esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.2.2 Starfm  
STARFM is a moving-window-based temporal and spatial resolution fusion algorithm that can not only be applied to the fusion of surface reflectance but can also be directly used for the fusion of NDVI and achieve good results [30; 45].  
Without considering geographic errors and the atmospheric correction error, the algorithm assumes that, under the condition that the land cover type and the systematic error do not change with time, the high-resolution surface reflectance image at time \(t_{k}\) can be predicted using just a pair of surface reflectance images consisting of a high-resolution image. A coarse-resolution image at the known reference time \(t_{0}\) and another coarse-resolution image at time \(t_{k}\) can also be predicted. First, a moving window of size \(w\) is set up to capture the neighboring similar pixels, and the weighting function \(W_{ij0}\) is determined by calculating the spectral difference \(S_{ij0}\), temporal difference \(T_{ij0}\), and spatial difference \(D_{ij0}\) between a pair of high-resolution and coarse-resolution scenes at time \(t_{0}\). Then, a prediction is made for a high-resolution image at time \(t_{k}\) based on the medium- and low-resolution image at time \(t_{k}\). The calculation method is as follows:  
\[G(X_{w/2},Y_{w/2},T_{k})=\sum_{i=1}^{w}\sum_{j=1}^{w}\sum_{k=1}^{k}W_{ijk} \times\left(L\left(X_{i},Y_{j},T_{k}\right)+G\left(X_{i},Y_{j},T_{0}\right)- L\left(X_{i},Y_{j},T_{0}\right)\right) \tag{2}\]  
\[S_{ij0}=\left|G\left(X_{i},Y_{j},T_{0}\right)-L\left(X_{i},Y_{j},T_{0}\right)\right| \tag{3}\]  
\[T_{ij0}=\left|G\left(X_{i},Y_{j},T_{0}\right)-L\left(X_{i},Y_{j},T_{k}\right)\right| \tag{4}\]  
\[D_{ij0}=1+\sqrt{\left(X_{w/2}-X_{i}\right)^{2}+\left(Y_{w/2}-Y_{j}\right)^{2} }/A \tag{5}\]  
\[W_{ij0}=S_{ij0}\times T_{ij0}\times D_{ij0} \tag{6}\]where \(G\) and \(L\) denote the high-resolution image and coarse-resolution image, respectively, \(X_{i}\), \(Y_{i}\) denote homogeneous image elements, \(w\) denotes the moving window size, and \(A\) is a constant.
## 2 Preliminary Consideration  
The wires in straight wire ropes under tensile load are mainly strained by tensile stresses [11]. In most practical cases, nominal rope tensile stress is merely a division of the rope tensile force and the wire rope cross section. However, the real tensile stress in individual wires can vary from this nominal value mainly according to the different lay angles of the wire and the strand layers. Real tensile stresses can then be calculated according to [12]:  
\[\sigma_{\text{tk}}=\frac{\frac{\cos^{2}\alpha_{\text{k}}}{1+v_{\text{k}}\sin^ {2}\alpha_{\text{k}}}E_{\text{k}}}{\sum\limits_{i=0}^{n}\left(\frac{z_{\text {i}}\cos^{2}\alpha_{\text{i}}}{1+v_{\text{i}}\sin^{2}\alpha_{\text{i}}}E_{i} A_{\text{i}}\right)}S \tag{1}\]  
where index \(i\) stands for the individual wire and \(k\) for the layers; \(E\) is the modulus of elasticity, \(A\) is the cross-sectional area of the wire, \(v\) is Poisson's ratio, \(\alpha\) is the lay angle, \(n\) is the number of wire layers, \(z\) is the number of wires in the layer, and \(S\) is the tensile force.  
The fatigue process generally involves the formation of a crack initiation point at preexisting flaws and crack propagation under the influence of a variable load. Besides variable tension load, factors such as fretting, friction, wear, or corrosion also influence the speed of the fatigue process. The fracture of strands in the wire rope is gradual; the rope maintains functionality even if some of the strands are broken. Mechanisms that affect load variation in wire ropes can be tension fatigue, bending-over-sheaves, free bending, and torsion fatigue.  
