esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Beamforming Efficiency Utilizing Taguchi Optimization and Neural Network Acceleration
###### Abstract
Phased antenna arrays play a pivotal role in the evolution of communication networks such as 5G and the future advancements toward 6 G. These antennas offer essential directionality and adaptability to meet the escalating demands for data throughput and connectivity [17, 18] in these next-generation communication networks [19, 20]. In 5G, phased arrays are utilized for dynamic beamforming, enabling communications with high speed and low latency and improving coverage in dense environments. Moving towards 6G, these antennas are poised to become even more critical, leveraging higher frequencies and emerging technologies to achieve faster speeds, ultra-low latency, and groundbreaking applications such as extended reality, holographic communications, and more. Phased antenna arrays [21] will be instrumental in maximizing spectral and spatial efficiency to address the technological challenges inherent in these advancements towards next-generation wireless networks [22, 23]. The rest of this paper is structured as follows. Section 1 introduces the topic of our study, providing context and outlining its significance. In Section 2, we delve into phased antenna arrays, offering a detailed theoretical formulation and discussing their relevance in the field. Section 3 elaborates on the Taguchi method, providing a thorough explanation of its implementation and showcasing synthesis results for various types of antenna arrays (10, 16, and 24 linear antennas). Section 4 focuses on the synthesis of radiation patterns using neural networks, detailing how the Taguchi method is employed for training and validation in this context. Finally, Section 5 concludes our work by summarizing the findings of our study and outlining potential future research directions. Table 1 provides a comprehensive comparative analysis of the various approaches used in synthesizing antenna array radiation patterns. Examining the advantages, disadvantages, and performance of each method allows for a detailed evaluation of the available options. Thus, this comparative study offers valuable insights that can guide the selection of the most appropriate method based on the specific needs of each application. Further  
more, identifying the strengths and limitations of each approach paves the way for future research with the aim of improving existing techniques and exploring new avenues for optimal synthesis of antenna array radiation patterns while considering parameters such as efficiency, complexity, and precision.
###### Abstract
Another assessment that has not been visible is understanding the impact of LH\({}_{2}\) utilization on aero-engine hot section blade life compared with jet fuel. Creep is one of the most common failure mechanisms that reduce component life [32; 33; 34]. [PERSON] et al. [35] provided a comparison for hydrogen and natural gas using an industrial gas turbine. The study shows a longer life to rupture for hydrogen against natural gas, which is in accordance with the expectation documented in references [10; 14; 36; 37; 38]. However, this has not been demonstrated for an aero engine, comparing LH\({}_{2}\) and kerosene impact on creep life assessment, which this paper aims to discuss. To this end, this paper provides performance and creep life assessment when comparing LH\({}_{2}\) with jet fuel in a turbofan aero engine. The benefit of this assessment is to justify the use of LH\({}_{2}\) from both the performance and hot section component life points of view. As mentioned in the previous paragraph, there are no publications to the authors' knowledge that compare LH\({}_{2}\) and kerosene for turbofan aero engines that look at cycle performance, exergy, and creep life assessment, as well as highlighting the merits and drawbacks of LH\({}_{2}\). The cycle's performance analysis was simulated with an in-house performance modeling technique developed by the authors [39], while the exergy assessment used a thermodynamic relationship for each component. This paper provides a comprehensive evaluation of LH\({}_{2}\) compared with jet fuel from performance, exergy, and creep life viewpoints, which has not been covered in previous work. Knowing the HPT blade failure mode resulting from the fuel usage will minimize the level of uncertainty in fuel choice for aero engines, and the average time (hours) between overhaul or refurbishment of the HPT blade can be estimated in hindsight. This paper has hance been divided into three focal points of discussion. The overarching results align with the advantages that LH\({}_{2}\) offers over jet fuel as discussed in other research works. This advantage is mainly associated with the high specific fuel heating value of liquid hydrogen over kerosene. Aside from performance and life assessment, other factors will drive the feasibility and technology maturity for LH\({}_{2}\), especially with other biofuels competing favorably. The drawbacks to LH\({}_{2}\) becoming mainstream aviation fuel have been discussed extensively in this paper. The paper also recommends future work that can be done for comprehensive exergy and creep life assessment. One of the limitations of this work is that the authors did not look at combustor performance computation analysis, which would be addressed in future work. This combustor performance assessment will give depth of insight into the operational characteristics of LH\({}_{2}\), as this would have an impact on the chosen control and fuel metering strategy of the turbofan engine. This paper did not address emission as there has been much ongoing discussion on combustor design and emission control, which is outside the scope of this study.
