esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Load Test and Contact Force Analysis of the AERORail Structure
## 3 AERORail Dynamic Load Test and Analysis
### Dynamic Load Identification
#### 3.2.2 10 m Span Dynamic Load Identification  
A similar method is used to process the acceleration and displacement data measured in the 10 m span, and some data are displayed according to the same method. The wavelet decomposition filtering method is used to filter the relevant displacement signals, and the dynamic deflection curves of the filtered 10 m span under different speed loads can be obtained. Using the time domain identification method and the filtered displacement signal, the actual movement and loading start and end times in the test are identified, and the acceleration signal actually used for calculation is intercepted.  
After resampling, substitute the fundamental frequency data of the 10 m span in Table 3. Using the speed and time information obtained by identification and the filtered mid-span acceleration signal, the vehicle-rail contact force-time curve shown in Figure 14 can be identified and obtained.  
The general rules of the 10 m span results are as follows: (1) The two absolute peaks of the contact force at the front and the rear of the curve gradually disappear after the speed increases (over 10 km/h); (2) The contact force still has a certain volatility, but it is decreasing; (3) At higher speeds, a longer and gentler trough appears in the middle of the curve. The fusion of the two contact force peaks may be due to the time lag of the first impact generated by the contact force when the speed is faster and the second impact to advance instead, causing the vibration effects to overlap. This is caused by the stability of the algorithm and the fluctuation of the signal itself. When the span increases, the contact force has relative stability in the mean value and time of the impact force on the AERoRail.  
To further analyze the results of the 10 m span and draw more instructive general conclusions, the average and maximum values of the above results are also calculated, and the results are shown in Table 5.  
Figure 14: The contact force identification result of the 10 m span. (**a**) 5 km/h, (**b**) 10 km/h, (**c**) 15 km/h, (**d**) 20 km/h, (**e**) 25 km/h.  
The data with a span of 10 m do not have obvious linear reduction characteristics, and the data volatility is obvious. Specifically, it has the following three characteristics: the average contact force fluctuates between 3800 and 4600 N with the speed change; the maximum contact force fluctuates to a certain extent with the change in vehicle speed; there is no obvious correlation between the statistical properties of the contact force curve and the vehicle speed.
# BVLOS Unmanned Aircraft Operations in Forest Environments
## 5 Taking a Risk-Based Approach to BVLOS Operations in Forest Environments
### Taking a Risk-Based Approach to BVLOS Operations in Forest Environments: Executive Summary  
* A risk-based approach will require incorporation of Detect and Avoid (DAA) technologies, consideration of C2 links, the detailing of operational procedures, and carefully managing human factors.
* The best DAA technology will depend upon the operating environment and the airspace classifications above that forest environment. In the case of varying operating environments, a combination of DAA technologies will likely be necessary.
* It is important to ensure that BVLOS is actually necessary and that alternatives, such as EVLOS+, will not be sufficient for the task.
* The operating manuals would need to clearly detail risk mitigations, such as JSA's, training requirements, fatigue risk management, and drug and alcohol policies.
