esa-sceva/satcom-chunk-collection huggingface dataset:
# Approximate Networking for Universal Internet Access
## 5 Discussion Issues
### HCI Issues: User Perceptions of the Approximation  
Previous human-computer-interaction (HCI) research has shown that humans' subjective knowledge can be flawed (relatively fast service may be judged to be unacceptable unless it is also predictable, visually appealing and reliable [74]). More research is needed on which kinds of weaknesses are most perceived by users. This knowledge can then be used to determine the precise approximate networking tradeoff to adopt so that the users perceive least inconvenience. If it turns out that the Pareto principle or the 80/20 rule is applicable, then we can optimize for the approximation that is responsible for 80% of user perceived QoS.  
In view of the user's experience, high network latency has an obvious detrimental effect on latency-bound applications such as gaming, voice, and web applications. While bandwidth is admittedly important for QoS (especially for bulk transfer and video applications), it turns out that the round-trip-time (RTT) can dominate performance more than bandwidth for short, bursty HTTP connections [75], especially over long-distance wide-area networks (WANs) [76]. It has been shown in human-computer interaction (HCI) literature that increments of only 100 ms can decrease sales by 1% and human can sense and do react to small differences in the delays of operations [50].
## 2 Science Data Products, Calibration/Validation, and Algorithm Management  
### Science Data Products  
The JPSS data products include Temperature Data Records (TDRs), Sensor Data Records (SDRs), and Environmental Data Records (EDRs). Raw Data Records (RDRs) refer to the raw data generated by the sensors on the satellites. Science algorithms that perform calibration and geo-location are applied on the RDRs to produce geo-located, radiometrically and spectrally calibrated radiances (or reflectance) with annotated quality indicators to generate SDRs and TDRs. EDRs are geophysical parameters that are derived by applying retrieval algorithms to the SDRs. EDRs provide global measurements of quantities, such as temperature and moisture profiles, ozone and trace gases, aerosols, clouds, wind speeds, land surface properties, snow and ice cover, sea surface temperature, ocean color, and other products.  
Figure 1 shows a list of the JPSS TDR, SDR, and EDR operational products (hereafter referred to as xDRs). The NESDIS operational EDR products derived from Global Change Observation Mission-Water (GCOM-W)/JAXA are also part of the JPSS product suite. Based on the utility of these science data products by customers and user community, ATMS TDRs, CrIS SDRs, and some of the VIIRS imagery EDRs are prioritized as Key Performance Parameters (KPPs). The remaining are prioritized as priority 2, 3, and 4 products. Documents, such as the Algorithm Theoretical Basis Documents (ATBDs), Cal/Val Plans, algorithm requirements and status documents, user guides and other science documents related to all of the SDR/EDR products are available at the JSTAR website [4]. The SDRs and imagery EDR products identified as Mission Unique Products are produced at the Interface Data Processing Segment (IDPS). The other EDRs are produced at the NOAA Data Exploitation (NDE) using enterprise algorithms.  
Figure 1: Sensor and Environmental Data (xDR) products from the S-NPP/NOAA-20/GCOM-W1 instruments suite.
## 6 Discussion  
Here, we analyze and compare results of the Tables 3-6 together. From Table 3, we found that RF is the best algorithm (with 79.15% accuracy/recall, 80.94% precision and 78.68% f1_score) and, after RF, DT is ranked second (with 78.96% accuracy/recall, 81.09% precision and 78.64% f1_score). However, as Table 4 shows, training phase in RF (with 0.215 s) is more time consuming than DT (with 0.0015 s). In Table 5, we have already seen that the combination of DT, RF and CNB can provide 79.81% accuracy/recall, 81.65% precision and 79.11% f1_score with 0.2205 s training time. And, in Table 6, we can see that the combination of five KNN, DT, RF, CNB and SVM algorithms can provide the best cross validation scores (with 79.93% accuracy/recall, 81.97% precision, 79.10% f1_score and 0.2534 s training time) in comparison to all other ensemble algorithms.  
