esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Edge Computing and Related Computing Paradigms
### Edge Computing
#### 2.4.1 Cloudlet Computing  
The first edge computing concept bringing computation/storage closer to user equipment (UE)s was proposed in 2009 by Carnegie Mellon University [43, 46]. Cloudlet is sometimes referred to as micro data center (MDC) in some studies [47]. The idea behind the cloudlet is placing a computer or a cluster of computers with trusted high resources and computation power and a strong connection to the Internet at strategic locations close to edge devices to provide both computation resources and storage for the UEs in the vicinity [46]. Cloudlets are small-scale data centers (miniature clouds) that are usually located one hop from mobile devices. The purpose of cloudlet computing is offloading computation from mobile devices to virtual machine (VM)-based cloudlets placed on the edge of the network and in the local area [48].  
Cloudlet is a small cloud in close proximity to mobile devices. Cloud service providers who have a plan to offer accessible services in the proximity of mobile devices can be operators of cloudlet computing. Cloudlets can be enabled by network infrastructure owners, such as AT&T, Nokia, etc., with virtualization capacity placed in close proximity to mobile devices, with small-scale hardware sizes in contrast to huge data centers in cloud computing. Computing resources in cloudlets are more moderate because of the small sizes of cloudlets, but latency and energy consumption are lower compared with cloud computing.  
A disadvantage of cloudlets is that they are predominantly accessible through Wi-Fi connection, and mobile UEs have to switch between Wi-Fi and the mobile network to use cloudlet services [4]. In addition, cloudlets are not an inherent part of the mobile network, and Wi-Fi only has local coverage with limited mobility support, so it is hard to attain good quality of service (QoS) for mobile UEs, the same as in the case of MCC.  
Mobile cloudlets are a similar concept to cloudlets. In mobile cloudlets, cloudlets are a group of close mobile devices connected wirelessly, for instance, using Wi-Fi or Bluetooth [49]; mobile devices here can be providers or clients of computing services.
## 4 Results and Discussions
### Analysis of Variance--ANOVA  
In order to quantitatively conclude the impact of each parameters and their interactions on the responses, Analysis of Variance is necessary. Table 5 reveals the Analysis of Variance in case of \(u_{2}\) response. It is observed that F-values of some parameters of A, F, K, E, L, G, AK, AL, EK, AE, KL, H, EL, AF, AJ, and JL exhibit the F-value higher than 50, and it can be concluded that these parameters have static significance. The R-square value in this case is high when the lowest R-square approaches 92%. In a similar way, we can also identify the high F-value of parameters in case of the \(u_{3}\) response, such as K, L, G, KL, F, and J. the lowest value of R-square reaches 92%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Term** & **Effect** & **Coef** & **SE Coef** & **T-Value** & \(p\)**-Value** & **VIF** \\ \hline Constant & & 4.2002 & 0.0224 & 187.27 & 0.000 & \\ Xba1 & 0.0797 & 0.0398 & 0.0224 & 1.78 & 0.078 & 1.00 \\ Xba3 & 0.0600 & 0.0300 & 0.0224 & 1.34 & 0.184 & 1.00 \\ AS1 & 0.0656 & 0.0328 & 0.0224 & 1.46 & 0.146 & 1.00 \\ AS2 & \(-0.3956\) & \(-0.1978\) & 0.0224 & \(-8.82\) & 0.000 & 1.00 \\ AS3 & 0.6103 & 0.3052 & 0.0224 & 13.61 & 0.000 & 1.00 \\ Tout & 0.0881 & 0.0441 & 0.0224 & 1.96 & 0.052 & 1.00 \\ Cgh & \(-0.2981\) & \(-0.1491\) & 0.0224 & \(-6.65\) & 0.000 & 1.00 \\ Cg & \(-1.1550\) & \(-0.5775\) & 0.0224 & \(-25.75\) & 0.000 & 1.00 \\ Cs & 1.0922 & 0.5461 & 0.0224 & 24.35 & 0.000 & 1.00 \\ Xba1*Tout & \(-0.1294\) & \(-0.0647\) & 0.0224 & \(-2.88\) & 0.005 & 1.00 \\ Xba1*Cs & 0.1228 & 0.0614 & 0.0224 & 2.74 & 0.007 & 1.00 \\ Xba3*AS3 & \(-0.1031\) & \(-0.0516\) & 0.0224 & \(-2.30\) & 0.023 & 1.00 \\ AS1*AS3 & \(-0.1294\) & \(-0.0647\) & 0.0224 & \(-2.88\) & 0.005 & 1.00 \\ AS1*Cg & 0.1284 & 0.0642 & 0.0224 & 2.86 & 0.005 & 1.00 \\ AS3*Cg & \(-0.1294\) & \(-0.0647\) & 0.0224 & \(-2.88\) & 0.005 & 1.00 \\ Cgh*Cg & 0.2016 & 0.1008 & 0.0224 & 4.49 & 0.000 & 1.00 \\ Cg*Cs & \(-0.4819\) & \(-0.2409\) & 0.0224 & \(-10.74\) & 0.000 & 1.00 \\ \hline \multicolumn{6}{c}{Coded Coefficients.} \\ \hline
**S** & & **R-sq** & **R-sq(adj)** & **R-sq(pred)** \\ \hline
0.253752 & 94.10\% & 93.19\% & 92.02\% \\ \hline \hline \multicolumn{6}{c}{Model Summary.} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Estimated effects and coefficients for \(u_{3}\).
## 4 Shear Strength Prediction Using Neural Hybrids of SFLA-ANN and WDO-ANN
### Accuracy Assessment Criteria  
Apart from the RMSE, MAE was also used to measure the error performance. Moreover, the coefficient of determination (\(R^{2}\)) was used to measure the correlation between the observed and estimated shear strengths. Let \(Z_{i\text{ {predicted}}}\) and \(Z_{i\text{ {observed}}}\) be the estimated and observed shear strengths, and let \(\overline{Z}_{\text{ {observed}}}\) be the average of \(Z_{i\text{ {observed}}}\) values; then, Equations (14)-(16) express the formulations of the RMSE, MAE, and \(R^{2}\), respectively.  
\[RMSE=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}\left[\left(Z_{i\text{ {observed}}}-Z_{i\text{ {predicted}}}\right)\right]^{2}}\,, \tag{14}\]  
\[MAE=\frac{1}{N}\sum\limits_{i=1}^{N}\lvert Z_{i\text{ {observed}}}-Z_{i\text{ {predicted}}}\rvert, \tag{15}\]  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{N}\left(Z_{i\text{ {predicted}}}-Z_{i\text{ {observed}}}\right)^{2}}{\sum\limits_{i=1}^{N}\left(Z_{i\text{ {observed}}}-\overline{Z}_{\text{ {observed}}}\right)^{2}}\,, \tag{16}\]  
where \(N\) is the number of data.  
Figure 5: (**a**) The results of the executed sensitivity analysis; (**b**,**c**) the convergence curves of the elite SFLA-artificial neural network (ANN) and wind-driven optimization (WDO)–ANN, respectively.
# A Numerical Study on Computational Time Reversal for Structural Health Monitoring
## 4 Applications with Numerical Examples
### Optimal Placement of Sensors
#### 4.3.1 One-Dimensional Beam  
For the one-dimensional beam case, we studied sets of receivers chosen from a maximum number of 12 possible equidistant receivers. Starting from a single receiver, then the number is gradually increased by one, randomly chosen by the set from the fixed possible 12 sensors. Results for the SNR are shown in Figure 7 where it is obvious that an increasing number of receivers SNR is improved (increased). The plots indicate an almost linear relation with respect to the receivers' number with deviations that occurred because of ghost superposition or cancellation. For the case of axial motion, the case of the left plot in  
Figure 6: Imaging using the norm for axial (**left column**) and transverse (**right column**) displacement (m) DOFs; the green line is the location of receiver while red line is the location of the source.  
Figure 7a, the factor of the linear evolution with the number of receivers is that of two as it was expected [36], while in the case of transverse and rotational motion depicted in the plot of Figure 7b, the corresponding coefficient is smaller.
