esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Fecal VOC in Gastrointestinal DISEASES
### Infectious Diseases
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Analytical Technique** & **Advantages** & **Disadvantages** \\ \hline \multirow{6}{*}{FAIMS} & Sensitivity in ppm range & \multirow{2}{*}{Identification of specific individual VOCs not possible Real-time measurements not possible Real-time measurements not possible \\  & Mobile system and suitable for clinical use & \\  & Easy to use & \\  & Low cost & not possible \\ \hline \multirow{6}{*}{SIFT-MS} & Real-time measurements possible & \multirow{2}{*}{Identification of specific individual VOCs is not possible Mobile system} \\  & & \\ \cline{1-1}  & Real-time measurements possible & \\ \cline{1-1}  & Fast & \\ \cline{1-1}  & Sensitivity in ppb range & \\ \cline{1-1}  & Mobile system & \\ \hline \hline \end{tabular} Abbreviations: eNose, electronic nose; GC-MS, gas chromatography mass spectrometry; FAIMS, field asymmetric ion mobility spectrometry; SIFT-MS, selected ion flow tube mass spectrometry; VOC, volatile organic compound; ppb, parts per billion; ppm, parts per million.  
\end{table} 
Table 1: _Cont._clinical application. In addition, identification of discriminant VOCs in _C. difficile_ positive patients is needed to develop tailor-made sensors, before clinical application of fecal VOC analysis is possible. Other Infectious Diseases  
A pilot study on detection of adult cholera patients by fecal VOC analysis using GC-MS, demonstrated the presence of two different VOCs in cholera positive fecal samples, whereas these were absent in healthy controls (Table 2). Overall, chromatograms of cholera samples showed a lower number of individual VOCs: 23 in cholera cases (\(n=6\)) vs. 36 in healthy controls (\(n=3\)). However, sample size in this study was too small to draw conclusions [29]. [PERSON] and colleagues studied fecal VOC profiles of samples of 53 children from Malawi, comprising 27 rotavirus positive and 26 stool samples of children with unspecified gastrointestinal problems, by means of GC-MS. In particular, two VOCs were found with a higher frequency and an higher abundance in rotavirus infected samples (Table 2) [25]. Another study found one specific VOC to be ubiquitous in rotavirus positive fecal samples, namely ethyl dodecanoate, however only five stool samples were analyzed [23]. [PERSON] et al. compared fecal VOCs from 16 adult patients infected with _Giardia lamblia_ with 17 controls with diarrhea without _Giardia lamblia_ by means of GC-MS. The two groups could be separated based on five different VOCs listed in Table 2, with AUCs ranging from 0.80 to 0.93. Additional larger studies by means of GC-MS are needed to assess the potential of fecal VOCs as a bedside diagnostic tool for rotavirus and _Giardia lamblia_ detection [26]. In a study on VOC profiles in _Campylobacter jejuni_ positive fecal samples, it was found that the presence of one specific VOC was positively associated with _C. jejuni_[8]. Another study demonstrated the absence of two types of VOCs were strongly associated with _C. jejuni_, as listed in Table 2[10].
# Paint Removal with Pulsed Laser: Theory Simulation and Mechanism Analysis
## 3 Results and Discussion
### XPS Analysis  
The changes in the surface composition of the paint after laser cleaning were determined by XPS. Figure 7 presents the spectra of (a) the cleaned surface and (b) the untreated surface. Peaks of 1s core level of C, O, N and 2p core level of Ti, Al are clearly visible, and the peaks of C, O, and N were found to have changed significantly before and after the cleaning. The results show that the peaks of C and N in the cleaned surface had decreased, while the peak of O had increased. The reason for this difference is most likely due to the following analysis: The surface of the residual paint layer was no longer smooth, and the surface area had obviously increased. In addition, nanoparticle additives such as TiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\) were found exposed on the surface. The polymer molecular chain was thermally decomposed and burned by absorbing laser energy, illustrating that the paint had released C and N atoms as simple gas or volatile species of CO and NO\({}_{2}\) during the cleaning process. The peaks of C, N were found to be lower on the cleaning surface and the intensity of the peak of O was enhanced. This is probably due to chemical reactions taking place while the pulsed laser was removing the paint.  