Table 7 shows the top first single algorithm (i.e., RF), the top first ensemble learning with three algorithms (i.e., DT, RF and CNB) and the top first ensemble learning with three algorithms (i.e., KNN, DT, RF, CNB and SVM). We also put DT, as a simple algorithm, to enrich our discussion. As Table 7 shows, training time of the simple DT algorithm (i.e., 0.0015 s) is much more less than the computing-intensive ensemble algorithm with the combination of five KNN, DT, RF, CNB and SVM algorithms (i.e., 0.2534 s), while there is not a noticeable difference between performance of them (e.g., accuracy difference between them is less than 1%).  
Considering the limited resources of AIoT end and even edge devices in smart parkings and also real-time nature of many AIoT applications, using simple algorithms such as DT looks more reasonable than the complex ensemble learning algorithms.
# Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications
## 3 Formulation
### Problem Formulation
#### 3.3.2 Data Gathering while Hovering  
If the SNs are likely to be outside the UAV's connectivity owing to the high speed, then the UAV hovers with \(v=0\) for a specific time. Therefore, the sensors can upload the remaining data to the UAV during the hovering time, thereby minimizing packet loss significantly. The time spent hovering above the location is concise and is expressed as \(H(t)\) = \(t_{h,n}(x_{n},\ y_{n},z_{n})\). As the UAV is static at this point, the CH's energy is simplified to the following  
\[t_{h,n}(x_{n},\ y_{n},z_{n})\times E_{trans}\leq E_{T}. \tag{12}\]  
Figure 2: Data collection models in UWSNs: (**a**) data gathering while flying and (**b**) data gathering while hovering.
## 2 Materials and Methods
### Framework for GPP Generation
#### 2.3.1 The Improved GPP Estimation Model
The leaf-internal CO\({}_{2}\) concentration can be estimated as follows [8]:  
\[[C_{i}](x,y,t)=[CO_{2}](x,y,t)\times\alpha \tag{6}\]  
\[[C_{i}]^{ref}(x,y,t)=[CO_{2}]^{ref}(x,y,t)\times\alpha \tag{7}\]  
where \([CO_{2}]\) (\(x\), \(y\), \(t\)) is the atmospheric CO\({}_{2}\) concentration at the current level and \([CO_{2}]^{ref}\) (\(x\), \(y\), \(t\)) is the atmospheric CO\({}_{2}\) concentration at the reference level, which is set to 281 ppmv [2; 24] in this study. \(\alpha\) is the ratio of leaf internal to atmospheric CO\({}_{2}\) concentration and dimensionless, which was set to 0.8 [25]. It is worth noting that the leaf-internal CO\({}_{2}\) concentration can be inferred from the ambient CO\({}_{2}\) concentration (ppm), which has been suggested to approximate the canopy CO\({}_{2}\) concentration. Researchers have indicated that the canopy CO\({}_{2}\) concentration can be computed from the atmospheric CO\({}_{2}\) concentration using the decoupling coefficient, which signifies the extent of separation of the canopy from the surrounding air. This study assumed a perfectly smooth surface and a decoupling coefficient of 1 [4]. In this study, a unique maximum light energy utilization value (\(\varepsilon_{max}\)) was set for different land cover types to meet the surface characteristics of terrestrial ecosystems (Table 2). The metrics including coefficient of determination (R\({}^{2}\)) and root mean square error (RMSE) were used to evaluate the performance of the improved GPP estimation model.
