esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Distributed Autonomous Timekeeping Algorithm for Low-Earth-Orbit Constellation
## 4 Experiment and Results Analysis  
The noise in inter-satellite direct comparison measurements is less than 0.2 ns, having a minimal impact on data accuracy [26]. However, the comparison between invisible satellites through relay satellites will lead to the superposition of the measurement noise. Currently, many GNSS satellites are equipped with rubidium and passive hydrogen clocks, demonstrating the reliability of existing onboard atomic clocks. Therefore, equipping future LEO constellation time-keeping satellites with clocks that have similar characteristics to those on GNSS satellites is a feasible option at this stage. In this paper, we simulate low-orbit satellite scenarios using the clock error products of the GPS and BeiDou system, and we add 0.2 ns to 0.8 ns of white noise to these clock error data to represent the link noise. The settings for the satellite data and the standard deviation (STD) of the link noise are detailed in Table 1. The experimental data were collected using precise clock error data, with a sampling interval of 300 s, provided by the Center for Orbit Determination in Europe (CODE) and were collected from 14 May 2023 to 24 June 2023. In the future, the use of optical links and optical clocks is expected to yield experimental results that surpass those presented in this paper.
## 3 Results
### Temperature Sweep  
In order to determine the temperature dependence of the complex modulus (\(G^{\bullet}\)) and the phase angle (\(\delta\)) of the WB and nano-TiO\({}_{2}\) binders subjected to various times of UV aging, a temperature sweep test was conducted in the study, with a temperature range of 30 to 90 \({}^{\circ}\)C at an interval of 2 \({}^{\circ}\)C/min. Figure 3 shows the temperature sweep test results for different durations of UV-aged binders. As shown in Figure 3, a constantly increased complex modulus was observed in the isochronal plots over the entire range of temperatures for all aged binders. However, the phase angle in the isochronal plots had an opposite trend, increasing as temperature increased. This result is consistent with other researchers' findings [5,25]. In addition, the complex modulus \(G^{\bullet}\) did not exhibit any significant change as the percentage of nanoparticles increased over the temperature domain, regardless of the aging time. In other words, control-aged binders generally have similar \(G^{\bullet}\) values with a binder containing nano-TiO\({}_{2}\) regardless of aging time.  
The phase angle (\(\delta\)) is generally considered to be more sensitive to the chemical structure; therefore, the modification of bitumen is greater compared to the complex modulus [26]. The figures clearly illustrate the temperature dependence of the modified binders and the effectiveness of the aging time. With increased temperatures, the phase angles for all aged bitumen present a plateau region between 45 \({}^{\circ}\)C and 65 \({}^{\circ}\)C. It has been reported that the plateau phenomenon can be attributed to the formation of a polymer network in SBS-modified asphalt binders [1,26,27]. This indicates that the spatial structure of SBS was not destroyed with the increase in UV aging time for all aged binders with or without nano-TiO\({}_{2}\).  
In the case of Figure 3a, the addition of nanoparticles resulted in a decrease in the phase angle of all aged bitumen at high temperatures. However, in Figure 3b,c, there is a similar increase in the phase angle towards more viscous behavior for all aged binders with  
Figure 2: Viscosity of UV-aged binders at 135 \({}^{\circ}\)C.  
the addition of nanoparticles at high temperatures. This significant increase in modification is a result of the UV aging resistance of WB with nanoparticles.
# Local Batch Normalization-Aided CNN Model for RSSI-Based Fingerprint Indoor Positioning
## 3 Proposed LBN-Aided Positioning Scheme
### Local Batch Normalization
When applied directly to CNN models for indoor localization, traditional BN fails to account for localized variations, leading to reduced robustness and accuracy. Building on the concepts of FedBN, this study proposes an LBN technique specifically designed for RSSI-based indoor positioning systems. Unlike FedBN, which operates within a federated learning framework, the proposed LBN is integrated directly into a CNN model, optimizing it for the specific challenges of RSSI signal processing. By applying normalization at a localized level, aligned with the spatial and positional characteristics of RSSI data, LBN enhances the model's sensitivity to location-specific features. This improves overall positioning accuracy and robustness. In doing so, LBN overcomes the limitations of traditional BN in indoor localization, offering a pathway to more accurate and reliable positioning systems.