The electronic state and existing forms of C, O, N, Ti, and Al can be further studied by examining the fine spectra of each element on the surface of the paint layer before and after laser cleaning. The profiles of the C1s, O1s, N1s, Ti2p and Al2p spectra on the surface of the paint layer before and after laser cleaning are shown in Figures 8-11, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Wave Number** & **Vibration Type** & **Remark** \\
**(cm\({}^{-1}\))** & **Vibration Type** & **Remark** \\ \hline
3673–4000 & the stretching of O-H & free state \\
3372.89 & the stretching of -OH & \\
3027.69 & the stretching of C-H & \\
2927.41 & the stretching of C-H & in the -CH\({}_{3}\) \\
2854.13 & the stretching of C-H & in the -CH\({}_{2}\)- \\
1727.91 & the stretching of -C=O & in the -(O-)C=O \\
1525.42 & the stretching of C = N & \\
1452.14 & the bending of -CH\({}_{2}\)- & \\
1382.71 & the stretching of -CH\({}_{3}\) & asymmetry and symmetry \\
1180.22 & the stretching of C-O-C & in the -(O-)C=O \\
1120.11 & alkyl group & long chain \\
1072.23 & the stretching of C-OH & Asymmetry \\
1020.16 & C-C single bond skeleton & \\
881.31 & the bending of C-H & out-of-plane \\
701.96 & the stretching of C-C & long carbon chain saturated hydrocarbons \\ \hline \hline \end{tabular}
\end{table}
Table 2: FT-IR absorption spectrum of the untreated surface of the paint layer.  
Figure 8: Curve-fitted C1s XPS spectra of (**a**) the cleaned surface, (**b**) the untreated surface.  
Figure 10: Curve-fitted N1s XPS spectra of (**a**) the cleaned surface, (**b**) the untreated surface.  
Figure 7: XPS spectra of (**a**) the cleaned surface; (**b**) the untreated surface.  
Figure 9: O1s XPS spectra of (**a**) the cleaned surface, (**b**) the untreated surface.
## 4 DRCE-IS Combined with D-Vine Copula and SNMM to Assess the Power System Reliability
### Pre-Simulation Stage of DRCE-IS
#### 4.2.1 IS PMFs of Transmission Lines  
The PMFs of the transmission lines are shown as:  
\[p(x_{l})=(\omega_{l})^{1-x_{l}}(1-\omega_{l})^{x_{l}}\ l=1,\dots,n_{L}, \tag{23}\]  
where \(\omega_{l}\) is the optimal failure probabilities of transmission lines. \(x_{l}\) is the RV representing the states of the \(l\)-th transmission line, whose potential values are 0 and 1, indicating failure state and operation state, respectively. \(n_{L}\) is the number of transmission lines.  
When we update \(\omega_{l}\), only the terms containing \(\omega_{l}\) in (21) affect the IS PMF. Therefore, we retain them in the objective function, while other terms are replaced by a constant C. Then, the objective function is shown as:  
\[\underset{\omega_{l}}{\operatorname{max}}\sum\limits_{s=1}^{n_{s}}I(x_{ss,s}) Q(x_{ss,s})\ln p(x_{l,s})+C=\underset{\omega_{l}}{\operatorname{max}}\sum \limits_{s=1}^{n_{s}}I(x_{ss,s})Q(x_{ss,s})\ln\Big{[}(\omega_{l})^{1-x_{l,s}}( 1-\omega_{l})^{x_{l,s}}\Big{]}+C. \tag{24}\]  
In the \(t\)-th iteration, draw \(n_{s}\) new samples and make the derivative of (24) equal to zero, i.e.,  
\[\frac{1}{\omega_{l,t}}\sum\limits_{s=1}^{n_{s}}I(x_{ss,s})Q(x_{ss,s})(1-x_{l,s} )+\frac{1}{\omega_{l,t}-1}\sum\limits_{s=1}^{n_{s}}I(x_{ss,s})Q(x_{ss,s})x_{l,s }=0. \tag{25}\]  
The solution of (25) is the optimal failure probabilities of transmission lines, which is shown as:  
\[\omega_{l,t}=1-\sum\limits_{s=1}^{n_{s}}I(x_{ss,s})Q(x_{ss,s})x_{l,s}/\sum \limits_{s=1}^{n_{s}}I(x_{ss,s})Q(x_{ss,s})\,. \tag{26}\]Equation (26) is valid for any \(l=1,\ldots,n_{L}\). The set \(\{x_{l}\,|\,l=1,\ldots,n_{L}\}\) forms the vector \(\mathbf{x}^{L}\) in (22). According to (21), LRF is defined as the ratio of the original PDF (or PMF) to the IS one. Hence, the LRF of transmission lines \(Q(\mathbf{x}^{L})\) is given as:  
\[Q(x^{L})=\prod_{l=1}^{n_{L}}\left(1-\theta_{l}\right)^{x_{l}}(\theta_{l})^{1- x_{l}}/\prod_{l=1}^{n_{L}}\left(1-\omega_{l}\right)^{x_{l}}(\omega_{l})^{1-x_{l}}\,, \tag{27}\]  
where \(\theta_{l}\) is the original failure probability of the transmission line \(l\).
