esa-sceva/satcom-chunk-collection huggingface dataset:
# Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection
## 4 Clutter Statistics and Detection
### Tri-Modal Discrete (3 MD) Texture Model  
A new model called tri-modal discrete texture model (3 MD) was recently proposed. This model is able to detect moving targets with a very low PFA. The model was mainly invented for spaceborne fully focused SAR data. It is based on the idea of the statistical modeling of the sea texture in a discrete form. The PDF of the 3 MD model for \(L\) number of looks can be written as [46; 47]  
\[f(I,\odot,L,\rho_{\text{c}})=\frac{L^{L}}{\Gamma(L)}I^{L-1}\sum_{d=1}^{D}c_{ \text{d}}\frac{\exp\!\left(-\frac{LI}{\rho_{\text{c}}a_{\text{d}}^{2}+\rho_{ \text{n}}}\right)}{\left(\rho_{\text{c}}a_{\text{d}}^{2}+\rho_{\text{n}} \right)^{L}}. \tag{24}\]  
where \(\odot=[c_{\text{d}},\ a_{\text{d}}]\), \(D\) corresponds to the number of discrete scatterers in a single pixel and \(\Gamma(\cdot)\) is the gamma function. The variables \(\rho_{\text{c}}\) and \(\rho_{\text{n}}\) are the normalized clutter and noise variances, respectively, whose sum is unity. The parameters \(a_{\text{d}}\) and \(c_{\text{d}}\) are the discrete texture intensity levels and their corresponding relative weightings, respectively, where \(\sum_{d=1}^{D}c_{\text{d}}=1\), \(c_{\text{d}}>0\).  
Figure 18: Binary detection map based on the K-distribution obtained from the same RC F-SAR X-band HH polarized data shown in Figure 10. The detected ship signal (left) and high sea clutter spikes can be clearly observed.  
The PFA for a given threshold \(\eta\) is given as  
\[P_{\rm fa}\ (\eta)=\sum_{d=1}^{D}c_{\rm d}\frac{\Gamma\!\left(L,\frac{L\eta}{ \rho_{\rm c}a_{\rm d}^{2}+\rho_{\rm n}}\right)}{\Gamma(L)}. \tag{25}\]  
The value of \(D\) after testing even highly heterogeneous sea state was found to be 3 [47]. Therefore, for the 3 MD clutter model there are eight unknown parameters: \(\odot=[c_{\rm d},a_{\rm d}],\rho_{\rm c}\) and \(L\). These unknowns are estimated using the non-linear least squares fit as explained in Section 4.1.2.
# Antennas for Licensed Shared Access in 5G Communications with LTE Mid- and High-Band Coverage
## 5 Measured and Predicted Results  
This section presents the measured reflection coefficient, efficiency, gain, and radiation patterns of the wideband antenna (Ant#3), which are compared with the predicted results. The radiation performance of the fabricated antenna was measured using the NSI/00S-50 near-field range at the Australian Antenna Measurement Facility, CSIRO, Marsfield, N.S.W., and the reflection coefficients were measured using an Agilent Network Analyzer PNA-X N5242A at Macquarie University, Australia. The top and bottom views of the prototype are shown in Figure 16.  
Figure 16: Fabricated wideband antenna (Ant#3) for mid and high bands: (**a**) top view of the antenna with whole mobile device substrate (antenna is shown in the inset), and (**b**) bottom view (whole substrate).  
Figure 15: Surface current distributions of the wideband antenna (Ant#3) at (**a**) 1.8, (**b**) 2.6, and (**c**) 3.6 GHz.
