esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Algorithm Design
### Parameter Initialization in MOEA/D-Mfar  
The algorithm parameters are initialized following the fundamental MOEA/D framework. Firstly, \(M\) is defined as the population size. Secondly, \(M\) uniformly distributed unit vectors \(\lambda^{1},\ldots,\lambda^{M}\) are generated using Chebyshev distance. Thirdly, for each unit vector \(\lambda^{m},M\) neighborhoods \(\mathbf{B}(m)=i_{1},\ldots,i_{s}\) are determined based on the Chebyshev distance. \(S\) is the size of the neighbor, and \(i_{1},\ldots,i_{s}\) is the subscript of the \(T\) unit vectors closest to \(\lambda^{m}\). Other symbols are defined as follows:  
1. \(Q\) is defined as the number of objective functions.
2. \(z=(z_{1},\ldots,z_{q},\ldots,z_{Q})\), where \(z_{q}\) is the best performance value of the \(q\)-th objective function.
3. \(FV_{1},\ldots,FV_{m},\ldots,FV_{M}\), where \(FV_{m}=[fv_{1}(\Gamma_{m}),\ldots,fv_{q}(\Gamma_{m}),\ldots,fv_{Q}(\Gamma_{m })]\), and \(fv_{q}(\Gamma_{m})\) is the value of the \(m\)-th individual \(\Gamma_{m}\) on the \(q\)-th objective function.  
Figure 3: New solution generation logic of MOEA/D-MFAR.  
Figure 2: Resource allocation logic diagram.  
The target value calculation formula of the \(m\)-th sub-problem is as follows:  
\[g^{le}(\Gamma_{m}\mid\lambda^{m},z)=\max_{1\leq q\leq Q}\{\lambda^{m}_{q}\mid fv_ {q}(\Gamma_{m})-z_{q}\} \tag{12}\]  
EP is defined as the set of feasible solutions eventually obtained by the algorithm, and PF is the set of target values corresponding to the solutions in EP.
## 4 Data Classification Results  
### Data Source  
The measured time-history data from the Hwatae Bridge, a cable-stayed bridge located in the city of Yeosu, Republic of Korea, were used for the analysis of data anomaly detection. This bridge was constructed in 2015 with two 130-meter-high pylons and a 500-meter span between the two pylons. The data consisted of acceleration, displacement, and wind data. Only the acceleration data of the bridge were used for anomaly detection. To collect the acceleration data, 36 channels were placed at various parts of the bridge, such as piers, decks, pylons, and cables. The sensor layout of the 36 accelerometers on the bridge is shown in Figure 9. Detailed information on the positions of these channels is presented in Table 1. Acceleration data were recorded every 10 min at a sampling frequency of 100 Hz.  
Considering that the data for the _Minor_ and _Square_ classes do not exist in the measured data, the data for these two classes were artificially produced. The data for the _Minor_ class  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Channels** & **Position** \\ \hline
1, 2 & Top of the left pylon \\
3–5 & Foundation of the left pylon \\
6–8 & Girder at \% span \\
9, 10 & Girder at \% span \\
11–21, 34 & Cables \\
22, 23 & Top of the right pylon \\
24–26 & Foundation of the right pylon \\
27–29 & Girder end \\
30–32 & Free field \\
33, 35, 36 & Girder at \% span \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information about selected channels for anomaly detection.  
Figure 9: Schematic of accelerometers installed on the Hwatae bridge.  
were generated by quantizing random signals of the original acceleration data. Similarly, the _Square_ class was artificially prepared using the square wave phenomenon. A total of 5105 _Minor_ and 5000 _Square_ class samples were generated. The artificially generated _Minor_ and _Square_ class examples are shown in Figure 10.  
Rule- and CNN-based methods were applied to the data measured from the bridge to investigate their applicability and performance. First, a rule-based method was applied to the measured data, which allowed us to identify class ambiguity in the data. Based on these results, labeled data were prepared for the CNN-based method. Furthermore, the CNN-based classification results were obtained for the labeled dataset. Given that data labeling is conducted using the rule-based classification, in this study, we do not intend to compare the accuracy of the two methods but to investigate their characteristics and associated issues. An in-depth discussion of the classification results is presented in the next section.
## 4 Experimental Analysis
### Comparison  
For a comprehensive assessment, we conducted a comparative analysis of the developed DCGAN + Mean-CAM-CNN approach against established ear recognition methodologies. The results of this comparison are detailed in Table 5. The Rank-1 recognition rate was employed as the primary evaluation metric for both MAI and AWE datasets, adhering to predefined protocols tailored to each dataset.  
