esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimating Maize Yield from 2001 to 2019 in the North China Plain Using a Satellite-Based Method
## 2 Materials and Methodology
### Methodology
#### 2.3.3 Calibration of the Harvest Index  
The HI, which represents the percentage of above-ground biomass that is converted to economic yield and indicates the percentage of the biomass allocated to harvesting organs, is a significant factor influencing crop yield. Numerous elements, including cultivars, management, and growing circumstances, might affect the HI. In order to take into consideration the temporal and spatial volatility of the HI, we referred back to the method used by [PERSON] et al. [51] to calculate the HI for each county based on the estimated GPP and statistical yield in the prior year. Then, the calibrated HI was used to convert the GPP estimated by the model into the crop yield for the year. For the counties where the HI was missing, we used the HI mean from the neighboring counties instead.
## 3 Operation and Models of the VANET Testbed  
Figure 2 is a simple example of a VANET consisting of cars communicating their video information with each other and with the Internet (IP cloud) via the RSUs, which can be implemented as a WiMAX-enabled Base Station. The server is to forward or to archive the streaming video from the cars. Each car is acting as a WiMAX Mobile Station (MS) capable of receiving, processing and forwarding data to the destination and consequently to the backhaul network via the WiMAX Base Station.  
In our VANET testbed, each vehicle has the ability to communicate with any neighboring vehicles. Depending on the nature of the messages, they will either stay within the VANET or exit the backhaul network via the RSU. For instance, tailgating, collision and braking (from preceding cars) are warning messages that can remain within the VANET network, while detailed regional weather forecast, notification of traffic jams and collision are usually exchanged between the backhaul network and the VANET. In many video applications we envision that video messages from the vehicular camera are usually required to be forwarded from the point of interest (such as traffic congestion, road block, and accidents) to a control center in the backhaul network to aid various parties (such as traffic personnel, emergency agents, insurance companies) to respond to the situations more effectively. We shall refer to these types of traffic as \"safety driving video\" or simply \"safety video\" later on.  
To implement a testbed for the study safety driving video traffic among vehicles, we shall provide the operations at different layers as well as different models used in the testbed in the following two sections.
# Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers
## 4 Model Application and Results
### The Suspended Sediment Load (SSL) Estimation through Local Linear Regression Models
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Location**} & \multirow{2}{*}{**Comb.**} & \multicolumn{2}{c}{**R**} & \multicolumn{2}{c}{**R\({}^{2}\)**} & \multicolumn{2}{c}{**RMSE (t/day)**} & \multicolumn{2}{c}{**PBIAS (\%)**} \\ \cline{3-10}  & & **Training** & **Testing** & **Training** & **Testing** & **Training** & **Testing** & **Training** & **Testing** \\ \hline \multirow{8}{*}{Pirna gauging station} & 1 & 0.98 & 0.94 & 0.96 & 0.89 & 455.78 & 229.57 & 0.24 & \(-\)1.42 \\ \cline{2-10}  & 2 & 0.99 & 0.97 & 0.97 & 0.95 & 391.50 & 160.26 & \(-\)0.13 & \(-\)0.73 \\ \cline{2-10}  & 3 & 0.97 & 0.95 & 0.94 & 0.90 & 569.36 & 216.51 & \(-\)1.29 & \(-\)1.57 \\ \cline{2-10}  & 4 & 0.95 & 0.91 & 0.91 & 0.82 & 695.51 & 290.96 & 0.39 & \(-\)0.31 \\ \cline{2-10}  & 5 & 0.96 & 0.93 & 0.92 & 0.86 & 660.47 & 259.17 & 0.28 & 0.93 \\ \hline \multirow{8}{*}{Magdeburg-Strombrücke gauging station} & 1 & 0.96 & 0.95 & 0.92 & 0.90 & 472.87 & 248.26 & 0.05 & \(-\)0.46 \\ \cline{2-10}  & 2 & 0.96 & 0.95 & 0.92 & 0.90 & 469.61 & 253.17 & \(-\)0.54 & \(-\)0.81 \\ \cline{1-1} \cline{2-10}  & 3 & 0.96 & 0.94 & 0.92 & 0.89 & 454.82 & 263.57 & 0.21 & \(-\)0.69 \\ \cline{1-1} \cline{2-10}  & 4 & 0.96 & 0.94 & 0.92 & 0.89 & 481.09 & 263.71 & \(-\)0.06 & \(-\)0.12 \\ \cline{1-1} \cline{2-10}  & 5 & 0.96 & 0.95 & 0.92 & 0.90 & 463.92 & 252.03 & \(-\)0.21 & 0.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The performance evaluation of the different WANN models. WANN model as the most precise model when reproducing the observed SSL pattern at both the stations, notwithstanding minor discrepancies. The best WANN model predictions in the testing phase was compared using scatter plots and time series (sediment) graphs of observed versus predicted SSL, as shown in Figures 15 and 16, for the two stations, respectively.
