esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Properties of Mechanical
#### 3.1.2 Compressive Strength Test Results  
Figure 7 shows the average compressive strength test results of MFRCM and HyFRCM compared to PM. As shown in Figure 7, the average compressive strength of PM at the age of 28 days was 45.9 MPa, and the average compressive strength of MFRCM containing only SF or CF was 45.3 MPa and 35.6 MPa, respectively. However, the average compressive strength of HyFRCM containing 0.75% SF and 0.25% CF was 46.9 MPa, the highest compressive strength that was ever obtained in this experimental investigation. The reason for the increase in compressive strength is that it is reinforced with micro-CF, which is relatively small compared to SF. In the case of MFRCM containing only CF, however, the average compressive strength was 35.6 MPa, a decrease by about 22.4% compared to PM. This seems to be because the compressive strength was lowered as the interfacial bonding force between the fibers and the matrix after curing became weak due to the non-hydrophilicity of the fiber surface [32]. If a large amount of CF was used, particularly, the dispersibility was lowered, and the workability and compressive strength were greatly affected due to the fiber balls phenomenon. Therefore, considering that the HyFRCM containing 0.75% SF and 0.25% CF was shown as the optimum fiber combination for obtaining the maximum compressive strength in this experimental investigation, it is considered to be the most appropriate mixing ratio to improve the compressive strength.  
Figure 6: Flexural strength of MFRCM and HyFRCM versus PM.  
Figure 7: Compressive strength of MFRCM and HyFRCM versus PM.
# A Multiple-Input Neural Network Model for Predicting Cotton Production Quantity: A Case Study
## 2 Related Work
Additionally, based on their detailed experiments, the authors stated that the use of remote sensing-based ANN models has a great potential to provide reliable and accurate predictions of cotton yield maps. [PERSON] et al. [15] proposed a spatial-temporal multi-task learning model for predicting within-field crop yield. The proposed model was based on a deep dense neural network enforced with dropout layers and a new weighted regularization technique to improve the prediction performance. It exploits different spatial-temporal features by integrating multiple heterogeneous data from difference sources. The data used in their study were collected from a cotton field in west Texas from 2001 to 2003 and include soil properties, weather data, normalized difference vegetation index data, and spectral data. Their proposed model was evaluated against state-of-the-art models such as linear regression, decision tree regression, support vector regression as well as ensemble models, such as random forest and XGBoost. Their experimental analysis provided empirical evidence about the superiority of the proposed model against traditional models; hence, the authors stated that it could effectively assist the field of crop prediction. In this research, we propose a different approach for the development of an accurate model for the prediction of cotton's yield production. More specifically, we propose a new multiple-input neural network model, which exploits mixed features as inputs from three different kinds of data: soil data, cultivation management data, and yield management data. The contribution of our approach is that the proposed neural network architecture processes and handles each type of input in an independent way, which benefits the prediction performance more, since mixed features of data usually require separate treatment. To the best of our knowledge, none of the mentioned approaches considered exploiting information from three different kinds of cotton data and developing a prediction model by handling them separately. An advantage provided by the proposed architecture is the considerable flexibility and adaptivity for low computational effort, compared to that of a fully connected neural network with two or more hidden layers.
# Clustering Optimization of IPMSM for Electric Vehicles: Considering Inverter Control Strategy
###### Abstract  
The actual performance of driving motors in the electric vehicle (EV) powertrain depends not only on the electromagnetic design of the motor itself but also on the driving condition of the vehicle. The traditional motor optimization method at the rated point is difficult to deal with because of the mismatch between its high-efficiency area and the actual operation area. This paper systematically proposes an optimal design method for driving motors for EVs, considering the driving conditions and control strategy to improve motor efficiency and passengers' riding comfort. It uses cluster analysis to identify representative points and related energy weights to consider motors' comprehensive performance in different driving cycles. Three typical operation conditions are selected to implement the proposed optimization process. In the design process, by using the sensitivity analysis method, the significance of the structural parameters is effectively evaluated. Moreover, the semianalytical efficiency model and torque model of permanent magnet driving motors based on finite element analysis results are deduced to consider the influence of magnetic saturation, space harmonics, and cross-coupling between d-axis and q-axis magnetic fields. Based on the driving system demands of an A0 class pure EV, the whole optimization design is divided into four steps and three scales, including the motor scale, control scale, and system scale. By using the multi-objective optimization method, Pareto optimality of motor efficiency and torque ripple is achieved under the city driving cycle and highway driving cycle. Compared to the optimization only at the rated condition, the proportion of motor sweet region increased about 1.25 times and 3.5 times by the proposed system-scale optimization under two driving cycles, respectively. Finally, the effectiveness of the proposed optimization method is verified by the prototype experiments.  