## 3 Performance Analysis
### Statistical Properties of Channels
Generally that \(m_{SR_{m}}\) is an integer, with the utilizing of [38], \(1_{F1}(m_{SR_{m}};1;\delta_{SR_{m}}x)\) is represented as:  
\[{}_{1}F_{1}(m_{SR_{m}};1;\delta_{SR_{m}}x)=e^{\delta_{SR_{m}}x}\sum_{n=0}^{m_{ SR_{m}}-1}\frac{\left(-\delta_{SR_{m}}\right)^{n}\left(1-m_{SR_{m}}\right)_{n}}{ \left(n!\right)^{2}}x^{n}, \tag{30}\]  
where \(\left(x\right)_{y}\) is the pochhammer function [39]. According to Equations (29) and (30), the PDF and CDF of \(\left|h_{SR_{m}}\right|^{2}\) are obtained as:  
\[f_{\left|h_{SR_{m}}\right|^{2}}(x)=\alpha_{SR_{m}}\sum_{n=0}^{m_{SR_{m}}-1} \frac{\left(-\delta_{SR_{m}}\right)^{n}\left(1-m_{SR_{m}}\right)_{n}}{\left(n! \right)^{2}}x^{n}e^{-\left(\beta_{SR_{m}}-\delta_{SR_{m}}\right)x}, \tag{31}\]  
\[F_{\left|h_{SR_{m}}\right|^{2}}(x)=1-\alpha_{SR_{m}}\sum_{n=0}^{m_{SR_{m}}-1} \sum_{k=0}^{n}\frac{\left(-\delta_{SR_{m}}\right)^{n}\left(1-m_{SR_{m}}\right) _{n}}{\left(k!\right)\left(\beta_{SR_{m}}-\delta_{SR_{m}}\right)^{n-k+1}}x^{k} e^{-\left(\beta_{SR_{m}}-\delta_{SR_{m}}\right)x}. \tag{32}\]
## 2 Monosaccharide Composition, Combinations, and Bioactivities of Mushroom Polysaccharides
### Bioactivities of Mushroom PSs with Differing Monosaccharide Composition/Combinations
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Species** & **Biological Activity** & **Active Component** & **Reference** \\ \hline _Inonotus obliquus_ & antioxidant and anticancer activity & Man & [53; 54; 55] \\ _Tremella mesenterica_ & cytokine-stimulating activity & Man & [47] \\ _Ganderman luculum_ & antioxidant activity & Rha & [34] \\ _Flammulating sculptes_ & antioxidant activity & Rha & [57] \\ _Agrosye cylindracea_ & antioxidant activity & Glc, Gal & [58] \\ _Sarcodon asgratus_ & macrophage activation & Man, Rha & [59] \\ _Inonotus obliquus_ & antioxidant activity & Man, Rha, Xyl & [8] \\ _Hirustella sp._ & antioxidant activity & Man, Glc & [60] \\ _Pleurotus ergvili_ & antioxidant activity & Man, Rha, GalA & [61] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Biological activities of active components in mushroom PSs. (MLR), support vector machine (SVM), and artificial neural network (ANN) methods to establish an optimum quantitative structure-activity relationship (QSAR) model for the antioxidant activity of PSs. In MLR and ANN models, Ara and GalA played major roles in determining EC\({}_{50}\) of DPPH (1,1-Diphenyl-2-picrylhydrazyl)-scavenging activity, and Fuc, Rha, and uronic acid had the greatest effects on the hydroxyl radical scavenging activity of PSs. Such measurement methods are useful for predicting performance and evaluating relationships between PS properties and biological activities [7]. Monosaccharide composition/combinations clearly play an important role, and are strongly correlated with biological activity. Chemically and biologically uniform PSs with specific monosaccharide composition/combinations could be used for controllable biosynthesis. However, we know little about the detailed mechanisms of the bioactive effects relating to the monosaccharide composition of PS, although the bioactivities of PSs are well established.
