esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Gravity Method  
### Gravity Data Acquisition and Reduction  
The gravity survey was conducted concurrently with the MT survey using a Lacoste and Romberg G-655 unit. The survey covered an area of 500 km\({}^{2}\) and 319 gravity stations were distributed (Figure 3). The distance between each measurement point ranged between 250 and 500 m. Most of the measurement stations were concentrated near Limboto Lake and the location of the geothermal surface manifestations. The base station for the gravity survey was located in Limboto Village at X = 498,445.535 and Y = 68,914.608 (UTM coordinates of WGS4, Zone 51) at an elevation of 5653 mash. The gravity value of the base station was first tied to the nearest national gravity value at Jalaluddin Airport, Gorontalo City at X = 483,587 and Y = 70,571, situated at an elevation of 30 mash with a gravity absolute value (Gabs) of 978,093.59 mGal. The Gabs of the base station was found to be 978,100.244346 mGal.  
The gravity method measures variations in gravity acceleration on the surface due to variations in the rock density values below the surface. The measured data are affected by the drift effect, tidal effect, latitude, altitude from the surface, and terrain effect. Data reduction must be applied to obtain a complete Bouguer anomaly (CBA). The CBA (in mGal) at a certain position was estimated using the following equation [48]:  
\[BA=g-\gamma+\beta h-2\pi G\rho h+\rho T \tag{7}\]  
where \(g\) (mGal) is the observed absolute gravity, \(\gamma\) (mGal) is the normal gravity value, \(\beta h\) (mGal/m) is the free-air correction, and \(T\) is the terrain correction per unit average density. \(G\) (m\({}^{3}\) kg\({}^{-1}\) s\({}^{-2}\)), \(\rho\) (kg m\({}^{-3}\)), and \(h\) (m) are the universal gravitational constant, density, and elevation, respectively. To obtain accurate Bouguer and terrain corrections, a proper estimation of the rock density values is needed. The rock density was determined before the Bouguer correction using the Parasnis method. The Bouguer anomaly is described as a function of density. Density values were estimated by minimizing the sum of the differences between the average Bouguer anomalies from the entire area with respect to the Bouguer anomaly at each observation point [49]. Based on these estimation results, a density correction value of 2.5 g/cm\({}^{3}\) was used in gravity data processing. Terrain corrections were calculated using a 50 m digital elevation model (DEM). The corrected data are the CBA used in the gravity analysis.
# A Source Anonymity-Based Lightweight Secure AODV Protocol for Fog-Based MANET
## 3 Preliminary Knowledge and Analysis
### Aodv
#### 3.1.4 Management of the Routing Table  
All items in the routing table not only maintain the routing information about the source node, the destination node, the source node sequence number, and the destination node sequence number, but also hold some other routing information that has little relationship to the communication. This information is called the soft state of the routing. Each routing message maintains a timer, and all routing information expires or becomes invalid after a timeout and is removed from the routing table. The length of valid time determines the size of the network structure.
## 3 Methods
### OS-Net Performance Evaluation  
Four performance evaluation metrics were employed to assess the OS-Net performance, including accuracy (Equation (3)), precision (Equation (4)), recall (Equation (5)), and F1 score (Equation (6)). Accuracy is the ratio of correctly predicted observations to the total observations. Precision is the ratio of correctly predicted positive observations to the total predicted positive observations. Recall is the ratio of correctly predicted positive observations to all observations in actual class. F1 Score is the weighted average of precision and recall [42] and is a reliable and robust indicator for accurate model measurements. The range of the F-1 score is from 0 to 1, with 0 being the worst possible and 1 being the best.  
\[\text{Accuracy}\;=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{FN}+ \text{TN}} \tag{3}\]  
\[\text{Precision}\;=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{4}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{5}\]\[\text{F1 score }=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{6}\]  
where TP refers to true positives, TN refers to true negatives, FP refers to positives, and FN refers to false negatives.
# Performance Evaluation of Routing Protocols for Underwater Wireless Sensor Networks
## 3 Performance Evaluation and Simulation Results
### Results
#### 3.2.1 Energy Consumption  
Figure 2 shows the energy consumption of all evaluated routing protocols. Compared to other routing strategies, the RACE-SM scheme applies the least energy. The RACE-SM uses the sink movement method to obtain data from the sensor nodes and send them directly to the source and the sink node. Additionally, direct data transmission between the source and sink nodes uses the least energy because only one sensor node is involved.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Value** \\ \hline Width & 1000 m \\ Breadth & 1000 m \\ Depth & 1000 m \\ Deployed Sensor Nodes & 200 \\ Sink Nodes & 10 \\ Transmission Range & 220 m \\ Total number of rounds & 8000 \\ Data rate & 150,000 \\ Packet Size & 1000 bits \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters Used in Simulation.  
