esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Risk Assessment Method for Land Subsidence in Tangshan Based on Vulnerability Zoning
## 3 Hazard Assessment Results for the Study Area
### Construction of Hazard Assessment Index System for Ground Subsidence
#### 3.1.1 Natural Factors
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Station Number** & **Longitude** & **Latitude** & **Groundwater Level (m)** & **Well Depth (m)** \\ \hline
130201210446 & 118.23 & 39.7 & 10.07 & 208 \\
130200210377 & 118.21 & 39.69 & 13.28 & 63 \\
130201210458 & 118.19 & 39.68 &  
of friction and drag folds, indicative of its normal fault nature. The Tangshan-Fengnan Fault (F2-2) forms a complex anticline and syncline trending northeast in the Tangshan-Fengnan region, composed of Ordovician limestone. (iii) The Tangshan Earthquake Fault Zone (F3), which is the seismogenic fault of the 1976 Tangshan earthquake: Its southwestern segment exhibits a NE trend of approximately 30\({}^{\circ}\). Upon passing through Tangshan Xiao Shan, it bifurcates northward into two branches, the Tangshan-Guye Fault Segment (F3-1), with an NE trend of approximately 50\({}^{\circ}\), and the Tangshan-Majiagou Mine Segment (F3-2), with an NE trend of approximately 20-30\({}^{\circ}\). The total length of the fault zone is about 30 km. The Tangshan-Majiagou Mine Fault (F3-3), located northeast of Tangshan City and west of Kaiping, generally trends towards the northeast and is characterized by the inversion of Paleozoic marine sedimentary rocks, akin to the inverted syncline of the Tangshan Mine in the southern Nanhu area, as shown in Figure 12.  
The geological tectonic zones beneath the urban road surface exhibit extensive karst development. At several fault zone turning points within the study area, the presence of well-developed water conduits, formed by these faults, fosters karst development and heightens the susceptibility to collapse. The study area's proximity to these fault zones was classified into five levels, as depicted in Figure 13.  
(3) The distance-to-water system  
The distance-to-water system was delineated using ArcGIS to establish buffers of 0-100 m, 100-200 m, 200-300 m, and greater than 300 m, as illustrated in Figure 14a,b. These buffers were subsequently merged and trimmed to the extent of the study area. The polygons were then converted into raster data with a pixel size of 30 m \(\times\) 30 m utilizing the conversion tool. Upon the classification of each indicator, the collapse point data, sourced from an Excel spreadsheet containing latitude and longitude coordinates, were imported into ArcGIS. The \"Extract Multi Values to Points\" tool was employed to assign the classification indicators of each single factor to the attribute table of the hazard points. ArcGIS software was subsequently used to summarize and calculate the statistical results for each classification indicator of every evaluation factor. The results are compiled in Table 9. The information quantities for the different levels of each factor were also computed. Figure 12: Fault zone distribution in study area.
## 3 Results and Discussion
### Superheat at Discharge as a Controlling Parameter  
In general, EEV varies to meet a specified superheat with a varying pressure and temperature at the compressor suction; however, when a two-phase flow (vapor phase and liquid phase) exists in the suction line of the compressor, the superheat is near zero (see Figure 15). It is thus difficult to achieve the required quality, as a controlling parameter of the expansion valve causes the hunting of TXV (Thermostatic Expansion Valve) [23].  
Figure 16 shows the superheat at the discharge line as a function of the quality of different EWT of OD HEX. The superheat at the discharge line is calculated by subtracting the saturation temperature corresponding to the pressure measured at the discharge line, from the temperature measured at the same place. The optimum quality is determined with the maximum COP, and it was ca. 0.94 to 0.90, when the EWT of OD HEX was 10 \({}^{\circ}\)C, 5 \({}^{\circ}\)C, and 0 \({}^{\circ}\)C, respectively. As shown in Figure 16, the superheat at the discharge line was nearly constant, at ca. 22 \({}^{\circ}\)C. Therefore, from this result, the superheat at the discharge line could be proposed as a proper controlling parameter to adjust the quality at the suction line, by varying the opening of the expansion valve during wet compression; although, this requires a more extensive study to confirm the findings.  
Figure 15: Superheat at the suction line as a function of quality for different EWT of OD HEX.
