esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Section
### Lacase Immobilization by PILs  
Amino-functionalized PILs (named PIL-NH\({}_{2}\)) immobilized lacase (Lac) by covalently binding glutaraldehyde (GA). The activated PIL (1)-NH\({}_{2}\) and PIL (2)-NH\({}_{2}\) (20 mg) were dispersed in a sodium citrate buffer (pH = 3, 10 mL, 100 mM) under ultrasound, and then lacase (1 mg-ml\({}^{-1}\)) was added. Then, glutaraldehyde (2% \(v/v\)) was added drop-wise to the mixture of PIL-NH\({}_{2}\) and lacase. The new mixture was cross-linked at room temperature for a certain time (e.g., 1, 2, 3, 4, or 5 h). The immobilization efficiency was estimated using Equation (1). Drawing BSA standard curve [59]: Deionized water was used to prepare BSA solution with a concentration of 1-10 \(\mathrm{\SIUnitSymbolMicro g}\)/mL. BSA (4 mL) was mixed with Coomassieie brilliant blue G-250 protein reagent (1 mL), and the absorbance was measured at 595 nm with a microplate reader after 3 min. Coomassieie brilliant blue G-250 staining method was used to determine the ability of amino-functionalized PILs to immobilize lactose. After the enzyme was cross-linked with the support for a certain time, the supernatant was taken and combined with the Coomassieie brilliant blue G-250 reagent. The absorbance was determined by colorimetry at 595 nm, and C (final concentrations of protein) was obtained according to the standard curve.  
\[\mathrm{M=}\frac{(\mathrm{C_{0}-C)V}}{\mathrm{m}} \tag{1}\]  
Here, \(\mathrm{M}\) is the efficiency of immobilized lactose of PIL-NH\({}_{2}\) (mg/g); \(\mathrm{C_{0}}\) and C are the initial and final concentrations of proteins (\(\mathrm{\SIUnitSymbolMicro g}\)/mL), respectively; \(\mathrm{V}\) is the volume of solution (mL); \(\mathrm{m}\) is the mass of PIL-NH\({}_{2}\) (mg). The lactose immobilized by PIL-NH\({}_{2}\)-GA-Lac under different initial concentrations of lactose and different cross-linking times was calculated.
# A Momentum-Based Local Face Adversarial Example Generation Algorithm
## 5 Experiments
### Attack Method Evaluation Indicators  
The accuracy of a face recognition model intuitively reflects the predictive ability of the model. The attack success rate (ASR) is calculated as follows:  
\[ASR=1-Acc \tag{22}\]  
The higher the ASR, the more vulnerable the model is to adversarial attacks; the lower the ASR, the more robust the model is to adversarial attacks and is able to withstand a certain degree of adversarial attacks.  
In order to evaluate the magnitude of the difference between the generated adversarial example and the original face image after the attack, this experiment used the peak signal-to-noise ratio (_PSNR_), and structural similarity (_SSIM_) [34], which are two metrics to measure the image quality of the adversarial example.  
The _PSNR_ is defined and calculated by the mean squared error (_MSE_). The following equation calculates the _PSNR_ for a given image \(I\).  
\[PSNR=20\cdot log_{10}(MAX_{I})-10\cdot log_{10}(MSE) \tag{23}\]  
where \(MAX_{I}\) is the maximum pixel value of the image; \(MSE\) is the mean square error. The larger the _PSNR_, the less distortion and the better quality of the adversarial example [3].  
Considering human intuition, we adopted the evaluation index of structural similarity (_SSIM_), which takes into account the three factors of brightness, contrast, and structure. Given images \(x\) and \(y\) with the same dimensions, the structural similarity is calculated as follows.  
\[SSIM(x,y)=\frac{\left(2\mu_{x}\mu_{y}+c_{1}\right)\left(2\sigma_{xy}+c_{2} \right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+c_{1}\right)\left(\sigma_{x}^{2}+\sigma _{y}^{2}+c_{2}\right)} \tag{24}\]  
Among them, \(\mu_{x}\), \(\mu_{y}\) are the mean values of image \(x,y\); \(\sigma_{x}^{2}\), \(\sigma_{y}^{2}\) are the variance of image \(x\), \(y\); \(\sigma_{xy}\) is the covariance, and \(c_{1}\) and \(c_{2}\) are used to maintain stability. _SSIM_ takes values in the range of [\(-1\), \(1\)], and the closer the value is to \(1\), the higher the structural similarity between the adversarial example and the original image. To a certain extent, it can indicate the more imperceptible the perturbation applied to the adversarial example is to humans.