Tension fatigue, the most common mechanism of wire rope fatigue, involves changes in axial tensile load that affect the stress distribution in ropes. It primarily occurs in fixed wire ropes, lifting, and hoisting applications. Change in mass and acceleration is the primary source of axial load variation. Given that a dominant parameter for this type of fatigue is load range, the number of load cycles for tension fatigue can be estimated using a simple power law equation [12]:  
\[N=\left(\frac{c}{F_{\text{eqr}}}\right)^{m} \tag{2}\]  
where \(c\) stands for an experimentally obtained constant, \(m\) is an empirically determined power number, and \(F_{\text{eqr}}\) is the equivalent load range:  
\[F_{\text{eqr}}=\frac{S_{\text{max}}-S_{\text{min}}}{F_{\text{max}}-S_{\text{ m}}}\cdot 100 \tag{3}\]  
with \(S_{\text{max}}\), \(S_{\text{min}}\), and \(S_{\text{m}}\) being the maximum, minimum, and mean load, and \(F_{\text{max}}\) the ultimate breaking load (Figure 1).
# A Survey of Forex and Stock Price Prediction Using Deep Learning
## 5 Discussion
### Analysis Based on the Method Used
#### 5.1.3. LSTM  
The following points were worth discussing from reviewing the LSTM papers:  
1. According to the datasets used, 3 papers used a combination of technical analysis, and sentiment and news analysis to predict the stock. The rest used technical analysis, only with the exception of one paper which used expert recommendations.
2. For the variables, the closing price was the choice of 23 LSTM-based models, there were 8 papers that used closing price only, and 12 papers included close price, open price, high price, and low price in their input.
3. It could be found that 15 of the papers changed the traditional LSTM model to pursue a higher performance in prediction. Attention-based LSTM and LSTM with RNN were the most frequent models, showing up in three different papers. Two papers chose the method of LSTM with GRU to improve the model.
4. The metrics used in each paper were different; there were 17 metrics used for measuring the performance of the LSTM based model.
5. Multiple articles selected RMSE, MAPE, MAE, accuracy, and MSE. For RMSE, we found that paper [36], paper [65], and paper [38] were in the lowest order of magnitude, with paper [36] achieving the lowest; paper [32] was in the second-lowest order of magnitude; and papers [39; 45; 46; 47] were in the third lowest order of magnitude. Paper [37] was in the fourth lowest order of magnitude, and paper [35] had the highest order of magnitude.
6. For MAPE, paper [57] had the lowest order of magnitude followed by papers [10; 38; 38; 41; 60; 62; 66]. Paper [40] had the highest order of magnitude for MAPE.
7. For MAE, papers [38; 40; 42; 57] had the lowest order of magnitude, with paper [57] having the best performance; papers [32; 37] were in the second-lowest order of magnitude, while paper [41] was in the third lowest order of magnitude. Paper [35] had the highest order of magnitude.
8. For MSE, paper [59] had the lowest MSE and was in the lowest order of magnitude; papers [33; 40; 42] were in the second-lowest order of magnitude, while papers [32; 39] were in the third lowest order of magnitude. Papers [41; 43] were in the fourth lowest order of magnitude, paper [38] was in the fifth-lowest order of magnitude, and paper [35] had the highest order of magnitude.
9. For accuracy, paper [36] had the highest accuracy, followed by [34; 45; 50; 52; 53; 54; 61], and paper [56] had the lowest accuracy.
# Product Demand Prediction with Spatial Graph Neural Networks
## 4 Experiments  
In our investigation, we answer the research questions (RQs) proposed in Section 1, which aim at evaluating the efficacy of our spatial GNN model in the scenario of geographic product demand prediction. These questions are designed to probe the model's performance, the incremental value of its components, and its robustness across different settings.
## 6 Results and Discussion  
This section presents and discusses the comparative performance of the six NMPC configurations (C, CW, P, PW, I, and IW) detailed in Section 5.2. The evaluation focuses on agronomic outcomes (biomass yield, constraint satisfaction), economic efficiency (net profit, operational costs), and computational performance (solver time). The discussion is organized based on two primary timescales: short-term (first 3 days) and full-cycle (40 days), followed by a general discussion of trade-offs and controller selection.
