esa-sceva/satcom-chunk-collection huggingface dataset:
# A Networked Sensor System for the Analysis of Plot-Scale Hydrology
## 2 Materials and Methods
### Equipment
#### 2.1.1 Initial Equipment (MICAz, IRIS, MDA300)  
The network was built on an existing investment in WSN hardware, manufactured by Crossbow Technology (now MEMSIC, Inc., Andover, MA, USA), which includes the MPR2400 (MICAz) and XM2110 (IRIS) processor and radio boards (i.e., wireless motes) and the MDA300 sensor board. The wireless motes are powered using rechargeable nickel-metal hydride (NiMH) batteries (size AA and D). The batteries, after recharging, are sorted based on their rested voltages to avoid deploying partially charged or uncharged batteries (see Section 3.5.1 for details). The data collection software, including data sampling and packet routing, is developed based on the state-of-the-art open-source WSN platform TinyOS [36].
# Adaptive Point Learning with Uncertainty Quantification to Generate Margin Lines on Prepared Teeth
## 3 Methods
### Training Details  
Supervised training was used to train the model. The training set contained 913 samples augmented 20 times with a 80/20 split for training and validation. The test set included 134 samples. Our data, i.e., die scans, comes from 20 different dental clinics. The margin line extraction and crown design were achieved by two different dental technicians. To augment the training set, we used 20 different randomly generated augmentation matrices per case to change orientation, position (translation over \(x\), \(y\) or \(z\) axis), and scale (stretch or expand over the axis it is applied to). The augmentation ranges were [\(-\)180, 180] degrees for rotation over the \(z\) axis, [\(-\)1, 1] for translation for all axes, and [0.9, 1.1] scaling for all axes. Because our dataset is orientated towards the \(z\) axis, we limited rotations around the \(x\) and \(y\) axes to [\(-\)30, 30] degrees to avoid flipping the die completely. Figure 4 shows an example of a training case augmented 20 times.  
A batch size of 8 was used to train the AdaPoinTr model with an output resolution of 1536 points. The AdamW optimizer was used with a weight decay of 0.0005 and a learning rate of 0.0001 combined with a lambda scheduler triggered every 21 epochs with a decay coefficient of 0.9 and a minimum value for learning rate of 0.000002. We used a batch normalization scheduler that triggers every 21 steps as well with decay coefficient of 0.5 on the moving average, a high momentum of 0.9, and the lowest value possible for a moving average as 0.01 of its initial value.  
Training was conducted using a cluster with GPU NVIDIA A100-SXM4-40 GB, 32 GB of RAM allocated and six CPU cores over three days (no limit on epochs). The loss converged after approximately 60 epochs for training and 70 epochs for validation (Figure 11). The  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted Confidence Metric**} \\
**Confusion Matrix CD versus Confidence Metric** & **Predicted Positive** & **Predicted Negative** \\  & **(Outliers \textless{} 0.65\%)** & **(Outliers \textgreater{} 0.65\%)** \\ \hline \hline \multirow{2}{*}{Actual CD} & Positive (CD \textless{} 0.2) & TP & FN \\  & Negative (CD \(\geqslant\) 0.2) & FP & TN \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix for correlations of CD with the proposed confidence metric.  
Figure 4: One case augmented 20 times.  
model of the last epoch was chosen as the best model to avoid overfitting. For the loss, we used a linear combination of CD and normalized infoCD. The value of infoCD was multiplied by \(10^{-4}\). The combined loss was used to adjust the gradients for backpropagation during the training. Five models were trained and validation set was varied among them.
## 2 Materials and Methods
### Electrochemical Impedance Spectroscopy (EIS)  
Electrochemical impedance spectroscopy (EIS), with an in-plane cell configuration, was used to measure the proton conductivities of the prepared PEMs. For the in-plane measurements, rectangular shape samples were cut from the films and placed in a commercial four-electrode cell (BT-112, Scribner Associates Inc.) fitted between the anode and cathode flow field of a fuel cell test hardware (850C, Scribner Associates Inc.). Impedance response of the cell was recorded at OCV with an oscillating potential of about 10 mV in the frequency range 1 Hz-1 MHz by using a PGSTAT 30 potentiostat/galvanostat/FRA. The data were analyzed using Metrohm Autolab NOVA software and the electrolyte resistance was extracted as the intercept of the high frequency signal in the Nyquist plot. A humidification system (Fuel Cells Technologies, Inc., Albuquerque, NM, USA) directly connected to the cell allowed for investigating the proton conductivity under variable T and RH.
