esa-sceva/satcom-chunk-collection huggingface dataset:
# Single-Shot Coherent X-Ray Imaging Instrument at PAL-XFEL
###### Abstract  
We developed a single-shot coherent X-ray imaging instrument at the hard X-ray beamline of the Pohang Accelerator Laboratory X-ray Free Electron Laser (PAL-XFEL). This experimental platform was established to conduct a variety of XFEL experiments, including coherent diffraction imaging (CDI), X-ray photon correlation spectroscopy (XPCS), and coherent X-ray scattering (CXS). Based on the forward-scattering geometry, this instrument utilizes a fixed-target method for sample delivery. It is well optimized for single-shot-based experiments in which one expects to observe the ultrafast phenomena of nanoparticles at picosecond temporal and nanometer spatial resolutions. In this paper, we introduce a single-shot coherent X-ray imaging instrument and report pump-probe coherent diffraction imaging (PPCDI) of Ag nanoparticles as an example of its applications.  
X-ray free electron laser; single-shot coherent X-ray imaging instrument; coherent diffraction imaging; pump-probe imaging; ultrafast dynamic imaging; ultrafast phenomena; temporal synchronization; fixed-target; thermal melting +
Footnote †: journal:  
Article
## 4 Analytical Prediction of the Suppressive Impact of MRCFM on Sideband Constituents  
### Analysis and Prediction of Sideband Current Harmonic Suppression Effect  
In this paper, a vector control model of PMSM is established in JMAG via field-circuit coupling. Table 3 displays the main parameters of the motor. Considering that the operational range of the test prototype is 500-1500 r/min and 2-6 Nm, 1000 r/min and 4 Nm are therefore chosen as the steady running conditions to simulate and analyze the harmonic currents before and after the adoption of the MRCFM method.  
The second carrier frequency domain is beyond the range of the sensitive frequency band of the human ear; therefore, the simulation prediction and verification in this paper only focus on the sideband components near the first carrier frequency domain. Figure 9 displays the simulated frequency spectra of the sideband harmonics in the phase current for six pulse-width modulation techniques, using \(\omega_{c}\) = 8 kHz and \(\omega_{0}\) = 83.33 Hz as the carrier frequency. The characteristic frequencies corresponding to the main-order components of DPWMMIN and DPWMMAX are \(\omega_{c}\pm\omega_{0}\). The sideband current harmonic distributions of DPWM0 and DPWM2 are located in \(\omega_{c}\pm 2\omega_{0}\) and \(\omega_{c}\pm 4\omega_{0}\). The main-order components of DPWM1 and DPWM3 are extended to farther bands, located at \(\omega_{c}\pm 3\omega_{0}\), \(\omega_{c}\pm 5\omega_{0}\) and \(\omega_{c}\pm 7\omega_{0}\). As shown in Figure 10, after adopting the MRCFM method, the peak values of sideband current harmonics for various PWM strategies decrease significantly. The harmonic peak values of DPWMMIN/MAX, DPWMO/2 and DPWMI/3 decrease by 33.0%, 33.3% and 38.5%, respectively.  
Figure 8: Results of randomized parameters with PSO algorithm: (**a**) The error of the objective function; (**b**) The value of the transfer probability; (**c**) The value of the random gain.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Values** & **Parameters** & **Values** \\ \hline Number of slots & 12 & Rated torque & 8 Nm \\ Number of poles & 10 & \(d\)-axis inductance & 1.2 mH \\ Rated speed & 2000 r/min & \(q\)-axis inductance & 3.4 mH \\ Rated power & 3 kW & DC link voltage & 300 V \\ \hline \hline \end{tabular}
\end{table}
Table 3: Key parameters of the prototype.
