esa-sceva/satcom-chunk-collection huggingface dataset:
# Multidisciplinary Aerodynamic Design of a Rotor
## 2 Methodology
### Modeling
#### 2.2.2 Rotor Direct Power Loss  
The rotor power loss is derived in order to determine an objective function in this study. The incremental torque can be expressed as  
\[dQ=\widetilde{r}dF_{x} \tag{15}\]  
where \(dF_{x}\) is blade section backward force.  
Combine Equation (2) and Equation (15), and represent \(dQ\) as two parts:  
\[dQ=\widetilde{r}\tan\varphi dF_{z}+\widetilde{r}\frac{dD}{\cos\varphi}=dQ_{i}+ dQ_{o} \tag{16}\]  
where \(dF_{z}\) is blade section normal force; \(dQ_{i}\) reflects the cost to provide lift perpendicular to the blade section; \(dQ_{o}\) reflects the cost to rotate the blade in a viscous fluid.  
Operate the rotor backward force \(dH\) in a similar way  
\[dH=(\tan\varphi\sin\psi-\sin\beta\cos\psi)dF_{z}+\frac{\sin\psi}{\cos\varphi} dD=dH_{i}+dH_{o} \tag{17}\]  
Since there is no y-direction quantity in a hub wind coordinate, the quantities are rearranged with respect to hub wind direction, and the quantity \(R\mu dH_{i}+dQ_{i}\) as \(dQ_{1}\) is defined as  
\[dQ_{1}=(R\mu\sin\psi+\widetilde{r})\tan\varphi dF_{z}-R\mu\sin\beta\cos\psi dF _{z} \tag{18}\]  
Combine Equation (1), and note that \(\tan\varphi=u_{p}/u_{t}\),  
\[dQ_{1}=\frac{\dot{\beta}}{\omega}(r-\varepsilon R)dF_{z}+\lambda_{h}R\cos \beta dF_{z} \tag{19}\]  
The integral within one revolution of first part in Equation (20) must be zero, since the \(dF_{z}\) and flap angle \(\beta\) are periodic in a steady trim.  
Rewrite \(dQ\) with the relationship \(\lambda_{h}=\lambda_{i}-\mu_{x}\alpha_{f}\) in the hub reference system,  
\[dQ=\lambda_{i}R\cos\beta dF_{z}+(R\mu dH_{o}+dQ_{o})-R\mu dH-\mu_{x}\alpha_{f}R \cos\beta dF_{z}+\frac{\dot{\beta}}{\omega}(r-\varepsilon R)dF_{z} \tag{20}\]  
Rotor power loss is expressed as  
\[P=\omega Q=P_{i}+P_{o}+P_{f} \tag{21}\]where \(P_{i}=\frac{\omega m_{B}R^{2}}{2\pi}{\int_{0}^{2\pi}}\int_{e}^{1}\lambda_{i}dT\), \(P_{o}=\frac{\omega m_{B}R^{2}}{2\pi}{\int_{0}^{2\pi}}\int_{e}^{1}\left(\mu dH_{o} +\frac{1}{R}dQ_{o}\right)\), \(P_{f}=-\frac{\omega m_{B}R^{2}}{2\pi}{\int_{0}^{2\pi}}\int_{e}^{1}\mu dH+\mu_{x} \alpha_{f}dT\).  
The component \(P_{i}\) is defined as induced power, which reflects the power consumption to produce rotor thrust, with the pattern of the dot product of induced velocity and section lift. The component \(P_{o}\) is defined as profile power, which reflects the power consumption to rotate the rotor in the air. The component \(P_{f}\) is determined by the entire helicopter trim state, fuselage equivalent drag area and gross weight.  
Define  
\[P_{rot}=P_{i}+P_{o} \tag{22}\]  
as the objective function in this study. It can be seen that the term \(P_{rot}\), called direct power loss (DPL), is a component representing rotor design merit and rotor performance. It is also advantageous to adopt DPL as the objective function, since it can represent hover FM (\(FM=P_{iid}/P_{rot}\)) and rotor performance in forward flight in one equation.