## 4 Experimental Results
### Stiffness Degradation  
The secant stiffness (\(K_{\text{g}}\)), as in the \"Specification for seismic test of buildings (JGJ/T 101-2015)\" [52], reflects the stiffness degradation of the structure caused by an increasing horizontal displacement. \(K_{\text{g}}\) is calculated using Equation (3), where the \(\pm F_{i}\) is the positive or negative peak load and \(\pm X_{i}\) is the corresponding positive or negative peak displacement during the \(i\)-th cycle.  
\[K_{g}=\frac{|+F_{i}|+|-F_{i}|}{|+X_{i}|+|-X_{i}|} \tag{3}\]  
\(K_{\text{g}}\)[52], as modified in the literature [36], can be calculated using Equation (4).  
\[K_{\text{s}}=\pm F_{i}/\pm X_{i} \tag{4}\]  
The stiffness degradation (\(K_{\text{s}}\)-\(d\)) curves of the IAP and GIAP specimens are compared in Figure 15. Under a positive \(d\), the initial \(K_{\text{s}}\) for the IAP and GIAP specimens are 3009 and 1830 kN\(\cdot\)m\({}^{-1}\), respectively. When \(d\) is +40 mm, the positive \(K_{\text{s}}\) for the IAP and GIAP specimens are 1098 and 1327 kN\(\cdot\)m\({}^{-1}\), with reductions of 63.5% and 27.5%, respectively. Under a negative \(d\), the initial \(K_{\text{s}}\) for the IAP and GIAP specimens are 261 and 555 kN\(\cdot\)m\({}^{-1}\), respectively. When \(d\) is \(-40\) mm, the negative \(K_{\text{s}}\) for the IAP and GIAP specimens are 69 and 199 kN\(\cdot\)m\({}^{-1}\), with reductions of 73.6% and 64.1%, respectively. It was found that the \(K_{\text{s}}\) for the GIAP specimen is smaller than that for the IAP specimen under a positive \(d\), while the \(K_{\text{s}}\) for the GIAP specimen is larger than that for the IAP specimen under a negative \(d\). The reduction in \(K_{\text{s}}\) for the GIAP specimen is smaller than that for the IAP specimen under any \(d\). Therefore, the initial \(K_{\text{s}}\) under a positive \(d\) and the reduction in \(K_{\text{s}}\) under any \(d\) in the GIAP specimen are overestimated, while the initial \(K_{\text{s}}\) under a negative \(d\) in the GIAP specimen is underestimated if the girder is not considered.
## Materials and Methods
### Remote Sensing Data
#### 2.5.1 Pre-Processing of MODIS Time Series  
Terra MODIS Level-1B data were obtained from the National Aeronautics and Space Administration (NASA)'s Level 1 and Atmosphere Archive and Distribution System (LAADS) and from the receiving station of the FMI in Sodankyla for the period from February to October in 2003 to 2010. The pre-processing consisted of calculation of Top-of-Atmosphere (TOA) reflectance and geometric projection of images into a latitude/longitude grid (datum WGS-84). MODIS observations with solar zenith angles greater than 73 degrees and sensor zenith angles greater than 50 degrees were excluded.  
For the detection of DBF_SOSat, we calculated the Normalized Difference Water Index (NDWI) from MODIS TOA reflectance data at 0.005\({}^{\circ}\) spatial resolution (corresponding approximately to a pixel size of 280-190 m (at 60\({}^{\circ}\) N and 70\({}^{\circ}\) N) by 550 m) as:  
\[\mathrm{NDWI}=\frac{\mathrm{R}_{\mathrm{NIR}}-\mathrm{R}_{\mathrm{MIR}}}{ \mathrm{R}_{\mathrm{NIR}}+\mathrm{R}_{\mathrm{MIR}}} \tag{1}\]  
where \(\mathrm{R}_{\mathrm{NIR}}\) and \(\mathrm{R}_{\mathrm{MIR}}\) refer to near-infrared and mid-infrared reflectance in MODIS band 2 (841-876 nm) and MODIS band 6 (1628-1652 nm), respectively.  
For the detection of ENF_SOSat, we calculated the FSC based on the SCAmod algorithm by [PERSON] et al. [65; 66] at the same spatial resolution as the NDWI. Clouds in daily FSC and NDWI products were masked based on an automatic cloud masking algorithm developed at the Finnish Environment Institute.
