esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Magnetism Analysis  
The value of saturated magnetization is an important parameter of magnetic materials, which reflects the ability of magnetic materials to respond to an external magnetic field. The data of coercivity and remanence demonstrates that magnetic microbeads exhibit superparamagnetism and the Fe\({}_{3}\)O\({}_{4}\) nanoparticles remain in microspheres. From the curves in Figure 2a, the saturation magnetization of Fe\({}_{3}\)O\({}_{4}\) nanoparticles was 45.5 emu/g. It is observed in Figure 2b,c that the saturated magnetization values of magnetic polymer microspheres (MPMs) and EDA-MPMs are 7.34 and 6.32 emu/g, respectively. Since Fe\({}_{3}\)O\({}_{4}\) particles on the surface of the microbeads are converted to Fe\({}_{2}\)O\({}_{3}\) during ring-opening reactions, saturated magnetization value between the two magnetic microbeads is different. With such saturation magnetization, EDA-MPMs could be easily and quickly separated from the aqueous solution by an external magnetic field.  
Figure 1: Electron and optical microscopy images of the products: (**a**) transmission electron microscope (TEM) image of Fe\({}_{3}\)O\({}_{4}\) nanoparticles); (**b**) Optical microscopy of ethylenediamine-functionalized magnetic microspheres (EDA-MPMs); (**c**,**d**) scanning electron microscope (SEM) images of EDA-MPMs; (**e**) The histograms of size distribution of EDA-MPMs.  
Figure 2: The magnetic hysteresis loop of magnetic nanoparticles (**a**); MPMs (**b**) and EDA-MPMs (**c**) at room temperature.
# Modeling and Analysis of Staged Constellation Deployment from a Single-Unit System
## 3 Methodology
Since the ballistic coefficients are constant up to \(k=(k_{max}-1)\), the corresponding phase durations are minimized while ensuring adequate separation. The problem is therefore reduced to evaluating candidate values for \(\theta_{(k_{max}-1),\;i_{f}}\) and the final-stage timing parameters \(\left(\Delta t_{a},\;\Delta t_{b}\right)_{k_{max}}\) across a range of \(\Delta v\). This parameter sweep defines the performance envelope of the disengagement mechanism and enables identification of the configuration yielding the shortest total phasing time.
## 2 Materials and Methods  
### Data Source  
Satellite clock corrections or satellite clock bias is a deviation of the satellite's clock reading from the adopted GNSS time scale realised by the reference clock. In this work, we focused on BeiDou clock products provided by the Wuhan University IGS Analysis Centre (WUM) in the framework of the Multi GNSS Experiment (MGEX) project ([[https://www.igs.org/mgex/data-products/#products](https://www.igs.org/mgex/data-products/#products)]([https://www.igs.org/mgex/data-products/#products](https://www.igs.org/mgex/data-products/#products)), access date: 1 September 2021). Since October 2020, WUM has been providing final clock correction data for C38-C46 BDS-3 satellites, and thus precise clock correction for all active BeiDou satellites has become available from IGS MGEX data centres (Figure 1). Our analysis covers the last 88 days of 2020 (5 October 2020-31 December 2020) and was performed for BDS-2 and BDS-3 satellite clocks. We focused on periodic effects connected with the orbital period of different types of satellites (MEO, GEO, and IGSO). The resulting analysis presents the periods in the range of 0.8-36 h. The lower limit was set to 0.8 h in order to take into account the higher harmonics of the periodic changes of the clock correction. A summary of the analysed satellite, its orbit, and clock type information are provided in Appendix A.
## Preliminaries
### Density Peaks Clustering  
The DPC finds the peak density points by calculating the local density \(\rho_{i}\). The DPC uses them as cluster centers, and then the leftover points are allocated directly to the cluster where the denser and closest points are located. For any point \(x_{i}\), the local density of \(x_{i}\) is \(\rho_{i}\), as shown in Equation (1).  
\[\rho_{i}=\sum_{j=1}^{n}\chi\big{(}d_{ij}-d_{c}\big{)} \tag{1}\]  
where \(n\) is the number of points, \(d_{ij}\) is the distance between \(x_{i}\) and \(x_{j}\), \(d_{c}\) is the cutoff distance, and \(\chi(x)\) is the logical function when \(x<0\), \(\chi(x)=1\), otherwise \(\chi(x)=0\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Description** \\ \hline \(X^{D}\) & global sample points \\ \(X^{\prime}\) & a subset of sample points in the universe \\ \(x_{i}\) & \(i\)-th sample point \\ \(\rho_{i}\) & local density of sample point \(i\) \\ \(\delta_{i}\) & distance from \(x_{i}\) to the closest point with \\ \(\delta_{i}\) & higher density than it \\ \(\theta\) & isolation partition \\ \(C_{b}^{P}\) & the core domain of the \(r\)-th cluster \\ \(C_{b}^{P}\) & the boundary domain of the \(r\)-th cluster \\ \(C_{l}^{N}\) & the noise domain of the \(r\)-th cluster \\ \hline \hline \end{tabular}
\end{table}
Table 1: Symbols and descriptions.  
