esa-sceva/satcom-chunk-collection huggingface dataset:
# A Machine Learning Approach for the Classification of Refrigerant Gases
## 7 Conclusions  
Concluding, we explored the efficacy of integrating Internet of Things (IoT) technology with machine learning algorithms to classify refrigerant gases, specifically R32 and R134a. The combination of these advanced technologies enables continuous, real-time monitoring and analysis, significantly enhancing the precision and efficiency of gas detection systems.  
Our approach utilized an array of gas sensors connected via an IoT network, which facilitated the collection of a comprehensive dataset under controlled conditions. This dataset was then used to train various machine learning models, including Random Forests, Support Vector Machines (SVM), and Neural Networks (MLP). Among these, the Random Forest algorithm demonstrated superior performance due to its robustness, accuracy, and computational efficiency.  
The integration of IoT and machine learning for refrigerant gas classification presents a promising solution for real-time environmental monitoring and safety applications. By enabling accurate and rapid detection of hazardous gases, this approach not only enhances operational efficiency but also contributes to environmental sustainability and regulatory compliance.  
Future work will focus on expanding the dataset to include more types of refrigerant gases and exploring additional machine-learning techniques to further improve classification accuracy. Additionally, implementing these systems in real-world scenarios will provide valuable insights into their practical viability and performance under diverse conditions. Also, we are committed to acquiring new signals to further enhance the dataset. Expanding the dataset to include additional types of refrigerant gases and increasing the number of samples will allow us to refine and validate our model further, improving its accuracy and robustness in diverse conditions.  
Conceptualization, J.K.; Methodology, N.A., N.D., S.T. and P.K.; Software, N.A., J.K. and P.K.; Investigation, C.A., N.D. and S.T.; Writing--original draft, N.A., J.K., S.T. and P.K.; Writing--review & editing, C.A., N.D. and P.K.; Supervision, P.K. All authors have read and agreed to the published version of the manuscript.  
This research received funding through the project \"Circular economy ecosystem to Recover, Recycle and Re-use F-gases contributing to the depletion of greenhouse gases (LIFE Retradables) from the LIFE Programme of the European Union under grant agreement LIFE19 CCM/AT 001226--LIFE Retradeables.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Authors [PERSON] and [PERSON] were employed by the company mat4 nrg GmbH. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Multisensor Array Layout Scheme Based on the Spatial Distribution Characteristics of Rail Strain
### Layout Scheme of Multisensor Arrays in the Rail Cross-Section
#### 2.2.2 Influences of Wheel-Rail Contact States on Different Layout Schemes  
In addition to the vertical wheel loads, the rail is also subjected to bending moments and torques caused by the lateral wheel-rail forces and wheel eccentricity which makes the strain distribution in the rail cross-section much more complicated. Therefore, the selected method is supposed to be immune to these interferences, and therefore is better able to capture the vertical impacts caused by wheel flats.  
To determine the influences of lateral forces, the model established in Section 2.1 was used to simulate the outputs of different sensor layout schemes during an intact wheel passage. Different levels of lateral forces (0, 10, 20, 30, and 40 kN) were exerted to the model during the process. Figure 8 shows the effects of the different lateral forces on the outputs of the rail web bending moment method and the rail web compression method. We concluded that the existence of lateral forces plays an important role in the measurement of the rail web bending moment method, while on the contrary, it has little effects on the test results of the rail web compression method.  
Similarly, we analyzed the influences of wheel eccentricity on different layout schemes. In the analysis, wheel eccentricity is defined as the distance between the wheel-rail contact center and the symmetry axis of the rail. Figure 9 shows a comparison of the output signals of these two methods under different wheel eccentricity conditions (0, 5, 10, and 15 mm). Apparently, wheel eccentricity has a greater impact on the rail web bending moment method.  
Figure 10 concludes the influences of different wheel-rail contact states on these two layout schemes. The Y-axis represents the errors under different interferences. The result shows that the rail web compression method is the optimal solution for the sensor layout in the rail cross-section.  
Figure 8: Comparison of influences of lateral forces on different layout schemes: (**a**) Rail web bending moment method; (**b**) rail web compression method.  
Figure 9: Comparison of influences of wheel eccentricity on different layout schemes: (**a**) Rail web bending moment method; (**b**) rail web compression method.
