esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Responses of GRS Walls with Secondary Reinforcements Subjected to Earthquake Loading
## 4 Results
### Effects of Secondary Reinforcement Length
#### 4.1.3 Acceleration Amplification  
Figure 13 shows the effect of the secondary reinforcement length on variations of acceleration amplification in the reinforced zones at 20 and 30 m distances from the facing, over the height of the backfill. The acceleration in the reinforced zone was based on a 1.0 m distance from the back of the facing blocks. The acceleration amplification is defined herein as the ratio of the peak response acceleration at the measured location to the peak acceleration on the base. It can be seen that the amplification is similar at distances of 20 and 30 m from the wall facing. The similar acceleration responses at these two distances prove that the wall length used in this study is large enough to prevent unrealistic waves from the side boundary. This conclusion applies to all of the five cases (i.e., no secondary reinforcement (0 m) and secondary reinforcement lengths of 0.9, 1.2, 1.5, and 1.8 m). To save space, only the results of the GRS walls without the secondary reinforcement are presented herein.  
Figure 12: The distributions of (**a**) the connection load; (**b**) the maximum reinforcement load in the primary reinforcement.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Secondary Reinforcement Length (m)** & **Connection Load (kN/m)** & **Maximum Reinforcement Load (kN/m)** \\ \hline
0 & 163.81 & 165.95 \\
1.2 & 134.22 & 144.58 \\
1.5 & 117.65 & 132.65 \\
1.8 & 107.47 & 120.75 \\
2.1 & 101.90 & 113.401 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Sum of connection loads and maximum reinforcement loads of walls in each primary reinforcement layer (effect of secondary reinforcement length).  
As shown in Figure 13, the acceleration amplification out of the reinforced zone increases nonlinearly over the backfill height. The acceleration amplification can be considered negligible or uniform in the lower part (vicinity of the foundation) and intensifies in the upper part of the backfill. A comparison of different secondary reinforcement lengths showed that the influence of the secondary reinforcement length on the acceleration amplification was negligible; however, the acceleration amplification in walls without secondary reinforcement was slightly larger than in the walls with secondary reinforcement, which is concluded to be due to the slight increase in relative lateral facing displacement.
## 3 Algorithm and Methods
### Hybrid Positioning Model  
The hybrid positioning model combines CNN location classification model and path-loss model. In the online stage, we will use hybrid positioning model when the test points satisfy Equation (13). Otherwise, the CNN model will work alone. We first substitute the multiple measurements of the test point into the Equation (12) to obtain  
```
0:\(\Omega\): The first \(K\) reference points set with high probability estimated by CNN; \(p_{k}\): The probability of the first \(K\) reference points; \((x_{k},y_{k})\): The coordinates of the first \(K\) reference points; \(d_{k}\): The distance between the first \(K\) reference points and the base station;  Path-loss model curve and measurements of the test points; Output: Estimated position of the test point; 1. Initialize parameters \(N_{0},\ \eta,\ \delta\); 2. Substitute the multiple measurements of the test point into the Equation (12) to obtain the \(N\) value; if\(|N-N_{0}|>\eta\)then  continue; else  Substitute the average RSRP of the test point into Equation (11) to obtain the estimated distance \(d^{{}^{\prime}}\) between the test point and the base station; for\(d_{k}\) of the \(k\)th reference point do  Obtain the absolute value of the difference \(\Delta d_{k}\) between \(d_{k}\) and \(d^{{}^{\prime}}\); endfor if\(\Delta d_{k}>\delta\)then \(s_{k}=p_{k}\); else \(s_{k}=p_{k}+\frac{\sum_{k=1}^{K}\Delta d_{k}}{\Delta d_{k}}\); endif  Update the probability of the \(k\)th reference point \(p_{k}^{{}^{\prime}}\) using Equation (16) endif 3. Use the Equation (17) to obtain the estimated position of the test point.