## 4 Numerical Results and Discussion
### Comparative Discussion  
In this subsection, the presented architecture is compared with previous works evaluating similar architectures [34; 35; 36; 37], in terms of the type of implemented RNs in the architecture, the working frequency of the backhaul and access links, and if the architecture under analysis was implemented by hardware, as shown in Table 4. In general, in these works, the operation of the A&F and D&F protocols were analyzed and compared, showing that the use of D&F RNs presents an improvement in the performance of the architecture compared to A&F RNs. A limitation found in the analyzed studies is the considered signal model. In general, in the analyzed studies, the considered signal model is not a signal model standardized by the 3 GPP [16], as proposed in this work. This paper also proposes a hardware implementation of the relay nodes through the design of mmWave transmitter and receiver chains with off-the shelf, having considered in the simulations the non-linearities of the used subsystems. On the other hand, our proposal has the disadvan  
Figure 13: Throughput vs. SNR of the cooperative system; 256-QAM signal.  
tage of working in Half Duplex mode, compared to the Full Duplex operation of the other architectures considered [34; 35; 36; 37], which will be addressed in further research.
# Characteristics of Moduli Decay for the Asphalt Mixture under Different Loading Conditions
###### Abstract  
In order to explore the moduli decay patterns of asphalt mixtures under different loading conditions, the nonlinear fatigue damage model was implemented in order to simulate the moduli decay patterns. Then, the direct tensile, indirect tensile, and uniaxial compression fatigue tests were employed under four kinds of stress levels with four parallel tests. The specimens of AC-13C Styrene-butadiene-styrene (SBS) modified mixtures were manufactured. Based on the test results, the decay patterns of the moduli during fatigue tests under different stress states were revealed, and the parameters of the damage model under different test conditions were obtained. By changing the values of the model parameters under a certain loading condition, fatigue curves were obtained. Then, the fatigue properties of asphalt mixtures under different stress states could be compared and analyzed directly. The result indicated that the evolution curves of fatigue damage for the direct tensile test, the indirect tensile test, and the uniaxial compression test all experienced three stages, which indicates that the fatigue damage characteristic of asphalt mixtures is non-linear. The decay patterns of the direct tensile moduli and the tensile moduli measured by the indirect tensile test are similar. The decay patterns of the uniaxial compression and the compression moduli measured by indirect tensile test are similar. The decay patterns of tensile and compressive moduli are obviously different. At the same cycle ratio state, the position of the decay curve for the compression moduli is higher than that of the tensile moduli. It indicates that the tensile failure is the main reason of the fatigue damage for asphalt mixture. The new analysis method of fatigue damage was proposed, which provides a possibility to compare the fatigue results that were obtained from different loading conditions and different specimen sizes.  
