esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Impedance Estimation: Challenges and Considerations
## 4 Dynamic Impedance Estimation  
The online estimation of the network impedance is a fundamental step in the study of harmonic propagation, attribution of harmonic responsibility, identification of losses in the network, among others [17; 38; 39; 40; 41; 42; 43; 44; 45; 46]. In recent years, research has focused on estimating impedance at the PCC, both on the load side and the source side [38; 39]. However, more information would be obtained if, instead of knowing the PCC's impedances, it was possible to find the system transfer functions. For example, Figure 9 shows a small section of a real transmission system, with harmonic sources in buses A and C. Knowing the transfer from A to D and from C to D, it would be possible to understand the harmonic propagation in the network, as well as to establish the responsibilities of each source.  
While in the Norton model in Figure 10, there is no need for satellite synchronization of measurements, since all measurements are taken from the same geographical point, the transfer functions' estimates will require synchronized measurements.  
Figure 8: Maximum and average TVE of harmonic phasors under off-nominal frequency.
## 3 Results and Analysis  
### Wind Turbine Model  
Based on the beam theory, a commercial 6 MW offshore wind turbine is modeled. The specific model parameters are given in Table 1. The first and second natural frequency of blades and the tower are calculated. The total simulation area of the typhoon is 200 m \(\times\) 200 m, which is discretized into 256 nodes, covering the whole rotor, as shown in Figure 3. The position and direction of the blades are also indicated with the state of parking. In the process of dynamic calculation, the flow direction facing the rotor is 0 degrees, clockwise, it is positive and counterclockwise, it was negative. In this paper, the aeroelastic response of the complete wind turbine is calculated in seven cases at yaw angles of 0, 30, 60, 90, 120, 150 and 180 degrees.
# Lossless Compression of Sensor Signals Using an Untrained Multi-Channel Recurrent Neural Predictor
## 4 Experiments
### Datasets  
We select four public sensor datasets with different signal characteristics:  
BLOND [27] provides continuous energy measurements of a typical office environment at high sampling rates. This dataset contains the voltage and current measurements of 16 common electrical appliances in 50 days. In this paper, we use the voltage measurement of the first 5 electrical appliances measured within 2 min starting at 10 a.m. on 30 June 2017.  
ESC [28] is a labeled collection of 2000 environmental audio recordings (with 50 semantical classes). The audio recordings are sampled at 44,100 Hz with 16 bits per sample without compression. Since the file size of each audio recording is too small, we merge the audios of each class into one single sensor signal without the file header. We use the first five classes of audio recordings for this experiment.  
MPACT [29] contains radio frequency (RF) signals from different brands and models of drone remote controls (RC). The RF signals transmitted by the drone RCs to communicate with the drone are recorded by a passive RF surveillance system. There are 17 drone RCs from eight different manufacturers, and each RF signal contains 5000 k samples (spanning a period of 0.25 ms). We use the raw data of the first RF signal file of drones from five different manufacturers for the experiment.  
MPACT-14 bits contains the same data as MPACT, except that the lowest 2 bits of each sampled value are removed. Thus, the ADC resolution of this dataset decreases to 14 bits. This dataset is constructed to verify all compressors' performance on different \(R_{p}\).  
The detailed information of these sensor datasets is listed in Table 2.  
Figure 2: Structure of the proposed multi-channel recurrent unit.
## 4 Proposed Method
### Combined Local and Global Contrast Enhancement  
As shown in Figure 2, we first utilize an image enhancement technique to improve the image quality and increment the color intensity of significant objects as compared to surrounding regions. In [18], we implemented a global color contrast improvement tool to upgrade shadowy scenery and low-level pixel intensities caused by non-natural illumination sources. In this study, we employed a robust and unique feature enhancement strategy, utilizing both local and global contrast improvement information for reducing the time consumption and generating better outcomes as compared to other image improvement techniques. When applying only one method, it not possible to enhance an input image using its local brightness features, as gray levels having a very high frequency dominate gray levels having a very low frequency.  