# A Cyber-Physical System for Wildfire Detection and Firefighting
## 1 Introduction
### Proposed CPS for Wildfire Detection
Factors such as communication capabilities, terrain accessibility, and atmospheric conditions in which the robots can effectively operate are crucial aspects that require further investigation and evaluation in the real world. By presenting this CPS architecture and its simulation outcomes, our goal is to establish a solid foundation for future research and development in the field of wildfire prevention and management based on IoRT. This approach can enable fire stations and other wildfire suppression organizations to adopt a more efficient and effective approach to combating wildfires through the combination of innovative technologies and a collaborative framework. The paper is structured as follows: Section 2 examines some related work. Section 3 presents the design and implementation of the cyber-physical system for wildfire detection. Section 4 presents a simulation, and Section 5 presents a discussion. Finally, Section 6 draws conclusions, and Section 7 presents future research.
# ML-Empowered Microservice Workload Prediction by Dual-Regularized Matrix Factorization
## 3 Methodology
### Problem Definition  
In modern microservice architectures, workload prediction is a critical task aimed at optimizing resource allocation and system performance. The primary goal is to accurately forecast future workload fluctuations based on historical data so as to enable efficient resource scheduling and system maximize system performance.  
Let the workload data be represented as a matrix \(Y\in\mathbb{R}^{n\times T}\), where \(n\) denotes the number of microservices, \(T\) represents the historical time steps, and \(Y_{i,t}\) indicates the workload of the \(i\)-th microservice at time \(t\). The prediction task can be formulated as learning a mapping function \(f(\cdot)\), which maps historical workloads \(Y_{1:T}\) to future workloads \(Y_{T+1:T+h}\):  
\[f:Y_{1:T}\to Y_{T+1:T+h},\]  
where \(f(\cdot)\) represents the learned prediction model.  
To facilitate workload prediction, we make the following assumptions:  
* The workload matrix \(Y\) can be approximated in a low-dimensional latent space, i.e., \(\text{rank}(Y)\ll\min(n,T)\), based on the inherent correlations of microservice interactions and workload patterns (low-rank assumption).
* Workload changes exhibit temporal continuity, where adjacent time steps have strong dependencies.  
Workload prediction in microservice systems faces multiple challenges. On one hand, workload data exhibit multi-scale temporal dynamics, encompassing short-term fluctuations, long-term trends, and periodic patterns, which require accurate modeling without overfitting. On the other hand, the complex and dynamic interdependencies among microservices introduce significant modeling challenges, necessitating the extraction of key interactions to enhance efficiency and interpretability. Furthermore, in cold-start or data-scarc scenarios, models must demonstrate robustness to handle uncertainty caused by limited historical data.
# Timing Performance Testing and Regularity Analysis of eLoran System
## 3 Measuring Equipment and Tests  
### Measuring Equipment  
A test platform was constructed to verify the validity and reliability of the two eLoran timing monitoring receivers. The platform was designed to assess the timing performance of the receivers. Two commercial eLoran navigation timing monitoring receivers were used in the test. Both receivers were activated simultaneously, and their timing signals were monitored and compared for consistency using monitoring software (ELEGANT_UN15X_v8.2.3 USA). The two receivers used in this test are existing commercial eLoran receivers. A diagram of the test equipment is shown in Figure 1.  
The test equipment provides a precise demodulation of the time, frequency, and data channels based on an eLoran C or Enhanced eLoran system. This receiver is equipped with a serial port, GPIO interface, and both 1 PPS and 10 MHz inputs and outputs. It uses a long-wave receiving antenna to capture the long-wave space signal, which is then fed into the receiver host via a coupler. The receiver host captured and identified the long-wave signal and also tracked the third weekly over-zero point of the first pulse in the pulse group. The receiver performed real-time timing monitoring by decoding the messages. This device provided three timing-pulse 1 PPS outputs and can communicate via its RS232 serial port or LAN network port to deliver timing monitoring information.
# Variable Neighborhood Search for the Two-Echelon Electric Vehicle Routing Problem with Time Windows
## 6 Conclusions and Future Research Directions  
This study presented the two-echelon electric vehicle routing problem with time windows as a valuable concept for sustainable city logistics. A three-index node-based mixed-integer programming model was developed and solved using CPLEX for small instances. In addition, we proposed a variable neighborhood search metaheuristic making use of a wide range of classical and large neighborhood search operators. Moreover, our algorithm allows visiting unfeasible solutions, which is achieved by means of an extended objective function for the evaluation of both feasible and unfeasible solutions. The local search step of our variable neighborhood search approach uses a variable neighborhood descent algorithm. Experimental tests were performed using new problem-specific instance sets generated based on available data sets from the literature. While CPLEX was able to solve the proposed mathematical model only for small problem instances with 5 and 10 customers, it started to struggle deriving even feasible solutions for larger instances. Our variable neighborhood search approach was able to find optimal or near-optimal solutions faster than CPLEX for all small problem instances. Moreover, numerical results showed that destroy-and-repair-type operators generally increased the algorithm's performance.  