# Cellulose Recovery from Agri-Food Residues by Effective Cavitational Treatments
## 5 Conclusions  
The high crystallinity of cellulose and its sheathing by hemicellulose-lignin matrix provide the resilient structure of lignocellulosic biomass. Hence, pretreatment is a pivotal step for conversion of agri-food waste biomass to sugars and further valuable bioproducts and biofuels. Extensive research for biomass valorisation has been carried out experimenting with acoustic and hydrodynamic cavitation. This survey shows the effectiveness of cavitation as pretreatment technique for cellulose recovery owing to its potential to simplify the disruption of recalcitrant lignocellulosic matrix. US and HC enhance the mass transfer of the pretreatment process and could enhance the accessibility of lignocellulosic biomass through microjet erosion, cell wall disruption suitable for cellulose recovery.  
Despite the promising results reported in lab scale pretreatment for cellulose recovery, US and HC remain underexploited so far. In this innovative field a relevant technical breakthrough is expected in the near future. Nowadays, cavitation activity still strongly depends on the device used, and it is difficult to correlate results obtained with different instrumentation and biomass sources. The efficient design of dedicated cavitational reactors will certainly boost the US and HC application for biomass pretreatment. Cavitation can be described as a complex nonlinear phenomenon, which means that quantitative estimation is a challenging aspect. New capital investments for the scaling up design also require a careful overall economic evaluation (both CAPEX and OPEX).  
Following the recent advances in large-scale cavitation technology, we can highlight the easier applicability of HC-based methods that may represent the solution of choice for the efficient pretreatment of agri-foods and forestry waste toward cellulose recovery.  
This work was supported by the University of Turin (Ricerca locale 2020) and by the Ministry of Science and Higher Education of the Russian Federation within the framework of state support for the creation and development of a World-Class Research Centers \"Digital Biodesign and Personalized Healthcare\".075-15-2020-926.  
The University of Turin and the Ministry of Science and Higher Education of the Russian Federation are acknowledged for the financial support.  
The authors declare no conflict of interest.  
Figure 18: HC rotor stator devices for flowtrought biomass pretreatment in kg scale (max. flow rate 30,000 L/h). E-PIC s.r.l. [115].
# A Novel Method of Seismic Signal Detection Using Waveform Features
## 2 Theory and Methods
### Optimizing Hyper-Parameters  
The hyperparameters in the Equation (11) are the length-scale \(l\), the signal variance \(\sigma_{f}^{2}\), and the noise variance \(\sigma_{n}^{2}\), which are unknown and need to be inferred from the observations. Obtaining the hyper-parameters is maximizing the marginal (log) likelihood through a optimization tool of gradient ascent. Using the training set \(D=\{(X_{i},y_{i})|i=1,2,...,n\}\) and the hyper-parameters vector \(\theta\) (\(\theta=(l,\sigma_{f}^{2},\sigma_{n}^{2})\)), the log marginal likelihood is  
\[\begin{split}\text{L}(\theta)&=\text{log}p(y|X, \theta)\\ &=-\frac{1}{2}y^{T}K_{y}^{-1}y-\frac{1}{2}\text{log}|K_{y}|-\frac{ n}{2}\text{log}2\pi,\end{split} \tag{14}\]  
with \(K_{y}=K(X,X)+\sigma_{n}^{2}I\). The equation is composed of three terms, denoting a fit penalizing measure. The term \(-\frac{1}{2}y^{T}K_{y}^{-1}y\) shows degree of data-fit. The term \(-\frac{1}{2}\text{log}|K_{y}|\) is related to the kernel and observation inputs, displaying the complexity penalty. The term \(-\frac{n}{2}\text{log}2\pi\) is a normalization constant.  
