esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Verification of Control Algorithm
### Closed-Loop Experiment of Fixed Speed Signal  
For the closed-loop experiment of fixed speed signal, the fixed speed signal of 0.1 mrad/s was input into the pitch axis control system of the ground-based laser communication servo turntable. Under the control of the proposed chatter-free sliding mode controller, the Ref. [22] chatter-reduced sliding mode controller, or the incremental digital PID controller, the motor operation was compared, as shown in Figure 9.  
Comparison reveals that all three algorithms can guarantee operation of the motor at the target speed of 0.1 mrad/s. However, there are obvious nonlinear noises in the operation curve of the digital PID controller, the maximum absolute speed error of the PID control accuracy is 37.3 \(\upmu\)rad/s, with an absolute error root of mean square (RMS) of 8.3 \(\upmu\)rad/s and an average absolute error of 6.5 \(\upmu\)rad/s. The nonlinear noise was obviously suppressed for both the proposed method and the method described in Ref. [22]. The maximum absolute speed error of the proposed SMC is 6.9 \(\upmu\)rad/s, the absolute error RMS is 1.5 \(\upmu\)rad/s and the average absolute error is 1.2 \(\upmu\)rad/s. These values are much higher than those obtained for classical PID algorithm, and are slightly smaller than that obtained for the Ref. [22] algorithm, which exhibited a maximum error of 8.3 \(\upmu\)rad/s, an average error of 2.3 \(\upmu\)rad/s, and RMS of 2.9 \(\upmu\)rad/s. Therefore, highest control accuracy was obtained for the proposed chattering-free algorithm, with slightly reduced the performance of the chatter-reduced algorithm described in Ref. [22] because of chattering.
## 8 Accuracy and Error Measures for Predictive Models
### Error and Accuracy Measures of Predictive Models  
Many error and accuracy measures were developed to assess the accuracy of predictive models for numerical data [65; 109; 110]. Some of these error and accuracy measures were assessed and their limitations were previously discussed [3; 18]. Of these error and accuracy measures, VECv (i.e., variance explained by a predictive model based on cross-validation) measures how accurate the predictive model is and was proven to be independent of unit, scale, data mean, and variance [18; 111], while root mean squared error (RMSE) measures how wrong the predictive model and the resultant predictionscan be. Therefore, VEcv and RMSE are recommended for numerical predictions. Legates and McCabe's E1 (E1) was recommended for numerical data as well [111]. The commonly used measure, \(r\) or \(r^{2}\), is not recommended because it is an incorrect measure of predictive accuracy [111].  
For categorical data, correct classification rate (CCR), kappa (kappa), sensitivity (sens), specificity (spec), and true skill statistic (TSS) are often recommended [112; 113]. RMSE is also used for presence and absence data [114]. One commonly used measure, area under the curve (AUC) (or receiver operating characteristics (ROC)), is not recommended for reasons previously highlighted [112; 115].
## 3 Economic Motivations, Applications and Requirements for VSNs
Positioning represents another important aspect, since actual GPS accuracy could not be sufficient when automated connected vehicles will be on the road. In fact, from Table 1, it can be observed that typical applications require a position accuracy of about 30 cm, whereas the actual GPS system allows an accuracy around 7 m (1-1.5 m of accuracy in the best cases) [21]. Through the C-ITS Platform, the EU also identified a list of services as candidates for early deployment. The so-called Day 1 services are considered fully mature and relevant for realizing the societal benefits in terms of road safety and reduced emissions. Day 1.5 services follow closely, up to longer term Day 4 services [18]. In Day 1 services, we can summarize applications such as hazardous location notifications, emergency brake light, probe vehicle data, whereas in Day 1.5, connected and cooperative navigation, vulnerable road user protection, etc. In Figure 2, three main steps provided by an always higher level of communication and penetration are sketched up to Day 4 services. Figure 1: Achieved profit in Euro for different scenarios in a 10-year projection. