esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Dental Model Height on Thermoformed PET-G Aligner Thickness--An In Vitro Micro-CT Study
## 3 Results
### Comparison of Thickness Values at Measurement Locations
Figure 5: Boxplot showing the local thickness values at reference points for group high (H) and narrow (N). The local thickness values were aggregated by tooth measurement location. Highest values were seen at cusp tips, and palatal as well as palatal cervical faces. \begin{table}
\begin{tabular}{c c c c} \hline
**Comparison** & **Est. Difference (SE)** & **T-Ratio** & \(p\)-Value** \\ \hline cusp tip–facial & 0.129 (0.003) & 38.735 & \(<\)0.001 *** \\ cusp tip–facial cervical & 0.150 (0.003) & 44.989 & \(<\)0.001 *** \\ cusp tip–incisal & \(-\)0.055 (0.003) & \(-\)16.118 & \(<\)0.001 *** \\ cusp tip–occlusal & 0.087 (0.003) & 26.071 & \(<\)0.001 *** \\ cusp tip–palatal & 0.040 (0.003) & 12.000 & \(<\)0.001 *** \\ cusp tip–palatal & 0.045 (0.003) & 13.485 & \(<\)0.001 *** \\ cervical & 0.021 (0.003) & 6.254 & \(<\)0.001 *** \\ facial–incisal & \(-\)0.184 (0.003) & \(-\)53.726 & \(<\)0.001 *** \\ facial–occlusal & \(-\)0.042 (0.003) & \(-\)12.664 & \(<\)0.001 *** \\ Group H & facial–palatal & \(-\)0.089 (0.003) & \(-\)26.735 & \(<\)0.001 *** \\ facial–palatal cervical & \(-\)0.084 (0.003) & \(-\)25.250 & \(<\)0.001 *** \\ facial cervical–incisal & \(-\)0.205 (0.003) & \(-\)59.798 & \(<\)0.001 *** \\ facial cervical–oclusal & \(-\)0.063 (0.003) & \(-\)18.918 & \(<\)0.001 *** \\ facial cervical–palatal & \(-\)0.110 (0.003) & \(-\)32.989 & \(<\)0.001 *** \\ facial cervical–palatal & \(-\)0.105 (0.003) & \(-\)31.504 & \(<\)0.001 *** \\ cervical & & & \\ incisal–occlusal & 0.142 (0.003) & 41.431 & \(<\)0.001 *** \\ incisal–palatal & 0.095 (0.003) & 27.768 & \(<\)0.001 *** \\ incisal–palatal cervical & 0.100 (0.003) & 29.211 & \(<\)0.001 *** \\ occlusal–palatal & \(-\)0.047 (0.003) & \(-\)14.071 & \(<\)0.001 *** \\ occlusal–palatal & \(-\)0.042 (0.003) & \(-\)12.586 & \(<\)0.001 *** \\ palatal–palatal cervical & 0.005 (0.003) & 1.485 & 0.752 \\ \hline \end{tabular}
\end{table} 
Table 5: Within-group comparison of local thickness values (mm) for group high (H) and narrow (N) aggregated for measurement location. Comparison was achieved using multiple comparison post hoc test with Tukey-method for \(p\)-value adjustment (family of 7 estimates), respectively. * \(p\)\(<\) 0.05, *** \(p\)\(<\) 0.001.