# Parameters Sensitivity Analysis of Position-Based Impedance Control for Bionic Legged Robots' [PERSON]
## 1 Introduction
[14] built a fourth-order linear mathematical model for the position control system of valve-controlled cylinders. In the model, the valve's pressure-flow nonlinearity is partly linearized. Based on [PERSON]'s research, he showed the first-order sensitivity analysis results of an extra seven parameters, and proposed four assessment methods to quantify the sensitivity of state variables when parameters changed by 1%. [PERSON] et al. [15] built a fifth-order linear mathematical model for the position-based control system, and studied the effect on system output when 14 system parameters changed by 1%. Based on the first-order trajectory sensitivity, [PERSON] et al. [16] deduced the method of second-order trajectory sensitivity and analyzed the effect on system output when 14 system parameters changed from 1% to 20%. Moreover, experiments are also conducted to verify the effect. In the position-based impedance control system, the above achievements adopted first-order and second-order sensitivity analysis methods to study the effect on control characteristics when parameters change. However, the above papers didn't analyze the impedance control methods on HDU. In addition, the trajectory sensitivity analysis methods they used, particularly the second-order trajectory sensitivity analysis method, are very difficult to compute. To solve the two problems, this paper is organized as follows. First, based on the mathematical model of HDU position control system, the method of position-based impedance control is deduced and tested by experiments. Then, to solve the difficulties in sensitivity computing, matrix sensitivity analysis, an easier method, is adopted to analyze the sensitivity of four main control parameters. Further, to obtain the optimal method, the results from matrix sensitivity analysis are compared with the results from trajectory sensitivity analysis. Moreover, the quantifational analysis results of the four main parameters are shown in this paper. Finally, the sensitivity analysis results are verified by experiments.
## 3 Index System Screening
### Finalizing the Index System
#### 3.2.4 The Final Index System  
According to the analysis of questionnaire results, the large majority of indicators have been proven to be effective. The indicators with mean values below 3.0 were filtered out, namely 3D printing technology (A18), the utilization of land resources (A34) and number of the employees (A44), which indicated that these three indicators are relatively less important for evaluating the development level of RCI with regards to reasonability and operability. The final composition of the index system is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dimension** & **Indicator** & **Mean** & **Std. Dev** & **CV** & **Rank** & **Group Mean** & **Group Rank** & **Verification** \\ \hline \multirow{6}{*}{Technical dimension (A1)} & A11 & 4.540 & 0.61 & 0.13 & 4 & & & Pass \\  & A12 & 4.628 & 0.58 & 0.13 & 2 & & & Pass \\  & A13 & 4.211 & 0.78 & 0.19 & 9 & & & Pass \\  & A14 & 4.352 & 0.82 & 0.19 & 7 & 4.383 & 1 & Pass \\  & A15 & 4.171 & 1.21 & 0.29 & 10 & & & Pass \\  & A16 & 4.685 & 0.46 & 0.10 & 1 & & & Pass \\  & A17 & 4.095 & 0.86 & 0.21 & 11 & & & Pass \\ \cline{2-8}  & A18 & 2.875 & 1.14 & 0.40 & 23 & & & No \\ \hline \multirow{6}{*}{Economic dimension (A2)} & A21 & 4.376 & 0.49 & 0.11 & 6 & & & Pass \\  & A22 & 3.157 & 0.78 & 0.25 & 22 & & & & Pass \\  & A23 & 3.866 & 0.50 & 0.13 & 15 & 3.774 & 4 & Pass \\  & A24 & 3.925 & 0.83 & 0.21 & 14 & & & Pass \\  & A25 & 3.548 & 0.71 & 0.20 & 17 & & & Pass \\ \hline \multirow{6}{*}{Sustainable dimension (A3)} & A31 & 3.430 & 0.83 & 0.24 & 19 & & & & Pass \\  & A32 & 3.961 & 0.66 & 0.17 & 13 & 3.892 & 3 & & Pass \\  & A33 & 4.285 & 0.87 & 0.20 & 8 & & & & Pass \\ \cline{2-8}  & A34 & 2.601 & 0.81 & 0.31 & 24 & & & & No \\ \hline \multirow{6}{*}{Enterprise development dimension (A4)} & A41 & 3.361 & 0.74 & 0.22 & 20 & & & & Pass \\  & A42 & 4.587 & 1.05 & 0.23 & 3 & 4.002 & 2 & & Pass \\ \cline{1-1}  & A43 & 4.059 & 0.67 & 0.17 & 12 & & & & Pass \\ \cline{1-1}  & A44 & 2.473 & 0.71 & 0.29 & 25 & & & & No \\ \hline \multirow{6}{*}{Development environment dimension (A5)} & A51 & 3.774 & 0.48 & 0.13 & 16 & & & & Pass \\ \cline{1-1}  & A52 & 3.471 & 0.85 & 0.24 & 18 & & & & Pass \\ \cline{1-1}  & A53 & 4.469 & 0.45 & 0.10 & 5 & & & & Pass \\ \cline{1-1}  & A54 & 3.254 & 0.62 & 0.19 & 21 & & & & Pass \\ \hline \hline \end{tabular}
\end{table}
Table 5: Scores and ranking of the indicators.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 4 Solutions to the Issues Faced by RPL during Mobility
### RSSI Based Solutions
#### 4.3.5 mRPL  
Recall that RPL faces issues related to the disconnection of mobile nodes and making closed loops as the mobile nodes may select the neighbor as a new parent, which previously was the child of the current mobile node. Hence, the authors [46] proposed a Smart-Hop algorithm, which has two main phases i.e., the data transmission phase and the discovery phase. In the first phase, a reliable link is assumed between Mobile Node (MN) and an Access Point (AP). Upon receiving reply packets from AP, MN monitors the link quality. AP replies back with the average RSSI value of the packets received in a given window. AP takes no action if no data packets are received, which can lead to disconnection. In mRPL approach, reply packets in unicast DIO message is received immediately after transmission of predefined data packets. Filtering of asymmetric links is done by the DIO message containing the average RSSI level.  
The data transmission phase is processed by the MN upon detecting the good quality of the link. The Discovery phase is initiated upon detection of the ARSSI degradation. After finding the better AP, connections with the serving AP are resumed. A burst of DIS messages are broadcasted to assess the potential parents. The average RSSI value embedded in the DIO message is compared with the threshold level by the MN. If the ARSSI value is below the threshold, MN continues broadcasting DIS burst and if the ARSSI value is above the threshold, the discovery phase stops and data communication with the new Preferred Parent starts. Loops are avoided in the network after getting all replies from all neighboring APs and ignoring messages from previous child nodes. Therefore, child nodes are excluded from the set after creating alternative parents sets. The simulation results show that mRPL guarantees connectivity by providing a reliable hand-off process. Within 80-xxx83 ms mRPL detects and performed the hand-off which is much faster than RPL. Similarly, 100% PDR is provided by mRPL because of the fast hand-off process; high data rates of 30 pkts/s mRPL show higher overheads as compared to RPL.
