esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Experiments  
To validate the efficiency and effectiveness of the proposed algorithm, AIS data from Zhoushan Port from June 2016 to December 2016 were used. The experimental range was between (30\({}^{\circ}\)01\({}^{\prime}\) N, 121\({}^{\circ}\)43\({}^{\prime}\) E) and (30\({}^{\circ}\)27\({}^{\prime}\) N, 122\({}^{\circ}\)07\({}^{\prime}\) E). The data were gathered through a network of AIS base stations set up by VTS along the Zhoushan coast of China. During this period, due to the construction of Yushan Bridge, from 20 September 2016 to 31 March 2019, ships were prohibited from passing through the bridge. This made the original traffic flow in the area disappear. The experimental area and the location of Yushan Bridge are given in Figure 7. Our experiment used the proposed algorithm for the cluster trajectory to see whether the cluster result could adjust traffic patterns with changes in the traffic environment.
## 2 Hybrid PLC and VLC Channel Based on Distinct Optical Beam Configurations
### Hybrid Channel Based on Baseline Lambertian Optical Beam Configuration
\begin{array}{cc}\frac{A_{R}}{2\pi d ^{2}}(m_{\text{Lam}}+1)\cos^{m_{\text{Lam}}}(\phi)\cos(\theta),&0\leq\theta \leq\theta_{\text{FOV}}\\ 0,&\theta>\theta_{\text{FOV}}\end{array} \right.. \tag{8}\]  
As for the optical channel gain of diffuse signals in (2), the expression of \(\eta_{\text{DIFF}}\) can be given by the following [13]:  
\[\eta_{\text{DIFF}}=\frac{A_{R}\rho}{A_{ROOM}(1-\rho)}, \tag{9}\]  
where \(A_{R}\) and \(A_{ROOM}\) denote the effect of the photodiode (PD)-based receiver area and the internal surface area of the room, while \(\rho\) is the average indoor surface reflectivity. Figure 1: Schematic of hybrid PLC and VLC based on baseline Lambertian emission beam for the coming 6G IoT network: (**a**) the respective application scenario, (**b**) the respective 3D illustration of beam pattern. Overall, the final hybrid PLC and VLC channel, based on a baseline Lambertian optical beam configuration for one certain receiver, possesses the superposed effects of all the LED optical sources and can be given by the following:  
\[H_{\text{Lam}}(f)=\sum_{i=1}^{N_{\text{LED}}}H_{i,\text{PLC}}(f)H_{i,\text{VLC }}^{\text{Lam}}(f), \tag{10}\]  
where \(H_{i,\text{PLC}}(f)\) denotes the PLC channel between the PLC modulator and the \(i\)-th LED optical source, \(H_{i,\text{VLC}}^{\text{Lam}}(f)\) represents the VLC channel between the \(i\)-th Lambertian LED optical source and the optical receiver, and \(N_{\text{LED}}\) represents the number of the LED sources that the optical receiver could detect.
# A Compact Broadband Planar Inverted-F Antenna with Dual-Resonant Modes
## 3 Principle and Analysis
### Loading of Slots 1  
#### 3.1.1 Closing the Resonant Frequencies of the Two Modes  
Figure 3 shows the simulated reflection coefficients (\(|\)S\({}_{11}\)\(|\)) of the three reference antennas, where we can learn that with Slots 1 loaded, the resonant frequency of the  
\begin{table}
\begin{tabular}{c c|c c} \hline
**Parameters** & **Values (mm)** & **Parameters** & **Values (mm)** \\ \hline \(G\) & 42 & \(W\)s\({}_{1}\) & 0.4 \\ \(Hz\) & 2.54 & \(D_{1}\) & 6.5 \\ \(L\) & 39 & \(D_{2}\) & 11.5 \\ \(Ls\) & 4.5 & \(D_{3}\) & 7.25 \\ \(W\) & 22.2 & \(Cp\) & 19.25 \\ \(W_{1}\) & 0.5 & \(R\) & 0.5 \\ \(W\)s & 11.5 & & \\ \hline \end{tabular}
\end{table}
Table 1: The detailed dimensions of the proposed antenna.  
