esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Verification
### Discussion  
In this section, we verify the feasibility of the stress wave communication system, including both the hardware and the software. In the experiment, we used a galvanized steel pipe as the transmission channel, and the information-containing stress waves were successfully modulated via the software and emitted by the proposed devices. All the received waveforms in the experiments were clearly refocused and interpreted so that we consider the stress wave communication system developed in this paper to have achieved the initial objective. The bit error rate (BER) as a function of the signal-to-noise (SNR) or interference [64; 65] requires the investigation of channel characteristics, which will be the focus of our future work on stress wave communication since this paper mainly focuses on the system design. Corrosion, especially that caused by chloride diffusion in subsea environment, may cause micro damages to the structure and may introduce unwanted disturbance to the communication signals [66; 67]. The impact of the structural damages to the stress wave communication signal will be further studied. Since the stress wave can be used to detect structural damage via the active sensing method [68; 69; 70; 71; 72], which is similar to stress wave communication in the sense that a PZT emitter and a PZT sensor are used, we will also explore damage detection by using the stress wave communication signals.
# Examination of Viscosity Effect on Cavitating Flow inside Poppet Valves Based on a Numerical Study
## 4 Discussion
### Thin Layer Feature  
In the oil-flow case, the vortex cavitation within the free shearing layer displays more evidently the feature as a thin layer. Such a difference could be interpreted by the higher viscosity of mineral oil, which contributes to a stronger shearing effect under condition of the same velocity gradient. Actually, the thin layer feature is also quite evident in the water-flow case for inception vortex cavitation at both free shearing layer as shown in Figure 5a and wall shearing layer as shown in Figure 7a. But the fast development of 3-dimensional vortices significantly attenuates the shearing intensity on the cavity structure. Such thin layered feature of vortex cavitation bears great resemblance to the stress-induced cavitation proposed by [PERSON] [22]. [PERSON] stated a case of cavitation inception through direct rupturing of a liquid by sufficiently strong shearing. However, the vortex cavitation in the current study cases exhibits a bit difference that a vortex is created first with the subsequent development to trigger cavitation in the core. It should be mentioned that the vortex structure is visualized with different magnitude for oil and water case (in Figures 5-8), indicating the higher velocity gradient, i.e., circulating strength, in the water-flow case. But the wall-shearing-induced cavitation is primarily more evident in the oil-flow case, as a consequence of the higher viscosity.
## Appendix A Throughput under Changing Data Traffic
### Average Delivery Delay under Changing Data Traffic  
Besides a reduction in throughput as data traffic rises, Figure 21 shows that average delivery delay also increases in the process. With other conditions remaining the same, higher data traffic increases  
Figure 22: Transmission overhead and average energy consumption under increasing data traffic in the Helsinki scenario.  
buffer occupancy, which in turn reduces the chances of completing the forwarding transactions for an encounter opportunity. As a result, messages take longer to reach their destinations, thereby increasing average delivery delay.  
The average delivery delay without a replication technique begins to surpass that of fixed-quota and ADS as data traffic increases. This is mainly due to fixed-quota and ADS also losing messages due to buffer overflow caused by higher data traffic. The differences in the delays of fixed-quota and ADS are relatively small, which is reasonable as they are both limited to generating 4 copies per message. As for our proposals, incorporating LAMeD into PRoPHET allows nodes to retain some messages (i.e., messages for which they can make better delivery contributions) for longer periods, thereby increasing average delivery delay. Further incorporating our proposed replication mechanisms reduces buffer occupancy and message loss by controlling replication. This is especially significant under less data traffic, as nodes can retain messages for longer periods of time in their buffers, consequently increasing average delivery delay. Higher data traffic however, increases message loss due to buffer overflows and reduces average delivery delay. This explains why the difference between the delays incurred by LAMeD and LARep reduces as data traffic increases.
## 3 Materials, Methods, and Equipment
### Classical Bearing Capacity Methods
#### 3.6.1 [PERSON] (1943) Methods  
In all methods, the effective vertical stress (\(\sigma^{\prime}_{v}(z)\)) is a fundamental quantity. For a soil with a water table located at depth (\(z_{w}\)) and a pile embedded up to a total depth (\(L\)), the effective stress is defined as follows:  
Above the water table (\(0\leq z\leq z_{w}\))  
\[\sigma^{\prime}_{v}(z)=\gamma_{d}z\]  
where (\(\gamma_{d}\)) is the dry unit weight of the soil.  