```  
**Algorithm 1** Hybrid positioning.
## 3 Results and Discussions
### A Comparison with Actual Measured Data  
In this study we have screened out days with the same atmospheric environment for analysis. Our temperature data collection lasted for nine months (from October 2011 to June 2012). Out of a sample size of 274 days, the authors have picked 10 days with a similar atmospheric environment. The weekday temperatures of classrooms located at the east side of the building were taken to be the basic  
Figure 9: VGS and cloud management system (user interface of iEN-ASP).  
temperatures of a classroom (without VGS) and the indoor temperatures were monitored with air conditioners working. Those located at the west of the building were taken to be the energy saving temperatures of a classroom (with VGS) and the indoor temperatures were monitored with air conditioner working. On weekends and winter and summer breaks, the temperatures of classrooms were taken without air conditioners working on both the east and west sides.  
The east side of the building was in its original form without a VGS while the west side had the VGS. Therefore, after a day's exposure to the Sun, this allowed the authors to compare the temperatures of the east and west sides and verify the energy saved. For the ten days the authors have screened out, the west side's (with VGS) average temperature was 22.275 \({}^{\circ}\)C, and the east side without VGS had an average recorded temperature of 23.880 \({}^{\circ}\)C, which indicated a room temperature reduction of 6.721% with the implementation of VGS.  
We have also picked out days on weekends and winter and summer breaks that have the same atmospheric environment with the selected weekdays (similar temperature, humidity, solar radiation amount, _etc._). The west side had an average temperature of 20.677 \({}^{\circ}\)C and the east was 22.453 \({}^{\circ}\)C. The temperature reduction was 7.910% without anthropogenic interference.  
The results for with/without anthropogenic interference were compared. The average room temperature of a classroom on the west side, without anthropogenic interference was 1.598 \({}^{\circ}\)C lower than with anthropogenic interference.
# Fog-Based CDN Framework for Minimizing Latency of Web Services Using Fog-Based HTTP Browser
## 3 Proposed Fog-Based CDN Framework
### Fog-Based CDN Framework Workflow
#### 3.2.3 The Fog Browser Workflow and the Fog Service Locator (Fsl) Role  
The Fog Browser enables edge devices to utilize the proposed framework and communicate with nearby fog nodes (FNs). It has three main roles: discovering the nearby fog nodes, normal browsing, and retrieving the resources from the nearby fog nodes. First, the Fog Browser sends a broadcast message in the network asking if there are any available fog nodes reachable in the local network. If the _FW_ is operating with any fog node nearby, it will respond back with the UUID they received during the _FR_ registration. To keep the Fog Browser updated, it keeps listening for any new fog node that may join the network. The Fog Browser keeps updating the list of UUIDs of the nearby fog nodes. In any browsing request, the Fog Browser adds two headers to the request: X-FOG-ENABLED and X-FOG-NODES. The fog-enabled header will have a Boolean value that indicates whether the browser has any nearby fog nodes registered or not. If true, the fog nodes header will have a comma separated list of UUIDs of the fog nodes. The Fog Service Locator (_FSL_) is a layer between web servers and web services. When a request is received with a fog-enabled header set to true, the _FSL_ queries the _FR_ asynchronously with the received UUIDs and service name. After the request gets processed, it adds a response header X-FOG-NODES-QUERY with UUIDs that have the service resources offloaded. The _FSL_ acts as a proxy in case the fog-based CDN headers do not exist or the fog-enabled header is set to false. Figure 7 shows a flowchart of the _FSL_ processes. After that, the Fog Browser receives the response, and, based on the fog node query header, it pulls the requests from the nearby fog nodes. The fog node might get turned off while the request is processing. Here, the Fog Browser uses the original resources as a fallback.  
Figure 6: The web service registration steps.
## 2 Taxonomy and Design Drivers
### Data Aggregation  
Based on the data stored in sensors, DCS networks can facilitate data aggregation in a fully distributed way. By using data aggregation, traffic generated by producer nodes can be reduced before answering consumer queries. Monitoring building integrity during earthquakes by engineers, habitat monitoring by biologists, monitoring temperature and power usage in data centers by a cluster computer administrator are examples of sensor applications depending on the ability to extract summary (aggregate) data rather than raw data from the network.
