esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Neural Network Equalization for Optical Short Reach Communication
## 2 Principles of Nonlinear Equalizer
### General Volterra Equalizer  
With \(y(n)\) and \(\hat{y}(n)\) representing system input and output, respectively, the \(P\)th-order discrete time Volterra series with \(M_{p}\) memory taps for order \(p\) is given as [8]  
\[\hat{y}(n)=\sum_{p=1}^{P}\sum_{m_{1}=0}^{M_{1}}\cdots\sum_{m_{p}=0}^{M_{p}}h_{ p}(m_{1},\cdots,m_{p})\prod_{k=1}^{p}y(n-m_{k}) \tag{1}\]  
It is the most complete model for nonlinearities with memory, as the \(p\)th-order Volterra kernel \(h_{p}(m_{1},\cdots,m_{p})\) includes _all_ possible combinations of a product of \(p\) time shifts of the input signal up to memory depth \(m_{p}\).  
Figure 1 illustrates this mapping of memory and nonlinearity in respect to the input signal \(y(n)\), which can be either real- or complex-valued. A VNLE architecture with given memory size and order is fully described by its so-called kernels \(h_{p}(m_{1},\cdots,m_{p})\). Before operation, they have to be identified, i.e., configured upon training data, in order to match the nonlinearities of interest. To this means, iterative approaches exist, such as least mean squares (LMS) or recursive least squares (RLS) kernel extraction. They are good solutions if little training data is available at a time or if channel dynamics call for frequent kernel reconfiguration. Typical O/E channels, however, are static enough to allow for a one shot configuration on a sufficiently large data set. The optimal solution in terms of the least squares (LS) error criterion to match the equalizer output to the desired signal (training signal) is achieved with a single operation LS solver. It involves inverting matrices, built from transmitted and received training data [9]. It has been shown [10] that these matrices can be ill-conditioned, resulting in a huge ratio between the highest and lowest eigenvalues. As a result, this matrix becomes nearly singular. With growing matrix complexity, i.e., increasing \(P\) or \(M\), the inverse of the matrix is computed  
with less and less accuracy. This calls for a trade off between matrix conditioning and the number of kernels for maximum performance.
## 2 Related Work  
This subsection brings a set of works dealing with the various studies made around the communication within the heterogeneous ad hoc networks. In [10], the authors have proposed an ad hoc Traversal Routing \"ATR\" solution resolving the obstacle of communication created by the lack of interoperability between the different routing protocols existing inside a heterogeneous ad hoc architecture composed by nodes belonging to networks MANET, VANET, and Mesh. The ATR is a mechanism implemented in some nodes representing gateways that convert control messages from one network to another according to the routing protocols used. [PERSON] and his colleagues [11] have integrated this ATR solution in their work, named the autonomous clustering-based inter-domain routing protocol. It consists of three protocols: a local routing protocol for routing in each MANET, an ATR that ensures the communication among multiple MANETs, and an autonomous clustering that allows managing NwGW nodes. Their idea has been based on self-governing clustering that creates groups in each MANET. Each array has a head that selects according to the information collected on the other networks, one or more NwGws endowed by the ATRalgorithm for playing the role of gateway between the different clusters. [PERSON] et al. [12] have undertaken studies on the heterogeneous MANET system by considering the conventional border gateway protocol as a new routing layer architecture that applies commercial concepts of virtual routers toward transparent connect heterogeneous networks even in the presence of mobility. The work achieved by [PERSON] and [PERSON] [13] have devised an analytical modeling strategy where the main objective is to ensure the establishment of sustainable routes for mobile nodes in a heterogeneous mobile ad hoc network integrated into the IoT.  
Analyzing all these proposals, we notice that they all use a gateway to ensure the communication between nodes of distinct networks. This concept has a limitation which is reflected in the consumption of memory space and energy at the bridge node, in addition to a high risk of data loss due to an eventually broken link between the intermediate neighbors.  
On the other hand, [PERSON] and colleagues [17] propose a new strategy for efficient routing within the heterogeneous network composed of vehicles and drones (UAVs) inside an urban area that makes data delivery reliable and guarantees robust paths in case of timeout of each discovered route. This solution is based on ad hoc cooperation between the VANET and FANET components. However, it remains limited to a well-defined type of network and architecture.  
