esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimentation and Results Analysis
### Evaluation Indicators and Experimental Setup  
The evaluation metrics normalized discounted cumulative gain (NDCG) @N and hits ratio (HR) @N were applied. They are used to compare the differences between the recommended list and the user's real interaction list.  
Each site in the list of recommendations has a certain correlation between the use and the score form of the expression of the correlation that exists. For example, for gain, if a user's rating is (5-10 points), the gain is 5-10; if the analysis is of the user's implicit behavior, such as observing whether the user clicks on the site, with 0 indicating click and 1 indicating not clicked, then the gain is 0 or 1. Add the gain between each site to obtain the Cumulative Gain (Cumulative Gain); let each site gain and its discount value (target site list position) be divided to obtain the discounted cumulative gain DCG (Discounted Cumulative Gain), which indicates that the site list position is more backward the higher the discount and the lower the value. Using the DCG to determine whether the user reference project recommendation is good or bad, but knowing that the DCG between different customers cannot be compared, it is necessary to use the normalized Discounted Cumulative Gain NDCG to obtain the best Discounted Cumulative Gain (Ideal Discounted Cumulative Gain, IDCG); combined with the project gain sorting for ranking, the best DCG calculation is IDCG. The greater the results of the calculation, the higher the value of the project list. The closer the calculation result is to 1, the more reasonable the ordering is. NDCG@N is defined by the Equation (26):  
\[NDCG\text{@}N=\frac{DCG\text{@}N}{IDCG\text{@}N} \tag{26}\]  
The hit rate HR is the recommended stronger evaluation metric. The HR@N Equation is:  
\[HR\text{@}N=\frac{1}{N}\cdot\sum_{i=1}^{N}hit(i) \tag{27}\]  
where, \(N\) indicates the number of times the site is recommended; the \(hit\) function indicates whether the \(hit\) is indicated by 1, if not, 0.  
Higher values of the corresponding data indicate more accurate final recommendations. Non-interaction sites were randomly selected as negative samples and interaction sites as positive samples.  
Set the number of base layers of the graph volume to 4, the \(\lambda_{1}\) in the prediction score to 0.7, the L2 normalization factor \(\mu\) to 0.0001, and the temperature coefficient \(\tau\in\{0.07,\ 0.1,\ 0.2,\ 0.5,\ 0.8,\ 1.0\}\).
# Development, Experimental Assessment, and Application of a Vacuum-Driven Soft Bending Actuator
###### Abstract  
This study presents the design, development, and experimental assessment of soft pneumatic actuators for achieving bending motion utilizing vacuum pressure, with their final application to soft robotic grippers. A novel soft actuator design is introduced, satisfying the following design requirements: safe operation without the risk of explosion, the ability to achieve large angular bending while overcoming significant forces, and the use of soft materials that are resistant to material fatigue. A vacuum-driven soft bending actuator (VSBA) was designed, incorporating a cylindrical ribbed bellow geometry and an integrated limiting element within its structure. Two variations of the VSBA were fabricated, each differing in the materials and manufacturing processes employed. The first version employs a cylindrical ribbed bellow made of thermoplastic rubber (TPR), while the other versions utilize heat-shrinkable polymer materials, resulting in an innovative manufacturing process capable of producing actuators in various sizes and shapes. This contributes to the analysis of how actuator geometry affects performance and enables its miniaturization. The performance of the novel VSBAs were experimentally assessed through measuring the bending angle, blocking force, and angular velocity-angle characteristics. The results confirmed a maximum bending angle of 140\({}^{\circ}\) corresponding to a bending ratio of 78%, a maximum blocking force of 110 N, and maximum angular velocity of 520\({}^{\circ}\)/s at a vacuum pressure of \(-\)0.8 bar. Finally, a soft robotic gripper was developed, consisting of three newly designed VSBAs. Experimental assessments demonstrated the gripper's capability to grasp objects of various shapes, with a maximum holding force of 28 N.  