sphalt mixture; service life; fatigue test; moduli decay; loading condition; stress state Article
# Dynamic Performance of a New-Type PSC I-girder for Railway Bridge Application
## 2 Dynamic Performance Evaluation of New Type of PSC I-girder Bridge
### Dynamic Performance Evaluation of New PSC I-girder Bridge  
#### 2.3.1 Natural Frequencies  
Since the I-girder develops lower torsional rigidity than the box girder, its torsional mode will occur at proximity of the flexural mode in the lower order mode range, which presents the risk for these two modes to superpose and amplify the responses [9]. Table 5 lists the natural frequencies of the PSC I-girder bridge with the corresponding mode according to the type of train with their critical speeds per considered span length of the bridge. The first flexural mode appears to be the first mode for the span lengths of 35 m and 40 m whereas the first mode for the span lengths of 30 m and 45 m is the torsional mode. In addition, when the first mode is the flexural mode, the second mode is the torsional mode and happens closely. Figure 8; Figure 9 shows the first and second mode shapes for the span lengths of 30 m and 35 m. The first flexural mode related to the critical speed ranges between 3.39 Hz and 4.5 Hz.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Span**} & \multirow{2}{*}{**Mode**} & \multirow{2}{*}{**Natural**} & \multicolumn{6}{c}{**Critical Speeds (km/h)**} \\ \cline{3-8}  & & & **Freq. (Hz)** & **KTX** & **HEMU** & **EMU** & **Freight** & **Saemaeul** \\ \hline \multirow{4}{*}{30 m} & 1(Torsion) & 4.14 & 278.81 & 362.31 & 319.07 & 207.99 & 350.38 \\  & 2(Flexure) & 4.50 & 302.73 & 393.39 & 346.44 & 225.83 & 380.44 \\  & 3(Torsion) & 7.16 & 482.26 & 626.68 & 551.89 & 359.76 & 606.05 \\  & 4(Torsion) & 12.74 & 857.66 & 1114.50 & 981.49 & 639.80 & 1077.80 \\ \hline \multirow{4}{*}{35 m} & 1(Flexure) & 4.27 & 288.02 & 374.27 & 329.60 & 214.86 & 361.95 \\  & 2(Torsion) & 4.67 & 314.87 & 409.16 & 360.33 & 234.89 & 395.69 \\  & 3(Torsion) & 8.21 & 552.79 & 718.33 & 632.60 & 412.38 & 694.68 \\  & 4(Torsion) & 13.14 & 885.10 & 1150.16 & 1012.90 & 660.28 & 1112.30 \\ \hline \multirow{4}{*}{40 m} & 1(Flexure) & 3.56 & 239.92 & 311.77 & 274.56 & 178.98 & 301.51 \\  & 2(Torsion) & 3.76 & 253.23 & 329.06 & 289.79 & 188.91 & 318.23 \\  & 3(Torsion) & 7.70 & 518.63 & 673.95 & 593.52 & 386.89 & 651.76 \\  & 4(Torsion) & 11.35 & 763.81 & 992.55 & 874.10 & 569.80 & 959.87 \\ \hline \multirow{4}{*}{45 m} & 1(Torsion) & 3.29 & 221.51 & 287.84 & 253.49 & 165.24 & 278.37 \\  & 2(Flexure) & 3.39 & 228.47 & 296.89 & 261.46 & 170.44 & 287.11 \\ \cline{1-1}  & 3(Torsion) & 7.66 & 515.39 & 669.73 & 589.80 & 384.47 & 647.68 \\ \cline{1-1}  & 4(Torsion) & 10.12 & 681.48 & 885.56 & 779.88 & 508.38 & 856.41 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Analytically obtained natural frequencies and critical speeds per train type.  
Figure 7: Model of PSC I-girder railway bridge with two traffic lines for moving load analysis.
## 2 Materials and Methods
### Data Sources
#### 2.2.2 Climatic Data  
For the estimation of GPP in rubber plantations over Hainan Island, we used input datasets of photosynthetically active radiation (PAR) from the Global Land Surface Satellite (GLASS) product [49] ([[http://www.glass.umd.edu/index.html](http://www.glass.umd.edu/index.html)]([http://www.glass.umd.edu/index.html](http://www.glass.umd.edu/index.html)), accessed on 7 April 2022), as well as air temperature (T\({}_{\rm a}\)) and relative humidity (RHJ) data from the National Earth System Science Data Center (NESSDC) ([[http://www.geodata.cn/data/](http://www.geodata.cn/data/)]([http://www.geodata.cn/data/](http://www.geodata.cn/data/)), accessed on 7 April 2022). The spatial resolution of PAR is 0.05\({}^{\circ}\), and we used the linear interpolation method to obtain the 1 km data. The monthly PAR was calculated by averaging daily values. T\({}_{\rm a}\) and RH were spatially downscaled to a spatial resolution of 1 km from the reanalysis datasets of ERA-5 [50]; and the temporal step was monthly.
# Influence of Rock Slide Geometry on Stability Behavior during Reservoir Impounding
## 1 Introduction
This study focuses on the impact of the geometry and kinematics on the stability behavior during reservoir impounding of deep-seated rock slides in the surroundings of large dam reservoirs. The first part of this study deals with the underlying mechanisms that occur during the interaction of a dam reservoir and the valley flanks by building an analytical block model based on GNU Octave [16]. In the second part, the limit-equilibrium method is applied to case-study-related geometries to determine the change in the factor of safety during the initial impounding of a reservoir. A relationship between the reservoir level and the corresponding factor of safety for the different geometries is presented.
# Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks
## 3 The Proposed Mechanism
### Preliminary Formation for the Proposed Work
#### 3.1.7 The Reputation of Hop Level Neighbors  
Some nodes may not cooperate due to their selfish behavior. Such nodes are enlisted during the data transmission by a source node. If a node does not reply to a route request, then it is considered a selfish node. The selfish nodes are not served by other relay nodes for data transfer requests. In detail, if a node continues non-cooperative behavior for a long period of time T, then it is black-listed. In addition, the blacklisted nodes are not requested for relaying services. A counter Cr is used when the source node declares a node as a blacklisted one after being selfish. A list of selfish and blacklisted nodes is broadcasted among the neighbors so that they might be contacted adaptively by all neighbor nodes. The states of selfish and blacklisted nodes can revert to normal after a specified period of time.  
Due to the existence of selfish nodes in the WSN, unfavorable situations may exist. For instance, it is possible that a node may not be able to communicate or transfer its data to the sink due to the presence of one or more selfish nodes along the route. In the worst case, a node can meet relay nodes that are all selfish. In such cases, these nodes cannot communicate with their destinations [9]. Such nodes typically attempt to retransmit data repeatedly during a particular period of time. In this study, we assumed that multiple alternate paths for transmissions were available in order to cope with such network partition by selfish nodes. Due to the fact that node intelligence encourages selfish behavior, a node must be able to select only the reliable ones among multiple relays to prevent excessive packet drops.
## 7 Results Analysis
### Complexity of PDP Based ML and Scaling Issues  
The distributed and adaptable policy decision point (PDP) based on the ML algorithm can be parallelized across multiple processors or nodes, making it simple to scale. However, the experiments in this unbalanced dataset have scaling issues, and the number of allowed and denied policies in the dataset may vary considerably. Therefore, it will negatively affect the model's performance; however, to solve the scaling issue, use the adaptive synthetic sampling method (ADASYN) to generate balanced datasets. Additionally, the proposed method has complexity in terms of time and space. Therefore, we will compute the complexity of the proposed method using the RF because it produces satisfactory outcomes and has an efficient algorithm for the proposed solution. Where \(n\) = the number of training examples, \(m\) = the number of features, \(k^{\prime}\) = the number of trees, \(k^{\prime}\) = the number of trees, and the maximum height \(O(logn)\), the complexity of an algorithm is computed using the following formulas:  
\[\text{{Train Time Complexity}}=O(k^{\prime}*n*log(n)*m) \tag{17}\]  
\[\text{{Test Time Complexity}}=O(m*k^{\prime}) \tag{18}\]  
\[\text{{Space Complexity}}=O(k^{\prime}*depth\text{ {of tree}}) \tag{19}\]
# Entropy and Statistical Complexity in Bioelectrical Signals: A Literature Review
## 3 Results
### Probability Distribution Function P  
Signals, being continuous, can be treated discretely through a three-step process: (i) sampling, (ii) probability distribution P, and (iii) quantification. First, sampling involves taking measurements of a continuous signal at regular time intervals. Second, the signal is characterized through a probability distribution function, which, according to the literature, has been referred to in various ways; for example, as the Probability Distribution Function (PDF) P [5], ordinal distribution, or distribution of ordinal patterns [6]. It has also been termed the discrete probability distribution function [1], all of which are universally denoted as P.  
It is important to highlight that the informational content of the system is typically evaluated through this probability distribution [6]. Additionally, this distribution, denoted as P, describes the probability of a given physical quantity, represented by an observable, occurring at a specific time t. In other words, P reflects how the possible occurrences of the observable are distributed over time. This is fundamental not only for understanding the associated uncertainty but also for modeling the variability in the behavior of a physical system under study. Furthermore, from the perspective of information theory and thermodynamics, such a distribution plays a crucial role in quantifying and meaningfully extracting information about the studied system based on the observed data [5].  
It is worth noting that such a distribution allows for detailing the values of the observable quantity under study, such as any given bioelectrical signal. Essentially, measuring the informational content of a specific observable quantity is largely equivalent to describing its associated P in detail [1].  