In this paper, we propose a new multipath routing protocol concept. It applies in a heterogeneous network comprising all ad hoc nodes types while respecting the characteristics of each of them. Our design presents a relevant solution that addresses both limitations of gateway-based communication and the predefined customized architectures.
## 3 Materials and Methods
### MOCell-Based QoS Optimal Path
In genetic algorithms, an additional operator is used to avoid falling local optima and maintain genetic diversity from one population to another, called the mutation operator. We employ the swap _mutation_ operator, which proposes the random selection of a pair of genes in the chromosome and the exchange of values between them [41]. Figure 5 shows the operators used. Fitness function representation: The optimization problem is formulated with three objective functions, \(f_{1}\), \(f_{2}\), and \(f_{3}\), represented by QoS parameters such as delay, packet loss, and available bandwidth, respectively. The purpose of the first is to minimize the cost of delay and latency covered by a specific path. The purpose of the second is to minimize the loss of data, and that of the third is to minimize the loss of quality of service when the demand for bandwidth consumption is not satisfactory for the correct functionality of the flow. The \(f_{1}\) function (Equation (9)) consists of the summation of all delay costs for the path. The QoS parameters, such as delay, have an additive metric composition rule for the end-to-end path. Figure 5: Genetic operators. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Chromosome** & **5** & **8** & **2** & **1** & **4** & **10** & **6** & **7** & **3** & **9** \\ \hline Nodes & R1 & R2 & R3 & R4 & R5 & R6  
The \(f_{2}\) function (Equation (10)) indicates the packet loss on the path, represented as the sum of the data losses in the links. The \(f_{3}\) function (Equation (8)) consists of the bandwidth parameter; the end-to-end path selects the minimum capacity bandwidth of the network links. We consider a standard configuration for the algorithm parameters: a population size of 100 for domains A and B and 400 for domain C, considering twice the number of individuals concerning the number of nodes in each domain; a neighborhood size of 8; and the tournament selection method with a crossover probability pc of 0.6 and operator SPX and a mutation pm of 0.6 with operator swap.
# Intrusion Detection Based on Gray-Level Co-Occurrence Matrix and 2D Dispersion Entropy
## 4 Results
### Computing Times  
In the following Table 7, we report the computing time of the proposed approach with the application of ML directly on the data set in a similar way to what was done in the paper [5]. The approach proposed in this paper implements a dimensionality reduction and the computing time to execute the machine learning algorithm on the reduced set is minimal. On the other side, the time requested to calculate the GLCM is significant (34 s in average for the DDoS attack and 31 s in average for the PorScan attack) as shown in Table 7. The average time needed to calculate the 2 DDE entropy measure is also relatively  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ER** & **FPR** & **FNR** & \
\begin{tabular}{c} **Optimal Values and/Or** \\ **Notes** \\ \end{tabular} &
\begin{tabular}{c} **Approach-Attack** \\ \end{tabular} \\ \hline
0.0016 & 0.0223 & 0.0008 & \(N_{B}=12\) & This approach (Decision Tree), DDoS \\ \hline
0.0084 & 0.0354 & 0.0069 & \(\gamma=2^{7},C=2^{10}\) & This approach (SVM), DDoS \\ \hline
0.0248 & 0.02 & 0.0251 & none & This approach (Naive Bayes), DDoS \\ \hline
0.0045 & 0.0023 & 0.0476 &
\begin{tabular}{c} Online Kernel Online \\ Anomaly Detection \\ (KOAD) \\ \end{tabular} & [10], DDoS \\ \hline
0.0016 & N/A & 0.0016 &
\begin{tabular}{c} Deep Belief Network \\ (DBN) and Bidirectional \\ Gated Recurrent Unit \\ (BiGRU) \\ \end{tabular} & [7] DDoS \\ \hline
0.0015 & 0.0017 & 0.0068 &
\begin{tabular}{c} Deep Neural Networks \\ (DNN) \\ \end{tabular} & [6], DDoS \\ \hline
0.0048 & 0.0618 & 0.0009 & \(N_{B}=17\) & This approach (Decision Tree), Port Scan \\ \hline
0.0082 & 0.0653 & 0.0038 & \(\gamma=2^{8},C=2^{9}\) & This approach (SVM), Port Scan \\ \hline
0.0339 & 0.0571 & 0.0322 & none & This approach (Naive Bayes), Port Scan \\ \hline N/A & 0.0094 & 0.0078 &
\begin{tabular}{c} cost-sensitive differential \\ evolution classifier \\ \end{tabular} & [8], Port Scan \\ \hline
0.0051 & 0.004 & 0.0016 & LSTM & [9], Port Scan \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary table of the ER, FPR and FNR results obtained with this approach (different machine learning algorithms) and the results from literature.  
