esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Processing Blocks in SDR Transmitter  
In this section, we demonstrate the proposed symbolic framework through the implementation of NOMA link-level simulation, by illustrating how the transmitter's side of the link can be succinctly implemented via symbolic language. Before providing the details of the transmitter configuration, we first introduce notations for several important parameters for SDR implementation: The bit rate and bit duration are denoted by \(R_{b}\) and \(T_{B}=1/R_{B}\), respectively; sampling frequency and sampling interval are denoted with \(f_{s}\) and with \(T_{S}=1/f_{s}\), respectively; and the oversampling factor is denoted by \(L=T_{B}/T_{S}\), which represents the number of samples corresponding to a single quadrature symbol. We denote the carrier frequency with \(f_{C}\), the roll-off factor, or excess bandwidth coefficient, for the square-root raised-cosine pulse shaping, with \(\alpha\), the group delay with \(G\), and the power coefficients with \(p_{i}\), \(i=1,2,\ldots,n\), where \(n\) is the number of users and \(\sum_{i=1}^{n}p_{i}=1\). The overall transmitter is schematically depicted in Figure 1. The input bit stream is first converted from serial to parallel (S/P) by converters that receive input bit streams from each multiplex channel and output bit vectors. The bits are then mapped to symbols via look-up tables (LUTs), and superposition coding with power coefficients \(p_{n}\) is applied. The resulting symbols are then input to pulse-shaping filters, mixers, and output adder. The local oscillator circuit generates a cosine waveform whose output is split into two branches, one of which includes a 90-degree phase shifter that produces the imaginary part of the complex exponential. The following subsections show how to implement these steps symbolically.
## 4 Results
### Trace and Rare-Earth Elements
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & **Zr/Nb** & **La/Nb** & **Ba/Nb** & **Ba/Th** & **Rb/Nb** & **K/Nb** & **Th/Nb** & **Th/La** & **Ba/La** \\ \hline Primitive & & & & & & & & & & \\ mantle * & 14.8 & 0.94 & 9 & 77 & 0.91 & 323 & 0.117 & 0.125 & 9.6 \\ N-MORB * & 30 & 1.07 & 1.7–8.0 & 60 & 0.36 & 210–350 & 0.025–0.071 & 0.067 & 4 \\ E-MORB * & & & 4.9–8.5 & & & 205–230 & 0.06–0.08 & & \\ Continental & & & & & & & & & \\ crust * & 16.2 & 2.2 & 54 & 124 & 4.7 & 1341 & 0.44 & 0.204 & 25 \\ HIMU–OIB * & 3.2–5.0 & 0.66–0.77 & 4.9–6.9 & 49–77 & 0.35–0.38 & 77–179 & 0.078–0.101 & 0.107–0.133 & 6.8–8.7 \\ EM1–OIB * & 1.2–11.5 & 0.86–1.19 & 11.4–17.8 & 103–154 & 0.88–1.17 & 213–432 & 0.105–0.122 & 0.107–0.128 & 13.2–16.9 \\ EM2–OIB * & 4.5–7.3 & 0.89–1.09 & 7.3–13.3 & 67–84 & 0.59–0.85 & 248–378 & 0.111–0.157 & 0.122–0.163 & 8.3–11.3 \\ \hline Jeju Island alk. & & & & & & & & & \\ volcanics & 3.9–7.4 & 0.86–1.35 & 9.4–18.3 & 49–154 & 0.87–1.96 & 394–652 & 0.108–0.204 & 0.111–0.177 & 6.9–20.1 \\ Average of Jeju & 5.5 & 1.05 & 12.9 & 88 & 1.22 & 501 & 0.152 & 0.146 & 12.7 \\ \hline \end{tabular}
* Data are from [PERSON] et al. (1988) and [PERSON] (1991a) [40,41].  