Figure 2: Design process of the proposed antenna: (**a**) Antenna I: traditional PIFA; (**b**) Antenna II: PIFA with Slots 1; (**c**) Antenna III: PIFA with Slots 1 and Slots 2.  
TM\({}_{3/2,0}\) mode can be greatly shifted down to be very close to that of the TM\({}_{1/2,2}\) mode. Table 2 shows the current distributions, and the corresponding radiation patterns of the two target modes. Here, it should be noted that the current intensity increases from 2.2 \(\times\) 10\({}^{-3}\)(A/m) to the maximum, as can be seen from the color maps. In addition, each radiation pattern is normalized by subtracting its peak gain from the gain in each direction. For quantitative comparison, the peak gains are marked separately in the table. From the current distributions, it can be found that the currents of the TM\({}_{1/2,2}\) mode are mostly parallel to the \(y\)-axis, while that of the TM\({}_{3/2,0}\) mode are basically parallel to the \(x\)-axis. With the introduction of Slots 1, the currents of the TM\({}_{3/2,0}\) mode are cut off, resulting in an increment in the current path, thereby leading to the resonant frequency reduction from 4.16 GHz to 3.6 GHz. On the other hand, since Slots 1 are parallel to most currents of the TM\({}_{1/2,2}\) mode, there is no significant effect on the frequency of the TM\({}_{1/2,2
## 4 Experimental Results and Discussions
### Flexural Performance of Concrete Pedestrian Decks
#### 4.2.3 Cracking Response  
Figure 11 shows the crack patterns of all tested SFRC decks near the maximum flexural loads. In general, only single or double cracks were formed in the specimens at the low and moderate volume fractions of 0.37 and 0.6%, regardless of the hooked-end fiber type. However, at the high volume fraction of 1.0%, the number of cracks obviously increased and all SFRC decks showed multiple flexural cracks. These cracking behaviors are related to the post-cracking load-carrying capacity. In order to achieve the deflection-hardening characteristic, the steel fibers at the crack surface must endure an increased pullout force after matrix cracking without pullout and rupture failures. This indicates that the maximum pullout load, applied to the steel fiber at the point of matrix cracking, was smaller than its pullout resistance (bond strength). The increased pullout load at the crack surface was thus transferred to the matrix surrounding the fibers, causing formations of additional cracks when the  
Figure 10: Summary of (**a**) load ductility index and (**b**) deflection ductility index.  
transferred stress exceeded its tensile strength. For this reason, deflection-hardening SFRC beams generally form multiple flexural cracks as reported by previous studies [31; 32]. On the other hand, the SFRC decks with fiber volume fractions of 0.37% and 0.6% exhibited a deflection-softening or vague deflection-hardening behavior (\(P_{cr}\approx P_{pu}\)); hence, the multiple cracking behavior was not observed. This is because the pullout load applied to the steel fibers at the point of crack formation was higher than or similar to their pullout resistance, so that they started pulling out from the matrix just after the crack formation, leading to a decrease in load. Thus, the tensile stress transferred to the surrounding matrix could not exceed the tensile strength causing crack localization without formation of multiple flexural cracks. For the specimens with 4D and 5D hooked-end fibers, larger crack widths were observed in the case of the moderate volume fraction of 0.6% than in the low volume fraction of 0.37% because the post-cracking ultimate load was slightly higher than the cracking load. Therefore, the deflection capacities of the former increased significantly than the latter, leading to greater values of curvature as well as crack width. Because flexural cracks initiate from the weakest part where fibers are fewer and the orientations are poorer, some specimens produced flexural cracks that deviate from the center where the maximum moment occurs.  
