esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Instantaneous frequency (IF) of shaft rotation is pivotal for bearing fault diagnosis under variable speed operations. However, shaft IF cannot always be measured as tachometers are not allowed to be installed in every case due to design reasons and cost concerns. Extracting the shaft IF ridge from time frequency representation (TFR) of vibration signals, therefore, becomes an alternative. Linear transform (LT), such as short time Fourier transform (STFT), has been widely adopted for such a purpose. Nevertheless, the accuracy of extracted IF ridges relies on the readability of TFR. Unfortunately, readability of TFR from STFT is often impaired by the smearing effect caused by non-synchronous frequencies between bases and signal components and limited time frequency resolution capability, which in turn adversely influences the accuracy of IF ridge extraction. To accurately extract IF ridges from vibration signals, this paper focuses on the first factor, which causes the smearing problem, and proposes a method named frequency matching linear transform (FMLT) to enhance the TFR, where transforming bases with frequencies varying with the shaft IF are constructed to alleviate the smearing effects. To construct the transforming bases with frequencies synchronous with shaft IF, a fast path optimization (FPO) algorithm, which generates all possible optimization paths among amplitude peaks and thereby ensures the continuity of extracted IF ridges, is adopted for IF pre-estimation. The TFR with improved readability can be subsequently obtained via FMLT, paving the way for accurate IF ridge extraction. Then, multiple IF ridges can be iteratively extracted using the FPO algorithm. The accuracy of extracted IF ridges before and after TFR enhancement is compared, indicating that the proposed FMLT can enhance the readability of TFR and lead to more accurate IF ridge extraction for bearing condition monitoring.  
Keywords:instantaneous frequency estimation; time frequency representation; bearing condition monitoring; time-varying speed condition +
Footnote †: journal: Applied Sciences
## 4 DRL for Resource Management
However, in larger networks, complex states and optional actions make Q-learning maintain a large Q-table and slow convergence speed, which limits the application scenario. Thus, we apply Q-learning techniques with a DNN parameterized by \(\theta\) as the action-value function approximator to learn the optimal policies, thus called DQN [35]. To accelerate the learning convergence rate, we adopt double DQN [36] with three key techniques. (1) Replay Memory: At each time step, BS observes the state \(\mathbf{s}_{t}\), determines resource allocation \(\mathbf{a}_{t}\), and broadcasts it to all V2V users. After the execution of the V2V links, the BS gets the corresponding reward \(r_{t+1}\), and the environment reaches the next state \(\mathbf{s}_{t+1}\). In this way, experience samples are formed, that is, (\(\mathbf{s}_{t}\), \(\mathbf{a}_{t}\), \(r_{t+1}\), \(\mathbf{s}_{t+1}\)). Experience samples are stored in the replay memory. The replay memory accumulates the agent's experiences over many episodes. After that, a mini-batch is uniformly sampled from memory for neural network training. (2) Fixed Q-Target: As shown in Figure 2, our proposed scheme consists of double DQN, a target DQN and a training DQN, which have the same structure. The weight value \(\theta^{\text{target}}\) in the target network is updated by the training network weight \(\theta^{\text{train}}\) at regular intervals. This improves network stability and convergence. (3) Action Selection-Evaluation Decoupling: In double DQN, the training DQN is used to select the optimal action for the next state, then the action selected by the training DQN is input and the next state into the target DQN to generate the target value to calculate the training loss. By changing the action of target DQN selection to training DQN selection, the risk of over-estimation of the Q value can be reduced. After gathering sufficient experience samples, a mini-batch that consists of \(D\) experience samples is retrieved from the buffer to minimize the sum-squared error  
\[\sum_{t\in D}\Big{[}y_{t}-Q\Big{(}\mathbf{s}_{t},\mathbf{a}_{t}\Big{|}\theta^{ \text{train}}\Big{)}\Big{]}^{2} \tag{18}\]  
where \(y_{t}\) is the target \(Q\)-value, accordingly, the \(y_{t}\) can be respectively given by  
\[y_{t}=r_{t+1}+\gamma Q\Bigg{(}\mathbf{s}_{t+1},\ \underset{\mathbf{a}_{t+1}}{ \text{argmax}}Q\Big{(}\mathbf{s}_{t+1},\ \mathbf{a}_{t+1}\Big{|}\theta^{\text{train}}\Big{)} \big{|}\theta^{\text{target}}\Bigg{)} \tag{19}\]  
Then, the updating process for the BS double DQN can be written as [36]  
\[\theta^{\text{train}}=\theta^{\text{train}}+\beta\sum_{t\in D}\frac{\partial Q \big{(}\mathbf{s}_{t},\mathbf{a}_{t}\big{|}\theta^{\text{train}}\big{)}}{ \partial\theta^{\text{train}}}\Big{[}y_{t}-Q\Big{(}\mathbf{s}_{t},\mathbf{a} _{t}\big{|}\theta^{\text{train}}\Big{)}\Big{]} \tag{20}\]  
where \(\beta\) is a nonnegative step size for each adjustment. Algorithm 1 summarizes the training process of the proposed scheme.
