esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials, Equipment and Context
### Arthur Clarke Mars Greenhouse
At other times, the communications, command and control computer can receive commands to initiate transfer of images and data via a year-round low-speed satellite modem (SDX-1100, Infosat) for eventual transmission by satellite. GFP images, along with all greenhouse data, are received by an operations center at Simon Fraser University in Vancouver, British Columbia, through a second SDX-1100 system operated via an autonomous command and control system operating on a Unix (Sun Microsystems Solaris-based) network-distributed mission critical server architecture, where it is made available to researchers through the Internet. To minimize technology deployment risk to the Arctic greenhouse any system destined for ACMG is first trialed in a duplicate greenhouse located at the CSA headquarters in Longueuil, Quebec. This duplicate greenhouse was constructed after several years of ACMG operations when it was realized that confidence in new hardware and software could be gained prior to integration into the ACMG. This duplicate greenhouse also provides an opportunity for greenhouse researchers to increase their work efficiency during the Arctic field season by first carrying out simulated deployments in the south.
## 3 The Construction of Dataset
### The Charged Particle Waveform Acquisition System  
In the actual experiment, ions accelerated by the GIC4117 2 \(\times\) 1.7 MV series accelerator at the Analytical and Testing Center of Beijing Normal University were measured on a semiconductor detector. To ensure signal integrity, the flux and beam spot size were controlled to avoid pulse accumulation. The silicon detector was mounted on a support mechanical structure and placed in a vacuum chamber to collect charged particles. To ensure that particles of different types and energies could be fully deposited in the detector, after simulation calculations with SRIM [20], we used a 300 \(\upmu\)m thick ORTEC U-013-150-300 ion-implanted silicon detector. During the experiment, a fixed biased voltage of 100 V was applied, while the detector's depletion voltage was around 60 V. The preamplifier used in the experiment was the CANBERRA 2003 BT charge-sensitive preamplifier, which has a very low noise level. For silicon, the FWHM noise at 0 pF was only 2 keV. A key reason for using this preamplifier is that it has two independent outputs. One output is the Energy Output, which integrates the charge generated by each particle event, while the other output is the Timing Output, which has a differential current output corresponding to the particle event. The rise time of Timing Output is less than 3 ns at 0 pF, making it possible to measure current pulse signals with a very short rise time. The main amplifier used was the ORTEC9302, and the power supply was a custom-made board providing \(\pm\)24 V, \(\pm\)12 V, and GND outputs. The acquisition equipment used was either an oscilloscope or a PXIe-1075 fast acquisition circuit. In the series accelerator laboratory, the entire setup used shielded cables, and the length of the cables from the detector to the preamplifier was minimized, ensuring that the noise could be controlled to around 2 mV, as shown in Figure 4.
# Influence of Drift on Robot Repeatability and Its Compensation
## 3 Data Acquisition
### Drift Compensation Calculation  
The compensation equation, which predicts the TCP position error based on the current temperature of the robot joints, is implemented and tested using Matlab Regression Learner. Seven inputs are defined for this prediction, each representing the average temperature of the robot's joints and the ambient temperature. The application output is defined as the prediction of the positioning errors for individual TCP axes. Figure 7 presents the single-axis output of the regression model (yellow points) and the originally measured drift (blue points). The temperature-based position error prediction corresponds to the actually measured position error.  
Figure 6: TCP Z-axis drift measurement, Target 1, maximum speed. Data recorded during the initial measurement: purple—joint 1 temperature, light blue—joint 2 temperature, yellow—joint 3 temperature, black—joint 4 temperature, orange—joint 5 temperature, dark blue—joint 6 temperature, red-dots express the trend of TCP Z-axis drift.  
After the created regression model is exported from Regression Learner to MatLab, the coefficients for the calculation can be extracted. An equation was then implemented in the form of the robot's RAPID program running in a parallel task. The task is configured to recalculate the drift compensation once per second based on all twenty-five input temperatures measured at the joints.  
The final equation represented by the program in the robot controller is then:  
\[n_{Z}=\left(I+A\times T+\overline{nI_{1}}\times J_{1}+\overline{nI_{2}}\times J _{2}+\overline{nI_{3}}\times J_{3}+\overline{nI_{4}}\times J_{4}+\overline{nI_ {5}}\times J_{5}+\overline{nI_{6}}\times J_{6}\right)-m. \tag{1}\]  
\(T\)[0.25] input array consists of all the values from the temperature sensors. The 25 th value represents the ambient temperature. The value of intercept \(I\), Ambient \(A\) and \(J_{1}\)-\(J_{6}\) are constants given by the regression learner (see Table 2). \(\overline{nI_{1}}-\overline{nI_{6}}\) are the average values of the joint temperatures (since each joint is equipped with four temperature sensors). The value of \(m\) is the maximum value of the drift. This calculation has to be performed three times--for each TCP-axis separately.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Value for:** & **Estimation [\(-\)]** \\ \hline \(I\) [Intercept] & \(-\)407.3077 \\ \(A\) [Ambient] & \(-\)0.1637 \\ \(J_{1}\) & 2.9261 \\ \(J_{2}\) & 9.7893 \\ \(J_{3}\) & \(-\)9.3647 \\ \(J_{4}\) & 9.0107 \\ \(J_{5}\) & 2.3683 \\ \(J_{6}\) & 4.4422 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Regression coefficients.  