## 2 The Role(s) of Non-Collagenous Bone Matrix Proteins in Bone
### Effects of Non-Collagenous Proteins on Bone Structure and Mechanics
In studying the Bgn-Dcn\({}^{-/-;-/-}\) double knockouts, the aforementioned effects from single knockouts were found to be additive and, in fact, synergistic in bone tissues. The onset of the osteogenic state was much earlier in Bgn-Dcn\({}^{-/-;-/-}\) mice compared to in Bgn\({}^{-/-}\) mice in spite of no apparent bone loss in Dcn\({}^{-/-}\) mice [115]. Thus, understanding the mechanism for this observed synergy is of considerable importance in light of the fact that functional synergies between NCPs are not guaranteed, as seen in OPN-OC double-deficient mice, and currently remain unpredictable. A rather limited number of studies report findings on other double-knockout models, including osteopontin-bone sialoprotein\({}^{-/-;-/-}\) (OPN-BSP\({}^{-/-;-/-}\)), biglycan-e-eiphycan \({}^{-/-;-/-}\), biglycan-fibromodulin\({}^{-/-;-/-}\), and TNAP-NPP1\({}^{-/-;-/-}\) [73, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103]. For OPN-BSP double-deficient mice, subtractive or compensatory effects were observed for the whole-bone architectural parameters: as an example, OPN\({}^{-/-}\) mice possessed significantly higher BMD, and BSP\({}^{-/-}\) mice possessed lower BMD compared to WT. In OPN-BSP\({}^{-/-;-/-}\) mice, the BMD was still lower than in WT, but the value was in between those of single NCP deficiencies [73]. This suggests potential functional compensation between the two NCPs. Biglycan-epiphycan double-deficiency is implicated in osteoarthritis, as are its single-knockout counterparts. In a male mice model lacking both SLRPs, the severity of osteoarthritis proved significantly higher in the double-deficient mice than both singly-deficient mice [76]. These examples thus again highlight the necessity of studying double knockouts in the context of bone physiology and pathology. Yet, there is a paucity of ultrastructural details in these studies--for example, the description of collagen fibril orientation, arrangement, and organization--and mechanical characterization of bones at both the micro and tissue levels. We are still far from understanding the effects of such deficiencies on bone structure and mechanics.
# Hyperspectral Imaging for the Detection of Bitter Almonds in Sweet Almond Batches
## 3 Results and Discussion
### Identification of Bitter Almonds in Adulterated Sweet Almond Batches
Thus, in those cases where the number of kernels in contact or the overlap between them was high, the separation between the almonds was not successful, and this affected the accuracy of the method. These results agreed with those reported by [PERSON] et al. [27], who developed an algorithm based on morphological mathematical operations able to discriminate between clusters composed of up to three kernels of wheat. These authors arranged the wheat grains in an orderly manner, having a minimal contact surface between them. Obviously, for their implementation in the processing lines, this is not a realistic situation for the almond industry. It should be noted that the present work is only the first approach to study the feasibility of using the HIS technology for the identification of bitter almonds within processed batches, and further research is in progress. Although the validation carried out in this work allows a fairly accurate identification of individual bitter almonds, in the almond industry, large flows of the product are processed, so more complex mathematical approaches are required previously to its real implementation in the processing lines.
## 3 Results
### Experimental Setup and Performance  
As shown in Table 2 the proposed method exhibited a clear advantage over CNN, TCN, gated recurrent unit (GRU), bidirectional GRU (BiGRU), and BiLSTM in both Bearing 1 and Bearing 2 test sets. It demonstrated lower MAE and RMSE compared to the other models. The network model outperformed the baseline models in direct prediction, highlighting its enhanced generalization. Adaptability was a main advantage of the proposed network architecture, allowing it to select different scales for various temporal dynamics. This adaptability enabled it to effectively capture the complex temporal patterns present in different datasets, demonstrating superior generalization ability. Table 3 and Figure 8 show the performance comparison of the model under different parameters.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Method**} & \multicolumn{2}{c}{**CNN**} & \multicolumn{2}{c}{**TCN**} & \multicolumn{2}{c}{**GRU**} \\ \hline \multicolumn{1}{c}{**Metric**} & \multicolumn{1}{c}{**MAE**} & \multicolumn{1}{c}{**RMSE**} & \multicolumn{1}{c}{**MAE**} & \multicolumn{1}{c}{**RMSE**} & \multicolumn{1}{c}{**MAE**} & \multicolumn{1}{c}{**RMSE**} \\ \hline \multirow{8}{*}{Bearing 1} & 3 & 0.161 & 0.193 & 0.108 & 0.122 & 0.102 & 0.133 \\  & 4 & 0.105 & 0.128 & 0.105 & 0.142 & 0.096 & 0.1