## 4 Series Expansions  
### Small-\(\omega\) Expansion  
For small and for large values of \(\omega\), \(F_{\beta}(\omega)\) can be determined from series expansions [1, 2, 26, 27, 28]. For small \(\omega\), we expand \(\exp(i\omega t)\) in Equation (4), substitute \(x=t^{\beta}\), and use the defining equation of the gamma function,  
\[\int_{0}^{\infty}\!{\rm d}x\,x^{\mu-1}{\rm e}^{-x}=:\Gamma(\mu) \tag{11}\]  
to one obtain the Taylor series (in [28] traced back to Cauchy 1853):  
\[F_{\beta}(\omega)=\frac{1}{\beta}\sum_{k=0}^{\infty}A_{k}(i\omega)^{k} \tag{12}\]  
with  
\[A_{k}:=\frac{\Gamma((k+1)/\beta)}{\Gamma(k+1)} \tag{13}\]  
Separating real and imaginary parts, we get  
\[\begin{array}{rcl}Q_{\beta}(\omega)&=&\frac{1}{\beta}\sum_{k=0}^{\infty}{(- 1)}^{k}A_{2k}\omega^{2k}\\ V_{\beta}(\omega)&=&\frac{1}{\beta}\sum_{k=0}^{\infty}{(-1)}^{k}A_{2k+1} \omega^{2k+1}\end{array} \tag{14}\]  
The expansion of \(P_{\beta}\) is obtained most easily by integrating that of \(Q_{\beta}\),  
\[P_{\beta}(\omega)=\frac{1}{\beta}\sum_{k=0}^{\infty}{(-1)}^{k}\frac{A_{2k}}{2k +1}\omega^{2k+1} \tag{15}\]  
The expansions of \(Q_{\beta}(\omega)\), \(V_{\beta}(\omega)\), and \(P_{\beta}(\omega)\) shall be summarized as  
\[Y=\sum_{k=0}^{\infty}{(-1)}^{k}\frac{A_{2k+\kappa}}{(2k+1)^{\mu}}\omega^{2k+ \kappa+\mu} \tag{16}\]with switches  
\[\kappa,\mu:=\left\{\begin{array}{ll}0,0&\mbox{for $Q$}\\ 1,0&\mbox{for $V$}\\ 0,1&\mbox{for $P$}\end{array}\right. \tag{17}\]  
All these series are useful only for small \(\omega\); otherwise large alternating terms prevent efficient summation. For \(\beta\geq 1\), they converge for all values of \(\omega\). For \(\beta<1\), they are _asymptotic expansions_, which means [32, 33] they diverge, but when truncated at the right place they nevertheless provide useful approximations.
# Effect of Tomato Juice and Different Drying Methods on Selected Properties of Courgette
## 3 Results and Discussion
### Bulk Density (\(p_{b}\))  
The analysis of the density of the dried courgette obtained using different drying methods revealed significant differences in density values expressed in kg/m\({}^{3}\). The highest density was recorded for the fresh courgette after impregnation (351.78 kg/m\({}^{3}\)) and the fresh courgette not subjected to pretreatment (262.95 kg/m\({}^{3}\)). The density of dried samples ranged from 15.42 kg/m\({}^{3}\) for courgettes without pretreatment after freeze drying to 51.27 kg/m\({}^{3}\) for vacuum-dried courgettes after impregnation. By analyzing the bulk density, it is possible to determine the effect of the drying process on the parameter under study. In particular, vacuum-dried samples showed a higher density, which may be due to the characteristics of this process (Figure 3). In this method, water is removed more intensively, which can lead to greater shrinkage of the material and, as a result, to a higher density, despite the formation of a porous structure [14].  
Tomato juice impregnation increased the density of the courgette in all tested materials, both fresh and after drying, as a result of the absorption of the impregnating liquid. Fresh samples that were impregnated showed an increase in density of 33% on average compared to non-impregnated samples. For the courgette dried using various methods after vacuum impregnation, the average increase in density was about 15% compared to the corresponding samples dried without pretreatment. In addition, it was observed that higher convection drying temperatures resulted in a lower density of the courgette,  
Figure 3: Bulk density of courgette (C) and courgette after vacuum impregnation (CT), fresh (Fresh), freeze-dried (FD), vacuum-dried (VD), and convection-dried at 50 \({}^{\circ}\)C (CD50), 60 \({}^{\circ}\)C (CD60), and 70 \({}^{\circ}\)C (CD70).  
indicating that temperature is crucial to the drying process, significantly affecting shrinkage and dehydration of the material [31].
