esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Energy-Based Damage Index
### Solution Method of the Total Energy Dissipation \(E_{\rm C}\)  
In order to guide the seismic design, it is necessary to establish an effective relationship between the seismic design parameters and the energy-based damage index \(D_{k}\). Therefore, the total energy dissipation \(E_{\rm C}\) of the single degree-of-freedom structure was obtained by [PERSON] [23] and [PERSON] [24] as follows:  
\[E_{\rm C}=\frac{0.3455m\eta E_{\rm s}}{n} \tag{8}\]  
with  
\[\eta=1.13\frac{(\mu-1)^{0.82}}{\mu} \tag{9}\]  
Figure 2: \(D_{k}\)–\(\rho_{\rm s}\) relationship.  
\[E_{\rm s}=0.5{(\psi{V_{\rm PG}})}^{2} \tag{10}\]  
\[\psi=\begin{cases}\psi_{\rm p}(\frac{2T}{T_{\rm g}}-(\frac{T}{T_{\rm g}})^{2})&T<T _{g}\\ \psi_{\rm p}(\frac{T}{T_{\rm g}})^{-\gamma}&T>T_{g}\end{cases} \tag{11}\]  
\[\psi_{\rm p}=\frac{0.25A_{\rm PG}}{V_{\rm PG}}\sqrt{t_{\rm d}T_{g}}\sqrt{\frac {\gamma+0.5}{2\gamma+2}} \tag{12}\]  
\[T_{g}=2\pi\frac{\tau_{\rm p}V_{\rm PG}}{\tau_{a}A_{\rm PG}} \tag{13}\]  
where \(m\) is mass of the structure, \(\eta\) is the ductility ratio parameters, \(E_{\rm s}\) is the seismic energy per unit mass in elastic stage, \(n\) is the number of reinforced concrete column members, \(\mu\) is the ductility coefficient, \(\psi\) is the displacement parameter for the input energy, \(\psi_{\rm v}\) is the peak displacement parameter, \(T\) and \(T_{g}\) represent the fundamental period and the characteristic period, respectively, \(\gamma\) is the parameter of ground motion (\(\gamma\) = 0.5 for the parameter of ground motion was used in this paper [25]), \(t_{\rm d}\) is the earthquake duration, and \(V_{\rm PG}\) and \(A_{\rm PG}\) represent the peak velocity of ground and the peak acceleration of ground, respectively. The parameter \(\tau_{\rm p}\) was set to 1.9, and the parameter \(\tau_{a}\) was set to 2.4 [26].  
According to Formulas (8)-(13), the total energy dissipation \(E_{\rm C}\) of the single degree-of-freedom structure can be obtained.  
\[E_{\rm C}=h(\mu,m,n,T,A_{\rm PG},V_{\rm PG},t_{\rm d}) \tag{14}\]  
It can be found from Formula (14) that the relationship between the total energy dissipation \(E_{\rm C}\) and the ductility coefficient \(\mu\), the number of reinforced concrete column members \(n\), the fundamental period \(T(m)\), the seismic intensity (\(A_{\rm PG}\) and \(V_{\rm PG}\)), and the earthquake duration \(t_{\rm d}\) is established.
###### Abstract  
Long-range radio (LoRa) communication is a widespread communication protocol that offers long range transmission and low data rates with minimum power consumption. In the context of solid waste management, only a low amount of data needs to be sent to the remote server. With this advantage, we proposed architecture for designing and developing a customized sensor node and gateway based on LoRa technology for realizing the filling level of the bins with minimal energy consumption. We evaluated the energy consumption of the proposed architecture by simulating it on the Framework for LoRa (FLORa) simulation by varying distinct fundamental parameters of LoRa communication. This paper also provides the distinct evaluation metrics of the the long-range data rate, time on-air (ToA), LoRa sensitivity, link budget, and battery life of sensor node. Finally, the paper concludes with a real-time experimental setup, where we can receive the sensor data on the cloud server with a customized sensor node and gateway.  
