esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 6G and Dependability
### Dependability in 6G Networks
#### 4.2.1 Reliability  
Sixth-generation networks are expected to offer extremely high reliability. EdgeAI supports the vision of 6G through offering more computational power near users or services while reducing overall latency. Reliability requires checking the necessary requirements instead of assuming that these are fulfilled and constantly monitoring the network [52]. Although in terms of performance, EdgeAI supposes a step forward, its distributed nature, combined with the high number of servers required, might well introduce other issues. First, we have asynchronism. As the number of edge servers rises, they are also expected to be capable of working in unison; this means being synchronized. Synchronization is improved when servers are aware of the status of neighboring servers; in other words, the exchange of information, such as available memory or processing power, is shared in a timely manner.  
Another issue is the heterogeneity of software and hardware at the nodes. Although it brings benefits in the long run, the adoption of heterogeneous solutions might also pose challenges. As an example, heterogeneous EdgeAI servers might have different power consumption and performance levels due to non-identical CPU architectures. In the same manner, distinct feature support could hinder synchronism. Scalability could also be a problem for networks, as it increases the complexity of management, and it might also create issues with synchronism. As 6G networks will be highly scalable, fault tolerance is also important in order to ensure reliability. As a system scales to be hundreds of nodes in size, a fault tolerant system will enable the operations or services to continue at a reduced level, though not stopping completely.
# Overrelaxed Sinkhorn-Knopp Algorithm for Regularized Optimal Transport
## 3 Regularized Nonlinear Acceleration of the Sinkhorn-Knopp Algorithm
### Discussion  
The nonlinear regularized acceleration algorithm involves relevant numerical accelerations without convergence guarantees. To build an algorithm that ensures the convergence of iterates, but also improves the numerical behavior of the SK algorithm, we now propose to follow a different approach using Lyapunov sequences, which is a classical tool to study optimization algorithms. The new scheme proposed here uses the specific form of the SK algorithm with the two variables \(\alpha\) and \(\beta\). It performs two successive overrelaxations (SOR) at each step, one for the update of \(\alpha\) and one for \(\beta\). The algorithm does not use any mixing scheme, but the simple structure allows defining a sequence, called a Lyapunov sequence, which decreases at each step. This Lyapunov approach allows ensuring the convergence of the algorithm for a suitable choice of the overrelaxation parameter.  
Figure 2: Comparison of RNA schemes on an optimal transport problem regularized with the entropic parameters \(\varepsilon=0.01\) (**top line**) and \(\varepsilon=0.003\) (**bottom line**). The convergence of RNA scheme \(N\in\{1,2,8\}\) is illustrated for different relaxation parameters \(\omega\in\{0.5,1,1.5,1.9\}\). Higher values of \(\omega\) lead to larger improvements in the case \(N=1\) (first column). When \(N>1\) (as in the middle column for \(N=2\) and the right column for \(N=8\)), it is not possible to conclude and to suggest a choice for the parameter \(\omega\) with the obtained numerical results.  
The algorithm can be summarized as follows:  
\[\alpha^{\ell+1} =(1-\omega)\alpha^{\ell}+\omega\operatorname*{argmax}_{\alpha}E( \alpha,\beta^{\ell}) \tag{18}\] \[\beta^{\ell+1} =(1-\omega)\beta^{\ell}+\omega\operatorname*{argmax}_{\beta}E( \alpha^{\ell+1},\beta). \tag{19}\]  
Our convergence analysis relied on an online adaptation of the overrelaxation parameter \(\omega\in[1,2)\). As illustrated by Figure 3, in the case for \(\varepsilon=0.01\), the proposed SK-SOR method was not as performant as high RNA orders \(N>1\) with \(\omega=1.5\). It nevertheless gave an important improvement with respect to the original SK method, while being provably convergent.