## 4 Conclusions  
The adoption of the GNSS for railway signaling applications requires a safe and liable determination of train position and this paper has analyzed one of the core algorithms of the augmentation network. A study of the sensitivity of the LMMSE algorithm to modeling errors regarding the a priori spatial correlation of the true total pseudorange DC is undertaken. The impact of the correlation distance modeling errors decrease with the increase of the true correlation distance to RS distance separation ratio, and converges to an asymptote beyond a certain threshold. On the other hand, noise variance modeling errors have a low impact on the estimation error variance. It is shown that for fairly high correlation distance to RS distance separation ratio values, and for a wide range of modeling errors, the LMMSE algorithm further decreases the noise inherent in pseudorange DC measurements generated at each RS for every satellite. Conversely, for very low ratio values, and for some correlation distance modeling errors,  
Figure 10: Estimation error variance corresponding to a sum of three Gauss-Markov processes and different \(\Delta\sigma^{2}\) modeling errors.  
Figure 9: Estimation error variance corresponding to a sum of three Gauss-Markov processes and different \(\Delta D\) modeling errors.  
the LMMSE algorithm increases the noise inherent in pseudorange DC measurements. In this sense, this paper has presented a methodology to assess the required distance between RSs that make up a GNSS augmentation system given a specific correlation distance that characterizes a certain environment. Furthermore, this impacts on the costs for deploying and maintaining the augmentation network.
# A Geospatial Approach to Improving Fish Species Detection in Maumee Bay, Lake Erie
## 3 Results  
### Hot Spot Determination  
Areas with high species richness were determined for all three gear types using 2013-2016 sampling data and the significance of species richness clustering varied among gear types (Table 1). Data for electrofishing (\(n=24\)) was below the recommended minimum number of samples (\(n=30\)) and should be interpreted with caution. Additionally, bottom trawl data from 2014 was not included because sampling took place much later in the year and may have influenced the number of fish captured. The number of species captured by any gear type ranged from 0 to 25 with a mean number of species by gear type ranging from 4.2 to 10.2 (Table 1). Paired fyke nets generally captured more species relative to the other gear types (Table 1). The Moran's I statistic indicated clustering among species richness locations for paired fyke nets (\(p=0.02\)) and bottom trawls (\(p<0.01\)), but not significantly for electrofishing (\(p=0.08\)). The search distance radius (first peak) used for the hot spot analysis varied among gear types and decreased in length when the number of samples increased (Table 1).  
With a limited number of exploratory samples for electrofishing, a small targeted hot spot zone was determined near the one significant (\(p\leq 0.05\)) identified hot spot in the western portion of Maumee Bay (Figure 1) so that targeted sampling could still occur using this gear type despite nonsignificant clustering. Paired fyke nets had five significant hot spots and two significant cold spots (i.e., low species richness). The sites with high species richness were located nearshore in the western portion of the bay, while the cold spots were clustered offshore (Figure 1). Bottom trawls had six significant hot spots and 21 significant cold spots. Sites with high species richness were clustered in the southeastern portion of the bay, while the cold spots were clustered in the northern portion of the bay (Figure 1). The targeted hot spot zones for 2017 sampling were in similar locations for electrofishing and paired fyke nets in the western portion of the bay, and offshore near the mouth of the shipping channel for bottom trawls (Figure 1).
## 2 Materials and Methods
### Evaluating the Models  
Once the model is trained, the critical question is how good the model is. The following metric is used to evaluate the model:  
* Mean Square Error (MSE): is the average of the squared differences between prediction and actual observation. In other words, the MSE is the variance of the error [41]. It is calculated by Equation (11): \[MSE=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}\] (11)
* Root Mean Square Error (RMSE): is the square root of the MSE. In other words, the RMSE is the standard deviation of the error [41]. In the MSE metric, the errors are first squared before averaging, resulting in a high penalty for large errors. Therefore, the RMSE is useful when large errors are undesirable.
* Mean Absolute Error: is the average of the absolute differences between predictions and actual observations and is calculated by Equation (12) [41]: \[MAE=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}|\] (12)  
Figure 7: Validation loss vs. time using Bayesian optimization.  
Mean Bias Error (MBE): captures the average bias in the prediction. A positive bias in a variable means that the data from the datasets are underestimated, while a negative bias means that the model overestimates the observed data. It is calculated by Equation (13): \[MBE=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})\] (13)
* \(R^{2}\): is a statistical measure that calculates the variance explained by the model over the total variance. The higher \(R^{2}\) is, the smaller the differences between the actual observations and the predicted values. It is calculated by Equation (14) [19]: \[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}- \overline{y})^{2}}\] (14) where \(y_{i}\)'s, \(y_{i}\)'s, and \(\overline{y}\) are the actual observations, the values predicted by the model, and the mean value of the actual observations, respectively.
