esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Multi-Objective Unit Commitment Model
## 2 Model
### Constraints  
These objectives must be minimized over a set of constraints.  
1. System power balance 1. For any given time intervals, the total power production must equal to the overall loads. \[D_{t}=\sum_{i}u_{i,t}p_{i,t}\] (4) \(D_{t}\) is the load demand at time interval \(t\). 2. System reserve constraints During each time interval, sufficient spinning reserve must be available. \[\sum_{i}u_{i,t}p_{i,max}\geq D_{t}+R_{t}\] (5) where \(R_{t}\) is the spinning reserve requirement at time \(t\), and \(p_{i,max}\) is the rated upper generation limit of unit \(i\). In our numerical examples. \(R_{t}\) is set to 0.05 times of \(D_{t}\).
2. Unit constraints 1. Generation limits \[P_{i,min}\leq p_{i,t}\leq P_{i,max}\] (6) where \(P_{i,min}\) is the rated lower generation limit of unit \(i\). 2. Unit minimum up/down time \[\begin{cases}u_{i,t}=1&T_{t,on}^{i}<T_{i,up}\\ u_{i,t}=0&T_{t,off}^{i}<T_{i,down}\\ u_{i,t}\in\{0,1\}&\text{otherwise}\end{cases}\] (7) \(T_{i,up}\), \(T_{i,down}\) are minimum up/down time for unit \(i\), and \(T_{t,on}^{i}\) are the time the unit \(i\) has been running before \(t\). 3. Unit ramp constraints \[-ramp_{i}\leq p_{i,t}-p_{i,t-1}\leq ramp_{i},t>1\] (8) We assume that the ramp constraint can always be met when \(t=1\).
3. Binary constraint \[u_{i,t},y_{i,t}\in\{0,1\}\] (9)
4. Tie-line constraint \[|p_{t}^{(tie)}|\leq P_{max}^{(tie)}\] (10) The tie-line between subsystems should not be overloaded. The power flow of tie-line is defined as a sequence \(P^{(tie)}=\{p_{1}^{(tie)},p_{2}^{(tie)},\ldots,p_{T-1}^{(tie)},p_{T}^{(tie)}\}\), and the tie-line power flow in any time interval should not exceed \(P_{max}^{(tie)}\).
# A Computational Platform for Automatic Signal Processing for Bender Element Sensors +
## 2 Conventional Interpretation Techniques for Bender Element Tests
### Time Domain Methods
#### 2.1.3 Second Arrival of the Output Wave  
In order to minimise the uncertainty in the definition of the arrival time, a method based on the second arrival of the shear wave was reported in reference [8]. After the first passage through the receiver, the shear wave travels back to the transmitter and is reflected to the receiver for a second time. The time elapsed between the first and the second arrivals at the receiver is equal to twice the travel time of the wave between the two extremities of the sample [8]. This method solves uncertainty not only in travel time but also in travel distance: the travel distance between the first and second events is always twice the total  
Figure 1: Idealised shape of transmitted (dashed line) and received (solid line) signals.  
length of the sample. However, second arrivals are often much harder to detect because of the dissipation of the shear wave as it travels through the sample.
# Applicability of Infrared Thermography for the Detection of Phase Transitions in Metal Alloys
## 3 Results & Discussion  
Figure 3 shows the evolution of the third and fourth time-eigenfunctions as well as the DSC curve for a sample of pure Gallium (Figure 3a) and for a sample of 54.9 mol%In-45.1 mol%Ga (Figure 3b). It should be mentioned that because the DSC can only be cooled down to room temperature, the eutectic transition for the mixture cannot be retrieved with DSC. Therefore, the heating curve does not present a clear baseline. Hence, it has been decided to compare IRT results to the DSC cooling curve. In the case of non-eutectic alloys, this choice does not drastically impact the transition temperature as they rarely exhibit supercooling tendencies. For the IRT method, the abrupt variations that could be associated with phase transitions are highlighted by blue dashed lines and an etiquette indicating the corresponding temperature. A red dashed line is used for the transitions detected with DSC.  