The partial derivative of \(\theta\) plays a vital role in seeking the optional value of gradient ascent, which is obtained by the Equation (14),  
\[\begin{split}\frac{\partial}{\partial\theta_{j}}\text{log}p(y|X, \theta)&=\frac{1}{2}y^{T}K_{y}^{-1}y-\frac{1}{2}\text{tr}\left(K_ {y}^{-1}\frac{\partial K_{y}}{\partial\theta_{j}}\right)\\ &=\frac{1}{2}\text{tr}\left((ww^{T}-K_{y}^{-1})\frac{\partial K_ {y}}{\partial\theta_{j}}\right).\end{split} \tag{15}\]
# A Visual Odometry Pipeline for Real-Time UAS Geopositioning
## 4 Experiments
### Evaluation  
To assess the results qualitatively, we show the predicted UAS trajectories based on the pseudo-perspective projection under different scenarios. The yellow trajectories represent the predicted trajectory compared with the red ones generated from reference GPS sensor readings. Quantitative accuracy and the details of the flight plans from the dataset are given in Table 4. Each flight plan is provided in a column with the information given in the first four rows: traversed distance, initial attitude (orientation) and altitude, and the altitude change for each plan. This is followed by two additional blocks of four rows detailing the geopositioning accuracy with the minimum, maximum, mean absolute error (MAE), and the standard deviation of the predicted horizontal and vertical geoposition against UAS reference geoposition during the flight. Due to the limited reference sensor geopositioning accuracy mentioned in Section 4.1, we reported all quantitative results except the standard deviation in decimeters. For all the scenarios, the MAE for the proposed approach is below 7.1 m horizontally and below 4.5 m vertically, and is within the uncertainty of the onboard GPS sensor positioning spec  
Figure 10: Example images from the dataset containing flight plans for scenarios including residential, university campus, surface parking, and farmland.
# Performance and Scalability Analysis of SDN-Based Large-Scale Wi-Fi Networks
## 3 Background Knowledge
### OpenFlow  
The \"Open Network Foundation\" developed the communication protocol known as \"OpenFlow\". It grants access to a network's switches and routers' data planes. The southbound API, known as OpenFlow, is in charge of enabling communication between the controller and other network devices. The OpenFlow controller, which regulates network switch behavior, makes use of the OpenFlow protocol. Ports, OpenFlow channels, and OpenFlow tables make up OpenFlow switches. Through an OpenFlow channel, the link between the controller and switch is made [38]. Figure 1 illustrates the differences between an SDN switch and a conventional switch.  
According to the OSI model, the data connection layer is in charge of the operation of the switch. In an environment based on SDN, the job of switches is to communicate data from one place to another. Within a network, switches have the ability to communicate data to a large number of other network devices. A switch has a database with a name-forwarding table that stores the MAC addresses of other network devices. This table is accessible through the switch's web interface. The \"Media Access Control\" (MAC) address of a device is equivalent to its hardware address [7].  
OpenFlow is a protocol that is implemented by SDN controllers so that they can connect with network nodes. The process of forwarding data through an OpenFlow switch is determined by \"flows\", which are sequences of packets that conform to a set of predefined rules [10].