Figure 11: Cracking patterns and failure mode.
# Integrated DVB-X2 Receiver Architecture with Common Acceleration Engine
## 3 DVB-X2 Receiver Architecture  
The common accelerator consists of de-interleavers such as frequency, time, and cell, and a forward error correction (FEC) decoding block such as a constellation de-mapper, a bit de-interleaver, and an LDPC/BCH decoder. The frequency de-interleaver is performed within one OFDM symbol using a  
Figure 3: Block diagram of DVB-X2 receiver.  
permutation function. The time de-interleaver is operated in the interleaving frame, which is composed of a number of FEC blocks. The cell de-interleaver performs the de-interleaving process within a single FEC block with a size of 64,800 or 16,200 using a permutation function. These de-interleavers perform simple tasks such as memory read and write operations. However, they require a large amount of memory, and need to generate different types of permutation addresses caused by diverse operation modes and parameters. Thus, the frequency, time, and cell de-interleavers are more effective implemented in S/W than in H/W. The decoding process of the LDPC decoder is performed with complex operations between variable nodes and check nodes. Because the error correction performance of the LDPC decoder improves through repetitive calculations, the LDPC decoder requires a large amount of high-speed calculations. Therefore, the LDPC decoder is effective implemented in H/W in order to improve the reception performance. The BCH decoder concatenated with the LDPC decoder calculates syndromes to find errors of the input data, determines the error location through the error location polynomial and outputs after revising the delayed original input data.  
The proposed DVB-X2 receiver architecture is composed of a tuner block, a H/W-based receiver engine, a S/W-based frame processor, and an A/V decoder as shown in Figure 4. The H/W-based receiver engine is composed of functional blocks that require real-time operation. On the other hand, the frame processor does not require real-time operation, and is composed of functional blocks, which support many different modes and parameters. The H/W-based receiver engine includes a DVB-T2/C2 demodulator, a DVB-S2 demodulator, and a channel decoder. The frame processor performs three main functions, such as the channel estimation function for the DVB-T2/C2 demodulator, various de-interleaving functions, and the frame decoding function such as the frame de-mapping and frame decoding.
## 3 Results and Discussion
### Effect of Nitrogen Fertilizer Reduction on Volatile Metabolomics of Suijing 18
#### 3.2.5 Analysis of Key Differential Volatile Metabolites  
Figure 6 shows a violin plot of the differences in the relative content of volatile metabolites between each group. The different metabolites are roughly divided into two groups in Figure 6, which are dictated by the trends in the application of nitrogen fertilizer. The first trend involves a gradual reduction in nitrogen fertilizer after an initial increase, while the other involves only a decrease. The relative content of the key sweet compounds 4-methyl-benzeneacetaldehyde, hexyl acetate, and 2-methylnaphthalene first increased and then decreased; the sweetness of the resulting rice was highest when nitrogen fertilizer was reduced by 20%. At the same time, the influence of 4-methyl-benzeneacetaldehyde on flavor cannot be ignored, as this compound has a distinct green grass flavor. The compound 4-methyl-phenylacetaldehyde also has a green-grass-like flavor. Hexyl acetate has a significant impact on fruit aroma, while 2-methylnaphthalene has a significant effect on floral aroma. Through an analysis of multiple differential volatile metabolites, we inferred that the flavor of Suijing 18 would be improved with a reduction in nitrogen fertilization, which was also consistent with the results of the eating quality evaluations.  
Figure 6: Violin plots of differential volatile metabolites within nitrogen fertilizer treatment groups: (a) 4-methyl-benzeneacetaldehyde; (b) hexyl acetate; (c) 2-methylnaphthalene.