## 2 Results
### Differential Transcript Expression and Enrichment Analysis  
Pairwise combinations identified 4184 transcripts exhibiting significant differential expressions in leaf vs. stem (1894: Ups: 962; DOWNs: 902), leaf vs. root (1637: Ups: 1139; DOWNs: 498), and stem vs. root (683: Ups: 514; DOWNs: 169) (Figure 2A, Table S2). Furthermore, the clustering of 1136 key DE transcripts was categorized into four subclusters representing 373 (Subcluster 1), 49 (Subcluster 2), 614 (Subcluster 3), and 100 (Subcluster 4)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S. No.** & **Sample Name** & **Raw Reads** & **Filtered Reads** &
\begin{tabular}{c} **Retained High-Quality** \\ **Reads (\%)** \\ \end{tabular} \\ \hline
1 & Leaf & 43,893,016 & 40,144,376 & 91.4 \\
2 & Stem & 39,814,592 & 36,737,576 & 92.2 \\
3 & Root & 35,495,806 & 32,757,628 & 92.2 \\  & Total reads & 119,203,414 & 109,639,580 & 91.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Total raw and filtered reads generated for leaf, stem, and root tissues of _A. glauca_.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **De Novo-Assembly Statistics** \\ \hline  & **Before Clustering** & **After Clustering** \\ \hline Total transcripts & 1,61,836 & 1,17,214 \\ Total genes & 81,570 & 81,162 \\ Percent GC & 39.69 & 39.62 \\ Contig N50 & 1751 & 1607 bp \\ Median contig length & 715 & 526 bp \\ Average contig length & 1071.45 & 923.32 bp \\ \hline \hline \end{tabular}
\end{table}
Table 2: De novo-assembly statistics of _A. glauca_ transcriptome.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Database** & **No. of Transcripts** & **Percent (\%)** \\ \hline NCBI nr & 69,731 & 59.49 \\ TAIR & 58,982 & 50.31 \\ KOG & 55,513 & 47.36 \\ Swiss-Prot & 53,957 & 46.03 \\ Plant TFDB & 33,040 & 28.18 \\ KEGG & 8945 & 7.63 \\ Total & 72,120 & 61.53 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Functional annotations of transcripts with various public databases.  
transcripts. Subclusters 2 and 3 represent the transcripts significantly enriched in the leaf, while Subclusters 1 and 4 were enriched solitarily in the stem and root tissues, respectively (Figure 2B, Table S3).
## 5 Material and Methods
### Scenario for Legal Network Traffic  
The Iperf tool was utilized to load the network and evaluate its capacity. Iperf is a tool that employs a client-server model, generating traffic in the form of data streams between server and client nodes. It produces random traffic, meaning that a host sends packets to any other host in the network with equal probability. Iperf can create TCP or UDP streams to load the network. In TCP mode, an Iperf client transmits an infinite amount of data to the server via a TCP stream. After a specified duration, the Iperf tool cancels the TCP connection and displays various statistics and the total amount of data successfully transmitted on the screen.  
Random flows among users were generated using Iperf in conjunction with the Mininet Flow Generator. For each flow, one user is randomly selected as the server and another user is chosen as the client. The randomly selected users establish TCP and UDP connections using Iperf.  
ApacheBench measures the performance of a web server by saturating it with HTTP requests and recording latency and success metrics, thereby conducting an HTTP bench mark. A large number of HTTP requests are sent using ApacheBench. In the simulation, all users run a Python HTTP server and an Iperf server to generate TCP and UDP traffic flows.  