## 3 Emission Reduction Measures at Ports
### Landside Interface
Digitalisation measures: In July 2023, Hamburger Hafen und Logistik AG (HHLA) deployed a digital solution at Hamburg to allow truckers to access its terminals in a more efficient way; this solution used a passify app to replace the existing truck card (www.portstrategy.com, accessed on 20 December 2023). As a result, the waiting time of external trucks at the port has been reduced. Two companies, driveMybox and IBEXUS, formed a partnership in November 2023, aiming at developing a secure method to store and validate emission data for container trucks by utilising blockchain technology and a digital container tracking platform (www.portstrategy.com, accessed on 20 December 2023). Predictive models such as deep belief net and support vector machine have been used to predict external truck arrivals, which can improve the terminal efficiency [53]. The Port of Antwerp initiated the Intermodal Solution and Connectivity Platform, which is an online tool designed to inform users about transport options to and from the port of Antwerp. As a result, a better synchronisation between port stakeholders and a smoother connectivity between the port and the hinterland can be achieved [52]. Policy and collaboration measures: Taiwan set up an emission standard regulation for diesel vehicles in 2013, under which all heavy-duty diesel vehicles must pass the test of the A5 standard to obtain a one-year pass to access seaports [54]. California's air regulator issued a new rule in 2023 that trucks bought for use in the state's ports and rail yards must be zero-emission (e.g., electric model powered by batteries or hydrogen fuel cells) from 2024, and every California drayage vehicle must be a zero-emission vehicle by 2035 (www.wired.com, accessed on 20 December 2023). The modal shifting policies can take international, national, and local perspectives. For example, internationally, the EU White Paper on Transport provided a roadmap towards a resource-efficient transport system and cut CO\({}_{2}\) emissions, including modal shifts, with the goals of shifting 30% of road freight to rail or water transport by 2030 (eur-lex.europa.eu, accessed on 20 December 2023). Nationally, the French Government initiated projects to promote modal shifts for the freight from the Port of Marseille-Fos to the region (www.marseille-port.fr, accessed on 20 December 2023). Locally, the Port of Rotterdam has included modal split obligations in concession contracts with container terminals since 2015 in order to promote modal shift to less polluting modes of transportation [55].
# Precoder and Decoder Co-Designs for Radar and Communication Spectrum Sharing +
### Contributions of This Work  
In this paper, precoders and decoders are designed, optimized and adapted jointly using interference alignment (IA) to manage interference in coexisting multicarrier radar [20] and multiuser-multicarrier communication systems. DoF exploitation and mutual interference between radar and communication systems are studied for the coexistence of radar and multiple communication users.  
The main contributions of this paper are as follows:  
* The multicarrier model of [20] is extended to a more general setting in which multicarrier radar and communication systems coexist. Compared with [20], the resulting generalized multicarrier radar-communication signal model is able to capture a multicarrier radar-communication coexistence scenario and the differences between multicarrier radar and communication waveforms.
* A joint precoder-decoder design is proposed using the max-SINR criterion and IA theory for a multicarrier-multiuser radar-communication coexistence scenario. Specifically, at each user, the signal space and interference space are spanned by columns of the decoder. Consequently, mutual interference between radar and multiuser communication systems can be almost completely eliminated by the proposed joint design.
* For \(K\) communication users and one radar user interference channel and assuming that the IA constraint is feasible, the proposed joint precoder-decoder design is able to achieve \(N_{\text{sc}}(K+1)/2\) total DoFs, which is known as the achievable DoF upper bound for the \((K+1)\)-user interference channel with \(N_{\text{sc}}\) subcarriers [21]. In other words, if radar waveforms and communication codebooks are appropriately designed, this proposed IA-based design is able to achieve the optimal total information throughput for the entire radar-communication coexistence system. Radar users could obtain better detection performance, diversity gain and interference-free DoFs compared to a subspace-based precoder-only design.
## 3 The Proposed Protocol for Energy Efficient Routing in WSNs
### Description of the Adopted Energy Model  
Currently, there is a great deal of research in the area of low-energy radios. Different assumptions about the radio characteristics, including energy dissipation in the transmit and receive modes, affect the performance of different protocols.  