## 2 Materials and Methods
### Measurement of Cell Migration Using Scratch Assay  
To evaluate the migration of BJ and MCF-7 cells exposed to 0.1%, 1%, and 10% honey samples, a scratch assay, also known as wound healing assay, was performed. Due to the lack of significant differences between the concentrations of 1% and 10%, only the effects caused by the two lowest concentrations will be discussed in the following sections. Cells at a density of 5 \(\times\) 10\({}^{4}\) cells/mL were incubated overnight until 95% of confluency. Two scratch lines were made in three replications for each sample through the confluent monolayer using a sterile 200 \(\mu\)L pipette tip. After washing cells twice with Dulbecco's Phosphate Buffered Saline (D-PBS, ATCC 4-X, Manassas, VA, USA), fresh EMEM containing 5% FBS with or without tested honey samples was added to cells at indicated concentrations. Photographs of each wound were captured using an Olympus IX83 inverted microscope (Olympus, Shinjuku, Japan) at indicated times. The different duration of the experiment resulted from the observation of cell migration in untreated control wells. The analysis was carried out until the cells of a specific cell line without the tested honey reached 100% confluence and was completed after 64 h and 24 h for BJ and MCF-7 cells, respectively. The experiment was conducted in triplicate and two independent series (total \(n=6\)). Obtained images were analyzed using ImageJ public domain software, and cell migration into the wound (%) was calculated according to Equation (2) [38]:  
\[\text{Cell\,Migration}\ [\%]=\left(\frac{\text{A}_{t=0}-\text{A}_{t=\Delta t}}{ \text{A}_{t=0}}\right) \tag{2}\]  
where A\({}_{t=0}\) is the initial scratch area and A\({}_{t=\Delta t}\) is the scratch area after \(n\) hours of the initial scratch, both in \(\upmu\)m\({}^{2}\).
# Enhancing Street-Crossing Safety for Visually Impaired Pedestrians with Haptic and Visual Feedback
## 5 Discussion
### Implications for Haptic and Visual Feedback Design  
Our study highlights the distinct advantages of haptic and visual feedback in different street-crossing contexts: visual feedback enhances decision efficiency at traffic lights, while haptic feedback proves especially useful in vehicle-based scenarios. Based on these findings, we offer the following design recommendations:  
* Visual feedback significantly improved decision-making efficiency, conveying urgency that enabled users to act quickly even at lower confidence levels. Designers targeting individuals with light perception should leverage visual cues to foster timely and effective decisions.
* Haptic feedback markedly enhanced decision efficiency, particularly for users with higher confidence, who could respond more rapidly than with visual feedback. Assistive systems requiring quick, confident decisions should prioritize haptic feedback to facilitate swift user reactions.
* Visual feedback induces more cautious behavior among users, owing to its stronger sense of urgency, particularly under high-frequency cues. This approach is beneficial for scenarios that demand heightened risk awareness, such as crossing busy multi-lane roads.
* Across both scenarios, a notable user preference emerged for haptic feedback, with participants citing comfort and intuitive information delivery. Given the respective strengths of both modalities, assistive systems should allow users to select their preferred method based on personal needs and specific crossing circumstances.
## 5 Damage Detection in Wavefield Images Based on NCA-RPCA Algorithm
### Wavefield Image Damage Identification of Single Defects Based on NCA-RPCA Algorithm
#### 5.2.2 Defects at the Edge Positions  
To closely simulate real-world scenarios, this study further investigates the identification results when defects are positioned at the edges of the plate. In the experiment, a new aluminum plate model was established, as illustrated in Figure 20. A rectangular edge defect with a width of 4 mm, depth of 8 mm, and height of 0.5 mm was placed on the  
Figure 19: Recognition results in the original image.  
Figure 17: The decomposition results of the NCA-RPCA algorithm.  
Figure 18: The sparse matrix after denoising.  
aluminum plate, altering the defect's location and size. After applying excitation, a time frame of 32 was selected for capturing the wavefield image, at which point the Lamb wave propagation at the location of the defect had concluded, as shown in Figure 21.  