Figure 2: Flowchart of the proposed method: (**1**) Input image sequences, (**2**) image enhancement based on local and global methods, (**3**) object segmentation and noise elimination process, (**4**) shadow removal based on geometry features, (**5**) foreground mask reconstruction for object tracking.  
Therefore, in our proposed method, we utilized a combination of global contrast enhancement and local contrast improvement techniques for such types of data, i.e., those that exist in the less dynamic region of the image, as in [19]. This is done by subtracting two consequent grayscale values of the image.  
\[O=I+\frac{M+C_{i}}{\sigma_{i}}[m_{i}-m_{i+1}] \tag{1}\]  
in the above, \(O\) is a pixel's intensity value in the improved image, corresponding to the \(I\) pixel's gray value in the original image. \(M\) is the global contrast gain control, with a range of [0-1.5]. \(C_{i}\) is the local contrast control, with a range of 0-1. \(\sigma_{i}\) is the local standard deviation of the window. \(m_{i}\) is the pixel's grayscale value present in the original image and \(m_{i+1}\) is the subsequent pixel's gray scale value in the original image. This method iterates pixel-by-pixel over the entire image.
## Oil Leakage Mechanism of SPRG in CMBW Systems
### Existing Standard Evaluation Methods and Limitations  
There are national standard test methods such as British Standard European Norm (BS EN) and American Society for Testing and Materials (ASTM) designed to test the materials based on the viscosity and settlement tendency of bitumen emulsions. While empirical data obtained from these test methods can provide a detailed understanding of the materials, it does not provide enough information on their practical performance with respect to waterproofing concrete structures. For example, ASTM D 2170 _Standard Test Method for Kinematic Viscosity of Asphalts (Bitumen)_[15] evaluates the time required for a fixed volume of a bitumen sample to vertically flow through containers linked by narrow capillaries, where kinematic viscosity is calculated by multiplying the efflux time in seconds by the viscometer calibration. This method can provide empirical data on the rheological property of SPRG based on  
Figure 3: Oil leakage mechanism of SPRG-based CMBW, illustrated.  
Figure 2: Illustration of SPRG breaking stages.  
the diameter sizes of the tube, and SPRG manufacturers test their materials in compliance with these standard test methods to validate their products' performance. However, actual concrete structures have complex degradation environmental factors, and laboratory experimentation has limitations in accurately simulating these conditions. As illustrated in Figure 3, the compressive force from the protective concrete layer and top soil will cause the segregated oil layer to migrate through an opening and leak through a crack. Unless the below-grade environment is simulated in the experiment and only standard methods are used, an accurate assessment of SPRG oil leakage tendency may be difficult [16].  
**3. Materials: SPRG in a Composite Modified Bituminous Waterproofing (CMBW) System**  
Refer to Table 1 for a typical composition of SPRG used in Korea.  
For this study, 8 different types of Korean SPRG products were selected. Individual SPRG mixture ratios differ in accordance to their own manufacturing methods. Each of the SPRG samples were tested for their viscosity and basic composition. Each sample's viscosity was measured in compliance with the method outlined in KS M ISO 2555: 2002 with a Brookfield viscometer, and the general composition ratio was measured in compliance with KOREA LAND & HOUSING CORPORATION 42531: 2015. Refer to Table 2 below for details.
# Field Trial and Performance Evaluation of Soybean-Based Bio-Fog Seals for Asphalt Rejuvenation
## 2 Materials and Methods
### Bio-Fog Seal Formulation  
The bio-fog seals used in this study were formulated from two soybean-based additives: SESO and BioMag. SESO, derived from partially epoxidized soybean oil, which contains triglyceride molecules and plays a key role in asphalt rejuvenation. The epoxy rings formed chemically during processing react with asphalt molecules, addressing asphaltene aggregation, a consequence of aging, and restabilizing the structure [26,27]. Additionally, SESO modifies asphaltene molecules, enhancing binder solubility and flexibility and ultimately reducing the stiffness of aged binders [28]. BioMag, a 50/50 blend of SESO and the thermoplastic biopolymer poly(acrylated epoxidized high oleic soybean oil) (PAEHOSO), further enhances performance. The SESO component acts as a solvent, while PAEHOSO is synthesized through RAFT polymerization [29].  