Multi performs better than EERBCR, Mulsi-Co, GCORP, EH-UWSN, and EEDG cooperative routing protocols because of the placement of the sink node in the optimal position, which decreases the route length and forwarding nodes to transmit the data from the sender to receiver. The EH-UWSN uses less energy than the EERBCR, Mulsi-Co, GCORP, and EEDG. The EH-UWSN recharges the sensor node batteries from the surrounding objectives to improve the network lifetime by decreasing energy consumption. All other routing schemes, such as MulSi-Co, EERBCR, and GCORP, have the same energy consumption due to their cooperative nature. To increase the reliability of the network, cooperative methods have been utilized. In the cooperation process, data are transferred through multiple nodes; hence, it uses more energy than direct data transfer. Table 2 addresses the energy consumption of all evaluated routing schemes.
# Research on a Service Load Prediction Method Based on VMD-GLRT
## 4 Conclusions  
This paper proposes a service load prediction model, VMD-GLRT, based on VMD, to address the issue of inaccurate prediction results of current deep learning models. The model uses VMD to decompose the unstable time series data into modal components that tend to be stable, thus facilitating the extraction of features. Additionally, the self-attention mechanism and residual network are introduced to improve the accuracy of the prediction results. Ablation experiments and comparison experiments were conducted to verify the effectiveness of the model, and the results show that the proposed model has better generalization ability, improves the accuracy of load prediction, and meets the demand of industry for service load prediction, allowing for reasonable resource allocation of services in advance.  
Conceptualization, J.Z.; Methodology, J.Z.; Validation, Y.P., C.S., W.C. and Y.H. ([PERSON]); Writing--original draft, [PERSON] ([PERSON]); Writing--review & editing, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MSE** & **RMSE** & **MAE** & **MAPE** \\ \hline CNN-LSTM [22] & 31.0664 & 5.5737 & 3.8658 & 5.8844 \\ CNN-BiLSTM [24] & 33.3091 & 5.7714 & 3.9759 & 6.1116 \\ ARIMA-LSTM [25] & 20.8844 & 4.5699 & 3.4509 & 5.3942 \\ TCN-Attention [26] & 25.3666 & 5.0365 & 3.8517 & 6.0587 \\ GLSTM [27] & 19.8621 & 4.4567 & 3.2288 & 5.0600 \\
**VMD-GLRT** & **7.3306** & **2.7075** & **1.9393** & **3.0494** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental comparison results.  
**Funding:** This research was funded by the Scientific and Technological Progress and Innovation Program of the Transportation Department of Hunan Province (201927), the Natural Science Foundation of Hunan Province (2021 JJ30456, 2022 JJ40514, 2022 JJ90018, 2022 JJ30624), the Open Fund of National Engineering Research Center of Highway Maintenance Technology (KFJ220107), the Open Research Project of the State Key Laboratory of Industrial Control Technology (No. ICT2022B60), National Defense Science and Technology Key Laboratory Fund Project (2021-KJWPDL-17).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The dataset for this study is available on GitHub at [[https://github.com/alibaba/clusterdata](https://github.com/alibaba/clusterdata)]([https://github.com/alibaba/clusterdata](https://github.com/alibaba/clusterdata)) (accessed on 1 July 2022).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
This approach could enable monitoring of the condition of components--such as pipeline segments--directly in their installed state, allowing for safe, predictive and cost-effective replacements. The present study discusses the dependencies of the micromagnetic features on different combinations of thermal and mechanical loadings. It has to be noted that the non-destructive measurements were not performed in situ. The microstructural changes induced by the variations in temperature and mechanical loading, and the associated changes in the mechanical material properties (e.g., Vickers hardness) were correlated with the micromagnetic parameters. Moreover, the relationship between these changes in mechanical properties and micromagnetic parameters and changes in microstructural features (e.g., number and density of (Fe\({}_{2}\)Mo) Laves phase particles) was determined and interpreted [12; 13]. Additionally, the applicability of different ML algorithms to predict mechanical properties and microstructural characteristics based on the micromagnetic feature space was examined.