## 6 Conclusions  
The conclusions are as follows:  
(1) Fifty-one underground powerhouses in China were summarized in detail, including their engineering scale, size, lithology, rock classification and in situ stress level. The complex stress environment and excavation technologies in China are also introduced. The detailed summaries provide convenience for analysis of excavation procedure and design parameters.  
(2) Excavation procedure methods and key parameters of three typical excavation zones (top arch zone, rock anchor beam zone, high side wall zone) in main powerhouse are influenced by in situ stress levels. The priority of each excavation procedure is different under different in situ stress levels. In addition, there are basically linear relationships between in situ stress levels and the key parameters.  
(3) Considering the bearing capacity of surrounding rock, the strength-stress ratio (SSR) is introduced to evaluate the cavern excavation stability comprehensively and recommend the excavation design. The results show that different SSR conditions correspond to different design parameters, and the parameters should be adjusted to guide the actual construction design quantitatively and control the cavern stability according to the specific SSR. Meanwhile, the suggestions on blasting excavation design are supplemented to control the stress transient unloading effect under high in situ stress.  
Besides, the downward excavation of large-span powerhouse leads to an increasing construction depth. It means increasing stress conditions to a certain degree, which also deserves further consideration. The rationality and accuracy of the study is determined by analyzing the influencing factors and comparing with the specification. So, the recommendations are relatively more accurate and scientific. It is believed that the summary and recommendations above will provide a good reference for the excavation procedure optimization of underground powerhouse under high in situ stress.  
Conceptualization, P.Y. and M.C.; methodology, X.L. and P.Y.; formal analysis, X.L., P.Y. and S.L.; investigation, X.L. and A.L.; writing--original draft preparation, X.L. and P.Y.; writing--review and editing, W.L., G.W. and S.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 51779192), Open Research Fund Project of Changjiang River Scientific Research Institute (No. CKWV2019742/KY), the Outstanding Youth Fund project of Hubei Natural Science Foundation (No. 2019 CFA060) and the Key Program of the National Natural Science Foundation of China (No. 51939008).  
The authors would like to thank the WSM Team and the PowerChina HuaDong Engineering Corporation Limited for the support of stress environment drawing and engineering investigation. In addition, this work is completed with the contributions of many researchers and their achievements. The authors wish to express thanks to all of them. In case of any omission, we express thanks to all supporters.  
The authors declare no conflict of interest.
# Measuring Maritime Paint Thickness under Water Using THz Cross-Correlation Spectroscopy
## 3 Results and Discussion
### Attenuation Coefficient of Silicone and Measuring Underwater  
The silicone patch required for underwater operation introduced additional complications of signal attenuation by THz absorption in the silicone. The frequency-dependent attenuation coefficient for our silicone is shown in Figure 4D and was recorded using a transmission setup. The spectrum showed an increasing absorption towards higher frequencies. For a given output power of any THz system, it was therefore optimal to have a high power spectral density at low THz frequencies to maximize the total power arriving at the receiver. Figure 4B shows that the THz-CCS system had a maximum output at 0.1 THz and that 90% of the total electric field resided below 0.4 THz. In this regime, the attenuation coefficient was \(\leq\)10 cm\({}^{-1}\), which for comparison was 20 times lower than for water [24]. To determine the efficiency of the silicone contact patch, two measurements through the contact patch onto a metal surface in dry conditions and submerged in water in Figure 5A could be considered, where there seemed to be no difference between the two measurements. Under the assumption that all materials did not have a magnetic response to the incident THz field, then the square of the electric field would be proportional to the intensity. From this, we could compute a measure \(I\)(t) which was proportional to the energy of the reflected signal by summing over the squared electric field amplitudes \(A(t)\). This could be used to calculate a measure of the total energy loss between a dry and submerged environment:  
\[\text{Energy loss}=1-\frac{I_{\text{water}}(t)}{I_{\text{dry}}(t)}=1-\frac{ \sum_{t}A_{\text{water}}(t)^{2}}{\sum_{t}A_{\text{ref}}(t)^{2}}=1-98\%=2\% \tag{9}\]  
This resulted in a 2% energy loss, which could be a result of a thin layer of water between the silicone and the sample. An energy loss of 2% translated to a water layer between the contact patch and the sample of 0.5 um, as the beams traveled through the layer twice in the reflection setup.  