# Assessing the Applicability of Machine Learning Models for Robotic Emotion Monitoring: A Survey
## 4 Results and Discussion
Therefore, our study could not directly compare different models and sources; rather we set up a priority list of sources and ML models to work for robotic emotion monitoring. Moreover, most of the neural network-based methods outperformed other traditional ML methods. However, it was also noticed that, in a lot of cases, the experiments suffered from a lack of data. As per our survey, NN-based LSTM was the highest-performing method for valence-arousal data. However, only a few experiments used NN. Therefore, we still need to explore the applicability of NN models in emotion recognition. Another limitation would be that, even though facial imaging data had the highest level of accuracy among other data sources, most of the works did not consider fake expressions. Humans are capable of faking an expression, which may alter the results. Further, humans are capable of having more than one feeling in a moment, which was not considered in any of the experiments. Therefore, fake emotions and multiple emotions also need to be considered for future experiments in this field.
## 5 Experimental Results
### Result Analysis & Validation
Therefore, both methods have different advantages and disadvantages, as shown in Table 5. The reduction ratio of fault candidates is the ratio of the size of fault candidates divided by the size of the updated memory. There is no significant difference between the reduction ratios of the two methods (22.43% and 19.21%). In addition, a fault candidate region identified by the stack includes symbols related to external ECU communication. However, a fault candidate region identified by the memory map includes symbols related to the internal ECU state. Therefore, the memory-map method is more useful for finding the fault location directly. However, a fault candidate region identified by the stack includes more symbols related to input signals that one identified by the memory map. In conclusion, since only indirect clues are available for finding the cause of a fault by the stack method, whereas it is possible to find the symbol directly causing the fault by the memory-map method, additional work is required to track the exact location of the symbol causing the fault. However, although the stack method requires further work, it can be applied independently of developer even in an environment without a memory map where HiL testing is performed. Figure 15: Differences of symbol types in fault candidates (memory map and stack).
# Prediction of Manufacturing Quality of Holes Based on a BP Neural Network
###### Abstract  
In order to improve the manufacturing quality of holes (\(\Phi\)3-\(\Phi\)8 mm) and to optimize the hole drilling process in T300 carbon fiber-reinforced plastic (CFRP) and 7050-T7 Al alloy stacks, a prediction model of multiple objective parameter optimization was proposed based on a back propagation (BP) neural network algorithm. Four parameters of feed rate, spindle speed, drilling diameter, and cushion plate were taken as the input layer parameters to study the manufacturing quality of holes in four stack types: CFRP/Al, Al/CFRP, Al/CFRP/Al, and CFRP/Al/CFRP. Delamination and tearing defects often appear in the drilling process; thus, a certain degree of defects in CFRP was selected as the output parameter, in an effort to build a prediction model of drilling quality. After the neural network model of the optimized hole-making process of an 8-14-1 three-layer topology was corrected by 170 steps, the error was reduced to 0.00016882, the regression fitting was 0.99978, and the fitting error of training samples was \(10^{-2}\)-\(10^{-5}\). The prediction model of the number of defective holes provided basically similar results to the experimental data. This indicates that the prediction model based on a BP neural network has good prediction ability. Based on the prediction of parameters, verification tests were performed, and the number of defective holes in CFRP was reduced while the manufacturing quality of the holes was improved significantly; the qualified rate of manufactured holes reached 97%.  
neural network algorithms; CFRP; drilling; quality prediction Article
# A Scheduling Scheme for Improving the Performance and Security of MU-MIMO Systems
## 4 Scheduling Schemes
### Suboptimal Schemes from Previous Works
#### 4.1.2 \"Fair\" Channel Allocation Algorithm  
This scheme was proposed in [26] and, similar to the algorithm based on Frobenius norms, the system chooses, in a first transmission (first time slot), the \(U\) users whose channel gain vectors have the highest squared Frobenius norms. Then, in the subsequent transmission, the algorithm chooses the \(U\) users whose channel gain vectors have the smallest squared Frobenius norms. Subsequently, the process is repeated. Alternate selection of users with higher and lower channel gains ensures fair access to the radio resources among all active users.  