multi-objective optimization; control strategy; driving cycle; electric vehicle +
Footnote †: journal: Applied  
Article  
Clustering Optimization of IPMSM for Electric Vehicles:  
Considering Inverter Control Strategy  
Jiabao Bu  
The State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710049, China; [EMAIL_ADDRESS] (J.B.); [EMAIL_ADDRESS] (S.Y.)  
* Correspondence: [EMAIL_ADDRESS]  
* Current address: Power Dispatch and Control Center of Guangqi Power Grid, Nanning 530023, China.
## 3 Results and Discussion
### Antenna Performance  
The return and transmission losses of the antenna are displayed in Figure 8 respectively. Through their analysis, it is possible to conclude that the antenna, when placed inside biological tissue, operates best at 2.4 GHz, as its S\({}_{11}\) parameter is the lowest at that frequency and the S\({}_{21}\) parameter is the highest. Operation at 2.4 GHz, i.e., the ISM band, is an attractive feature of the antenna, as common communication protocols, e.g., Wi-fi and Bluetooth, also utilize this band, thus making it easier to establish a communication link with the implanted device.  
Further analysis of the experimental results, namely the S\({}_{21}\) parameter, indicates that the fabricated antenna is capable of receiving \(-\)45 dB of power from the transmission antenna at 2.4 GHz when they are 25 cm apart.  
In order to determine the suitability of the antenna for communication in the presented scenario, the Specific Absorption Rate (SAR) of the antenna must be considered. The SAR is a measurement of how much energy from the electromagnetic wave propagating through the tissue is absorbed by said tissue. In order to guarantee the safety of the patient, the SAR must be below a predefined limit, in this case, 2 W/kg, as values higher than this can lead to significant heating of the tissues. Since communication modules that can be used outside the body have sensitivities of \(-\)90 dBm, \(-\)125 dBm and even as low as \(-\)150 dBm [10], the transmitter inside the body can be transmitted with an extremely low power level, for example, \(-\)10 dBm, thus guaranteeing a low SAR. With such an output power, \(-\)55 dBm would still reach the receiver (in the previously described scenario of Figure 5), a value well above the mentioned sensitivities.  
Thus, we can state that given that communication modules can easily achieve sensitivities of \(-\)120 dBm, and assuming the implanted antenna will transmit data with a very conservative power of \(-\)10 dBm, the S21 could be as low as \(-\)110 dB for \(-\)120 dBm of power (the detection limit) to reach the communication module. As it is \(-\)45 dB, we have 65 dB of margin, which means we receive signals with power more than six orders of magnitude higher than the minimum that the receivers can detect. Furthermore, the  
Figure 8: S\({}_{11}\) (**top**) and S\({}_{21}\) (**bottom**) parameters measured through the VNA for a frequency range of 1 to 5 GHz, which demonstrates that the antenna is better suited to operate at 2.4 GHz.  
test performed on the antenna allowed mimicking the real scenario with the use of biological tissue. Thus, considering the setup in which the antenna was tested and the high margin of power achieved, it is clear that the developed antenna is suitable for wireless communications from inside the body.
## 2 Measurements and Quality Control Processes
### Longwave Upward Radiation Parameterization  
The estimation of the upward emitted longwave irradiance from the ground surface is based on the Stefan-Boltzman equation:  
\[L_{up}=\varepsilon_{\text{lg}}*\sigma*T_{g}^{4} \tag{19}\]  
Figure 17: Comparison of estimated and measured \(L_{dtn}\) for the all day time interval for the data set of the period 2016–2017.  