Figure 4: **(A)** Measurement of sample and a reference measurement at \(\theta\) = 20 and s-polarization. The reference is a measurement of the backside of the paint sample. The circles mark the arrival time of each reflection. **(B)** FFT of **(A)**. The shaded blue area contains approx. 90% of the signal power. The vertical blue line represents the maximum power output at 0.1 THz. **(C)** Fit using the two measurements from figure **(A)**, results in a paint thickness of 232.8 μm. **(D)** Attenuation coefficient of silicone in the THz frequency domain, measured in a transmission setup.
# Maintenance Method for Rail Corrugation Considering Wheel-Rail Interaction Force
## 3 Wheel-Rail Interaction Force  
To evaluate the effect of rail corrugation on the track, wheel-rail interaction forces were utilized as shown in Figure 8. According to previous studies [20; 21; 22; 23; 24; 25; 26; 27; 28; 29], the rail surface roughness in contact with the wheel increases the wheel-rail interaction force, thus resulting in an increase in the inertia forces such as the dynamic wheel-rail contact force. [PERSON] and [PERSON] [20] proposed a dynamic model considering the wheel-rail interaction when the train is running at a speed of V, as shown in Figure 8.  
[PERSON] [15] proposed that the maximum dynamic wheel-rail interaction force (\(F_{dyn,\ max}\)) is expressed as follows:  
\[F_{dyn,\ max}=\beta\frac{M\
u^{2}}{L_{0}}\frac{dz}{dx_{norm}}\cdot QI, \tag{1}\]  
where \(\beta\) is the dimensionless calibration factor, \(L_{0}\) is the reference wavelength, \(m\) is the equivalence mass (wheel-rail system), and \(z\) is \(z_{0}\) sin(\(2\pi vt/L\)).  
The measurements of rail surface roughness on Section A before and after the grinding are shown in Figure 9a. Based on these roughness measurements and Equation (1), \(F_{dyn,\ max}\) was estimated, as shown in Figure 9b,c. To develop the distribution of the estimated wheel loads, the maximum values of \(F_{dyn,\ max}\) within every 100 mm interval were selected. Figure 9b shows these values for five intervals of 1000 mm. It is worth noting that the maximum value of \(F_{dyn,\ max}\) was estimated to be approximately 220 kN, which exceeded the design wheel load of the urban transit system (Seoul Metro subway) (i.e., 80 kN). However, the maximum value of \(F_{dyn,\ max}\) decreased to approximately 200 kN (approximately 9% reduction) after the grinding, which satisfied the design wheel load.  
Figure 8: Mass–stiffness combination in the wheel–rail system considering rail roughness.  
Figure 9: _Cont._The distributions of the estimated and measured wheel loads are compared in Figure 10. The Gaussian distributions of the measured wheel loads were constructed based on the data shown in Figure 7. The maximum wheel loads estimated for Section A (Figure 10) were used to develop the Gaussian distributions. Note that the maximum wheel loads for Section B were estimated in the same manner. It turned out that the distributions for the estimated and measured wheel loads for both sections are in good agreement. The mean and standard deviation values for the estimated and measured wheel loads were within 3.2% difference.  