# Experimental Approaches in Delineating mTOR Signaling
## 4 Identification and Characterization of mTOR Signaling Pathway Components
### Immunofluorescence-Mediated Identification of mTOR Signaling Components
#### 4.3.2 LRS Was Identified as an Intracellular Leucine Sensor by Immunofluorescence  
Leucyl-tRNA synthetase (LRS) ligates leucine to its cognate transfer RNA. In an effort to search for tRNA-independent function of LRS, [PERSON] et al. performed cell fractionation and immunofluorescence assays and found a portion of LRS localized to lysosome at basal levels [76]. More importantly, amino acids stimulated LRS enrichment on lysosome to colocalize with mTOR, suggesting a possible role of LRS in regulating mTOR. Further biochemical assays indicated that associations of LRS with mTOR (by mTOR immunoprecipitations in HEK293T cells) were only observed in leucine-stimulated but not leucine-deprived conditions [76]. Further, LRS pulldowns in HEK293T cells showed that LRS specifically interacted with RagD in the presence of leucine and acted as a RagD GAP. The essential role of LRS as a leucine sensor for mTORC1 activation was confirmed by two types of LRS mutants: (1) an LRS-F50 AL52A mutant deficient in binding leucine was not able to activate mTORC1 and (2)a tRNA transferase activity-deficient LRS-K716 AK719A mutant did not affect mTORC1 activation [76].
## 3 Results
### Scan Result Analysis
Consequently, the scanning conditions can be regarded as adequate for the purpose of comparing the quality of point clouds using multiple functions of the program. Additionally, Figure 15. presents the registration of the laser pattern within the binary images that underwent thresholding. This scan results in three point clouds. Figure 12: Comparison of obtained point clouds within the proposed program functions. (**a**) Front view of initial scan, (**b**) front view of results of computing arithmetic mean of points, (**c**) front view of results of computing based on averaging the acquired 3D coordinate, (**d**) right view of initial scan, (**e**) right view of results of computing arithmetic mean of points, (**f**) right view of results of computing based on averaging the acquired 3D coordinate. Figure 13: Representation of scanning a single laser beam on the surface of a cardboard box. A comparative analysis of the resulting point clouds for these functions, conducted through visual inspection, revealed that the highest degree of precision was observed in Figure 16. The second function depicted in Figure 16b employed the arithmetic mean of the image points present within the binary image. Consequently, this function was selected for subsequent utilization in scans and within the final device design. Figure 16: Comparison of the resulting point clouds. (**a**) Initial function, (**b**) second adjusted function, (**c**) third adjusted function. Figure 11.0 ESP32-CAM images used for point cloud calculation. Figure 11.1 Rectified and thresholded binary images used for point cloud computation. As previously mentioned, it is more appropriate for subsequent research to employ a laser line that moves along the object without undergoing rotation. Following the digitization process, Figure 17 illustrates the coverage of a more extensive area of the digitized surface achieved through the movement of the laser line over the object's surface. This finding lends further credence to the hypothesis that the proposed principle of digitizing with shifting of the projected laser line is more suitable for the prototype digitizing device than the principle of digitizing with rotation of the laser projector.
# Enhanced Curvature-Based Fabric Defect Detection:
## 7 Conclusions  
In conclusion, the modified discrete curvature method demonstrates strong potential, although it does not yet match the power of deep learning-based methods. This modified approach, combined with the newly applied techniques, shows promise in enhancing performance and accuracy while maintaining computational efficiency. The results from the Gabor transformation were also highly successful, largely due to parameter selection tailored to the faults, with most defects occurring horizontally and vertically. However, the success of these methods may vary with different fabric types. The modified discrete curvature method is particularly suited for local defect detection with minimal CPU usage and low power consumption.  
In the future, the discrete curvature algorithm could be integrated with the Gabor approach to further improve accuracy and reduce execution time. Additionally, the system is expected to operate at higher frame rates (FPS) with enhanced accuracy, and future work will focus on developing noise detection algorithms to identify noise artifacts in the images. As a result, the developed system has the potential to serve not only the textile industry but also other sectors by detecting a wide range of surface defects.  
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [M.E.]. The first draft of the manuscript was written by [M.E.] and all authors commented on previous versions of the manuscript. Review and editing are conducted by [M.D.]. All authors read and approved the final manuscript.  
This research received no external funding.  
**Data Availability Statement**: Some sample images from the data-set are shown in Figure 2. The datasets and software used during the current study are available from the corresponding author on reasonable request.  