# Green Synthesis, Characterization, and Antifungal Efficiency of Biogenic Iron Oxide Nanoparticles
## 2 Materials and Methods
### Physicochemical Characterization of the Biogenic IONPs  
Various techniques were employed to characterize the biogenic IONPs, including UV-Vis spectroscopy, which was utilized to determine the optical properties of the IONPs. The shape and particle size distribution of the biosynthesized IONPs were examined utilizing a Transmission Electron Microscope (TEM) (model JEM1011, JEOL, Tokyo, Japan). Furthermore, the elemental composition of IONPs was determined via the utilization of an Energy-Dispersive X-ray (EDX) analysis. Additionally, a Fourier transform infrared spectroscopy (FTIR) analysis was employed to identify the primary functional groups present in the biofabricated IONPs. The biogenic IONPs underwent a X-ray powder diffraction (XRD) examination to affirm their crystalline structure and detect their crystalline size. The Zeta sizer instrument (Malvern Instruments Ltd.; zs90, Worcestershire, UK) was utilized to assess the zeta potential value and hydrodynamic diameter of IONPs.
# Provision of Energy- and Wavelength-Efficient Traffic
## 2 Scenario and Models of Traffic Grooming in the DSCNs
### Scenario Description  
To clearly depict the DSCNs, a bidirectional graph \(G(V,E)\) is established to describe the physical topology, where \(V\) (\(V=\{V_{1},V_{2},\cdots,V_{U}\}\)) denotes the satellite nodes and \(E\) represents the ISLs. Equipped with multiple independent antenna units, the single satellite can communicate with its neighboring nodes using sparse WDM-enabled large-capacity ISLs. It is assumed that each ISL can modulate up to \(W\) wavelengths and the optical antenna unit is tunable to guarantee the continuity of wavelengths. Traffic requests in the DSCNs exhibit the characteristic of multiple granularities, and the bandwidth demand matrix can be expressed as \(\mathcal{B}\in\mathbb{R}^{U\times U}\), where each vector represents the bandwidth demand of a traffic request, i.e., \(\mathcal{B}[s,d]=\left\{b_{s,d}^{f}|s,d\in V,1\leq f\leq F_{s,d}\right\}\) and \(F_{s,d}\) is the number of traffic requests from \(s\) to \(d\).  
Under given traffic demands, there are two objectives to be optimized, i.e., the minimum energy consumption and the minimum number of wavelengths. To calculate the energy consumption, we use \(E_{oe}\), \(E_{oo}\), \(E_{agg}\), and \(E_{edfa}\) to represent the energy consumption of each port for optical/electric conversion, electric/optical conversion, traffic aggregation, and EDFA amplifier, respectively. The transmitting energy can be expressed as \(E_{TX}\). Other notations are given as follows.  
Figure 1: A group of U satellites is interconnected by sparse WDM-enabled wireless laser links, providing services for users within its coverage, where three data flows traverse from the sources to destinations.