The bio-fog seals were formulated as water-based and fast-setting emulsions, 100% derived from soybean oil with 0% residual bitumen. Their composition includes 30% solids, 2% surfactant, and 1% emulsion aid, resulting in a stable and highly penetrating product. The viscosity is set below 20 cP at 35 \({}^{\circ}\)C, and the density is approximately 0.97 g/cm\({}^{3}\). The surfactant emulsifies the rejuvenator and polymer components into micron-sized droplets within the water, while also reducing surface tension to facilitate rapid penetration into the pavement.
# Fine-Grained Action Recognition by Motion Saliency and Mid-Level Patches
## 3 Mid-Level Patch Mining Based on Motion Saliency  
To benefit from the space context, spatial pooling divides a video using fixed segmentation grids and pools the features locally in each grid cell [22; 23; 24]. Though the performance has been improved, different action instances of the same category with various human localizations in spatiotemporal volume [46; 47] can result in a non-uniform distribution of features. Furthermore, one interaction component may be divided into different cells due to the fixed segmentation. Therefore, the key problem is how to divide the interaction components accurately in different instances. To avoid manual annotation, we use mid-level patches (detected from object proposals) to represent the interaction components. Most algorithms apply Bing [16] or Selective Search [22] to extract object proposals in a fixed way. All the objects in the scene are regarded as proposals, including dynamic ones and static ones unrelated to the action. In our algorithm, object proposals are extracted based on the motion saliency [25], which can automatically exclude the static regions.
# A Comprehensive Survey on Knowledge-Defined Networking
## 7 Application Plane
### Application Scenarios
#### 7.3.2 Network Management
In [478], an integer linear programming optimization model is used to optimize the energy consumption of IoT nodes by activating an optimal number of network function virtualization nodes and assigning regular nodes to those activated by network function virtualization. A task assignment and scheduling platform that is formulated as a deep Q learning process strives to maximize energy efficiency by saving battery power under the constraints of application dependence, thus minimizing energy consumption considering the requirements of the applications [479].
## 4 Resource Allocation under Separate Power Constraints
### Power Allocation  
Similarly, the Lagrangian function of Equation (16) is constructed as  
\[L =\sum_{i=1}^{N}\sum_{j=1}^{N}\rho_{i,j}r_{i,j}+\lambda_{1}\left[P_{S }-\sum_{i=1}^{N}p_{s,i}\right]+\lambda_{2}\left[P_{R}-\sum_{j=1}^{N}p_{r,j} \right]+\mu_{1}\left[Q_{1}-\sum_{i=1}^{N}p_{s,i}c_{i}\right]+\mu_{2}\left[Q_{2} -\sum_{j=1}^{N}p_{r,j}d_{j}\right]\] \[=\sum_{i=1}^{N}\sum_{j=1}^{N}\rho_{i,j}\left[r_{i,j}-\lambda_{1}p _{s,i}-\lambda_{2}p_{r,j}-\mu_{1}p_{s,i}c_{i}-\mu_{2}p_{r,j}d_{j}\right]+ \lambda_{1}P_{S}+\lambda_{2}P_{R}+\mu_{1}Q_{1}+\mu_{2}Q_{2} \tag{17}\]  