cloud server; customized sensor node; customized gateway; FLoRa simulation; LoRa range radio; solid waste management +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Volatile Compounds  
Volatile compounds were extracted from SFOH and SFCONV samples using a headspace solid-phase microextraction technique (SPME), at each sampling point from gastrointestinal compartments. The fiber used was a DVB/CAR/PDMS (divinylbenzene/carboxy/polydimethylsiloxane) 50/30 mm from Supelco (Bellefonte, PA, USA). A Varian gas chromatograph, Varian CP-450 (Walnut Creek, CA, USA), equipped with a Varian Saturn 240 MS (Walnut Creek, CA, USA) mass spectral detector was used for the identification and quantification of the volatile compounds. The column used for the volatiles separation was a FactorFour VF-WAXms 15 m length \(\times\) 0.15 mm of internal diameter \(\times\) 0.15 \(\upmu\)m of film thickness column, from Varian (Lake Forest, CA, USA). Briefly, 1 g of SFOH and SFCONV were dissolved in 5 mL of ethanol solution (10%), in a headspace screw vial, and spiked with 20 \(\upmu\)L of 3-octanol (50 mg/L). Samples were preincubated in a CombiPLAN oven at 40 \({}^{\circ}\)C and 150\(\times\)_g_ for 5 min, and the fiber was exposed after 15 min at 150\(\times\)_g_ for extraction. Desorption of the volatile compounds in the injector was performed at 220 \({}^{\circ}\)C for 10 min. All mass spectra wereacquired in the electron impact (EI) mode. Compound identification was achieved by comparing retention times and mass spectra obtained from a sample containing pure, authentic standards or by National Institute of Standards and Technology (NIST) database. Compound quantitation was performed based on standard calibration curves using _m/z_ quantifiers. 1,2-dimethylindole--equivalents of linalool; 2,6-dimethylbenzaldehyde--equivalents of linalool; benzoic acid--pure standard; \(\beta\)-cyclocitral--pure standard; 3,4-diethenyl-1,6-dimethyl--pure standard; camphenol--pure standard; linalool--pure standard.  
Recovery and Bioaccessibility Indexes of Polyphenic and Carotenoids Compounds Throughout In Vitro Gastrointestinal Digestion  
Bioaccessibility is defined as the percentage of the bioactive compound solubilized after intestinal dialysis step; this index defines the proportion of the bioactive compound that could become available for absorption into the blood system:  
\[\text{Bioaccessibility index (\%)}=\text{(BCS/BCDSI)}\times 100 \tag{1}\]  
where BCS is the bioactive content (mg) in the digested sample after the dialysis step (OUT) and BCDSI is the bioactive content (mg) in the digested sample after the small intestinal step.  
The recovery index determines the main compound concentration during each step of gastrointestinal digestion following the equation:  
\[\text{Recovery index (\%)}=\text{(BCDF/BCTM)}\times 100 \tag{2}\]  
where BCDF represents the bioactive content (mg) in each digested fraction and BCTM is the bioactive content (mg) quantified in the test matrix.
## 3 Fault Detection Algorithm
To avoid confusion, the variables \(x\), \(\theta\), \(\eta\) in the noise-free system are denoted by \(x_{e}\), \(\theta_{e}\), \(\eta_{e}\), respectively. Then, we can obtain \[\left\{ \begin{array}{l}\dot{\hat{x}}_{e}=(A-S^{-1}C^{T}C)\hat{x}_{e}+\tilde{ \Phi}_{0}(\hat{x}_{e},x_{e})+\tilde{\Phi}_{1}(\hat{x}_{e},x_{e})\theta+\Phi_{1} (\hat{x}_{e},u)\tilde{\theta}_{e}+\mathrm{Y}(t)\dot{\hat{\theta}}_{e},\\ \dot{\hat{\theta}}_{e}=-P(t)\mathrm{Y}^{T}(t)C^{T}C\tilde{x}_{e},\\ \dot{\eta}_{e}=(A-S^{-1}C^{T}C)\eta_{e}+\tilde{\Phi}_{0}(\hat{x}_{e},x_{e})+ \tilde{\Phi}_{1}(\hat{x}_{e},x_{e})\theta.\end{array} \right. \tag{9}\]  