For the pure Gallium sample, the melting temperature is measured at 30.1 \({}^{\circ}\)C with our DSC. [PERSON] et al. [17] compiled all phase transition data for the Ga-In binary system. The average melting temperature for pure Gallium is there stated at 29.8 \({}^{\circ}\)C which is within acceptable tolerance from our measurement. Figure 3a shows that a transition is detected around 31.6 \({}^{\circ}\)C with the IRT method. However, slightly overestimating, this value is rather acceptable considering the estimative nature of the method which is primarily designed for fast measurements. Another possible transition is detected around 23.4 \({}^{\circ}\)C. This transition could not be retrieved with DSC measurements and is not mentioned in available literature data. According to Figure 3b, it appears that a transition has also been detected around this temperature for the mixture sample. This transition temperature is also inconsistent for this composition which would indicate that the variation in the time-eigenfunctions around this temperature is most likely due to an unexpected event. This event could be related to an unwanted phase formation (e.g., oxide, hydroxide, reaction with the substrate) during the experiment or a potential isolated malfunction of the experimental setup. In order to understand this phenomenon, additional chemical analyses (such as X-ray diffraction measurements) must be performed. For the 54.9 mol%In-45.1 mol%Ga sample, the liquidus temperature is measured at 71.1 \({}^{\circ}\)C with the DSC. The IRT method appears to detect a transition around 69.3 \({}^{\circ}\)C which would once again be a satisfactory approach. In [17], a sample of 55.6 mol%In-44.4 mol%Ga shows a liquidus temperature of 70.8 \({}^{\circ}\)C which is in line with both of our measurements. Although the eutectic transition could not be retrieved with our DSC measurement, literature [17] positions this transition around 15.3 \({}^{\circ}\)C. Our IRT experiment detects a transition around 16.5 \({}^{\circ}\)C which is a fairly close estimation given the purpose of the method.
# Analyzing Data Reduction Techniques: An Experimental Perspective
## 8 Experimental Evaluation
### Lossy Data Reduction Algorithms
#### 8.1.4 Principal Component Analysis  
In our evaluation of PCA, we integrated the sci-kit learn PCA component [45] into our dataset processing workflow. A critical aspect of this integration was to adjust the temporal structure of the data to improve compatibility with the algorithm. Our preprocessing involved converting different date formats into a universal timestamp to streamline data ingestion. We then determined the number of dimensions for the PCA algorithm using the elbow method, a simple approach to determining the optimal number of principal components (PCs) to retain. By plotting the explained variance ratios against the number of PCs, we search for the mark where the explained variance stabilizes, forming an elbow shape. This point indicates the optimal number of PCs to retain. More sophisticated implementations would require a careful evaluation of the intrinsic characteristics of the dataset to identify and prioritize the most influential data for dimensionality reduction.  
After selecting the dimensionality, we applied the PCA _fit_transform_ function to derive the PCs, effectively transforming the original dataset. We reverted the temporal changes from the universal timestamp to the original date formats to ease its interpretation. This approach ensures the successful application of PCA but also recognizes the importance of nuanced dimensionality selection for meaningful analysis, providing a comprehensive solution to our experiments.  
Table 9 shows the performance of the algorithm on datasets of different sizes. The algorithm showed a robust performance on larger datasets, demonstrating its scalability and efficiency, having the opposite effect when applied to simpler datasets. Understanding and addressing such nuances in the algorithm's behavior across different dataset complexities is critical to its applicability and to ensure consistent performance across distinct scenarios. In terms of performance, this algorithm performed relatively well. The algorithm took less time to process \(1\) MB on the Smoke Detection dataset, which contains numeric data, taking \(0.0276\) ms to process \(1\) MB. Considering percentage variation, the best result is a reduction of \(-20.81\%\) of the Accelerometer original dataset in \(0.160\) ms.
# Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
## 6 Conclusions  
In this work, we analyzed a novel dataset of BIRDS CubeSats. We investigated anomalies in the CubeSat solar panel system through an experimental ML study to compare proximity-based algorithms and linear model algorithms using on-orbit fault data obtained from four different CubeSats. Further, we introduced the windowed averaging method, standardization, and PCA in the pre-processing stage. The evaluation results on F-score metrics indicate that LDA and OC-SVM are the best models for detecting these particular anomalies within the dataset. LDA and OC-SVM models were tested on Raspberry Pi to confirm their technical viability for future onboard implementation. The results confirm their feasibility for onboard applications and prove that LDA outperforms OC-SVM in terms of execution time and power consumption.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app12178634/s1](https://www.mdpi.com/article/10.3390/app12178634/s1)]([https://www.mdpi.com/article/10.3390/app12178634/s1](https://www.mdpi.com/article/10.3390/app12178634/s1)). TSURU.xlsx: TSURU satellite dataset; NEPAL-ISAT.xlsx: NEPALSAT satellite dataset; RAAVANA.xlsx: RAAVANA satellite dataset; UGUISU.xlsx: UGUISU satellite dataset.  
**Author Contributions:** Conceptualization, A.J.J.C. and B.H.B.P.; methodology, A.J.J.C.; software, A.J.J.C. and B.H.B.P.; validation, A.J.J.C., B.H.B.P., A.H. and M.C.; formal analysis, A.J.J.C. and B.H.B.P.; investigation, A.J.J.C. and B.H.B.P.; resources, M.C.; data curation, A.J.J.C.; writing--original draft preparation, A.J.J.C.; writing--review and editing, B.H.B.P., A.H. and M.C.; visualization, A.J.J.C. and B.H.B.P.; supervision, M.C.; project administration, M.C.; funding acquisition, M.C. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by JSPS Core-to-Core Program B: Asia-Africa Science Platforms (JPJSCCB20200005).  
Not applicable.  
The authors would like to express their appreciation for the valuable comments of the associate editors and anonymous reviewers. This work was supported by BIRDS-3 and BIRDS-4 engineering team members. In addition, the authors would like to acknowledge the support provided by [PERSON], [PERSON], [PERSON], and [PERSON].  
The authors declare no conflict of interest.
## 2 Physical and Mathematical Model
### Channel Model  
Continuity equation [39]:  
\[\overrightarrow{V}\cdot\overrightarrow{V}=0 \tag{1}\]  
where \(\overrightarrow{V}\) is the velocity in all directions.  
Momentum conservation equation:  
\[\overrightarrow{\
abla}\cdot\left(\overrightarrow{\rho V}\overrightarrow{V} \right)=-\overrightarrow{V}P+\overrightarrow{V}\tau+\overrightarrow{S} \tag{2}\]  
where \(\rho\) is the density of fluid, \(P\) is the pressure, \(\overrightarrow{\tau}\) is the stress tensor, and \(\overrightarrow{S}\) is the momentum source term, which is 0 in all regions except the porous medium. It mainly includes the loss terms caused by viscous resistance and inertial resistance, as described by the Darcy equation.  
Darcy-Forchheimer equation [40]:  
\[\overrightarrow{S}=\frac{\mu}{k}v_{w}+\beta\rho v_{w}^{2} \tag{3}\]  
where \(\mu\) is the viscosity of the fluid; \(k\) is the permeability; \(\beta\) is the Forchheimer coefficient, which represents the nonlinear flow effect; and \(v_{w}\) is the wall velocity. For high flow velocity in porous medium, Re is calculated to be between 1 and 10. It should be noted that Forchheimer's extension in the Darcy equation is included to consider the nonlinear flow effect.  
For the model limitations and potential errors, it should be noted that this simulation neglects the detailed microscopic structures of the GPF along both the axial and the radial directions due to catalyst coating and the particle-loading process. It only employs different permeability and porosity to simulate the velocity and pressure within the GPF, which may result in discrepancies between the simulated and experimental flow characteristics.
## 3 Results and Discussion
### Results and Discussion of Performance Test
#### 3.2.3 Analysis of the Influence of Interaction Factors on the Ear Damage Rate
When the spring stiffness was set at a certain level, as the forward speed increased, the ear damage rate gradually increased. When the forward speed was 1.11-1.53 m\(\cdot\)s\({}^{-1}\) and the spring stiffness was 27.0-46.3 N\(\cdot\)mm\({}^{-1}\), the ear damage rate was noted to be low.