\end{table} 
Table 2: Representative incompatible trace-elemental ratios of Jeju Island alkaline volcanic rocks, with primitive mantle, N-MORB, E-MORB, continental crust, HIMU-OIB, EM1-OIB, and EM2-OIB values for comparison. Figure 7: Ba/Nb vs. Th/Nb ratios. The data for the primitive mantle (PM), E-MORB, N-MORB, HIMU, EM1, EM2, and continental crust (CC) are from compilations by [PERSON] et al. (1988), [PERSON] (1991a), [PERSON] (1998), and [PERSON] et al. (2014) [40, 41, 42, 43]. Symbols are the same as in Figure 2.
## 6 Conclusions  
In this paper, conventional cyclic tension-compression tests and dynamic tests were performed to characterize the LVE behaviour of AM on a large range of frequencies and temperatures. Two different AM and five specimens were tested with both tests and results were analysed using the 2S2P1D model.  
Two methods were studied to back-calculate the complex modulus of AM from dynamic measurements at each temperature. It is shown that the same shift factors are found with the two methods. Moreover, the two methods give very similar complex modulus values (less than 4% of difference for the norm of the modulus and 0.03\({}^{\circ}\) for the phase angle) and the differences observes may be due to the different assumptions on the Poisson's ratio value. Therefore, the second method, which is a new and simpler approach, appears to be a good option to obtain the complex modulus of AM from FRFs.  
The results of dynamic tests were also compared to the results of tension-compression tests. The shift factors from both tests are very close for the low temperatures and shift factors from dynamic tests are little higher for temperatures higher than 10 \({}^{\circ}\)C. The complex modulus obtained from dynamic tests have a higher norm and a lower phase angle than the ones determined with the conventional approach using cyclic tests. The differences observed between the two tests are very limited for the high frequencies or low temperatures (less than 5% for the norm and 0.2\({}^{\circ}\) for the phase angle) and are more important for the low frequencies or high temperatures (around 20% for the norm and 2\({}^{\circ}\) for the phase angle at 15 \({}^{\circ}\)C and 1 Hz). Since the strain level is approximately 500 times lower in the dynamic tests, the nonlinearity of AM with the level of strain amplitude may explain a part of the differences. Ageing of the materials between the tension-compression and the dynamic tests may also have an impact on the complex modulus evaluation.  
The agreement between dynamic tests and the tension-compression tests is still satisfactory on the whole frequency range. The combination of the two tests methods is useful to improve the characterization of the LVE behaviour of AM on a wider frequency range because dynamic tests give access to very high frequencies. The presented research shows that dynamic tests, which have the great advantage of being cheap and rapid, can be back-analysed with a very simple model and can provide accurately the complex modulus of AM on a wide range of frequencies and temperatures.  
Methodology, J.-C.C., H.D.B., and C.S.; experimental tests, J.-C.C.; formal analysis, J.-C.C.; writing--original draft preparation, J.-C.C.; and writing--review and editing, H.D.B. and C.S. This research received no external funding. The authors declare no conflict of interest.
# Optimizing Ambulance Allocation in Dynamic Urban Environments: A Historic Data-Driven Approach
## 3 Problem Description and Mathematical Model  
### Problem Description  
In this study, we first partitioned the boroughs into subzones, which are represented as hexagonal cells. We utilized Uber's H3 package to generate these hexagonal cells ([PERSON], 2016). We set the size of the hexagonal cells to be 100 m from the center of the hexagon to the vertices. The size of Seoul, which we analyzed in this paper, is 605 km\({}^{2}\). We determined the size of the hexagonal cell to encompass road and terrain information suitable for ambulance operations by conducting interviews with the person in charge of ambulance operations. We chose hexagonal cells because they have six sides and more neighboring sides than rectangular cells, allowing them to represent more ambulance routes.  
Our model aims to determine, at each time point within a given time horizon, the number of ambulances to be allocated to each of these cells. Ambulance relocation can occur between existing fire stations and what we term \"potential centers\", which include community centers and administrative buildings.  
The purpose of the relocation is to efficiently respond to dynamically changing demand. However, this strategy comes with two significant costs. First, placing an ambulance in a potential center incurs a cost because the center was not originally designed to house the ambulance. Second, there is a cost for the relocation itself at each point in time.  