# Angular Trajectory Design for MR-SPS Using Bezier Shaping Approach
## 1 Introduction
MR-SPS is an SPS scheme located in GEO proposed by CAST, which utilizes a number of mutually independent rotating solar arrays continuously oriented to the sun to receive solar energy and converts it into electric energy that is transmitted to the power transmission bus through a number of independent low-power conductive rotating joints. The transmission medium of the energy is microwave. There was some research on the attitude of the MR-SPS. In [14], the flexible multi-body attitude dynamic model including external disturbances is derived, and a hybrid high/low bandwidth robust controller is proposed to achieve the attitude control of the SPS. Refs. [15; 16] proposed a switched iterative learning controller (ILC) for a flexible SPS to periodically track the earth and the sun. The previous research established the dynamic model of MR-SPS based on analytical mechanics which need complicated derivatives with respect to generalized coordinates. The angular trajectory was not optimized which can cause quite large control torque. In view of the dynamic characteristics of MR-SPS, if the microwave antenna is accurately oriented to the ground and the solar arrays are accurately oriented to the sun when the energy receiving efficiency is 100%, there will be a slew at some time around the vernal and autumnal equinox, which requires large control torques. However, subject to the total weight and degradation of the actuators, the torque generated by the actuators may fail to meet the orientation requirements. The previous attitude planning for SPS did not consider the constraints of dynamics. Aiming at this problem, an MR-SPS dynamic model was established. Using the method of Bezier shaping approach, the angular trajectory is optimized under the condition of limited control torque, which ensures the accurate earth orientation of the antenna and the quasi sun orientation of solar arrays as well as a high energy receiving efficiency. Simulation verifies the feasibility of the optimization method. The rest of this paper is arranged as follows: In Section 2, the model of MR-SPS is presented; the angular trajectory planning based on Bezier shaping approach is described in Section 3; in Section 4, the simulation results are given. The results are summarized in Section 5.
## 2 Overview of Shunt Regulation Design for Satellite Power Supply Systems
When a specific S3R shunt regulation circuit is in the shunt state, the thermistor at the temperature measurement point of that circuit generates heat, causing the corresponding thermistor telemetry temperature to rise. When a specific S3R shunt regulation circuit is in the cut-off state, the corresponding thermistor telemetry temperature decreases. The principal block diagram of the shunt regulation circuit is shown in Figure 2 below. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline
**Shunt Circuit Stages** & **PC1** & **PC2** & **PC3** & **PC4** & **PC5** & **PC6** & **PC7** & **PC8** & **PC9** & **PC10** & **PC11** & **PC12** \\ \hline Shunt Threshold & & & & & & & & & & & \\ Voltage(V) & 12.952 & 13.553 & 14.146 & 14.763 & 15.356 & 15.958 & 16.559 & 17.160 & 17.753 & 18.370 & 19.174 & 20.048 \\ \hline \end{tabular}
\end{table} 
Table 1: Measured data table of shunt circuit stages and corresponding shunt threshold voltages. From the principle of the shunt circuit, it is known that the temperature of the battery array when supplying the load is lower than that when shunting, and shunting generates heat dissipation in the diode.
## 4 Combining Ascending and Descending Sentinel-1 Images to Analyze
### Integration of Ascending and Descending Measurements  
Having ascending and descending velocity vectors in the same geodetic projection the following step is intended to combine them in a common dataset. The high precision of the Sentinel-1 orbit together with the high resolution of the sensor and the high density of the measurements make us consider a priori all the Vlos values equally valuable, including unfavorable geometries. Therefore, because of the scale and precision of the case study, our strategy is to preserve and exploit as many measurements as possible until the end of the post-processing. We start by considering all calculated velocities equally valid, and then we apply the following 3 steps:  
1. Once ascending and descending datasets are in the same projection, we merged them into a common dataset of 8824 values (Figure 8). Then, ascending and descending can be jointly interpreted. However, the new dataset contains artifacts and outliers that need to be inspected.
2. Among the values projected to the slope (Vlos), those with negative cos\(\sigma\) and Vlos have been removed, 25 of them, because this combination causes artifacts (Figure 8). Values of cos\(\sigma\) < 0 entail LOS-slope angles greater than 90\({}^{\circ}\), with this geometry, only positive Vlos (displacements approaching the satellite) can be projected downslope direction. On the contrary, the negative Vlos can be only projected in an upslope direction, that is why we call climbers to these artifacts produced by unfavorable geometries. We draw attention to the fact that by convention displacements approaching the satellite are considered positive.
3. We also removed isolated outliers, 61 of them, mainly located in the active part of the mine. The outliers have been mapped and removed when they conflict with neighboring values (Figures 8 and 9).  
In the end, we got a final clean dataset of geo-projected velocities that combine both ascending and descending values (Figure 8). The proposed integration strategy has the advantage of preserving ascending and descending DInSAR processing results to the maximum. There are no a priori geometric restrictions. Then, artifacts (climbers and outliers) are removed from the final dataset. On the other hand, the downside is that some low-quality data may have escaped and integrated into the final dataset.