pneumatics actuators; bending motion; soft robotics; robotic gripper +
Footnote †: journal: Applied sciences  
Article
## 3 The Proposed Model
### Feature Extraction  
At this stage, the segmented images are passed into the SqueezeNet model to produce feature vectors. Generally, the CNN comprises a pooling layer, full connection layer, and convolutional layer. At first, the feature is extracted using multiple convolutions and pooling layers. Next, the feature mapping from the final convolutional layers is transformed into a one-dimensional vector. At last, the output layer classifies the input image. The network reduces the square variance amongst the predictable output and classification outcomes and changes the weight variable by means of BP. The neuron in all the layers are well-arranged in three dimensions: depth, height, and width, whereby depth represents the amount of input feature mappings or channel amount of input image, and height and width refers to the size of a neuron. The convolution layer contains several convolutional filters and extracts features from the image through the convolutional technique. The convolutional filter of the existing layer convoluted the input feature mapping to extract local features and accomplishes the output feature mapping. Next, the nonlinear feature mapping is accomplished by the activation function. The pooling layer, or subsampling layer, was behind the convolution layers. It implements a down-sampling method and has a particular value as output in a certain region.  
As the variable count for AlexNet and VGGNet increases, the SqueezeNet network architecture was introduced that has the lowest variable while maintaining accuracy [19]. The fire module becomes the essential module in SqueezeNet, as well as its architecture, as given in Figure 2. This module can be divided into Expand and Squeeze architecture. The \(1\times 1\) convolution layer received considerable attention in the network architecture. The work explains from the perceptive of cross channel pooling whereas multilayer perceptron (MLP) was equivalent to the cascaded cross channel parametric pooling layer behind the traditional kernel, thus achieving data integration over the channel and linear incorporation of several feature maps. Once the amount of input and output channels were greater, the convolutional kernels become large. Then, adding \(1\times 1\) convolutions to every single inception mechanism reduces the number of input channels, as well as the complexity operation and convolution kernel variables are decreased. Lastly, add \(1\times 1\) convolutions to increase the feature extraction and the number of channels. Once the sampling reduction method can be delayed, a larger activation graph was provided to the convolution layer, whereby the larger activation graph preserves further data and provides better classification performance.  
Figure 2: Structure of SqueezeNet.
## 3 Results and Discussion
### Plant Response at Microcosm Scale
Figure 1: Fresh weight (g) of shoots of plants grown on different soils (A–D) compared to control (CT) during microcosm tests. Data reported as the mean of replicates with the related standard deviation. (+) indicates the addition of PGPB. spread of the contamination. Considering as a reference the natural value of biogenic origin obtained, which in the case of _Z. mays_ is 122 \(\pm\) 67.0 mg kg\({}^{-1}\) of C \(>\) 12 (control soil in Table 2), it is noted that higher concentrations in plants were detected in the presence of contaminated soil (in some cases over 300 mg kg\({}^{-1}\)), which could indicate a passage of the hydrocarbons into the plant. However, this aspect is not particularly evident when considering the average value of all microcosms, given the significant variability in the individual concentrations found. Even for _M. sativa_, an increase is noted that would seem quite significant with respect to the biogenic content, although the inherent variability of the biological system should always be considered. With regard to _L. albus_, by contrast, the values found in plants grown on the contaminated soil fall substantially within the range of the concentrations found in the control plants grown on organic farming soils. Also, in the tests with PGPB, the hydrocarbon concentration values were generally not significantly different from those found in plants grown on control soils. This seems to confirm that the uptake of these compounds from the soil, if any, is very small and that the essential action of plants in the phytoremediation process is that of rhizodegradation, which is further promoted by the presence of PGPB [63]. The only exception seems to be _M. sativa_, which reconfirms a more pronounced difference in hydrocarbon content than plants grown in the control soil. However, the considerable data variability invites some caution in interpreting the results. In fact, as will be shown in the next section, examining the residual amounts of hydrocarbons in the soil after plant growth does not reveal particularly different reductions compared to the other two plant species considered.
# Privacy-Aware Access Protocols for MEC Applications in 5G +
## 4 Results and Discussion
### Solution
#### 4.1.3 Access to APPIFY in MEC
APPIFY sends the certificate \(cert(APPIFY)\) to the UE of [PERSON] during the TLS handshake. The UE verifies this certificate by using the verification key of the main server of APPIFY, \(VK_{main}\). The TLS connection is established between [PERSON] and APPIFY after successfully completing the handshake. 9. The TLS handshake ensures that [PERSON] is talking with the correct entity. For the mutual authentication, [PERSON] reveals her identity to APPIFY by initiating a post-handshake client authentication with APPIFY. In this phase, [PERSON] sends her certificate \(cert(Alice13)\) to APPIFY, and APPIFY verifies it with the verification key of the main server of APPIFY, \(VK_{main}\). 10. [PERSON] sends a service request to APPIFY through the TLS connection. 11. APPIFY replies with the service response to [PERSON] through the TLS connection. 12. The communication between [PERSON] and APPIFY continues as in Steps 10 and 11. The DTLS and TLS connections stay open during the session. When the session ends, both DTLS and TLS connections are terminated. If [PERSON] wants to contact APPIFY again after the session is closed, the procedure starts again from Step 1 or Step 2.