The description of the time series (t) through Information Theory assumes that \"the underlying P of the time series is already given a priori. However, a key part of the analysis is extracting P from the time series, for which no unambiguous procedure exists\" [7] (p. 49). Various procedures have been proposed to address the question of how to appropriately select P, including (i) approaches based on amplitude statistics or histograms, (ii) representation through symbolic dynamics, (iii) Fourier analysis, (iv) wavelet transform, and (v) the use of permutation probabilities, among other methods [8].  
It is important to note that, in some cases, the calculation of entropy is intrinsically dependent on the probability distribution of the system's states or events. In other words, it is derived from or understood based on the probability distribution of all possible states the system may experience [5].
###### Abstract  
To understand the possibility of monitoring the crustal stress and tectonic activities via satellite remote sensing technology, an experimental study focused on the thermal infrared variation was performed for cyclic loaded rock in the outdoor condition with two types of strong interference background. The stress-induced infrared radiation was extracted using wavelet analysis. The results showed that due to the significant effect of the ambient temperature, the weak stress-induced infrared signal was indistinguishable from the original infrared radiation. However, after wavelet decomposition, the infrared radiation concurrent with the change in stress became clear, and the correlation coefficient with the stress increased significantly with the value of 0.91 after decomposition. Additionally, the amplitude of the extracted stress-induced infrared signal was close to the theoretical result, indicating that the wavelet analysis method can extract the weak infrared signals induced by cyclic loading in the background of strong interference to some degree. The results provide an experimental basis and ideas for monitoring crustal stress and tectonic activities using thermal infrared remote sensing.  
remote sensing rock mechanics; stress detection; interference; thermal infrared measurements +
Footnote †: journal: Applied Sciences
# Efficient Time-Series Clustering through Sparse Gaussian Modeling
## 4 The Sparse Time-Series Clustering Framework
### Clustering Stage  
The second and final stage of our framework is to partition the dataset \(\mathcal{X}^{\prime}=(X_{1}^{\prime},\ldots,X_{N}^{\prime})\), consisting of \(N\) sparse time series with \(m\) inducing points each, into \(k\)-clusters using the \(k\)-means algorithm for time series, with the DTW Barycenter Averaging (DBA) algorithm [19] used for updating the cluster representatives in each iteration of \(k\)-means.  
We apply \(k\)-means with the \(\beta\)-DTW distance, defined in Section 2.2, with \(\beta=\frac{\pi T}{m}\). We use \(\alpha\in\{0,10^{-4},10^{-3},10^{-2}\}\) in our experimental evaluation. The intuition behind setting \(\beta\) to a multiple of \(T/m\) is that we expect that each inducing point serves as a representative of approximately \(T/m\) points, with increasing density of inducing points in time intervals where the time series exhibits more complex behavior. Hence, we want to (mildly) penalize with a multiple of \(T/m\) cases where different time series exhibit a high density of inducing points in distant time intervals.  
The time complexity of \(k\)-means with the \(\beta\)-DTW distance and the DBA algorithm for updating the cluster representatives is \(\Theta(INkm^{2})=O(INk\log^{2}T)\), where \(I\) is the number of iterations and \(O(m^{2})\) is the time required to compute the \(\beta\)-DTW distance between a pair of sparse time series, and \(\Theta(Nkm^{2}T)=\Theta(Nk\log^{2}T)\) is the time complexity of a single iteration of \(k\)-means. In our experimental evaluation, we run \(k\)-means and DBA for a maximum of \(I=100\) iterations.
# Two-Channel OTDM System for Data-Center Interconnects: A Review
## 4 Discussion
In the previous experiments, the transmission distances of the 150 Gb/s and 200 Gb/s OTDM signals were limited to <2 km (in the C-band region) even when using the electrical equalizer because the electrical equalizer cannot directly compensate for the CD in an IM/DD system [1]. Thus, it is worth developing ways to compensate for the CD in an optical domain to further increase the transmission distance (even if the O-band region is utilized). However, the use of the DCF is not considered an attractive solution for short-reach systems because of its large footprint [1]. Instead, it is necessary to demonstrate transmission experiments using other optical CD compensation methods, such as the use of a delay-line-based optical equalizer [38] or the fiber Bragg grating [39].