high (63 s for the DDoS attack and 83 s for the PortScan attack). These calculated times are based on \(W_{S}=100\) since this was the window size with the minimum ER and the optimal selection of features presented in Table 4. In this study, it was used a laptop with Intel i7 85550U CPU running at 1.8 GHz with 16 GBytes of RAM and no GPU.
# Fatigue of Friction Stir Welded Aluminum Alloy Joints: A Review
## 1 Introduction  
Friction Stir Welding (FSW) is a solid-state welding technology invented by The Welding Institute (TWI), UK, in 1991. As shown in Figure 1, the FSW tool rotates at high speed and plunges into the workpiece. Heat generated by friction and material deformation softens the surrounding material, when the tool moves along the weld direction, the material is stirred and forced from advancing side to retreating side to fill the space behind the tool, consequently forming the weld. Compared with conventional fusion welding technologies, FSW can avoid defects such as porosity, lack of penetration, hot cracking, and distortion [1; 2; 3; 4; 5].  
Aluminum alloy is widely used in aerospace, automotive and marine industries. [6] According to relevant data [7; 8] fatigue failure of parts accounts for 50 to 90% of all failures in manufactured parts in general. The FSW method improved the fatigue performance by ~2.4 times in comparison with the single riveting method commonly used in aerospace, and reduced the overlap area in the double riveting method, while ensuring the fatigue strength and mechanical properties. [9; 10; 11; 12; 13; 14; 15] The main application fields of FSW such as aerospace and transportation have very high safety requirements, and fatigue resistance of a welded joint is of great significance in ensuring overall structural integrity. The International Institute of Welding (IIW) guideline [16], Eurocode 9 standards [17], and GB/T15248-94 [18] have provided procedures to assess the fatigue. The weld fatigue design curves given in these standards are relatively conservative and there is no in-depth description of the weak locations or defects that occur during the welding process [19; 20].  
In the literature, the research on fatigue performance of FSW joints was mainly focused on aspects of fatigue life, fractographic analysis, effects of weld defects, welding parameters, and test conditions. This paper provided a systematic review of the fatigue analysis of friction stir welded joints.
# Optimization of Acoustic Communication Links for a Swarm of AUVs: The COMET and NEMOSENS Examples
###### Abstract  
Underwater acoustic communication is a key point for performance improvement in an autonomous underwater vehicle (AUV) swarm. The communication process is essential for improving the AUVs localization accuracy for navigation and is a convenient way for sharing information among the AUVs in a network. The objective of this work, which was developed in the COMET and NEMOSENS projects, is to address the communication process required in a mobile underwater wireless network, with a focus on the proposal of an adaptive physical layer methodology. We discuss about the employed channel access method, the frame structure, and we propose the usage of an adaptive guard interval in order to ameliorate the network usage rate. We explain the physical layer aspect of the communication: the data processing at the transmitter and receiver side. In addition to that, we propose the usage of smart communications among AUVs. We design a method for adapting some physical layer parameters. The proposed approach relies only on the knowledge of the transmission geometry, and it optimizes the number of subcarriers and the cyclic-prefix length of the Orthogonal Frequency-Division Multiplexing (OFDM) system. The obtained results show a performance improvement in terms of bit-error rate when compared with the case of random parameters selection. These results corroborate the benefits of our adaptive parameters approach.  