The comparison reveals satisfactory Rank-1 recognition rates for most of the analyzed papers, such as [18, 20, 21, 22, 23, 24, 25, 26, 27, 28], achieving rates surpassing 93% with the MAI dataset. Conversely, our approach exhibits the highest and most competitive performance, reaching a Rank-1 recognition rate of 100.00%. The favorable results attained by several papers using the MAI dataset may be attributed to the dataset's simplistic and constrained image nature. In contrast, the majority of the compared papers, both employing handcrafted and deep learning methods, face greater challenges with the AWE dataset due to its complex and unconstrained image nature. However, our proposed approach demonstrates highly acceptable and competitive results, yielding a Rank-1 recognition rate of 76.25% with the AWE dataset. The Mean-CAM method enables the CNN to focus solely on valuable information for recognition, eliminating irrelevant details, complemented by the colorization of grayscale images and enhancement of dark shades through the trained DCGAN model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**AMI**} & \multicolumn{2}{c}{**AWE**} \\ \cline{2-6}  & **Rank-1 (\%)** & **Rank-5 (\%)** & **AUCMC (\%)** & **Rank-1 (\%)** & **Rank-5 (\%)** & **AUCMC (\%)** \\ \hline
**Without preprocessing** & 99.67 & **100.00** & **100.00** & 74.50 & 89.50 & 98.93 \\ \hline
**With preprocessing** & **100.00** & **100.00** & **100.00** & **76.25** & **91.25** & **99.96** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative analysis of ear recognition outcomes: Evaluating the influence of preprocessing on Mean-CAM-CNN performance.
## 3 Methodology
### Encryption Logic Deployment in SDN Environment and Performance Evaluation  
The deployment of encryption logic is carried out in an SDN environment using Mininet. The network topology consisted of a central encryption controller that managed policy deployment across the network. Two switches and two hosts are connected to each switch as shown in Figure 6.  
The SDN environment is assessed for performance using key metrics of transmission time, instantaneous throughput, average packet loss, and average jitter to diagnose network bottlenecks and to analyze the impact of encryption on overall network performance. In addition, a thorough cryptographic analysis has been conducted in relation to entropy and pattern analysis, involving advanced techniques such as differential and linear cryptanalysis, to evaluate the robustness of the encryption scheme.
###### Abstract  
The organic fraction of municipal solid waste (OFMSW) is recognized as a suitable substrate for the anaerobic digestion (AD) process and is currently considered a mature technology. A promising strategy to enhance biogas yield and productivity is the co-digestion of OFMSW with other organic biomass, such as green waste (GW), a mixture of leaves, grass, and woody materials originated from private yards and public greenspace management. The main limitation to the use of GW for biogas production is the high percentage of the lignocellulosic fraction, which makes necessary a pretreatment of delignification to dissolve the recalcitrant structure. In this study, a new strategy of sustainable bio-delignification using the white-rot fungi _Bierkandera adusta_ (BA) in comparison with other chemical pretreatments were investigated. Untreated and treated GW were, respectively, submitted to anaerobic co-digestion with OFMSW. AD processes were carried out in a lab-scale plant for 30 days in thermophilic conditions (55 \({}^{\circ}\)C). Biogas cumulative production was increased by about 100% in the case of treated GW compared with that of just OFMSW, from 145 to 289 Nm\({}^{3}\) CH\({}_{4}\)/ton SV, and productivity almost doubled from 145 to 283 Nm\({}^{3}\)/ton FM \({}^{*}\) day. The measured average methane content values in the cumulative biogas were 55% from OFMSW and 54% from GW. Moreover, over 95% of the biogas was produced in 20 days, showing the potential opportunity to reduce the AD time.  