Below the water table (\(z_{w}\leq z\leq L\))  
\[\sigma^{\prime}_{v}(z)=\gamma_{d}z_{w}+\gamma^{\prime}_{s}(z-z_{w})\]  
with the submerged unit weight defined as \(\gamma^{\prime}_{s}=\gamma_{\text{sat}}-\gamma_{tw}\), where (\(\gamma_{\text{sat}}\)) is the saturated unit weight and (\(\gamma_{tw}\)) is the unit weight of water.  
The lateral friction along the pile is modeled by assuming that the unit skin friction, \(\tau(z)\), is proportional to the effective normal stress at that depth, as shown in Equation (2).  
\[\tau(z)=K\sigma^{\prime}_{v}(z)tan\delta \tag{2}\]  
where  
\(K\) = lateral earth pressure coefficient (often parameterized as \((K=0.5+0.008 Dr)\) as per [PERSON]'s (1983) equation to estimate \(K\) with (\(D_{r}\)) representing the relative density in percent);  
\(\delta=\) interface friction (or adhesion) angle between the pile composite materials and the soil.  
For an infinitesimal pile element of length (\(dz\)), the incremental frictional resistance is given by Equation (3).  
\[dQ_{s}=\tau(z)(\pi D)dz \tag{3}\]  
where (\(D\)) is the pile diameter. Integration over the entire length yields Equation (4).  
\[Q_{s}=\pi DK\tan\delta\int_{0}^{L}\sigma_{v}^{\prime}(z)dz \tag{4}\]  
Due to the discontinuity in effective stress at the water table, the integration is commonly split into two segments:  
Segment 1 (0 to (\(z_{w}\))):  
\[I_{1}=\int_{0}^{z_{w}}\gamma_{d}zdz=\frac{\gamma_{d}z_{w}^{2}}{2}\]  
Segment 2 ((\(z_{w}\)) to (\(L\))):  
\[I_{2}=\int_{z_{w}}^{L}[\gamma_{d}z_{w}+\gamma^{\prime}{}_{s}(z-z_{w})]dz=\gamma _{d}z_{w}(L-z_{w})+\frac{\gamma^{\prime}{}_{s}(L-z_{w})^{2}}{2}\]  
Thus, the complete expression becomes Equation (5).  
\[Q_{s}=\pi DKtan\delta[I_{1}+I_{2}] \tag{5}\]  
As in Equation (6), the effective vertical stress is seen at the pile tip (depth (\(L\))).  
\[\sigma^{\prime}{}_{v,\text{tip}}=\gamma_{d}z_{w}+\gamma^{\prime}{}_{s}(L-z_{w}) \tag{6}\]  
For a circular pile, the base area is as follows:  
\[A_{b}=\frac{\pi D^{2}}{4}\]  
The end-bearing capacity is then expressed as shown in Equation (7):  
\[Q_{b}=N_{q}\sigma^{\prime}_{v,\text{tip}}A_{b} \tag{7}\]  
The bearing capacity factor is commonly computed as shown in Equation (8):  
\[N_{q}=\frac{e^{2(3\pi/4-\phi^{\prime}/2)\tan\phi^{\prime}}}{2\cos^{2}(45+\phi ^{\prime}/2)} \tag{8}\]  
(\(\phi\)) represents the soil's internal friction angle.
# A Decentralized Multi-Agent-Based Approach for Low Voltage Microgrid Restoration
## 1 Introduction
### Contributions  
This paper introduces a decentralized multi-agent-based approach for restoration of an LV MG. In the proposed scheme, the MG local controllers are assigned to specific agents. The agents only know their own local information and communicate with their neighboring agents to access the required global information. The communication among the agents for sharing the local information and accessing to the global information is based on ACA. After completing the sharing information process, all agents take a common decision based on the discovered information to determine which load or generation unit should be chosen and connected for maximizing the amount of the restored load in the shortest possible time.  