**Conflicts of Interest**: The authors declare that they have no financial interests or conflicts of interest related to this research.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **RPI4 CPU** & **Jetson Nano CPU** & **Jetson Nano GPU Acc.** \\ \hline CA & 4.9 fps & 5.5 fps & - \\ MDCA & 3.38 fps & 3.8 fps & - \\ GT & 9.1 fps & 10.2 fps & - \\ CNN & \textless{}1 fps & 1 fps & 8.33 fps \\ \hline \hline \end{tabular}
\end{table}
Table 8: Hardware Comparison of Algorithms CA (Curvature Algorithm), MDCA (Modified Curvature Algorithm), GT (Gabor Transform), CNN (Convolutional Neural Network).
## 2 Research Contribution  
In the present study, an architecture of HEMS is presented for automated scheduling of appliances with an objective of reducing the peak-power consumption and the total electricity cost. It is assumed that the bi-directional communication between the house and the power grid is present, which enables the HEMS to receive/send data and control signal from the power utility. HEMS is employed in real-time electricity pricing environment.  
The HEMS is designed to operate in two modes of operation (MOO) based upon the load category (thermal/non-thermal) (see Figure 1). Mode 1 remains in operation if any of the non-thermal appliances is active. On the other hand, in Mode 2, only the thermal appliances are activated. At each time step, an optimization problem is formulated into a linear-program (LP) with appropriate objective function and constraints. The HEMS functions in a receding horizon framework, i.e., at each time step, updated system states and predicted information is provided to the controller and the optimization process is repeated. In both the modes, soft constraints on the maximum power consumption of the appliances is also imposed. This way, the total peak power consumption is maintained under the specified peak-consumption capacity. The constraint values are decided by the utility company or the aggregator based upon the demand of electricity and operational cost of the grid. These values are communicated to the smart-meter in homes for a fixed duration in advance and updated at each time step. The detailed functionalities of HEMS are described in later sections.  
The implemented scheduling algorithm does not require a priori knowledge of the load-demand. The non-thermal appliance can be activated at any time by the customer. The customer is required to set a deadline at the time of activation by which the task assigned to the appliance should be finished. The HEMS outputs a warning if the deadline set by the customer is shorter than the running time of that appliance.  
Figure 1: System architecture of HEMS. MILP = mixed-integer-linear-programming.
## 2 Assimilation Method and Experiment Configuration
### Satellite and Radar Data Assimilation Scheme  
According to the optimal solution in (10), the construction of MPs matrix and OPs matrix is essential to the implementation of POD-4 DEnVar method. For satellite data assimilation, since the satellite-observed brightness temperature (TB) is not model variables, the observation operator is required to calculate TB. Within the WRF Data Assimilation system (WRFDA), the Community Radiative Transfer Model (CRTM) is used as the observation operator [30]. CRTM calculates model-simulated TB from WRF temperature and moisture profiles. In the system, CRTM is run to produce the simulated TB for ensemble members and background field, then making up the OPs matrix.  
Like satellite data assimilation, the radar-observed radial velocity and reflectivity are also not model variables. In this case, an observation operator for radar is needed in order to assimilate them directly. The observation operator for radial velocity is  
\[V_{r}=u\frac{x-x_{r}}{r}+v\frac{y-y_{r}}{r}+(w-V_{Tm})\frac{z-z_{r}}{r}, \tag{11}\]  
in which, \((u,v,w)\) is the three-dimensional wind field, \(r\) is the direction from model grid point \((x,y,z)\) to radar location \((x_{r},y_{r},z_{r})\). \(V_{Tm}\) is the terminal velocity of the precipitation, given by [PERSON] and [PERSON] (1997, 1998) [17; 18].  
\[V_{Tm} =5.40 aq_{r}^{0.125}, \tag{12}\] \[a =\left(\frac{P_{0}}{\overline{P}}\right)^{0.4}, \tag{13}\]  
where \(\overline{P}\) represents the base-state pressure and \(P_{0}\) is the surface pressure.  