Let \(V_{1}(\eta_{e}(t))=\eta_{e}{}^{T}(t)S\eta_{e}(t)\), \(V_{2}(\theta_{e}(t))=\theta_{e}{}^{T}(t)P^{-1}(t)\theta_{e}(t)\), where \(S\) and \(P(t)\) are given by Equation (5), we define the Lyapunov function \(V(\eta_{e}(t),\theta_{e}(t))=V_{1}(\eta_{e}(t))+V_{2}(\theta_{e}(t))\). Based on Equation (8), it is derived that  
\[\begin{array}{l}\dot{V}_{1}(\eta_{e}(t))=\dot{\eta}_{e}{}^{T}(t)S\eta_{e}(t )+\eta_{e  
\[\left\{ \begin{array}{l}\frac{d(E(\tilde{x}))}{dt}=(A-S^{-1}C^{T}C)E(\tilde{x})+E( \Phi(\tilde{x},x))+E(\tilde{\Phi}_{1}(\tilde{x},x))\theta+\Phi_{1}(\tilde{x},u)E (\tilde{\theta})\\ \hskip 28.452756 pt+\Upsilon(t)E(\hat{\theta})+(E(w(t))+S^{-1}C^{T}E(v(t))),\\ \frac{d(E(\tilde{\theta}))}{dt}=-P(t)\Upsilon^{T}(t)C^{T}CE(\tilde{x})+P(t) \Upsilon^{T}(t)C^{T}E(v(t)),\end{array} \right. \tag{18}\]  
where \(E\) is an operator for calculating the mean value. As \(w(t)\) and \(v(t)\) are Gaussian distribution signals with zero mean, it can be derived that  
\[\left\{ \begin{array}{l}\frac{d(E(\tilde{x}))}{dt}=(A-S^{-1}C^{T}C)E(\tilde{ x})+E(\Phi(\hat{x},x))+E(\tilde{\Phi}_{1}(\hat{x},x))\theta+\Phi_{1}(\hat{x},u)E (\tilde{\theta})+\Upsilon(t)E(\hat{\theta}),\\ \frac{d(E(\tilde{\theta}))}{dt}=-P(t)\Upsilon^{T}(t)C^{T}CE(\tilde{x}).\end{array} \right. \tag{19}\]  
Then we have \(E(\tilde{x})\to 0,E(\tilde{\theta})\to 0\) when \(t\rightarrow\infty\). The proof is completed. According to Theorem 1, it is known that the mean value \(E(\hat{\theta})\) of observed parameters \(\hat{\theta}\) can converge to the true value of \(\theta\). Therefore, the proposed detection algorithm is available for the fault detection of n-DoF robot manipulator.
## 2 Methodology
### Spectral Dimension Reduction Module  
There is a significant amount of redundant information in the original hyperspectral image. Using dimension reduction modules, it is possible to significantly reduce the computational cost without significant performance loss. The 1 \(\times\) 1 convolutional layer has the ability to remove useless spectral information and increase nonlinear characteristics. Moreover, it is usually used as a dimension reduction module to remove computational cost, as shown in Figure 2. In the FCGN network, hyperspectral images are first processed using two 1 \(\times\) 1 convolutional blocks. Specifically, each 1 \(\times\) 1 convolutional block contains a BN layer, a 1 \(\times\) 1 convolution layer, and an activation function layer. The role of the BN layer is to accelerate the convergence of the network, and the activation function layer can significantly increase the network's nonlinearity to achieve better expressiveness. The activation function in this module adopts Leaky ReLU.  
Figure 1: The framework of the FCGN algorithm. **(a1,a2)** Feature conversion module; **(b1,b2)** SE attention module.  
Figure 2: Dimension reduction module. Each 1\(\times\)1 convolution block contains the above three parts.  
We have:  
\[\mathbf{X}_{\text{h,w,n}}^{*}=\sigma(\sum_{\text{x,y,b}}\mathbf{W}_{\text{x,y,b,n}} \widetilde{\mathbf{X}}_{\text{h,w,b}}+\mathbf{B}_{\text{x,y,b,n}}) \tag{1}\]  
where \(\mathbf{X}_{\text{h,w,b}}\) denotes the input feature map, \(\widetilde{\mathbf{X}}_{\text{h,w,b}}\) denotes the batch-normalized input feature map, \(\mathbf{X}_{\text{h,w,n}}^{*}\) denotes the output feature map, \(\mathbf{W}_{\text{x,y,b,n}}\) denotes the convolution kernel of the input feature map in row x and column y, \(\mathbf{B}_{\text{x,y,b,n}}\) denotes bias, and n is the number of convolution kernels. \(\sigma\) represents the Leaky ReLU activation function.