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 2 Historical Review
### Characteristics of Research Output
#### 2.1.5 Research Foci  
SUHI research has covered a wide range of topics, including SUHI variations at a local scale, SUHI variations among cities, the relationship between SUHI and air UHI, and others (Figure 3c). Local-scale SUHI variations were of the most interest to researchers (64%). Impacts of urban expansion and landscape structure on the SUHI had similar coverage, which accounted for 18% and 17% of the total publications, respectively. In contrast, less attention (14%) has been paid to the SUHI variations among cities at a regional or global scale. The correlations between surface and air UHIs have also become one of the most important topics of SUHI research (8%) because of their contrasting observation altitudes and methods. The remaining literature (14%) concentrated on other issues such as data quality, quantification methods, and health risks. These SUHI-related studies were published in 107 different journals, which suggests a wide variety of interests of scholars from various disciplines, particularly those in the fields of remote sensing, environment, urban landscape, and sustainability. Further details on the active journals can be found in Supporting Materials Text S1.
## 2 Patients and Methods
### Molecular Testing  
Between 2012 and 2018, Sanger sequencing or NGS was utilized for detecting _EGFR_ mutations. Sanger sequencing focused on identifying mutations in exons 18-21 of the _EGFR_ gene. The extraction of genomic DNA was conducted from formalin-fixed, paraffin-embedded (FFPE) tissue acquired from biopsy or resection samples utilizing the QIAamp DNA FFPE tissue kit (Qiagen, Hilden, Germany), in compliance with the guidelines provided by the manufacturer. An automated single-capillary genetic analyzer was then used to conduct further sequencing (ABI 310; Applied Biosystems, Foster City, CA, USA) with forward and reverse primers. The identification of nucleotide alterations was carried out according to a comparison of the sequences acquired with the database of the National Center for Biotechnology Information (reference sequence: NM_005228.2). Beginning in 2018, molecular analyses of the samples were conducted to detect insertions/deletions and point mutations in 19 genes, including _EGFR_, using the Qiagen GeneReader NGS System (Qiagen GeneReader Platform, Hilden, Germany) with the GeneRead QIAact Lung DNA UMI Panel. The analysis process involved using QCI-Analyze to import reads, trim primers, verify quality, map reads to the human reference genome, call variants, and filter according to coverage. QCI-Interpretation was then employed to import and filter variants, followed by annotation of the identified variants on the basis of the clinical outcomes.  
The evaluation of presumed tumors in each of the patients was performed using contrast-enhanced CT. Either a 16-row detector CT (Siemens Somatom Sensation16, Forcheim, Germany), 64-row detector CT (Toshiba Aquillon 64), or 320-row detector CT (Toshiba Aquillon ONE, Otawara-shi, Japan) was utilized when conducting the chest CT examinations. The parameters for acquisition included a detector collimation of 0.5, 0.5, or 0.625 mm; tube voltage of 120 kVp; gantry rotation time of 0.5 s; reconstructed section thickness of 1, 1, or 1.5 mm; and reconstruction intervals of 0.8, 0.8, and 1 mm. Prior to the examinations, patients received an intravenous injection of 60-100 mL (1-1.5 mL/kg) of nonionic contrast agent (350/100 Omnipaque, GE Healthcare, Oslo, Norway). A workstation was utilized for the analysis of multiplanar reformatted images (GE Healthcare, Waukesha, WI, USA).
## 3 Results and Discussion
### Analysis of Strain Characteristics  
#### 3.4.1 Physiological, Biochemical, and Phenotypic Characterizations of Strain Q3-6  
Physiological and biochemical characterizations of the isolate Q3-6 were detected in this study. It was Gram-stain-positive, rod-shaped (1.0-2.5 \(\upmu\)m long and 0.5-1.0 \(\upmu\)m wide) (Figure S3). After 24 h of growth at 37 \({}^{\circ}\)C, the colonies were creamy white or yellowish, raised, smooth, rounded, with intact margins, and 1.0-5.0 mm in diameter on the BPM agar.  
The strain can grow at 15-45 \({}^{\circ}\)C, and its optimal growth temperature is 30-40 \({}^{\circ}\)C. The growth temperature of _Bacillus_ spp. studied by [PERSON] et al. [53] ranged from 25 \({}^{\circ}\)C to 50 \({}^{\circ}\)C. The growth temperature of _Bacillus_ spp. isolated from drinking water treatment plants by [PERSON] et al. [54] ranged from 15 \({}^{\circ}\)C to 37 \({}^{\circ}\)C. The strain grows at pH 4-7.5 (optimal pH 5.5-7.5) range. Compared with others in the same genus [55; 56; 57; 58], strain Q3-6 has a significantly wider optimal growth temperature range as well as an optimal pH range, and it is better adapted to low temperatures, which also suggests that strain Q3-6 has stronger environmental adaptability in treating sewage pollutants. Strain Q3-6 can tolerate NaCl in the range of 0-5%, and the optimal salinity conditions are 0-2%.  