## 4 Results Discussion
### The Dimension of Melt Pool from Experiment, FE and Analytical Solution
\[E=\frac{P}{v} \tag{14}\]  
It can be seen from Figure 5 that the Rosenthal equation calculation results and the FE simulation results have large errors with the experimental results at higher heat input. Therefore, these methods are obviously not suitable for predicting the situation when the laser power is too large or the scanning speed is lower. However, by the analytical method with temperature-dependent absorptivity, the prediction accuracy in this area is obviously higher. Figure 5: The dimension of melt pool of In718 by SLM processing at high heat input from experiment by [PERSON] [12], FE modeling and analytical solution (equation from this paper and Rosenthal equation). It can be seen from Figure 6 that, as the laser scanning speed increases, the length of the melt pool increases from 0.336 mm at 600 mm/s to 0.476 mm at 2500 mm/s. However, the depth and width of the melt pool are reduced. The width of the melt pool is reduced from 0.14 mm to 0.084 mm and the depth of the melt pool is reduced from 0.06 mm to 0.028 mm. It can be seen that when the laser power is constant, due to the increase of the laser scanning speed, the action time of the laser at one point becomes smaller, which causes the dimension of the melt pool to decrease. [PERSON] [24] proposed that presintering can be used to minimize the effect of sputter. The preheating of the substrate will increase \(T_{0}\) in Equation (1). Based on the calculation results, it can be found that pre-sintering can increase the size of the molten pool and more easily cause the existence of keyhole defects. Therefore, in order to improve the quality of additive manufacturing parts, the temperature of the substrate can be calculated based on this model. Figure 6: Melt pool depth and length of In718 by SLM processing at different scanning speed from experiment, FE modeling and analytical solution (laser power is 200 W). (**a**) Depth of melt pool, (**b**) length of melt pool.
# Lithium Battery SoC Estimation Based on Improved Iterated Extended Kalman Filter
## 3 IEKF Algorithm Based on LM
### The Procedure of the IEKF Algorithm Based on LM Optimization
(5) Calculate the Kalman gain matrix:  
\[K_{k}^{(i)}=\frac{\overset{\cdot(i)}{P_{k|k-1}}H_{k}^{\mathrm{T}}}{\overset{ \cdot(i)}{\cdot(i)}} \tag{21}\]  
(6) Calculate the posterior estimate and update the error covariance matrix:  
\[\overset{\cdot(i)}{x_{k|k}}=\overset{\cdot(i)}{x_{k|k-1}}+K_{k}^{(i)}\left[z _{k}-h(\overset{\cdot(i)}{x_{k|k-1}},u_{k})\right] \tag{22}\]  
\[P_{k|k}^{(i)}=\left(I-K_{k}^{(i)}H_{k}\right)\overset{\cdot(i)}{P_{k|k-1}} \tag{23}\]  
Define the cost function as follows:  
\[q_{k}^{(i)}=\frac{1}{2}\left(z_{k}-h(\overset{\cdot(i)}{x_{k|k-1}},u_{k}) \right)^{\mathrm{T}}\cdot R_{k}^{-1}\left(z_{k}-h(\overset{\cdot(i)}{x_{k|k- 1}},u_{k})\right) \tag{24}\]  
By calculating the cost function results after two adjacent iterations, it can be judged whether the LM algorithm has a positive effect on the estimation results in the iteration. When \(q_{k}^{(i)}<q_{k}^{(i-1)}\), it means that the estimation result is convergent, and the direction of iteration is correct. We recorded the calculation result and set \(\alpha=0.5\alpha\) to continue the iterated calculation. On the contrary, it is proved that the direction of iteration is opposite to the trend of convergence. We let \(\alpha=4\alpha\) continue to iterate. Define the stopping condition as follows:  
\[\frac{\overset{\cdot(i)}{\overset{\cdot(i)}{x_{k|k}}-\overset{\cdot(i-1)} {x_{k|k}}}\|}{\overset{\cdot(i-1)}{x_{k|k}}}<\varepsilon \tag{25}\]The parameter \(\varepsilon\) represents the error between the results of the two iterations. If the value is less than a certain limit, the result of the iteration can be considered to have converged to the extreme point, and the iteration can be terminated. In this paper, the value of \(\varepsilon\) is set to 0.00001. If the condition of Equation (25) is not satisfied under the limited number of iterations, the iteration will stop the IKEF iteration process after the set number of iterations is reached. The maximum number of iterations is set to 20 in this paper. The flow of the LM-IEKF algorithm based on LM optimization is shown in Figure 4.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### ICT for in-Home and Building Energy Management
#### 4.3.2 E2 SoHo  
In the E2 SoHo project the energy consumption reduction in social housing has been examined by implementing an ICT solution for energy management. In addition, the project dealt with interoperability issues between smart meters from different vendors. The ICT system has been composed of a communications and data processing platform, a user interface for tenants and a user interface for building managers. This solution can be integrated with smart metering systems that are used throughout Europe. From the technological point of view, the communication between the sensors and the data loggers is done through wired (RS485) or wireless (Zigbee) solutions. Afterwards, the data logger has been connected wired or wirelessly to a router, which communicated the data to a server through TCP/IP [82].