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 1 Introduction
### Types of Accelerometers
#### 1.3.6 Surface Acoustic Wave (SAW)  
Surface acoustic wave sensors are small devices that use sound waves on their surface to detect physical changes. They are a type of microelectromechanical system (MEMS). Unlike an electrical signal, the sensor transduces an input electrical signal into a mechanical wave, which physical phenomena can easily influence. This device subsequently transduces the wave back into an electrical signal. Variations in amplitude, phase, frequency, or time delay between input and output electrical signals can be utilized to detect the presence of the targeted phenomenon [13]. These sensors are of marginal importance and are not mass-produced.
## 4 Energy Consumption of Different Transport Schemes
### Energy Consumption per Unit Coverage  
To establish a more realistic and practical evaluation on power consumption for fiber-wireless network, we have also studied the energy consumption per base station coverage [41]. Here we model the mobile wireless network as an infinite regular grid of sites characterized by the size distance, generating equally sized hexagonal cell structures of side length \(d\) as shown in Figure 9a. Each hexagonal cell represents the coverage of a base station which is located in the center of the cell at a distance of \(3\sqrt{3}\,/\,2d^{2}\,\). To ensure that the entire cell is covered, the transmitting power (\(P_{TX}\)) has to be greater than the customer's receiver sensitivity (\(P_{RX}\)) after taking into account the propagation loss (\(PL\)) over a distance of \(d\). The \(PL\) quantifies the wireless signal deterioration due to path loss, shadowing and multipath fading which can be formulated into Equation (2) [42]. The value is dependent on man-made structures [42,43] and hence, is different for different areas. Using the estimated parameters in [44] and Equation (2), we are able to estimate the propagation losses for rural and urban areas which are quantified by Equations (3) and (4). Figure 9b shows the maximum cell side  
Figure 8: Base station power consumption as a function of transmitting power for the five different transport schemes investigated.  
Figure 7: Base station architectures for (**a**) Analog RF-over-fiber (RFoF) (**b**) Analog IF-over-fiber (IFoF) (**c**) Digitized RF-over-fiber (DRFoF) and (**d**) Digitized IF-over-fiber (DIFoF).  
lengths (\(d\)) at different transmitting power in both rural and urban areas for a typical receiver sensitivity of \(-120\) dBm. It is obvious that the cell size is much larger for rural area compared to urban area for the same base station transmitting power. Since the propagation models are different for urban and rural areas, we will investigate the power consumption per square meter as a function of cell size for both rural and urban areas separately.  
\[PL=PI\big{(}d_{o},f\big{)}+10n\log(d/d_{o}) \tag{2}\]  
\[PL_{wall}(dB)=49+43.5\log(d) \tag{3}\]  
\[PL_{wall}(dB)=77+48\log(d) \tag{4}\]
### Reduced Length of Modelled Component  
Reducing the length of the modelled component is also effective for improving efficiency, since it can reduce the number of elements in the FEM model. However, it is questioned whether a short model can represent the behaviour of a large full-scale model. An early study by [PERSON] et al. [49] examined rolling contact with friction, and it was found that short models are sufficient to \"stabilize analyzed quantities\" [49] and \"further wheel travel does not effectively change the pictures of stresses and strain in tested area\" [49]; in other words, the reduced length is sufficient for the model to reach a steady state of rolling.  
[PERSON] et al. [33] used an FEM model with significantly shorter length (465 mm) compared to the actual workpiece (750 mm) that was rolled during experiments, so as to reduce computational time, and the short model predicted similar results to the full scale model. For rolling in a WAAM component, [PERSON] et al. [34] developed a model with a length of 100 mm for a deformable component, which was rolled along the minimal length under steady state, and they found that a further increase of the rolling distance in analysis did not make any marked difference in the result. Short models have also been used in other rolling scenarios. For instance, [PERSON] et al. [42] and [PERSON] et al. [43] used short segments for simulations of deep rolling of a circular component. [PERSON] et al. [46] developed a 62 mm long model for rolling simulation of quenched aluminium blocks. [PERSON] et al. [41] and [PERSON] et al. [51] employed short models for deep rolling and burnishing.  
The main criterion to determine the shortest length for the rolled component is that the shortened component should be able to reach a steady state of rolling. The manual of the FEM analysis software, Abaqus, suggests that the equivalent plastic strain, rolling force and torque should be used as the norms for steady-state detection. Steady-state rolling is reached when the reaction force and moment are stabilised, while the equivalent plastic strain in the rolled material becomes constant or fluctuates around a certain value [52].