Figure 7: MatLab TCP \(Z\) Drift prediction, Target 1, maximal speed. Presented trends: blue points—measured robot drift, yellow points—drift prediction calculated by MatLab regression learner.
# Asymmetric Encryption of Invisible Structured Light 3D Imaging
## 2 Invisible Structured Light 3D Imaging System
### Obtaining the Eight Images for Encryption  
The structured light imaging is detailed as shown in Figure 1. After passing through a half wave plate (HWP), the laser beam is divided into two beams in an orthogonal  
Figure 1: Invisible structured light 3D imaging system. HWP: half wave plate; QWP: quarter wave plate; PBS\({}_{1}\), PBS\({}_{2}\): polarization beam splitter; BE\({}_{1}\), BE\({}_{2}\): beam expander; M\({}_{1}\), M\({}_{2}\): mirror; L\({}_{1}\), L\({}_{2}\), L\({}_{3}\): lens.  
polarization state by a polarization beam splitter (PBS1), namely, horizontally polarized light and vertically polarized light. The horizontally polarized light is expanded by the beam expander (BE2) and the reflector (M2), passes through a variable pupil that is adopted to adjust the size of the light, and irradiates on the polarizing beam combiner (PBS2) through the lens (L1). At the same time, the vertically polarized light is expanded by the beam expander (BE1) and the reflector (M1), and then irradiates on the polarization beam combiner after passing through the phase modulator, which is applied to control the phase. Vertically polarized light and horizontally polarized light pass through a quarter wave plate (QWP) and finally form an invisible structure light with an orthogonal polarization direction and controllable phase and size. It is then transmitted to the object under test by the lens (L2). Because the two beams are orthogonal polarization states, the interference structure light pattern cannot be seen on the target plane, and can only be detected at the receiving end, so it is called \"invisible structure light\".  
Using the invisible structure light-emitting system, after emitting 2 orthogonal polarized lights to irradiate the target plane, the expressions of 4 sinusoidal fringes with phase shifts of 0, \(\pi\)/2, \(\pi\), and 3\(\pi\)/2 can be obtained using a convergent lens (L3) and polarization camera, which can be expressed as:  
\[I_{0}(x,y)=\frac{a_{0}^{2}}{z^{2}}[1+\sin(\frac{kxd}{Mz}+\frac{kd^{2}}{2z})] \tag{1}\]  
\[I_{\frac{\pi}{2}}(x,y)=\frac{a_{0}^{2}}{z^{2}}[1+\cos(\frac{kxd}{Mz}+\frac{kd^ {2}}{2z})] \tag{2}\]  
\[I_{\pi}(x,y)=\frac{a_{0}^{2}}{z^{2}}[1-\sin(\frac{kxd}{Mz}+\frac{kd^{2}}{2z})] \tag{3}\]  
\[I_{\frac{3\pi}{2}}(x,y)=\frac{a_{0}^{2}}{z^{2}}[1-\cos(\frac{kxd}{Mz}+\frac{kd ^{2}}{2z})] \tag{4}\]  
where \(a_{0}\) represents the amplitude at a unit distance from the point light source, \(z\) represents the distance between the L2 focus position and target plane, \(k\) represents the wave number, \(d\) represents the distance of the convergence point of two orthogonal polarized lights after passing through the lens L2, and \(M\) represents the imaging magnification of the camera.
###### Abstract  
The expansion of tunnel scale has led to a massive demand for inspections. Light Detection And Ranging (LiDAR) has been widely applied in tunnel structure inspections due to its fast data acquisition speed and strong environmental adaptability. However, raw tunnel point-cloud data contain noise point clouds, such as non-structural facilities, which affect the detection of tunnel lining structures. Methods such as point-cloud filtering and machine learning have been applied to tunnel point-cloud denoising, but these methods usually require a lot of manual data preprocessing. In order to directly denoise the tunnel point cloud without preprocessing, this study proposes a comprehensive processing method for cross-section fitting and point-cloud denoising in subway shield tunnels based on the Huber loss function. The proposed method is compared with classical fitting denoising methods such as the least-squares method and random sample consensus (RANSAC). This study is experimentally verified with 40 m long shield-tunnel point-cloud data. Experimental results show that the method proposed in this study can more accurately fit the geometric parameters of the tunnel lining structure and denoise the point-cloud data, achieving a better denoising effect. Meanwhile, since coordinate system transformations are required during the point-cloud denoising process to handle the data, manual rotations of the coordinate system can introduce errors. This study simultaneously combines the Huber loss function with principal component analysis (PCA) and proposes a three-dimensional spatial coordinate system transformation method for tunnel point-cloud data based on the characteristics of data distribution.  