For ambulance maintenance, ambulances are ideally managed from a base center, such as a fire station. It is also efficient for paramedics to return to the fire station where they work to rest after a call. From a demand management perspective, the most-effective relocation strategy is to place ambulances in hard-to-reach areas within the golden time window for emergency medical response. However, locating ambulances away from these base centers can be operationally inefficient. Centralizing ambulances at locations like fire stations ensures regular maintenance, inspection, and restocking, keeping them always ready for emergencies. Additionally, this centralized approach enhances dispatch efficiency, ensuring quicker response times. We evaluated the cost of relocation based on the distance required for the ambulance to return to the base center.
# Advancing Nighttime Object Detection through Image Enhancement and Domain Adaptation
## 3 Methodology
### LightImg
```
Require: Nighttime Image \(I\)  Ensure: Enhanced Image \(I_{enhanced}\) \(R\), \(G\), \(B\)\(\leftarrow\) decompose\(\_\)channels\((I)\) \(L_{R}\)\(\leftarrow\)\(\textit{Gaussian}(R)\), \(L_{G}\)\(\leftarrow\)\(\textit{Gaussian}(G)\), \(L_{B}\)\(\leftarrow\)\(\textit{Gaussian}(B)\) \(R_{R}\)\(\leftarrow\)\(\log(R)-\log(L_{B})\), \(R_{G}\)\(\leftarrow\)\(\log(G)-\log(L_{G})\), \(R_{B}\)\(\leftarrow\)\(\log(B)-\log(L_{B})\) \(C_{R}\)\(\leftarrow\)\(R_{R}\times(R+G+B)\)\(\div\)3, \(C_{G}\)\(\leftarrow\)\(R_{G}\times(R+G+B)\)\(\div\)3, \(C_{B}\)\(\leftarrow\)\(R_{B}\times(R+G+B)\)\(\div\)3 \(\textit{I}_{enhanced}\)\(\leftarrow\)\(\textit{merge}\_\textit{channels}(C_{R}\), \(C_{G}\), \(C_{B}\)) Return\(\textit{I}_{enhanced}\)
```  
**Algorithm 1****LightImg Image Enhancement**  
```
Initialize \(S\) with daytime images Initialize \(T\) as EMA of \(S\) for each iteration do  Enhanced\(\_\)Night\(\_\)Images \(\leftarrow\)LightImg(Dn)  High\(\_\)Confidence\(\_\)Labels \(\leftarrow\)GenerateAndFilterLabels(T,Enhanced\(\_\)Night\(\_\)Images)  Candidate\(\_\)Boxes \(\leftarrow\)Merge(Candidate\(\_\)Boxes, High\(\_\)Confidence\(\_\)Labels)  ClassifyAndRegress(S, Combined\(\_\)Boxes)  Consistency\(\_\)Loss \(\leftarrow\)ComputeConsistencyLoss(S, T, Combined\(\_\)Boxes)  UpdateNetworks(S, T, Consistency\(\_\)Loss) endfor return Trained Student Network S
```  
**Algorithm 2****NUDN**
## 2 Materials and Methods
\begin{table}
\begin{tabular}{c c c c c} \hline
**Name of Material** & **Shape** & **Type** & **Manufacturer** & **Composition** \\ \hline \hline \multirow{4}{*}{NX3 light-cure (NX3L)} & \multirow{4}{*}{Yellow} & \multirow{4}{*}{Light-cure resin cement} & \multicolumn{3}{c}{Uncured methacrylate ester} \\  & & & & monomers, minor filler, \\  & & & & pigments, radiopaque agent \\  & & & & 20–40\%, \\  & & & & wt 63\%, vol 47\% \\ \hline \multirow{4}{*}{NX3 dual-cure (NX3D)} & \multirow{4}{*}{Yellow} & \multirow{4}{*}{Dual-cure resin cement} & \multirow{4}{*}{Kerr, Orange, CA, USA} & Uncured methacrylate ester \\  & & & & monomers 20–40\%, \\  & & & & wt unknown, vol 43.3\% \\ \hline \multirow{4}{*}{RelyX Veneer (RXL)} & \multirow{4}{*}{A3} & \multirow{4}{*}{Light-cure resin cement} & \multirow{4}{*}{3M ESPE, St. Paul, MN, USA} & TEGDMA or BisGMA, particles \\  & & & & of zirconia or silica and \\  & & & & colloidal