# Effects of Atmospheric Turbulence on Optical Wireless Communication in NEOM Smart City
## 5 Channel Model and BER Analysis
### G-G Channel  
The G-G distribution is used to model an optical channel with strong turbulence. The PDF of G-G channels can be defined as [59]  
\[f_{h}(h)=\frac{(\alpha\beta)^{\frac{\alpha+\beta}{2}}}{\Gamma(\alpha)\Gamma( \beta)}h^{\frac{\alpha+\beta}{2}-1}G_{0,2}^{2,0}\Bigg{(}\alpha\beta h\Bigg{|} \begin{subarray}{c}\alpha-\beta\\ \frac{\alpha-\beta}{2},\end{subarray}\overset{\text{-}}{\frac{\alpha}{2}} \Bigg{)}, \tag{14}\]  
where \(G_{p,q}^{n,nt}[.]\) is the Meijer G-function, \(\Gamma(.)\) is the Gamma function, and \(\alpha\) and \(\beta\) are the effective number of large-scale and small-scale eddies, respectively. The corresponding values for a plane wave are as follows:  
\[\alpha=\bigg{[}\exp\Bigg{(}\frac{0.49\sigma_{I}^{2}}{(1+1.11\sigma_{I}^{\frac{ 12}{2}})}-1\bigg{]}^{-1} \tag{15}\]  
\[\beta=\bigg{[}\exp\Bigg{(}\frac{0.5\sigma_{I}^{2}}{(1+0.69\sigma_{I}^{\frac{ 12}{2}})}-1\bigg{]}^{-1} \tag{16}\]  
The average BER for FSO communication system with a G-G channel can be expressed as [59]:  
\[P_{e}=\frac{2^{\alpha+\beta-3}}{\pi^{\frac{3}{2}}\Gamma(\alpha)\Gamma(\beta)} G_{5,2}^{2,4}\Bigg{(}a\frac{4\gamma}{(\alpha\beta)^{2}}\Bigg{|}\begin{subarray}{c} \frac{2-\alpha}{2},\frac{1-\alpha}{2},\frac{2-\beta}{2},\frac{1-\beta}{2},1 \\ 0,\frac{\beta}{2}\end{subarray}\Bigg{)} \tag{17}\]  
The outage probability of the proposed system over the gamma-gamma channel can be calculated using [60]  
\[P_{out}=\frac{1}{\Gamma(\alpha)\Gamma(\beta)}\times G_{1,3}^{2,1}\Bigg{[} \frac{\alpha\beta}{\sqrt{a}P_{M}}\Bigg{|}\begin{subarray}{c}1\\ \alpha,\beta,0\end{subarray}\Bigg{]}. \tag{18}\]  
The availability of the system can be calculated using (13).
# Seismic Blind Deconvolution Based on Self-Supervised Machine Learning
## 2 Methodology
### Algorithm Framework  
Figure 1 shows the framework of the proposed SSL-BD algorithm, which mainly includes three modules: zero-phase wavelet estimation, wavelet phase inversion network, and reflectivity inversion network. We will introduce these three modules in detail in the following subsections. Here, we briefly explain the steps of the proposed algorithm.  
Figure 1: Framework of the proposed SSL-BD algorithm.  
1. Input the observed seismic data \(\mathbf{Y}_{\text{obs}}\) into the zero-phase wavelet estimation module, wavelet phase inversion network, and reflectivity inversion network to obtain initial zero-phase wavelet \(\mathbf{w}_{0}\), initial predicted wavelet phase \(\theta_{\text{pred}}\), and initial predicted reflectivity \(\mathbf{R}_{\text{pred}}\), respectively;  
2. Rotate the initial zero-phase wavelet \(\mathbf{w}_{0}\) with the predicted phase \(\theta_{\text{pred}}\)  to obtain the seismic wavelet \(\mathbf{w}_{\text{pred}}\), and apply relative sparsity process to the reflectivity \(\mathbf{R}_{\text{pred}}\)  to obtain the sparsity-improved reflectivity \(\bar{\mathbf{R}}_{\text{pred}}\) ;  
3. Calculate the convolution of seismic wavelet \(\mathbf{w}_{\text{pred}}\)  and reflectivity \(\bar{\mathbf{R}}_{\text{pred}}\)  to obtain reconstructed seismic data \(\tilde{\mathbf{Y}}_{\text{pred}}\).  