# Automated Classification of the Tympanic Membrane Using a Convolutional Neural Network
## 4 Discussion
However, in an otoendoscopic examination, the examiner holds an endoscopic camera with the right hand regardless of the ear side. By pulling the patient's auric outward, the external ear canal would be straightened and the endoscope could be advanced in the external ear canal [23]. Due to this practice, the approach angle and captured images would be taken less consistently for the left side compared to the right side. Therefore, this practice is considered to be one of the factors affecting the accuracy of classifying the left side TM images. Several factors, including sufficiently large volumes of data, improvement of graphic processing and development of a deep learning method, have contributed to the success of deep learning models [24; 25; 26]. For classifying otitis media, [PERSON] et al. proposed deep learning models built on two CNN architectures, namely InceptionV3 and Mo-bileNet. The accuracy of Inception V3 was 82.2% and that of MobileNets was 80% [27]. The number of layers in InceptionV3 was 42 and that in MobileNet was 28 layers. Usually, deeper neural network models perform better than thin neural network models. However, the gradient may vanish with the depth of the layers. ResNet by Microsoft was one of the famous deep neural network architectures that overcame gradient vanishing. At the planning step of this study, we compared the performance of ResNet and our CNN model (data not shown). The accuracy of our CNN model was comparable with that of the ResNet model. However, since our model was trained to perform only binary classification, it can be considered to be no different from a deep model such as ResNet. Hence, we plan to develop a real-time decision model as our next step. To improve this model in the next study, an increase of the data volume, understanding the symmetry and use of different kinds of diseases will be needed. To expand the dataset volume, image transform technics can be useful. The transform techniques, such as mirroring, rotation, flip and even generative adversarial networks (GANs), can be used [28; 29; 30]. Also, understanding the symmetric feature can improve the exact diagnosis [29]. Additionally, using different and more problematic cases will be required. Tympanic membranes with retraction, perforation with discharge and cholesteatoma are more difficult to detect than clear perforation. Taken together, further research will be needed to create models that could analyze the whole spectrum of otoscopic pictures in clinical practice. The limitation of this study is that only two classes are considered, while the postoperative condition and attic lesions that may be more difficult to interpret are excluded. In addition, real-time diagnosis of otitis media is desirable. Therefore, further research and development of the proposed deep learning model is required.
## 3 Results
### Studies of Fixed-Bed Column  
#### 3.4.1 Concentration Effect of the Influuent Phenol on the Curve of Breakthrough  
The breakthrough curves at variable concentrations of influent phenol are presented in Figure 4. It was revealed that the process of adsorption rapidly reached the stage of saturation and the breakthrough time deteriorated according to the increase in the concentration of influent phenol. The influent phenol concentration's increase caused a shorter breakthrough along with higher adsorption capacity. Table 2 shows that the time for breakthrough decreased from 153 min to 35 min, whereas the equilibrium uptake (\(q_{\text{eq}}\)) increased from 17.49 mg\(\cdot\)g\({}^{-1}\) to 45.75 mg\(\cdot\)g\({}^{-1}\) as the inlet concentration increased from 400 mg\(\cdot\)L\({}^{-1}\) to 2000 mg\(\cdot\)L\({}^{-1}\). This occurred because, at lesser inlet phenol concentrations, curves of breakthrough showed scattered; hence, a slower breakthrough occurred. Sharper curves of breakthrough were observed once the influent concentration has boosted [45]. Additionally, a minor concentration gradient produced gentler conveyance owing to the diffusion coefficient's reduction or the coefficients of mass transfer. As the concentration of inlet elevated, the breakthrough curve's slopes and shorter times are obtained [45; 46].  
Figure 4: Concentration effect of the influent phenol on the curve of breakthrough of phenol adsorption of RHAC (rate of flow given as 10 mL\(\cdot\)min\({}^{-1}\); bed depth given as 8.5 cm).  
Figure 3: X-ray diffraction pattern of (**a**) RHAC and (**b**) silicic acid.
# Lightweight Micro-Expression Recognition on Composite Database
## 4 Results and Discussions
### Experiment 3: Imbalanced Dataset Problem Can Be Circumvented by Algorithm Tuning  
The effect of using multi-class focal loss (FL) using the proposed class weightage is compared to using standard cross-entropy loss (CE). Both Efficient-ME models used were pre-trained using a balanced FER2013 dataset. From the results in Table 6, it can be noted that using multi-class focal loss with class weightage improves the UF1 and UAR scores on the imbalanced composite dataset by a considerable margin with UFI score of 0.98 when using FL compared to UFI score 0.916 when using CE, respectively. Drilling into the per-class F1 scores, Efficient-ME with CE performed worst in recognizing class _Surprise_, which has the least number of samples in the composite dataset. On the contrary, the per-class F1 scores for Efficient-ME with FL are more balanced and even. This shows the efficacy of simple algorithm tuning such as loss function manipulation to deal with an imbalanced dataset by emphasizing the importance of minority class without requiring additional samples generation.  