# Two-Layer Network Caching for Different Service Requirements
## 3 Model
### Caching Problems
#### 3.3.3 Case 3: Guaranteed Download Time  
In this case we introduce an additional performance guarantee. Our aim is to maximize the hit ratio and ensure a maximum download time \(\tau\) for the whole item. For this purpose, it is necessary to introduce other constraints. Also in this case we include the possibility of downloading different chunks of the same content simultaneously, from different layer-2 caches. Given the need to ensure the download time, also the bandwidth of links between layer-2 caches and users is an issue generating a constraint. Essentially, it is necessary to ensure download time for all chunks, while respecting the limitation on the output bandwidth at all cache servers. This limitation is due to technology used, the bandwidth of links, and the operating conditions, such as thermal noise and interference at the receiver site in case of radio links. The minimum bandwidth necessary for transmitting a chunk is \(b_{min}=\frac{size(\xi)}{\tau}\). Let \(b_{i,j}\) be the bandwidth used to transfer contents from cache \(j\) to customer \(i\). Clearly \(B_{j}\geq\sum_{i}b_{i,j}\), where \(B_{j}\) is the output bandwidth of the \(j\)-th cache server. The problem can be formulated as follows:  
**Problem 6**. : \[\max_{S_{j}\xi}\left\{\sum_{i=1}^{W}\sum_{\xi=1}^{C}\left(\bigvee _{j=1}^{|K_{2}|}\phi_{i,j}\beta_{j,\xi}\right)P_{MZ,i}(\xi)\right\}\] (16) \[\text{st}\] \[\sum_{\xi=1}^{C}g_{j,\xi}\leq K_{2,j}\quad\forall j\in[1,|K_{2}|]\] \[\sum_{\xi=1}^{C}g_{j,\xi}\leq\frac{B_{j}}{b_{min}}\quad\forall j \in[1,|K_{2}|]\] \[\sum_{\xi=1}^{C}\phi_{i,j}\beta_{j,\xi}\leq\frac{b_{i,j}}{b_{min}} \quad\forall i\in[1,W]\forall j\in[1,|K_{2}|]\] \[\sum_{\xi=1}^{C}\bigvee_{j=1}^{|K_{2}|}\phi_{i,j}\beta_{j,\xi} \xi(\xi,m)=|m|\quad\forall m\in[1,Q],\forall j\in[1,|K_{2}|]\] \[g_{j,\xi}\quad\in\{0,1\}.\]  
Note that for each user all items of an aggregate have the same popularity. Therefore, if sufficient bandwidth and storage are available, they are selected sequentially. Note also that the first two sets of constraints of Problem 4 are reduced to a single set, relevant to the minimum thresholds. In Algorithm3 (see Code 4), the elements of the \([W\times|K_{2}|]\) matrix **FB** are the free bandwidth of links connecting a user \(i\) and a cache \(j\). As for the asymptotic complexity of the algorithm, all changes due to the additional constraints introduce a complexity of the same order as that present in Algorithm1. Thus the complexity of Algorithm3 is the same as Algorithm2 bis. For what concerns the performance, all constraints of the Problem 6 map into uniform matroids. Theorem A1 in the Appendix shows that the intersection of independents of uniform matroids are independent sets of a uniform matroid. The submodular maximization function is the same as Algorithm1. Following the same steps of the proof for Algorithm1, it is easy to find that the guaranteed approximation ratio of Algorithm3 is the same as Algorithm1.
## 3 Results
### A Quantitative Evaluation of the Impact of Irrigation on CWILE in Cropland
#### 3.2.2 Quantitative Evaluation of CWUE for Irrigated and Non-Irrigated Cropland  
The quantitative evaluation of cropland CWUE for irrigated and non-irrigated areas in Henan Province from 2001 to 2019 is presented in Figure 7. Throughout this period, CWUE in non-irrigated cropland consistently exceeded that of irrigated cropland. The multi-year averages of the WUE\({}_{\text{GPP}}\), WUE\({}_{\text{NPP}}\), and CUE for irrigated cropland were 2.51 g C m\({}^{-2}\) mm\({}^{-1}\), 1.08 g C m\({}^{-2}\) mm\({}^{-1}\), and 0.43, while those for non-irrigated cropland were slightly higher at 2.55 g C m\({}^{-2}\) mm\({}^{-1}\), 1.13 g C m\({}^{-2}\) mm\({}^{-1}\), and 0.44, respectively. The peak WUE\({}_{\text{GPP}}\) values for irrigated and non-irrigated cropland both occurred in 2006, reaching 2.88 g C m\({}^{-2}\) mm\({}^{-1}\) and 2.93 g C m\({}^{-2}\) mm\({}^{-1}\), respectively. Similarly, the highest WUE\({}_{\text{NPP}}\) values were observed in 2006 at 1.30 g C m\({}^{-2}\) mm\({}^{-1}\) for irrigated cropland and 1.34 g C m\({}^{-2}\) mm\({}^{-1}\) for non-irrigated cropland. The maximum CUE values were recorded in 2003, with 0.49 for irrigated cropland and 0.50 for non-irrigated cropland.  