The centralized restoration scheme uses the information about the last generation-consumption scenarios of the MG to determine the sequence of restoration actions. The information is periodically sent by the MG local controllers to the MGCC. After a blackout, the MGCC performs service restoration based on the latest updated information stored in a database and determines all restoration actions. Since the database information is gathered during a certain period of time before the blackout, in the case of changing in the load or generation scenario or lack of preparedness for the restoration of a generation unit or load during the restoration process, the restoration scheme will be in trouble [8; 22]. Thus, there is a need for a decentralized restoration approach that uses the online information of the generation/consumption of the MG and determines a proper sequence of restoration actions to restore the maximum possible amount of loads in the shortest possible time. The proposed decentralizedmulti-agent based restoration approach uses the online information of the generation/consumption of the MG during the restoration process and determines a proper sequence of restoration actions.
## 2 Chirp-Based FHSS Scheme  
The basic chirp signal used in the proposed method is expressed in Equation (1) as  
\[\mathrm{s}(t)=\exp\left[j2\pi\left(f_{0}t+\frac{k}{2}t^{2}\right)\right], \tag{1}\]  
where \(f_{0}\) is the initial frequency and \(k\) is the chirp rate. Equation (1) can be rewritten to express the chirp signal as follows,  
\[s_{u}(t)=\exp\left[j2\pi t(f_{min}+\frac{f_{max}-f_{min}}{T}t)\right], \tag{2}\]  
\[s_{d}(t)=\exp\left[j2\pi t(f_{max}+\frac{f_{min}-f_{max}}{T}t)\right], \tag{3}\]  
where \(f_{min}\) is the minimum frequency and \(f_{max}\) is the maximum frequency. \(T\) is the duration of the symbol. Equation (2) represents a signal that has an up-slope frequency with an initial frequency \(f_{min}\). Equation (3) represents a signal that has a down-slope frequency with an initial frequency \(f_{max}\). To express the chirp signal in real numbers, the above equations can be expressed as  
\[S_{u}(t)=Re\{s_{u}(t)\}, \tag{4}\]  
\[S_{d}(t)=Re\{s_{d}(t)\}. \tag{5}\]  
The chirp-based scheme selects one of the chirp symbols of Equation (4) using the binary sequence \(b_{i}\). Therefore, the chirp-based signal \(s_{c}(t)\) with the symbol period \(T\) is expressed as [28]  
\[s_{c}(t)=\left\{\begin{array}{ll}S_{u}(t),\;if\;b_{i}=0\\ S_{d}(t),\;if\;b_{i}=1\end{array}\right.,\;nT\leq t\leq(n+1)T. \tag{6}\]  
This equation implies assigning a binary value \"1\" to a signal with an up-slope frequency, and assigning a binary value \"0\" to a signal with a down-slope frequency. During the duration of the symbol, the chirp signal is mapped by the binary bit stream. The received signal is usually used by a correlator receiver.  
Figure 1 shows the basic structure of the hopping scheme in a chirp-based FHSS. In this case, since the hopping duration is longer than the symbol duration, there are several symbols in the single hopping interval.  
Figure 1: Basic scheme of the chirp-based FHSS.
###### Abstract  
Maize gross primary productivity (GPP) contributes the most to the global cropland GPP, making it crucial to accurately estimate maize GPP for the global carbon cycle. Previous research validated machine learning (ML) methods using remote sensing and meteorological data to estimate plant GPP, yet they disregard vegetation physiological dynamics driven by phenology. Leaf nitrogen content per unit leaf area (i.e., specific leaf nitrogen (SLN)) greatly affects photosynthesis. Its maximum allowable value correlates with a phenological factor conceptualized as normalized maize phenology (NMP). This study aims to validate SLN and NMP for maize GPP estimation using four ML methods (random forest (RF), support vector machine (SVM), convolutional neutral network (CNN), and extreme learning machine (ELM)). Inputs consist of vegetation index (NDVI), air temperature, solar radiation (SSR), NMP, and SLN. Data from four American maize flux sites (NE1, NE2, and NE3 sites in Nebraska and ROI site in Minnesota) were gathered. Using data from three NE sites to validate the effect of SLN and MMP shows that the accuracy of four ML methods notably increased after adding SLN and MMP. Among these methods, RF and SVM achieved the best performance of Nash-Sutcliffe efficiency coefficient (NSE) = 0.9703 and 0.9706, root mean square error (RMSE) = 1.5596 and 1.5509 gC-m2-d-1, and coefficient of variance (CV) = 0.1508 and 0.1470, respectively. When evaluating the best ML models from three NE sites at the ROI site, only RF and CNN could effectively incorporate the impact of SLN and NMP. But, in terms of unbiased estimation results, the four ML models were comprehensively enhanced by adding SLN and NMP. Due to their fixed relationship, introducing SLN or NMP alone might be more effective than introducing both simultaneously, considering the data redundancy for methods like CNN and ELM. This study supports the integration of phenology and leaf-level photosynthetic factors in plant GPP estimation via ML methods and provides a reference for similar research.  