This algorithm is characterized by its low computational complexity, since only the Frobenius norms of the vectors that contain the channel gains for the \(U_{t}\) users must be calculated. Thus, calculating the squared Frobenius norm of a vector of dimensions \(A\times 1\) requires \(2A\) complex additions and \(2A\) complex multiplications. Therefore, the total number of complex operations required by this algorithm is equal to  
\[\text{O}_{\text{j}}=4 AU_{t}. \tag{16}\]
# Part Qualification Methodology for Composite Aircraft Components Using Acoustic Emission Monitoring
## 1 Introduction
However, more progress is needed to gain the confidence required to integrate AE into structural qualification test standards [50; 51]. In general, AE analysis and post-processing can leverage the recorded waveforms and a number of useful parameters extracted from them. Common AE waveform parameters are described in Shull [13] and ASNT's NDT Handbook [52]. Certain AE patterns can provide information about the test specimen's structural integrity, beyond pass/fail load criteria. For example, AE activity may not be observed until load levels are reached that previously induced damage. Specifically, if a structure is progressively loaded, new damage may not be initiated until the prior maximum load has been exceeded. This observation is known as the Kaiser effect [13; 53], which, however, may not hold for composite materials similar to those evaluated in this manuscript [54; 55]. In cases where AE activity is detected at loads that are lower than the previous maximum, then a specific number is computed called the Felicity ratio (FR)--defined as the load at which significant AE reinitiates, divided by the previous maximum load. Materials that consistently emit AE at loads below their previous maximum, thus, have a FR value of less than one, and investigations have shown that they exhibit progressive damage behavior. In fact, changes in observed Kaiser and FR effects as well as other AE activity patterns have been correlated with the presence of damage [56; 57; 58; 59]. Based on this introduction, this research seeks to evaluate the progressive failure of full-scale, composite aircraft spars using AE during structural testing, and associate such information with ultimate failure prognosis. The insight gained from AE is intended to augment current test methodologies in order to capitalize on readily-available data and decrease the time taken to qualify novel airframe materials. The overall approach described includes the following parts: (1) characterization of damage progression by examining AE activity trends; (2) identification of probable damage regions; (3) ultimate failure load prognosis; and (4) statistical evaluation of the spar static strength requirements using AE data.
## 4 Conclusions  
This study explores the effects of cross-linkers with varying hydrophilicity on ionomers in PP substrates for water electrolysis applications. Unlike previous research focused on hydrophilic electrolyte monomers, two cross-linkers, E3-C10 (hydrophilic) and E3-C11 (hydrophobic), were utilized with a common electrolyte monomer to assess their impact on ionomer properties. Key findings include the successful creation of void-free PFAEMs, enhanced ion conductivity, and improved mechanical strength when hydrophilic and ester- and ether-free C10 was used. The PFAEMs demonstrated low hydrogen permeability and high chemical stability, particularly the E3-C10 PFAEM, which exhibited better alkaline stability. Performance tests showed that E3-C10 PFAEMs had higher ion conductivity and lower degradation compared to the E3-C11. However, membrane-electrode interfacial resistance affected the overall electrolysis performance, which was improved by coating electrodes with an ionomer layer. The study suggests that optimizing membrane-electrode interactions is crucial for improving the efficiency and durability of water electrolysis devices.  
This study demonstrates that in energy conversion devices like water electrolysis, the electrolyte is assembled to the electrode layer. In this process, optimizing the compatibility of ion, electron, and gas transfer between the electrolyte and the electrode is essential to achieve optimal performance. Therefore, it is recommended that future research focus on identifying the specific physical properties of the electrolyte membrane under development.  
Conceptualization, J.-H.P. and J.-S.P.; methodology, J.-H.P.; validation, J.-S.P.; formal analysis, J.-H.P., Y.P., T.-S.J. and Y.S.; investigation, J.-H.P., Y.P., T.-S.J. and Y.S.; resources, J.-S.P.; data curation, J.-H.P., Y.P., T.-S.J. and Y.S.; writing--original draft preparation, J.-H.P.; writing--review and editing, J.-S.P.; supervision, J.-S.P.; project administration, J.-S.P.; funding acquisition, J.-S.P. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.
