esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Analysis
### Data Description  
The bearings testbed is shown in Figure 5. Detailed information about the platform and experiments can be found in [40].  
As tabulated in Table 1, 15 rolling element bearingss were tested under three different operating conditions. The first two bearings and the last bearing in every operating condition were regarded as a training set and the others were used as a testing set.  
The change trend of the original time domain and frequency domain characteristics with the monitoring time is shown in Figure 6. The amplitude of vibration signal varies with time, which indicates that vibration signal plays an important role in the evaluation of bearings performance degradation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Operating** & **Rotating Speed** & **Radial Force** & **Bearings Dataset** \\
**Condition** & **(rpm)** & **(kN)** & **Bearings Dataset** \\ \hline \hline \multirow{4}{*}{Condition A} & \multirow{4}{*}{2100} & \multirow{4}{*}{12} & \multirow{4}{*}{BearingA\_1} \\  & &
## 3 Preliminaries and System Model
### Security Requirement  
The proposed protocol has the following security assumptions:  
* Assuming that the NCCs and 6 SCs are trusted by UE, LEOs, and GSs. During the initialization phase, NCCs can send secret parameters for future use to the UE and LEOs over trusted channels (e.g., offline channels);
* Assuming that during satellite authentication, the channels between NCCs and GSs, GSs and LEOs, and NCCs and NCCs are trusted, it can be built using SSL or TLS. While using AKA, the satellite channels from UE to gNBs are untrusted, and the channels between gNBs and 6 GCs are trusted;
* It is assumed that no trust relationship has been established between the UE and LEOs before access authentication;
* It is assumed that in an unlinkable authentication scenario, regenerative LEOs may track the message of the UE and try to reveal its real information;
* The proposed protocol also needs to meet the following security requirements:
* Forward and Backward Secrecy: Due to the changes in the geographical location and requirements of UE, the proposed scheme should ensure forward and backward security; that is, an attacker cannot obtain the current session key through the information of the previous session [34]. In addition, if the current session is compromised, the attacker cannot affect the security of the previous channel [35];
* Mutual Authentication: The proposed scheme should satisfy mutual authentication; that is, regenerative LEOs can detect and refuse access to an illegal UE. In addition, a UE can also know the legitimacy of access nodes in the system, to avoid potential malicious attacks;
* Key Establishment: The proposed scheme should ensure that the session keys negotiated by the protocol are only shared between the UE and regenerative LEOs;
* User Privacy: In the unlinkable authentication scenario, the UE requires unlinkability; that is, others cannot know whether the information comes from the same UE. In the  
Figure 2: SGIN protocol model.  
batch authentication scenario and roaming authentication phase, the UE is linkable, but others will not be able to learn its identity information.
# Effect of Plate Curvature on Heat Source Distribution in Induction Line Heating for Plate Forming
## 4 Results and Analysis
### Effect of Initial Curvature
Figure 11: Plastic strain distribution with different initial curvatures in section S\({}_{\text{B}}\)–S\({}_{\text{B}}\): (**a**) out-of-plane strain in the X direction; (**b**) out-of-plane strain in the Y direction; (**c**) in-plane strain in the X direction; and (**d**) in-plane strain in the Y direction. Figure 10: Heat generation in section \(S_{1}\)–\(S_{1}\). be described as \(max(\left(\left(\varepsilon-\varepsilon_{K=0}\right)/\varepsilon_{K=0}\right)\). The result shows that the curvature has the greatest influence on the out-of-plane strain in the X direction and the smallest influence on the in-plane strain in the Y direction. When the curvature \(K<0.2\), the variation of the plastic strain Joule heat generation is mainly below 10%, which means that the effect of the curvature is not apparent. The total transverse shrinkage and transverse angular distortion are reduced. With an initial curvature \(K\)=1, the transverse shrinkage is reduced by about 11%, and the transverse angular distortion is reduced by about 6%. After comparing the results of different plate thicknesses, the same pattern can be concluded. When the curvature is below 0.5 m-1, the effect of the curvature on the out-of-plane strain is more significant for thicker plates. In practical applications, if the heat sources do not consider the change of plate curvature, the calculated plastic strain distribution will exhibit a significant deviation. According to the above calculation results, when the curvature is greater than 0.2, the influence of curvature on the distribution of the heat source should be considered. A compromise scheme is to use a heat source distribution under an intermediate curvature for calculation, so as to reduce the deviation caused by the curvature change.