## 4 Conclusions  
In this study, we analyzed the application of OCT to the fields of brain tissue structural and functional imaging using a model of mesh-based Monte Carlo for OCT (MMC-OCT). We first verified that the MMC-OCT model used in this paper is an effective analytical tool for brain researches through simulations of structural imaging. Then, through theoretical analysis and simulation of functional sensing, we found that the measurement accuracy of hemoglobin concentration \(C\), oxygen saturation \(Y\), and scattering coefficient \(S\) can be optimized by reasonable selection of wavelengths. Finally, we proposed the following optimization schemes for imaging vascular structure and cerebral cortex in brain tissue: When imaging small-sized blood vessels, we can reasonably select three wavelengths to resolve \(C\), \(Y\), and \(S\). When imaging the cerebral cortex and the spatial resolution requirement is not high, the method of fitting with multi-wavelength data should be used; when the spatial resolution requirement is high, the measurement accuracy of \(C\), \(Y\), and \(S\) can be improved by roughly predicting their ranges in advance to select the appropriate wavelengths for calculation. What is more, these methods are also suitable for other complex 3D bio-tissues. In particular, the method of wavelength selection is also applicable to other optical imaging modalities such as photoacoustic imaging [37], near infrared multispectral imaging [19], etc. In the future, we will provide the whole set of codes and a small guidance for reproducing the results of simulations presented in this manuscript, which provides an analytical method for studying the brain tissue and other complex 3D bio-tissues.  
Conceptualization, L.Y.; formal analysis, L.Y.; methodology, L.Y. and L.S.; software, L.Y., M.Z. and B.H.; supervision, L.S.; validation, L.Y. and L.S.; writing--original draft, L.Y.; writing--review and editing, L.S., M.Z. and B.H.  
This research received no external funding  
L.Y., M.Z., and B.H. acknowledge the State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instruments, Tsinghua University for support through a PhD studentship.  
The authors declare no conflict of interest.
###### Abstract  
The organic Rankine cycle (ORC) is a popular technology used in waste heat recovery and low-grade heat utilization, which are two important measures to solve the problems brought by the energy crisis. The economic performance of ORC system is an important factor affecting its application and development. Therefore, the economic analysis of ORC is of great significance. In this study, R123 and R245 fa, two frequently-used working fluids during the transition period, were selected for calculating and analyzing the economic performance of an ORC used for recovery of waste heat with a low flow rate and medium-low temperature. Five traditional economic indicators, namely total cost, net earnings, payback period, return on investment, levelized energy cost, and present value of total profit in system service life, which is a relatively new indicator, were used to establish the economic analysis model of ORC. The variation effects of evaporation temperature, condensation temperature of working fluid, flue gas inlet temperature, and mass flow rate of flue gas on the above six economic indicators were analyzed. The results show that the optimal evaporation temperature of R123 is 125 \({}^{\circ}\)C, the optimal condensation temperature is 33 \({}^{\circ}\)C, and the optimal heat source temperature is 217 \({}^{\circ}\)C. For R245 fa, the optimal evaporation temperature is 122 \({}^{\circ}\)C, the optimal condensation temperature is 27 \({}^{\circ}\)C, and the optimal heat source temperature is 177 \({}^{\circ}\)C. The economic performance of an ORC demonstration project was reported and used for comparison with the estimation and analysis. It was found that the single screw expander has an excellent economy performance, which greatly reduces the proportion of expander cost in the ORC system.  
organic Rankine cycle; economic indicators; economic performance analysis; R123; R245 fa; demonstration project +
Footnote †: journal: Applied Sciences
## 4 Experiments and Result Analysis
### Efficiency Analysis  
In terms of computing time, the operation of Google Colab under the same parameter scale fails to run the final result due to the limited computing power of Google Colab. Small-scale hyperparameter combinations are chosen for comparison in order to perform traditional grid search hyperparameter optimization and improved grid search hyperparameter optimization. Table 8 depicts the configuration space for small-scale hyperparameter combinations.  
