\]  
where \(e\) is the flatness, and \(L\) and \(B\) are the maximum and minimum Feret's diameters, respectively. Roughness is expressed by \(r\), \(P\) is the perimeter of the grain, and \(Pc\) is the perimeter of the smallest circumscribing polygon of the grain.  
According to Equations (1) and (2), the flatness and roughness of the three mineral grain groups mentioned above were calculated, and the results are shown in Figure 5. As can be seen from Figure 5a, the flatness of the strip-shaped grain group was significantly higher than that of the triangular and square grain groups. In the strip-shaped grain group, from S1 to S6, the flatness increased rapidly with the increase of \(d/R\). The flatness of the square grain group was slightly higher than that of the triangular grain group, and the flatness increased slightly from T1 to T6 and R1 to R6. It can be seen from Figure 5b that the square grain group had the highest surface roughness, followed sequentially by the triangular and the strip-shaped grain group. With the increase of \(d/R\), the roughness of the three grain groups all increased. When the \(d/R\) value was low (shape numbers 1-3), an increase in the roughness was not obvious; however, it increased faster when the \(d/R\) value was higher (shape numbers 4-6).  
Figure 4: Basic dimension parameters of irregular grains used for calculating flatness and roughness.
## 1 Introduction
[28] assessed the low-cost ZED-F9P GNSS receiver along with two different types of affordable antennas in horizontal displacement estimation using different static observation periods. It was found that the low-cost system can be used effectively in structural health monitoring applications. The main innovation point of our paper is to explore the positioning accuracy of real-time single-constellation PPP solutions (i.e., GPS, GLONASS, Galileo, and Beilou PPP) with a ZED-F9P device utilizing satellite orbit and clock corrections from BKG, CNES, IGS, GFZ, and WHU instantaneous data streams, which has not been addressed yet in previous studies; additionally, our proposed real-time PPP scenarios are validated in the troposphere precipitable water vapor (PWV) domain. Since the examined station does not belong to the IGS stations, a new method is developed to create PWV reference values; then, these reference values are used in PWV validation. In our research, the real-time PPP algorithm is elaborated in Section 2; thereafter, the PWV estimation method is discussed in Section 3, and the data processing scheme is presented in Section 4. Additionally, the obtained results are analyzed and interpreted in Section 5; after that, the findings are summarized in Section 6, and the conclusion is finally drawn in Section 7.
# Nanosponges for Water Treatment: Progress and Challenges
## 3 Conclusions and Future Outlooks  
Compared to other nanosystems/materials, nanosponges exhibit high porosity, easy functionalization, simplicity, and cost-effectiveness, which make them attractive candidates for capturing and removing organic/inorganic pollutants/contaminants (e.g., dyes, pharmaceuticals, and heavy metals) from water and wastewater. Notably, cyclodext-based nanosponges with unique physicochemical properties and architectures, including good biocompatibility, low/non-toxicity, easy surface functionalization, and bio-absorbent features, can be employed for eliminating a wide variety of pollutants from water via adsorption/inclusion. However, more elaborative studies are warranted to critically evaluate and overcome some crucial aspects/challenges, such as adsorption mechanisms, functionalization processes, removal activities/efficiencies, toxicity/biosafety issues, hydrophobic interactions, electrostatics, and complexation agents for improved targeting of pollutants. Additionally, the type of applied materials (e.g., native/modified polymers), adsorbent dosage, contact time, competing ions, adsorption isotherm models, kinetics, thermodynam  
Figure 4: (**A**) Halloysite nanotubes (HNT)-cyclodextrinrin derivatives (CDs) nanosponge hybrids. (**B**) Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) images of the designed nanosponge hybrids. (**C**) The preparative process of HNT-CDs nanosponge hybrids. Adapted with permission from Ref. [54]. Copyright 2017, American Chemical Society.  
ics, and reaction/experimental conditions (e.g., molar ratios, temperature, and pH solution) are important factors affecting the adsorption and removal of pollutants using nanosponges. The physicochemical properties, optimization/fabrication processes, complexation behaviors, low-cost production techniques, commercialization, structural variations, and safety evaluations of these nanosystems are essential for their future appliances in water treatment. For antimicrobial and antiviral potentials of these nanosponge-based systems, more elaborative studies are still required for clarifying the mechanism of activities and their efficacy, as well as their long-term biocompatibility and biosafety issues.  
S.I. and R.S.V.: conceptualization, writing-review and editing. All authors have read and agreed to the published version of the manuscript.  
This study was self-funded.  
Not applicable.