# A Novel 3D Topography Stitching Algorithm Based on Reflectance and Multimap
## 5 Conclusions  
Stitching makes the acquisition of high resolution over a large field of view 3D topography possible, which is helpful for multiscale analysis and to understand physical phenomena. However, stitching algorithms in 3D topography, especially of 3D optical profilometers, are either too sensitive to noise or too coarse. This paper proposes a novel reflectance-based multimap stitching algorithm, the rising sun stitching algorithm, in the form of three variants: the naive reflectance, the exhaustive enumeration reflectance and the two-step reflectance stitching algorithms. These three variants were compared to three existing 3D topography stitching algorithms (geometric, cross-correlation-based and global optimization of differences) with the help of four test cases, composed of measured topographic elementary maps, acquired with four 3D profilometers (two focus variation microscopes from AlconaTM, an interferometer from BrukerTM and an interferometer from ZygoTM). Five criteria are used for comparison of stitching algorithms: visual inspection (qualitative criterion), run time, memory usage, mean repositioning error and stitching error estimator SEE (four quantitative criteria and indicator). Results show that the rising sun stitching algorithm and its variants provide the best result for all test cases, especially if stitching quality is important. A decision-helping tool, the stitching gain lift plot, was then proposed to select the best stitching algorithm for a given set of elementary topographic maps: this helps users to graphically solve this optimization problem and sums up all results from the five criteria for stitching comparison. Overall, the two-step reflectance  
Figure 17: SGL plots for each test case.  
(TSR) stitching algorithm should be preferred except in the case of pathologic morphology (perfectly isotropic surface, periodic surface without noise), in which case the naive reflectance (NR) stitching algorithm should be chosen.  
Conceptualization, J.L.; formal analysis, J.L. and M.B.; investigation, J.L.; methodology, J.L. and R.G.; software, R.G.; supervision, M.B.; validation, J.L., R.G. and M.B.; visualization, J.L. and R.G.; writing--original draft preparation, J.L. and R.G.; writing--review and editing, J.L., R.G. and M.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the ELSAT2020 project, co-financed by the European Union with the European Regional Development Fund, the French state and the Hauts-de-France region.  
Not applicable.  
The authors thank the MorphoMeca platform for the use of the measuring apparatus.  
The authors declare no conflict of interest.
## 4 Discussion
### Comparison of Test Results and Analytical Simulations  
In a parallel research study, the structural response and demand of the concrete pile under high pressure originated from the energy storage was investigated through finite element modeling and simulations. The study covered two structural design parameters, including the number of building  
Figure 14: Regression analyses for predicted vs. actual strength values.  
stories and pile inner diameters. A full description of the study can be found in [PERSON] et al. [26]. As observed in the study, the concrete pile as an energy storage medium was subjected to considerable high circumferential tensile stresses, which might result in cracking and air pressure leakage in normal-strength concrete. Thus, it is important to ensure that the tensile strength of the RPC tested in this paper is higher than the maximum tensile stress demand obtained from the simulations.  
Figure 15 compares the maximum tensile stress demand and the tensile strength of the RPC. The maximum tensile stress demands in the pile, which are plotted as solid lines, were obtained from the simulations for different design cases by varying number of stories (2-story, 6-story, and 10-story buildings) and the inner diameters of the piles. The tensile strength that was calculated based on the test results is plotted as horizontal dotted trend lines for (a) different SF contents (at WB = 0.22) and (b) different WB (at SF = 20%). The tensile strength was calculated by dividing the 28-day normal curing flexural strength (refer to Figures 8 and 10) by a factor of 2.5, as proposed in [PERSON] and Tahah [40]. As seen in Figure 15, the RPCs that are studied in this paper generally have higher tensile strength than the maximum circumferential tensile stress demand obtained from the simulations, which indicates that the RPC is potentially suitable for the energy storage pile application. The mixture design with a combination of WB = 0.22 and SF = 20% provides the highest tensile strength of the RPC.