## 4 Results and Discussion
### Two-Class Classification  
We also calculated the accuracy, receiver operating characteristic curve (ROC), and area under curve (AUC) in two-class classifications: BAPL3 + BAPL2 versus BAPL1 and BAPL3 versus BAPL1 + BAPL2. In the first case, both BAPL3 and BAPL2 were treated as positive, whereas in the second case, only BAPL3 was treated as positive. The ROC curves and AUCs of BAPL3 + BAPL2 versus BAPL1 and BAPL3 versus BAPL1 + BAPL2 are shown in Figure 6, and the two-class classification accuracies are given in Table 7. Both the AUCs and accuracies of cases 1 and 2 were significantly higher for the proposed joint loss function (\(\lambda=0.0025\)) than for the conventional loss function (\(\lambda=0.00\)).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Method** & & **Precision** & **Recall** & **F1 Score** & **Accuracy** \\ \hline \multirow{6}{*}{VGG19} & \multirow{3}{*}{\(\lambda=0\)} & BAPL1 & 88.16\% & 91.60\% & 89.85\% & \multirow{3}{*}{81.50\%} \\ \cline{3-5}  & & BAPL2 & 40.25\% & 17.50\% & 24.39\% & \\ \cline{3-5}  & & BAPL3 & 78.52\% & 89.40\% & 83.61\% & \\ \cline{3-5}  & & BAPL1 & 87.96\% & 93.20\% & 90.50\% & \\ \cline{3-5}  & & BAPL2 & 100\% & 51.50\% & 67.99\% & \\ \cline{3-5}  & & BAPL3 & 87.51\% & 94.90\% & 91.06\% & \\ \hline \multirow{6}{*}{ResNet50} & \multirow{3}{*}{\(\lambda=0\)} & BAPL1 & 92\% & 92.60\% & 92.40\% & \multirow{3}{*}{88.90\%} \\ \cline{3-5}  & & BAPL2 & 73.70\% & 40.20\% & 52.02\% & \\ \cline{3-5}  & & BAPL3 & 81.90\% & 94.10\% & 87.58\% & \\ \cline{3-5}  & & BAPL1 & 93.63\% & 92.00\% & 92.81\% & \\ \cline{3-5}  & & BAPL2 & 67.80\% & 62.10\% & 64.82\% & \\ \cline{3-5}  & & BAPL3 & 89.80\% & 92.00\% & 90.89\% & \\ \hline \multirow{6}{*}{DenseNet121} & \multirow{3}{*}{\(\lambda=0\)} & BAPL1 & 97.38\% & 88.30\% & 92.62\% & \multirow{3}{*}{88.06\%} \\ \cline{3-5}  & & BAPL2 & 67.79\% & 53.00\% & 59.49\% & \\ \cline{3-5}  & & BAPL3 & 89.76\% & 99.40\% & 94.33\% & \\ \cline{3-5}  & & BAPL1 & 100\% & 97.70\% & 98.84\% & \\ \cline{3-5}  & & BAPL2 & 85.32\% & 72.70\% & 78.51\% & \\ \cline{3-5}  & & BAPL3 & 91.57\% & 98.90\% & 95.09\% & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of classification performances between the proposed joint loss function and the conventional loss function in the three network architectures.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **BAPL1 versus (BAPL2 + BAPL3)** & **(BAPL1 + BAPL2) versus BAPL3** \\ \hline \(\lambda=0\) & 90.91\% & 92.42\% \\ \hline \(\lambda=0.0025\) & 96.97\% & 95.55\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of two-class classification accuracies.
# Copper Nanowires for Transparent Electrodes: Properties, Challenges and Applications
###### Abstract  
Transparent electrodes are essential elements of devices bearing a screen or display, as well as solar cells, LEDs etc. To overcome the drawbacks presented by indium tin oxide, nanomaterials have been proposed for a long time as alternatives. Metal nanowires are particularly interesting for their high intrinsic electrical conductivity. Copper nanowires have attracted wide interest due to the low cost and high abundancy of the starting material. However, they are easily oxidized thus suitable strategies must be devised to prevent it. This review discusses the fundamental properties and challenges of copper nanowires, focusing on the efforts made to make them longer and thinner then the strategies to prevent oxidation and to join them in the network are presented. After that, mechanical properties are summarized and applications are presented, before conclusions and perspectives are finally given.  
copper nanowires; transparent electrodes; mechanical properties; welding; oxidation +
Footnote †: journal: Applied Sciences  
12 August 2021  
Accepted: 26 August 2021  
Subject: 30 August 2021  
as oxidation over time under ambient conditions, which hinder the conductivity and the stability. Common methods, like annealing, were proposed but had low compatibility with processing, for example in flexible electronics.  