When normal traffic data are transmitted across the network, the Ryu controller records flow statistics in a local file in CSV format upon receiving an \"OFPC_FLOW_STATS_REPLY\" message from the OpenFlow switches. The recorded attributes are augmented by adding a traffic class, increasing the number of features. In this class, normal traffic is labeled as \"0\".
## 5 Experiments and Performance Evaluation  
To implement the WADGPS system in Taipei flight information region (FIR), the stable RSs collection of dual-frequency GPS observations are essential. This paper uses the e-GPS observation stations in Taiwan as the WADGPS RSs, and the e-GPS observation stations are operated by Taiwan Ministry of Interior. The WADGPS RSs send four types of raw dual-frequency GPS observations to the WADGPS MS including:  
Range data: it is composed of L1-L2 dual-frequency pseudorange, carrier phase, Doppler frequency, and signal to noise ratio of each satellite in view by the network [1]. The data update rate is 1 Hz.  
Ephemeris data [15]: it includes GPS orbit parameters and satellite clock correction coefficients of each satellite in view by the network. It is updated every 50 seconds.  
Almanac data [15]: it consists of the simplified GPS orbit parameters. It is updated every 500 seconds.  
The Klobuchar model coefficients [15]: it provides the common ionospheric model for single frequency users. It is updated every 500 seconds
## 3 System Model and Symbol Definitions
### RACH Procedure Scenario
#### 3.1.2 Success and Collision Probability  
In the 5G scenario with massive MTDs, we assume many MTDs will select the same preamble, causing the collision [5]. The NR base station will refuse access to the contenting devices, creating a massive signal overload. The failed devices will still try to re-transmit after a predefined back-off window [37]. The success of an attempt exists if and only if one device utilizes the \(w^{\text{th}}\) preamble. Accordingly, the three probabilities are defined as follows:  
* With the maximum preamble \(W=54\), the probability that the \(w-th\) preamble is unclaimed is given by \[\mathbb{P}(a)=\binom{N}{0}\bigg{(}\frac{1}{W_{\mathcal{P}a}}\bigg{)}^{0} \bigg{(}1-\frac{1}{W_{\mathcal{P}a}}\bigg{)}^{N}\] (2) where \(\binom{N}{0}\) is the binomial coefficient.
* The probability that only one device claimed the \(w^{\text{th}}\) preamble is \[\mathbb{P}(b)=\binom{N}{1}\bigg{(}\frac{1}{W_{\mathcal{P}a}}\bigg{)}^{1} \bigg{(}1-\frac{1}{W_{\mathcal{P}a}}\bigg{)}^{N-1}\] (3)
* The probability that two or more devices are contending for the \(l\)-th preamble and colliding is \[\mathbb{P}(c)=1-(\mathbb{P}(a)+\mathbb{P}(b))\] (4)  
Considering the system model, we can summarize the scenario as a massive number of devices concurring to a select few numbers of available preambles and ever-increasing transmission attempts from previously failed devices wasting energy to perform the transmission without any guarantee of success. Given this situation and the delay-intolerant characteristics of some MTDs, exploring an intelligent way to guarantee the successful access and data transmission of the MTC devices according to the latency characteristics of the applications requesting them is critical.