## 3 Results and Discussion
### Ultrasonic Testing (UT)  
PAUT was previously shown to be very adept at detecting defects on a simulated tank in the laboratory under controlled conditions [8]. However, PAUT inspections of a full-scale tank yield significantly greater background noise, which is attributed to the signal attenuation and the fact that the interior of the tank is not necessarily smooth (e.g., some tanks have a wavy texture on the interior wall surface). In general, it was found that the shallower defects were more easily detected by PAUT. The only defect that could not be measured by PAUT was R1C6, which had a depth so small it could not be measured with traditional tools (i.e., calipers and pulse-echo UT). The defects surrounding R1C6, which were R1C5, R2C5, and R2C6, could all be measured using PAUT. As these three defects are the smallest in the set, the authors conclude that all other defects can also be measured using PAUT.  
There were three types of results when using PAUT to find defects over locations where defects were known to be: (1) a confident result (Figure 15), where the returned signal strength was strong enough to easily distinguish from the background noise; (2) a less confident result (Figure 16) that returned a weaker signal strength but that was still distinguishable from the background noise, and (3) a returned signal strength indistinguishable from the background noise (Figure 17). A skilled, trained operator can distinguish a return signal from a defect in the third result type. When the phased array probe is moved back and forth over the location of a small defect, the return signal behaves differently from the background noise, which is how it can be distinguished.  
Figure 14: Impact of close heating on the defect detection (**a**) localized and non-uniform heating (**b**) uniform heating.  
Figure 15: An example of a confident signal return detected using PAUT.
## 3 Results and Discussion
### Time-Harmonic Analysis  
The geometry described in Section 3.1 was first studied by means of a time-harmonic analysis at \(60\,\mathrm{Hz}\), using the complex MVP described in Section 2.1.2. This allowed the induced interference levels in normal operating conditions of the power line to be examined. The described formulation allowed the value of \(J_{0}\) to be enforced for each element of the domain. \(J_{0}\) represents the current density that one would find in a given element for a stationary problem, i.e., neglecting the effects of [PERSON]'s law. For a problem with frequency \(f\
eq 0\), the total current density is given by  
\[J(t)=J_{0}(t)-\sigma\frac{\partial A_{z}(t)}{\partial t}, \tag{17}\]  
which for the time-harmonic formulation becomes:  
\[\mathbf{J}=\mathbf{J_{0}}-j\omega\sigma\mathbf{A_{z}}. \tag{18}\]  
To simulate a single section of a \(250\,\mathrm{A}\) balanced three-phase system using a current-driven approach, we set \(\sigma=0\) on the phase conductors and we enforce \(\mathbf{J_{0}}=250/S_{\mathrm{phase}}e^{-i\Omega Z/3\pi}\), where \(k=1,\dots,3\) for phase conductors \(1,2,\) and \(3\), respectively. \(S_{\mathrm{phase}}\) is the phase conductor cross-section. The same result can be obtained with a voltage-driven approach, using the real electrical conductivity values for the three phase conductors and by subsequently setting \(J_{0,z}\) such that the total phase currents (determined by \(J_{0,z}\) and \(-j\omega\sigma A_{z}\)) have the desired value.  
Since FLARE is coded in MATLAB(r), the obtained finite element solution for a given problem can be easily visualized using the trisurf function. Field lines can be also drawn using the streamslice utility. The magnitude of the computed MVP near the three-phase conductors and around the buried pipeline is plotted in Figure 4. The magnetic field lines are also plotted at \(t=1\,\mathrm{ms}\). A distortion of the field lines path can be noticed at the OGWs and pipeline location, caused by the local effects of the magnetic field produced by the induced currents. One can also see that, at this frequency and for the considered value of soil resistivity, the magnetic field screening effect exerted by the soil was low. This was expected, since the soil penetration depth \(\delta=\sqrt{(\pi f\sigma\mu)^{-1}}\) was on the order of \(10^{2}\) m for the considered conditions.  