Conceptualization, M.A.A., C.B.K., C.B. and O.P.; Methodology, M.A.A., C.B.K. and O.P.; Data curation, M.A.A.; Software, M.A.A.; Writing--original draft, M.A.A. and C.B.; Writing--polishing, M.A.A. and C.B. All authors have read and agreed to the published version of the manuscript.  
Grant PID2019-104156 GB-100 funded by MCIN/AEI/10.13039/501100011033, grant 119M236 funded by the Technological Research Council of Turkey (TUBITAK). The corresponding author was funded by the Ministry of National Education, Turkey (Scholarship program: YLYS-2019).  
Figure 8: Critical difference plots concerning the results for large instances. The graphic in (**a**) considers all large instances, while the other graphics consider subsets of the set of large instances. (**a**) All large instances; (**b**) clustered instances; (**c**) random instances; (**d**) random-clustered instances; (**e**) instances R1*; C1* and RC1*; and (**f**) instances R2*, C2*, and RC2*.
## 4 Methodology  
The proposed method consists of two stages: (i) selection of the best machine for each process by applying a heuristic approach; (ii) selection of the best process and optimum allocated tolerance for each component using combined whale optimization algorithm and univariate search method. In the first stage, the process tolerance is divided into nd number of discrete values using Equation (10) and the allocated tolerance (\(t_{ejk}\)) is calculated using Equation (11). The tolerance cost (\(TC_{ejk}\)) and machining time (\(MT_{ejk}\)) for \(t_{ejk}\) are calculated using Equations (2) and (3), respectively. [PERSON] et al. (2018) explained that the distance method is used to combine the two different objective functions into a single one. For each discrete value, points are plotted on a graph where the \(x\)-axis and \(y\)-axis represent tolerance cost (\(TC_{ejk}\)) and machining time (\(MT_{ejk}\)), respectively. Assuming point (\(x1\), \(y1\)) as the origin and point (\(x2\), \(y2\)) as discrete tolerance cost and machining time, and substituting (\(x1\), \(y1\)) as (0,0) and (\(x2\), \(y2\)) as (\(TC_{ejk}\), \(MT_{ejk}\)) in Equation (12), then the distance equation becomes Equation (13). The detailed steps of the heuristic and univariate search methods are shown in Figures 1 and 2. The pseudocode for the combined whale optimization algorithm and a univariate search method is presented in Section 5.  
\[td_{j}=\frac{t\text{max}_{j}-t\text{min}_{j}}{nd} \tag{10}\]  
\[t_{ejk}=t\text{min}_{j}+(e-1)td_{j} \tag{11}\]  
where \(e\) is the index for discrete point of tolerance and takes from 1,2,3... \(nd\).  
\[dis=\sqrt{\left(x2-x1\right)^{2}+\left(y2-y1\right)^{2}} \tag{12}\]  
\[dis_{ejk}=\sqrt{\left(TC_{ejk}-0\right)^{2}+\left(MT_{ejk}-0\right)^{2}}= \sqrt{TC_{ejk}{}^{2}+MT_{ejk}{}^{2}} \tag{13}\]Figure 1: Heuristic approach to determine the best machine for each process.
# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 4 Results
### Performance Evaluation Metrics
#### 4.1.5 Average Packet Delivery Ratio (\(\bar{\mathcal{R}}\))  
The average packet delivery ratio per data-gathering cycle (\(\bar{\mathcal{R}}\)) measures the reliability of a system as it measures the proportion of packets that were delivered out from the total packets sent. Equation (23) can be used to calculate the packet delivery ratio. The packet source is either a vehicular node or an RSU node.  
\[\bar{\mathcal{R}}\ =\ \frac{100}{\mathcal{K}}\sum_{m=1}^{\mathcal{K}}\frac{ \mathit{Total\ packets\ received\ at\ destination}}{\mathit{Total\ packets\ sent\ from\ the\ source}} \tag{23}\]  
The destination is the controller for hybrid and centralized SDVN architectures, while the destination is another vehicular or RSU node for the distributed architecture. Reliability refers to the maximum tolerable packet loss rate at the application layer [45]. An \(\bar{\mathcal{R}}\) value close to 100 is desirable.  