maize; GPP; machine learning; specific leaf nitrogen; phenology 2024
# On Modeling Antennas Using MoM-Based Algorithms: Wire-Grid versus Surface Triangulation
## 2 Materials and Methods
### General Mathematical Formulation
Then, the unknown function \(f\) is represented as a linear combination of \(N\) basis functions \(f_{n}\) in the domain of the integral operator  
\[f=\sum\limits_{n=1}^{N}\alpha_{n}f_{n}, \tag{11}\]  
where \(\alpha_{n}\) are unknown coefficients. Substituting (10) into (9), we obtain an approximate solution to the integral equation  
\[\sum\limits_{n=1}^{N}\alpha_{n}Lf_{n}\approx g. \tag{12}\]  
The difference between the exact and approximate solutions is determined as  
\[R=g-\sum\limits_{n=1}^{N}\alpha_{n}Lf_{n}. \tag{13}\]  
The objective of the MoM is to reduce the difference between the exact solution and the approximation, which can be achieved by using suitable functions \(w_{n}\) for weighting or testing. Once a suitable inner product is identified for the problem, the equation takes the following form  
\[\sum\limits_{n=1}^{N}\alpha_{n}(w_{m},Lf_{n})\approx(w_{m},g). \tag{14}\]  
As a result, the linear integral equation is reduced to a linear algebraic equation. By repeating this procedure for a set of the selected independent test functions, a SLAE of the following form is obtained  
\[\mathbf{H}^{\mathrm{scat}}(\mathbf{r})=\frac{1}{\mu}\
abla\times\mathbf{A}( \mathbf{r}). \tag{3}\]0  
where matrix elements \(z_{mn}=(w_{m},Lf_{n})\) determine the impedance of the structure, and vector \(v_{m}=(w_{m},g)\) is the applied excitation. The solution of the obtained SLAE allows finding an approximate solution for the unknown coefficients of the unknown current functions, from which the antenna characteristics are subsequently determined.
## 3 Experimental Results
### Comparison with Other Predictive Models  
To compare the predictive performance of MoLE-Lasso, MoLE-EN, and MoLE-RR with other predictive algorithms, all models were run in the training dataset of 25 batches and tested on the test dataset. For comparison purposes, the following models were implemented: a multivariate linear regression model with Lasso, EN, and RR penalties, an artificial neural network (ANN), a support vector regression, with radial basis kernel (SVR), with hyper-parameters \(\gamma\) (kernel width) and \(C\) (Gain), a decision tree (DT), and partial least squares (PLS). The parameters of Lasso, EN, RR, and PLS were selected so as to minimize the BIC criterion. The number of hidden nodes \(N_{h}\) of the ANN and the regularization parameter \(\gamma_{\text{LS-SVR}}\) and the Gaussian kernel parameter \(\sigma_{\text{LS-SVR}}\) of the SVR were determined using a 10-fold cross validation. Table 3 shows the parameters obtained for the PLS, ANN and SVR models.  
Table 4 shows the prediction performance on the resin acidity number and resin viscosity, for all models, on the test dataset. Table 4, shows that the regularized MoLE models perform better among all datasets. On the other hand, nonlinear models such as ANN, SVM and DT have the worst results. Indeed, according to the results, the performance results have shown that for viscosity this is essentially a linear modeling problem, while for the acidity, there is a clear benefit of using the regularize MoLE methods. As the MoLE models can capture the different phases of the process, it could increase the performance over the linear and nonlinear models.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dataset** & **PLS** & **ANN** & **LS-SVR** \\ \hline Viscosity & LV \(=10\) & \(N_{h}=3\) & \(\gamma_{\text{LS-SVR}}=50\), \(\sigma_{\text{LS-SVR}}=10\) \\ \hline Acidity & LV \(=17\) & \(N_{h}=3\) & \(\gamma_{\text{LS-SVR}}=50\), \(\sigma_{\text{LS-SVR}}=25\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters selected for PLS, ANN, and SVR models for each data set.