## 6 Results  
### Classification Performance  
To evaluate sports images classification results objectively, we compared the proposed CNN-TL-DE method with the CNN method for training the CNN model in an conventional manner and CNN-TL method that performs transfer learning upon the same CNN architecture. For the conventional CNN method, we used the VGG19 [52] CNN architecture and trained the models from scratch. For both the CNN-TL and CNN-TL-DE methods, we applied the transfer learning approach on the same VGG19 CNN architecture, but pre-trained on the ImageNet dataset [53]; however, in the CNN-TL-DE method, the hyper-parameters were automatically optimized using the described DE approach. In this way, the classification performance results depend only on different training methods used. The results were obtained through the conducted experiments on the sports images dataset using the 10-fold cross-validation.  
The obtained classification performance results are summarized in Table 4. Our proposed CNN-TL-DE method achieved the best scores by a significant margin in six performance metrics (accuracy, AUNP, precision, recall, \(F\)-1 score, and Cohen's kappa), while lagging behind in terms of consumed time and number of epochs. From the compared two methods, the CNN-TL method performed much better than the conventional CNN in all aspects but the number of consumed epochs, where the difference was not very dramatic.  
Figure 2(a) presents test accuracy results obtained by the three compared methods for all 10 folds on the sports images dataset. From 10 folds, the CNN-TL-DE method was the most accurate in eight cases and the CNN-TL in three cases (there was one tied win between CNN-TL and CNN-TL-DE), while the CNN method lag quite distinctively behind. The great advantage of the CNN-TL-DE method with regard to accuracy can be well seen on a violin plot of the comparison of the three methods  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **CNN** & **CNN-TL** & **CNN-TL-DE** \\ \hline max number of epochs & 100 & 100 & 5100 \\ batch size & 32 & 32 & 32 \\ learning rate & 1 \(\times 10^{-5}\) & \(1\times 10^{-5}\) & - \\ optimizer type & RMSprop & RMSprop & - \\ SpatialDropout2D probability & 0.5 & 0.5 & - \\ number of neurons in last FC & 1024 & 1024 & - \\ dropout probability & 0.5 & 0.5 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter settings for the performed experiments.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Dimension of the problem & 5 \\ Population size \(Np\) & 10 \\ Scale factor \(F\) & 0.5 \\ Crossover rate \(CR\) & 0.9 \\ Number of function evaluations & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Used parameter values for the DE optimization method.  
average of 0.7508. The CNN-TL method achieves on average fair to good agreement, while the CNN-method achieves on average a poor agreement (see Table 4).
# Layer-Wise Compressive Training for Convolutional Neural Networks
## 3 Compressive Training  
The compressive training technique is a modified version of the SGD algorithm illustrated in Section 2.1. The entire procedure is based on three sequential stages. Firstly, the CNN model undergoes a _preliminary training_. This is accomplished through a standard training procedure during which the weights are updated using the standard SGD algorithm. Once trained, the weights show a typical bell distribution as shown in the leftmost plot of Figure 2. Secondly, an amplitude-based _weights pruning_, as described in Section 2.2, is applied. This stage is performed iteratively in order to gradually achieve the desired amount of sparsity, i.e., the percentage of zero-weights. Every pruning iteration is followed by a short retraining phase thus to adjust the remaining connections. The central plot in Figure 2 illustrates a pruned weights distribution where almost half of the original weights are collapsed to zero. Thirdly, the \(\sigma\)-_constrained compression_ algorithm is applied; during this phase, weights are bounded layer-wise into the \((-\sigma,0,+\sigma)\) subspace; the weights distribution will then resemble the one depicted in the rightmost plot in Figure 2. It is worth noticing that each layer may have its own optimal \(\sigma\).