## 3 Space--Time Dynamic Analysis Method Based on DCM
### Space-Time Discretization and Numerical Scheme of DC Dynamic Analysis
\right]_{(m-2)\times(N-2n-m+2)}\]  
Equation (16) is abbreviated as the following form  
\[\widetilde{\mathbf{F}}=\mathbf{F}+\mathbf{W}_{0}\mathbf{y}_{0}+\mathbf{W}_{00}\dot{\mathbf{y}}_{0} \tag{21}\]  
where  
\[\mathbf{W}_{0}=\frac{\rho_{\mathbf{\zeta}}^{\prime}}{\Delta T^{2}L^{2}}\mathbf{W}_{B_{i},B _{N}}^{(\xi^{2})}\mathbf{W}_{B_{N},B_{12}}^{(\tau)}\mathbf{W}_{B_{12},B_{3}}^{(\tau)}- \frac{EI}{L^{4}}\mathbf{W}_{B_{i},B_{3}}^{(\xi^{2})}\mathbf{W}_{B_{3},B_{N}}^{(\xi^{2})} \mathbf{W}_{B_{N},B_{3}}^{(\xi^{2})}-\frac{\rho}{\Delta T^{2}}\mathbf{W}_{B_{i},B_{12}} ^{(\tau)}\mathbf{W}_{B_{12},B_{3}}^{(\tau)} \tag{22}\]  
\[\mathbf{W}_{00}=\frac{\rho_{\mathbf{\zeta}}^{\prime}}{\Delta TL^{2}}\mathbf{W}_{B_{i},B_{N}}^ {(\xi^{2})}\mathbf{W}_{B_{N},B_{3}}^{(\tau)}-\frac{\rho}{\Delta T}\mathbf{W}_{B_{i},B_{3 }}^{(\tau)} \tag{23}\]  
Substituting Equations (22) and (23) into Equation (19), we can obtain  
\[\mathbf{Y}_{B_{4}}=\mathbf{\Gamma}\widetilde{\mathbf{K}}^{-1}\big{(}\mathbf{F}+\mathbf{W}_{0}\mathbf{y }_{0}+\mathbf{W}_{00}\dot{\mathbf{y}}_{0}\big{)} \tag{24}\]\[\dot{Y}_{B_{4}}=\frac{\mathbf{\Gamma}}{\Delta T}\bigg{(}\mathbf{W}_{B_{i},B_{i}}^{(\tau)} \widetilde{K}^{-1}\mathbf{F}+\bigg{(}\mathbf{W}_{B_{i},B_{i}}^{(\tau)}\widetilde{K}^{-1} \mathbf{W}_{0}+\mathbf{W}_{B_{i},B_{3}}^{(\tau)}\bigg{)}\mathbf{y}_{0}+\mathbf{W}_{B_{i},B_{i}}^ {(\tau)}\widetilde{K}^{-1}\mathbf{W}_{00}\dot{\mathbf{y}}_{0}\bigg{)} \tag{25}\]  
Equations (24) and (25) can be written as the following recursive format  
\[\left\{ \begin{array}{l}\mathbf{Y}_{B_{4}}\\ \dot{\mathbf{Y}}_{B_{4}}\end{array} \right\}=\mathbf{Q}\left\{ \begin{array}{l}\mathbf{y}_ {0}\\ \dot{\mathbf{y}}_{0}\end{array} \right\}+\mathbf{J}_{1} \tag{26}\]  
where \(\mathbf{Q}\) is a transfer matrix, and  
\[\mathbf{Q}=\left[ \begin{array}{cc}\Gamma\widetilde{K}^{-1}\mathbf{W}_{0}&\Gamma \widetilde{K}^{-1}\mathbf{W}_{00}\\ \frac{\mathbf{\Gamma}}{\Delta T}\bigg{(}\mathbf{W}_{B_{i},B_{i}}^{(\tau)}\widetilde{K }^{-1}\mathbf{W}_{0}+\mathbf{W}_{B_{i},B_{3}}^{(\tau)}\bigg{)}&\frac{\mathbf{\Gamma}}{ \Delta T}\mathbf{W}_{B_{i},B_{i}}^{(\tau)}\widetilde{K}^{-1}\mathbf{W}_{00}\end{array} \right]\]  
\[\mathbf{J}_{1}=\left\{ \begin{array}{l}\Gamma\widetilde{K}^{-1}\mathbf{F}\\ \frac{\mathbf{\Gamma}}{\Delta T}\mathbf{W}_{B_{i},B_{i}}^{(\tau)}\widetilde{K}^{-1} \mathbf{F}\end{array} \right\}\]
### UHI Studies with Satellite Data
Significant relationships were also found between land cover (built-up region) and surface temperature in a conventional regression analysis (R\({}^{2}\) = 0.578) of an urban heat island effect over Taoyuan, Taiwan [86]. The effect of land cover characteristics (such as building roof-top areas, vegetation index, water) and other factors (solar radiations, sky view factor) on UHI were studied over high-density city areas using satellite (LST, NDVI) and LIDAR (model of city) grid datasets. It was found that an increase in building roof-top areas increases LST, while an increase in vegetation and water lead to decreases in LST [87]. The analysis of diurnal surface temperature variations to explore the UHI effect over Jaipur and Ahmedabad using MODIS-sensor-derived LST, showed the influence of thermal properties of different surfaces on LST [88]. In another study, SUHI intensity and its seasonal, diurnal and spatial variability for 84 urban and surrounding rural areas in India, and its correlation with evapotranspiration, was analyzed from MODIS sensor data. This study attached the day/night and seasonal behavior of SUHI to the differences in evapotranspiration between urban and rural areas [89]. Similarly, the daily, seasonal, and annual fluctuations in SUHI intensity across 150 major cities in India, each located in distinct climatic regions, was analyzed by employing MODIS data spanning from 2003 to 2018, which found that daytime SUHI was tightly linked to vegetation, evapotranspiration, and thermal inertia, while nighttime SUHI was linked with built-up intensity, white sky albedo, and thermal inertia [90].