# Design of an IMCPID Optimized Neural Network for Stepless Flow Control of Reciprocating Machinery
## 4 Algorithm Simulation and Results
### Step Response Test and Validation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Algorithm**} & \multicolumn{1}{c}{**M\({}_{p}\) (\%)**} & \multicolumn{1}{c}{**t\({}_{s}\) (s)**} & \multicolumn{1}{c}{**IAE(\(\times\)10\({}^{3}\))**} & \multicolumn{1}{c}{**ISE(\(\times\)10\({}^{5}\))**} & \multicolumn{1}{c}{**ITAE(\(\times\)10\({}^{5}\))**} & \multicolumn{1}{c}{**ITSE(\(\times\)10\({}^{6}\))**} \\ \hline \multirow{4}{*}{Output1} & PID & 2.73 & 272.96 & 5.42 & 4.88 & 2.71 & 7.10 \\  & IMCPID & 9.73 & 319.01 & 11.53 & 9.24 & 8.65 & 38.42 \\  & FLSMC & 0 & 73.68 & 5.09 & 3.21 & 1.91 & 7.08 \\  & IMCPIDNN & 0 & 61.91 & 2.62 & 1.98 & 0.42 & 1.96 \\ \hline \multirow{4}{*}{Output2} & PID & 7.63 & 435.42 & 16.70 & 18.96 & 15.99 & 65.07 \\  & IMCPID & 18.27 & 278.99 & 14.48 & 18.42 & 9.59 & 53.10 \\ \cline{1-1}  & FLSMC & 0 & 102.54 & 16.47 & 19.43 & 10.70 & 73.88 \\ \cline{1-1}  & IMCPIDNN & 0 & 82.83 & 10.60 & 14.05 & 4.01 & 31.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of the four algorithms used for concurrent step responses. Figure 6: Comparison of step responses with concurrent changes in set-points: (**a**) Output1; (**b**) Output2. t = 100 in the set-point of the first input caused a negative effect on the second output, which is undesirable. It indicates that the system coupling is serious with no decoupling method. The FLSMC controller eliminates the coupling of the system by nonlinear method, but its performance in step response is not as good as that of the IMCPIDNN controller. Compared with the other three controllers, the proposed IMCPIDNN controller performs better in set-point tracking. Neither the first step change nor the second step change has a large effect on the other loop. The performance comparison of the four algorithms used for sequential step responses are presented in Table 4. For loop 1 and 2, the settle times \(t_{\text{s}}\) obtained using the proposed controller are 61.91 s and 146.78 s respectively, and the overshoots \(M_{p}\) are both zero. Meanwhile, the results of the smallest four error evaluation indexes obtained by the IMCPIDNN controller can ensure the set-point tracking accuracy. Obviously, the IMCPIDNN controller performs better in terms of facing the interactions.
## 5 Result and Discussion
### Influence of the Structural Parameters on the Release Load  
The magnitude and accuracy of the release load of the holdback bar will affect the catapult-assisted takeoff performance of the aircraft, and the structural form and structural parameters of the holdback bar will affect the sensitivity of the release load. Here, according to the determined structural form, the influence of the structural parameters of the holdback bar on the release load is studied, and the angular parameters of the contact surface in the release mechanism are taken as the object of study. Five angles--30\({}^{\circ}\), 40\({}^{\circ}\), 50\({}^{\circ}\), 60\({}^{\circ}\), and 70\({}^{\circ}\)--are selected to study the displacement changes of the release mechanism relative to the load transfer bar at different angles, as shown in Figure 9. When the load gradually increases from 0, the release mechanism has a small displacement relative to the load transfer bar; when the load reaches the release load, the lock mechanism unlocks and the displacement of the release mechanism reaches its maximum value; it can be seen from the figure that the holdback bar lock mechanism can be unlocked normally when the angle of the contact surface is taken from 30\({}^{\circ}\) to 70\({}^{\circ}\).  