Figure 3: Geometry of the considered right-of-ways (not to scale).  
In order to validate the time-harmonic solver, we compared the obtained solution with that yielded by the well-established COMSOL Multiphysics(r) software. More details of the simulation in COMSOL Multiphysics(r) are provided in Section 3.4. The results are summarized in Table 1, where the magnitude and phase of the induced current are compared for the considered conductors. The column marked with \(\Delta_{r}\%\) shows the relative percentage difference between the two codes for the given quantity. This quantity is defined as  
\[\Delta_{r}\%=|(I_{\text{FLARE}}-I_{\text{COMSOL}})/I_{\text{COMSOL}}\cdot 100|. \tag{19}\]  
For the current magnitude and phase, the largest differences were below \(1\%\).
# An Application of an Unequal-Area Facilities Layout Problem with Fixed-Shape Facilities
## 2 Mathematical Model
### Small Problem Instance  
The MILP model for the UA-FLP will be used to solve a small problem instance taken from [PERSON] et al. [9]. The data for the problem instance is given in Table 1. For instance, the length and width of facility 4 is 1 and 2 distance units, respectively. Also, there are 40 units of materials flowing from facility 2 to facility 3. Recall, to prepare \(f_{ij}\) for the MILP model, we obtain new \(f_{ij}=f_{ji}+f_{ji}\). The MILP model is solved optimally using the CPLEX solver (version 12.4). The optimal solution is given in Table 2. For example, the center of facility 1 is located at point (4,4) on the plant floor and its length and width is 2 and 4 distance units, respectively. Notice facility 1 is vertically oriented since \(o_{l}=1\) and the width of the facility is greater than its length. Recall, that to be vertically oriented means the longer side of the facility is parallel to the \(y\)-axis. Also, all the other facilities are horizontally oriented. See the actual layout of the plant floor in Figure 1 where the total weighted distance (i.e., the total distance materials travel) is 1842.5 distance units. The optimal solution was obtained in 1.25 s using the CPLEX solver on Microsoft Windows 10 running on an Intel Core processor with a CPU speed of 3.20 GHz equipped with 16 GB of RAM. Although the small problem instance with six facilities was solved optimally in 1.25 s, it took 21.3 h to solve a problem instance with eight facilities, which will be discussed later. Since the UA-FLP at the TMMWV plant required solving a 20-facility problem, a genetic algorithm is developed for the problem.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \begin{tabular}{c} **Facility** \\ **(i)** \\ \end{tabular} & \begin{tabular}{c} **Length** \\ **(\(a_{i}\))** \\ \end{tabular} & \begin{tabular}{c} **Width** \\ **(\(b_{i}\))** \\ \end{tabular} & \begin{tabular}{c} \\ **1** \\ \end{tabular} & \begin{tabular}{c} \\ **2** \\ \end{tabular} & \begin{tabular}{c} **3** \\ \end{tabular} & \begin{tabular}{c} **4** \\ \end{tabular} & \
\begin{tabular}{c} **5** \\ \end{tabular} &
\begin{tabular}{c} **6** \\ \end{tabular} \\ \hline
1 & 2 & 4 & - & 51 & 0 & 23 & 17 & 29 \\
2 & 2 & 3 & 25 & - & 40 & 10 & 35 & 0 \\
3 & 2 & 2 & 0 & 15 & - & 0 & 60 & 12 \\
4 & 1 & 2 & 20 & 30 & 10 & - & 0 & 50 \\
5 & 1 & 3 & 36 & 0 & 43 & 0 & - & 23 \\
6 & 3 & 4 & 16 & 48 & 0 & 19 & 0 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data for small UA-FLP instance.
