esa-sceva/satcom-chunk-collection huggingface dataset:
# Hydrogeochemistry of Fault-Related Hot Springs in the Qaidam Basin, China
## 4 Discussion
### Chemical Characteristics of the Hot Spring Gas
#### 4.2.2 Isotopic Characteristics of Helium and Neon
A previous study on the rare gas isotopic signature of the northern margin of the Qaidam Basin in the eastern portion of the study region revealed \({}^{3}\)He/\({}^{4}\) He values in the range of (1.43-5.0) \(\times\) 10\({}^{-8}\) and R/Ra values between 0.001 and 0.04, which are typical of upper crustal-sourced helium [76]. Faults are efficient conduits for gas transport, and the anomalous release of helium is often monitored before seismic activity, which, in addition to facilitating helium transport, serves as a physical factor influencing helium release in the formation [77; 78]. Moreover, it is reported that the enrichment of ore bodies in the Qaidam Basin is controlled by the fracture system, which suggests that fractures, besides serving as good gas transport channels, are also a major influence on the generation of helium source rocks [79]. The study area is located at the intersection of the Altun Tagh fault and the East Kunlun fault belt, which is a large left-slip fault with deep basement penetration and a north-to-east trend, and this fault was active late in the Eocene and also underwent slip movement after that time [80]. The helium isotope content and ratio in the study region are largely comparable to those in the surrounding region. The source was from the mantle, which could be related to the fracture's distribution and activity. Future continuous observations of hot spring gas in the study region would, therefore, have research implications for monitoring seismic activity in the region.
## 3 The Bias Compensation-Based Parameter and State Estimation Algorithm
### The Parameter Estimation Algorithm
Define the noise information vectors:  
\[\hat{\mathbf{\phi}}_{\text{n}}(k) :=[-\hat{e}(k-n),-\hat{e}(k-n+1),\cdots,-\hat{e}(k-1),0,0,\cdots,0 ]^{\text{T}}\in\mathbb{R}^{2n}, \tag{35}\] \[\hat{\mathbf{\phi}}_{\text{v}}(k) :=[\hat{v}(k-1),\hat{v}(k-2),\cdots,\hat{v}(k-n_{e})]^{\text{T}} \in\mathbb{R}^{n_{e}}. \tag{36}\]  
From Equations (14) and (16), we have:  
\[\hat{e}(k) =y(k)-\mathbf{\phi}_{\text{s}}^{\text{T}}(k)\hat{\mathbf{\sigma}}_{\text {C}}(k)-\hat{\mathbf{\phi}}_{\text{n}}^{\text{T}}(k)\hat{\mathbf{\sigma}}_{\text{C}}(k), \tag{37}\] \[\hat{v}(k) =\hat{e}(k)-\hat{\mathbf{\phi}}_{\text{v}}^{\text{T}}(k)\hat{\mathbf{ \sigma}}_{\text{v}}(k). \tag{38}\]  
Using the least squares principle, the estimate \(\hat{\mathbf{\sigma}}_{\text{v}}(k)\) of \(\mathbf{\phi}_{\text{v}}\) in Equation (16) can be computed by:  
\[\hat{\mathbf{\sigma}}_{\text{v}}(k) =\hat{\mathbf{\sigma}}_{\text{v}}(k-1)+\mathbf{P}_{\text{v}}(k)\hat{\mathbf{ \sigma}}_{\text{v}}(k)[\hat{e}(k)-\hat{\mathbf{\sigma}}_{\text{v}}^{\text{T}}(k) \hat{\mathbf{\sigma}}_{\text{v}}(k-1)], \tag{39}\] \[\mathbf{P}_{\text{v}}^{-1}(k) =\mathbf{P}_{\text{v}}^{-1}(k-1)+\hat{\mathbf{\sigma}}_{\text{v}}(k)\hat {\mathbf{\sigma}}_{\text{v}}^{\text{T}}(k). \tag{40}\]  
Thus, Equations (26)-(40) form the bias compensation-based parameter estimation (BC-PE) algorithm for identifying the parameter vector \(\mathbf{\vartheta}\). The BC-PE algorithm is an interactive estimation process: we can compute the estimates \(\hat{\mathbf{\vartheta}}_{\text{v}}(k)\) and \(\hat{\delta}(k)\); with these obtained variable estimates, we derive the estimate of \(\mathbf{R}\) and \(\mathbf{r}\) and then update the unbiased estimate \(\hat{\mathbf{\vartheta}}_{\text{C}}(k)\) using Equations (26)-(29).