## 4 Conclusions  
This paper presents a deep learning model, SpectraViT, which combines convolutional neural networks and Transformers for the classification task of jade UV spectra. The model leverages convolutional neural networks to extract local features of the spectra and Transformers to capture global dependencies, achieving efficient identification of jade samples. Experiments on the jade UV spectral dataset validated the effectiveness and superiority of the model, outperforming traditional machine learning algorithms and other neural network models. The experimental results indicate that the model achieves an accuracy of 99.24% in jade classification tasks. Additionally, different preprocessing methods for spectral data were compared, revealing that interpolation and resampling significantly improve classification accuracy, while other preprocessing methods may lead to feature loss. The proposed model provides a new approach and method for the UV spectral classification of jade, but there are still some limitations and areas for improvement. The dataset used in this study is relatively small, which may pose a risk of overfitting. Future work could involve using larger and more diverse spectral datasets for training and testing to enhance the model's generalization ability and robustness.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Acc (\%)** & **Recall (\%)** & **Precision (\%)** & **F1 (\%)** \\ \hline SpectraViT & 98.47 & 98.49 & 98.47 & 98.48 \\ SVM & 92.93 & 92.93 & 93.00 & 92.93 \\ PLS-DA & 93.88 & 93.88 & 93.95 & 93.82 \\ AlexNet & 97.96 & 97.95 & 97.99 & 97.96 \\ DeepSpectra & 95.41 & 95.41 & 95.49 & 95.38 \\ SpectraTr & 97.45 & 97.45 & 97.43 & 97.44 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance comparison on the fruit-puree-spectra datasets.  
**Author Contributions:** conceptualization, J.C. and X.L.; methodology, X.L.; software, J.C.; validation, J.C., X.L. and J.F. ; formal analysis, X.L.; investigation, J.C.; resources, X.L.; data curation, J.C.; writing--original draft preparation, J.C.; writing--review and editing, X.L.; visualization, J.C.; supervision, X.L.; project administration, X.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 51827901.  
**Institutional Review Board Statement:** Ethical review and approval were waived for this study because the study was conducted on Jade ultraviolet spectral dataset and publicly available spectral datasets and did not involve experimental studies in humans.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The ultraviolet spectral dataset presented in this study is unavailable due to privacy reasons, while the fruit puree dataset is publicly available, reference number [31].  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Results
### Spatial Distribution of Urban-Rural Fringes  
Figure 3 shows the identification results of the urban-rural fringe in Beijing, where green grids represent the rural area, orange grids represent the urban area, and yellow grids represent the urban-rural fringe. As the study area, Beijing was divided into 272,528 grids (250 m \(\times\) 250 m), with a total area of about 16,410.54 square kilometers [54]. Of these, 233,629 were classified as rural grids, covering an area of approximately 14,068.20 square kilometers and accounting for roughly 85.73% of the total. Urban grids numbered 11,123, encompassing about 669.78 square kilometers, which represented 4.08% of the area. The urban-rural fringe contained 27,776 grids, spanning approximately 1672.56 square kilometers and constituting 10.19% of the total area.  
From the perspective of spatial distribution, Beijing as a whole presented a layout of urban, urban-rural fringe, and rural areas from the inside to the outside. The urban area was concentrated in the core area within the Sixth Ring Road, which was the key area of Beijing's urban development and had the characteristics of high urbanization. In addition, the urban area was also scattered in the center of several suburbs, such as Yanqing, Huairou, Miyun and Pinggu in the north, and Fangshan, Daxing and Tongzhou in the south. Although the suburbs were not as prosperous as the city center, their centers still had significant urbanization characteristics and were therefore identified as urban areas. Rural areas were widely distributed outside the Sixth Ring Road, and most of them were sparsely populated with undeveloped land such as forests, grasslands, and farmland. The urban-rural fringe was widely distributed between the edge of urban built-up areas and rural areas and was the transition zone generated by the rapid expansion of urban areas. They are frontier areas where urbanization is taking place and play a unique role in the process of urbanization [55]. The urban-rural fringe is a special geographical space that is neither entirely urban nor rural, but both, as discussed in more detail later.