According to the KKT rule, taking the partial derivative of \(L\) with respect to \(p_{s,i}\) and \(p_{r,j}\), we get  
\[\frac{\partial L}{\partial p_{s,i}}= \frac{a_{i}b_{j}^{2}p_{r,j}^{2}+a_{i}^{2}g_{i}p_{s,i}^{2}+b_{j}^{ 2}g_{i}p_{r,j}^{2}+2a_{i}b_{j}g_{i}p_{s,i}p_{r,j}}{2\ln 2\left(a_{i}p_{s,i}+b_{j}p_{r,j} \right)\left(a_{i}p_{s,i}+b_{j}p_{r,j}+a_{i}b_{j}p_{s,i}p_{r,j}+b_{j}g_{i}p_{s,i}p_{r,j}+a_{i}g_{i}p_{s,i}^{2}\right)}-\lambda_{1}-\mu_{1}c_{i}\] \[= 0 \tag{18}\]  
\[\frac{\partial L}{\partial p_{r,j}}= \frac{a_{i}^{2}b_{j}p_{s,i}^{2}}{2\ln 2\left(a_{i}p_{s,i}+b_{j}p_{r,j }\right)\left(a_{i}p_{s,i}+b_{j}p_{r,j}+a_{i}b_{j}p_{s,i}p_{r,j}+b_{j}g_{i}p_{s,i}p_{r,j}+a_{i}g_{i}p_{s,i}^{2}\right)}-\lambda_{2}-\mu_{2}d_{j}\] \[= 0 \tag{19}\]  
After some algebraic operations, the optimal power allocation is given by  
\[p_{s,i}^{\star} =\left[\frac{a_{i}b_{j}^{2}f_{i,j}^{2}+a_{i}^{2}g_{i}+2a_{i}b_{j} g_{i}f_{i,j}+b_{j}^{2}g_{i}f_{i,j}^{2}}{2\ln 2\left(a_{i}+b_{j}f_{i,j} \right)\left(\lambda_{1}+c_{i}\mu_{1}\right)\left(b_{j}g_{i}f_{i,j}+a_{i}b_{j }f_{i,j}+a_{i}g_{i}\right)}-\frac{a_{i}+b_{j}f_{i,j}}{b_{j}g_{i}f_{i,j}+a_{i}b_ {j}f_{i,j}+a_{i}g_{i}}\right]^{+} \tag{20}\] \[p_{r,j}^{\star}= f_{i,j}p_{s,i} \tag{21}\]  
where  
\[f_{i,j}=\begin{cases}\frac{a_{i}\sqrt{\left(\lambda_{2}+d_{i}\mu_{2}\right) \left(a_{i}b_{j}\left(\lambda_{1}+c_{i}\mu_{1}\right)+b_{j}g_{i}\left(\lambda_ {1}+c_{i}\mu_{1}\right)-a_{i}g_{i}\left(\lambda_{2}+d_{j}\mu_{2}\right)\right) }-a_{i}g_{i}\left(\lambda_{2}+d_{j}\mu_{2}\right)}{b_{j}\left(a_{i}+g_{i} \right)\left(\lambda_{2}+d_{j}\mu_{2}\right)}&\text{if }b_{j}\left(\lambda_{1}+c_{i}\mu_{1}\right)\\ 0&>g_{i}\left(\lambda_{2}+d_{j}\mu_{2}\right)\\ 0&\text{otherwise}\end{cases} \tag{22}\]  
In the separate power constraints, the decision criterion for whether to relay becomes \(b_{j}\left(\lambda_{1}+c_{i}\mu_{1}\right)>g_{i}\left(\lambda_{2}+d_{j}\mu_{ 2}\right)\). If the condition \(b_{j}\left(\lambda_{1}+c_{i}\mu_{1}\right)>g_{i}\left(\lambda_{2}+d_{j}\mu_{ 2}\right)\) holds, \(SS\) prefers to communicate with \(SD\) via the help of the relay station \(SR\). Otherwise, direct transmission is more beneficial. Next, let's look at an interesting special case. When there is only one subcarrier, the optimal power allocation is given by \(p_{s}=\min\left(P_{S},Q_{1}/c\right)\) and \(p_{r}=\min\left(P_{R},Q_{2}/d\right)\).
# Blended Filter-Based Detection for Thruster Valve Failure and Control Recovery Evaluation for RLV
## 2 Blending Filter-Based Thruster Fault Detection
### Thruster Model Based on Valve Failure  
Figure 1 shows the basic principle of the thruster. By supplying power to the coil, the electromagnet generates a magnetic force that drives the thruster valve to either switch on or off, so the fuel combines with the catalyst and, after heating, generates injection gas to provide reaction thrust to the RLV.  