The observation operator for reflectivity is [19]  
\[Z=43.1+17.5\log(\rho q_{r}), \tag{14}\]  
where \(\rho\) is the air density, and \(q_{r}\) is the rainwater mixing ratio. According to the observation operators developed for radar, radial velocity and reflectivity can be calculated from the wind field, rainwater mixing ratio and other model variables, then making up the OPs for radar data assimilation.
Even though in hospitals the assessment of muscle fatigue based on EMG monitoring has been rarely used for practical applications, some studies are available. Muscle activity has been monitored in ICU patients during cycling activities, demonstrating the feasibility of EMG assessment and its potential in rehabilitation evaluation [21]. EMG has been used also for assessing diaphragm function in patients who are mechanically ventilated [22]. Moreover, muscle fatigue analysis has been used to compare stroke patients at different stages [23] and in observational studies to compare patients to healthy participants in order to evaluate the feasibility of using EMG in ICU [24]. These studies showed measures that provide general information of muscle activity; however, the assessments are usually not exploited to directly guide the early rehabilitation intervention. To our knowledge, few studies have investigated muscle fatigue using EMG in patients at the bed side, especially with multi-domain approaches. Only some preliminary studies have been conducted for assessing patient rehabilitation with EMG and NIRS [25]. Muscle fatigue can be an indicator of motor recovery for inpatients and it can be used to assist the physical therapist in implementing a patient-tailored exercise prescription [24] and provides feedback on training activity and motor recovery [20]. Patients with hip fracture are usually old and frail subjects that need time after orthopedic surgery to restore their mobility [26]. Therefore, rehabilitation intervention for stimulating muscles also during hospitalization may have beneficial effects on the motor recovery and quality of life of older inpatients [27]. However, the effects of early rehabilitation for inpatients that underwent an orthopedic surgery have been rarely assessed in the literature with muscle fatigue. In this scenario, many studies have proposed a multi-domain assessment for patients with orthopaedic or neurological diseases [6; 28] whereas there are very few instrumental measurements of fatigue in patients with motor disability. In this study, EMG data are coupled with force evaluation for a description of the course of rehabilitation of inpatients, as a preliminary outcome of a multi-domain approach also including Time Domain (TD) NIRS, and force and measurements. Therefore, the purposes of our study were twofold and expand upon recently adopted approaches [24]. First, we assessed the feasibility of collecting EMG on inpatients to measure their recovery in the framework of a multi-domain evaluation, and second, we investigated the variation in muscle fatigue during isometric submaximal contractions comparing the performances of a group of hospitalized patients, before and after a rehabilitation therapy.
# Accuracy of Guided Endodontics in Posterior Teeth
###### Abstract  
The purpose of this ex vivo study was to determine the accuracy of template-based guided endodontics for access cavity preparation and root canal detection in posterior teeth. First, three maxillary and four mandibular models were constructed using 67 premolars and molars, with a total number of 135 main root canals. Cone beam computed tomography (CBCT) and three-dimensional 3D surface scans of each model were performed and matched in order to plan access cavity preparation and design templates virtually. Template-guided access cavity preparation was then performed for each tooth, followed by postoperative CBCT scanning. Deviations between planned and prepared access cavities were measured after superimposition of the pre- and postoperative CBCT scans, and they were analyzed using descriptive and multivariate statistics. All root canals (135/135) were detected utilizing guided endodontics. The mean angle deviation was 1.4 degrees, and the mean deviations at the tip and base of the bur were 0.24-0.31 mm and 0.26-0.29 mm, respectively. This study demonstrated that guided endodontics is an accurate and predictable method for endodontic access cavity preparation in posterior teeth.  