# Pollution and Climatic Influence on Trees in the Siberian Arctic Wetlands
## 2 Materials and Methods
### Dendrochronological Analysis  
Tree cores were mounted on wooden backing. Cores and disks were finely sanded and treated with contrast powder to enhance visualization of ring boundaries. The measurements were carried out on a LINTAB-6 platform with an accuracy of 0.01 mm. The cross-dating quality was assessed by COFECHA software ([[https://www.ldeo.columbia.edu/tree-ring-laboratory/resources/software](https://www.ldeo.columbia.edu/tree-ring-laboratory/resources/software)]([https://www.ldeo.columbia.edu/tree-ring-laboratory/resources/software](https://www.ldeo.columbia.edu/tree-ring-laboratory/resources/software)); [13] (accessed on 1 December 2022)). From the measured series, we constructed a growth index (GI) chronology for each TP from raw tree-ring width in mm to unitless GI values by a detrending method with program ARSTAN ([14]; [[https://www.ldeo.columbia.edu/tree-ring-laboratory/resources/software](https://www.ldeo.columbia.edu/tree-ring-laboratory/resources/software)]([https://www.ldeo.columbia.edu/tree-ring-laboratory/resources/software](https://www.ldeo.columbia.edu/tree-ring-laboratory/resources/software)) (accessed on 1 December 2022)). We removed low frequency trends by fitting tree-ring series with a negative exponential curve or line with a negative slope. For statistical analysis, the GI chronology was normalized by conversion to Z-scores with a mean of zero and standard deviation of 1.0.  
Figure 2: Study area and test plots sites (TP) location. TP within heavily polluted and background area indicated by red and white dots, correspondingly. The legend: 1—swamps with open larch stands and bush–grass–moss communities, 2—bogs (grass–moss–small bushes communities), 3—tundra bogs, 4—barren, 5—water bodies.
# DTN Trustworthiness for Permafrost Telemetry IoT Network
## 4 Trustworthiness Model Specification
### Trustworthiness Layers Relationships  
Figure 6 synthesizes our trustworthiness model actors. Blue-colored elements form part of our model baseline layers, and orange-colored elements form part of the extension layers. The primary goal is to increase the \(STR\) to provide better trustworthiness. Three main factors directly help increase the \(STR\): (1) Mitigate/tolerate Byzantine errors; (2) decrease the \(FSR\); and (3) increase the \(PDR\). These factors can be seen as secondary goals that leverage the success of the final goal to provide trustworthiness. Each of these secondary goals can be accomplished by implementing a set of actions or countermeasures. Each of these countermeasures commonly affects only one of the goals. Moreover, two transversal actions impact more than one secondary goal. These transversal actions implement the extension layers of our model: the social trustworthiness layer and the consensus layer.  
In Figure 6, continuous-line arrows indicate a positive outcome, discontinuous-line arrows indicate a negative outcome, and dotted-line arrows indicate an uncertain outcome. On the one hand, the use of social trustworthiness can reduce network congestion thanks to the ostracism of nodes with the worst reputation by only sending the values from nodes with the highest reputation to the control center. In addition, social trustworthiness also helps to reduce the \(FSR\) thanks to the ostracism of bad reputation nodes. It also leverages the mitigation of Byzantine errors because only values from high reputation nodes (leaders)are trusted. On the other hand, implementing a consensus mechanism mitigates Byzantine errors thanks to the general agreements reached by all nodes from a consensus group. Contrarily, the consensus layer can negatively affect the \(PDR\), given that it introduces a considerable amount of extra traffic to the network, which could lead to link congestion.