silica, \\  & & & & wt 66\%, vol 47\% \\ \hline \multirow{4}{*}{RelyX Ultimate (RXD)} & \multirow{4}{*}{A3} & \multirow{4}{*}{Dual-cure resin cement} & \multirow{4}{*}{3M ESPE, St. Paul, MN, USA} & Methacrylate monomer, alkaline \\  & & & & filler, initiator components, \\  & & & & stabilisers, pigments, rheological \\  & & & & additives, fluorescence dye, \\ \hline \multirow{4}{*}{\begin{tabular}{c} Enamel Plus \\ HRi Flow \\ (MF) \\ \end{tabular} } & \multirow{4}{*}{UD3} & \multirow{4}{*}{Light-cure flow resin composite} & \multirow{4}{*}{Micerium, Genova, Italy} & BisGMA, BDDMA, UDMA, glass \\  & & & filler, highly dispersed SiO, \\  & & & wt 77\%, vol unknown \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Preheated Enamel \\ Plus HRi \\ (MH) \\ \end{tabular} } & \multirow{4}{*}{UD3} & \multirow{4}{*}{Preheated light-cure  high-viscosity resin composite} & \multirow{4}{*}{Micerium, Genova, Italy} & DiUDMA, BisGMA, BDDMA \\  & & & filler, particles of zirconium \\  & & & oxide and glass, \\  & & & wt 80\%, vol 63\% \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Bis-GMA = bisphenol-A glycidyl methacrylate, BDDMA = 1,4-butanoidlidmethacrylate, TEGDMA = triethylene glycol \\ dimethacrylate, UDMA = urethndimethacrylate, = wt number \% = weight percent, vol number \% = volume percent. \\ \end{tabular} } \\  & & & & wt 80\%, vol 63\% \\ \hline \end{tabular}
\end{table} 
Table 1: Materials used in this study. In this study, the perceptibility threshold for colour differences (PTC) was set to 1.2 [19]. The translucency was evaluated using the comparative translucency parameter (TPc), since the backgrounds used during the measurements of the colour parameters were different from the ideal backgrounds (ideal white background, L = 94.3, a = \(-\)0.4, b = \(-\)0.6; ideal black background, L = 0.2, a = 0.4, b = \(-\)0.6) [20].
## 3 Mathematical Model
### Center Plane Offset  
The center plane offset is used to check if there is a shift at the center plane. For this purpose, the y-axis \(aa3_{y}\) taken from the center point \(aa3(aa3_{x},aa3_{y},aa3_{z})\) is regarded as the center standard plane.  
In Figure 4, two check points, i.e., \(cc1\) and \(cc22\), coordinates at the S/T rotating shaft are expressed as:  
\[cc1(cc1_{x},cc1_{y},cc1_{z})\qquad\qquad cc22(cc22_{x},cc22_{y},cc22_{z})\]  
\(cc\) is defined as the center point between \(cc1\) and \(cc22\) as:  
\[cc\bigg{(}\frac{cc1_{x}+cc22_{x}}{2},\frac{cc1_{y}+cc22_{y}}{2},\frac{cc1_{z}+ cc22_{z}}{2}\bigg{)}\]  
\(w1\) shown in Equation (1) is defined as the center plane offset at the S/T rotating shaft, and it is the distance between the y axis coordinate of the \(cc\) point and the center plane.  
\[w1=\bigg{|}\frac{cc1_{y}+cc22_{y}}{2}-aa3_{y}\bigg{|} \tag{1}\]  
Figure 3: The center plane (**left**) and point (**right**) of bike frame.
## 5 Experiments
### FASHION Classification Experiment with DCNN  
Data set with images of clothing: 60,000 pairs in the training data set and 10,000 for testing. They are available through the TensorFlow module: tf.keras.datasets.fashion_mnist. The entries to the neural network are of dimension 28 \(\times\) 28, corresponding to grayscale images, to classify them into ten different classes. The DCNN has the characteristics that were described at the beginning of this section.  