While other reviews have been published on the topic of Cu NW based transparent electrodes [21; 22], Ref [21] while quite comprehensive is already five years old and Ref. [22] only focusses on applications. While the Cu NW synthesis is not the main scope of this review, synthetic strategies used for the fabrication of transparent conductors, especially those presenting an advantage in terms of electrical and optical properties will be briefly covered. More comprehensive reviews on the synthesis of Cu NWs can be found in Refs. [23; 24]. This paper aims at giving a comprehensive overview on the topic including the most recent results, and is structured as follows:  
* Electrical and optical properties of Cu NWs
* Oxidation prevention
* Joining of nanowires
* Mechanical properties
* Applications of copper nanowires
* Conclusions and perspectives
## 3 Results and Discussion
### Correlation between [Ni]/[DOC] Ratios and Optical Indicators  
Table 3 shows the correlation coefficients between [Ni]/[DOC] ratios and selected indicators for AEOM and DOM solutions, respectively. The ratios of ([Ni]/[DOC])\({}_{\text{AEOM}}\) had a significant correlation with the indicators SUVA\({}_{254}\), FI, and BIX (\(p\) < 0.001). However, ([Ni]/[DOC])\({}_{\text{DOM}}\) ratios had a weak correlation with the optical indicators. [PERSON] et al. [12] studied natural and anthropogenically-influenced DOM water. The DOM tests demonstrated that the SUVA\({}_{254}\) values of the natural water DOM had a significant positive correlation with the ratios of [Ni]/[DOC]; however, the anthropogenic-affected DOM SUVA\({}_{254}\) values had no correlation with the [Ni]/[DOC] ratios. In the soil AEOM solutions [22],  
the LMW ([Ni]/[DOC])\({}_{\text{AECM}}\) had a positive correlation with the SUVA\({}_{\text{254}}\) values and molecular weight index (S\({}_{\text{R}}\)). The HMW ([Ni]/[DOC])\({}_{\text{AECM}}\) was insignificantly correlated with the AEOM indicators due to complex composition of HMW AEOM.  
The ratios of ([Ni]/[DOC])\({}_{\text{DOM}}\) were insignificantly correlated with the indicators, which implied a complex composition of DOM, with aromatic content (SUVA\({}_{\text{254}}\)); sources of DOM (FI and BIX) cannot predict the Ni and DOM binding affinity. The indicators had a strong correlation with ([Ni]/[DOC])\({}_{\text{AECM}}\) ratios, which suggested the AEOM chemical properties have the potential to represent the binding affinity of Ni. One investigation showed that the exchange conceptual model assumed that the POM  
Figure 3: Optical indicators of size-fractionated DOM and AEOM: SUVA\({}_{\text{254}}\) (**a**), fluorescence index (FI) (**b**), and Biological index (BIX) (**c**).  
in the sediment underwent hydrolysis and oxidation cleavage started from low molecular weight POM [2; 5]. The decomposed POM dissolved into the liquid phase becoming DOM. The exchange between DOM and POM in sediment may result in Ni and OC being distributed in the LMW fractions and the LMW DOM had high [Ni]/[DOC] ratios. The optical indicators were poor correlated with ([Ni]/[DOC])\({}_{\text{DOM}}\) ratios. However, the LMW POM had decomposed, which resulted in the Ni and OC being predominantly distributed in the HMW AEOM. The HMW AEOM had a higher [Ni]/[DOC] ratio than LMW AEOM. In addition, the hydrolyzed POM yield had stable chemical properties of AEOM solution. The optical indicators of AEOM had a strong correlation with each other as well as with the([Ni]/[DOC])\({}_{\text{AEOM}}\) ratios.
## 7 Results
### Attestation Token Size
In Figure 7, we show how the token size varies with the number of encoder layers for the evaluated AE architecture. Depending on the model architecture, this trend can accelerate further. For instance, an LSTM layer becomes \(2.2\times\) smaller when integer-encoded claim keys are used instead of string-encoded claim keys. This demonstrates that the impact \begin{table}
\begin{tabular}{l c c} \hline \hline
**Layer** & **String-Encoded Claim Keys** & **Integer-Encoded Claim Keys** \\ \hline Activation & 158 & 95 \\ Add & 125 & 70 \\ BatchNormalization & 660 & 327 \\ Conv2D & 544 & 243 \\ Dense & 427 & 207 \\ Dropout & 166 & 98 \\ Flatten & 159 & 95 \\ LSTM & 801 & 358 \\ MaxPooling2D & 210 & 117 \\ Multiply & 135 & 80 \\ Input & 89 & 49 \\ ReLU & 182 & 101 \\ Softmax & 139 & 82 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Size comparison (bytes) of integer-encoded and string-encoded claim keys for different Keras-generated layers. of the encoding method on token size can be more pronunced for certain layers, highlighting the importance of encoding choices in optimizing token sizes.