## 4 Results and Discussion
### Push-Shear Force-Push-Shear Displacement Characteristics  
By analyzing the two loading curves (Figure 7), the shear force displacement characteristics of carbonaceous mudstone were obtained, which are manifested as follows:  
(1) Under natural conditions, the push-shear deformation process of carbonaceous mudstone specimens in Group A basically shows the full stress-strain curve, and the curve can be roughly divided into four stages: linear elastic deformation stage, yielding stage, strengthening stage, and local deformation stage. In the linear elastic deformation stage, the push-shear force has a higher growth rate compared with the displacement, and the change between stress and strain shows a linear relationship. After entering the yielding stage, the rate of increase of push-shear force decreases significantly, and cracks of different sizes and lengths begin to develop slowly on the specimen surface, and there are irregular curve segments on most of the curves. In the strengthening stage, the push-shear force gradually reaches the peak value, and the specimen shows a large displacement. After the destruction of the specimen after the local deformation stage, the displacement of the specimen continued to increase, while the push-shear force decreased, and the rate of decline gradually slowed down.  
(2) After saturated water immersion, the push-shear deformation process of carbonaceous mudstone specimens of Group B went through elastic-plastic deformation stage and plastic deformation stage successively. After shearing, the specimen enters the elastic-plastic deformation stage, at which the shear force grows slowly, and the growth rate gradually becomes smaller, and the horizontal shear displacement also grows. For specimens in the shear after entering the plastic deformation stage, this stage of the push-shear force appears to be a small decrease.  
(3) Observing and comparing the displacement relationship between the two groups A and B in Figure 7, it is found that the horizontal push-shear displacements of the specimens in Group B are increased compared with those in Group A. This is mainly due to the fact that when the carbonaceous mudstone is saturated with water, there will be pore water pressure generated in its interior. The pore water pressure is larger at the beginning of the test, but as the test proceeds, it gradually dissipates, which leads to a slow increase in the shear strength of the specimens.  
Figure 7: Push–shear force–push–shear displacement relationship curves of specimens under different water environments: (**a**) first loading curve; (**b**) second loading curve.
## 3 Methods
### GPS Data Processing
GPS provides displacement measurements in three directions: North-south (NS), east-west (EW), and up-down (UD) directions. The DD solutions are referred to the base station. The baseline is approximately 1.7 km. The PPP solutions are referred to a stable regional reference frame. A smoothed trend line is also plotted for each time series. The trend is obtained by a moving-average filter with a sliding window of 11 measurements, current point plus 5 points before and after the current point. It is clear that the PPP solutions retain a larger scatter than the DD solutions. Further analysis indicates that the RMS-accuracy of the PPP solutions was approximately 3 mm in the horizontal directions and 7 mm in the vertical direction; the DD solutions achieved 1.5-mm RMS-accuracy in the horizontal directions and 2.5-mm in the vertical direction (Figure 4b,d,f).
## 2 Materials and Methods  
### Fabrication of an Open-Ended Coaxial Four-Point Probe  
A four-point EIS probe was custom-designed, as shown in Figure 1a and Supplementary Figure S1, with four open-ended RG58/U coaxial cables (Cnals, Jiangsu Elesun Cable Co., Ltd., Zhenjiang, Jiangsu, China) (45 cm long, copper core diameter of 0.9 mm, polyethylene insulation with an outer diameter of 2.95 mm, maximum capacitance of 101 pF/m, and impedance of 50 \(\pm\) 2 \(\Omega\)). BNC connectors were fitted to one end of each cable, while the opposite ends were modified to expose 4 cm of insulation and 1.5 cm of the copper core. These open ends were mounted in a polycarbonate block with evenly spaced holes to maintain a core-to-core distance of 0.3 cm between the exposed cores in a linear arrangement. Likewise, the setup minimized displacement and optimized contact during the measurements.