## 2 International Standards for the Sizing of PAUT  
Table 1 below shows the international standards that are used in the flaw sizing of PAUT in weldments. API Standard 620 is referred to in this study because the standard indicates the methods used to design and construct LNG storage tanks.  
Amongst the standards, API 620 Annex U presents the acceptance criteria for the flaws in weldments. The acceptability of the flaws can be determined according to the acceptance criteria presented in API 620 Annex U, since the acceptance criteria are the output of fracture mechanical experiments. ISO 13588 is referred to because it is an international standard for the use of automated PAUT. ISO 22825 is referred to because it contains the PAUT procedure for austenitic steels and nickel-based alloys. Table 2 presents the acceptance criteria listed in API 620 Annex U. Moreover, the sizing methodology of the flaw image obtained by PAUT must follow the flaw sizing methodologies introduced in API 620 Annex U and ISO 11666.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Standards** & **Title** & **Edition** \\ \hline API Standard 620 [15] & Design and Construction of Large, Welded, Low-Pressure Storage Tanks & 12 th Ed., 2013, Addendum 2, 2018 \\ \hline ISO 13588 [4] & Non-destructive testing of welds—Ultrasonic testing—Use of automated phased array technology & 2 nd Ed., 2019-02 \\ \hline ISO 22825 [17] & Non-destructive testing of welds—Ultrasonic testing—Testing of welds in austenitic steels and nickel-based alloys & 3 rd Ed., 2017-09 \\ \hline ISO 17640 [18] & Non-destructive testing of welds—Ultrasonic testing—Techniques, testing levels, and assessment & 4 th Ed., 2018-10 \\ \hline ISO 19285 [19] & Non-destructive testing of welds—Phased Array Ultrasonic Testing (PAUT)—Acceptance levels & 1 st Ed., 2017-08 \\ \hline ISO 11666 [16] & Non-destructive testing of welds—Ultrasonic testing—Acceptance levels & 2 nd Ed., 2018-01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The international standards of PAUT flaw sizing technique.
## 2 Principles of Rainfall Field Retrieval by ESLs and HMLs
### Principle of Rainfall Retrieval by Esl
Therefore, according to Equations (9) and (10), the rain attenuation \(A_{R}\) can be rewritten as  
\[A_{R}(t)=\text{SNR}_{no-rain}-\text{SNR}. \tag{11}\]The rain attenuation \(A_{R}\) can be measured at the receiver terminal of ESL by the method described above. In addition, according to the ITU-R model [40; 41], the relationship between rain specific attenuation \(\gamma_{R}\) (dB/km) and rain attenuation is obtained by  
\[\left\{ \begin{array}{l}\gamma_{R}=\alpha R^{\beta}\\ A_{R}=\gamma_{R}\cdot h_{0}+0.36/\text{sin}\theta\end{array} \right., \tag{12}\]  
where \(h_{0}\) is the height of atmospheric \(0^{\circ}\)C-Layer, \(\theta\) is the elevation of ESL, and the values of coefficients \(\alpha\) and \(\beta\) are obtained by ITU-R P.838-3 [40]. Therefore, combined with Equations (11) and (12), the average rain rate \(R\) (mm/h) retrieved by ESL can be written as  
\[R=\left[\frac{(\text{SNR}_{no-rain}-\text{SNR})\text{sin}\theta}{(h_{0}+0.36) \alpha}\right]^{\beta^{-1}}. \tag{13}\]  
In order to further verify the corresponding relationship between SNR and rain rate, we used the ESL at Ku band built in Nanjing, China, to record the change process of SNR during rainfall. A rainfall event in June 2020 is selected to analyze the correlation between ESL signal and rain intensity. As shown in Figure 2, the SNR decreases significantly when rainfall occurs. This change becomes more obvious as the rain rate increases. In addition, the SNR can fluctuate rapidly due to noise, even when no rainfall is present. This is mainly due to the movement of the atmosphere. However, on the whole, rainfall is the main factor leading to the decrease in SNR of ESL. Further calculation shows that the correlation coefficient (CC) between SNR and rain rate is \(-0.612\). This indicates that there is a strong negative correlation between the two and validates the feasibility of using ESL to retrieve rainfall.