In this work, the energy model adopted is as follows: the radio dissipates 50 nJ/bit (\(E_{\text{elec}}\)) to run the transmitter or receiver circuitry and 100 pJ/bit/m\({}^{2}\) (\(E_{\text{amp}}\)) for the transmit amplifier [11]. The energy that a node dissipates for the radio transmission \(E_{\text{Tx}}\)(\(k\), \(d\)) of a message of \(k\) bits over a distance \(d\) is due to running both the transmitter circuitry \(E_{\text{Tx-elec}}(k)\) and the transmitter amplifier \(E_{\text{TX-amp}}(k\), \(d)\) and is expressed by the following:  
\[E_{\text{Tx}}\left(k,d\right)=E_{\text{Tx-elec}}\left(k\right)+E_{\text{Tx-amp}} \left(k,d\right)=E_{\text{elec}}\cdot k+E_{\text{amp}}\cdot k\cdot d^{2} \tag{1}\]  
where \(E_{\text{elec}}\) is the transmitter circuitry dissipation per bit--equal to the corresponding receiver circuitry dissipation per bit--and \(E_{\text{amp}}\) is the transmit amplifier dissipation per bit per square meter. Moreover, by using multi-hop routing, \(E_{\text{Tx}}(k\), \(d)\) is reduced when compared to that in single-hop routing. Similarly, the energy dissipated by a node for the reception \(E_{\text{Rx}}(k)\) of a message of \(k\) bits is due to running the receiver circuitry \(E_{\text{Rx-elec}}(k)\) and is expressed by the following equation:  
\[E_{\text{Rx}}\left(k\right)=E_{\text{Rx-elec}}\left(k\right)=E_{\text{elec}}\cdot k \tag{2}\]
# A Hybrid Data-Fusion System by Integrating CFD and PNN for Structural Damage Identification
###### Abstract  
Recently, a variety of intelligent structural damage identification algorithms have been developed and have obtained considerable attention worldwide due to the advantages of reliable analysis and high efficiency. However, the performances of existing intelligent damage identification methods are heavily dependent on the extracted signatures from raw signals. This will lead to the intelligent damage identification method becoming the optimal solution for actual application. Furthermore, the feature extraction and neural network training are time-consuming tasks, which affect the real-time performance in identification results directly. To address these problems, this paper proposes a new intelligent data fusion system for damage detection, combining the probabilistic neural network (PNN), data fusion technology with correlation fractal dimension (CFD). The intelligent system consists of three modules (models): the eigen-level fusion model, the decision-level fusion model and a PNN classifier model. The highlight points of this system are these three intelligent models specialized in certain situations. The eigen-level model is specialized in the case of measured data with enormous samples and uncertainties, and for the case of confidence level of each sensor is determined ahead, the decision-level model is the best choice. The single PNN model is considered only when the data collected is somehow limited, or few sensors have been installed. Numerical simulations of a two-span concrete-filled steel tubular arch bridge in service and a seven-storey steel frame in laboratory were used to validate the hybrid system by identifying both single- and multi-damage patterns. The results show that the hybrid data-fusion system has excellent performance of damage identification, and also has superior capability of anti-noise and robustness.  
data fusion; damage identification; probabilistic neural network; fractal dimension +
Footnote †: journal: Applied Sciences  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# Fault Diagnosis of Rolling Bearings Based on Acoustic Signals in Strong Noise Environments
## 6 Conclusions  
Compared with vibration sensors, microphones have the advantages of non-contact detection, high sensitivity, low cost, and easy installation. In order to solve the problem of complex components and serious interference of rolling bearing sound signals, a rolling bearing acoustic signal fault diagnosis method based on the Secretary Bird Optimization Algorithm and SBOA optimization (Feature Mode Decomposition, FMD) was proposed. The method was verified through simulation and test signals. The following conclusions were obtained:  
1. This paper proposes a method for bearing fault diagnosis based on sound signals. Compared with vibration signals, sound signals have the advantages of non-contact, economical price, and convenient remote monitoring. However, sound signals are extremely susceptible to noise interference, so noise reduction processing of sound signals is crucial.
2. The rolling bearing sound signal fault diagnosis method based on parameter-adaptive FMD studied in this article preprocesses the original sound signal through parameter-adaptive FMD, which can reduce the impact of noise and other interference components on diagnosis and then obtain the envelope spectrum for fault diagnosis.
3. Simulation and experimental analysis show that compared with the fixed parameter FMD and parameter adaptive VMD methods, the method studied in this paper can effectively reduce noise interference, accurately extract fault information from the bearing sound signal, and realize bearing fault diagnosis.  