# Normalized Weighting Schemes for Image Interpolation Algorithms
## 2 Materials and Methods
### IQA Metrics
#### 2.5.1 FR-IQA Metrics  
Here, FR-IQA metrics were used due to the need for quantifying the closeness or similarity of interpolated images against their corresponding source or ground-truth images [49]. Those FR-IQA metrics selected included the mean-squared error (MSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). The MSE is the mean square error between the test or input image and the ground truth or reference image. The PSNR is calculated using the following Equation (8), where K is the peak value either specified by the user or taken from the range of the image data type.  
\[PSNR=10*log_{10}\bigg{(}\frac{K^{2}}{MSE}\bigg{)} \tag{8}\]  
The SSIM is calculated based on the computation of 3 terms, namely: luminance (I), contrast (c), and structural (s) terms [62]. The resulting SSIM is a multiplicative combination of 3 terms, as shown in Equation (9), whose details can be found in [62].  
\[SSIM(x,y)=[l(x,y)]^{a}\times[c(x,y)]^{\beta}\times[s(x,y)]^{\gamma} \tag{9}\]  
Figure 8: A simplified flowchart showing the image interpolation procedure followed by TB (Figure 2), MD (Figure 3), HR (Figure 4), AT (Figure 6), and AC (Figure 7) methods.
# Minimum Interference Channel Assignment Algorithm for Multicast in a Wireless Mesh Network
## 1 Introduction
This paper is organized as follows: Section 2 reviews the relevant literature regarding channel assignment for multicast in WMNs; we describe our proposed MICA algorithm and explain three procedures in detail in Section 3; Section 4 shows our simulation results, focusing on comparing the performance of MICA with that of MCM; and the last section finalizes the paper by providing conclusions.
# Two-Level-Composite-Hashing Facilitating Highly Efficient Anonymous IoT and D2D Authentication
## 5 Security Analysis and Performance Evaluation
### The Performance Evaluation
In Table 2 of (24), they list the execution time of the pairing, the exponentiation, and the scalar multiplication for such a setting; in Table 5 of (44), they list the execution cycles of several operations including the encryption, the hashing, and the scalar multiplication. Combining the two tables, we can estimate the required execution times of the operations for comparison. Table 5 lists the computational costs (in ms) for such a setting. The 3 rd column of Table 4 lists some estimated cost (in ms) when we apply the figures from Table 5; for example, our D2S authentication demands \(D_{i}\)\(6T_{h}+2T_{ECCM}\) (0.597 ms), where 0.597 ms represents the estimated time when we apply the figures from Table 5 into \(6T_{h}+2T_{ECCM}\); AAKA-D2D's D2S2D authentication demands 29.056 ms. Figure 13 shows the bar charts of the estimated computation time (in ms), using the figures from Table 5. The left four items are the proposed schemes. From Figure 13, we can see that the computation times of our schemes, [PERSON]'s schemes (25), and [PERSON] et al. 's level-2 D2S group authentication are almost negligible, compared to those of Se-DS (15) and AAKA-D2D (16). We can see that those pairing-based schemes (15,16,22) demand much larger computational costs. Of course, we have to note that the cost of Se-DS (15) include the pairing-based signatures and verifications operations for additional data sharing. In a short summary of the performance comparison both our proposed schemes, [PERSON]'s schemes [11; 25], and [PERSON] et al. 's level-2 D2S authentication [12] demand almost negligible computational cost, compared to other PKC-based schemes. However, [PERSON]'s schemes [11; 25] do not provide anonymity, [PERSON] et al. 's level-2 D2S authentication [12] does not consider unlinkability, and the D2S group authentication [12] is vulnerable to the single-compromised-device-impersonation attack. Those pseudonym-bound-certificate schemes [29; 31; 32] and the pseudonym-bound-CLPK schemes [36] demand much larger computational overhead, which make them much less attractive to the resource-limited devices; additionally, they only provide anonymity but not unlinkability; the process of periodically renewing the certificates or the CLPKs is also very costly.