Due to the defect being located at the edge of the plate, the propagation time of Lamb waves is longer compared to the previous scenario. The reflections and scattering signals from the plate boundary introduce greater interference to anomaly detection. Applying NCA-RPCA to this image for defect detection, as seen in the decomposed result in Figure 22, still allows for a relatively clear identification of the sparse defect region. To achieve more accurate defect identification, an adaptive thresholding method is still needed to remove other noise sources, as depicted in Figures 23 and 24.  
Figure 21: Wavefield image of edge defect.  
Figure 20: Edge defect structure diagram.  
Figure 22: The decomposition results of the NCA-RPCA algorithm at the edge of the plate.  
When the defect is located at an edge position, due to the presence of more reflections and scattering signals around the edges of the plate, compared to defects in the central location, there will be more interference signals, significantly enhancing the difficulty of detection. Moreover, the size of the defect becomes smaller. However, the algorithm is still capable of accurately decomposing the original data image into effective low-rank and sparse abnormal value components. This allows for the rapid and accurate detection of defects within the image. This demonstrates that regardless of the defect's size, shape, or position, the algorithm can effectively identify it, further confirming the validity and practicality of the approach.
# Research of Calibration Method for Industrial Robot Based on Error Model of Position
###### Abstract  
Based on the current situation of high precision and comparatively low APA (absolute positioning accuracy) in industrial robots, a calibration method to enhance the APA of industrial robots is proposed. In view of the \"hidden\" characteristics of the RBCs (robot base coordinate system) and the FCS (flange coordinate system) in the measurement process, a comparatively general measurement and calibration method of the RBCs and the FCS is proposed, and the source of the robot terminal position error is classified into three aspects: positioning error of industrial RBCs, kinematics parameter error of manipulator, and positioning error of industrial robot end FCS. The robot position error model is established, and the relation equation of the robot end position error and the industrial robot model parameter error is deduced. By solving the equation, the parameter error identification and the supplementary results are obtained, and the method of compensating the error by using the robot joint angle is realized. The Leica laser tracker is used to verify the calibration method on ABB IRB120 industrial robot. The experimental results show that the calibration method can effectively enhance the APA of the robot.  
industrial robot; robot calibration; error model of position; parameter error identification; absolute positioning accuracy
## 4 Discussion
Lower estimates of between-day reliability commonly feature in the literature [8, 35], where some of the additional error may be due to initial sensor orientation. The mathematical correction demonstrated here suggests that this error can be resolved, removing the variability and effect of differing sensor orientations. The magnitude of resulting errors are within those expected from data specification sheets and match those of other motion analysis methods [36, 37, 38, 39, 40]. Using normative ROM data from [PERSON] et al. [41], example error for lumbar flexion was reduced by our algorithm from 10.6% in the sagittal plane to 1.4% following correction for a 20\({}^{\circ}\) tilt and twist initial orientation. The results of this study also showed no differences in error due to ROM size as limits of agreement across all conditions were below 1.7\({}^{\circ}\). Furthermore, the mathematical correction results in the removal of the relationship between error and velocity, suggesting that accelerometers can be used with confidence within the parameters tested in this study. Accelerometers have some inherent advantages over other methods. They operate without concern for metallic interference and do not suffer from integration drift; therefore, they are ideal for a breadth of environments and operate well across time. Therefore, accelerometers can offer an attractive option for motion analysis with excellent reliability and small errors, allowing for confidence of use in any environment to measure joint angles. The knowledge gained from this study allows for confidence in the use of accelerometers for a range of joint angle measurements if the initial orientation is corrected. Initial sensor orientation error goes some way towards explaining the errors between different operators (inter-operator error) and the error associated with removing and reattaching measurement devices (i.e., pre-post attachment orientation error). Therefore, the removal of this source of error means that accelerometers can be used to quantify changes in ROM from interventions (i.e., physiotherapy or surgery) or across time (i.e., with progressive conditions) in a range of locations and environments.