## 4 Performance Evaluation  
In this paper, we adopt Eclipse [29] to run the simulation with parameters listed in Table 3. This simulation is conducted on the road network (10.87 km \(\times\) 10.60 km) of Shanghai, China, which is shown in Figure 8, by randomly choosing several road segments to suffer communication link failures, i.e., the event, in the disaster. The _event percentage_ (\(EP\)) parameter in Table 3 is the ratio of road segments, denoted by two adjacent blue bullet points, suffered communication link failures in Figure 8. Hence, the number of events (\(N_{ev}=N_{rs}\times EP\)) in this simulation is equal to the product of the number of total road segments (\(N_{rs}\)) and the event percentage \(EP\). According to [25], \(\mu_{LOS}\) and \(\mu_{NLOS}\) for calculating the value of Equations (4) and (5) are 1 dB and 20 dB, respectively in an urban environment. Further, B and C in Equation (6) are set as 9.6 and 0.28, respectively. The following figures show average simulation results of five schemes on two metrics for twenty times. These five schemes are EAMUD with the Drop exception handling method (_EAMUD-D_), EAMUD with the Postpone method (_EAMUD-P_), EAUD with the Drop method (_EAUD-D_), EAUD with the Postpone method (_EAUD-P_) and the related work _USP_, respectively. Here, EAUD is a variant of EAMUD without performing the EBD algorithm in Step 5 when the number of UAVs needed for this sub-event is less than or equal to the number of available UAVs. Hence, EAUD is not able to check whether communication durations of UAVs already dispatched for all other sibling sub-events will overlap with the communication duration of the UAV dispatched for this sub-event. Consequently, it cannot achieve the high event communication time as EAMUD does. The following two metrics, i.e., the average ratio of event link communication time and its histogram, are depicted with respect to the UAV dispatch energy threshold, the number of UAVs and the event percentage. As mentioned in Section 2.3, if the UAV in the GCS has charged to an energy level, which is not less than the UAV dispatch energy threshold (\(E^{DTH}\)), it can be dispatched to serve a sub-event if needed As mentioned in Section 3, if each sub-event \(Ev_{j,i}\) of \(Ev_{j}\) at time \(t\) can be served by a dispatched UAV, the communication link of \(Ev_{j}\) at time \(t\) is said to be recovered. Then, the event link communication time of \(Ev_{j}\) is formulated by Equation (10) and its ratio of event link communication time is equal to \(\left(\sum\limits_{l=1}^{T_{ev}}\chi_{j}^{t}\right)/T_{ev}\). Further, the average ratio of event link communication time is defined as the average ratio value of all events in the simulation, which is formulated as follows:  
\[\frac{\sum_{j=1}^{N_{ev}}\left[\left(\sum_{t=1}^{T_{ev}}\chi_{j}^{t}\right)/T_ {ev}\right]}{N_{ev}} \tag{11}\]  
where \(T_{ev}\) is the event duration and \(N_{ev}\) (\(=N_{rs}\times EP\)) is the number of events.  
Figure 8: Shanghai map in the simulation.
## 3 Methods
### Principal Component Analysis  
PCA is a statistical technique used to better understand and analyze multivariate data. The primary goal of PCA is to explain the largest variance in a dataset by creating new variables, called principal components, which are linear combinations of the original variables [109]. PCA begins by calculating the covariance matrix of the variables in the dataset, which shows the relationships and shared variance between them. Using this covariance matrix, eigenvalues and eigenvectors are computed. Eigenvalues represent the variance of the principal components, while eigenvectors determine the direction of these components. The eigenvector with the largest eigenvalue defines the first principal component and explains the most variance in the dataset. The eigenvector with the second-largest eigenvalue defines the second principal component, explaining less variance and being orthogonal to the first component. This process continues until as many principal components as the number of variables are created. Since the principal components are linear combinations of the original variables, understanding the relationship between these components can help reveal structural features and hidden patterns in the dataset. However, PCA has some limitations. For instance, it only considers linear relationships and may require data normalization [110]. Additionally, interpreting the principal components can be challenging because they are complex combinations of the original variables.
# The Infestation of Olive Fruits by _Bactrocera oleae_ ([PERSON])
## 3 Materials and Methods
### Extraction and Analysis of VOO Volatile Compounds  
Olive oil samples (0.5 g of oil in 10 mL vials) were placed in a vial heater at 40 \({}^{\circ}\)C and after a 10 min equilibration, volatile compounds were adsorbed onto a SPME fiber DVB/Carboxen/PDMS 50/30 \(\upmu\)m (Supelco Co., Bellefonte, PA, USA) for 50 min. Desorption of volatile compounds trapped in the SPME fiber was performed directly into the GC injector. Identification of volatile compounds was carried out on a 7820A/GC-5975/MSD system (Agilent Technologies, Santa Clara, CA, USA) equipped with a DB-Wax capillary column (60 m \(\times\) 0.25 mm i.d., film thickness, 0.25 \(\upmu\)m, J&W Scientific, Folsom, CA, USA). Quantitation of VOO volatile compounds was carried out on a HP-6890 gas chromatography apparatus (Agilent Technologies, Santa Clara, CA, USA), equipped with the same column and operated under similar conditions (three analyses per sample). Details on the operating conditions used for both systems 7820A/GC-5975/MSD and HP-6890, as well as the quantification method have been fully described in a previous paper [48]. The volatile compounds were individually quantitated and also clustered into different groups and subgroups according to their origin in the LOX pathway branch (C6 and C5 compounds) from LA and LNA, as well as terpen, and branched-chain (BC) volatile compounds from amino acid metabolism:  
* C6/LnA aldehydes: (\(E\))-hex-2-enal, (\(Z\))-hex-3-enal, (\(Z\))-hex-2-enal, (\(E\))-hex-3-enal.