## 2 Related Works
### Magnetic Anomaly Detection Technology
The frequency components of the pseudo-random transmission sources are rich with a noticeable [35; 36] suppression equivalence effect. However, this method requires moving the transmitting source and constructing a transmitting and receiving array [37]. The conventional working mode of the electrical source for the transient electromagnetic method is the long-offset transient electromagnetic method (LOTEM). It implements several kilometers of grounded wire to transmit switched-on bipolar square wave current underground to observe the electromagnetic field response at an offset range greater than three times the detection depth. As the grounded wire source can generate induced currents in both horizontal and vertical directions underground, it has a stronger discrimination ability for both low- and high-resistant underground targets so that it can play an important role in the field of crustal research, oil and gas exploration, geothermal investigation, and other areas at extreme depths [38]. Nevertheless, the larger the offset, the greater the construction quantity is, which leads to a higher requirement from the transmitter for power and performance, along with the factor of continuous waveform current, which increases the difficulty of data processing. Recently, the short-offset transient electromagnetic method (SOTEM) [39] was developed to conduct precision detection for deep targets 1.5 km underground. It uses the electromagnetic field excited by a switched-off bipolar current to observe the pure quadratic field at an offset range of fewer than two times the detection depth. The received signal strength is strong, as the transmitting and receiving distance is short. The SOTEM method improves detection accuracy and construction efficiency to a certain extent. The current mainstream electromagnetic instruments, such as V8 and GDP32, are all available for SOTEM measurement, providing favorable conditions to popularize this method. The SOTEM method has developed rapidly, with a relatively mature system of methods having been formed in the forward modeling, apparent resistivity calculation, inversion interpretation, and construction technology [40; 41], and is widely and successfully applied in the areas of metal ore and coal hydrogeology [42; 43; 44; 45]. The power frequency magnetic field-based detection approach suggested in this study overcomes the limitations of the preceding technologies and does not necessitate a huge transmitting and receiving array. It detects magnetic anomaly signals by using the world's alternating current (AC) transmission grid as the transmission source, and only requires an industrial frequency magnetic signal as the background field.
# Techno-Economic Analysis of Flare Gas to Hydrogen: A Lean and Green Sustainability Approach
## 1 Introduction
### Problem Statement  
Despite efforts by the Federal Government and other International Communities to reduce the flaring rate, gas flaring continues at an alarming rate in Nigeria. According to data from the World Bank, Nigeria ranks among the top 10 countries with the highest flaring rates in the world [49]. The Federal Government has introduced various policies and programs, such as the Nigerian Gas Flare Commercialization Program, the National Gas Policy, Flare Gas (Prevention and Waste Control) Regulations, and the most recent one, the Petroleum Industry Bill 2021, which has been signed into law [50]. The challenges associated with flaring stem from the government's lack of stringent actions against violators, insufficient technology, and inadequate managerial control by oil companies [51; 52]. As the world shifts toward cleaner energy systems, the potential of hydrogen to decarbonize various industries has resulted in a growing demand for this clean energy carrier. Transforming flare gas into value-added products, such as hydrogen, offers a viable path for sustainable energy production and carbon reduction, effectively addressing waste, lowering emissions, and generating clean energy. However, the techno-economic viability of this conversion, particularly in the context of lean and green practices, has not been thoroughly explored in Nigeria.  
This study addresses this gap by conducting a comprehensive simulation of hydrogen production from Nigerian flare gas using Aspen HYSYS. The research applies DEA and inverse DEA for performance benchmarking. The study also evaluates the levelized cost of hydrogen (LCOH) and performs a cost-benefit analysis. By adhering to lean and green sustainability principles, the study presents a viable pathway for utilizing flare gas as a strategic asset for Nigeria's low-carbon future.  
The rest of the paper is organized as follows: Section 2 outlines the method employed in this study. Section 3 presents the results. Section 4 discusses the results, and the conclusion is presented in Section 5.