Hydrolysis of casein was positive. The result suggests that strain Q3-6 possesses a high-activity tyrosinase enzyme, rendering it proficient in treating effluents with organic wastes. Some other strains [59] with high-activity tyrosinase enzyme were also used in treating dairy processing plant effluents and restaurant effluents. And hydrolysis of Tween 20, 40, and 60 were weakly positive.  
By using the API 20E, Argin dihydro, Lysine decarboxylase, Ornithine decarboxylase, Citrate, Tryptophan deaminase, and the Voges-Proskauer reaction were positive for Q3-6, which confirms the utilization of gelatin and glucose. By using the API ZYM, alkaline phosphatase, Esterase (C4), Lipid esterase (C8), Leucine Arylaminase, Valine arylaminase, pancreatic reennet, acid phosphatase, Napthol-AS-BI-phosphate hydrolase, and \(\alpha\)-Glucosidase were positive for Q3-6. Strain Q3-6 produces pancreatic reennet, which can selectively cleave amino acid fragments from protein chains, indicating that this strain can effectively treat protein-rich sewage. The alkaline phosphatase and acid phosphatase produced by this strain catalyze the hydrolysis of organic phosphorus in sewage [60]. By using the API 50 CH, acid production from glycol, d-ribose, d-glucose, d-fructose, d-mannose, \(N\)-Acetylglucosamine, amygdala, ARBULIN, Hepatitis C7H7 and Iron citrate, salicin, d-cellobiose, d-maltose, d-Sucrose, d-Alginose, d-Gentian disaccharide, d-turanose, d-tagatose, Potassium gluconate, potassium 2-ketogluconate, and potassium 5-ketogluconate were positive. Detailed results of the enzyme activity of strain Q3-6 are shown in Table S2.
## 4 Mathematical Model of the Damper  
### Structure of the Drag Force  
The phenomenological model of the drag force is based on several assumptions:  
1. If the area of open slits \(\delta(C,a)\) is fixed, the drag force is assumed to be proportional to the square of the velocity (due to the high viscosity of the fluid).
2. The velocity determines the shape of the flat circular membrane with slits. The higher the velocity, the larger the area of open slits (the larger the parameter \(C\)).
3. The higher the area of open slits, the smaller the drag force. Note that \(S(C,a)\) is an even function in respect to \(C\). Therefore, according to Assumption 2,  
\[C=\Omega\dot{x} \tag{13}\]  
where \(\Omega\) is the coefficient of proportionality between \(C\) and \(\dot{x}\).  
According to Assumption 3, the drag force is inversely proportional to the area of open slits. Combining all three assumptions results in the following expression of the drag force \(D\):  
\[D\big{(}\dot{x}\big{)}=h\cdot sign\big{(}\dot{x}\big{)}\cdot\big{(}\dot{x} \big{)}^{2}\cdot\frac{1}{\delta\big{(}\dot{x},a\big{)}} \tag{14}\]  
This structure of the drag force has a singularity point at \(\dot{x}=0\). Therefore, we add the following fourth assumption:  
1. The drag force is not infinite when all the slits are closed (when the flat circular membrane is in the state of equilibrium).  
Combining the fourth assumption yields the final phenomenological model of the drag force:  
\[D\big{(}\dot{x}\big{)}=h\cdot sign\big{(}\dot{x}\big{)}\cdot\big{(}\dot{x} \big{)}^{2}\cdot\frac{1}{\delta\big{(}\dot{x},a\big{)}+\varepsilon} \tag{15}\]  
where \(\varepsilon\) is a small positive number determining the drag force when all slits are closed.  
First, using the above assumptions, properties of the damper were researched. The force-velocity characteristic of the slit damper in comparison with a conventional linear damper is shown in Figure 4. An increased damping efficiency can be observed in the lower velocity range.  
Figure 3: The relationship between the area of open slits \(\delta(C,a)\) and \(C\) at \(a=0.01\).