## 1 Introduction and Problem Formulation
### Realization of the Concurrent Control System within FPGA  
Finally, the designed Petri net-based concurrent control system is implemented within an FPGA device. This process can be divided into two main steps. Firstly, the HDL code is synthesized logically independent of the internal device structure (logic synthesis), while the second step refers to the physical implementation of the system within the FPGA (translate, map, and place and route process). The whole process is strictly executed according to the vendor rules of the destination FPGA device. In this paper, guidelines for Xilinx devices are applied; however, the technique is similar for different FPGA vendors.  
Figure 7: Searching for shared \(g\) functions: (**a,b**) functions \(f_{1}\) and \(f_{2}\) and corresponding circuit partitions, (**c**) merged multiterminal diagram \(f_{\text{f}}\)/\(g_{2}\), (**d**) column patterns, (**e**) compatibility graph, (**f**) circuit with shared \(g_{1}^{\prime}\).  
Initially, the optimized Verilog code of the designed concurrent control system is logically synthesized. Such a code is achieved as the result of the previous step of the presented design flow (c.f. Section 2.3), where logic equations are decomposed and optimized. Logical synthesis transforms the Verilog code into the low-level (also called \"gate-level\") representation. Such a description is further implemented, that is, mapped, placed, and routed to the particular FPGA internal structure. As the result of the implementation, a programming _bitstream file_ is produced. This file is used for the final configuration (physical implementation) of the FPGA. Since the presented technique is aimed at the reduction in the logic blocks of the programmable device, the most important values are obtained during the logical implementation step. Those results are essential and fundamental in the comparison with the traditional design flow (without applying the proposed functional decomposition method). The utilization of the device resources clearly indicates the usefulness of the introduced technique. Let us explain it in detail by a real-life example of the photovoltaic control system shown in the subsequent section.
## 4 Application
### Application of the ACSN to Optimize this Process
The ACSN's features can additionally be illustrated using the event of a requested change to a delivery date as an example - an event that occurs at least once a shift with 46% of the participants in the study by [PERSON] et al. [PERSON] et al. (2018). The delivery date can either be postponed, which does not necessarily require countermeasures but allows manufacturing capacity to speed up other orders, or preponed (cf. Figure 10). In particular, preponed delivery dates can be challenging to ensure overall delivery reliability despite their realization to satisfy customer requirements. With the ACSN, the production planner and the supply network planner do no longer have to time-consumingly negotiate possible countermeasures and their exact configuration as well as manually adjust the corresponding control values via the line manager. Instead, knowing their dependencies and effects countermeasures and even complex combinations of these can be designed automatically by the ACSN controller. Subsequently, changed control values even for many orders are set automatically via the interface to the MES. Therefore, the ACSN is in summary a suitable complement to forecasts, mitigating the consequences of their deviations to actual demand represented by supply network events effectively and efficiently.
# EDC-Net: Edge Detection Capsule Network for 3D Point Clouds
## 4 Experimental Results  
### Dataset  
We performed a statistical analysis over the ABC [35] and ShapeNet [34] datasets. ABC [35] is a large-scale CAD dataset containing ground-truth annotations for edge points. The ABC ground-truth classification was produced using the vertices each associated with a sharp feature. The sharp feature labels of ABC dataset are based on the distances to the closest feature lines of point clouds. In our experiments, in order to train a point-wise segmentation model, we required hard labels to classify edge and non-edge points. Therefore, we set a threshold (\(\tau\)) as 0.1 (\(\tau=0.1\)); if the distance (\(d\)) of a point to the closest feature lines of point clouds was smaller than the threshold (\(d<\tau\)), then the point was assigned as an edge point; otherwise, (\(d>\tau\)) the point was assigned as a non-edge point. The threshold value (\(\tau\)) is not a sensitive hyperparameter for training processes, since this value is rather a parameter for setting up the dataset and the same value was applied on the training set, validation set and test set of ABC dataset.  
ABC dataset contains 81,920 high resolution annotated point clouds for the sharp feature detection task. We split this dataset into 57,344 point clouds for training (70%), 8192 point clouds for validation (10%) and 16,384 point clouds for testing (20%). Each point cloud provided by the ABC dataset contains 4096 points; in our experiments we sampled 1024 and 2048 points from each point cloud via uniform point sampling and then transferred the annotation model to the point clouds.  