# PV-Powered Charging Station with Energy Cost Optimization via V2G Services
## 2 PV-Powered Charging Station with V2G Service
### Electric Vehicles
#### 3.4.2 EV Charging Mode  
If the EV user does not want to participate in the V2G service, Constraints (24)-(27) are used for charging the EV battery, where the charging power is determined by the charging mode selected by the EV user as follows:  
\[\mathit{if}\ M_{v}=1\ \mathit{then}\ 0\leq p_{EV_{v}}(t_{i})\leq P_{EV\_fast\_max} \ \forall t_{i}\in[t_{arr_{v}},t_{dep_{v}}]\ \mathit{with}\ \mathit{v}=\{1,2,\ldots,N_{v}\}, \tag{24}\]  
\[\mathit{if}\ M_{v}=2\ \mathit{then}\ 0\leq p_{EV_{v}}(t_{i})\leq P_{EV\_ \mathit{aver\_max}}\ \forall t_{i}\in[t_{arr_{v}},t_{dep_{v}}], \tag{25}\]  
\[\mathit{if}\ M_{v}=3\ \mathit{then}\ 0\leq p_{EV_{v}}(t_{i})\leq P_{EV\_ slow_max}\ \forall t_{i}\in[t_{arr_{v}},t_{dep_{v}}], \tag{26}\]  
\[p_{EV_{v}}(t_{i})=0\ \forall t_{i}\
otin[t_{arr_{v}},t_{dep_{v}}], \tag{27}\]  
where \(N_{v}\) is the total number of EVs, \(P_{EV\_short}\) is the average charging power at maximum, and \(P_{EV\_slow\_max}\) is the slow charging power at maximum. The total EV charging power in (28) and the SOC calculation with its constraints in (29)-(34) are given:  
\[p_{PVCS}(t_{i})=\sum_{v}^{N_{v}}p_{EV_{v}}(t_{i})\ \forall t_{i}\in[t_{arr_{v}},t_{dep_{v}}], \tag{28}\]\[SOC_{EV\_min}\leq soc_{EV_{v}}(t_{i})\leq SOC_{EV\_max}\,\forall t_{i}\in[t_{ arr_{v}},t_{dep_{v}}], \tag{29}\]  
\[soc_{EV_{v}}(t_{i})=0\,\forall t_{i}\
otin[t_{arr_{v}},t_{dep_{v}}], \tag{30}\]  
\[soc_{EV_{v}}(t_{i})=SOC_{EV\_arr_{v}}(t_{i})\,\forall t_{i}=t_{arr_{v}}, \tag{31}\]  
\[SOC_{EV\_arr_{v}}(t_{i})\geq SOC_{EV\_min}\,\forall t_{i}=t_{arr_{v}} \tag{32}\]  
\[soc_{EV_{v}}(t_{i+1})=soc_{EV\_arr_{v}}(t_{i})\,+\frac{1}{3600\cdot E}\int_{t_ {arr_{v}}}^{t_{dep_{v}}}\,\,\,\,p_{EV_{v}}(t_{i})\cdot dt, \tag{33}\]  
\[SOC_{EV_{v}}(t_{i})\leq soc_{EV\_des_{v}}\,\forall t_{i}=t_{dep_{v}}, \tag{34}\]  
where \(SOC_{EV}\) is the SOC of vehicle \(v\), and \(SOC_{EV\_min}\) and \(SOC_{EV\_max}\) are the minimum and maximum battery SOC of vehicle \(v\), respectively. The dynamic SOC evolution \(soc_{EV_{v}}\) is given by (33). Similar to the stationary storage, Constraint (29) determines the SOC limit. Additionally, Constraint (30) refers to the absence of an EV, while Constraint (31) assigns the SOC of an EV at its arrival time, requiring it to be greater than the limit specified in Constraint (32). Finally, Constraint (34) refers to the SOC of an EV at its departure time, which should be lower than or equal to the desired SOC of the EV at departure.