The PDR in centralized and hybrid SDVN implies how much the controller is aware of the underlying data plane. However, in a VANET (distributed SDVN), the meaning of the PDR is how much information is exchanged between the neighbors. This information collection (in a centralized and hybrid SDVN) and exchange (in a VANET) lays the foundation for improved network functions such as routing.
# Side-Illuminating Optical Fiber for High-Power-Density-Mediated Intraluminal Photoacoustic Imaging
## 2 Materials and Methods
### Coupling PA Laser into Fiber  
A Q-switched Nd:YAG OPO tunable laser (LAZR, FUJIFILM VisualSonics, Inc., Toronto, ON, Canada) was used as the PA laser source. The laser system produces 40 mJ pulses with a duration of 5-10 ns in the near-infrared range (680-970 nm) at 20 Hz repetition rate. For multispectral photoacoustic (PA) imaging using the Vevo 3100 system (FUJIFILM VisualSonics, Inc., Toronto, ON, Canada), up to 550 frames can be collected during one acquisition, with 4 pulses required for each frame. Thus, a total of 2200 pulses can be fired during one PA acquisition. To demonstrate the fiber's mechanical resilience, it was tested by coupling more pulses than needed for one PA acquisition; thus, a total of 6000 shots at 18 mJ pulse energy (\(\lambda\) = 750 nm) were transmitted into the fiber. A custom coupling assembly was developed as described in our previous studies [28] and had an NA of 0.10, as used in the characterization setup. The light from the coupling assembly was directed into a 1-meter-long patch cable of the same specification as the fabricated side-fire fibers. The fabricated fibers were then butt coupled to this long fiber connected to the PA laser with an index matching gel (G608N3, Thorlabs, Inc., Newton, NJ, USA); we measured an energy loss of 10-16% due to the butt connection. The patch fiber was spatially overfilled by placing the coupling end slightly beyond the focal point of the focusing lens. The per-pulse energy emitted from the window region was measured using a power/energy meter (Maestro; Gentec, OR, USA; 5 cm diameter) before and after PA laser pulse transmission as part of standard measurement prior to imaging.
# An FPGA Design with High Memory Efficiency and Decoding Performance for 5G LDPC Decoder
## 3 The Proposed Decoder Architecture
### Variable Node Units and A Posteriori Blocks  
As shown in Algorithm 1, VNNs of the proposed decoder calculate \(\alpha_{m,n}\) of the VN processing step, while APBs compute \(\widetilde{\gamma}_{n}\) messages of the AP update step. It is worth noting that the main difference between these two blocks is that the VNU uses the subtractor while the APB uses the adder. Hence, we merge their logical functionalities into a VNU/APB, controlled by the \"sel\" signal. The controller generates this control signal, which selects VNU mode during the first clock and APB mode during the second clock. The detailed architecture of VNNs/APBs is depicted in Figure 6. In the current iteration, during the first clock cycle, the control signal sel = 0, and at the input two multiplexers are selected VNU mode (\(\widetilde{\gamma}_{n}^{old},\beta_{m,n}^{old}\)). The Subtractor/Adder block chooses the subtraction operation, thus, at the output, VTC messages are received. Each VN messages is calculated by \(\alpha_{m,n}^{new}=\widetilde{\gamma}_{n}^{old}-\beta_{m,n}^{old}\), where \(\widetilde{\gamma}_{n}^{old}\), \(\beta_{m,n}^{old}\) are the AP information values and the appropriate CN message values read from the AP-MB and CN-MB, respectively. Moreover, at the first clock cycle, these received \(\alpha_{m,n}^{new}\) messages provide to CNNs to create CN messages \(\beta_{m,n}^{new}\). During the second clock cycle, the control signal sel = 1, APB mode is executed and AP information update values (\(\widetilde{\gamma}_{n}^{new}\)) are updated by \(\widetilde{\gamma}_{n}^{new}=\alpha_{m,n}^{new}+\beta_{m,n}^{new}\).  
Figure 4: Check Node Unit (CNU) architecture with \(d_{\text{cmax}}=19\) (Min & Index finder architecture is shown in the gray dashed box).  
Figure 5: The architecture of (**a**) 2\(-\)mVU and (**b**) 3\(-\)mVU.  