OFMSW; green waste; biogas; anaerobic co-digestion; bio-delignification; _Bierkandera adusta_
## 3 Materials and Methods  
### Machine Learning Methods  
#### 3.1.1 Adaptive Networks-Based Fuzzy Inference System  
The adaptive neuro-fuzzy inference system (ANFIS) is an artificial intelligence-based algorithm developed by [PERSON] [56] in the early 1990s. By combining features of artificial neural networks (ANN) [57; 58; 59] along with fuzzy logic (FL) principles, the method has a powerful potential for predicting complex non-linear processes [60; 61]. The ANFIS hybrid learning algorithm could apply neural networks to optimize its algorithm, so it has a higher learning capability to adapt to its environment [62]. In the ANFIS, the number of nodes is connected by directional links in the ANN, and the fuzzy parameters of fuzzy logic algorithm can be estimated [63]. The main principle of ANFIS is to construct a set of fuzzy \"if-then\" rules with suitable membership functions to create the stipulated input and output variables [55]. In a specific manner, it is supposed that there are two input parameters (\(x\) and \(y\)) and one output (\(f\)). The FL-based \"if-then\" rules used in ANFIS can be expressed as follows [64]:  
\[\text{If }x\text{ and }y\text{ are }A_{1}\text{ and }B_{1}\text{, respectively, then}f_{1}=p_{1}x+q_{1}y+r_{1}\text{ (Rule 1),} \tag{1}\] \[\text{If }x\text{ and }y\text{ are }A_{2}\text{ and }B_{2}\text{, respectively, then}f_{2}=p_{2}x+q_{2}y+r_{2}\text{ (Rule 2).} \tag{2}\]  
where \(p_{1}\), \(q_{1}\), and \(r_{1}\) and \(p_{2}\), \(q_{2}\), and \(r_{2}\) are the linear variables of Rule 1 and Rule 2, respectively, while \(A_{1}\), \(A_{2}\), \(B_{1}\), \(B_{2}\) are the functions of \(x\) and \(y\).  
Such typical ANFIS model with one output (_f_) and two inputs (_x_ and _y_) can be visualized in Figure 1, including five main layers [56; 65]:  
**Layer 1:** In this layer, every node is a squared node with a function as follows:  
\[O_{1,i}=\mu_{A_{i}}(x) \tag{3}\]  
\[O_{1,i}=\mu_{B_{i}}(y) \tag{4}\]  
where \(\mu_{A_{i}}(x)\) and \(\mu_{B_{i}}(y)\) are the membership functions of linguistic labels \(A_{i(x)}\) and \(B_{i(y)}\) of the inputs \(x\) and \(y\), respectively.  
**Layer 2:** In this layer, every node, which is fixed, multiplies the incoming signals and sends the output:  
\[O_{2,i}=\mu_{A_{i}}(x)\times\mu_{B_{i}}(y). \tag{5}\]  
**Layer 3:** In this layer, every node is also fixed, and outputs of this layer are normalized as follows:  
\[O_{3,i}=\frac{w_{i}}{\sum_{i=1}^{2}w_{i}}. \tag{6}\]  
**Layer 4:** Every node of this layer is an adaptive node, with the node function indicated as  
\[O_{4,i}=\overline{w}_{i}(p_{i}x+q_{i}y+r_{i}) \tag{7}\]  
where \(w_{i}\) infers the outputs of Layer 3, while \(f_{1}\) is defined by Equations (1) and (2) (I = 1, 2).  
**Layer 5:** In this layer, every node is a single fixed node; it sums up all incoming signals to compute the overall output as follows:  
\[O_{5,i}=\frac{\sum_{i=1}^{2}w_{i}f_{i}}{\sum_{i=1}^{2}w_{i}}. \tag{8}\]
# A Review on Millimeter-Wave Hybrid Beamforming for Wireless Intelligent Transport Systems
## 3 An In-Depth Analysis of the mmWave Hybrid Beamforming Landscape
### Carrier  
Hybrid beamforming techniques are compatible with OFDM as well as NOMA waveforms.
## 5 Practical Applications  
The adoption of vertical stiffness measurements could be a valuable tool to assess the neuromuscular load experienced by athletes. This approach presents several advantages: (1) it offers a practical and non-intrusive method to assess mechanical load without the need for additional, exhaustive testing, and (2) it allows for the identification of load differences between training sessions and competitions, providing objective insight into how athletes respond to various demands. By regularly monitoring vertical stiffness, coaches and physical trainers can fine-tune recovery strategies and better tailor training loads to each athlete's current condition, ultimately enhancing performance outcomes and reducing the risk of injury.