## 5 Case Study of Combined Positioning in Tunnels
### Analysis of Lateral Positioning Error  
In order to quantitatively analyze the lateral accuracy of GNSS/INS/OD combined positioning constrained by lane lines inside the tunnel, the experimental results in the opposite direction are analyzed and statistically processed below. In this experiment, the vehicle's driving speed inside the tunnel was approximately 60 km/h, as shown in Figure 11. Figure 12 provides the lateral error of a 9.2 km tunnel, with the elliptical marked portion representing a 4.6 km half-length tunnel. The method used to calculate lateral error is as follows: at each comparison moment, calculate the vertical distance between the positioning point and the lane line, then subtract the actual distance of the vehicle from the lane line at the corresponding moment (this distance is obtained by subtracting the lane line distance from the tunnel wall distance measured by LiDAR).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Positioning Method** & **Reverse 1** & **Reverse 2** \\ \hline GNSS/INS & 251.53 & 454.55 \\ GNSS/INS/OD & 141.82 & 329.78 \\ Lateral Distance Measurement + Lane Markings + GNSS/INS/OD & 1.43 & 1.23 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the horizontal accuracy between the tunnel positioning points and the first fixed RTK point outside the tunnel (unit: m).  
Figure 11: Vehicle speed inside the tunnel.  
Figure 10: Vehicle speed inside the tunnel.  
As can be seen from Figure 12, with the increase in driving distance in the tunnel, the positioning error shows a gradually diverging trend. In the second half of the tunnel, the positioning error diverges severely, reaching a maximum of 1 m. The positioning error results for the 4.6 km midpoint of the tunnel are summarized in Table 3. The table includes the arithmetic mean, absolute error mean, root mean square error, maximum error, minimum error, and the 95 th percentile of the absolute error. In Table 3, a total of 61,001 positioning points were analyzed. It can be observed that the arithmetic mean of the lateral error is 0.013 m, and the absolute error mean is 0.294 m, which characterizes the absolute lateral accuracy of vehicle positioning. The maximum error and minimum error are 0.671 m and \(-\)0.865 m, respectively, with the 95 th percentile of the absolute error being 0.612 m, indicating that further improvement is needed in the stability of vehicle positioning in the tunnel.
## 4 Auxiliary Problem Principle Method with Self-Adaptive Penalty Parameter
### Motivation  
[PERSON] proposed a modified alternating direction method to deal with problem (8). To be more exact, this method adjusts the penalty parameter \(c\) based on the iterate message [17].  
Now we give the details on how to adjust the penalty parameter \(c\) for better convergence performance. In fact, the solution of problem (8) is equivalent to finding a zero point \(e(w^{k})\):  
\[e\left(w^{k}\right)=\begin{bmatrix}e_{x_{1}}\left(w^{k}\right)\\ e_{x_{2}}\left(w^{k}\right)\\ e_{x_{2}}\left(w^{k}\right)\end{bmatrix}=\begin{bmatrix}x_{1}^{k}-P_{\alpha 1} \left\{x_{1}^{k}-\left[\
abla f\left(x_{1}^{k}\right)-A^{r}A^{s}\lambda^{s} \right]\right\}\\ x_{2}^{k}-P_{\alpha 2}\left\{x_{2}^{k}-\left[\
abla g\left(x_{2}^{k}\right)-B^{r} \lambda^{s}\right]\right\}\\ A\lambda_{1}^{s}+Bx_{2}^{s}\end{bmatrix} \tag{17}\]  
where  
\[w=\begin{pmatrix}x_{1}\\ x_{2}\\ \lambda\end{pmatrix},\ W=\Omega_{1}\times\Omega_{2}\times R^{r},\ w^{k}\in W \tag{18}\]  
\(P_{\alpha}(\cdot)\) is the projection on \(\Omega\). \(\forall(\cdot)\) and \(\
abla g(\cdot)\) denote the gradient of \(f(\cdot)\) and \(g(\cdot)\) respectively.  
Equation (17) offers us an inspiration on how to adjust penalty parameter \(c\). For the sake of balance, we should adjust the penalty parameter \(c\) such that \(\bigv\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!  
Based on above discussion, the proof of Lemma 1 is completed.  
Furthermore, (20) can be rewritten as follow.  