For the weakly-supervised learning process, we utilized ShapeNetCore.v2, which is a subset of the full ShapeNet [34] dataset. ShapeNetCore.v2 contains 51,127 pre-aligned 3D shapes from 55 common object categories categories, which are split into 35,708 (70%) shapes for training, 5158 (10%) shapes for validation and 10,261 (20%) shapes for testing.  
In our experiments, we sampled 1024 and 2048 points from ShapeNet models. We followed the iterative farthest point sampling (FPS) configuration of [7] to sample points uniformly from the point clouds in both the ABC and ShapeNet datasets.
###### Abstract
* With the implemented IBFD system, the resultant bit error rate (BER) can achieve \(10^{-3}\) order under a moderate signal-to-noise ratio (SNR), which validates the effectiveness and practicality of our proposed IBFD system implementation design. The rest of this paper is organized as follows. The system overview is described in Section 2. The SI cancellation method is introduced in Section 3. Experiments are conducted to validate the designed IBFD system in Section 4. Finally, some conclusion remarks are given in Section 5. In addition, all the mathematical notations used in the following sections are summarized in Table 1 for reference.
## 5 Analytical Methods  
### Liquid Chromatography  
The analysis of 4(5)MEI is most often conducted using LC-based methods. To detect 4(5)MEI, a number of researchers have used high-performance liquid chromatography (HPLC) with the UV absorbance set at 215 [17; 18; 21], 217 [19], 220 [20], 233 [23; 81], and 254 nm [22]. However, 4(5)MEI does not have a strong chromophore group, and impurities are absorbed at 210 nm. Therefore, most of the LC methods use mass spectrometry (MS) for detection (Table 2). Reversed-phase [10; 17; 22; 24; 26; 28; 31; 33; 34; 35; 38; 39; 40; 41; 78] and hydrophilic interaction chromatography (HILIC) columns [18; 21; 27; 29; 30] have been commonly used. The high polarity and solubility of 4(5)MEI in water has a negative impact on the separation from other co-extracted substances. This can be overcome by the use of an alkaline mobile phase with the addition of ammonium hydroxide, ammonium formate or ammonium acetate. Several researchers have also employed the Hypercarb column, with a porous graphitic carbon stationary phase, which is specifically applied for the retention of very polar analytes [19; 32; 37].  
The lowest LOQ from the employment of HPLC with UV detection was determined at 5.64 \(\upmu\)g L\({}^{-1}\)[81]. However, lower LOQs have been acquired with MS detection. The lowest LOQ has been observed in a study by [PERSON] et al. [82] with the use of an ion trap-LC  
Figure 3: Derivatization reaction of 4(5)MEI with isobutyl chloroformate.  
MS, which was 0.2 ng mL\({}^{-1}\). Similar LOQs have been obtained by the use of LC-MS/MS (0.3 ug L\({}^{-1}\)) [28], LC-MS in SIM mode (0.4 ng mL\({}^{-1}\)) [17; 18], and LC-QQ-MS (0.5 ng mL\({}^{-1}\)) [82].
# Transmission Power Control in Wireless Sensor Networks Using Fuzzy Adaptive Data Rate
## 3 Adaptive Fuzzy Control Algorithm and Analysis of Power Consumption
### Fuzzy Control Algorithm Design
On the contrary, it attempts to be close to sensitivity for decreasing the power consumption when errors are close to L or VL and error interval is close to H or VH. The relations of inputs and output according to the center of gravity (COG) method for defuzzification are illustrated in Figure 8. To reduce the real-time computational effort, the fuzzy surface illustrated in Figure 8 is transferred to a look up table for on-line implementation. The look up table is organized by an error count that ranges from 0 to 11 and an error interval ranging from 0 to 127, and its size is 1536 bytes. According to the analysis of transmission performance, power consumption, and fuzzy controller, a fuzzy-based data rate and transmission power control algorithm was proposed. The flow chart for the control algorithm is illustrated in Figure 9. After receiving the response packet, it performs statistics and analysis about the packet. Furthermore, all the input parameters are substituted into the fuzzy controller to obtain a crisp value for determining data rate and transmission power. If there is any interferece during the transmission time, it will be reflected in the value of SNR and PER. As a result, these two parameters are used to adjust the input of the fuzzy system. We calculate the differences in RSSI and sensitivities of eight data rates as  
\[RSSI-Sensitivity_{i}\geq Guard,\text{ where }i=0,\,1,\,\ldots,\,7 \tag{7}\]  
Figure 6: Membership functions for antecedent parts: (**a**) error count and (**b**) error interval. The algorithm chooses the highest data rate that satisfies the condition via Equation (7); and it adopts \(TP=TP_{Max}-Difference\) to calculate the transmission power; where \(TP_{Max}\) represents the highest transmission power of the device. Finally, the data rate and transmission power are updated to the configuration table. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Error******Cont** & **VL** & **L** & **M** & **H** & **VH** \\ \hline VL & M & H & VH & VH & VH \\ L & L & M & H & VH & VH \\ M & L & M & M & H & VH \\ H & VL & L & M & H & H \\ VH & VL & VL & L & M & H \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Fuzzy rules table. Figure 8: Relations of error count, error interval, and guard. Figure 7: Membership functions for Guard.