For this study, the possible thruster faults caused by the failure conditions of the thruster valve are:  
Fault A: The thruster valve is closed; a control signal is activated to turn on the valve, but the thruster valve does not open and a pipe jam fault occurs, that is:  
\[F_{i}\equiv 0,when\left\{\begin{array}{l}U(k-1)=0\\ U(k)=1\end{array}\right.. \tag{3}\]  
Fault B: The thruster valve is open; a control signal is applied to turn off the valve, but the thruster valve does not close and the pipeline cannot be closed, that is:  
\[F_{i}\equiv F,when\left\{\begin{array}{l}U(k-1)=1\\ U(k)=0\end{array}\right.. \tag{4}\]  
Fault C: The thruster valve is open; after a control signal is given, the valve-closing performance of the fuel pipe attenuates so that it is not completely executed in accordance with the control signal, and the valve only partially closes, that is:  
\[\left\{\begin{array}{l}F_{i}(k-1)=F,\;U(k-1)=1\\ F_{i}(k)=\tau F,U(k)=0\end{array}\right., \tag{5}\]  
where \(\tau\) refers to the ratio of the incomplete closing distance to the width of the nozzle after performance degradation.
## 2 Fundamentals of Pricing Schemes: Uniform, Zonal, and Nodal Pricing
### Pricing Mechanisms
#### 2.2.2 Extended Locational Marginal Price (ELMP)  
Electricity markets, formulated using mixed integer linear programming (MILP), face challenges owing to their nonconvexity. As previously discussed, traditional marginal cost pricing may not allow some generators to recover their full production costs, leading to MWPs. These payments can distort price signals and reduce market transparency [34; 35].  
The ELMP has been developed as a practical alternative to address these issues, approximating convex hull pricing, while balancing accuracy with computational feasibility. This method allows fast-start resources (FSRs) to set prices and reduce side payments by adding their quasi-fixed costs to their production costs and allowing partial commitment [30]. Figure 1 illustrates the ELMP adopted by the Midcontinent Independent System Operator (MISO), which is a representative electricity market using this price mechanism [36]. Owing to the computational complexity of node-level UCED, the process is structured into two primary parts: security-constrained unit commitment (SCUC), followed by security-constrained economic dispatch (SCED) and SCED-pricing.  
The SCUC is formulated as an MILP problem and optimized over a 24 h horizon. It incorporates transmission losses exogenously and only checks the transmission flow constraints for the watchlist transmission set wherein binding or violations are anticipated. Consequently, the resulting hourly generator commitment information is passed on to the SCED and SCED-pricing.  
Figure 1: SCUC, SCED, and SCED-pricing procedure in MISO.  
The SCED is formulated as an hourly linear programming (LP) problem that determines the optimal dispatch based on fixed generator commitments from the SCUC. Unlike SCUC, SCED conducts a more extensive evaluation of transmission flow constraints, encompassing a broader set of transmission lines beyond the predefined watchlist. The objective function excludes start-up and no-load costs as sunk costs, whereas the transmission losses are determined endogenously through marginal loss factors. The SCED outputs the generator dispatch information used in the system operations.  
SCED-pricing, executed independently of the SCED, is also modeled as an hourly LP problem. It shares fundamental characteristics with SCED. The key distinction is that SCED-pricing facilitates partial commitment for committed FSRs by relaxing their binary variables to 0-1. This approach facilitates these resources in setting the market prices and incorporate certain no-load and start-up costs. The market prices derived from the SCED-pricing, calculated using the LMP formulation in Equation (7), are defined as ELMP. In MISO, FSR-qualified generators are defined as units that can start up within 1 h or less, with a minimum run time of 1 h or less.
# A Computational Platform for Automatic Signal Processing for Bender Element Sensors +
## 2 Conventional Interpretation Techniques for Bender Element Tests
### Time Domain Methods
#### 2.1.4 Cross-Correlation Method  
The cross-correlation method was first introduced in reference [26] in the context of bender element tests in soils. Cross-correlation measures the correspondence between signal \(x\) and time-lagged copies of signal \(y\), as the latter is slided by the former with time shift \(\tau\).  