# Multi-Criteria Decision Making in the PMEDM Process by Using MARCOS, TOPSIS, and MAIRCA Methods
## 5 Multi-Criteria Decision Making
### Using TOPSIS Method  
The application of TOPSIS method to multi-objective decision making is guided in Section 2.2. Accordingly, the normalized values of \(\text{k}_{\text{ij}}\) are calculated by Equation (12), and the normalized weighted values \(\text{l}_{\text{ij}}\) are determined by Equation (13). Similarly, the A\({}^{+}\) and \(A^{-}\) values of Ra and MRS are achieved by Equations (14) and (15). It is noted that Ra and MRS are equal to 0.0583 and 0.2058 for A\({}^{+}\) and 0.2681 and 0.0212 for \(A^{-}\). In addition, the \(\text{D}_{i}^{+}\) and \(\text{D}_{i}^{-}\) values were found according to Formulas (16) and (17). Finally, the ratio \(\text{R}_{\text{i}}\) was calculated by Equation (18). Table 4 illustrates the results of the calculation of several  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Trial** & \(K^{-}\) & \(K^{+}\) & _f(K\({}^{-}\))_ & _f(K\({}^{+}\))_ & _f(K\({}_{j}\))_ & **Rank** \\ \hline A1 & 0.060611 & 0.058234 & 0.49 & 0.51 & 0.0396 & 8 \\ A2 & 0.048251 & 0.046359 & 0.49 & 0.51 & 0.0315 & 12 \\ A3 & 0.042628 & 0.040956 & 0.49 & 0.51 & 0.0278 & 15 \\ A4 & 0.060355 & 0.057989 & 0.49 & 0.51 & 0.0394 & 9 \\ A5 & 0.073787 & 0.070893 & 0.49 & 0.51 & 0.0482 & 5 \\ A6 & 0.022154 & 0.021285 & 0.49 & 0.51 & 0.0145 & 18 \\ A7 & 0.06754 & 0.064892 & 0.49 & 0.51 & 0.0441 & 7 \\ A8 & 0.068236 & 0.06556 & 0.49 & 0.51 & 0.0446 & 6 \\ A9 & 0.056476 & 0.054261 & 0.49 & 0.51 & 0.0369 & 11 \\ A10 & 0.081773 & 0.078567 & 0.49 & 0.51 & 0.0534 & 4 \\ A11 & 0.037312 & 0.035848 & 0.49 & 0.51 & 0.0244 & 17 \\ A12 & 0.040619 & 0.039026 & 0.49 & 0.51 & 0.0265 & 16 \\ A13 & 0.090423 & 0.086877 & 0.49 & 0.51 & 0.0591 & 1 \\ A14 & 0.044125 & 0.042934 & 0.49 & 0.51 & 0.0288 & 14 \\ A15 & 0.047812 & 0.045937 & 0.49 & 0.51 & 0.0312 & 13 \\ A16 & 0.082495 & 0.07926 & 0.49 & 0.51 & 0.0539 & 3 \\ A17 & 0.082767 & 0.079521 & 0.49 & 0.51 & 0.0541 & 2 \\ A18 & 0.056708 & 0.054484 & 0.49 & 0.51 & 0.0370 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Calculated results and ranking of alternatives by MARCOS method.  
parameters and the ranking of the alternatives when using the TOSIS method. It was found that alternative A13 is the best alternative among the given options.