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Application** & **Communication Type Rate (Hz)** & **Update** & **End-To-End Latency (ms)** & **Data Rate (kb/s)** & **Positioning (cm)** \\ \hline Emergency electronic & & 1–10 & 100 & 1–10 & - \\ brake lights & & 10 & 20 & 20–25,000 & \textless{}0–100 \\ Pre-crash sensing & & 50 & 50 & 20–25,000 & 30 \\ Pre-collision brake assist & & 50 & 50 & 20–25,000 & 30 \\ Emergency vehicle warning & & 10 & 100 & \textgreater{}100 & - \\ Overtaking vehicle warning & & 2-10 & 100 & 10–5000 & 30 \\ Lane change assistance & Periodic broadcast & 2–10 & 100 & 10–5000 & 30 \\ Cooperative state reduction & & 2 & 100 & \textgreater{}10 & - \\ Merging traffic turn & & 2-10 & 100 & 10–5000 & - \\ collision risk warning & & 10 & 100 & \textgreater{}10 & 30 \\ Cooperative collision warning & & 1–10 & 100 & 10–2000 & \textless{}100 \\ Cooperative navigation & & 1–10 & 100 & 10–2000 & \textless{}100 \\ Adaptive cruise control & & 1–10 & 100 & 10–2000 & \textless{}100 \\ Highway platooning & & \textgreater{}2 & \textgreater{}10 & \textgreater{}10 & 30 \\ \hline Emergency or slow vehicle warning & & 10 & 100 & \textgreater{}10 & - \\ Wrong way driving warning & & \textgreater{}1 \& \textgreater{}10 & 100 & 1–10 & \textless{}100 \\ Stationary vehicle warning & & \textgreater{}1 \& \textgreater{}10 & 100 & 1–10 & \textless{}500 \\ Traffic condition warning & & 1–10 & 100 & 1–10 & \textless{}500 \\ Intersection warning & & 10 & 100 & 10 & \textless{}100 \\ Post-crash warning & & 10 & 100 & \textgreater{}10 & \textgreater{}100 \\ Cooperative adaptive cruise control & & 1–10 & 100–300 & 10–2000 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Example requirements for typical V2V-related applications.
## 1 Introduction
When the particle sizes were 140 and 200 mesh, the prepared cementitious materials had higher 28 day compressive strength than others. However, the effect of powdered SAP on the early-age strength of mortar was not mentioned. Researchers have noted that the addition of SAP reduces the mortar porosity via microCT [20]. [PERSON] et al. [21] pointed out that the types of SAP, the mixing amount and the mixing method all affect free water and defects in mortar, which in turn, affects mechanical properties of mortar. When the SAP was added in the form of dry powder (with the same SAP-adding amount), its adverse effect on early-age strength was more significant. An early-strength accelerant can significantly improve the early setting and hardening of concrete and rapidly improve early mechanical properties of concrete [22]. There are three major categories of traditional early-age strength accelerants: organic small molecules, inorganic salts, and composite early-age strength accelerants [23]. The effects of different early-age strength agents on cement-based materials are different, and they can be concluded in four aspects, namely providing crystalline nuclei for cement hydration, increasing pH value of the cement paste, speeding up the dissolving of cement components and forming complexes with hydration products of Ca(OH)\({}_{2}\)[24]. Each of them can speed up early cement hydration reactions and improve early mechanical properties of cementitious materials. Traditional alkanolamines are TEA, TIPA and so on. Some studies [25] have shown that TEA can enhance the early strength of mortar, and TIPA can enhance the later strength of mortar. When alkanolamine is used as a chemical additive, it chemically enhances the mineral hydration in the cement; thus, it is considered to be added to the mortar in combination with SAP to improve early-age strength. The purpose of this paper is to find a way to inhibit the decrease in early-age strength of internal curing of mortar without affecting the crack resistance of SAP itself. In order to achieve this requirement, SAP as modified using different types of alkanolamines to form composite early-age strengthen internal curing agents. Their effects on the early-age strength and drying shrinkage of the mortar were also observed. The early-age strength can be improved by adding early-strength components to promote cement hydration.
## 2 Materials and Methods
### Characteristics Investigated for the Selected Materials
#### 2.2.2 HPLC Determination of Residual Monomers  
Samples Preparation  
After 60 days, the storage medium (distilled water/TISAB III) in which the samples were immersed in order to perform the fluoride release assessment, was frozen and then lyophilized in a Model Alpha 1-4 LDPLUS until the liquid was completely removed. The residual monomers from restoration composites were determined from the lyophilized storage medium (water) and the residue was re-suspended in 0.6 mL of acetonitrile, filtered in 0.22 \(\upmu\)m PTFE filters and analyzed by HPLC.  