## 6 Results  
In this section, the results obtained in the typical Amazonian forest, where the vegetation has an average height of 24 m, and the transmitter heights are defined at 6 m (tree trunk), 12 m (beginning of the tree canopy), and 18 m (mid-canopy), will be presented. The influence of these antenna's heights and polarizations in signal propagation will be analyzed; following this, comparisons will be made between the proposed RNA-based models with other propagation loss models such as CI, FI, Weissberger, Early ITU-R, Cost235, and FITU-R models, aiming to demonstrate the efficiency of using ML techniques such as MLP and GRNN in estimating propagation loss in wooded environments.
## 2 Materials and Methods
### Methods
#### 2.2.3 Multilevel Feature Adaptive Fusion Module Based on Multi-Scale Fusion  
The design of the MFAF incorporates information about the integrity of a house at different scales [26]. Figure 4 displays the construction of the MFAF module. \(F_{i}\) represents the input features processed by the convolution layer and \(F_{j}\) represents the learned house integrity feature map. Global statistics of feature maps at different resolutions are captured through three different strategies. The first strategy involves the simultaneous modification of channel number and resolution by applying a 3 \(\times\) 3 convolution layer with a stride of 2. This downsamples the input feature map resolution to 1/2. The second strategy compresses the input feature map by employing a 1 \(\times\)1 convolution, preserving image information at the original scale. The third strategy increases the resolution to 2\(\times\) by employing a 1 \(\times\) 1 convolution and interpolation. This design captures global statistics of feature maps across various resolutions, aiding in the detection of buildings of different sizes in the input. Following the scaling operation, the output from each branch is processed through a fully connected network, resulting in a series of weighted feature vectors. The number of channels in the feature weight vector, denoted as \(l\), represents the feature weight vector diagram for the three branches. Subsequently, the SoftMax operation is applied to fix  
Figure 3: DFE module structure.  
the values of the feature matrix between 0 and 1. These values are then used to multiply the original feature map, weighting the house features to amplify the impact of complete information. The features at the corresponding level \(l\) are fused as follows:  
\[y_{ij}^{l}=\alpha_{ij}^{l},x_{ij}^{1\to l}+\beta_{ij}^{l},x_{ij}^{2 \to l}+\gamma_{ij}^{l},x_{ij}^{3\to l} \tag{1}\]  
where \(x_{ij}^{n\to l}\) denotes the feature vector at position \(i,j\) on the feature map, adjusted from level \(n\) to level \(l\). The terms \(\alpha_{ij}^{l}\), \(\beta_{ij}^{l}\), and \(\gamma_{ij}^{l}\) refer to the spatial importance weights from three different levels to level \(l\), which the network adaptively learns. Additionally, \(y_{ij}^{l}\) indicates the \(i,j\) vector of the output feature map between channels.
# Current Sensorless MPPT Control for PV Systems Based on Robust Observer
## 2 System Modeling  
The overall PV generation system is composed of a PV array, a DC-DC boost converter and a load, as illustrated in Figure 1. In order to design a robust observer based on AEM a quasi-Lipschitz model representation is required which states that the system is bounded in a particular way. Hence, this section details the mathematical model considered for boost converter and its quasi-Lipschitz representation.  
Figure 1: PV System’s configurations. (**a**) Conventional PV system with voltage and current sensors; (**b**) Proposed PV system with current estimation.