As mentioned above, the check node degree varies dramatically in the 5G LDPC codes. Therefore, control signal \"set\" is needed to inactivate (i.e., the input values are set to zero) in cases with the check node degree \(d_{\epsilon}<d_{\text{cmax}}\). In this case, the last \((d_{\text{cmax}}-d_{\epsilon})\) CTV inputs are inactivated (set = '0').  
It is important to emphasize that for the HOMS algorithm, VN processing is modified by applying an offset factor (\(\delta>0\)) to the VTC message. Before sending into the Subtractor/Adder block, the \(\beta_{m,n}^{old}\) magnitude is reduced by the offset parameter \(\delta>0\) or set to zero if the resulting value is less than zero. The SMU transforms a sign-magnitude binary number in \(\widetilde{q}\)-bits (i.e., 1-bit sign and \(\widetilde{q}-1\) magnitude) to \(\widetilde{q}\)-bit in two's complement format.
## 3 Finite Element Analysis Simulation  
### Input Material Properties  
The mechanical properties of investigated material have been taken from the literature directly to perform the numerical simulation. The mechanical properties of Armox 500T and 7.62 AP bullet has been taken from [PERSON] et al. [11] and [PERSON] et al. [12] studies and mentioned in Table 2. The multi-pass SMAW procedure of 25 mm thickness Armox 500T plate with ASS and LHF consumables are explain by [PERSON] et al. [3; 4; 5; 6] in detail. Further, the mechanical properties of SMAW joints prepared with LHF consumables (Weldment-1) and ASS consumables (Weldment-2) are also taken from [PERSON]'s [12] research work and mentioned in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Description** & **Notation** & **Base Metal [11]** & **Weldment-1 [12]** & **Weldment-2 [12]** & **7.62 AP Bullet [11]** \\ \hline Modulus of elasticity & \(E\) (N/m\({}^{2}\)) & \(201\times 10^{9}\) & \(201\times 10^{9}\) & \(200\times 10^{9}\) & \(200\times 10^{9}\) \\ \hline Poisson’s ratio & \(\
u\) & \(0.33\) & \(0.3\) & \(0.32\) & \(0.3\) \\ \hline Density & \(\rho\) & \(7850\) & \(7850\) & \(7850\) & \(7850\) \\ \hline Yield stress constant & \(A\) (N/m\({}^{2}\)) & \(1372.488\times 10^{6}\) & \(331.47\times 10^{6}\) & \(345.19\times 10^{6}\) & \(1657.71\times 10^{6}\) \\ \hline Strain hardening & \(B\) (N/m\({}^{2}\)) & \(835.022\times 10^{6}\) & \(331.47\times 10^{6}\) & \(523.900\times 10^{6}\) & \(20\),\(855.6\times 106\) \\ \cline{2-6} constant & \(n\) & \(0.2467\) & \(0.4454\) & \(0.2819\) & \(0.651\) \\ \hline Viscous effect & \(C\) & \(0.0617\) & \(0.122\) & \(0.096\) & \(0.0076\) \\ \hline Thermal sensitivity & \(m\) & \(0.84\) & \(1\) & \(1\) & \(0.35\) \\ \hline Reference strain rate & \(\dot{\epsilon}_{0}\) (s\({}^{-1}\)) & \(1\) & \(10^{-3}\) & \(10^{-3}\) & \(1\) \\ \hline Melting temperature & \(T_{m}\) (K) & \(1800\) & \(1800\) & \(1800\) & \(1800\) \\ \hline Reference temperature & \(T_{m\prime}\) (K) & \(293\) & \(293\) & \(293\) & \(293\) \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Fracture strain \\ constant \\ \end{tabular} } & \(D_{1}\) & \(0.04289\) & \(0.0510\) & \(0.0463\) & \(0.0301\) \\ \cline{2-6}  & \(D_{2}\) & \(2.1521\) & \(2.0426\) & \(2.341\) & \(0.0142\) \\ \cline{2-6}  & \(D_{3}\) & \(-2.7575\) & \(-2.8342\) & \(-2.567\) & \(-2.192\) \\ \cline{2-6}  & \(D_{4}\) & \(-0.0066\) & \(-0.0509\) & \(-0.0408\) & \(0.0\) \\ \cline{2-6}  & \(D_{5}\) & \(0.86\) & \(0\) & \(0\) & \(0.35\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material parameters of investigated materials used in Numerical Simulations.