## 4 Evaluation and Test Cases  
HEPSPEC06 is based on a benchmark specific high-energy physics (HEP) suite derived from the SPEC06 CPU benchmark [26]. This benchmark suite delivers better performance, as it uses 64-bit mode mainly. The HEPSPEC06 benchmark is used to test CPU core performance. Before this benchmark set, SPECint 2000 [26] was regarded as the most common benchmark in the high-energy physics community, withdrawn in February 2007. This benchmark was introduced in the period of 2007-2008 by HEPiX CPU Benchmark working group [8]. HEPiX group puts efforts together to launch this benchmark that is considered as the replacement of SPECint 2000. It replaced the outdated kSI2k metric. Briefly, this benchmark is used to measure a CPU's performance. It is suitable for the operations required for floating point calculations.  
The HEPSPEC06 test performs different tests on a single machine for each core at the same time. Since this test can perform integer operations and floating point closely linked to HEP codes, the scientific communities of physics and IT disciplines would likely use this benchmark. The HEPSPEC06 test also provides versatility on both 32-bit and 64-bit architecture to run the benchmarks. For optimizing CPU performance of virtual machines and containers in the cloud environment, HEPSPEC06 is the ideal benchmark. In this experimental setup, the HEPSPEC06 test suite is used to evaluate performance of VMs and containers supplied for scientific workload execution.  
As noted, the HEPSPEC06 benchmark is used to test the efficiency and performance of virtual machines and containers, so that high-power scientific workloads can be carried out on the experimental setup presented above. Throughout this evaluation process, the HEPSPEC06 benchmark runs on VMs or containers to analyze better performance and throughput as well. In this experiment, normal CLI (command line interface) commands on OpenStack are used for provisioning virtual machines and containers. Each virtual machine is initialized and controlled by an independently uploaded image that is installed with the basic operating system of CentOS 7.1, which works the same as the host operating system. On the other hand, the setup of containers also performed similarly in the cloud. Required necessary packages are also installed to run the HEPSPEC06 benchmark in virtual machines and containers. Three different test cases are evaluated in conjunction with techniques to increase VMs and containers' efficiency and throughput. In this section, we discuss the detailed results of all test cases.
## 3 QZS Analysis
### Comparative Analysis of Four Linear Spring Configuration Schemes to Achieve QZS  
The characteristic of QZS isolators suggests that the linear spring stiffness is zero at the equilibrium position. To meet the demand for vibration isolation at lower frequencies, a lower equivalent torsional stiffness distribution is needed. Accordingly, four configuration schemes, (a), (b), (c), and (d), are shown in Figure 5 (a-d, respectively). Here, the same stiffness coefficient and initial length (\(k=200\,\mathrm{N}/\mathrm{m}\), \(l_{5}=299.17\,\mathrm{mm}\)) were adopted, and in Figure 5c, two springs with symmetric distribution are used. Equivalent torsional stiffness can be obtained using the method described in Section 3.1.  
In Figure 5a, the spring chain is placed between two symmetric \"T\"-frame rocking rods, allowing changes in the equivalent torsional stiffness via changes in the stiffness coefficient of the linear spring and the distance variable, \(d_{3}\). In Figure 5b, the spring chain connects the adjacent sides of two parallelograms. \(\theta\) would change as the spring length increased in response to an input disturbance. For schemes (a) and (b), if the same \(k\) and \(d_{3}\) values were used, scheme (a) would yield less equivalent torsional stiffness, which would be beneficial to ultra-low-frequency vibration isolation. The conformations of schemes (c) and (d) depend on the installation method. In Figure 5c, the linear spring is connected to the basic platform, yielding a symmetric distribution, whereas in Figure 5d, the linear spring is connected to one rod within the mechanism.  
Figure 6 shows the distributions of equivalent torsional stiffness, corresponding to the four configuration schemes. Here, schemes (c) and (d) perform better when the initial length and stiffness coefficient are constant, and the rate of change in stiffness is primarily decided by \(l_{5}\) and \(d_{1}\).  
Figure 5: Four linear spring configuration schemes. (**a**) Spring-damping connects two main branches; (**b**) Spring-damping connects two parallelograms; (**c**) Spring-damping connects parallelogram and base platform; (**d**) Spring-damping connects vibration isolation platform and base platform.  
From the preceding analysis, scheme (a) yields a better high-static-low-dynamic performance and lower total stiffness than the other schemes, indicating the importance of spring stiffness distribution with regard to ultra-low-frequency vibration isolation. In addition, the linear spring chain configuration schemes should adhere to the principle of zero equivalent torsional stiffness at the equilibrium position, as well as of rod symmetry.