# Territorial Development as an Innovation Driver: A Complex Network Approach
## 3 Results
### WDI Country Communities  
As described in Section 2.4, we use two different algorithms, exploring a wide range of the related parameter spaces, to obtain a hierarchical community detection. The robust partition of the StartupBlink countries' WDI network found through this process consists of three communities that will be labeled henceforth as I, II, and III. The geographical distribution of countries in these communities is shown in Figure 3.  
Both Spin Glass and Leiden algorithms stop after two iterations of the hierarchical pipeline described in the Section 2.4 and provide the same results in each step. In the first iteration, the algorithms return a network subdivision in two communities, comprising 49 and 51 countries, respectively. Then, in the second iteration, the first community splits in two sets, composed of 22 and 27 countries, while the second community, made of 51 nodes, is no longer divisible. Therefore, the final partition of the country network consists of three  
Figure 3: StartupBlink network communities. Communities of StartupBlink countries determined from the similarity network based on WDI indicators. Community I (red) contains 22 countries; community II (green) contains 27 countries; community III (blue) contains 51 countries.  
communities. Country membership to the three final communities is explicitly reported below, with countries identified according to their ISO-3166 alpha-3 code standard [64]:  
* _Community I (22 countries)_: USA, GBR, CAN, ISR, AUS, NLD, SWE, CHE, DEU, FRA, FIN, IRL, DNK, SGP, JPN, BEL, NZL, AUT, NOR, LUX, ISL, MLT;
* _Community II (27 countries)_: ESP, EST, RUS, LTU, KOR, POL, CZE, ITA, CHN, PRT, CHL, UKR, BGR, SRB, ROU, HUN, GRC, LVA, SVN, SVK, HRV, BLR, MKD, MDA, CYP, PRI, BIH;
* _Community III (51 countries)_: IND, MEX, THA, COL, BRA, ARE, IDN, TUR, ARG, MYS, ZAF, KEN, PHL, NGA, PER, EGY, PAK, GEO, ARM, RWA, MAR, AZE, KAZ, URY, VNM, JOR, TUN, GHA, ECU, LKA, DOM, SAU, UGA, LBN, IRN, CMR, ALB, CRI, BGD, JAM, BWA, SLV, ZMB, VEN, TTO, BHR, PRY, QAT, BOL, DZA, ETH.  
Interestingly, as one can also observe from Figure 3, many states that are members of the same community share geographical proximity in addition to economic proximity. Comparison with the partitions determined by the the World Bank income groups [47] indicates, as reported in Figure 4, that communities are ordered in a descending manner from I to III in terms of income: therefore, we will use henceforth the expression _wealth communities_ when referring to them.
## 3 Results and Discussion
### Analysis of Direct Shear Test Results
#### 3.1.2 Effect of Grouting Parameters on Shear Strength  
The fitting curve of the shear strength of the soil around the permeation grouting piles under different cement content is shown in Figure 7.  
Figure 6: Curves of shear stress and shear displacement of silty clay. (**a**) \(\sigma=50\) kPa. (**b**) \(\sigma=100\) kPa. (**c**) \(\sigma=200\) kPa. (**d**) \(\sigma=400\) kPa.  
The shear strength of the soil around the permeation grouting piles increases with the normal stress and cement content and has positive linear correlations with the normal stress. This demonstrates that the shear failure of the soil around permeation grouting piles also obeys the Mohr-Coulomb criterion of failure, as shown in Equation (5).  
\[\tau_{t}=c+\sigma\tan\varphi \tag{5}\]  
where \(c\) is the cohesion of the soil around the piles, and \(\varphi\) is the friction angle.  