Instrumentation and Method  
The analyzes were performed on a Jasco HPLC chromatograph (Jasco International Co., Ltd., Tokyo, Japan) that was equipped with an intelligent pump PU-980, a ternary gradient unit LG-980-02, an intelligent column thermostat CO-2060 Plus, an intelligent detector UV-975, and an injection valve that was equipped with a 20 \(\upmu\)L sample loop (Rheodyne, Thermo Fischer Scientific, Waltham, MA, USA). The system was controlled and the experimental data analyzed with the ChromPass software (version v1.7, Jasco International Co., Ltd., Tokyo, Japan). Separation was performed on a Lichrosorb RP-C18 column (25 \(\times\) 0.46 cm) at a column temperature of 21 \({}^{\circ}\)C. The mobile phase was a mixture of acetonitrile (A, HPLC grade) and water (Milipore ultrapure water) and a gradient was applied according to the following method: 0-15 min, linear gradient 50-80% A; 15-25 min, linear gradient 80-50% A. The flow rate was 0.9 mL/min and the injected volume was always 20 \(\upmu\)L. UV detection was performed at 204 nm to monitor the elution of all analytes (Bis-GMA and TEGDMA) because it shows significant absorption at this wavelength. Stock solutions of Bis-GMA and TEGDMA reference standards (1 mg/mL) were prepared in acetonitrile and stored at 4 \({}^{\circ}\)C. The linearity of the response to the analytes was established with four concentration levels and the regression factors R2 were higher than 0.998. All of the analyses were perform in triplicates for both the standard and the samples.  
The residual monomer amount has been determined from the HPLC chromatograms of the extracts and it was calculated as percentage related to the initial amount of monomer in the sample and the weight of the sample, respectively [26].
## 2 Principle of the Miniature Michelson Interferometer (MMI)  
Figure 1a shows the configuration of a nanopositioning \(y\)-stage. The linear bearing between the linear slide and the moving table is to allow the installation of another \(x\)-stage to form a coplanar stage. For the sake of clarity, only the displacement feedback control along the \(y\)-direction is plotted. The ultrasonic motor is employed to actuate the linear slide through a friction force on the ceramic plate. Thus, the moving table can be moved by the linear slide in push-pull mode. The three-degree-of-freedom laser measurement (3 DOFLM) system, consisting of a miniature Michelson interferometer (MMI) kit for displacement measurement and feedback, a wavelength correction kit for the MMI kit, and a miniature autocollimator kit for angular error measurement, is mounted onto the baseplate of the stage and on the opposite side of the ultrasonic motor. The laser beam of the 3 DOFLM system is in line with the moving axis at the center of the stage. With this kind of arrangement, the Abbe principle is totally observed. The optical design of the 3 DOFLM system is shown in Figure 1b. A low-cost laser diode is employed as the laser source. The miniature autocollimator kit is employed to detect the pitch and yaw angular errors for the purpose of Abbe error compensation of the stage. The displacement of the stage detected by the MMI kit is used as a feedback signal to the ultrasonic motor. The laser diode wavelength of the MMI kit is corrected in real time by the wavelength corrector kit. The detailed principles of angular error measurement and displacement measurement with and without wavelength correction are introduced below.  
Figure 1: (**a**) Top view of configuration of the nanopositioning \(y\)-stage; (**b**) optical design of the three-degree-of-freedom laser measurement (3 DOFLM) system (_xy_ view).
# Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi
## 3 Experiment
### Experimental Results  
In this experiment, to verify the performance, five comparison networks were introduced. The first was used to confirm the accuracy of each method and the second was used to confirm the testing time of each method.
# A New Approach to Detect Hand-Drawn Dashed Lines in Engineering Sketches
## 3 Vectorization Overview  
Vectorization is a complex task, which commonly requires different stages aimed at producing 2D line-drawings, made of high semantic geometric elements, and intended as intermediate results to output 3D CAD models. The main stage involves fitting strokes into lines, i.e., skipping low-semantic-level geometric elements (typical output in raster to vector conversions) to identify the high-semantic-level primitives depicted by the strokes of a sketch. However, some sketch refinement is required:  
* Some users tend to redraw the lines of the sketch by overlapping different strokes. This \"sweeping the lines\" produces overtraced strokes, which require _grouping overtraces_, to identify each single stroke/line, which was intended to represent.
* Some users sketch non-solid lines that are drawn as sets of consecutive strokes (for instance, by drawing hidden edges as dashed lines). _Grouping non-solid strokes_ facilitates vectorization, while providing high-level information that may ease the subsequent reconstruction.