## 5 Conclusions  
In conclusion, our findings align with the existing literature, as no significant differences were observed between (VLPs) and (K-wires) in medium- and long-term functional outcomes, including range of motion, grip strength, and patient-reported outcomes, at a 5-year follow-up in patients aged 18-85 years of both sexes. However, VLPs demonstrated superior anatomical restoration, particularly in maintaining polar tilt and radial height, and provided enhanced fracture stability. This contributed to faster recovery of wrist functionality, shorter immobilization times, and earlier return to work, especially in cases of complex or intra-articular fractures. In contrast, K-wires remain a valuable option for simple extra-articular fractures or in patients with comorbidities where a less invasive approach is preferred. Consequently, we recommend that the choice of treatment be individualized, taking into account the type and complexity of the fracture, associated soft tissue injuries, bone quality (e.g., osteoporortic versus non-osteoporotic fractures), patient activity level, and overall health status to optimize outcomes.  
Conceptualization, G.R., P.P. and G.M.; Methodology, G.R., P.P. and G.M.; Software, V.D.L., A.T. and G.F.; Validation, V.D.L., A.T., G.F. and P.F.; Resources, F.L. and F.D.M. Writing--original draft, G.R., P.P. and G.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} MIPO & Minimally Invasive Plate Osteosynthesis \\ ORIF & Open Reduction and Internal Fixation \\ DRF & Distal Radius Fracture \\ PKW & Percutaneous Kirschner Wire \\ VLP & Polar Locking Plate \\ DASH & Disabilities of the Arm, Shoulder, and Hand \\ PRWE & Patient-Rated Wrist Evaluation \\ MAYO & Mayo Wrist Score \\ ROM & Range of Motion \\ CRPS & Complex Regional Pain Syndrome \\ FPL & Flexor Pollicis Longus \\ EPL & Extensor Pollicis Longus \\ IRB & Institutional Review Board \\ RCT & Randomized Controlled Trial \\ SD & Standard Deviation \\ \end{tabular}
# UAV-Cooperative Penetration Dynamic-Tracking Interceptor Method Based on DDPG
## 4 Experimental-Simulation Analysis
### Simulation Results and Analysis  
After 10,000 trainings, the simulation results are shown in Figure 7.  
Two UAVs can pass enough to bypass the dynamic interceptor and finally reach the target area at the same time, and the time to reach the target area will not exceed the simulation step (1 s).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameters** & **Value** \\ \hline \multirow{4}{*}{**UAV**} & Linear velocity \(v\) & 20 m/s \\ \cline{2-3}  & Angular velocity \(\omega\) & \(-0.5\) rad/s \(\sim\) 0.5 rad/s \\ \cline{2-3}  & Initial azimuth \(\theta\) & \(\frac{\pi}{4}\) \\ \cline{2-3}  & Initial position \((x,y)\) & \(x_{A},y_{A}=[50\) m, 50 m] \\  & \(x_{B},y_{B}=[50\) m, 500 m] \\ \hline \multirow{4}{*}{**Interceptor**} & Linear velocity \(v\) & 22 m/s \\ \cline{2-3}  & Initial azimuth \(\omega\) & \(\frac{\pi}{4}\) \\ \cline{2-3}  & Angular velocity \(\theta\) & \(-0.5\) rad/s\(\sim\) 0.5 rad/s \\ \cline{2-3}  & Initial position \((x,y)\) & Same as target \\ \hline
**Target** & Position \((x,y)\) & \(x_{t},y_{t}=[850\) m, 850 m] \\ \hline \multicolumn{3}{c}{Simulation step d\(t\)} & 1 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters.  
It can be seen from the figure that the UAVs carried out a wide-angle exercise, the interceptor's line of sight to the UAVs continued to increase, and the interception failed.  
Figure 10 shows the distance curve between the two UAVs and the interceptor. The black line at the bottom of the figure represents the distance captured by the interceptor.  