For muddy clay, the shear strength is significantly improved after adding cement slurry, and the maximum increase is at 5% cement content. However, the shear strength of silty clay is slightly less sensitive to cement. With the increase of cement content, the shear strength increases slightly, and the shear strength curves of different cement content are approximately parallel. The shear strength parameters of the soil around permeation grouting piles under different cement content are obtained by fitting curves (Figure 7), as shown in Table 5, in which \(c\) and \(\varphi\) represent cohesion and friction angle, respectively.  
The shear strength parameters of the soil around piles under permeation grouting are significantly higher than those of the non-grouted soil. Taking the muddy clay with 15% cement content as an example, its cohesion increased by 527.8%, and the friction angle increased by 226%. The cohesion of grouted muddy clay is greater than that of grouted silty clay at the same cement content. Concretely, the cohesion of the soil around the permeation grouting piles increases from 9.7 kPa to 60.91 kPa when the cement content is from 0 to 15%, and the friction angle increases from 5.0\({}^{\circ}\) to 16.3\({}^{\circ}\). Permeation grouting significantly increases the cohesion of the soil around piles and improves the shear strength.  
Figure 7: Shear strength of soil around the permeation grouting piles under different cement content. (a) Muddy clay. (b) Silty clay.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Cement Content**} & \multicolumn{4}{c}{**Muddy Clay**} & \multicolumn{4}{c}{**Silty Clay**} \\ \cline{2-7}  & \(\varphi\) (\({}^{\circ}\)) & \(c\) (\(\mathbf{kPa}\)) & \(\mathbf{R^{2}}\) & \(\varphi\) (\({}^{\circ}\)) & \(c\) (\(\mathbf{kPa}\)) & \(\mathbf{R^{2}}\) \\ \hline
0\% & 5.0 & 9.7 & 0.996 & 14.3 & 16.9 & 0.997 \\
5\% & 9.8 & 42.6 & 0.964 & 15.4 & 31.5 & 0.998 \\
10\% & 13.4 & 57.3 & 0.996 & 17.0 & 40.1 & 0.999 \\
15\% & 16.3 & 60.9 & 0.997 & 18.5 & 46.1 & 0.999 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Shear strength parameters of soil around piles with permeation grouting.
## 2 System Model
### Mutualism Scenario
#### 2.1.1 Energy Harvesting Protocol for Mutualism Scenario  
The total energy harvested by HT is given by the following equation:  
\[E_{\text{active}}^{1}=P_{\text{BS}}(1-z)|g_{\text{BS-HT}}|^{2}\beta_{1}+P_{1} \big{|}g_{\text{j-HT}}\big{|}^{2}(\beta_{1}+\beta_{2})+P_{\text{WIFI}}|g_{\text{ AW-HT}}|^{2}(\beta_{0}+\beta_{1}+\beta_{2}). \tag{7}\]  
The power of the signal sent by HT in active bit transmission is given as  
\[P_{\text{active}}^{1}=\frac{E_{\text{active}}^{1}+E_{\text{i}}-E_{\text{ss}}-E_ {\text{c}}^{1}}{\gamma\beta_{2}}. \tag{8}\]  
\(E_{\text{i}}\) is the initial energy at HT. The energy consumed during spectrum sensing is \(E_{\text{ss}}=P_{\text{ss}}\beta_{0}\). \(P_{\text{ss}}\) is the power consumed in spectrum sensing. The energy consumed in the circuit internal structure of the HT during \(\gamma\beta_{2}\) is given by \(E_{\text{c}}^{1}=P_{c}\gamma\beta_{2}\). Here, \(P_{c}\) represents the power consumed in the circuit.
# Improving the Energy Conversion Efficiency for Hydrokinetic Turbines Using MPPT Controller
###### Abstract  
The research presented in this paper involves the design of a power control system for a hydrokinetic turbine previously tested in real operating conditions. A maximum power point tracking (MPPT) algorithm was designed and simulated using the required parameters for a specific electric generator. The proposed system consists of a generator connected to the hydrokinetic turbine, a three-phase uncontrolled rectifier, a direct current (DC) boost converter with MPPT control to extract maximum available power, and a buck converter to control the amount of power delivered to the load. In order to test the MPPT algorithm, we built the individual blocks on the basis of the corresponding equations of each component. The algorithm considered the specific parameters of the previously tested turbine as input data and simulated the same water velocities for which the turbine had been tested. Thus, the simulation predicted a power output of 105 W for a water velocity of 1.33 m/s, 60 W for 1 m/s, and 30 W for 0.83 m/s. The efficiency of the control system was demonstrated when the instantaneous power value was maintained at a maximum point, regardless of the rotational speed according to the experimental power curves of the driving rotor obtained for certain water velocities.  