Figure 10: _Cont._  
Figure 9: Example of evaluation results of the dynamic wheel–rail interaction force analysis: (**a**) measurement result for rail surface roughness; (**b**) analysis result for dynamic wheel–rail interaction force; (**c**) maximum dynamic wheel–rail interaction force for each section.
## 2 Mathematical Model of the Hybrid MG System
### PV System  
The single diode model, known as the five-parameter model of the equivalent circuit of a solar cell, is mathematically used to represent the PV array. The output current of the PV module (\(I_{PV}^{mod}\)) is determined as follows [31]:  
\[I_{PV}^{mod}=N_{cell}^{PV,\ P}\times I_{ph}-N_{cell}^{PV,\ P}\times I_{d}^{rev} \left[exp\left(\frac{q\times\left(V_{PV}^{mod}+I_{ph}\times R_{s}\right)}{N_{ cell}^{PV,\ s}\times b\times K_{b}\times T}\right)\right] \tag{1}\]  
The output voltage is denoted as \(V_{PV}^{mod}\). \(I_{ph}\) represents the light-generated current, while \(I_{d}^{rev}\) refers to the diode reverse saturation current (in amperes). The diode ideality factor is denoted by \(b\). \(K_{b}\) is Boltzmann's constant, valued at 1.3805 \(\times\) 10\({}^{-}\)23\({}^{23}\) J/K, and \(q\) represents the electron charge, equal to 1.6 \(\times\) 10\({}^{-}\)19 Coulombs. \(N_{cell}^{PV,\ s}\) and \(N_{cell}^{PV,\ P}\) represent the number of cells connected in series and parallel, respectively. The cell's operating temperature, in Kelvin, is indicated by \(T\). Additionally, the photocurrent \(I_{ph}\) of a PV cell is influenced by temperature and solar irradiance, as described by (2):  
\[I_{ph}=I_{SC}+K_{Isc}\times(T-298)\times\frac{G}{1000} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Electrical Element** & **Location** & **Specifications** \\ \hline PV system & DC Bus & 1.5 MW \\ PEMFC & DC Bus & 0.1 MW \\ Transformer 1 & B1–B2 & 260 V/25 kV; 60 Hz \\ Transformer 2 & B2–B4 & 25 kV/600 V; 60 Hz \\ Transformer 3 & B2-DSTATCOM & 2.17 kV/25 kV; 60 Hz \\ Grid & B3 & 25 kV; 60 Hz \\ EV charging station & B4 & 0.5 MW; 600 V \\ Linear load & B4 & 1 MW, 0.2 MVar; 600 V \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data of the investigated hybrid MG.  
Figure 1: The layout of the hybrid MG investigated in this work.  
\(K_{Isc}\) is the temperature coefficient of the short-circuit current at \(I_{sc}\). The cell series resistance, measured in ohms, is represented by \(R_{s}\). The shunt resistance in the system is typically very high, and it is often reasonable to neglect it when conducting the analysis. This simplifies the overall analysis process.
# Dynamic Energy Management Strategy of a Solar-and-Energy Storage-Integrated Smart Charging Station
## 2 Description  
This research is situated in the context of EV charging infrastructure deployment in a densely populated island nation. Due to limited land availability, EVs often depend on centralized DC fast charging stations for their charging needs. Within densely inhabited urban regions, existing facilities like rooftops of wholesale stores and their parking areas emerge as promising sites for the development of solar-and-energy storage-integrated charging stations.
# From Nearly Zero Energy to Carbon-Neutral: Case Study of a Hospitality Building
## 4 Case Study
### Assessment of the Whole-Building Life Cycle Carbon Emissions
#### 4.4.3 Use Stage B4-6 (Construction Materials Replacement and Energy Use)  
The use phase accounts for most of the CO\({}_{2}\)-eq emissions in the lifecycle of buildings and generally reflects the building energy performance. The building location, occupation type, and energy source directly affect the energy consumption [68]. Associated to this stage, the replacement and the refurbishment works (B4-5) were considered in both the scenarios, according to the service life of each construction material, as declared in each EPD, and the impacts were calculated through One Click LCA software.  