* There is no univocal relationship between strokes and lines, since some users do not pen-up when a stroke is created just to pen-down to start the following stroke that begins exactly where the previous one ends. Instead, they sketch a single stroke that depicts multiple lines. This \"continuous\" stroke produces poly-strokes that must be segmented by the suitable _corner finding_ module, to bring up the different geometric primitives it contains.  
In our approach, the first step of vectorization is to calculate fits of all the strokes. The second step involves grouping the overtraced strokes. A set of strokes is replaced by a single stroke that encompasses them all. The fit of the resulting stroke is calculated and the strokes that have been grouped are removed from the sketch. The third step involves grouping non-solid strokes. The strokes of all the dashes are replaced by one single stroke that encompasses all of them (and the fit of this resulting stroke is calculated). The strokes that do not fit well with either straight lines or ellipse arcs are segmented, and their segments are fitted, thus obtaining an initial vectorized drawing, which is later refined.  
From the overall vectorization process, only the stage of grouping non-solid lines is studied here. Readers interested in fitting are recommended to explore the works by [PERSON] et al. [21; 22], [PERSON] et al. [23], and [PERSON] et al. [24], which introduced overtrace detection and clustering. [PERSON] et al. have also contributed with interesting advances [25; 26; 27] and the work by [PERSON] and [PERSON] [28] is useful to understand the problem of detecting corners.
# RM-Line: A Ray-Model-Based Straight-Line Extraction Method for the Grid Map of Mobile Robot
## 2 Materials and Methods
### The Ray Model  
In the above sections, edge points were extracted, and active points were marked. Next, we propose a ray model, combined with active points, to extract straight lines from the edge map.  
The ray model is shown in Figure 4.  
As shown in Figure 4a, \(p_{0}(x_{0},y_{0})\) is taken as the starting point and \(n\times\Delta\theta\) is taken as the direction to get the ray \(ray_{n0}\) and the reverse ray \(ray_{n1}\), where \(\Delta\theta\) is the set angle interval, and \(0\leq n\leq\frac{180}{\Delta\theta}\). As shown in Figure 4b, a series of scoring points are set at intervals of \(\Delta\)s on \(ray_{n0}\), denoted as \(\{p_{n00},\ldots,p_{n0k}\}\). Similarly, scoring points are set on \(ray_{n1}\), denoted as \(\{p_{n11},\ldots,p_{n1m}\}\), where \(\Delta\)s is the set distance interval, \(k\geq 0\), and \(m\geq 0\).  
As shown in Figure 4b, the target grid \(p_{t}\) is used as the center of the ray model. For \(ray_{n0}\), its score is calculated starting from the scoring point \(p_{n00}\). If the grid corresponding to the scoring point \(p_{n0k}\) is an OBSTACLE grid, the score of \(ray_{n0}\) is increased by 1, and the calculation continues. If it is not an OBSTACLE grid, the calculation is stopped, and the current scoring point \(p_{n0k}\) is coded as the stop-point of \(ray_{n0}\). Similarly, the score of \(ray_{n1}\) is calculated starting from the scoring point \(p_{n10}\). The score function of the ray is shown in Equation (1).  
\[score_{n}=\sum_{\xi=0}^{0}S(p_{n0k})+\sum_{m=0}^{e1}S(p_{n1m}), \tag{1}\]  
where \(0\leq n\leq\frac{180}{\Delta\theta}\); \(e0\) is the label of the stop-point of \(ray_{n0}\), and \(e1\) is the label of the stop-point of \(ray_{n1}\); \(S(p_{n0k})\) is the score of \(ray_{n0}\) and \(S(p_{n1m})\) is the score of \(ray_{n1}\). \(S(p)\) can be obtained using Equation (2).  
\[s(p)=\begin{cases}1&map(p)==OBSTACLE\\ 0&others\end{cases}. \tag{2}\]  
For the target grid \(p_{t}\), the scores of all rays are calculated, and \(ray_{t0}\) and \(ray_{k1}\) with the highest scores are selected as the output results, as shown by the red line in Figure 4c.  
Figure 4: (**a**) The ray model; (**b**) scoring points; edge map; (**c**) the best ray; (**d**) line segment.  
(5) As shown in Figure 5d, if \(p(x,y)\) is an active point and marked as free, then go to (6); otherwise, go to (8).  
(6) Grid cluster \(\{p_{0},p_{1},\ldots,p_{N}\}\) is obtained using a method based on the ray model, and then fitted to a straight line using the method based on the least-squares, as shown in Figure 5e.  