# Modeling and Analyzing Preemption-Based Service Prioritization in 5G Networks Slicing Framework
###### Abstract
As an important feature to solution QoS degradation, the number of production downtimes and the corresponding associated expenditures can be significantly reduced. In contrast, the paper [27] proposes a real test-bed of 5G stand-alone network deployment with a Service Prioritization scheme, then a NS-technology technique, and, finally, a flexible transparent allocation mechanism at the Radio level. The first aim is to mark a difference with other contributions remaining at the simulation level. The proposed approaches are evaluated in terms of performance given a scenario with saturated uplinks communications. Multiple data flows and various service requirements from external sources are considered in the contribution. The results obtained are then compared to others, from a scenario without NS-technology. In the current article, following the papers [27; 30] and in contrast with papers [25; 26], we focus on two key features of NS-technology: NSIs Resource isolation and Automated management. First, propose a Pre-emption-based Prioritization (PP) scheme \"merging\" the classical RR and Service Prioritization schemes. Second, and last, evaluate or estimate the proposed PP-scheme efficiency given a QS model analyzing the operation of multiple NSIs with various requirements at common 5G BSs. Thus, the remainder of this paper is outlined as follows. Section 2 discusses the main assumptions and considerations of the paper. Section 3 mathematically analyzes a given QS model and proposes formulas to find key performance indicators (KPIs). Section 4 gives numerical results, illustrative of the proposed PP-scheme efficiency. Section 5 presents the conclusions.
# Recent Progress on Vegetation Remote Sensing Using Spaceborne GNSS-Reflectometry
###### Abstract
Flexible Microwave Payload-2 (FMPL-2) is its payload and it is a novel CubeSat based instrument combining an L1/E1 GNSS-R and an L-band Microwave Radiometer (MWR) using software-defined radio. Coarse Soil Moisture (SM) estimation is one of FSSCat's objectives [12]. HydroGNSS is planned to be developed and launched before the end of 2024. This mission uses GNSS reflectometry to target four hydrological essential climate variables: soil moisture, wetland/inundation, freeze/thaw state, and above ground biomass [13]. Since GNSS-R is essentially a bistatic/multi-static radar operating in the L-band, and traditional microwave remote sensing has proven that this band is suitable for soil moisture observation [14; 15], its current applications in land surface parameters are mostly concentrated on soil moisture [16; 17; 18; 19; 20; 21]. Compared with traditional monostatic radar, the significant advantage of using this method to observe vegetation parameters is that the signal response to biomass will not be as easily saturated as in monostatic radar [22]. Therefore, the use of this remote sensing method to monitor vegetation parameters has advantages that traditional remote sensing methods cannot match. However, the changing observation geometry is one of the potential disadvantages of GNSS-R. In this paper, we will summarize the current research status of GNSS-R vegetation remote sensing in Section 2. Meanwhile, the existing problems are discussed and analyzed in Section 3. Based on the microwave scattering characteristics, the future development trend is discussed in Section 4. Finally, we will give a conclusion in Section 5.
# COMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation
## 3 Results
### Experimental Results  
#### 3.3.1 Comparison with State-of-the-Art Methods  
We compared the proposed network with eight existing methods [9; 17; 19; 20; 22; 25; 27; 46]. For unbiased comparison, we compared the proposed method to the state-of-the-arts [9; 17; 19; 20; 22; 25; 27; 46] using the prediction maps pre-computed by the existing study [27] and the scores obtained from the published papers [19; 22; 25]. As demonstrated in Table 1, COMMA achieved state-of-the-art performance on five evaluation metrics compared to previous methods. ResNet-50, as a backbone encoder, showed outstanding performance on the CVC-ClinicDB, whereas Res2 Net50 outperformed the existing methods on the Kvasir dataset. In terms of the generalization performance, as shown in Table 2, COMMA exhibited significant improvement on the three unseen datasets (i.e., CVC-ColonDB, ETIS, and CVC-T). Compared to the state-of-the-art method, PraNet (32.55 M), which is the previous outstanding approach, COMMA required fewer learning parameters (31.1 M), but the segmentation performance was improved. As we obtained networkrobustness by leveraging the explicit boundary propagation and large samples with data augmentation, COMMA could detect unknown polyps more elaborately.