## 4 Conclusions  
In summary, E-POSS was successfully prepared by the epoxidation of C=C bonds. With the blending method, the E51 + E-POSS, E51 + GF, and E51 + E-POSS + GF composites were prepared. The results show that the tensile strengths of binary composites of E51 + E-POSS and E51 + GF were higher than that of pure E51. In addition, the ternary composite of the E51 + E-POSS + GF sample showed the optimum tensile strength with the addition of E-POSS (10 wt%) and GF (16 wt%), compared with E51 + E-POSS (10 wt%), E51 + GF (16 wt%), and pure E51 samples. Moreover, the DMA results show that the E51 + E-POSS (10%) + GF (16%) sample exhibited the highest stiffness in comparison with the E51 + E-POSS (10%), E51 + GF (16%), and pure E51 samples. The TGA results indicate that T\({}_{\text{d5\%}}\) and residues of the E51 + E-POSS (10%) + GF (16%) sample were significantly improved compared with those of E51 + E-POSS (10%), E51 + GF (16%), and pure E51 samples,  
Figure 8: SEM image of the fracture surface. (**A**) E51, (**B**) E51 + E-POSS (10%), (**C**) E51 + GF (16%), and (**D**) E51 + E-POSS (10%) + GF (16%).  
revealing that the thermal stability of ternary composite was better than that of binary and pure E51 samples. It can be concluded that the incorporation of an appropriate amount of E-POSS (10 wt%) and GF (16 wt%) into E51 could significantly improve the mechanical and thermal properties of E51 resin. The improvement was not only related to interaction between E51/POSS and E51/GF, but also related to the synergistic effect between E-POSS and GF.  
Conceptualization, project administration, supervision, writing--review & editing, Y.F.; investigation, J.S.; methodology, investigation, writing--original draft preparation, J.J.; writing--review and editing, X.Y. and D.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Note applicable.  
There are no conflict to declare.
# Identification of Topographic Seismic Site Periods in Sloping Terrains
## 4 Conclusions  
In the present study, the dynamic response of an inclined terrain was evaluated with the objective of identifying peaks in the amplification ratio that may be associated with the discontinuity generated by the slope of the terrain to obtain a vibration period linked to its site topography.  
A parametric analysis focused on the influence of the terrain slope and identified a characteristic period at which an amplification peak occurs, which is not observed under the same soil stiffness conditions in horizontal terrains. Given that topographic amplification effects are frequency dependent, this finding opens the door to defining period ranges, in conjunction with the periods associated with the vibration of the surrounding soil, where structures located on sloping terrains may experience a variation in demand compared to that recorded on horizontal terrains. Therefore, it is recommended that future research evaluates the implications that the period \(T_{t}\) could have on the response spectra for the design of structures located on sloping terrains, extending to three-dimensional cases under different soil conditions.  
For all the analyzed cases that corresponded to homogeneous terrains, a pattern was observed in the response of the points located on the slope of the terrain, which included the presence of an amplification peak. This peak was generated due to the interaction between the vibration of the sloped area and the surrounding terrain and was associated with a characteristic seismic site period, \(T_{t}\), which is independent of the slope angle of the terrain.  
To determine the period \(T_{t}\), referred to as the topographic period \(T_{t}\), an expression was fitted with a coefficient of determination (R\({}^{2}\)) of 99%. This expression was defined as \(T_{t}=\frac{3H}{Vs}\), where \(H_{T}\) represented the height of the slope, and \(Vs\) represented the shear wave velocity of the soil.  
Finally, it was observed that, beyond a distance equivalent to \(3H\), measured from the crest of the inclined zone, \(X_{b}\) (Figure 2), the amplification peak associated with \(T_{t}\) significantly decreases.  
Conceptualization, E.G.D.-S.; methodology, E.G.D.-S.; software, J.E.O.-V.; validation, E.G.D.-S. and J.E.O.-V.; formal analysis, E.G.D.-S. and J.E.O.-V.; data curation, E.G.D.-S. and J.E.O.-V.; writing--original draft preparation, E.G.D.-S.; writing--review and editing, E.G.D.-S. and J.E.O.-V.; funding acquisition, E.G.D.-S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by VRIEA-PUCV, grant number 039.337/2022.  
Not applicable.  
Data Availability Statement: Data generated or analyzed during this study are available from the corresponding author upon reasonable request.  
The authors thank the Civil Engineering School of Pontificia Universidad Catolica de Valparaiso for the support provided in the development of the research, as well as the support in the publication of the article.  
The authors declare no conflicts of interest.