# An Overview of Fog Data Analytics for IoT Applications
## 3 Data Analytics
### Data Generation Sources
#### 3.1.3 Dynamic Data Sources  
The data sources are termed as smart data sources. Here, communication with the application takes place instead of just data passing. The communication is bidirectional and also dynamic or in real-time. We can take an example of a surveillance camera with a small code of face detection embedded into it. When a criminal's face is recognized, the camera can send an alarm message to the cloud-based criminal dataset program and communicate with it. Thus, these devices can not only send data but also alter the data format; for example, the camera does not send the whole image to the cloud, but only sends data in the form of whether the criminal has been identified.
## 2 Methods
### Double Interpolation of Optimization Local Mean Decomposition (LMD)
#### 2.1.2 The Interpolation-Based Local Mean Decomposition  
The envelope estimation function construction method has been an enduring research topic since LMD was first proposed. In the original LMD, the envelope estimation function is gained by the moving average method. Consequently, some repeats might result in phase inaccuracy of the functions. Inspired by EMD, the envelope estimation function can be constructed by using the cubic spline interpolation (CSI) envelopes of both the local minimum and maximum points to solve the phase difference problem, and the interpolation-based LMD method originating from EMD has become a widely used mode. CSI is a special case of spline interpolation; when compared to Lagrange and Newton polynomials, its interpolation polynomial is smoother and has less inaccuracy [28; 29], while the continuous second derivative of the CSI envelopes makes it susceptible to overshoot and undershoot issues for powerful nonstationary signals.  
Another common interpolation technique in LMD is called monotonic piecewise cubic Hermite interpolation (MPCHI), and it has a first-order continuous derivative [30; 31], which gives its envelope good flexibility. Its first-order derivative can be set monotonically; that is, the overshoot and undershoot problems in the CSI envelope for powerful nonstationary signals can be resolved.  
Instead of step (1) in the original LMD algorithm, the interpolation-based LMD uses the upper and lower interpolation envelopes of extreme points to calculate the envelope estimation function and the local main function, and then it continues to execute the rest of the original steps. The flowchart of interpolation-based LMD is shown in Figure 1.
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 6 Real Shipping Movement Model
### Results and Discussion  
Based on the results, there is little difference in connectivity between protocols, but the overheads are significantly different. A majority of the traffic in the Irish Sea is heading to and from Liverpool and has to call at the pilot boarding station in Liverpool Bay. This has a significant effect on the performance of MANETs in this simulation. Although the area covered does not have the same physical constraints, the shipping lanes nevertheless constrain the nodes to relatively narrow paths. The location of the four base stations is also critical to the performance in this example. Snaefell is on the Isle of Man, about 40 km north of the \"Belfast and Northern Routes\" shipping lanes. South Stack is near Holyhead, about 20 km south of the \"Dublin and Southern Routes\" shipping lane. Crosby Maritime Coastguard Agency (MCA) is on the mainland, about 20 km east of the pilot boarding station where the two main shipping lanes meet. Elsam Traffic is located in the Dee estuary, approximately 20 km south of the pilot boarding station. Because three of the base stations are relatively close to the shipping lanes, building a MANET in this scenario is largely dependent on establishing a chain of communication along the shipping lanes. The effect of the base stations' locations can be clearly seen from the packet delivery ratio results in this model, shown in Figure 12. Elsam Traffic and Crosby are both reached by all protocols with very high PDRs, over 97% for all the transmission ranges modelled. Ships can establish communications with each other along the shipping lanes, and there are enough nodes between the end of the shipping lanes and the base stations to make the final hops to these two base stations. Making a connection to South Stack requires a slightly longer radio range, because there are fewer ships in the area between the shipping lane and South Stack. So, there is no connection at all for a radio range of 10 km, but at all ranges above this, a very high PDR results, again over 97%. Snaefell is considerably further from the shipping lanes, and there are few ships making local journeys nearer to Snaefell than the northern shipping lane. Therefore, a considerably longer radio range is required in order to make the final hop to the base station and establish the connection.