\(\delta_{i}\) is the shortest distance between \(x_{i}\) and a point denser than it, as shown in Equation (2). If no point with a higher density is found, the local density is made to be the farthest distance in the dataset.  
\[\delta_{i}=\min_{i\in p_{i}<p_{i}}(d_{ij}) \tag{2}\]  
According to the above definition, by constructing a decision diagram of \(\delta_{i}\) relative to \(\rho_{i}\), the points are classified into three different types, i.e., density peaks, normal points, and noise. The density peaks are chosen as the cluster centers, and then the DPC allocates the leftover points to denser and the closest clusters.
## 6 Methodology
### Description and Implementation of the Model Under Test  
We assess the on-device token generation for models applied in anomalous sound detection based on the autoencoder (AE) architecture proposed in [40], which can be used in federated learning approaches. The AE comprises eight dense layers, each with 128 units, an 8-unit bottleneck, and ReLU activations after each layer except the final one. Additionally, we evaluate simplified architectures with 32 and 64 units, available in both quantized and non-quantized versions. The quantized models implement full integer quantization for weights and activations, utilizing int8 inputs and outputs to enhance on-device efficiency.  
The models are built with Keras 2.4.0, trained on parts of ToyADMOS and the MIMI Dataset [41] (normal/anomalous sounds of six toys and real machines) and converted to TensorFlow Lite (TFLite). Before conversion, batch normalization layers are folded into dense layers to improve efficiency and reduce model size during inference.  
The ML-EAT token is generated off-device by combining the COSE header, the generic claims from Table 2, and the model architecture, extracted from the summary of each network layer using Keras APIs. These data, structured in JSON format, are then serialized to CBOR using the cbor Python package. Since the token structure is created during the training phase, the device does not have a CBOR encoder firmware. At runtime, the device fills in the hash of the TFLite buffer data, the hash of the public key for model updates, and the signature at predefined offsets.
## 3 Results
### Model Interpretation Using SHAP
Figure 5 shows the force plot for the NS sample misclassified as APS. The sum of all the SHAP values for this instance combined with the expected base value is equal to 2.23, which is lower than the expected base value of 10.46. This indicates that the sum of all the contributions from the signal has a positive effect on the prediction of NS. However, ST contributed to the misclassification of the NS sample as APS; the negative standardized mean ST value of \(-0.672\) has a positive SHAP value of 0.45, causing the model to indicate APS. Figure 6 illustrates the dependency plot, showcasing the influence of GSR on the model's predictions of APS detection considering the interaction with HR. The plot shows the SHAP values for standardized GSR versus the standardized GSR values, with each data point representing an individual prediction from the testing dataset color-coded based on the HR values. As seen in the figure, there is a positive correlation between the standardized GSR values and the GSR SHAP values. Higher GSR values are associated with higher SHAP values, indicating a stronger influence of GSR on the model's predictions. Additionally, the color-coding based on standardized HR values demonstrates that elevated HR values (indicated by darker shades) are generally associated with higher GSR values. To quantify the relationship between GSR and HR, we calculated the [PERSON]'s correlation coefficient for the test set. The [PERSON]'s correlation coefficient, a measure of the linear correlation between GSR and HR, is 0.46 for the test set, a moderate positive correlation, suggesting that APS occurrence is associated with elevated GSR and HR values. Figure 5: Force plot for NS sample misclassified as APS. Figure 6: Dependency plot of GSR on APS detection with HR interaction.
## 3 Case Study
### Data Pre-Processing
#### 3.2.4 Normalization  
Because of the differences in the unit scale and order of magnitude of multivariate sensor data, it is easy to make the neural network difficult to converge and increase the training difficulty of the model, so this paper adopts the min-max standardization method to normalize the data.  
\[x^{*}=\frac{x-x_{min}}{x_{max}-x_{min}} \tag{7}\]  
where \(x^{*}\) is the normalized data of a certain ponding factor; \(x\) is the original data of a certain type of ponding factor; \(x_{max}\) is the maximum value of a certain type of ponding factor; and \(x_{min}\) is the minimum value of a certain type of ponding factor.