Ten training processes of three epochs are executed with each of the algorithms: Adam, SKAdam, REKF, and FDEKF. Batch size is 256, except for FDEKF. The loss function is the mean squared error (MSE). For REKF: \(\mathbf{P}_{0}=I\), \(\mathbf{Q}_{0}=I\cdot 10^{-5}\), \(\mathbf{R}=1\). For FDEKF: \(\mathbf{P}_{0}=I\cdot 5\times 10^{2}\), \(\mathbf{Q}_{0}=I\cdot 10^{-8}\), \(\mathbf{R}_{0}=2\times 10^{2}\), \(\mathbf{R}\) is adjusted every 256 samples based on the average of values in \(\mathbf{P}\) of the output node. Hyper-parameters settings for Adam and sKAdam: \(\alpha=0.001\), \(\beta_{1}=0.9\), \(\beta_{2}=0.999\), and \(\epsilon=10^{-8}\).  
The evolution of the loss function with each of the algorithms is shown in Figure 4. The graph depicts the loss value calculated at each iteration of the training process. The value of the loss function decays much faster with FDEKF. From the first iteration, after 256 training samples, the loss function decays rapidly with FDEKF. The performances of Adam and sKAdam are very similar to each other, both in the evolution of the loss function and in precision. Consistently, with these results, Table 1 shows the greatest accuracy of FDEKF.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Mean Accuracy** & **Standard Deviation** \\ \hline Adam & 0.8901 & 0.0020 \\ sKAdam & 0.8923 & 0.0021 \\ REKF & 0.8433 & 0.0257 \\ FDEKF & 0.9015 & 0.0025 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of Accuracy and Standard Deviation for Fashion Test Set in Ten Training Process.  
Figure 4: FASHION with DCNN experiment—loss.
## 5 Numerical Test and Results  
The availability of the proposed voltage regulation strategy is verified in the IEEE 33 node power system, which is shown in Figure 4. Node 1 is the reference node. The others are the 'PQ' nodes. According to IEEE Recommended Practice for Monitoring Electric Power Quality, the voltage of each node should be kept within [0.9, 1.1] p.u. [17].  
The simulation lasts a whole day, from 6:00 a.m. on the first day to 6:00 a.m. on the second day, which has 288 time slots, with each time slot set as 5 min. The penetration rates of the PVs and the ACs are 85% and 100%, respectively. The data of PVs and base load are obtained from the literature [18], which is shown in Figure 5. The outpower of the PVs reaches the maximum of 7229.7 kW at 11:10 and stops generating power at 19:20. The load fluctuates around 4623.8 kW and 2325.9 kW. The parameters of the thermal model are shown in Table 1.  
Figure 4: The model of the IEEE 33 node power system.  
Figure 5: The data of the PVs and loads.  
Three cases are considered in the simulation as follows:  
Case 1: The voltage regulation is not applied.  
Case 2: The voltage regulation is applied only with the OLTC.  
Case 3: The voltage regulation is applied with the OLTC and ACs.  
In Case 1, due to the unbalance power between supply and demand, the voltage problem happens without any voltage regulation. For Case 2, the OLTC provides voltage regulation when the nodal voltage exceeds the permissive range. For Case 3, the OLTC and the ACs participate in voltage regulation services when the nodal voltage exceeds the permissive range. The ACs exit voltage regulation after the action of the OLTC finishes.