## 3 Controller Design and Stability Analysis
### Yaw Controller Design  
This section designs a yaw controller based on the BLF constraint to prevent safety accidents. Define yaw rate tracking errors as:  
\[r_{e}=r-\alpha_{r}, \tag{39}\]where \(\alpha_{r}\) is the virtual control variable.  
Considering filter error, the yaw error lyapunov function is designed as:  
\[V_{3}=\frac{1}{2}\psi_{e}^{2}+\frac{1}{2}\xi_{r}^{2}. \tag{40}\]  
The differentiation of (40) is:  
\[\dot{V}_{3} = \psi_{e}\left(\dot{\psi}_{r}-r\cos(\varphi)\right)+\xi_{r}\dot{\xi }_{r} \tag{41}\] \[= \psi_{e}[\dot{\psi}_{r}-(\alpha_{r}+\Delta\alpha_{r}-r_{e})\cos( \varphi)]+\xi_{r}\left(-a_{r}\xi_{r}+b_{r}\Delta\alpha_{r}-\left(\frac{1}{2}b_{ r}^{2}\Delta\alpha_{r}^{2}+|\psi_{e}\Delta\alpha_{r}|\right)\xi_{r}^{-1}\right).\]  
Hence, the virtual control law for \(\alpha_{r}\) is designed as:  
\[\alpha_{r}=(k_{3}\psi_{e}+\dot{\psi}_{r}-c_{r}\xi_{r}+c_{r}^{2}\psi_{e}/2)/\cos (\varphi). \tag{42}\]  
Using the barrier Lyapunov function to constrain the yaw velocity error, the Lyapunov function is designed as:  
\[V_{4}=V_{3}+\frac{1}{2}I_{z}\ln\frac{k_{b}^{2}}{k_{b}^{2}-r_{e}^{2}}+\frac{1} {2}\dot{W}_{r}^{2} \tag{43}\]  
\[\dot{V}_{4} = \dot{V}_{3}+\frac{mr_{e}\dot{r}_{e}}{k_{b}^{2}-r_{e}^{2}}+\ddot{W }_{r}\dot{W}_{r} \tag{44}\] \[= -k_{3}\psi_{e}^{2}+r_{e}\psi_{e}\cos(\varphi)-(a_{r}-1)\,\dot{ \xi}_{r}^{2}-\frac{1}{2}(\xi_{r}-c_{r}\psi_{e})^{2}\] \[-\frac{1}{2}(\xi_{r}-b_{r}\Delta\alpha_{r})^{2}+\frac{mr_{e}}{k_ {b}^{2}-r_{e}^{2}}\left(\dot{r}-\dot{a}_{r}\right)\,+\,\ddot{W}_{r}\dot{W}_{r}\] \[= -k_{3}\psi_{e}^{2}+r_{e}\psi_{e}\cos(\varphi)-(a_{r}-1)\,\dot{ \xi}_{r}^{2}-\frac{1}{2}(\xi_{r}-c_{r}\psi_{e})^{2}-\frac{1}{2}(\xi_{r}-b_{r }\Delta\alpha_{r})^{2}\] \[+\,\frac{r_{e}}{(k_{b}^{2}-r_{e}^{2})}[-d_{41}u-d_{42}v-d_{44}r+N _{res}+f_{n}+\tau_{r}-m\dot{a}_{r}]+\ddot{W}_{r}\dot{W}_{r}.\]  
Combined with (19), yaw controller law can be designed as:  
\[\delta=N(\zeta_{r}(t))r \tag{45}\]  
\[\dot{r}=-\,k_{4}r_{e}+m\dot{a}_{r}-\left(k_{b}^{2}-r_{e}^{2}\right)\psi_{e}+d_{ 41}u+d_{42}v+d_{44}r-N_{res}-\ddot{W}_{r}H_{r}(x) \tag{46}\]  
\[\dot{\xi}_{r}=-r_{e}\rho_{r}\ddot{r} \tag{47}\]  
\[\dot{\dot{W}}_{r}=-\eta_{r}\dot{W}_{r}-(r_{e}/(k_{b}^{2}-r_{e}^{2}))H_{r}(x) \tag{48}\]
## 4 Test Results
### Permeability Test  
#### 4.2.1 Test Equipment and Principles  
The instrument used for the test is a low permeability measurement instrument designed using the standards of the American Petroleum Institute (API) [34] and Geology and Mineral Resources of China (Figures 8 and 9). It measures the permeability of the rock specimen under steady nitrogen (N\({}_{2}\)) gas flow. Changing the upstream pressure, the Klinkenberg permeability and slip factor \(b\) can be fitted by the permeability of gas obtained by multipoint measurements.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **Type** & **Mudstone Content (\%)** & **Particle Volume (cm\({}^{3}\))** & **Pore Volume (cm\({}^{3}\))** & **Porosity (\%)** \\ \hline a1 & Mixed & 0 & 23.35 & 1.21 & 4.9 \\ b1 & Mixed & 0 & 26.07 & 0.79 & 2.9 \\ c1 & Mixed & 20 & 24.66 & 1.69 & 6.4 \\ d1 & Mixed & 40 & 23.75 & 3.22 & 11.9 \\ e1 & Mixed & 50 & 23.02 & 2.67 & 10.4 \\ f1 & Mixed & 60 & 23.51 & 2.43 & 9.4 \\ g1 & Mixed & 80 & 21.69 & 3.47 & 13.8 \\ h1 & Mixed & 100 & 20.53 & 3.95 & 16.1 \\ i1 & Mixed & 100 & 21.39 & 5.11 & 19.3 \\ a2 & Layered & 0 & 26.11 & 0.70 & 2.6 \\ b2 & Layered & 20 & 24.89 & 1.77 & 6.6 \\ c2 & Layered & 40 & 23.83 & 1.71 & 6.7 \\ d2 & Layered & 50 & 23.65 & 1.99 & 7.8 \\ e2 & Layered & 60 & 23.79 & 2.34 & 9.0 \\ f2 & Layered & 80 & 22.66 & 2.77 & 10.9 \\ g2 & Layered & 100 & 21.56 & 4.23 & 16.4 \\ \hline \end{tabular}