# Investigation of the Characteristics of NLS-PNA: Influence of NLS Location on Invasion Efficiency
## 2 Materials and Methods  
### Materials  
Solvents and reagents were purchased from FUJIFILM Wako Pure Chemical Co. (Tokyo, Japan); Tokyo Chemical Industry Co., Ltd. (Tokyo, Japan); Merck (Darmstadt, Germany); Sigma-Aldrich Co., LLC. (St. Louis, MO, USA); nacalai tesque Inc. (Kyoto, Japan); and Kanto Chemical Co., Inc. (Tokyo, Japan); and were used without further purification. PNA monomers were purchased from ASM Research Chemicals (Hannover, Germany). Oligonucleotides were purchased from Fasmac (Kanagawa, Japan).  
PNAs with or without an NLS peptide were synthesised by standard Boc-chemistry-based solid-phase peptide synthesis according to a literature procedure [42], purified by reversed-phase high-performance liquid chromatography (HPLC) (Jasco Co.; Tokyo, Japan), and characterised by matrix-assisted laser desorption/ionization-time of flight mass spectrometer (MALDI-TOF MS) (ultraflex III, Bruker Daltonics; Billerica, MA, USA) (Figures S2 and S3). The concentration of PNAs and DNAs was determined based on their absorbance at 260 nm by using a UV-visible spectrophotometer  
Figure 1: (**a**) Chemical structure of peptide nucleic acid (PNA). (**b**) Stable invasion complex formation using nuclear-localisation-signal-modified PNAs (NLS-PNAs).  
(molar extinction coefficients for PNA monomers (in M\({}^{-1}\) cm\({}^{-1}\)): \(\varepsilon\)(D) = 7600; \(\varepsilon\)(C) = 6600; \(\varepsilon\)(G) = 11,700; \(\varepsilon\)(U) = 10,200).
# Automatic Detection of Airway Invasion from Videofluoroscopy via Deep Learning Technology
## 3 Results  
### Network Training  
User parameters that can be considered when designing a deep learning network are the learning rate, batch size, and network size. Since there is no general rule for determining the batch size, we empirically set the batch size. When we experimented with the batch sizes of 16 and 32 in the case of U-Net, the result of 32 was better; in the case of exception-based CNN, when the batch size was experimented with 2, 4, and 8, the result of eight was the best. To effectively train a deep neural network, if the batch size is large, the learning rate should be set to a high value, whereas when the batch size is small, a low learning rate should be set to mitigate the influence of defective data that may exist in each individual batch. Therefore, we set the learning rate to \(0.1*b/256\) by using the linear scaling learning rate method [30] to determine the learning rate in accordance with the batch size (\(b\) is 32 for the U-Net model in Section 2.3.1 and eight for the Xception module in Section 2.3.2).  
Since airway invasion occurs for only a short period of time during the entire run time of a video file, as shown in Table 1, there are more image frames classified as negative than image frames classified as positive. To alleviate the problem of data imbalance between these classes, we used the focal loss function [31]. The focal loss function assigns lower losses to classes that include many samples, and thus can be classified relatively frequently, in order to concentrate more on learning the characteristics of more difficult classes (with fewer samples) by increasing the corresponding loss updates. In both the global localization of the cervical spinal column (Section 2.3.1) and the CNN-based detection of airway invasion (Section 2.3.2), finding a positive sample can be considered a relatively difficult task compared to finding a negative sample. Therefore, instead of the cross-entropy loss used in general classification problems, we trained the network using the following focal loss function (\(FL\)):  
\[FL(p_{t})=-(1-p_{t})^{\gamma}log(p_{t}) \tag{2}\]  
Figure 7: Convolution filtering of \(\mathbf{r}\) and \(\mathbf{f}\) for the decision on airway invasion occurrence in a video.  
Here, \(p_{t}\) is the model's estimated probability of the positive class, whereas \(1-p_{t}\) is the estimated probability of the negative class, and \(\gamma\) is empirically set to two.