To make out the release load of the contact surface in the holdback bar release mechanism at different angles, the contact surface angle was selected from 30\({}^{\circ}\) to 70\({}^{\circ}\) to study the change of the release load, as shown in Figure 10, which shows the release load curves at different angles. From the analysis results it is easy to find out that as the contact surface angle increases from 30\({}^{\circ}\) to 50\({}^{\circ}\), the change of release load is small and remains around 19.5 tons, and when the angle increases from 50\({}^{\circ}\) to 70\({}^{\circ}\), the release load increases significantly, from 19.5 tons to 30 tons.  
Figure 9: Relative displacement of the release mechanism.  
The angle parameter of the contact surface of the holdback structure is between 30\({}^{\circ}\) and 50\({}^{\circ}\), the change of the release load is small, and the impact on the sensitivity of the release load is small; when the angle is between 50\({}^{\circ}\) and 70\({}^{\circ}\), the change of the release load is large, the release load increases gradually with the increase of the angle of the contact surface, and the impact on the sensitivity of the release load is large; in order to ensure the stability of the release load, the angle parameter should be selected between 30\({}^{\circ}\) and 50\({}^{\circ}\), so that the precise release load of the drawbar can be controlled within the range of 0-6%.
## 4 Aerodynamic Characteristics
### Strouhal Numbers  
The Strouhal numbers (St\({}^{t}\), St\({}^{b}\), and St\({}^{tu}\) for the train, bridge-deck, and whole train-bridge models, respectively) are calculated via the power spectral densities (PSDs) of the model lifts based on \(d\), \(D\), and \(d+D\), respectively.  
Figure 10a,b depicts the St\({}^{t}\) and St\({}^{b}\) in the range of \(\alpha=[-12^{\circ},12^{\circ}]\). The results of an infinite prism tested by [40] and the train model in the train-only case are also demonstrated here for comparison. Generally, the train-only results highly mimic those of [40], again suggesting a validation of the present investigation.  
The Strouhal numbers of the present models in the train-bridge case displays similar behavior but with different values as described in Figure 10a,b, respectively. That is expected because the train, bridge deck, and the whole train-bridge share a similar vortex shedding frequency.  
As shown in Figure 10, the behaviors of the Strouhal numbers in the train-bridge case in all the test ranges of \(\alpha=[-12^{\circ},12^{\circ}]\) can be classified into three aspects. In \(\alpha=[-12^{\circ},-10^{\circ}]\), the Strouhal numbers of the present models are about 0.04. In \(\alpha=[-8^{\circ},-6^{\circ}]\), no Strouhal numbers can be identified both on the train and bridge-deck models. If a rectangular bluff body is governed by the impinging leading-edge vortices flow pattern (as mentioned in Section 3.2.1) at large Reynolds number, the periodic vortex shedding from the model is almost imperceptible [31; 41]. For the present train-bridge system, the lower half bridge-deck is governed by the ILEV flow pattern in this range of \(\alpha\) as summarized in Section 3.2.1, and the inflow Reynolds number based on \(d\) is 1.25 \(\times\) 10\({}^{5}\) belonging to the large Reynoldsnumber [41]. Thus, the underlying mechanism of this Strouhal number disappearance shall be this ILEV flow pattern. In \(\alpha=[-4^{\circ},\,12^{\circ}]\), both the Strouhal numbers of the train and bridge-deck models decreases gradually.  
Upon comparing with the study of [34] with the train model mounted on the downstream track, the present study with the train model mounted on the upstream track can still identify the Strouhal numbers in \(\alpha=[0^{\circ},\,12^{\circ}]\). As pointed out by [42], the periodic vortex shedding of a circular cylinder tends to cease with an increase in Reynolds number in critical, transcritical, and supercritical regimes. As discussed in [34], the quasi-Reynolds number effect highly mimics the classical Reynolds number effect. Moreover, the above quasi-Reynolds number effect governs the main flow features of the train-bridge system with the train model mounted on the downstream track in \(\alpha=[0^{\circ},\,12^{\circ}]\). Thus, due to the disappearance of the quasi-Reynolds number effect in the present study, the detectable Strouhal numbers in \(\alpha=[0^{\circ},\,12^{\circ}]\) here are reasonable.