Further experiment was done to uncover the effect of FL and CE when the model is pre-trained on an imbalanced source database. This is done by taking the whole FER2013 samples without re-sampling, supplying 8989 samples for class Positive, 16,698 samples for class Negative and 4002 samples for class Surprise.  
As can be observed in Table 7, the specific loss function used has less effect when the model is pre-trained on a source database with matching class distribution. The full UFI score and the per-class scores when using FL do not significantly differ to CE although the score for CE is slightly higher. This is because the original imbalanced FER2013 also has the least samples for class _Surprise_ followed by class _Positive_ like the composite dataset.  
Hence, it can be summarized that manipulating loss functions for an imbalanced dataset is useful when the class distribution of the source database does not match the target database during transfer learning. This discovery opens the possibility of exploiting the deep learning algorithms to solve imbalanced dataset problems during transfer learning.
## 3 Results and Discussion
### LNC Estimation Based on PCA  
The training process of the BPNN model is to modify and update the weight of each neuron until the average sum squared error is optimal for all samples. However, the training function and the hidden layer size will influence the performance of the BPNN model, which results in the different accuracies of LNC estimation. In addition, different fluorescence characteristics were used to analyze LNC to compare the performance of the proposed fluorescence index for LNC estimation. Thus, the main fluorescence characteristics extracted by PCA were first used to estimate LNC based on the BPNN model, and the changes of \(R^{2}\) between the predicted and measured LNC with hidden layer sizes for different train functions are shown in Figure 3.  
Figure 2: Relationship between the proposed fluorescence characteristics and LNC. The solid line is the linear regression analysis.  
Figure 3 shows the changes of \(R^{2}\) between predicted and measured LNC with hidden layer size and different training functions based on fluorescence characteristics extracted through PCA. The black error bar represents one SD of mean values with 60 repetition times for each hidden layer size. The results show that the effect of hidden layer sizes on the LNC estimation can be ignored, and the training function is the major influencing factor by comparing the \(R^{2}\) values. By comparing the \(R^{2}\) and SD values, the \"trianrp,\" \"trainbr,\" and \"trainlm\" are superior to other training functions for  
Figure 3: Performance of back-propagation neural network (BPNN) for LNC estimation based on fluorescence characteristics calculated by principal component analysis (PCA) varying with hidden layer sizes for different training functions. The black error bar represents one standard deviation of mean values.  
LNC estimation. The overall robustness of train function \"trainrp\" is the best (Figure 3e). However, the BPNN model with the \"trainbr\" serves as the training function, and the hidden layer size set to three is optimal for LNC estimation based on the fluorescence characteristics extracted through PCA as input parameters. \(R^{2}\) and SD are 0.828, and 0.032, respectively (Figure 3l).
## 4 Discussion
### Discussion of Lateral Ultimate Capacity
#### 4.1.1 Monopile Bending Moment at Lateral Ultimate Capacity  
Examining bending-moment (BM) profiles for MP at lateral ultimate capacity can aid in assessing pile behaviour and determining structural failure. Figures 13 and 14 display the bending-moment profiles for two soils, namely soil 1 and 6. Figure 13 presents the BM profile against normalized depth, I/L, for different pile lengths in a soft clay at the maximum lateral loads (S\({}_{\text{uo}}\) = 4.2 kPa). As expected, the piles behave rigidly in all cases, with the pile showing a toe kick in behavior and zero BM at the pile toe. In contrast, the  
Figure 11: Effects of relative rigidity on normalized lateral ultimate capacity of the monopiles.  
Figure 12: Normalized lateral ultimate capacity of the monopiles versus L/D for different soil profiles considered.  
piles behave rigidly for small L values up to 40 m and start to show a negative moment when the soil profile is stiff as shown in Figure 14 (S\({}_{\text{uo}}\) = 354 kPa).
## 3 Low-Power Electronic Converters
### Switching DC/DC Regulators
#### 3.3.1 Structure and Operational Principle of Step-Down Converters  
A simplified circuit configuration of a step-down converter is given in Figure 23. In this circuit, the output voltage can take values \(0<V_{o}\leq V_{i}\) depending on the value of the duty cycle, and the switch \(S\) is represented as an ideal element, with zero resistance in the closed state and zero current when open. Additionally, the circuit contains a diode \(D\) (most commonly a Schottky diode, such as those proposed in the previous section), a coil \(L\), and a smoothing capacitor \(C\). When switch \(S\) is closed, the current through coil \(L\) increases as capacitor \(C\) charges with the polarity shown in the figure. When it opens, the current through the coil retains its direction, and the diode is turned on. The current through the coil decreases, and the voltage across the capacitor decreases until the switch closes again. The voltage and current timing diagrams illustrating the principle of operation of the converter are shown in Figure 24.  