## 3 Results and Discussion
### Effect of Soil pH on Volatile Compounds in Tea Leaves
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multicolumn{6}{c}{**Measured Values of Different Indexes**} & \multicolumn{6}{c}{**Fertility Levels of Different Indexes**} \\ \cline{2-9}  & **P1** & **P2** & **P3** & **P4** & **P1** & **P2** & **P3** & **P4** \\ \hline pH value & 3.29 \(\pm\) 0.13d & 4.13 \(\pm\) 0.17c & 4.74 \(\pm\) 0.12b & 5.32 \(\pm\) 0.09a & III & III & II & I \\ Organic matter (g/kg) & 8.32 \(\pm\) 0.24d & 9.57 \(\pm\) 0.18c & 12.08 \(\pm\) 0.26b & 17.96 \(\pm\) 0.53a & III & III & II & I \\ Cation exchange capacity (cmoL/kg) & 7.26 \(\pm\) 1.16d & 13.52 \(\pm\) 0.89c & 17.13 \(\pm\) 1.21b & 22.48 \(\pm\) 1.25a & III & III & II & I \\ Total nitrogen (g/kg) & 2.46 \(\pm\) 0.12a & 2.48 \(\pm\) 0.13a & 2.53 \(\pm\) 0.09a & 2.49 \(\pm\) 0.14a & I & I & I & I \\ Total phosphorus (g/kg) & 1.24 \(\pm\) 0.13a & 1.09 \(\pm\) 0.11a & 1.15 \(\pm\) 0.08a & 1.13 \(\pm\) 0.09a & I & I & I & I \\ Total potassium (g/kg) & 6.84 \(\pm\) 0.39a & 6.95 \(\pm\) 0.54a & 7.42 \(\pm\) 0.43a & 7.38 \(\pm\) 0.65a & II & II & II & II \\ Available nitrogen (mg/kg) & 35.23 \(\pm\) 1.87d & 46.12 \(\pm\) 2.46c & 84.95 \(\pm\) 1.74b & 91.38 \(\pm\) 2.86a & III & III & II & II \\ Available phosphorus (mg/kg) & 3.13 \(\pm\) 0.22d & 4.06 \(\pm\) 0.23c & 10.41 \(\pm\) 0.46b & 15.12 \(\pm\) 0.77a & III & III & I & I \\ Available potassium (mg/kg) & 49.25 \(\pm\) 2.13d & 66.18 \(\pm\) 2.45c & 113.24 \(\pm\) 2.94b & 127.95 \(\pm\) 2.75a & III & III & II & I \\ \hline \end{tabular} Note: I—the soil has good fertility; II—the soil’s fertility is moderate; III—the soil’s fertility is poor; Different lowercase letters indicate a significant difference at the \(p\) \(<\) 0.05 level among different samples.  
\end{table} 
Table 1: Basic chemical indexes of different soil samples.
## 5 Algorithms and Standard Products for Inferring Crop-Biophysical Characteristics  
Once field data are collected, the processing algorithm, designed to ingest massive amounts of data in an efficient and systematic manner, is invoked. The procedures are based on standardized macros and three basic \"levels\" of data-handling/analyses. During Level-1 processing, the raw spectral data are converted to percent reflectance, stored in an Excel spreadsheet, interpolated at a 1-nm interval, and compressed for archiving purposes. At this stage, mean values of reflectance for each sampled area and the standard deviations of reflectance are calculated and, thus, these statistics characterize each sampled area.  
Level-2 processing involves integration of Ocean Optics reflectance to simulate informative spectral regions for retrieval of crop biophysical characteristics including spectral bands of our own airborne AISA-Eagle scanner as well as existing space borne systems (e.g., TM Landsat, MODIS, MERIS). The final processing step, Level-3, addresses the estimation of biophysical and agronomic characteristics of crops using a developed suite of algorithms, as described below.  
Our suite of algorithms includes procedures for retrieval of (i) foliar pigment content including total chlorophyll, carotenoids, and anthocyanins [13; 14; 15] and (ii) crop biophysical characteristics including vegetation fraction (VF), fraction of photosynthetically active radiation absorbed by live vegetation (_f_APARgreen) [16], green LAI, green leaf biomass, and gross primary production (GPP).