\end{table}
Table 4: The results of the porosity test.
## 2 Materials and Methods
The custom USC root vector analysis program uses algorithms to calculate the torque values for all teeth automatically [22]. Subsequently, we reported the torque data in an Excel spreadsheet (version 2019; Microsoft, Redmond, Wash). The difference between pre-treatment and post-treatment torque values for each tooth and the percentage of torque variation were calculated using an Excel spreadsheet. Additionally, we analyzed the presence of external apical root resorption (EARR). The root length was measured by Dolphin software as the perpendicular distance between the most apical point of the tooth and the reference line at the cementoenamel junction, according to the studies reported by [PERSON] et al. [21] and [PERSON] et al. [2] (Figure 3). EARR was defined as a root length loss (mm) between T0 and T1 stages, and the percentage of EARR was calculated as (root length loss/original root length) \(\times\) 100% using an Excel spreadsheet. The severity of EARR was divided into the following three degrees according to the percentage of EARR: mild (<10%), moderate (10-20%), and severe (>20%). Two blinded observers, previously instructed to use Dolphin Imaging 3D software, measured the torque and root length values. All measurements were repeated for 10% of the sample after 4 weeks. Figure 1: Classification of root position relative to cortical plates according to [PERSON]: (**A**), Class I: the root is positioned against the labial cortical plate. (**B**), Class II: the root is centered in the alveolar housing without engaging the labial or palatal cortical plate at the apical third of the root. (**C**), Class III: the root is positioned against the palatal cortical plate. (**D**), Class IV: at least two-thirds of the root engages the labial and palatal cortical plates. (**E**), Class V: the root is positioned outside the labial cortical plate. Figure 3: Measurements of the root length of the right central incisor on the cross sections: (**A**), pre-treatment; (**B**), post-treatment. This tooth shows a slight root resorption of 0.5 mm, equal to 3.7% of the initial root length. Figure 2: Measurements of torque using root vector analysis software program. The torque and root length values before and after therapy were subjected to statistical analysis to establish whether torque and root length changes are attributable to orthodontic therapy with clear aligners. Intraclass correlation coefficients (ICCs) were used to estimate intra-rater and inter-rater reliabilities. Statistical analyses were performed using Microsoft Excel (version 2019; Microsoft, Redmond, Wash) and StatPlus (AnalystSoft Inc., Walnut, CA, USA). A paired \(t\)-test was selected to compare torque and root length variations before and after therapy. The level of significance was set at 5%.