\[\left\|{{v^{k}}-{v^{\prime}}}\right\|_{{{}_{{N}}}}^{2}-\left\|{{v^{k+1}}-{v^{ \prime}}}\right\|_{{{}_{{N}}}}^{2}\geq\left\|{{v^{k}}-{v^{k+1}}}\right\|_{{{}_{{ N}}}}^{2} \tag{31}\]  
where  
\[\begin{array}{c}{v=\begin{pmatrix}A\tau_{1}\\ B\tau_{2}\\ \lambda\end{pmatrix},N=\begin{pmatrix}(\beta-c)I&-cI&0\\ -cI&(\beta-c)I&0\\ 0&0&\dfrac{1}{c}I_{n}\end{pmatrix}}\end{array} \tag{32}\]  
It is clear that (31) is Fejer monotone, so we get [20]  
\[\begin{array}{c}{\lim_{k\to\infty}{\left|{Ax_{1}^{k+1}}-A\tau_{1}^{k}\right| }=0,\;\lim_{k\to\infty}{\left|{Bx_{2}^{k+1}-Bx_{2}^{k}}\right|}=0,\;\lim_{k\to \infty}{\left|{\lambda^{k+1}-\lambda^{k}}\right|}=0}\end{array} \tag{33}\]  
Interestingly, (33) is consistent with APP's stop criterion, so it is clear that if \(\left|{v^{\prime}}-{v^{\prime\prime}}\right\|_{{{}_{{N}}}}^{2}\), then \({w^{k+1}}\) can be denoted by \({w^{*}}\). Hence, the magnitude of \(\left|{v^{\prime}}-{w^{\prime\prime}}\right\|_{{{}_{{N}}}}^{2}\) can measure the error between \({w^{k}}\) and \({w^{*}}\).  
According to \(\beta\)=\(2c\) which is described in (16), we get,  
\[\left\|{{w^{k}}-{w^{k+1}}}\right\|_{{{}_{{N}}}}^{2}=\left\|{\left({Ax_{1}^{k+1 }-Ax_{1}^{k}}\right)-\left(Bx_{2}^{k+1}-Bx_{2}^{k}\right)}\right\|_{{{}_{{N}} }}^{2}+\left\|{\lambda^{k+1}-\lambda^{k}}\right\|_{{{}_{{}_{{c}}}}}^{2} \tag{34}\]  
Now, for the sake of balance, we only need to adjust penalty parameter \(c\) such that \(\left\|{Ax_{1}^{k+1}-Ax_{1}^{k}}\right\|+\left(Bx_{2}^{k+1}-Bx_{2}^{k}\right) \right\|_{{{}_{{N}}}}-\left\|{x^{k+1}-x^{k}}\right\|_{{{}_{{N}}}}^{2}\).
# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
## 3 Methodology  
As shown in Figure 1, our proposed system consists of three main phases. First, we extract data sequences from three missions in the dataset, followed by the necessary preprocessing steps, which include handling non-stationary features and converting the dataset into an appropriate shape. In the second phase, we build machine learning-based classifiers, namely decision tree, random forest, and XGBoost, to classify GPS signals into four categories: authentic signals, simple attacks, intermediate attacks, and sophisticated attacks. Finally, we train each classifier on each mission dataset through 50 experiments and evaluate them using various metrics: accuracy, precision, recall, and F1-score. For further assessment, we combine all the mission datasets into a single dataset to evaluate the consistency of each model on large-scale data.
## 3 Results and Discussion
### Synthesis of Storage Lipids and Fatty Acid Composition of Total Lipids and Lipid Fractions
In the lipid fraction of NL, the aforementioned fatty acids were present in low proportions, whereas \({}^{\Delta 9}\)C14:1 was present in higher amounts compared with TL (i.e., 13.1 \(\pm\) 0.4%, wt/wt against 4.0 \(\pm\) 0.0%, wt/wt). In general, a similar picture was found for both G and P fractions, but in the case of the lipid fraction G, the percentage of C17:0 (15.7 \(\pm\) 0.1%, wt/wt) is remarkable. The fatty acid C17:0 is a rare fatty acid that possesses pheromone and allomonic properties [62]. The presence of such C17:0 has been established for microalgae, though its relatively high percentage is rather interesting, as usually the content of C17:0, or other odd-chain and branched-chain fatty acids, does not exceed 3-5% of all FAs. However, it must be noted that C17:0 has been considered a marker of bacterial contamination of microalgae and cyanobacteria cultures [63], which probably was the case in our open culture bioreactor as well. The fatty acids occurring in higher percentages in the TLs of _M. gaditan_ VON5.3 were, in order, \({}^{\Delta 5,8,11,14,17}\)C20:5 (22.7 \(\pm\) 0.8%, wt/wt), \({}^{\Delta 9}\)C16:1 (31.5 \(\pm\) 0.5%, wt/wt), and C16:0 (15.5 \(\pm\) 1.1%, wt/wt). In general, these fatty acids were the dominant ones in all lipid fractions and at similar levels. However, in the case of the NL lipid fraction, \({}^{\Delta 5,8,11,14,17}\)C20:5 and \({}^{\Delta 9}\)C16:1 were detected at significantly lower percentages compared with the TL (i.e., 7.6 \(\pm\) 2.2%, wt/wt, and 17.9 \(\pm\) 4.9%, wt/wt, for \({}^{\Delta 5,8,11,14,17}\)C20:5 and \({}^{\Delta 9}\)C16:1, respectively), while in the G and P lipid fractions, higher values of \({}^{\Delta 9}\)C16:1 compared with the NL fraction were recorded. Finally, no production of any PUFA was recorded in the lipids of _N. pyriformis_ PAT2.7 under these culture conditions. Therefore, the lipids of this strain are a more suitable source for biodiesel production in comparison with the lipids of the other strains. Predominant fatty acids in both total lipids and their lipid fractions were \({}^{\Delta 9}\)C16:1 (exceeding 40%, wt/wt in TL) and C14:0. The great percentage of \({}^{\Delta 9}\)C16:1 is a finding of high interest, as it is a fatty acid that, in recent investigations, was attributed, among other things, with anti-inflammatory properties and the ability to reprogram gut microbiota [64]. Lastly, it is worth mentioning the high proportion of \({}^{\Delta 9}\)C18:1 (21.7 \(\pm\) 3.0%) in the lipid fraction of P.