## 2 Sliding Mode Control Law of the DFIG System
### Sliding Mode Control of DFIG (RSC)
It should be pointed out that, due to the most parts of disturbance \(\mathbf{d}_{\text{r}}\) are the AC components at the frequencies of \(2\omega\) and \(6\omega\), the added resonant regulators are tuned at the frequencies of \(2\omega\) and \(6\omega\). According to the condition of the equivalent control, viz. \(dS_{\text{r}}/dt=0\), the equivalent controls, which provide sufficient regulation under ideal conditions, are derived as  
\[\mathbf{u}_{\text{req}}=\sigma^{-1}{B_{0}}^{-1}\left(f_{\text{r}}-k_{\text{rs}} \mathbf{x}_{\text{re}}-\frac{d\mathbf{x}_{\text{r}}^{*}}{dt}\right) \tag{6}\]  
When disturbances arise, the system will drift from sliding surface only with the equivalent control. The switching control must be added for disturbance rejection. To weaken the chattering phenomenon, the sign functions is replaced by saturation functions sat() in this paper. Consequently, the switching controls are  
\[\Delta\mathbf{u}_{\text{r}}=-\sigma^{-1}{B_{0}}^{-1}k_{\text{ru}}sat(S_{\text{r}} )\ sat(y)=\left\{ \begin{array}{c}sign(y),\,|y|>\lambda\\ \
icefrac{{y/\lambda}}{{}},|y|\ \leq\lambda\end{array} \right. \tag{7}\]  
where, \(k_{\text{ru}}=[k_{\text{ru1}}\ 0;\,0\ k_{\text{ru2}}]\); \(k_{\text{ru1}}\) and \(k_{\text{ru2}}\) are positive control gains; \(\lambda\) is the width of boundary layer in RSC. Finally, the voltages to be applied to the rotor winding are  
\[\mathbf{v}_{\text{r}}(\mathbf{x}_{\text{r}},t)=\mathbf{u}_{\text{req}}+\Delta\mathbf{u}_{ \text{r}} \tag{8}\]
## 2 Experiment
### Film Patterning
#### 2.3.2 Etching Process  
A water bath was established for the etching to be conducted in a stable temperature environment. A trough was filled with deionized (DI) water and placed upon an Echo Thermal HP30 hotplate (Torrey Pines Scientific, Inc., Carlsbad, California). Around 300 mL of 30% KOH/diluted KOH was poured into a beaker, which was later transferred into the trough. A Teflon plate was added in between to avoid direct heating. Finally, a thermocouple was submerged into the KOH solution and connected to the hotplate through a proportional-integral-derivative (PID) control loop to adjust the solution temperature. With all these measures, the solution temperature was able to be stabilized within \(\pm\)1 \({}^{\circ}\)C during etching. The test sample was cleaved into an 8 \(\times\) 10 mm\({}^{2}\) die and clamped by a tweezer when dipped into the solution. When submerged in the KOH, samples experienced minimal agitation, and upon removal, they were rinsed under DI water and subsequently dried with N\({}_{2}\). Finally, the sample was cleaved from the middle (Figure 3a) and a cross-section was imaged in a FEI Quanta 600 Environmental Scanning Electron Microscope (ESEM) (FEI Company, Hillsboro, OR, USA) (Figure 3b).  
Figure 3: (**a**) A cleaved sample after etching. Black background is the vacancy after cleaving. (**b**) Cross-section SEM image of an Al\({}_{0.72}\)Sc\({}_{0.28}\)N film etched for 30 s in 30% KOH at 60 \({}^{\circ}\)C.
# An Intrusion Resistant SCADA Framework Based on Quantum and Post-Quantum Scheme
## 2 Related Work
### Post Quantum Digital Signature Scheme
#### 2.3.2. Winternitz One-Time Signature Scheme  
To address the large key size of LOTSS, Winternitz One-time Signature Scheme (WOTS), an optimized version of LOTSS, uses a parameter \(w\). A larger value of \(w\) means smaller signature size. However, the time cost of the WOTS scheme increases as \(w\) increases. The signature size decreases linearly, and the time cost increases exponentially [31].