## 6 Experiments
### Application 1: Image Stitching  
To validate ICOS in the real-world application of rotation search, we adopted ICOS to solve the image stitching problem over the _LunchRoom_ image sequence from the PASSTA dataset [55], with an experimental setup similar to [23]. We first used the 2D feature descriptor SURF [16] to detect and match the 2D keypoints across a pair of images with overlapping scenes. Then, we applied the inverse of the camera intrinsic matrix \(\mathbf{K}^{-1}\) to create the vector correspondences \(\mathcal{U}=\{\mathbf{u}_{i}\}_{i=1}^{N}\) and \(\mathcal{V}=\{\mathbf{v}_{i}\}_{i=1}^{N}\) over the two frames. Eventually, we used Algorithm 1 (with \(\sigma=0.02\)) to solve the rotation \(\boldsymbol{\hat{R}}\) between the two images and then obtained the homography matrix using matrix transformation: \(\mathbf{H}=\mathbf{K}\boldsymbol{\hat{R}}\mathbf{K}^{-1}\) to stitch them together. The correspondences and experimental results are shown in Figure 10. We found that ICOS always found the correct inliers among the correspondences and stitched the images successfully.  
Figure 9: Results with high noise. (**a**) Rotation search. (**b**) Unknown—scale registration. Right bottom: the left image is the “bunny” without noise, and the right one is that with noise \(\sigma=0.1\).  
Figure 10: Image stitching results over the _LunchRoom_ dataset [55]. Columns 1–3 display the raw correspondences matched by SURF [16] with the image indices and outlier ratios given on top, the inliers found by ICOS (the inliers found are labeled in green; the others are outliers, labeled in red), and the image stitching results using the rotation estimated by ICOS, respectively.
## 2 Preliminaries and Related Works  
### Link Correlation Metric and Measurement  
Metric: There are several metrics characterizing packet-level link correlation [13; 16; 17]. Among all of these metrics, conditional packet reception/loss probability (CPRP/CPLP) is the most widely-used, due to its meaningfulness and applicability for estimating upper layer performance, such as the expected number of transmissions (ETX). More specifically, the link correlation-aware opportunistic routing (CAR) [15] and collective flooding (CF) [16] uses CPRP as the link correlation metric, while correlated flooding [17] and CoCo [20] use CPLP as the link correlation metric.  
Measurement: Existing works employing beacon messages for measuring link correlation function as follows: Each node periodically transmits beacon messages and records the beacon receptions/losses from other nodes with bitmaps, a \"1\" denoting a packet reception and a \"0\" denoting a packet loss. Then, each node transmits the bitmaps to corresponding beacon senders. When a node obtains the bitmaps, it is able to calculate the link correlation (CPRP/CPLP) between its outbound links. For example, with one sender S and two receivers A and B, given two bitmaps indicating the packet receptions on S\(\rightarrow\)A and S\(\rightarrow\)B, \"10011\" and \"11001\", the link correlation can be calculated as the number of common packet receptions divided by packet receptions on S\(\rightarrow\)B, _i.e._, \(c_{A,B}^{S}=\frac{2}{3}\). This means S\(\rightarrow\)A will receive a packet with a probability of 2/3 given that S\(\rightarrow\)B receives a packet.  
The above beacon-based link correlation measurement has two main drawbacks, as follows: First and most importantly, it lacks accuracy. Beacon-based measurement uses the beacon receptions to measure the link correlation. As we analyze in Section 2.2, since both the packet length and transmission rate of beacons are different from those of data packets, the fractions of interference captured by beacon-based measurement are different from the interference that has a real impact on data transmissions and the measurement result lacks accuracy. Second, the periodic beacons incur considerable overhead. In [17], beacons are transmitted every 10 s. However, in typical sensor networks, data packets are transmitted every 10 min [1]. The beacons' overhead is unacceptable.  
Compared to the above commonly-used approach, LACE: (1) uses data reception bitmaps for link correlation measurement, which is inherently accurate (the elimination of beacons also greatly reduces the measurement overhead); and (2) combines both physical layer parameter and network layer statistics for flexible link correlation estimation (considering both long-term and short-term link behaviors for correlation measurement).
# A Systematic Revision of the NFIP Claims Hazard Data in Florida for Flood Risk Assessment
## 3 Results and Discussion
### NFIP Claims Hazard Revision
#### 3.1.2 Cause of Loss Revision  
The following fields were added to the original NFIP claim data: HURDAT2 storm identification, storm flag (tropical depression (TD), tropical storm (TS), hurricane (HU) or nontropical storm (NA)), revised catastrophe number, rainfall maximum, and distances to the nearest coast and body of water. Using these collected information permitted a revision of the cause of loss when the original loss cause was in clear error or ambiguous.  