guided endodontics; cone beam computed tomography; access cavity preparation
## 6 Results
### Model Validation  
In order to verify the robustness of the improved BP neural network model, the method in Section 4 was adopted to modify the interval time to 1 h and reacquire nine Yinghong images and moisture contents for verification. After 10 h of moisture loss, the real-time fresh weight and images of 10 groups of tea leaves were recorded. The test results showed that the moisture content of the tea leaves dropped from 73.68% to 60.27%, which was in line with the model for moisture content prediction. The improved BP neural network model, SVR model, and PLS model were used to predict the verification data, and the prediction effect is shown in Figure 11. Compared with the PLS model, the SVR model was closer to the real moisture content, but the fitting degree was still lower than the improved BP neural network model. The prediction indicators of each model are shown in Table 7. Compared with the SVR and PLS models, the improved BP neural network
# Hybrid Edge-Cloud-Based Smart System for Chatter Suppression in Train Wheel Repair
## 1 Introduction
Finally, continous spindle speed variation (CSSV) techniques increase stability by disrupting the regenerative effect [25; 26; 27; 28]. By applying the previous structured engineering thinking and techniques to the industrial use of AI or machine learning, a transformation from collected raw data to enriched information or smart data [29] will be generated. The smart data are responsible for detecting relevant signals and patterns through intelligent digital signal-processing algorithms. Smart data makes sense out of big data, providing actionable information and improving decision making. The present work addressed the implementation of a hybrid edge-cloud smart system in a portal lathe for optimal wheelset reprofiling. The edge computing expert system consisted of a chatter detection and suppression tool, which generated and evaluated the smart data. The process was continuously monitored through embedded sensors which decided when chatter was occurring and, depending on the severity, applied different chatter suppression strategies. Additionally, a cloud computing system obtained the fingerprint of wheel profile machining. Finally, the correct behavior of the system was experimentally validated through industrial real wheel reprofiling tests where the vibration of the process was removed through the smart system and the turning process was optimally performed. Moreover, the data provided from the system were collected, creating a fingerprint for each machining operation in the cloud. The data were remotely accessed and analyzed allowing a forensic examination.
## 2 Experimental Section  
The Sn-5 Zn-0.1 RE, Sn-10 Zn-0.1 RE, Sn-20 Zn-0.1 RE and Sn-30 Zn-0.1 RE filler metals used in this study were prepared by melting tin and zinc slugs of 99.99% purity and a rare earth element (RE) mixture of 99% purity in a vacuum arc furnace under a high-purity argon atmosphere. The mixed rare earth element with the advantage of low cost used in the study combined several characteristics of different rare-earth elements which had a composition by mass of 77.82% La, 16.84% Pr, 3.24% Ce, and 2.10% Nd. To ensure a homogenous composition within the filler metals, the alloys were stirred for 30 min. Each cast ingot was 20 mm in diameter, about 100 g in weight and was rolled into 200 \(\upmu\)m thick foil. The base materials used in the joining experiments were AZ31 magnesium alloy and 6061 aluminum alloy. The compositions of the two base materials are listed in Table 1.  
The materials to be joined were cut into strips of 30 mm \(\times\) 7 mm \(\times\) 3 mm. Prior to soldering, both the bonding and the filler metal surfaces were ground down to grade 1200 with SiC paper. The 200 \(\upmu\)m thick filler metal foils were then placed on base material surfaces to be melted on a heating plate. The Sn-xZn-0.1 RE (x = 5, 10, 20 and 30 wt.%) fillers had the same solidus temperature of about 199 \({}^{\circ}\)C. In order to prevent the oxidation and volatility of zinc, the temperature used for soldering was controlled at about 230 \({}^{\circ}\)C. After the molten solder was agitated for 30 s, the AZ31 magnesium and the 6061 aluminum alloy specimens were overlapped, with an overlap length of 5 mm. The joints were then removed from the heating platform and held firmly in place for 30 seconds to allow cooling to solidify the molten solder.  
Each set of specimens was aged at 150 \({}^{\circ}\)C in air and then cross-sectioned. A standard metallographic procedure was applied to all joints. At least 3 shear tests were carried out for each condition and the average shear strength values were determined. The geometry and dimensions of the soldered specimens subjected to shear testing were shown in Figure 1. Shear tests were carried out using a tensile testing machine (Hung Ta Instrument Co., Ltd. HT-2402, Taizhong, Taiwan). Fractured surfaces and the microstructures of the bonding interfaces were characterized with a field-emission scanning electron microscope (FE-SEM, Philips XL40) coupled with energy dispersive X-ray (EDX) to investigate the bonding mechanism and related interfacial reactions.