4. Calculate the mean square error of the input seismic data \(\mathbf{Y}_{\text{obs}}\) and reconstructed seismic data \(\tilde{\mathbf{Y}}_{\text{pred}}\)  as the network loss, and then update the network's parameters through gradient back-propagation algorithm to reduce the loss;  
5. After the network converges, output the final seismic wavelet and reflectivity.
## 6 Experimental Set-Up
### Results  
We conducted a set of experiments using three CH nodes and 12 leaf nodes. The configuration of the leaf nodes is the same as that proposed in the previous section. We forced a worst case tree creation scenario, where only one CH can communicate directly to the sink and the other CHs form a chain. This entails a distance of up to a 3-hop distance for a CH. The set-up has been working for two months and has not shown too many problems. No battery changes were required, including the CH nearest to the sink. The following issues arose from the experiments:  
Figure 9: Aspect of the CH node prototype.  
* TDMA de-synchronization: From time to time, CH lost access to some leaf nodes. We found that this occurred owing to an RTC inaccuracy that causes an incorrect timing scenario between the RX window timing instant in the leaf node and the TX polling time in the CH;
* Orphan nodes: Due to the time de-synchronization mentioned above, some spurious failures occurred, creating LF nodes with orphans.  
These issues were patched and incorporated in the EDETA protocol described in Section 2. To solve the TDM de-synchronization, we concluded that events must be scheduled based on calendar events and not in time delays. If the RTC clock is set correctly, then we can ensure the correct operation of the whole WSN.  
All timers are slightly corrected in each polling phase, and corrections are based on high precision clocks at the roots. Round-trip protocols for time synchronization can be considered in this implementation due to the waste of energy. In light of this, we decided to use a round-trip technique to estimate the delay, applying small local corrections in the nodes during normal operation in order to maintain the same temporary vision.  
As this mechanism will be applied in all the nodes of the system, a correction wave of sorts will take place spreading from the root to leafs. To solve the issue of orphan nodes, we added a special event in the scheduling calendar for the recovery of orphans, by opening a (sufficiently long) temporary reception window that all the nodes can locate. This has been incorporated in EDETA as a fault recovering mechanism for leaf nodes.
# Transparent CoAP Services to IoT Endpoints through ICN Operator Networks +
## 4 Design and Implementation
### Request Processing  
Algorithms 1 and 2 present the process of handling CoAP requests of different types as discussed in Section 4.3 in a cNAP and sNAP. Upon reception of a CoAP request from a CoAP client, cNAP checks the request packet to verify the request type. cNAP also looks up its state table to verify if there is an entry for this request. If no match is found, cNAP creates an entry for this request and publishes it to the ICN network. Otherwise, the cNAP aggregates the request and appends the requester in that entry of the state table. If there exist multiple resources hosted in different IoT devices which are connected to different sNAPs, ICN multicasts the request to those sNAPs, resulting in group communication. If the request type is CoAP observe, the cNAP follows the algorithm presented in [11]. The sNAP maintains the state of the cNAPs, which request the same resources in a situation, when the resource is not yet available. When the resource is available to the sNAP, it multicasts the response to all cNAPs. This case demonstrates the delayed response communication scenario.  
```
**Algorithm 1** CoAP Request in cNAP
``` isProxyOption \(\leftarrow\) [coap_request] \(coap\_header\)\(\leftarrow\) [coap_request] Token \(\leftarrow\) [coap_request] request_type \(\leftarrow\) [coap_request] ifrequest_type == = Confirmablethen  send empty ACK to requesting client request_type \(\leftarrow\) Non - confirmable endif ifisProxyOption == truethen \(proxyURI\leftarrow\) [coap_request] \(split\_proxy\)\(URI\)(host\(URI\), \(port\), resource\(URI\), query) isObserveOption \(\leftarrow\) [coap_request] apply request aggregation request \(\leftarrow\) new coap_packet() request \(\leftarrow\) coap_header request \(\leftarrow\) insertOption(host\(URI\), resource\(URI\)) ifport and query are not NULLthen  request \(\leftarrow\) insertOption(port) request \(\leftarrow\) insertOption(query) endif ifisObserveOption = = truethen  apply coap observe algorithm request \(\leftarrow\) insertObserveOption() endif ifcoap_request has payload then  request \(\leftarrow\) insertPayload() endif / * possibility of ICN multicast \(*\) multiple server register for this resource \(*\)/ creatependingICNpublicationList using Token \(SIDhost\leftarrow\)sha256_hash(host\(URI\)) \(SID\leftarrow\)COAP_SCOPE_ID + SIDhost \(RID\leftarrow\) generateRandomString() ICN_ID \(\leftarrow\) SID + RID update ICN_ID_to_client_token_list  publish request to ICN usingICN\({}_{l}\)D endif ```
**Algorithm 2** CoAP Request in cNAP