The fundamental conjecture is that the cross-correlation between output and input signals should have a maximum at a value of time shift \(\tau\) which is equal to the travel time. However, recent research has shown that the arrival of the shear wave may not correspond to the largest oscillation in the output signal [14], so the absolute maximum of the cross-correlation may not necessarily correspond to the arrival of the shear wave.  
The cross-correlation method is objective, meaning that its results do not depend on the experience of the analyst in the interpretation of output signals. For this reason, it is currently one of the most common signal interpretation methods for bender element experiments.
# Energy Consumption Model for Drilling Processes Based on Cutting Force
## 4 Results and Discussions
### Model Validation  
The setup of Experiment-II was used to verify the proposed model. The four sets of processing parameters, which were different from Experiment-I, are shown in Table 3, together with the corresponding measured actual power \(P\), and the \(P_{idle}\), \(P_{cutting}\), predicting power (\(P\)*), and accuracy were calculated under each setting.  
Among all the settings, the MRR was kept unchanged while the spindle speed was varied in four levels. It can be seen from the experimental data (Table 3) that, when the MRR was constant, the  
Figure 7: Flowchart of the drilling process energy consumption calculation.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline _No._ & \(n\) **(rpm)** & \(f\) **(mm/min)** & _MRR_ **(mm3/s)** & \(P\) **(W)** & \(P\) **(H)** & \(P\) **(cutting (W)** & \(P\) **(W)** & _Accuracy_ \\ \hline
1 & 400 & 76.4 & 100 & 1851.0 & 1290.0 & 472.1 & 1826.1 & 98.66\% \\
2 & 550 & 76.4 & 100 & 1925.7 & 1340.6 & 507.9 & 1917.3 & 99.57\% \\
3 & 700 & 76.4 & 100 & 2010.6 & 1404.7 & 543.9 & 2022.4 & 99.41\% \\
4 & 850 & 76.4 & 100 & 2105.2 & 1482.3 & 580.2 & 2141.1 & 98.30\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cutting conditions and power data in Experiment-II.  
varied with the different processing parameters. Therefore, the simple model for predicting power with the material removal rate was not accurate enough. On the other hand, under the same feed rate, the \(P_{idle}\) changed with the spindle speed, so it was also not accurate enough to use \(P_{cutting}\) to predict the power. It can be seen that the predicting power (\(P\) *) calculated by the prediction model was nearly 99% accurate compared with the four sets of experimental data, and it is proved that the model is reliable for predicting the energy consumption of drilling.
## 2 Materials and Methods  
### Study Area  
The studied area is Dobrogea, a region of 15,500 km\({}^{2}\) located in the South-East of Romania, between the Danube River and the Black Sea, between 27\({}^{\circ}\)15\({}^{\prime}\)15\({}^{\prime}\)-29\({}^{\circ}\)30\({}^{\prime}\)10\({}^{\prime}\) eastern longitude and 43\({}^{\circ}\)40\({}^{\prime}\)4\({}^{\prime}\)-45\({}^{\circ}\)25\({}^{\prime}\)3\({}^{\prime}\) northern latitude [16]. Its geographic structure is that of a plateau with a hilly aspect, the altitude decreasing from north to south. The climate is temperate-continental, but the region is subject to drought and desertification [16; 17].  
The study data consists of 51 series of 1-day maximum annual precipitation registered during the period January 1965-December 2005 at 10 main meteorological stations and 41 hydro-meteorological points in the region of Dobrogea, Romania (Figure 1). When working only with the data recorded at the main stations, the input data will be a table with 41 rows and 10 columns (in the A1 and B1 scenarios presented in Section 3), while when working with all data, the input will consist of a table with 41 rows and 51 columns (in the A2 and B2 scenarios presented in Section 3). The data were collected at the stations under the National Authority Romanian Waters; they are without gaps and they are reliable. The maximum annual series collected at the main meteorological stations are represented in Figure 2.  
Figure 1: The map of the Dobrogea region and the stations where the data series were recorded.