The longwave emittance of natural ground surfaces, \(\epsilon_{\text{lg}}\), is typically around 0.96 [44]. \(T_{g}\) is the ground surface temperature given in Kelvin. In a sunny weather, \(T_{g}\) is higher than the screen air temperature (\(T_{a}\)), but on clear winter nights it is below \(T_{a}\). Figure 18a shows the relationship between the estimated longwave irradiance and the measured one using Equation (19). The graph shows a strong correlation between the two variables (R\({}^{2}\) = 0.93), although a quadratic equation could give even higher correlation (R\({}^{2}\) = 0.96).  
Figure 18: (**a**). Comparison of measured and estimated hourly upward longwave irradiances for the whole day using Equation (19). (**b**). Comparison of measured and estimated hourly daylight upward longwave irradiances for the calibrated Dognieux and Lemoine model at Athalassa using the data set 2016–2017.  
[PERSON] and [PERSON] [45] have also estimated daytime upward irradiance as a function of ground surface temperature, global solar radiation (\(G\)) in W m\({}^{-2}\) and the ground albedo (\(\rho_{g}\)) which is defined as the ratio of the Reflected solar irradiance to Global solar irradiance:  
\[L_{up}=\sigma*[0.995*T_{g}+0.023*(1-\rho_{g})*G]^{4} \tag{20}\]  
The above equation was used for the estimation of the daytime upward irradiance of the European Solar Radiation Atlas and therefore it was tested under Cyprus environmental conditions. The yearly average value of the ground albedo at Athalassa is 0.214. In the current study, the hourly values of the albedo are used for the estimation of the upward longwave irradiance. The coefficients of Equation (20) were locally calibrated [9]. The original coefficients are very close to the calibrated ones. Figure 18b shows the relationship between the estimated daytime longwave irradiance and the measured one. The figure indicates that there is also a strong correlation between the two variables (R\({}^{2}\) = 0.942).
###### Abstract  
The prediction of travel time is challenging given the sparseness of real-time traffic data and the uncertainty of travel, because it is influenced by multiple factors on the congested urban road networks. In our paper, we propose a three-layer neural network from big probe vehicles data incorporating multi-factors to estimate travel time. The procedure includes the following three steps. First, we aggregate data according to the travel time of a single taxi traveling a target link on working days as traffic flows display similar traffic patterns over a weekly cycle. We then extract feature relationships between target and adjacent links at 30 min interval. About 224,830,178 records are extracted from probe vehicles. Second, we design a three-layer artificial neural network model. The number of neurons in input layer is eight, and the number of neurons in output layer is one. Finally, the trained neural network model is used for link travel time prediction. Different factors are included to examine their influence on the link travel time. Our model is verified using historical data from probe vehicles collected from May to July 2014 in Wuhan, China. The results show that we could obtain the link travel time prediction results using the designed artificial neural network model and detect the influence of different factors on link travel time.  
big probe vehicles data; data sparsity; spatiotemporal relationships; multi-factor influences; artificial neural networks; link travel time prediction 2018 8 279 1
## 2 Materials and Methods
### Soil Sampling and Laboratory Analyses  
The soil samples were collected from the upper 60-cm layer (where the main part of the olive trees' root system exists and nutrients are mainly taken up) [22] of a native marl soil from typical mature olive groves in an agricultural region around the city of Thessaloniki, Central Macedonia. After 5 soil samples were randomly collected, they were carefully mixed to homogenize them. Then, the homogenized soil samples were transferred to the experimental greenhouse to prepare the soil mixtures with the organic amendments (as described above in Section 2.1), fill the black plastic bags where the plants were grown, and establish the experiment. After all of the mixtures of the native marl soil and the organic amendments were prepared, in order to determine their initial fertility (before the beginning of the experiment), approximately 1.5 kg of each soil mixture (i.e., soil + SM, soil + EBLS, and soil + SM + EBLS) was transferred to a laboratory for chemical analyses.  