* C6/LnA alcohols: (\(E\))-hex-2-enol, (\(Z\))-hex-3-enol, (\(E\))-hex-3-enol.
* C6/LA aldehyde: hexanal.
* C6/LA alcohol: hexan-1-ol.
* C5/LNA carbonyls: pent-1-en-3-one, (\(E\))-pent-2-enal, (\(Z\))-pent-2-enal.
* C5/LNA alcohols: pent-1-en-3-ol, (\(E\))-pent-2-en-1-ol, (\(Z\))-pent-2-en-1-ol.
* PD: pentene dimers (seven isomers).  
C5/LA carbonyls: pentan-3-one, pentanal.
* C5/LA alcohol: pentan-1-ol.
* LOX esters: hexyl acetate, (Z)-hex-3-en-1-yl acetate, (_E_)-hex-2-en-1-yl acetate.
* Non-LOX esters: methyl acetate, ethyl acetate, methyl hexanoate, ethyl hexanoate.
* Terpenes: limonene, \(\beta\)-ocimene
* BC aldehydes: 3-methyl-butanal, 2-methyl-butanal
* BC alcohol: 2-methyl-butan-1-ol
# Optimization Technique for High-Gain CMOS Power Amplifier for 5G Applications
## 4 Conclusions  
We designed a PA operating between the 26.5-29.5 GHz band using a 65 nm RFCMOS. The designed PA achieved a high gain of 30.7 dB at 27.8 GHz by optimizing the size of the power cell and neutral capacitor \(C_{neu}\). Particularly, if a three-stage PA is configured, there is a rash risk due to high gain, and stability can be ensured only through strict EM simulations and analysis of parasitic components. The designed PA had a saturated power of 16.1 dBm--13.1 dBm at output P1 dB, and a maximum PAE of 29.7%.  
Conceptualization, H.J., H.L. and C.P.; methodology, B.P. and S.J.; Investigation, B.P. and S.K.; supervision, writing--original draft, H.J. and C.P.; writing--review and editing, H.J., H.L., B.P., S.J., S.K. and C.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (2019-0-00933, Development on multi-beam antenna technology).  
Not applicable.  
The data can be received from the authors on request.  
The authors declare no conflict of interest.
# Machine Learning Methods with Decision Forests for Parkinson's Detection
## 3 Decision Forests
### Systematically Developed Forest (SysFor)
#### 3.2.1 Identification of Potential Attributes  
The potential feature identification process starts with scanning the features of the dataset say \(M_{\text{D}}\). The process also additionally accepts user defined threshold points, i.e., a threshold for choosing the potential attributes, also known as goodness threshold (\(\infty\)), and a threshold for feature separation (\(\beta\)). Both these threshold points are responsible for a clear-cut distinction between potential and non-potential attributes. The gain-ratio of attributes plays a crucial role for identifying potential attributes. Both the numerical and categorical attributes are treated differently while evaluating gain-ratio; thus, making the entire process robust and scalable. At first, the whole non-class attributes are scanned one by one. For categorical attributes, the attribute itself and its gain ratio are added to two distinct sets separately \(A\) & \(G\), where \(A\) is the attribute sets and \(G\) represents the set of gain ratio. Similarly, the numerical splitting point for attributes is kept reserved to a negative number in a separate set \(P\). In this case, the authors in [27] uses \(-100\) as the splitting point. It is because unlike numerical attributes the categorical attribute does not have a splitting point. Similarly, for numerical attributes, the splitting point is determined by maintaining the following relation for all other available splitting point (\(P_{j}\)):  
\[\frac{abs\big{(}P_{j}-P_{k}\big{)}}{|A_{i}|}-\beta>0,\ \forall P_{k}\in\ P \tag{6}\]  
where, \(P_{j}\) is any available splitting points and \(P_{k}\) is the \(k^{th}\) splitting point in \(P\). After estimating all the splitting points, the split point having the highest gain ratio is added to the set \(P\) and \(G\). The process of adding gain ratio and corresponding splitting point continues till a single splitting point realized. It is because the size of \(P\) is inversely proportional to available splitting points. Finally, the attribute positions are sorted in the descending order of gain ratio, which gives an opportunity to select the potential attributes. The attributes having the gain ratio below the goodness threshold \(\propto\) are selected for building the C4.5 decision trees and SysFor decision forest.