It can be seen from the figure that the minimum distance between the two UAVs and the interceptors is above the black line, that is, they are not captured by the interceptors.  
Figure 11 is the distance between the two UAVs and the target point. In the figure, there is a certain gap between the initial distance between the two UAVs and the target area, which is about 10 m.  
Figure 10: The distance between the UAV and the interceptor.  
Figure 9: The parameter change curve of UAV B and Interceptor B during training.  
It can be seen from the figure that the final distance difference between the two UAVs and the target point is almost zero. At the beginning of the movement, the distance between the two UAVs and the target point is about 3 m. UAV A will deliberately go around to ensure that it is moving. Finally, the target point can be reached at the same time.  
The simulation experiment shows that the DDPG algorithm can complete the cooperative penetration of the dynamic-tracking interceptor through the training of the UAV cluster, which meets the high-performance requirements of the multi-UAV system and is a method with huge application potential.
_Applied_  
_sciences_  
_Article_  
**An Adaptive Approach for Multi-National Vehicle License Plate Recognition Using Multi-Level Deep Features and Foreground Polarity Detection Model**  
**[PERSON] 1,2,*, [PERSON] 1,*, [PERSON] 2,3** and  
**[PERSON] 4**  
Received: 14 February 2020; Accepted: 17 March 2020; Published: 22 March 2020  
**Featured Application: We present an adaptive framework for the recognition of multinational vehicles license plates. To make it generalized, this research does not require any prior knowledge of license plate layout and, furthermore, training data is not used from all of the targeted countries. These properties make this approach more suitable in order to get the registration identity of multinational vehicles.**  
**Abstract:** License plate recognition system (LPR) plays a vital role in intelligent transport systems to build up smart environments. Numerous country specific methods have been proposed successfully for an LPR system, but there is a need to find a generalized solution that is independent of license plate layout. The proposed architecture is comprised of two important LPR stages: (i) License plate character segmentation (LPCS) and (ii) License plate character recognition (LPCR). A foreground polarity detection model is proposed by using a Red-Green-Blue (RGB) channel-based color map in order to segment and recognize the LP characters effectively at both LPCS and LPCR stages respectively. Further, a multi-channel CNN framework with layer aggregation module is proposed to extract deep features, and support vector machine is used to produce target labels. Multi-channel processing with merged features from different-level convolutional layers makes output feature map more expressive. Experimental results show that the proposed method is capable of achieving high recognition rate for multinational vehicles license plates under various illumination conditions.  
**Keywords:** Intelligent transport system; license plate recognition; multinational vehicles; adaptive thresholding; deep features
# Throughput Prediction of 5G Network Based on Trace Similarity for Adaptive Video
## 2 Related Works
Extensive research has been conducted on managing concept drift, including using an ensemble learning approach, concept-drift-aware federated averaging, and model updating mechanisms. For a more detailed review, see [35]. In this work, we developed multiple forecasting models using the ensemble approach to generate the final forecasts. This technique should improve the prediction accuracy, as multiple dedicated models do not need to repeatedly reformulate their decision boundaries, in contrast to a single global model. To the best of our knowledge, no research has considered concept drift when predicting 5G traffic. This idea has only been considered for classification problems in a few studies, with the latest being [36]. As concept drift can be applied for general time series prediction [37], we were motivated to explore this technique for network traffic prediction.