In Table 9, computation time and roc_auc score are compared for grid search, genetic algorithm search and IGSA [29]. The roc_auc score of IGSA is close to the global optimum and higher than the genetic algorithm search. More importantly, the hyperparameter  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metrics** & **SVM** & **NB** & **DT** & **Work [18]** & **Work [28]** & **Proposed Method** \\ \hline Accuracy & 0.78 & 0.63 & 0.95 & 0.96 & 0.96 & **0.98** \\ Precision & 0.60 & 0.69 & 0.95 & 0.95 & 0.96 & **0.98** \\ Recall & 0.78 & 0.63 & 0.95 & 0.95 & 0.96 & **0.99** \\ F1-score & 0.68 & 0.65 & 0.95 & 0.96 & 0.96 & **0.99** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison of different models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameter** & **Range** & **Step** \\ \hline n\_estimators & [220, 270] & 10 \\ max\_depth & [20, 32] & 2 \\ min\_samples\_split & [2, 4] & 1 \\ min\_samples\_leaf & [1, 3] & 1 \\ max\_features & [2, 15] & 2 \\ bootstrap & \{true, false\} & \textbackslash{} \\ criterion & \{gini, entropy\} & \textbackslash{} \\ \hline \hline \end{tabular}
\end{table}
Table 8: Configuration space for small-scale hyperparameters.  
optimization speed is increased by 165 times compared to grid search and 5 times faster than genetic algorithm search, proving the method's effectiveness.
# Context-Aware Beam Selection for IRS-Assisted mmWave V2I Communications
## 1 Introduction  
The advent of millimeter-wave (mmWave) technology has sparked a transformative wave in next-generation wireless communication. In this dynamic landscape, Vehicle-to-Everything (V2X) communication emerges as a pivotal force poised to reshape the future of transportation systems [1]. By enabling seamless interaction among vehicles, infrastructure, pedestrians, and other road users, V2X holds the promise of fostering safer, more efficient, and smarter transportation networks [2]. However, leveraging mmWave for V2X introduces significant challenges due to the high-frequency nature of mmWave signals, including increased propagation loss, susceptibility to interference, and mobility constraints [3]. Additionally, mmWave small cells operate with highly directional narrow beams to maintain high data rates, but this results in frequent beam switching and handovers as vehicles rapidly move between beam sectors, adding further complexity to maintaining stable and reliable communication.  
To fully harness mmWave's potential for V2X communication, researchers and engineers are delving into innovative solutions aimed at enhancing signal propagation, coverage, and reliability. One particularly promising avenue gaining momentum is the integration of Intelligent Reflecting Surfaces (IRSs) into mmWave V2X systems [4]. Outfittedwith passive reflecting elements, these surfaces excel at redirecting signals when a direct link between User Equipment (UE) and a mmWave Base Station (BS) is obstructed [5]. IRS technology emerges as a focal point in the research community, holding the potential to significantly boost spectral efficiency and expand communication range [6].  
The efficacy of IRS-assisted mmWave V2X communication hinges not only on deploying IRSs but also on tailoring their functionality to the specific demands and dynamics of the mmWave V2X environment [7]. Context-awareness becomes paramount in this context, enabling the system to dynamically respond to real-time changes in vehicular mobility, traffic patterns, and environmental conditions [8]. Employing context-aware beam selection strategies alongside IRS technology proves instrumental in enhancing communication quality and coverage, minimizing latency, and ensuring the reliability of mmWave V2X communication across diverse scenarios [9].