## 5 Commercial and Environmental Compatibility  
The last 70 years have seen a rise in research and use of inorganic engineered nano-particle materials (ENMs) in electronics, automotive, aeronautical and construction industries [48]. Given the typically lighter, stronger and more energy efficient properties of ENMs, AECOM predicted that over 50% of building materials will contain nanomaterials by 2025 [49]. The share of nanotechnology in building materials, however, is generally likely to be smaller. This is mainly, due to the uncertainty and misinformation in relation to these materials, the confused definition of them across the construction industry and regional and national standards, current limited knowledge of them by constructional professionals, their uncertain environmental, toxicological and ecotoxicological implications [50], their likelihood of dissolution and spread into the environment and subsequent human exposure, difficulties in measurement and mitigations measures, and commercial viability. With the latter, among the 156 nano-enabled construction products developed, to-date [51], many fail to be converted into marketable products and have remained at early stages of development with little chance of commercial viability.  
Over the past 50 years, NS has been broadly used in pharmaceutical, food, textile, printer toner, cosmetics, paint and coating (silane, siloxane and polysilazane), medical and drug products [50], and has scope to be used at industrial level as low-viscose grouts for ground improvement purposes,  
Figure 13: Variation of critical state friction angle with; (**a**) NS content; and (**b**) combined roundness of particles forming binary mixtures. Note: FS and TS, Fujian and Toyoura sands ([PERSON], 2012); F161, base sand for present investigation.  
including compensation grouting for submerged excavations [52], permeation grouting for geological containment and storage of hazardous wastes, and corrective grouting in fractured rocks. In comparison with cement, NS grouts offer a control on the induction and gel time, and also better penetrability in finer soils. This, together with their suitable mechanical properties, allows their application to soil via non-disruptive low-gradient delivery [22]. The undrained mechanical properties of NS-treated sands were studied in previous sections. The economical and environmental viability of NS grout and their possible implications for application of a 5% solution in 1 L of saturated porous sand of 1530 kg/m\({}^{3}\) bulk density is briefly discussed below.
## 5 ENO Controller Design
### Controller Gain Tuning  
P, I, and D gains for the gain scheduled ENO controller were tuned by a three stage trial-and-error process. Firstly, an initial set of gains were selected based on a constant PVC model of 10 to simplify tuning. Battery capacity was initially modeled as 100 \(mA-hr\) such that impact of energy harvested and consumed on battery level could be correlated to changing gain values. Secondly, adjustments were then made to the initial set of gains to account for replacing a constant-valued PVC model with one dependent on a Sun cycle. Thirdly and finally, a second set of gains, selectable by a gain scheduler, were introduced to reduce fluctuation in data collection rate when battery SOC was within \(\pm\)10% of a set point. Battery information from a CTT proprietary database shows that tracking device life is typically increased when battery levels are consistently high, and therefore a set point of 80% SOC was selected. In addition, battery capacity was selected to be 800 \(mA-hr\) (typically used to power the CTT-1000a) while tuning the second set of gains.  
Derivative gain was initially tuned given a previously selected \(K_{P1}\) of 0.3 and \(K_{I1}\) was 0. Battery SOC and GPS fix intervals were evaluated at \(K_{D1}\) values of 500, 1000, and 2000 and, as seen in plot (a) of Figure 7, SOC had an overshoot of 3%, 2%, and 1% with times to reach an 80% reference point of 2, 4, and 5 h, respectively. Plot (b) shows a GPS fix interval of 25 s was reached in 6, 4, and 2 h for \(K_{D1}\) values of 500, 1000, and 2000, respectively. In summary, a \(K_{D1}\) value of 500 was selected because it had the shortest rise/steady state time of 2 h for the GPS fix interval.  
Figure 6: Block diagram of feedback control system.  
Figure 7: Constant PVC derivative tuning results (**a**) SOC with respect to time; and (**b**) GPS fix interval.  
Values for an initial set of PID gains were selected manually, given a constant 10 mA PVC model and a 100 \(mA-hr\) battery capacity. \(K_{P1}\), \(K_{I1}\), and \(K_{D1}\) were tuned one at a time by evaluating both battery SOC and GPS fix interval. \(K_{P1}\) was assigned a value of 0.3, \(K_{D1}\) was assigned 2000, and \(K_{I1}\) was not used. Plot (a) of Figure 8 shows simulation results for SOC that started at 100% SOC and reached a reference SOC point of 80% in one hour. GPS fix interval reached a steady state of 25 s in 2 h but SOC did not reach a steady state due to the GPS fix interval being restricted to integer values, where the value needed to achieve SOC steady state was between 24 and 25 s.