Conceptualization, H.W. and J.X.; methodology, H.W. and J.X.; software, H.W.; validation, H.W. and J.X.; formal analysis, H.W.; investigation, H.W.; resources, [PERSON]; data curation, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ningbo City Unveiled Project (2023T016).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflicts of interest.
###### Abstract  
To study the influence of obstacles on the premixed gas explosion process and provide a theoretical basis for the safe use of fuel and the space configuration within weakly constrained structures, experimental and numerical simulation studies were conducted to investigate the explosion behavior of H\({}_{2}\)/CH\({}_{4}\) premixed gas under different obstacle conditions. According to the angle and position of obstacles, 12 explosion working conditions were set up, and the flame kinetic behavior under the combined influence was obtained. The results show that increasing the direct contact area between the obstacle and the flame near the ignition source can effectively reduce the explosion effect. The explosion consequence is most serious when the obstacle is located in the middle position of the weakly constrained structure. When the obstacle is close to the vent, the later the flame reaches the vent, the more the explosion pressure peaks, and the explosion impact decreases as the angle of the obstacle decreases. In the numerical simulation, it was also found that when the flame passes through the obstacle near the ignition source, it takes on a special \"jellyfish\" shape toward the vent. In conclusion, the results of the study are useful for making reasonable assumptions about the location of the ignition source and the presence of obstacles based on the degree of damage to the weakly confined structure caused by the premixed gas explosion.  
deflagration dynamics; large eddy simulation; premixed combustion; obstacle location +
Footnote †: journal: applied sciences  
0000-0002-2310-782X][PERSON]  
1 School of Petroleum Engineering & Environment, Zhejiang Ocean University, Zhoushan 316022, China; [EMAIL_ADDRESS] (Y.H.); [EMAIL_ADDRESS] (Y.W.); [EMAIL_ADDRESS] (X.S.); [EMAIL_ADDRESS] (S.P.)
## 3 Google and Alibaba Traces
In Figure 4, the machine's contribution in the provisioning process for a static \(B\) is derived from the real trace cases, which means the amount of resources released must achieve the requested demand with good performance. For provisioning with an adaptive \(B\), a machine list is dynamically rearranged, and the best resources are chosen. A rational derivation for \(B\) values is to correlate the minimum and maximum demand values during a time interval (\(RD_{min}\), \(RD_{max}\)) with respect to the maximum and minimum provisioned values (\(PR_{min}andPR_{max}\)). The relationship is derived by finding the minimum value of the normalized averaged of the minimum demand and what was provisioned and the normalized average of the maximum demand and what was provisioned during the time window (\(T_{w}\)). The final boundary value is described in Equation (1) for each time window. \[B=\min\biggl{\{}\frac{PR_{max}+RD_{max}}{T_{w}\times RD_{max}},\frac{PR_{min}+ RD_{min}}{T_{w}\times RD_{min}}\biggr{\}}\times RD. \tag{1}\]The Alibaba data center log trace [41] has similar characteristics to Google trace, where the workload is submitted in a set of jobs. Each job has a set of tasks, and in each task, there are a set of instances. An instance is a copy of the task operation with different input data. A task at Alibaba has six states, where Ready, Waiting and Running need resources (scale out),  
Figure 4: Google data center with Constant and Adaptive B Ratios and Gradually Increasing Resources. Figure 3: Google data center Provisioned and Requested Workloads, Sample 2. Figure 2: Google data center Provisioned and Requested Workloads, Sample 1.  
and Terminated, Failed and Canceled do not need resources (scale in). For each instance, there is an average usage of RAM and CPU. Instances are used as the demand reference, and the states indicate the scaling direction because each instance is run in the task. In the schema, the workload is found by multiplying the number of instances by the CPU usage value for CPU demand and RAM usage value for RAM demand during the time window \(T_{w}\). Workload demand is found based on instance (Ready, Waiting, Running) states means that resources are requested, and other states mean resources are to be released. Again, values must be normalized, the RAM demand is already normalized, but the CPU demand is not, which is normalized during data preparation. For provisioning of the resources, the machine has three states (add, soft error, hard error). The adding machine is used to scale out. For scale in, it is not specified, but it is considered as soft or hard errors. In Alibaba trace, more Terminated states are shown, which gives a negative value because the trace is cut into small time durations of 12 hours and does not show the mapped start time and ID for the terminated instances. It is fixed by adding a biased value (negative minimum value) for both demand and provisioning, based on which scaling is found. Figures 5 and 6 depict the Alibaba data center scaling in reaction to workload demand.