As far as the energy-use stage of the building (B6) is concerned, as already mentioned, the calculation of the environmental impacts was carried out through SimaPro 8.5.2.0 to better define the CO\({}_{2}\)-eq emissions due to the 9 micro-CHP fuel-cell systems (considered in the CNB scenario), able to produce 0.9 kW\({}_{\text{TH}}\) and 1.5 kW\({}_{\text{EL}}\) (thermal and electric) each, with an hourly natural gas consumption of 0.25 m\({}^{3}\)/h. To assess the environmental impacts due to the thermal and electric energy production by the CHP systems, a 2 kW CHP system process present within the SimaPro 8.5.2.0 software was considered and adapted to the case study. The process was modeled according to two different scenarios for the allocation of environmental impacts, based on the products and coproducts generated by the CHP system. The scenarios considered were based, in one case, on the exergetic allocation of impacts, which considers the two CHP products (electricity and thermal energy), and, in the other, on the allocation of impacts considering only the electricity as a product of the CHP [65].  
The exergetic allocation produces 0.354 kgCO\({}_{2}\)-eq per each generated electric kWh\({}_{\text{EL}}\), and 0.275 kgCO\({}_{2}\)-eq per each generated thermal kWh\({}_{\text{TH}}\), while the second scenario (electricity allocation) produces 0.47 kgCO\({}_{2}\)-eq per each generated electric kWh\({}_{\text{EL}}\), and null emissions to produce thermal energy. To assess the environmental impacts related to the electric energy supplied by the national grid and used on site, an emission factor equal to 0.414 kgCO\({}_{2}\)-eq/kWh\({}_{\text{EL}}\)[67, 69] was used. Therefore, the CO\({}_{2}\)-eq emissions assessment of the B6 stage for the CNB scenario considered the emissions related to the natural gas consumption, i.e., the emissions related to the production of electric and thermal energy by CHP systems, and the emissions related to the remaining electric energy demand supplied by the national grid, used on site, and not compensated for through onsite RES. The nZEB scenario considers only the electric energy supplied by the national grid and not compensated through onsite RES.
# 5G/B5G mmWave Cellular Networks with MEC Prefetching Based on User Context Information +
## 2 System Model  
We extended the system level simulator of mmWave HetNet developed in [5] to assess the effectiveness of combining mmWave access and MEC in the case of limited backhaul capacity. For that purpose, we developed a new mobility model, traffic model, and prefetching algorithm. Figure 1 shows the schematic overview of our proposed mmWave Edge Cloud 5G cellular system.
## 2 Indoor Model Testing
### Model Testing Apparatus
#### 2.1.2 Model Pile  
Based on engineering practice, a model was created to simulate a pullout pile with a length of 20 m and a diameter of 0.5 m, made of pre-cast reinforced concrete, with a concrete strength grade of C30 and an elastic modulus of 30 GPa. Considering the operability of the experiment, the impact of data collection, and testing duration, this experimental study strictly follows the theory of similarity and uses a model test research method. Through similarity verification of the surrounding soil and the model pile used in the model test, the similarity ratio for the model is determined to be 1:20. A PC pipe is selected as the model pile, with an elastic modulus of 7.5 GPa. The embedment depths were selected as 600 mm, 900 mm, and 1100 mm, with pile diameters of 20, 25, and 40 mm. To facilitate the reading of experimental data, the total length of the model pile was made 100 mm longer than the embedment depth, as shown in Figure 1.  
The PC pipe is composed of two half-shells cut along the axis. Strain gauges are symmetrically pasted on the inner wall at intervals of 100 mm, starting from 100 mm below the pile top. The surface of the strain gauges is coated with a layer of epoxy resin for waterproofing. Finally, the two half-shells are bonded and fixed together using adhesive to form the model pile. The parameters of different model piles and soil depths are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pile Number** & **Pile Length** & **Depth of Pile** & **Pile Diameter** \\  & **_L/mm_** & **Embedment/mm** & **_D/mm_** \\ \hline
1* & 700 & 600 & 20 \\
2* & 1000 & 900 & 20 \\
3* & 1200 & 1100 & 20 \\
4* & 700 & 600 & 25 \\
5* & 1000 & 900 & 25 \\
6* & 1200 & 1100 & 25 \\
7* & 700 & 600 & 40 \\
8* & 1000 & 900 & 40 \\
9* & 1200 & 1100 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model pile parameters.  
Figure 1: Model box and model pile schematic diagram.