# Remote Speckle-Based Measurements of Backward Brillouin Acoustic Vibrations in Optical Fibers
## 1 Introduction
This property may be significant when we work with long fibers. Moreover, if the field of view of the camera that is capturing the speckle patterns matches to the length of the inspected fiber, then the mapping of the Brillouin frequency along the fiber can be obtained at once without the need to apply time-dependent scanning process. In previous work, we introduced primality results of the detection of Brillouin radial acoustic modes by using this method [23]. In this paper we show for the first time, remote detection of acoustic vibrations in optical fiber resulting from the backward Brillouin effect. Those measurements were performed by using a two pump waves configuration to decrease the frequency of the acoustic vibrations. First, this principle of measurement will be explained in detail. Then we introduce the experimental setup and the received results that show the ability to sense the Brillouin vibrations in an optical fiber by speckles analysis, and finally, we will discuss the meaning of the obtained results.
## 4 Results and Discussion
### PEMD Analysis
#### 4.2.1 Residential PEMD Analysis
**A 75% re-allocation scenario**: 75% of the reduced demand is re-allocated to other hours, and 25% is not used by the consumers, such as lighting, TV, or AC usage that users simply do not use again. Therefore, the summation of all cross elasticities in every column in the PEMD is equal in magnitude to 75% of the self-elasticity at every hour.
## 4 Experiments and Analysis  
To verify the effectiveness of the proposed technology, comprehensive experiments were conducted under different SNRs by adjusting the variance \(\sigma^{2}\) of Gaussian white noise n(t). The experimental layout and scenario are depicted in Figure 15. The experiment system mainly consists of the prototype of the PRBC-PAM sensor and the signal acquisition system, energized by a direct current (DC)-regulated voltage source, and interfaced with a horn antenna for signal radiation into open space. The apparatus for signal monitoring and analysis encompasses a suite of equipment: a double-ridge waveguide horn antenna, a signal generator, a quad-channel downconverter, a wideband digital receiver, an integrated embedded controller, and a display monitor. Within this system, the signal generator furnishes a downconverted reference signal for the local oscillator. After downconversion, the signal undergoes sampling and analysis by the wideband digital receiver. Furthermore, the embedded controller facilitates parameter configuration for each device in the setup.  
We fix the to-be-measured PRBC-PAM signal's frequency as \(\omega=100\) MHz. The code element width of the pseudorandom code is set to \(T_{\rm c}=100\) ns, the repetition period of the pulse signal is fixed as \(T_{\rm m}=T_{\rm c}=100\) ns, and the duty cycle of the pulse signal is set to 20%. A summary of the primary parameters is presented in Table 2.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Carrier frequency & 10 GHz & \(T_{\rm c}\) & 100 ns \\ \(T_{\rm m}\) & 100 ns & SNR & –30 dB-0 dB \\ Pseudorandom code width & 100 ns & Pseudorandom code width & 63/127 \\ Duty cycle & 20\% & Downconverter channels & 4 channels \\ Intermediate frequency & 100 MHz & Sample rate & 3.2 GS/s \\ Bandwidth of the antenna & 750 MHz–18 GHz. & Sample channels & 12 bits \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of main parameters.