###### Abstract  
The aim of this study was to evaluate a newly formulated mouth rinse containing cynenol in patients undergoing supportive periodontal care in terms of safety (primary outcome) and the impact on dental biofilm and gingival inflammation (secondary outcomes). This research was designed as a pilot, controlled, randomized, parallel, triple-blinded, single-center, clinical trial of a 12-week duration. Adverse events and product perception were assessed by a questionnaire. Clinical, patient-reported outcomes (PROs), compliance, tooth staining, dentin hypersensitivity and microbiological variables were also evaluated. Student T, Mann-Whitney-U and Chi-square tests were applied. Thirty participants (15 per group) were included, randomized and followed for 12 weeks. No adverse events were reported. The questionnaire showed an overall rating of 7.2 (out of 10) in the experimental group and of 8.2 in the control group (\(p=0.165\)) at 12 weeks. No statistically significant differences were observed in terms of gingival health, tooth staining, dentin hypersensitivity or microbiological outcomes between groups at baseline, 6- and 12-week visits. The adjuvant use of the new mouth urine formulation proved to be as safe as the control product and no significant differences were observed in terms of clinical efficacy.  
cymenol; mouth rinse; tolerability; dental biofilm; gingival inflammation
# Knowledge Graph Recommendation Model Based on Feature Space Fusion
## 3 Our Model
### Model Introduction  
We present a knowledge graph recommendation model based on feature space fusion (KGRFSF), as shown in Figure 3. There are four modules in the architecture of the recommendation model: (1) Behavioral feature module: for the input user \(u\) and item \(v\) to be recommended, the corresponding behavioral features are extracted from the UIG to construct the user's behavioral feature vector \(u_{\text{cf}}\) and item's behavioral feature vector \(v_{\text{cf}}\). (2) The content feature module: based on the attention mechanism, aggregates the \(L\)-order neighborhood information of the user interactive item set \(\{v_{1},\,v_{2},\,\ldots,\,v_{n}\}\) and item \(v\) in KG, and the content feature vectors \(u_{\text{kg}}\) and \(v_{\text{kg}}\) of users and items are obtained. (3) The feature space fusion module: projects the behavioral feature vectors (\(u_{\text{cf}}\) and \(v_{\text{cf}}\)) and the content feature vectors (\(u_{\text{kg}}\) and \(v_{\text{kg}}\)) obtained in the first two steps into the preference feature space through a projection operation. After the concatenation operation, it is input into the multilayer perceptron (MLP) to complete the fusion and obtain the final vector of the user, and the item represents \(u_{\text{f}}\) and \(v_{\text{f}}\). (4) Prediction module: the vector inner product of the two is calculated as the user's predicted click-through rate \(\hat{y}(u,\,v)\). The complete training process is shown in Algorithm 1.  
```
Input: user \(u\), item \(v\), user-item bipartite graph \(G_{1}=\{U,\,I\}\), knowledge graph \(G_{2}=\{V,\,E\}\), embedding dimension \(d\), learning rate \(\eta\), user entity set size \(S_{u}\), item entity set size \(S_{v}\). Output: score of the user on the item \(\hat{y}(u,\,v)\). Step 1:for\(n=0\) to epochdo. Step 2: According to Formulas (5) and (6), user behavioral feature embedding \(u_{\text{cf}}\) and item behavioral feature embedding \(v_{\text{cf}}\) are calculated. Step 3: User content feature embedding \(u_{\text{kg}}\) and item content feature embedding \(v_{\text{kg}}\) are counted according to Formulas (9)-(18). Step 4: According to Formula (19)-(25), calculate the score \(\hat{y}(u,\,v)\) of users. Step 5: Calculation Formula (26). Step 6: Calculate gradient and send back to update model parameters. Step 7:endfor Step 8:return\(\hat{y}(u,\,v)\)
```  
**Algorithm 1**Knowledge graph recommendation model based on feature space fusion  
Figure 2: User’s feature modeling process.
# Attention-Based Background/Foreground Monocular Depth Prediction Model Using Image Segmentation
## 4 Experiments
### Implementation Details  
#### 4.2.1 Data Augmentation  
Data augmentation is a common way to reduce overfitting in neural network models and improve their performance. We augmented the KITTI dataset via the following steps: we randomly cropped the images into \(704*352\) pixel segments from the original images; we then horizontally flipped the images and randomly rotated them in the range of \(-3\) to \(3\) degrees; then, the RGB values were multiplied globally by a random value from 0.9 to 1.1.