The soil mixtures and the control soil were dried at room temperature, their stones were removed, and then they were sieved through a 10-mesh screen before the analyses. The laboratory analyses included the following: determination of pH; particle size analysis; organic matter; [PERSON]; [PERSON]; exchangeable cations (Ca, Mg, and K); DTPA-extractable micronutrients (Fe, Mn, Zn, and Cu). These parameters were determined as follows: pH in the soil: distilled water paste 1:1 [23]; particle size analysis according to the [PERSON] method [24]; organic matter with the potassium dichromate method [25]; [PERSON] according to the method described by [PERSON] (1982) [26]; [PERSON] according to the method of [PERSON] (1982) [27]. The exchangeable cations (Ca, Mg, and K) were calculated based on the method of ammonium acetate [28], while the DTPA-extractable micronutrients (Fe, Mn, Zn, and Cu) were determined after the extraction of 10 g of soil with DTPA at pH 7.3 [29]. The concentrations of the exchangeable cations and extractable micronutrients (i.e., those of Ca, Mg, K, Fe, Mn, Zn, and Cu) were measured by ICP (OPTIMA 2100 DV, optical emission spectrometer, Perkin Elmer, Waltham, MA, USA) [30].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Organic Material** & **N** & **P** & **K** & **Ca** & **Mg** & **Fe** & **Mn** & **Zn** & **Cu** \\ \hline  & & & \% D.W. & & & & & mg kg\({}^{-1}\) D.W. & \\ \hline SM & 2.63 & 0.16 & 2.99 & 1.25 & 1.32 & 3142 & 283 & 81 & 18 \\ \hline EBLS & 1.25 & 0.08 & 0.31 & 1.11 & 0.36 & 4122 & 1205 & 90 & 16 \\ \hline \hline \end{tabular} SM = sheep manure; EBLS = litter from evergreen broadleaf species, i.e., from _Quercus_ sp.  
\end{table}
Table 1: Nutrient contents of the two organic amendments applied to the control soil.
# Multispectral Target Detection Based on Deep Feature Fusion of Visible and Infrared Modalities
## 4 Materials and Methods
### Datasets
#### 4.1.3 M\({}^{3}\)Fd  
The M\({}^{3}\)FD dataset is the most comprehensive multitarget detection (MOD) dataset available, encompassing the widest variety of scenes. The dataset contains 4200 pairs of aligned and labeled visible and infrared images, most of which have a high resolution of 1024 \(\times\) 768. It includes six target categories--\"people\", \"cars\", \"buses\", \"motorcycles\", \"trucks\", and \"traffic signals\"--which are commonly found in surveillance and autonomous driving scenarios. The images were captured by optical and infrared cameras mounted on vehicles and represent a diverse range of challenging scenarios, including daytime, cloudy, nighttime, and foggy conditions. As a result, the M\({}^{3}\)FD dataset is ideally suited for multitarget detection (MOD) research.
## 5 Discussion
### Current Research in EUS Development  
A multidisciplinary approach involving gastroenterologists, radiologists, and surgeons is often required to manage patients with pancreas divicum-related pancreatitis. This highlights the complexity of treating such cases and the need for standardized protocols or consensus guidelines.  
Beyond reducing operator dependency, artificial intelligence (AI) and machine learning algorithms are increasingly being integrated into EUS interpretation. AI-assisted image analysis has the potential to enhance the detection of subtle pancreatic ductal abnormalities, thereby improving diagnostic accuracy. Deep learning models trained on large datasetsmay help standardize interpretations, minimize interobserver variability, and provide real-time decision support during procedures.  
Fusion Imaging and Augmented Reality in EUS. Recent advancements in fusion imaging, combining EUS with MRI or CT, have been explored to improve pancreatic ductal visualization. This technology enhances spatial orientation and anatomical correlation, allowing for more precise identification of ductal anomalies in pancreas divisum. Additionally, augmented reality (AR) applications in EUS are under investigation to overlay real-time imaging with pre-acquired scans, potentially improving procedural accuracy.  
Beyond diagnosis, EUS is evolving as a tool for therapeutic interventions in pancreas divisum. Techniques such as EUS-guided pancreatic duct drainage and EUS-assisted minor papilla interventions offer minimally invasive alternatives when ERCP is not feasible. Studies evaluating the long-term efficacy and safety of these approaches are ongoing and could influence future management guidelines.  
The use of secretin-stimulated EUS is being investigated as a method to assess pancreatic exocrine function dynamically. By evaluating ductal dilation and pancreatic fluid secretion in real time, secretin-enhanced EUS may help differentiate functional from structural abnormalities in pancreas divisum, providing a more comprehensive evaluation of disease impact.  
Given the operator-dependent nature of EUS, efforts are being made to establish structured training programs and competency assessment criteria for endosonographers. The development of simulation-based training modules and certification programs may help improve diagnostic accuracy and ensure consistency across different clinical settings.