MPPT; hydrokinetic turbines; MPPT controller; energy efficiency; electric generator +
Footnote †: journal: Applied Sciences
## 4 Proposed Method
### Performing the Track-Touring Process
#### 4.2.3 Multiplier Factor (MF)  
MF is the neighborhood strategy of which the basic idea is to pull out the current solution from the local optimum by multiplying the current value in that position of the track by the learning multiplier factor. The new value in the track is obtained when multiplied by the multiplier using Equation (20):  
\[VT_{ih}^{LMF}=R_{ih}VT_{ih}^{N}, \tag{20}\]  
where \(R_{ih}\) is the random number that corresponds to position \(h\) of track \(i\), \(VT_{ih}^{MF}\) is the track after applying the MF strategy, and \(VT_{ih}^{N}\) is the track before applying the MF. An example of the MF method is shown in Table 6.  
\begin{table}
\begin{tabular}{|l|l|l|l|l|l|l|l|l|l|l|l|l|} \hline \multicolumn{1}{l}{**Type**} & \multicolumn{8}{c|}{**Job Element**} & \multicolumn{8}{c|}{**Machine Element**} \\ \hline
**Job** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **A** & **B** & **C** & **D** \\ \hline \(VT_{ih}^{N}\) & 0.58 & 0.49 & 0.52 & 0.5 & 0.6 & 0.98 & 0.24 & 0.72 & 0.26 & 0.09 & 0.18 & 0.88 & 0.26 \\ \hline \(R_{ih}\) & 0.20 & 0.31 & 0.12 & 0.67 & 0.12 & 0.35 & 0.75 & 0.95 & 0.66 & 0.18 & 0.56 & 0.70 & 0.03 \\ \hline \(VT_{ih}^{MF}\) & 0.12 & 0.15 & 0.06 & 0.33 & 0.07 & 0.34 & 0.18 & 0.68 & 0.17 & 0.02 & 0.10 & 0.62 & 0.01 \\ \hline \end{tabular}
\end{table}
Table 6: Example values of the MF method.  
Figure 1: Example of the track-transition method (TTM) procedure: (**a**) Tracks 1 and 2, and new random track; (**b**) Transition rate (TR); (**c**) Result of new transition.  
After the MF operation, \(VT_{ih}^{\text{MF}}\) uses the decoding method to obtain the solution for the proposed problem. The MF is iteratively applied to the track that selects this strategy. The predefined number of iterations was previously determined. In our method, 500 iterations were set as the stopping criterion. The MF algorithm is shown in Algorithm 6.  
```
Input: The value in track (\(VT_{ih}^{\text{N}}\)), number of jobs, number of machines Outputs: Track after (\(VT_{ih}^{\text{MF}}\)); Begin \(i=1\) While \(i\leq\) maximal number of iterations, Randomly generate the random track (\(R_{ih}\)) Multiply \(VT_{ih}^{\text{MF}}\) by \(R_{ih}\) and obtain \(VT_{ih}^{\text{MF}}\) Decode \(VT_{ih}^{\text{MF}}\) to obtain solution for the problem \(i=i+1\); end while End
```  
**Algorithm 6** Multiplier factor.  
Let \(S\) be the set of all feasible solutions and consider a solution \(Z_{ijt}\in S\). A neighborhood strategy associates each \(Z_{ijt}\in S\) with a neighborhood \(N_{k}\) (\(Z_{ijt}\)) \(\subseteq F\) of the solution \(Z_{ijt}\). For this paper, the three neighborhood structures are SDR, TTM and MF. The time complexity of neighborhoods (\(N_{1}(Z_{ijt})\), \(N_{2}(Z_{ijt})\) and \(N_{3}(Z_{ijt})\), respectively) are determined both by its respective structure and by the solution it is being applied to. The size of neighborhood \(N_{1}(Z_{ijt})\) is \(O(m\cdot n)\), neighborhood \(N_{2}(Z_{ijt})\) is \(O(m+n)\) and neighborhood \(N_{3}(Z_{ijt})\) is \(O(m+n)\).