## 5 Discussion and Conclusions  
In this study, for the ECT inspection of the steam generator tube, we used FEM to detect noise factors generated from curvature to improve fault-detection capabilities. The signal generated by the curvature was found to be one of the sources of noise, and the curvature position may vary in the inner and outer sections due to the bending of the pipe during manufacturing. Using CT analysis, the pipe cross-section was found to have an average oval of about 4.8% compared to the initial curvature. The shape was modeled using a 3D simulation to perform the electromagnetic analysis. The modeling showed that there was a relatively concentrated current density and an electric field in areas with high ovality. However, the ECT signal conversion results showed that the difference in current density does not have a significant effect on the noise signal. Noise signals tend to occur at the starting point of the U-bend in a straight-line tube, and residual stress in the tube is thought to significantly affect the section in which deformation begins. The manufacturing process of the curved tube was found to have a stronger effect on the structure of the inside of the pipe than on changes with a higher sectional ovality of the pipe. The CT analysis suggested that if the coil probe passes through an area where the inside of the coil is not held in a circular shape, a coil may be tilted or shaken. Due to this effect, the noise signal amplitude was greater at the boundary position (between straight and curved tubes) than at the curvature position. Further design-modeling technology development is needed in order to analyze this.  
[PERSON], and [PERSON], conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], K.K., analyzed the data; [PERSON], wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (2017M2A8A4015158).  
Not applicable.  
Data Availability StatementData available on request due to restrictions eg privacy or ethical The data presented in this study are available on request from the corresponding author. The data are not publicly available due to [The research data policy in company].  
The authors declare no conflict of interest.  
Figure 10: The amplitude variation with coil position of the simulation analysis results.
## 1 Introduction
### Background
As per [PERSON] et al., an essential step in addressing the above issue is the need to develop simplified methodologies for measuring UFP concentrations [8]. Currently, most investigations must rely on complex data collection processes and expensive equipment that makes research into the subject prohibitive. Linking UFP levels to diesel truck traffic counts offers an inexpensive option, especially for regions with minimal monitoring infrastructure [9]. Such an approach has the potential to enable more targeted mitigation strategies, such as traffic management or the implementation of green infrastructure, to reduce exposure. Regions such as Morgantown, WV, USA that have unique features like steep inclines and complex terrain present ideal locations for investigations into the influence of topography on the dispersion of UFPs. To address the issue of determining UFP concentrations in Appalachian regions like Morgantown that have varying complex topographies, the current study seeks to address the critical knowledge gaps in our understanding of UFP emissions in such regions. The findings from the data analyzed here will offer critical guidance into UFP exposure prevention through optimal urban planning strategies like better zoning and location of busy highways far from complex topographical locations. This research seeks to enhance the estimation of ultrafine particle (UFP) concentrations in complex topographical regions by creating a simplified, cost-effective methodology that associates heavy diesel truck traffic with UFP levels. This study will provide more information on the relationship between UFP concentrations and traffic emissions, offering critical insights for air pollution mitigation, transportation planning, air quality legislation, and public health risk evaluation.
# Ionospheric TEC Prediction in China Based on the Multiple-Attention LSTM Model
## 2 Data and Proposed Model
### Data Preprocessing
#### 2.2.3 Normalization of TEC Data  
After the first-order difference processing, the original TEC data become a stationary non-random time series and can be predicted. However, there are still great changes in the data of the whole data space, which will affect the prediction results, so the data needs to be normalized. In this paper, min-max normalization is used to map the first-order differential TEC data between 0 and 1. The calculation formula of min-max normalization is as follows:  
\[x_{i}=\frac{x_{i}-min(x)}{max(x)-min(x)} \tag{2}\]  
where \(x_{i}\) is the TEC observation value at time \(i\), and \(x\) is a vector composed of all TEC observations in a selected area.
## 4 Simulation and Water Tank Experiment Results
### Results of Water Tank Experiment
#### 4.2.3 Analysis of Experimental Results  
In order to compare the traditional pulse counting method with the deep learning method proposed by us, we adopted the deep learning method and the traditional pulse counting method to realize time slot synchronous clock and data recovery, respectively, in the water tank experimental system; the corresponding system communication BER is shown in Figure 14. It can be seen from the figure that the system communication BER using the deep learning method is lower. This phenomenon also proves that the time slot synchronous clock recovered by the deep learning method is more accurate. It is worth noting that the BER of the system using CPR-Net is lower than the BER of the system using CPD-Net, which directly shows that the method of using CPR-Net to predict the time slot synchronous clock phase value is better.