## 4 Solvents for CO\({}_{2}\) Capture  
Solvent-based technology for CO\({}_{2}\) separation is one of the most deeply studied fields in CO\({}_{2}\) capture. Whether it is for application in absorption, adsorption, or membrane-based separation processes, solvents are used to enhance the driving force between the gaseous and liquid phases, facilitating the selective transport of CO\({}_{2}\) to the liquid side. The principle behind the separation of CO\({}_{2}\) using a solvent depends on the binding mechanism of the latter with CO\({}_{2}\). To this end, solvents are mainly separated into physical and chemical.
# Application and Evaluation of Surrogate Models for Radiation Source Search
## 5 Surrogate Performance Results
The Gaussian processes surrogate models are constructed in a way to avoid this problem using anisotropic kernel functions. The Gaussian processes surrogate models are more accurate than the other models, but require a large amount of computation and evaluation time when compared with the other models. However, the combination of the computation and evaluation expenses required by the Gaussian processes surrogate models is still significantly less expensive than the evaluation of the physical model at those same test points. Radial basis function and neural network surrogate models also provide accurate approximations of the physical model with the trade off of requiring a moderate amount of time to compute the surrogate models. The evaluation time for the RBF and neural networks surrogate models is an order of magnitude smaller than the other surrogate models, excluding the less accurate MARS surrogate models, and substantially smaller than the physical model evaluation time, making them ideal for Bayesian inference and uncertainty quantification. We used MATLAB's built-in neural network and Gaussian processes packages to develop these two surrogate models, whereas we constructed the functions required to evaluate the radial basis functions and Legendre functions surrogate models. Additionally, we employed the third-party ARES toolbox [28] for the construction of the MARS surrogate models. Figure 6: Log of detector 1 model responses plotted for the first 50 test points versus the: (**a**) Legendre surrogate model computed via Equation (21); (**b**) Legendre surrogate model computed via Equation (26); (**c**) MARS surrogate model; (**d**) radial basis functions surrogate model; (**e**) Gaussian processes surrogate model; and (**f**) neural networks surrogate model.
## 5 Results and Discussion
### Fourier Series Transformation
Regarding the sensitivity to the chosen maximum frequency, no significant influence on the retrieved soil moisture is observed. By excluding frequencies higher than the revision time, some information about soil moisture is naturally lost in the backscatter time series. In principle, a diurnal cycle can be observed for soil moisture, with decreasing soil moisture one to two hours before sunrise and increasing soil moisture about two to four hours before sunset [88]. Since all available orbits are used, the original SAR time series includes soil moisture information from both morning (around 05:00 a.m. to 06:00 a.m.) and evening acquisitions (around 04:40 p.m. to 05:10 p.m.) for both study areas. Excluding high frequencies, information about the sub-daily dynamics of soil moisture is therefore filtered out. Nevertheless, the dynamics of changing vegetational water content and dew cycles during the day are also filtered out, which are also affecting the backscatter signal of C-band. Excluding high temporal frequencies also reduces the representation of short-term wetting and drying of topsoil due to precipitation events, while retaining weekly and seasonal patterns. Since the backscattering signal is not only affected by precipitation through soil moisture increase but also by intercepting water, these effects are also reduced. In doing so, a tradeoff can be made between losing temporal information about soil moisture and reducing backscatter changes unrelated to soil moisture. Thus, the exclusion of frequencies higher than the revisit time mainly links to the effect of the changing angle of incidence rather than the loss of soil moisture information. Looking at the power spectral density, calculated using [PERSON]'s average periodogram [89], a small peak at 3 days frequency is observable, which is the period between ascending and descending scenes (Figure 7). The next increase in power compared to the in-situ measured soil moisture power spectral density curve is starting from 5 days frequencies onward, which is again the period between ascending and descending scenes, while it is also peaking at 6 days frequency, which is the period between matching orbits of Sentinel-1 A and B. As the power spectral density of the Fourier Transformed soil moisture time series is reduced for these frequencies, it is in line with filtering out backscattering signals related to alternating incidence angles.