# Stability Evaluation of TBM Pilot Tunnels to Rear Blasting Using the Protection Shield
## 3 Numerical Analysis  
Because soil in the ground is an anisotropic material that behaves nonlinearly over time, its behavior is extremely difficult to predict without conducting a numerical analysis of the ground because its interaction with the ground structure is extremely complex. In this study, the simultaneous excavation of a TBM pilot and NATM enlargement using a protection shield was analyzed through Plaxis 3D, a geotechnical numerical analysis program developed by the Delft University of Technology in 1987. Plaxis 3D analyzes the deformation and stability of the ground based on finite element analysis, and it is often used for conducting deformation and stability analyses of ground structures such as tunnels, foundations, and levees [14].
## 4 Industrial Control Network Traffic Classifier Based on Feature Extraction Dataset
### Decision Tree Classifier Training and Testing Based on Feature Dataset  
The training process of the decision tree classifier is shown in Figure 3. The decision tree belongs to supervised classification algorithms. It is necessary to know the categories corresponding to different data in advance before training the model, so the data must be labeled first. Then, according to the features extracted by the feature extraction method, redundant data are removed from the original dataset to obtain a new feature dataset. Finally, 80% of the feature dataset is randomly selected as the training set, and 20% is used as the test set to train and test the decision tree classifier on the open-source machine learning platform.
# A Crowdsensing Platform for Monitoring of Vehicular Emissions: A Smart City Perspective
## 3 Platform Architecture
### Vehicle Connection  
In order to enable the collection of the desired data, the monitored vehicle must have an OBD-II communication interface. The mobile application uses this interface to obtain information from the vehicle control units.  
There are a number of Bluetooth-, Wi-Fi-, and serial-enabled scanners on the market today, among many others. Considering the cost-benefit ratio, the ELM327 scanner was adopted throughout the development of this project. It has a low cost, implements OBD-II Operating Mode 1, and still features a Bluetooth module, allowing any smartphone application to connect easily to it.  
The information needed to calculate vehicle pollution is completely provided by the same Engine Control Unit (ECU). From the information coming from several sensors, we can estimate how much carbon dioxide is generated for a certain amount of fuel that is burned.
###### Abstract  
Point absorber wave energy converter (WEC) control strategies often require accurate models for maximum energy extraction. While linear models are suitable for small motions, the focus is on the nonlinear model of an hour-glass shaped buoy undergoing large vertical displacements. Closed-form expressions for the static and dynamic Froude-Krylov forces are developed. It is shown that, in general, the dynamic and static forces are of similar magnitude, which is not the case for a spherical buoy. While the dynamic force reduces the amplitude of the net buoy force, its shape predicts a larger buoy response than if neglected, causing the nonlinear terms to have an even more significant effect. An input-state feedback linearizing controller is developed to show how the nonlinear model can be used in a control law. A 2.5 m buoy example is simulated to illustrate the approach of tracking an arbitrary displacement reference. For the case considered, the extracted power is 30% larger when the nonlinear dynamic FK force is used in the control law. The hourglass buoy is also compared to a spherical buoy to illustrate differences in their response to regular waves and energy extraction when using the same control laws. A spherical buoy diameter of 7.5 m was required to obtain the same power output as a 5 m tall hourglass buoy. A power-force-amplitude (PFA) metric is introduced to compare energy extraction performance and power take-off requirements. The hourglass buoy's PFA was 13% larger than the spherical buoy implying that it can produce similar power but with less control effort.  
wave energy; nonlinear model; Froude-Krylov force; input-state feedback linearization; power-force-amplitude Article