# Properties of Vector Embeddings in Social Networks
## 5 Experiments
### Parameter Settings  
* **DeepWalk:** Here, we apply the Skip-Gram model [13] on the node sequences generated by biased random walk. We set parameters as follows: the context size \(c=10\), the embedding size \(d=128\), the length of each node sequence \(t=40\), and the number of node sequences for each node \(\gamma=80\)[12].
* **loc:** Here, we apply Paragraph Vector [16] to learn embeddings for limited sequence of nodes, the same as paragraphs in text. In the Paragraph Vector Distributed Memory (PV-DM) model, optimal context size is 8 and the learned vector representations have 400 dimensions for both words and paragraphs [16].
* **node2 vec:** This algorithm operates the same as DeepWalk, but the hyper-parameter \(p\) and \(q\) control the walking procedure. With \(q>1\), the random walk is biased towards nodes close to the start node. Such walks obtain a local view of the underlying graph with respect to the start node in the walk and approximate BFS behavior in the sense that samples are comprised of nodes within a small locality. The parameter \(p\) controls the likelihood of immediately revisiting a node in the walk. If \(\mathrm{p}\) is low \((<\min(q,1))\), it would lead the walk to backtrack a step and this would keep the walk \"local\" close to the starting node. The optimal values of \(\mathrm{p}\) and \(\mathrm{q}\) depend a lot on the dataset [10]. In our experiment, we consider two settings: node2 vec\((1)\) keeps the walk local with \(p=2^{-8},q=2^{8}\), while node2 vec\((2)\) walks more exploratively with \(p=2^{8},q=2^{-8}\).
* **LINE:** LINE with first-order proximity, in which linked nodes will have closer representations, and LINE with second-order proximity, in which nodes with similar neighbors will have similar representations. In both settings, we consider the embedding size \(d=128\), batch-size\(=1000\), learning-rate \(\rho=0.025\).
# A Machine Learning Model for Predicting Noise Limits of Motor Vehicles in UNECE R51 Regulations
## 3 BPNN Model for Noise Limits Prediction during Vehicle Acceleration
### Architecture of the Proposed BPNN Model
#### 3.1.2 Development of Back-Propagation Neural Network  
The hidden unit in the BPNN can be set freely according to the objective of this study. A three-layer network can facilitate continuous functions with an acceptable precision set by a developer [24, 25]. In this study, according to the characteristics of the vehicle acceleration noise limits analysed above, the three-layer neural network is established, that is, the input layer, the hidden layer (only a single-layer neural unit), and the output layer. In the UNECE R51 regulations, the vehicle acceleration noise limits were revised seven times so far for six major vehicle categories. If the noise limits in the regulations is assumed as a function of time to describe its variations, new revised limits can be predicted based on past known limits. Therefore, in the BPNN model, the data of the previous six noise limits revisions can be extracted from all revisions as input data, and the output is the newly revised value, which would be the seventh minor revision value. Then the mapping between input data and output data can be conducted via the BPNN self-learning until the minimum root mean square value is achieved. There is no scientific model or formula to determine the number of hidden layer units. In this study, a trial-and-error method was used to determine the number of hidden layers, which was selected by the value of MSE results from training data, validation data and test data. The maximum MSE is lower than 2.0 from hidden layer units 12 to 24, while the maximum MSE is higher than 2.0 from hidden layer units 6 to 12, as shown in Table 2 (highlighted with grey colour), so the number of hidden layer neural units is 12, due to the minima of hidden layers. Hereby, the architecture of the BPNN model was proposed as 6-12-1 (6 inputs, 12 hidden layer units, and 1 output), as depicted in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number of** & \multicolumn{2}{c}{**Training Data**} & \multicolumn{2}{c}{**Validation Data**} & \multicolumn{2}{c}{**Test Data**} \\ \cline{2-7}
**Hidden** & **MSE** & **Regression** & **MSE** & **Regression** & **MSE** & **Regression** \\
**Layers** & **MSE** & **Values** & **MSE** & **Values** & **Values** \\ \hline
6 & 0.601 & 0.996 & 0.340 & 0.950 & 2.