underwater communication; underwater acoustic communication; autonomous underwater vehicle; AUV; swarm of AUVs; adaptive underwater communication; underwater OFDM system +
Footnote †: journal: Applied Sciences  
_Article_  
Optimization of Acoustic Communication Links for a Swarm of AUVs: The COMET and NEMOSENS Examples
# Consensus Tracking Control of Multiple Unmanned Aerial Vehicles Subject to Distinct Unknown Delays
## 3 Control Strategy
### Translation Control
#### Follower agent control
Then, the formation described by \(\bar{H}\) can be accomplished for the leader-follower system (41) via the protocol (40)._  
Proof. : Considering the quadratic function \(V(\bar{\xi})=\bar{\xi}^{T}(t)(I_{n}\otimes P)\bar{\xi}(t)\), one obtains the following dynamics:  
\[\dot{V}(\bar{\xi}) =\hat{\xi}^{T}(t)(I_{N}\otimes P)\bar{\xi}(t)+\bar{\xi}^{T}(t)(I_ {N}\otimes P)\hat{\xi}(t)\] \[=((I_{N}\otimes\bar{A}+\mathcal{L}_{1}\otimes BK)\bar{\xi}(t)+(I_ {N}\otimes B)\bar{u}(t))^{T}(I_{N}\otimes P)\bar{\xi}(t)\] \[\quad+\bar{\xi}(t)^{T}(I_{N}\otimes P)((I_{N}\otimes\bar{A}+ \mathcal{L}_{1}\otimes BK)\bar{\xi}(t)+(I_{N}\otimes B)\bar{u}(t))\] \[=\bar{\xi}(t)^{T}((I_{N}\otimes\bar{A}+\mathcal{L}_{1}\otimes BK )^{T}(I_{N}\otimes P)+(I_{N}\otimes P)(I_{N}\otimes\bar{A}+\mathcal{L}_{1} \otimes BK))\bar{\xi}(t)\] \[\quad+\bar{u}(t)^{T}B^{T}(I_{N}\otimes P)\bar{\xi}(t)+\bar{\xi}( t)^{T}(I_{N}\otimes P)B\bar{u}(t)) \tag{56}\]  
Then, Equation (56) can be expressed in matrix form as  
\[\dot{V}(\bar{\xi})= \begin{bmatrix}\bar{\xi}(t)\\ \bar{u}(t)\end{bmatrix} ^{T} \begin{bmatrix}I_{N}\otimes\bar{A}^{T}P+\mathcal{ L}_{1}^{T}\otimes K^{T}B^{T}P+I_{N}\otimes P\bar{A}+\mathcal{L}_{1}\otimes PBK&I_{N} \otimes PB\\ I_{N}\otimes B^{T}P&0\end{bmatrix}\begin{bmatrix}\bar{\xi}(t)\\ \bar{u}(t)\end{bmatrix}  \tag{57}\]  
Proceeding as in the stability analysis of the leader system, one can use Proposition 1, and making use of a non-singular transformation, one obtains the following LMI:  
\[ \begin{bmatrix}I_{N}\otimes P_{c}\bar{A}^{T}+\mathcal{L}_{1}\otimes W^{T}B^{T} +I_{N}\otimes\bar{A}P_{c}+\mathcal{L}_{1}\otimes BW+I_{N}\otimes\alpha_{1}P_{ c}&I_{N}\otimes B\\ I_{N}\otimes B^{T}&I_{N}\otimes-\alpha_{2}I_{N}\end{bmatrix} <0, \tag{58}\]  
where the control gain is \(K=WP_{c}^{-1}\) and the positive constants \(\alpha_{1}>\alpha_{2}>0\) guarantee the above LMI, which can ensure the exponential stability and the \(\mathcal{H}_{\infty}\) performance of the follower system in the presence of the unknown inputs, being affected by the unknown time-varying delays \(\tau_{i}\).