## 4 Experimental Results, Analysis, and Evaluation
### Security Evaluation
The entropy of [PERSON]'s method is formed from 2218 to 6.04 \(\times\) 10\({}^{5}\) dB (see Table 1). [PERSON] encrypted the features of the 3D CAD model with a random 3 \(\times\) 3 matrix generated by a secret key. Thus, we can consider that [PERSON]'s method is encrypted 3D CAD models based on features and a random matrix by a secret key \(\mathbf{K}\). Therefore, the entropy of this method is dependent on both the number of features and the 3 \(\times\) 3 matrix. In the experimental results, around 50% of the facets are selected as the features of the 3D CAD model. Based on the test models in Table 1, the entropy of [PERSON]'s method is formed from 2754 to 7.38 \(\times\) 10\({}^{5}\) dB. Figure 7 shows the entropy of the proposed method and the entropy of the previous methods according to the number of facets. The entropy of our method is always greater than the entropy of previous methods. Consequently, our method is superior, providing more security compared with previous methods. Figure 7: Entropy of the proposed method.
## 4 Design and Implementation
### BFSOOK Modulation  
Each bit is represented as one stripe in the captured image with OOK modulation. Although it provides a higher data rate but at the cost of increased sensitivity to noise. Additionally, the distance between transmitter and receiver is strictly related to the data rate. With a slight change in the distance, the number of observed stripes changes, and thus the decoded bits. Alternatively, in Frequency Shift OOK, the data is directly encoded in the form of frequencies of On-Off cycles. Therefore, by identifying the transmitting frequencies, the receiver can demodulate the signal. In Binary-FSOOK, two frequencies are used for representing logic 1 and 0 respectively.
## 6 Evaluation
### Evaluation Results
#### 6.2.2 Packet Loss Rate  
The packet loss rate (PLR) of each protocol is shown in Figure 14. PLR is expressed by:  
\[\text{PLR}=\frac{\sum_{i=1}^{N}(i_{\textit{Received Packet}})}{\sum_{i=1}^{N}(i_{ \textit{Detected Packet}})} \tag{10}\]  
where \(N\) is the number of nodes, \(i_{\textit{Received packets}}\) is the number of packets received by node \(i\), and \(i_{\textit{Detected packets}}\) is the number packets detected by node \(i\).  
Figure 14a shows the packet loss rates of AR-Proposal and AR-Basic. They are both about 0.05 when the number of nodes is as small as 5. However, when the number of nodes increased to 10 or 15, the PLR of AR-Basic increased more than that of AR-Proposal. The former was greater than the latter by more than 0.1 when the number of nodes was 15. Figure 14b compares the packet loss rates of RR-Proposal with that of RR-Basic. The difference between the PLR of the former and that of the latter was about 0.05 irrespective of the number of nodes. Both AR-Proposal and RR-Proposal provide better collision avoidance because they stagger message transmission timing. When AR-Basic or RR-Basic was used, i.e., when collision avoidance relied on CSMA/CA only, an increase in the number of nodes resulted in a dramatic increase in the number of messages. This made it difficult for the collision avoidance processing to catch up, resulting in a dramatic increase in the packet loss rate. Figure 14c compares the packet loss rates of AR-Proposal with that of RR-Proposal. As the number of nodes increased, the PLR of AR-Proposal increased less than that of RR-Proposal. This is because AR-Proposal does not use REQUEST messages. This means that fewer packets are transmitted, resulting in a higher communication efficiency.  
Figure 13: Number of packets that arose in each 10-ms interval during stray node detection processing in the case where the number of nodes was 15. (**a**) AR-Basic; (**b**) AR-Proposal; (**c**) RR-Basic; and (**d**) RR-Proposal.
# An Integrated Thermal-Electrical Model for Simulations of Battery Behavior in CubeSats
## 2 Methodology  
Initially, this section introduces the equations employed to model the CubeSat position in orbit and the satellite orientation. The transient irradiance and thermal balance are then described for a CubeSat geometry with a battery inside the satellite and six photovoltaic panels (PV) covering the external surfaces. In the sequence, the adopted electrical model includes the architecture of the satellite's electrical power system (EPS), the solar panels, the battery, and its coupling with thermal parameters.  
The models presented in this section are subjected to the following assumptions:  
* Circular orbit without perturbations;
* One node is attributed for each part of the satellite, namely, six PV (\(w=[1,2,3,4,5,6]\)) and a battery (\(w=7\));
* One normal vector will represent the orientation of each PV;
* PV does not generate heat;
* Thermal resistance among the solar panels is infinite;
* Every PV panel exchanges heat with the battery;
* Gray surfaces (absorptivity equal to emissivity);
* Constant material properties;
* Perfect vacuum condition;
* Experimental results regarding the battery internal losses are available.