The algorithms for the revision were as follows:  
* if the provided cause of loss was tidal water overflow (surge; cause number 1), but the distance to coast was greater than 20 km (based on personal communication with surge modeling experts), the rainfall maximum for the location was checked. If it was greater than 25 mm/day, the cause was revised to be accumulation of rainfall (cause number 4). Otherwise, it was designated as questionable, likely wrong address (see Figure 6);
* for hurricane events, if the original cause of loss was unknown for a property that was less than 20 km to the coast, and the precipitation maximum was greater than 25 mm/day, the cause of loss was marked as undetermined (cause number 5) but was either tidal water overflow or accumulation of rainfall, and further investigation was warranted. Otherwise, the cause of loss was marked as cause number 1;
* for nonhurricane events, if the original cause of loss was unknown for a property that was greater than 20 km to the coast and the precipitation maximum was greater than 25 mm/day, the cause of loss was marked as cause number 4;
* if the original cause of loss was 4 in inland areas, but the precipitation maximum was less than 5 mm/day, then the cause of loss was marked as questionable, possible error in address or date;
* for hurricane-only events, if the distance to the coast was less than 1 km, claims with cause numbers 2, 3, and 4 were revised to tidal water overflow (cause number 1). Figure 7 shows why this might be a practical revision algorithm. Hurricane Ivan (2014) hit the northwest coast of Florida (see Figure 3) and caused widespread surge damages according to FEMA. However, the cause of loss in many NFIP paid claims was marked as accumulation of rainfall (cause number 4), even in the apparent isolated island areas. All these areas were damaged by the storm surge according to the FEMA observed flood estimates.  
Figure 5: NFIP catastrophe event number update: Major Hurricane Mitch (period: 4 November 1998–6 November 1998); event number = 134 (see Table 1). Provided claim count = 8 (**red dots**) vs. revised claim count = 206 (**green dots**).
# Density Self-Adaptive Hybrid Clustering Routing Protocol for Wireless Sensor Networks
## 4 Performance Evaluation
### Simulation Settings  
In the experiments, 100 nodes are randomly deployed in topological areas of dimension \(100\) m \(\times\)\(100\) m. It uses the first-order radio communication model described in Section 3.1. As stated in the literature [17], the RSSI value is a logarithmic function of distance from the emitter. In simpler terms, the farther the distance, the smaller the RSSI value; the closer the distance, the greater the RSSI value. In this experiment, we use the distance to replace the critical value of \(-87\) dBm in Section 3.4. After many experiments, the critical value of distance (\(D\)) is set to \(30\) m because its efficiency is optimal. All of the sensor nodes have the same initial energy of \(0.5\) Joules. The energy for data aggregation is set as \(E_{DA}\). Table 3 presents the network parameters in detail.  
Additionally, the upper bound threshold \(T_{1}\) is set as \((1+0.2)\times N_{0}\), and the lower bound threshold \(T_{2}\) is set as \((1-0.2)\times N_{0}\). They are decided by the conclusion drawn from many experiments and their performances are better. In the experiment, we consider two cases of the BS located internally and externally. When the BS is inside, it is in the central position of the network (\(50\), \(50\)). When the BS is outside, its position is (\(50\), \(155\)). Figures 3 and 4 are the initial deployment of the nodes when the BS is in the internal or external, respectively.  
Figure 4: The initial deployment of the nodes when the BS is outside.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Initial energy of nodes & 0.5 J \\ \(E_{elec}\) & 50 nJ/bit \\ \(\epsilon_{\beta}\) & 10 pJ/bit/m\({}^{2}\) \\ \(\epsilon_{mp}\) & 0.0013 pJ/bit/m\({}^{4}\) \\ \(E_{DA}\) & 50 nJ/bit/signal \\ Data packet size & 4000 bits \\ Control packet size & 200 bits \\ \(D\) & 30 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Network parameters.  
Figure 3: The initial deployment of the nodes when the BS (base station) is inside.
## 6 Example of Verification of Recommended Equations  
### The Composite joints of SMRFs  
For verifying the reliability of Equations (7) and (8), the authors of this paper designed an FEM calculation example of the composite joint of an SMRF according to an actual project [31; 32]. Through analysis using ABAQUS software, the slab's effective width in the composite joint calculation example was obtained and then compared with the value of the FEM model suggested by Equations (7) and (8).