# Determining Optimal Power Flow Solutions Using New Adaptive Gaussian TLBO Method
## 2 OPF Problem Formulation
### Problem Constraints
#### 2.1.1 Equality Constraints  
There are two types of power supply constraints applicable to system buses--real and reactive [14]:  
\[P_{Gi}-P_{Di}=V_{i}\sum_{j=1}^{NB}V_{j}\big{(}G_{ij}\cos\delta_{ij}+B_{ij}\sin \delta_{ij}\big{)} \tag{4}\]  
\[Q_{Gi}-Q_{Di}=V_{i}\sum_{j=1}^{NB}V_{j}\big{(}G_{ij}\sin\delta_{ij}-B_{ij}\cos \delta_{ij}\big{)} \tag{5}\]  
\(V_{i}\) and \(V_{j}\) represent the magnitudes of the voltages, while \(i\) and \(j\) are the indices of the bus. \(PG_{i}\) and \(QG_{i}\) are the generated units' real and reactive power outputs, respectively. The real load demand is represented by \(PD_{i}\) and the reactive load demand by \(QD_{i}\). \(\delta_{ij}\) is the voltage phase angle difference between nodes \(i\) and \(j\). \(G_{ij}\) and \(B_{ij}\) are the conductance and susceptance of line \(ij\), respectively. \(NB\) is the number of buses.
## 5 Conclusions  
In this work, Pt-clusters decorated Ni@Pd hierarchically structured ternary metallic nanocatalyst were prepared on carbon black support by using \"self-aligned\" heterogeneous nucleation-crystal growth mechanism. As-obtained Ni@Pd-Pt NC efficiently catalyzes the HER with an ultra-low overpotential of 11.5 mV at the 10 mA cm\({}^{-2}\) cathodic current density in alkaline medium, which is 14.5 mV less compared to standard Pt-C catalyst. Of special relevance, the overpotential of Ni@Pd NC is suppressed by 161.5 mV by decorating \(\sim\)2.0 wt.% of Pt. Moreover, Ni@Pd-Pt NC achieves the Tafel slope value of 42 mV dec\({}^{-1}\), which is the same as that of a standard Pt/C catalyst. The as-prepared Ni@Pd-Pt NC also exhibits outstanding performance in the acidic medium. In brief, the obtained results outline an efficient strategy for the development of next-generation HER catalysts with ultra-low content of Pt.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/10/15/5155/s1](http://www.mdpi.com/2076-3417/10/15/5155/s1)]([http://www.mdpi.com/2076-3417/10/15/5155/s1](http://www.mdpi.com/2076-3417/10/15/5155/s1)). Figure S1: The XPS spectra at Pt-4f orbital of Ni@Pd-Pt NC, Figure S2: The XPS spectra at Ni-2p orbital of Ni@Pd NC, Figure S3: Stability test results of Ni@Pd-Pt NC, Figure S4: Cyclic voltammograms of experimental NCs, Table S1: ICP-OES determined composition ratios of experimental NCs, and Table S2: Benchmark Table for HER activity of Pt-based NCs.  
**Author Contributions:** Data curation, S.-P.W.; formal analysis, D.B., S.L. and C.Y.; funding acquisition, P.-C.C.; project administration, P.-C.C.; resources, P.-C.C.; supervision, K.-W.W.; validation, D.B.; writing--original draft, D.B.; writing--review and editing, P.-C.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** We acknowledge the funding support of the Ministry of Science and Technology, Taiwan (MOST 106-2112-M-007-016-MY3, MOST 107-2221-E-027-009-MY2, MOST 108-3116-F-007-001, MOST 107-3017-F-006-003, and MOST 109-3116-F-007-001).  
**Acknowledgments:** The authors thank [PERSON] and the staff of the National Synchrotron Radiation Research Center (NSRRC), Hsinchu, Taiwan (BL-01C1, BL-01C2, BL-07, and BL-17C) and Spring-8, Japan (BL-12B2) for helping in various synchrotron-based spectroscopies and diffraction analysis. Additionally, the authors acknowledge the Precision Research and Analysis Center at NTUT and financial supports from the Ministry of Education for the funding of \"Center for Neuromodulation Medical Electronics Systems\" from the featured areas research center program within the framework of the higher education sprout project.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Information Update and Fusion
### Environment Search Status Map  
The environment search status map that records the latest detected time for cells can be indicated as follows:  
\[\mathcal{E}_{i,t}=\left[\mathcal{E}_{i,t}^{x,y}\right]_{L_{x}\times L_{y}}, \tag{18}\]  
where \(\mathcal{E}_{i,t}^{x,y}\) denotes the latest observed time of cell \(G_{x,y}\) at timestep \(k\) in the understanding of UAV \(U_{i}\). And the initial value is set to 0, i.e., \(\mathcal{E}_{i,0}^{x,y}=0\).  