###### Abstract  
Shandong peninsula, the largest peninsula of China, is prone to severe land subsidence hazards along the coastline. In this paper, we provide, for the first time, multi-scale and multi-dimensional time series deformation measurements of the entire Shandong peninsula with advanced time series Interferometric Synthetic Aperture Radar (InSAR) techniques. We derive the spatiotemporal evolutions of the land subsidence by integrating multi-track Sentinel-1A/B and RADARSAT-2 satellite images. InSAR measurements are cross validated by the independent deformation rate results generated from different SAR tracks, reaching a precision of less than 1.3 cm/a. Two-dimensional time series over the Yellow River Delta (YRD) from 2017 to 2019 are revealed by integrating time series InSAR measurements from both descending and ascending tracks. Land subsidence zones are mainly concentrated on the YRD. In total, twelve typical localized subsidence zones are identified in the cities of Dongying (up to 290 mm/a; brine and groundwater exploitation for industrial usage), Weifang (up to 170 mm/a; brine exploitation for industrial usage), Qingdao (up to 70 mm/a; aquaculture and land reclamation), Yantai (up to 50 mm/a; land reclamation) and Rizhao (up to 60 mm/a; land reclamation). The causal factors of localized ground deformation are discussed, encompassing groundwater and brine exploitation, aquaculture and land reclamation. Multi-scale surveys of spatiotemporal deformation evolution and mechanism analysis are critical to make decisions on underground fluid exploitation and land reclamation.  
InSAR; Shandong peninsula; surface deformation; sentinel-1A/B; two-dimensional deformation; MSBAS +
Footnote †: journal: Applied Sciences
# In-Situ Nanoparticles: A New Strengthening Method for Metallic Structural Material
## 5 Optimizing Microstructure
### Grain Refinement  
Strong grain refinement effect can emerge by heterogeneous nucleation caused by in-situ nanoparticles in our investigated alloys. Several optical images and statistic results of as-cast Ni-Cr-Mo HSLA steel [58], hot rolled Q195 steel [33], as-cast tin bronze [64], pure Cu and Cu-Fe-Co alloy [69] are shown in Figure 13. Typical dendritic structure with various orders of developed dendrite arms can be seen in conventional as-cast HSLA steel, while in nanoparticle strengthened HSLA steel the amount of dendrites is reduced obviously and equiaxed grains appear [58]. For hot rolled Q195 steels, in-situ nanoparticles can be retained after deformation and the grains are much smaller and distribute more uniformly compared with the base alloy [33]. In tin bronze alloys, the as-cast structure is significantly refined by iron-rich particles and columnar to equiaxed transition (CET) apparently appears [64], indicating that iron-rich nanoparticles precipitate earlier in Cu melt and subsequently induced heterogeneous nucleation during solidification to refine the matrix grains. Similar phenomenon happens in as-cast pure Cu and Cu-Fe-Co alloys, with Fe, Co addition typical CET occurs. In addition, the size of equiaxed grains decreases with the increase of Fe + Co content [69]. However, it should be noted that grain refinement effect is not enhanced indefinitely with increasing Fe/Co content. [PERSON] et al. [71] found that only part of the nanoparticles can act as nucleation cores, and the latent heat released by recalescence [79; 80] during the growth of prior nucleus will affect the undercooling degree, wherefore the nucleation of particles nearby are hindered. Interestingly, grains in tin bronze are much finer than that in Cu-Fe-Co cast alloys. This is because that the recalescence is weakened by growth restriction [81; 82] through Sn, Zn elements in tin bronze, so heterogeneous nucleation can be effectively promoted.  
Figure 12: Schematic illustration of atomic arrangement and matching at interface between (**a**) FCC iron-rich particles and Cu matrix, (**b**) BCC iron-rich particles and Cu matrix based on TEM characterization results [71].
## 4 Simulation and Results  
In the simulation test, we used a software GPS receiver to realize the functions of Tx/Rx. The simulation test is performed in the following steps and illustrated in Figure 5:_Step 1_: Collecting real-world GPS data. The real-world GPS signal is collected by a front end (iP-solutions GPS/Galileo L1 RF recorder with OCXO) and logged as a data file in the computer.  
_Step 2_: Simulating TA distribution and user position, \(\delta_{i}^{RT}\) and \(d_{i}\). The distance \(\delta_{i}^{RT}\) is estimated according to the simulated distribution of TAs and the range \(d_{i}\) is estimated according to the simulated user position and TA position.  
_Step 3_: Separating GPS IF data. The software GPS receiver is used to demodulate the real-world GPS data. The navigation data, carrier and code with delay due to \(\delta_{i}^{RT}\) and \(d_{i}\) of each channel corresponding to one satellite are remodulated. The remodulated signal is used to simulate the indoor signal transmitted by the TA.  