## 3 Experiments and Discussions  
Figure 5 shows a photo of the constructed 3 DOFLM system. Its size, including the electric cables and the base plate for mounting the optical components, is 80 mm (\(x\)) \(\times\) 90 mm (\(y\)) \(\times\) 20 mm (\(z\)), which is much smaller than commercial He-Ne laser interferometers. The U-shaped holder was made to fix both FL and QPD in one place for better assembly accuracy. It should be noted that, due to the space limit of the nanopositioning stage, the dead path of the 3 DOFLM system, which is the distance between the MMI kit and the null point of the measurement position, was designed to be 36 mm. Therefore, the error due to the dead path is negligible [27].  
Figure 4: Optical layout of the wavelength corrector kit.  
Figure 5: Photo of the constructed 3 DOFLM system.  
In order to achieve accurate displacement of the nanopositioning stage, it is essential to investigate the performance of the designed 3 DOFLM system. It should be noted that all experiments were carried out in a non-environmental controlled open laboratory in which disturbances caused by temperature change and humidity variation cannot be avoided.
# The Quest for Low Loss High Refractive Index Dielectric Materials for UV Photonic Applications
## 4 Results
### Interaction Effects
Since the temperature increment is proportional to the intensity of the illuminating beam, the temperature scale is arbitrary and for comparison purposes, going from 0 to a maximum value that depends on this intensity [40]. The diamond dimer offers the highest near field enhancement, together with the smallest heat perturbation. However, this is not the case for Al + Al\({}_{2}\)O\({}_{3}\), where the field enhancement is four times smaller and significant heat generation is present, as expected from its absorption [53]. Although the results for gap = 10 nm are not shown, the overall effect of decreasing the gap is an increase of the near-field enhancement and temperature at the center of the gap, while keeping a very similar \(\left|\left.E\right|\right./\left|E_{0}\right|\) and \(\Delta T\) spatial distribution [2].
## 2 Methodology
### Improved SOBI
This is equivalent to forming a whitening matrix as follows:  
\[W=\left[\left(\lambda_{1}-\sigma^{2}\right)^{-\frac{1}{2}}h_{1},\ldots,\left( \lambda_{3}-\sigma^{2}\right)^{-\frac{1}{2}}h_{3}\right]^{T}. \tag{2}\]  
Figure 2: Flowchart of the improved second-order blind identification (SOBI) signal de-noising method for the obtained time-series displacement of bridges using ground-based synthetic aperture radar (GBSAR). (3) Denote \(W\) as a whitening matrix, and the original observation signals, \(X(t)\), are whitened to remove the second-order correlation between the signal components to obtain a unit covariance matrix, \(Z(t)\):  
\[Z(t)=W*X(t)=W*A*S(t)=V*S(t). \tag{3}\]  
(4) For fixed time lags, \(\tau\in\left\{\tau_{j}|j=1,2,..k\right\}\), the sample covariance matrix, \(R(\tau)\), of the whitened data is calculated using \(R(\tau)=E\left[Z(t)Z^{T}(t+\tau)\right]=AR_{Z}(\tau)A^{T}\). (5) For all \(R\left(\tau_{j}\right)\), the joint approximate diagonalization algorithm is used to derive the orthogonal matrix, \(V\). (6) The estimated value of the source signal is obtained using \(Y(t)=BX(t)=V^{T}WX(t)\), and the mixing matrix, \(A\), is estimated as \(A=W^{-1}V\), where \(W^{-1}\) is the pseudo inverse matrix of the whitening matrix, \(W\). (7) The fast Fourier transform (FFT) algorithm is used to perform a spectral conversion on the separated signal components to find the noise signal components for the zeroing processing combined with a frequency analysis. The estimated value of the mixing matrix, \(A\), is used for the reverse reconstruction:  
\[Y_{N}(t)=A*Y_{Z}(t), \tag{4}\]  
where \(Y_{N}(t)\) is the reconstructed signal component as the de-noised signal; \(A\) is the estimation of the mixing matrix; and \(Y_{Z}(t)\) is a source signal matrix obtained by zeroing the unwanted noise signal components in the estimated value of the source signal, \(Y(t)\).