# Cross-Project Defect Prediction Based on Domain Adaptation and LSTM Optimization
###### Abstract  
Cross-project defect prediction (CPDP) aims to predict software defects in a target project domain by leveraging information from different source project domains, allowing testers to identify defective modules quickly. However, CPDP models often underperform due to different data distributions between source and target domains, class imbalances, and the presence of noisy and irrelevant instances in both source and target projects. Additionally, standard features often fail to capture sufficient semantic and contextual information from the source project, leading to poor prediction performance in the target project. To address these challenges, this research proposes Smote Correlation and Attention Gated recurrent unit based Long Short-Term Memory optimization (SCAG-LSTM), which first employs a novel hybrid technique that extends the synthetic minority oversampling technique (SMOTE) with edited nearest neighbors (ENN) to rebalance class distributions and mitigate the issues caused by noisy and irrelevant instances in both source and target domains. Furthermore, correlation-based feature selection (CFS) with best-first search (BFS) is utilized to identify and select the most important features, aiming to reduce the differences in data distribution among projects. Additionally, SCAG-LSTM integrates bidirectional gated recurrent unit (Bi-GRU) and bidirectional long short-term memory (Bi-LSTM) networks to enhance the effectiveness of the long short-term memory (LSTM) model. These components efficiently capture semantic and contextual information as well as dependencies within the data, leading to more accurate predictions. Moreover, an attention mechanism is incorporated into the model to focus on key features, further improving prediction performance. Experiments are conducted on apache_lucene, equinox, eclipse_jdt_core, eclipse_pde_ui, and mlylyn (AEEEM) and predictor models in software engineering (PROMISE) datasets and compared with active learning-based method (ALTRA), multi-source-based cross-project defect prediction method (MSCPDP), the two-phase feature importance amplification method (TFIA) on AEEEM and the two-phase transfer learning method (TPTL), domain adaptive kernel twin support vector machines method (DA-KTSVMO), and generative adversarial long-short term memory neural networks method (GB-CPDP) on PROMISE datasets. The results demonstrate that the proposed SCAG-LSTM model enhances the baseline models by 33.03%, 29.15% and 1.48% in terms of FI-measure and by 16.32%, 34.31% and 3.59% in terms of Area Under the Curve (_AUC_) on the AEEEM dataset, while on the PROMISE dataset it enhances the baseline models' FI-measure by 42.60%, 32.00% and 25.10% and _AUC_ by 34.90%, 27.80% and 12.96%. These findings suggest that the proposed model exhibits strong predictive performance.  
cross project defect prediction; domain adaptation; bidirectional-gated recurrent units; bidirectional long short-term memory; attention mechanism +
Footnote †: journal: _Algorithms_
# Teleseismic Tomography for Imaging the Upper Mantle Beneth Northeast China
###### Abstract  
Tomographic imaging technology is a geophysical inversion method. According to the ray scanning, this method carries on the inversion calculation to the obtained information, and reconstructs the image of the parameter distribution rule of elastic wave and electromagnetic wave in the measured range, so as to delineate the structure of the geological body. In this paper, teleseismic tomography is applied by using seismic travel time data to constrain layered crustal structure where Fast Marching Methods (FMM) and the subspace method are considered as forward and inverse methods, respectively. Based on the travel time data picked up from seismic waveform data in the study region, the P-wave velocity structure beneath Northeast China down to 750 km is obtained. It can be seen that there are low-velocity anomalies penetrating the mantle transition zone under the Changbai volcano group, Jingpohu Volcano, and Arshan Volcano, and these low-velocity anomalies extend to the shallow part. In this paper, it is suggested that the Cenozoic volcanoes in Northeast China were heated by the heat source provided by the dehydration of the subducted Pacific plate and the upwelling of geothermal matter in the lower mantle. The low-velocity anomaly in the north Songliao basin does not penetrate the mantle transition zone, which may be related to mantle convection and basin delamination. According to the low-velocity anomalies widely distributed in the upper mantle and the low-velocity bodies passing through the mantle transition zone beneath the volcanoes, this study suggests that the Cenozoic volcanoes in Northeast China are kindred and have a common formation mechanism.  