# Improved MUSIC Method against Range Dimension Deceptive Jamming Based on FDA-MIMO
## 3 Model and Method of Range Dimension Deceptive Jamming  
### Deceptive Jamming Model for FDA-MIMO  
DRFM can generate dummy targets with obvious range offset by sampling, storing, jamming modulation, copying, and forwarding the received signal. The dummy target deceptive jamming produced after DRFM processing can be written as  
\[\begin{array}{rl}y_{m,n}&=\xi_{j}e^{j2\pi[-f_{0}\frac{2(r_{j}+\Delta r)}{c}-m \Delta f\frac{2(r_{j}+\Delta r)}{c}+\frac{m\sin\theta_{j}}{2}+\frac{n\sin \theta_{j}}{2}]}\\ &\quad=\xi_{j}e^{j2\pi[-m\Delta f\frac{2(r_{j}+\Delta r)}{c}+\frac{m\sin \theta_{j}}{2}+\frac{n\sin\theta_{j}}{2}]}\end{array} \tag{16}\]  
where \(\xi_{j}\) is the compound coefficient of the interference signal, \(\xi_{j}^{\prime}=\xi_{j}e^{-2\pi f_{0}\frac{2(r_{j}+\Delta r)}{c}}\).  
The dummy targets enter the received side of the FDA-MIMO radar and undergo signal processing, the signal processing flow associated with the range parameter is shown in Figure 3.  
Figure 3: The signal processing flow of the FDA-MIMO radar receiving end associated with the range parameter.  
The range parameter information of the desired target is determined by estimating the range bin, which is mainly defined by pulse repetition period \(T_{p}\) and time delay \(\tau\), thus  
\[r=\text{mod}(c\times\tau/2,R_{\text{max}}) \tag{17}\]  
In Equation (17), \(\text{mod}(a,b)\) denotes the remainder of \((a,b)\). After signal processing at the receiving end, its array factor can be written as  
\[AF=\sum_{n=0}^{N-1}\sum_{m=0}^{M-1}e^{j2\pi(-m\Delta f\frac{2\tau}{c}+m\frac{ \sin\theta}{2}+n\frac{\sin\theta}{2})} \tag{18}\]  
After normalizing its antenna pattern, it can be written as  
\[B(r,\theta)=\left|\frac{\mathbf{w}_{0}^{\text{H}}\cdot\mathbf{S}(r,\theta)}{ MN}\right|^{2}=\left|\frac{1}{N}\sum_{n=0}^{N-1}e^{j2\pi m\frac{(\sin \theta-\sin\theta_{0})}{2}}\right|^{2}\times\left|\frac{1}{M}\sum_{m=0}^{M-1} e^{j2\pi[-m\Delta f\frac{2(\tau-\tau_{0})}{c}+m\frac{(\sin\theta-\sin\theta_{0})}{2} ]}\right|^{2} \tag{19}\]  
When the MVDR adaptive beamforming technique is used to analyze the FDA-MIMO radar, it achieves maximum gain at the desired target location and minimum gain at the interference location, that is  
\[G=B(r,\theta)\Bigg{|}_{r=r_{s},\theta=\theta_{s}}=\left|\frac{1}{M}\sum_{m=1 }^{M}e^{j0}\right|^{2}\times\left|\frac{1}{N}\sum_{n=1}^{N}e^{j0}\right|^{2}=1 \tag{20}\]  
\[G=B(r,\theta)\Bigg{|}_{r=r_{j},\theta=\theta_{j}}=\left|\frac{1}{M}\sum_{m=1 }^{M}e^{j2\pi(-m\frac{2\Delta r}{c})}\right|^{2}\times\left|\frac{1}{N}\sum_{ n=1}^{N}e^{0}\right|^{2}<1 \tag{21}\]  
It can be seen from Equations (20) and (21) that after weighting processing in the FDA-MIMO radar receiver, different range information and angle information of the output signal will result in different gains.