The environment search status map \(\mathcal{E}_{i,t}\) is updated in two stages. The map \(\mathcal{E}_{i,t}\) is first updated by the individual detection of UAV \(U_{i}\) at each timestep \(t\), which can be represented as  
\[\mathcal{E}_{i,t}^{x,y}=t,\text{ if }G_{x,y}\in\mathbb{S}_{i,t}. \tag{19}\]  
Then, the environment search status maps from the neighboring UAVs can be integrated. The information fusion mechanism of the environment search status map can be denoted as follows:  
\[\mathcal{E}_{i,t}^{x,y}=\mathcal{E}_{j,t}^{x,y},\text{ if }\mathcal{E}_{j,t}^{x,y} >\mathcal{E}_{i,t}^{x,y}\text{ and }U_{j}\in\mathbb{N}_{i,t}. \tag{20}\]
## 5 Discussion
EP-COSI has a high degree of phase modulation across \(t_{1}\) that must be preserved during reconstruction. EP-JRESI does not have the same degree of phase variation across \(t_{1}\) as EP-COSI, so the signal phase is of less importance. Because TV applies the differential operators in magnitude mode and only imparts phase information into the reconstruction through the fidelity constraint, the phase characteristics of EP-COSI data are not adequately reconstructed. A phase sensitive TV reconstruction, in which separate TV regularizers are applied to the real and imaginary channels, may have improved results with 4D NUS EP-COSI reconstruction. Future research will further investigate this dependence on the sequence phase characteristics and the effect each reconstruction method has on it. As it is currently implemented, the Split Bregman GS problem converges in fewer outer iterations than the CS problem; however, it takes more time to compute per iteration because of the group-wise shrink. A typical 4D EP-COSI reconstruction of GS\({}_{2}\) takes 15 min to converge on a 64-bit dual core, 3.4 GHz Core i7 processor with 16 GB RAM, while CS takes 5 min. A more efficient implementation of group-wise shrink using distributed processing techniques is currently under development.
## 3 Results and Discussion
### The Concentration of Biogenic Amines in Lentil Wholemal Samples
Aromatic and heterocyclic biogenic amines (TRIP, PHE, HIS, and TYR) are produced from the respective amino acids (i.e., tryptophan, phenylalanine, histidine, and tyrosine, respectively) by decarboxylation with enzymes that are produced by bacteria [52]. Certain LAB, e.g., _Lactococci_, _Pediococcus_, and _Lactobacillus_, which are decarboxylase-positive, may result in biogenic amine synthesis during fermentation processes [56]. Some strains of _Pediococcus_ spp. have been found to accumulate cadaverine and tyramine in alcoholic beverages at low levels [52]. The aliphatic amine PUT can be formed from arginine via multiple pathways, e.g., arginine decarboxylation to agmatine, which is converted to PUT via agmatine deaminase and PUT carbamoyl transferase; arginine deanimization to citruline and conversion to PUT via ornithine carbamoyl transferase; arginine conversion to ornithine via arginase/arginine ureohydrolase and conversion to PUT via ornithine decarboxylase; and decarboxylation to agmatine and conversion to PUT via agmatinase/agmatine ureohydroidase. CAD is formed from lysine via lysine decarboxylase [57]. SPRMD is formed from PUT via spermidine synthase and SPRM is formed from SPRMD via spermine synthase [58]. The Food and Drug Administration (FDA) of the United Sates of America (USA) and the Food and Agriculture Organization (FAO) of the United Nations (UN), with the World Health Organization (WHO), proposed a maximum acceptable histamine limit of 50 mg/kg in fish and food products [59]. On the other hand, according to a rat model, the no observed adverse effect level (NOAEL) was set at 2000 ppm for tyramine, putrescine, and cadaverine, 1000 ppm for spermidine, and 200 ppm for spermine [60].