teleseismic tomography; Fast Marching Methods; subspace method; Northeast China 2020 10 4557 10.3390/app10134557
# Enhanced Design of a Soft Thin-Film Vibrotactile Actuator Based on PVC Gel
## 3 A New Design of an Improved Soft Thin-Film Vibrotactile Actuator  
As we mentioned above, the conventional PVC gel based vibrotactile actuator did not fully reflect the characteristics of the wave-shaped PVC gel. In this section, we present a new improved soft vibrotactile actuator that reflects the characteristics of the wave-shaped PVC gel. Figure 4 shows the fabrication process of the improved soft vibrotactile actuator based on PVC gel. First, we prepared a flexible polyimide film (Kepton) having 20 mm (H) \(\times\) 50 mm (W) \(\times\) 200 \(\upmu\)m (T), which has heat-resisting and insulating properties, flexibility, and high durability [18]. After that, we prepared an aluminum mask for sputtering electrodes onto the polyimide film. This aluminum mask, whose size is 20 (H) \(\times\) 50 (W) \(\times\) 1 mm (T), has 20 \(\times\) 20-arrayed rectangular holes in order to position all rectangular holes in the center of the corresponding ridges of the wave-shaped PVC gel. The fabricated aluminum mask was loaded onto the polyimide film, and then a 100 nm gold was sputtered onto the polyimide film and the mask. By removing the mask, the patterned electrode can be applied onto the polyimide film. We then carried out a laser cutting process for fabricating plate springs at both ends of the polyimide film (Figure 4e). These two plate springs were considered for helping the restoration of the actuator when the applied input voltage was removed. We named the polyimide with electrodes in Figure 4e a gold-sputtered polyimide film. A wave-shaped PVC gel was loaded onto the fabricated gold-sputtered polyimide film. Next, the wave-shaped PVC gel was covered by another gold-sputtered polyimide film as shown in Figure 4g. By joining two gold-sputtered polyimide films, we finally obtained a soft vibrotactile actuator (Figure 4h,i). According to the laser cutting process, the actuation part can easily move in an up and down direction with voltage inputs (Figure 5a,b). Due to this structure, the electric energy could be focused onto the ridges of the wave-shaped PVC gel.  
Figure 4: Fabrication process of the newly designed soft thin-film vibrotactile actuator based on wave-shaped PVC gel.  
Figure 3: Deformation ratio of the valleys and the ridges in the previous design. (**a**) Configuration without voltage input (Initial configuration); and (**b**) configuration under voltage input.
## 3 Results
### FQ Persistence Levels Depend on the Type of Starvation and FQ Used  
Persistence assays were conducted with LEVO or MOXI on cultures incubated in starvation media for 16 h and a growing M9 control with the same approximate OD\({}_{600}\) at the time of treatment. LEVO is the active isomer of ofloxacin (OFL), which is widely used in persistence studies [54]; and MOXI is a more recent FQ that has been identified as a more potent inhibitor of topoisomerase IV [55]. For both LEVO and MOXI, all nutrient  
Figure 1: Establishment of culturing conditions for overnight starvation to individual nutrients. (**A**) Exponentially-growing wild-type (WT) cultures were inoculated into complete media or media deprived of carbon (C), nitrogen (N), phosphate (P), or magnesium (Mg\({}^{2+}\)) at an optical density at 600 nm (OD\({}_{600}\)) of 0.02. After inoculation, OD\({}_{600}\) was measured at indicated time points, and all cultures in the nutrient-starved conditions exhibited growth inhibition. (**B**) After 16 h in nutrient-starved environments (0–16 h), the missing nutrients were supplied to cultures and incubated for an additional 16 h (16–32 h). Addition of the missing nutrient resulted in growth of all cultures, demonstrating single-nutrient limited growth. (**C**) Controls where autoclaved water was added in place of the missing nutrient and growth was monitored for an additional 16 h (16–32 h). Data points indicate the means of three biological replicates, and the error bars indicate the standard errors of the means. A schematic of the culturing procedure with respect to different assays is provided in Supplementary Figure S1. (**A**) One-way ANOVA with post-hoc Tukey tests were conducted to assess significance among the different treatment conditions. * Indicates significance with \(p\) < 0.05 with respect to the other treatment conditions at the same time point. (**B**,**C**) _t_-tests were conducted for significance analysis. * Indicates significance with \(p\) < 0.05 with respect to \(t\) = 0 h for the same treatment condition.  
starved cultures exhibited significantly higher persister levels compared to cultures that were exponentially growing in M9 media (Figure 2). Under LEVO treatment, N-starved cultures exhibited the highest level of persistence with survival approaching 50%, which was significantly higher than C- and Mg\({}^{2+}\)-starved persister levels (Figure 2A). With MOXI, P-starvation exhibited the highest level of persistence with survival approaching 25%, which was significantly higher than C-, N-, and Mg\({}^{2+}\)-starved persister levels (Figure 2B). Under both FQ treatments, Mg\({}^{2+}\) and C starvation gave similar levels of persistence. The observed differences in persistence among differently starved cultures suggest that persister levels depend on not just the type of starvation, but also the FQ used. Nutrient-starved cultures treated with water instead of FQ exhibited complete survival over the course of the experiments (Supplementary Figure S3).