Since the coil encloses an alternating magnetic flux, a voltage is induced in it, which has the form [46]  
\[V_{L}=-L\frac{dI_{L}}{dt}. \tag{25}\]  
The negative sign reflects [PERSON]'s rule, according to which the induced voltage has such a direction as to oppose the change in current.  
When the switch in the circuit shown in Figure 23 is closed (or during the ON-time interval), the voltage drop across the coil is \(V_{L}=V_{i}-V_{o}\), while when the switch is open, (or during the OFF-time interval), the voltage is \(V_{L}=-V_{o}\).  
By using (25), for a linearized current approximation, the following is obtained at the switching moment:  
\[\Delta I_{L}=\frac{1}{L}(V_{i}-V_{o})t_{ON}=\frac{1}{L}V_{o}t_{OFF}. \tag{26}\]  
Then, based on the above expression, the following is obtained for the output voltage value [46]:  
\[V_{o}=\frac{t_{ON}}{t_{ON}+t_{OFF}}V_{i}=\frac{t_{ON}}{T}V_{i}=\delta V_{i}, \tag{27}\]  
Figure 23: Simplified circuit configuration of a step-down converter.  
Figure 24: Voltage and current timing diagrams of a step-down converter.  
where \(T=t_{ON}+t_{OFF}=\frac{1}{f}\) is the period of the control signal, as the time interval \(t_{ON}\) coincides with the duration of the pulse \(t_{pulse}\) during which the switch is closed.  
The value of the output current is determined by the average value of the current through the coil, or \(I_{o}\approx\left(I_{L,\max}-I_{L,\min}\right)/2\) is obtained for the current.  
The inductance of the coil is chosen to be as large as possible so that when the switch is open current values below the minimum value \(I_{o,\min}\) (in which the current through the coil reaches zero during the time interval when the switch is open) are not reached [15; 32]:  
\[L=T\bigg{(}1-\frac{V_{o}}{V_{i}}\bigg{)}\frac{V_{o}}{2I_{o,\min}}. \tag{28}\]
# Complex-Valued Phase Transmittance RBF Neural Networks for Massive MIMO-OFDM Receivers
## 3 Proposed Approach
### Coding Scheme
The main issue of the proposed coding scheme is that we cannot define a simplified ML decoding method as in the former cases. Then, it is here that the system proposed in this paper takes shape, with the MM-PTRBF decoding, making the joint solution feasible. We have observed, by extensive simulations, that Equation (8) achieves half of the diversity presented by the orthogonal coding schemes but keeps full-rate (i.e., \(R=1\)), which is essentially the characteristics of the quasi-orthogonal scheme proposed by [11; 56].
## 4 Simulation Verification
### Fading Channel
#### 4.2.3 Summary of Simulation Experiment in Fading Channel  
The simulation results for both types of fading channels show that, under the same simulation conditions, the BER of the BPL decoding based on the path selection criterion using information geometry proposed in this paper is lower than that of the BPL decoding of polar codes based on traditional path selection criteria. This is because when channel conditions are poor, many decoders fail to meet criterion \(\hat{x}=\hat{n}G\), resulting in the BPL decoding degrading to BP decoding.  
Furthermore, the path selection criterion proposed in this paper does not require judgment of matrix operations in the finite field after each iteration, significantly reducing computational overhead. Taking \(E_{b}/N_{0}=2\) dB as an example, with a maximum of  
Figure 10: The bit error rate performance of BPLIG compared to BPL in Small-scale fading channel.  
Figure 9: The bit error rate performance of BPLIG compared to BPL in Large-scale fading channel.  
30 iterations, five decoders, and using an AMD Ryzen 5 5600H CPU, the average simulation runtime for the BPL decoding algorithm based on traditional path selection criteria over 100 runs is 71.61 s, while the average runtime for the BPL decoding algorithm based on information geometry proposed in this paper over 100 runs is 48.95 s. Thus, the simulation runtime of the proposed BPL decoding algorithm based on information geometry is reduced by 31.6%.