# Investigation of the Automatic Monitoring System of a Solar Power Plant with Flexible PV Modules
## 3 Results and Discussion
With flexible solar modules, PWM (pulse-width modulation) and MPPT (Maximum Power Point Tracking) charging technologies operate differently depending on weather conditions. PWM works most efficiently in sunny conditions, but can lose power when the solar modules are at a higher voltage than the battery. On cloudy or rainy days, the efficiency of PWM is reduced because it has not been able to use all the power it generates. MPPT continuously monitors the output parameters of the solar modules and optimizes energy recovery, making it perform better in changing weather conditions. Due to this feature, MPPT provides higher energy efficiency, especially in poor weather conditions, compared to PWM. Flexible solar modules that can adapt to uneven surfaces allow for more efficient use of the sun's rays, especially in bad weather conditions, as they can be quickly reoriented, which is especially useful when using MPPT technology. Meanwhile, rigid modules, which require a perfectly flat surface, can suffer efficiency losses if their  
Figure 2.5 Time dependence of the efficiency of PWM and MPPT charging controllers. position is not optimal, and their energy collection can be reduced on cloudy days [1; 2; 3; 4; 5]. With PWM, the efficiency of flexible modules can decrease when the intensity of sunlight decreases, while the inflexible modules can take longer to charge the batteries because they cannot use the full power generated. The automatic monitoring system for a solar power plant stands out for its performance and advantages compared to other solutions on the market, such as Solar-Log, SMA Sunny Portal and Enphase Envoy. For example, Solar-Log offers advanced monitoring of energy production, but our system allows for greater efficiency and adaptability on uneven surfaces using flexible modules and microcontrollers [1; 2; 3; 4]. In addition, compared to SMA Sunny Portal and Enphase Envoy, our solution provides better energy consumption analysis as it can accurately monitor battery charging processes in real time. This solution offers the possibility to adapt to different surfaces, thanks to the flexible solar modules that allow for more efficient use of solar energy even on uneven or unconventional surfaces. In addition, the automated monitoring system provides detailed analyses of energy production, battery charging and energy consumption, allowing for more efficient management of energy resources and optimizing their use. This solution outperforms others because its automated monitoring system and the use of flexible modules ensure greater energy efficiency, adaptability to various conditions and accurate real-time energy management compared to traditional solutions.
## 3 Machine Learning Techniques Based on Linear and Regularized Linear Models
### Linear and Regularized Linear Models
#### 3.1.2 Ridge Regression (RR)  
Ridge regression, also known as Tikhonov regularization, is a model designed to address the limitation of linear regression, where regression coefficients can become excessively large. It does so by adding an L2 regularization term to linear regression, ensuring that the model weights remain small. The cost function of ridge regression incorporates the L2 regularization term \(\lambda\sum_{j=1}^{m}\beta_{j}^{2}\), in addition to the cost function of linear regression. This regularization term helps reduce the weights during the training process [51, 52, 53]. The cost function of ridge regression is presented in Equation (3):  
\[RR_{cost}=\sum_{i=1}^{m}(y_{i}-\widehat{y_{i}})^{2}+\lambda\sum_{j=1}^{n} \beta_{j}^{2}=MLR_{cost}+\lambda\sum_{j=1}^{n}\beta_{j}^{2} \tag{3}\]  
where \(\lambda\) is the regularization parameter, controlled by the hyperparameter alpha. As \(\lambda\) increases, the regularization strength becomes greater, and the model's weights decrease [54].
# Legendre Multiwavelet Transform and Its Application in Bearing Fault Detection
###### Abstract  
Bearing failures often result from compound faults, where the characteristics of these compound faults span across multiple domains. To tackle the challenge of extracting features from compound faults, this paper proposes a novel fault detection method based on the Legendre multiwavelet transform (LMWT) combined with envelope spectrum analysis. Additionally, to address the issue of identifying suitable wavelet decomposition coefficients, this paper introduces the concept of relative energy ratio. This ratio assists in identifying the most sensitive wavelet coefficients associated with fault frequency bands. To assess the performance of the proposed method, the results obtained from the LMWT method are compared with those derived from the empirical wavelet transform (EWT) method using different datasets. Experimental findings demonstrate that the proposed method exhibits more effective frequency spectrum segmentation and superior detection performance across various experimental conditions.  
rolling bearings; fault diagnosis; Legendre multiwavelet decomposition; envelope spectrum analysis; spectral segmentation Article
## 3 Results and Discussion
### Assessment of Modelling Approaches Considering a 0D Description of the Junction
#### 3.2.2 Frequency Domain Assessment
The characteristic frequencies governing the reflection coefficient are not exactly captured, but the overall amplitude and the influence of the exciting port are reproduced by the numerical results.