(7) Mark the active points \(\{p_{0},p_{1},\ldots,p_{N}\}\) as locks.  
(8) If \(x<xsize\), then \(x++\); otherwise, \(x=0\), \(y++\).  
(9) Repeat (4)-(8) until all edge points are processed. The straight-line extraction result is shown in Figure 5f.
## 2 Related Research
### Multi-Model-Based Breast Cancer Ultrasound Image Channel Expansion
#### 3.1.3. Noise Reduction and Channel Expansion through Histogram Equalization  
In the next process, the brightness standardization is performed through histogram equalization. Histogram equalization redefines the brightness values distributed in the image, as shown in Equation (2). x and y in Equation (2) refer to coordinates of \(I[\cdot]\), and \(I[x,y]\) denotes a brightness value located at an arbitrary coordinate. \(i\) refers to the frequency at which the feature brightness value can appear and has a range of (\(0\leq l\leq L-1\)). \(Gmax\) refers the maximum frequency. \(N\) and \(M\) are the sizes of \(I[\cdot]\). H\((\cdot)\) is the histogram before normalization and h\((\cdot)\) is a normalized histogram. Equation (2) equalizes \(I[\cdot]\) and \(C1\). The color image of the expanded channel is created by combining the images generated in the above steps into one.  
\[\begin{split}\mathrm{H}(i)=|\{(x,y)|I[x,y]\}=i|\\ \mathrm{h}(i)=\left(\frac{Gmax}{N\times M}\right)\times\mathrm{H }(i)\end{split} \tag{2}\]  
Figure 7 shows the process of generating an expanded channel image through the method proposed in this paper. Figure 7a is the original image, (b) is the original image after histogram equalization, (c) is the image calculated through histogram equalization after adjusting the brightness range, and (d) is the image with the simplified brightness range. The image in (e) is the images of (b), (c), and (d) combined into one. The images with the expanded channel show stronger boundaries than the original image, and the weak and strong brightness areas are clearly distinguishable. The images produced through the process are used as training data for the YOLO model. It detects lesions in the breast ultrasound image, and the detected lesion area can be used in the RNN model through the dimensionality reduction technique.  
Figure 6. The image produced by simplifying the brightness value.  
Figure 7. Expanded channel image creation process. (**a**) original image, (**b**) histogram equalization, (**c**) adjusting the brightness range, (**d**) simplified brightness rang, (**e**) result image.
# Design and Analysis on Decoupling Techniques for MIMO Wireless Systems in 5G Applications
## 4 The Methods of Decoupling
### External Decoupling
#### 4.1.1 NL Technique  
The principle of NL techniques is based on conducting an out-phase current path to offset the original coupling between two antenna elements. The physical structure of NL is vital to the decoupling effect, which the length determines the inductance and the width represents the capacitance. Selecting the proper location of NLs is a design art. Although research [31] pointed out that the strip linked the feeding or the shorting line of plane inverted-F antennas (PIFAs) was an effective decoupling method, there is no common theory for arranging NLs available now.  
The neutralization line structure was used in compact MIMO systems with elements spacing of no more than one wavelength. To save space, a meandering NL was used in [32]. It can be observed from the surface current distribution that added NL prevented the current from flowing to other elements and the isolation was better than 16 dB. Moreover, as shown in Figure 1a, the multi-NLs structure can also be applied to multi-band MIMO [33]. The two U-shaped lines corresponding to different frequency decoupling was proposed. As shown in the comparison without and with decoupling structure in Figure 1b, the proposed new paths can effectively resist the mutual interference between antenna elements, and the isolation of tri-band MIMO can achieve better than 18 dB.  
Since the NL can be conformal with the radiation elements, it can be often found in portable devices' MIMO systems. In [34], the eight-port MIMO for 5G smartphones was  
Figure 1: MIMO antenna with NLs: (**a**) fabricated MIMO antenna; and (**b**) comparison of current distributions without and with grounding NLs (Results from [PERSON] et al. [33]).  
designed. Each antenna element consisted of a meandering monopole and a gap-coupled loop branch and was placed on a bezel with a height of 6 mm. The adjacent antenna elements were spaced only 10 mm apart, and NL was connected to the loop branch. The isolation was better than 11 dB. A meandering NL was used in the dual-port MIMO system applicable in 5G laptops [35]. Its isolation was enhanced by up to 8 dB.