## 1 Introduction
In addition, although the existing research produced some satisfactory conclusions on the temperature distribution in the CRTS-II ballastless track-bridge system under high-temperature conditions [29], the research considered only the direct solar irradiation in natural climates, and the results presented a non-uniform temperature distribution. Based on finite element model, [PERSON] et al. [31] concluded that the shadowing effect has a great influence on the temperature distribution in the box girder of ballastless tracks. However, there is currently a lack of research on the uniform temperature distribution of CRTS-II ballastless tracks on HSR bridges under sunlight occlusion or cloudy weather conditions. Moreover, no specification of the temperature profile of CRTS-II ballastless tracks on HSR bridges exists in the codes. In this paper, a 1:4 scaled specimen of simply supported concrete box girder bridge with ballastless track was constructed. The rapid extreme high temperature (EHT) test was carried out in winter in the national engineering laboratory of high-speed railway construction technology of Central South University. In summer of the same year, a typical high temperature day was selected, and conventional high temperature (CHT) test was carried out on the scaled specimen under no direct sunlight conditions. The results were analyzed and compared with the relevant literature to obtain meaningful conclusions. Using the temperature sensor system, the transverse and vertical temperature distributions in the track on a HSR bridge under different temperature conditions were studied, and the conclusions can provide references for the design of CRTS-II ballastless tracks and further understanding of the effect of temperature on CRTS-II ballastless tracks on HSR bridges.
# Water Segmentation for Unmanned Ship Navigation Based on Multi-Scale Feature Fusion
## 3 Experimental Data and Processing
### Introduction to Experimental Dataset  
This study utilizes the USVInland dataset for training and testing the proposed model. USVInland is a multi-sensor, multi-weather inland USV image dataset collected in real-world scenarios [37]. The dataset includes 27 raw data segments from various river scenes, with a total collection distance exceeding 26 km. It contains images for three tasks: SLAM, stereo matching, and waterline segmentation. Specifically, the USVInland waterline segmentation dataset comprises a total of 700 images, including 518 images with a resolution of 640 \(\times\) 320 and 182 images with a resolution of 1280 \(\times\) 640. Additionally, the dataset provides stereo image rectification methods, and after processing the raw images, the resolutions are adjusted to 640 \(\times\) 400 and 1280 \(\times\) 800, respectively.  
In this study, 518 images with a resolution of 640 \(\times\) 400 were selected as the training set, and 182 images with a resolution of 1280 \(\times\) 800 were selected as the test set. To effectively monitor overfitting during the training process, 20% (104 images) of the training set was randomly partitioned as a validation set, ensuring that the model could be appropriately fine-tuned during training and improving its final generalization capability. To further prevent overfitting and enhance the model's adaptability to different environments, the USVInland dataset underwent down-sampling and data augmentation preprocessing. Before being input into the model, the images were cropped to half their original size and augmented using random rotation (\(\pm\)15\({}^{\circ}\)), horizontal and vertical flipping (probability 0.5), and Gaussian blur (\(\sigma\) = 0.1-1.0) to improve the model's robustness to scale variations, rotational disturbances, and noise.  
Moreover, to optimize data distribution, the training set images were divided according to different river scene categories, ensuring an even distribution of samples from various aquatic environments, such as calm water, waves, and backlighting. This helped to reduce the risk of the model overfitting to specific scenes. Notably, the test set images retained their original high resolution and underwent no data augmentation, ensuring that the evaluation results would accurately reflect the model's generalization performance on unseen data.  
Through the aforementioned data preprocessing strategies, the dataset size was expanded to three times its original volume (totaling 1554 training images), further enhancing the model's generalization capability while accelerating the training and inference processes. Experimental results demonstrate that the proposed method maintains stable performance across different scenarios. This outcome proves that the adopted data preprocessing strategies effectively balance training efficiency and generalization capability, improving the model's robustness in practical applications.