## 2 Numerical Investigation
### Geometrical Model and Mesh  
The original plenum fan model PRL-560L0(LG Electronics, Korea) was used in this study. The geometric shapes are shown in Figure 1, and the specifications are shown in Table 1. Figure 2 shows the computational domain. The radius of the computational domain was three times the diameter of the plenum fan, and the height was set to 30 times the width of the plenum fan. In the figure, Block 1 is the area at which the flow enters through the bell mouse, Block 2 is the fan part and the rotating flow area, and Block 3 is the area where there is rotational flow. The flow field was treated as a wall in this work. As a boundary condition, the inlet condition was the pressure inlet condition, the outlet condition was the mass flow rate condition, and the air density corresponded to 25 \({}^{\circ}\)C. The size of the domain was considered large enough to avoid interference in the flow.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables** & **Units** & **Value** \\ \hline Flow coefficient & - & 0.675 \\ Static pressure & Pa & 392 \\ Static pressure efficiency & \% & 71.8 \\ Impeller diameter & mm & 640 \\ Impeller outlet breadth & mm & 167 \\ Number of blades & - & 7 \\ Revolving speed & rpm & 1100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rated variables of the plenum fan.  
Figure 3 shows the axial cross section of the original model's mesh system. Since the impeller and shrouds were curved and exhibited complex geometrical shapes, a high-quality structured grating could not be applied, so an unstructured grating was used. For analysis, 9.4 million gratings were constructed for the impeller and the bell mouth, and 7.4 million tetrahedra and 2 million wedges were applied.
# From DevOps to MLOps: Overview and Application to Electricity Market Forecasting
## 4 Case Study 1: Forecasting an Hourly Day-Ahead Electricity Market Using an MLOps Pipeline
### The Frequency Containment Reserves Market  
With the advent of smart grids and Virtual Power Plants (VPPs), various Distributed Energy Resources (DERs)--such as smart loads, batteries, photovoltaics (PVs), and wind power--are being exploited on various electricity markets, including frequency reserves [165; 166; 167; 168]. Frequency reserves with a fast response time for frequency deviations are generally called Primary Frequency Reserves (PFRs), and traditionally they consist of fossil-fuel-burning spinning reserves. These reserves are now being replaced with DERs in the push towards reducing carbon emissions [169]; due to this, under the current allowed delays for PFRs, the reduced grid inertia is becoming a threat to the stability of power systems [170]. The FCR-N market was selected among other ancillary services for this case study, as the FCR-N does not have hard real-time constraints and a minimum power bid for market participation. The FCR-N is a day-ahead market, and bidders must submit all of the bids for the hours of the next day before 6 p.m. of the current day [171; 172].  
If the day-ahead reserve market prices can be predicted, then the DER owners can anticipate variations in price peaks and low or zero prices. Thus, this case study presents a solution based on artificial neural networks, which are deployed online, so that the predictions are automatically updated and available before the bidding deadline of the day-ahead market. A transformer-based ANN model is exploited to predict the day-ahead ancillary energy market prices. The MLOps pipelines are configured to ingest the data at 1 p.m. on the current day, so the forecasts are available, e.g., at 2 p.m., so that the person or system doing the bidding has the forecast a few hours before submitting the bid.