Figure 6: Study area extraction of non-irrigated and irrigated cropland in Henan Province, 2001–2019.  
Quantitative assessments of the GPP, NPP, and ET for irrigated and non-irrigated cropland in Henan Province from 2001 to 2019 are shown in Figure 8. During this period, the GPP, NPP, and ET values for irrigated cropland consistently exceeded those of non-irrigated cropland. The multi-year mean values of the GPP, NPP and ET for irrigated cropland were 881.96 g C m\({}^{-2}\) y\({}^{-1}\), 379.17 g C m\({}^{-2}\) y\({}^{-1}\), and 359.84 mm y\({}^{-1}\), respectively, while those for non-irrigated cropland were slightly lower at 846.50 mm y\({}^{-1}\), 375.83 g C m\({}^{-2}\) y\({}^{-1}\), and 342.61 mm y\({}^{-1}\), respectively. The highest GPP values for irrigated and non-irrigated cropland occurred in 2015, reaching 1005.59 g C m\({}^{-2}\) y\({}^{-1}\) and 963.98 g C m\({}^{-2}\) y\({}^{-1}\), respectively. Similarly, the peak NPP values were observed in 2015, with 439.28 g C m\({}^{-2}\) y\({}^{-1}\) for irrigated cropland and 436.90 g C m\({}^{-2}\) y\({}^{-1}\) for non-irrigated cropland. The maximum ET values were recorded in 2017 at 437.74 mm y\({}^{-1}\) for irrigated cropland and 418.46 mm y\({}^{-1}\) for non-irrigated cropland.  
Figure 7: Quantitative evaluation of CWUE of irrigated and non-irrigated cropland in Henan Province, 2001–2019.
## 3 Proposed Methodology  
The SFO approach and the cross-layer based optimized opportunistic routing protocols (CORP) are used in this study. A new cross-layer-based optimized opportunistic routing strategy for WSNs decreases power consumption and balances it across nodes. K-medoid and sunflower optimization techniques reduce the detrimental effects of extreme values and determine the best medoid among the sensor nodes for the optimal clustering results. This technology can enhance energy efficiency and effectively increase the network's life by balancing the network burden.
## 3 Calculation Model for the Gas Pressure Regulator
### Thermo-Hydro-Mechanical Coupling Model  
The thermo-hydro-mechanical coupling problem involves the interaction among the fields (flow, pressure, temperature, structure deformation, etc.). In this paper, the one-way coupling method is chosen to solve the structure of the regulator, and the basic control equations should be satisfied in the solution process [14].  
The main parameters are set in the fluid domain as follows: (1) the specific pressure values of the pressure inlet and outlet are set according to Table 1; (2) the turbulent flow is selected, and the standard k-\(\varepsilon\) model is used; (3) the wall surface is set as a standard wall function; (4) the gravitational acceleration value is 9.81 m/s\({}^{2}\), and the direction is z-axis positive; (5) the hydraulic diameter is 0.2 m, and the turbulence intensity is 5%; and (6) the gas temperature is 20 \({}^{\circ}\)C.  
The main parameters are set in the structure domain: (1) the ambient temperature is 22 \({}^{\circ}\)C; (2) the material is selected as steel; and (3) the fixing surface is on the end face of the inlet and outlet of the regulator and the middle circular curved surface.  