# Non-Linear Creep-Relaxation Constitutive Damage Model for Aging Concrete
## 2 Theoretical Formulation
### Dissipative Processes  
#### 2.4.1 Dissipation Inequality  
Let us substitute Equations (2) and (35) into the Clausius-Duhem inequality for uniform temperature and isothermal processes, i.e.,  
\[\sigma_{ij}\dot{\varepsilon}_{ij}^{c}-\rho\dot{\Psi}\geq 0 \tag{40}\]  
where \(\dot{\varepsilon}_{ij}^{c}\) is the rate of creep strain and \(\rho\dot{\Psi}\) is the rate of the thermodynamic potential, Equation (35). Hence, with the Leibniz rule, by considering the symmetry of the damage tensor, by incorporating Equations (16) and (23), and taking \(\tau=\tau^{\prime}\) in the terms with \(\dot{\varepsilon}_{ij}^{v}\), and with Equations (36)-(40), this becomes  
\[Y_{ij}\dot{D}_{ij}+s_{ij}\dot{\varepsilon}_{ij}^{f}+B\dot{\beta}+\Lambda\geq 0 \tag{41}\]  
where \(\dot{D}_{ij}\) are the components of the evolution of the damage tensor, \(s_{ij}\) are the components of the deviatoric stress, \(\dot{\varepsilon}_{ij}^{f}\) is the rate of long-term creep strain, \(\dot{\beta}\) is the evolution of damage strengthening, and \(\Lambda\) is the rate of dissipation energy given by  
\[\begin{array}{ll}\Lambda=-\frac{1}{2}\int_{\tau^{\prime}=0}^{t}\int_{\tau=0}^ {t}&\dot{\widetilde{\lambda}}(t,\tau,\tau^{\prime})d\varepsilon_{ii}^{v}( \tau)d\varepsilon_{jj}^{v}(\tau^{\prime})-\int_{\tau^{\prime}=0}^{t}\int_{\tau= 0}^{t}\dot{\widetilde{\mu}}(t,\tau,\tau^{\prime})d\varepsilon_{ij}^{v}(\tau)d \varepsilon_{ij}^{v}(\tau^{\prime})\\ &-\int_{\tau^{\prime}=0}^{t}\int_{\tau=0}^{t}\dot{\widetilde{\lambda}}_{1}(t, \tau,\tau^{\prime})D_{ii}d\varepsilon_{jj}^{v+}(\tau)d\varepsilon_{kk}^{v+}( \tau^{\prime})\\ &-\int_{\tau^{\prime}=0}^{t}\int_{\tau=0}^{t}\dot{\widetilde{\lambda}}_{3}(t, \tau,\tau^{\prime})D_{ij}d\varepsilon_{kk}^{v+}(\tau)d\varepsilon_{ij}^{v+}( \tau^{\prime})\\ &-\int_{\tau^{\prime}=0}^{t}\int_{\tau=0}^{t}\dot{\widetilde{\lambda}}_{4}(t, \tau,\tau^{\prime})D_{ji}d\varepsilon_{ij}^{v+}(\tau)d\varepsilon_{ik}^{v+}( \tau^{\prime})-\frac{1}{2}\dot{k}_{f}\varepsilon_{ii}^{f}\varepsilon_{jj}^{f}- \frac{1}{2}\dot{k}_{D}\beta^{2}\end{array} \tag{42}\]  
where \(\dot{\widetilde{\lambda}}\) and \(\dot{\widetilde{\mu}}\) are the rate of the first and second Lame aging relaxation functions \(\widetilde{\lambda}\) and \(\widetilde{\mu}\), Equation (16), respectively; \(\dot{\widetilde{X}}_{l}\) (with \(I=1,2,\cdots,4\)) are the rate of the aging relaxation functions \(\widetilde{\chi}_{l}\), Equation (23); \(\dot{\hat{k}}_{f}\) is the rate of the bulk aging function \(\hat{k}_{f}\); and \(\dot{\hat{k}}_{D}\) is the rate of the aging hardening function \(\hat{k}_{D}\).
# Review of Renewable Energy-Based Charging Infrastructure for Electric Vehicles
## 6 Optimal Sizing
In a slightly different perspective, [82] presented the optimal design and comparative studies for an isolated EV charging station (EVCS) and a grid-connected EVCS as a smart energy hub configuration. This study's various supply options are diesel-based, solar PV with battery energy storage system (BESS)-based, and diesel-solar PV-BESS mix. The studies were carried out using HOMER software, which considered different dispatch strategies that yield the minimum project cost for each EVCS configuration. To adequately cater to the increased demand for EV charging resulting from the ever-rising EVs users, it becomes inevitable to provide fast and easily accessible charging infrastructures. Concerning the fast-charging concept, [PERSON] et al. [83] analyzed the technical and economic viability of an off-grid photovoltaic-battery energy storage system (PV-BESS) for fast-charging EVs. The whole system's optimum sizing was performed with HOMER software using the meteorological data and then enhanced the result using the principle of load shifting. With 281.52 kW PV modules, a total of 12 EVs' complete recharges of 35 k Wh for the period of 13.5 h per day were achieved using a 50 kW DC fast-charging device. Similarly, [84] proposed a strategy for BESS's sizing within a fast-charging station (FCS). The charging station's load has been estimated by the stochastic load profile of individual EVs registered with the FCS. The mathematical model result revealed that the FCS demand depends on the FCS charging level, the number of EVs in the FCS, the residual energy level of EVs, and the battery size of EVs registered with the FCS. Likewise, [85] developed a probabilistic planning model for optimal sizing and allocation of EVs' fast-charging stations. The load level of the EVs' charging demand was modeled based on queuing theory (QT). The exponential distribution function was employed to determine each charging device's service time while the EVs' charging demand was estimated using the poisoning process.