_Step 4_: Mixing the separated GPS IF data of different channels (_i.e._, different satellites). The GPS IF data of different channels are combined into one IF data stream to simulate the GPS data collected indoors using the user terminal.  
_Step 5_: Simulating the user terminal's function. A software GPS receiver is used as the GPS module in the user terminal to process the simulated GPS IF data and output pseudorange measurements \(\rho_{i}^{RGPS}\). The TA distribution, \(\delta_{i}^{RT}\) and \(d_{i}\) are obtained from Step 2, the pseudorange measurements of outside GPS signal \(\rho_{i}^{GPS}\) are obtained in Step 3. The user position is estimated according to Equation (5).  
The parameters of the Front End and the GPS software receiver used in the simulation are shown in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline
**GPS Signal** & **L1** \\ \hline Sampling frequency & 16.3676 MHz \\ Intermediate frequency & 4.1043 MHz \\ Integration time & 1 ms \\ PLL bandwidth & 10 Hz \\ DLL bandwidth & 1 Hz \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of front end and GPS software receiver.  
Figure 5: Simulation process.  
In the simulation, a real-world GPS signal was collected on the roof of the [PERSON] Che Woo building at the Hong Kong Polytechnic University in October, 2012. Four satellites' signal are randomly selected to generate \(\ RF_{i}^{\ RF_{i}^{\ RF_{i}}}\). Their distributions are shown in Figure 6. The figure shows that the four satellites (PRN 9, 18, 15 and 26) have bad geometry. All of them are distributed in the section with 322.3\({}^{\circ}\)-121.8\({}^{\circ}\) azimuth angle. In addition, one of them (PRN 26) has very low elevation angle reaching 11.7\({}^{\circ}\). As a result, the PDOP value reaches 4.43.
## 2 Materials and Methods
### Final Irrigation Protocol  
Fifteen specimens were randomly divided into three groups:  
* **Negative Control Group (n = 5):** Specimens were shaped and not subjected to final irrigant activation.
* EndoActivator Activation Group (n = 5) (EA Group): Specimens were irrigated with 5 mL of NaOCl at 5.25% preheated using TruNatomy irrigation needle (Dentsply Sirona). To sonically activate the irrigating solution, EndoActivator (Dentsply Sirona) was used. The EndoActivator is a sonically driven canal activation system. It is presented with a portable handpiece and disposable noncutting polymer tips. It has been shown to significantly better remove debris when compared to conventional syringe irrigation and passive ultrasonic irrigation (PUI). The smooth polymer tips have the advantage of being noncutting, hence reducing the risk of iatrogenic damage. The propylene tip was inserted at 1 mm from working length, and then the irrigant was activated for 2 \(\times\) 30 s at 10.000 cpm using the size 15.02 taper EndoActivator with the highest frequency (166 Hz). The tip was passively inserted with an in-and-out movement in the root canal. Then, the specimen was rinsed with 3 mL of saline solution for 30 s. Finally, the root canal was irrigated with 5 mL 17% EDTA for 30 s and sonically activated with the same procedures used for NaOCl.  
Figure 1: ProTrain and its exploded view.  
* Electric Current Activation Group (n = 5) (EB Group): The specimen was irrigated with 5 mL of NaOCl at 5.25% preheated using TruNatomy irrigation needle (Dentsply Sirona). For the electric current activation, ElectroBond (Patent No. WO03/002014 PCT/IB02/02565, Rome, Italy) was used. ElectroBond delivered a direct current (DC) between the endodontic dentin (working as cathode) and the irrigating solution (working as anode) [14]. The tested ElectroBond automatically induced an electric flow over 20 \(\mu\)A throughout the irrigating solution through the endodontic dentine during the application procedure. To permit electricity conduction under in vitro conditions, the tooth to be tested was surrounded by a transmitting medium inside the ProTrain (Figure 1).