Meanwhile, the mesh size of the fluid domain contact surface is 1 mm, the expansion layer is set to five layers, and the tetrahedral mesh is used to divide the mesh size by 3 mm. The mesh size of the structure domain contact surface is set to 3 mm, the rest of the solid part is set to 5 mm, and the tetrahedral mesh is used to divide the mesh, as shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Inlet** & **Outlet** & **Outlet Wall** & **Simulation** & **Relative Error of** & **Flow** \\  & **Pressure** & **Pressure** & **Temperature** & **Wall Surface** & **of Outlet** & **Wall Surface** & **Relative** \\  & **(MPa)** & **(MPa)** & **(\({}^{\circ}\)C)** & **Temperature** (\({}^{\circ}\)C) & **Flow** (m\({}^{3}\)/h) & **Temperature** (\%) & **Temperature** (\%) & **Error (\%)** \\ \hline
1 & 2.93 & 1.31 & 19.9 & 83,706.98 & 20.8 & 77,328.51 & 4.52 & 7.60 \\
2 & 2.93 & 1.39 & 20.5 & 85,942.66 & 19.8 & 79,631.89 & 3.41 & 7.34 \\
3 & 2.91 & 1.37 & 20.5 & 85,451.60 & 19.8 & 81,842.12 & 3.41 & 4.22 \\
4 & 2.93 & 1.69 & 21.5 & 97,148.79 & 20.3 & 87,761.30 & 5.58 & 9.66 \\
5 & 2.92 & 1.89 & 22.1 & 100,777.39 & 21.6 & 95,421.00 & 2.26 & 5.32 \\
6 & 2.92 & 1.56 & 20.9 & 94,991.37 & 19.7 & 84,284.89 & 5.74 & 11.27 \\
7 & 2.92 & 1.33 & 20.1 & 84,525.12 & 19.2 & 77,397.96 & 4.48 & 8.43 \\
8 & 2.91 & 1.46 & 21.4 & 93,508.88 & 20.3 & 87,475.69 & 5.14 & 6.45 \\
9 & 2.92 & 1.61 & 22.8 & 96,564.26 & 21.8 & 92,912.20 & 4.39 & 3.78 \\
10 & 2.93 & 1.47 & 20.7 & 93,557.04 & 19.0 & 87,399.12 & 8.21 & 6.58 \\
11 & 2.93 & 1.71 & 21.8 & 98,164.84 & 20.9 & 93,872.09 & 4.40 & 4.37 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between the operation data and simulation data.
# Simulation Model for Robotic Pick-Point Evaluation for 2-F Robotic Gripper
## 4 Results
### Test Object: Hygienic Door Opener
#### 4.4.1 Analysis of Pick-Point N\({}_{1}\)  
Figure 14 shows four steps in picking the selected object in the specific location and orientation: (1) prepick, (2) establishing initial contact with the robotic fingers, (3) reaching final contact, and (4) lifting of the object. In the event of the first pick-point, it can be seen that the gripper cannot pick the object successfully in simulation and on the physical test setup. The grasp score for the first pick-point is also low compared to two successful pick-points, indicating that the success metrics are calculated accordingly. Only in the last step are there minor differences between the simulated and the actual response due to the immediate lifting after establishing contact with the physical system. Therefore, the two figures do not match completely. Figure 15 shows contact forces at the described events for pick-point \(N_{1}\).  
\begin{table}
\begin{tabular}{c c} \hline Total pick-points/Selected pick-points & 50/20 \\ Correctly determined pick-points & 15 \\ Falsely determined pick-points & 5 \\ Highest grasp quality score & 3.19 \\ Lowest grasp quality score & 0.30 \\ \hline \end{tabular}
\end{table}
Table 4: Simulation results. Source: own.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pick-Point (N)** & \(X\) **(mm)** & \(Z\) **(mm)** & \(RotY\) (\({}^{\circ}\))** &
\begin{tabular}{c} **Grasp Quality** \\ **Score (S\({}_{\textit{grasp}}\))** \\ \end{tabular} \\ \hline
1 & \(-\)19.39 & 6.51 & 65.90 & 0.32 \\
2 & \(-\)3.38 & \(-\)9.79 & \(-\)150.05 & 1.97 \\
3 & 6.72 & \(-\)7.43 & \(-\)167.91 & 1.61 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Test pick-point location, orientation, and score. Source: own.  
Figure 14: Graphical analysis of the first pick-point. Source: own.  
As can be seen from Figure 14, the robotic gripper starts grasping the object at around \(t_{\rm sim}=5.39\) s, and the contact force increases to a small value of around \(C_{LF\cdot O}=0.05\) N. At around \(t_{\rm sim}=9.52\) s, the initial contact force is increased to around 49 N since the two bodies (the left gripper finger and the object) come into contact. In the next few milliseconds, the force controller controls the contact force and maintains it for another second. However, when lifting the object starts, the object begins to slip from the gripper fingers; therefore, the contact force \(C_{LF\cdot O}\) value suddenly drops to zero.