3D laser scanning technology; point-cloud denoising; Huber loss function; principal component analysis (PCA); cross-section fitting 2020 IEEEexample:BSTcontrol
## 3 Control Strategy
### Judge Rule of the Working Mode of the Boom  
The forklift is a speed-controlled system, and the handle opening represents the target velocity of the boom. When the boom descends, there is a certain energy loss of the energy regeneration unit composed of HMG and lithium battery. When the energy recovered by the external load is less than the loss generated by PERS itself, using the HMG controlled mode to control the descending velocity of the lifting cylinder will generate additional energy loss, which will reduce the energy regeneration efficiency of the whole machine. Therefore, there is a minimum load requirement for the external load under HMG controlled mode to recover the potential energy. The load is measured by detecting the rodless chamber pressure of the lifting cylinder. To prevent being overcharged by the lithium batteries,  
Figure 4: Schematic of the double motor-generators potential energy regeneration system (PERS).  
there is a requirement for the maximum allowable charging of SOC. Accordingly, the main factors influencing the decision of the control mode of lifting cylinder descending are the handle opening, the minimum rodless chamber pressure and the maximum SOC of the battery. Therefore, the judge rules for the descending mode of the lifting cylinder can be shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Working Mode**} & **Judge Rule** \\ \hline \multirow{2}{*}{HMG controlled mode with PERS} & Single HMG & \(p_{3}\geq C_{\text{mino}}\) and SOC \(<S_{\text{max}}\) and \(Y_{\text{min}}\leq Y_{p}\leq Y_{\text{s}}\) \\  & Double HMGs & \(p_{3}\geq C_{\text{mino}}\) and SOC \(<S_{\text{max}}\) and \(Y_{\text{s}}<Y_{p}\leq Y_{\text{max}}\) \\  & Throttling-controlled mode & \(p_{3}<C_{\text{mino}}\) or SOC \(\geq S_{\text{max}}\) and \(Y_{\text{min}}\leq Y_{p}\leq Y_{\text{max}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Decision rules of the working mode when boom downwards.  
Figure 5: Overview of the control strategy of the double hydraulic motor-generator (HMG) PERS.  
When the rodless chamber pressure satisfies \(p_{3}\geq C_{\rm{mimp}}\), and the residual charge of lithium battery satisfies \(\rm{SOC}<S_{\rm{max}}\) and the opening of the handle with CAN communication satisfies \(Y_{\rm{s}}<Y_{\rm{p}}\leq Y_{\rm{max}}\), the boom down system enters the double HMG mode.
3. When the rodless chamber pressure satisfies \(p_{3}<C_{\rm{mimp}}\) or the residual charge of the lithium battery satisfies \(\rm{SOC}\geq S_{\rm{max}}\) and the opening of the handle with CAN communication satisfies \(Y_{\rm{min}}\leq Y_{\rm{p}}\leq Y_{\rm{max}}\), the boom down system enters in the traditional throttling-controlled mode.
## 3 Methodology  
### Benchmark Construction  
The benchmark dataset for this study was constructed based on the question types used in the Korean Hanja Proficiency Test ([[https://www.hanja.re.kr/](https://www.hanja.re.kr/)]([https://www.hanja.re.kr/](https://www.hanja.re.kr/)) (accessed on 14 May 2025)), a state-certified examination conducted in South Korea to assess proficiency in Hanja. Among the six official testing institutions recognized by the Ministry of Education of South Korea, we referred to the Korean Language Society's Korean Hanja Proficiency Test. This test is structured into proficiency levels ranging from Grade 8 (beginner) to Grade 1 (advanced), with additional higher-tier classifications such as \"Special Grade\" and \"Semi-Special Grade\" for expert-level assessment.  
For this study, we focused on Grade 1 to Grade 6, as these levels are widely taken by test candidates and cover the common-use Hanja shared across multiple testing institutions. The Special Grade and Semi-Special Grade levels were excluded since they assess expert-level proficiency and follow different question formats compared to other levels.  
Among the common question types in Grades 1 to 6, we selected four primary question types for evaluation: four-character idioms, synonyms, antonyms, and homophones. The Hanja Proficiency Test includes different question types depending on the level, and it primarily focuses on assessing individual Hanja characters. However, questions involving four-character idioms, synonyms, antonyms, and homophones appear across Grades 1 to 6. These four types were selected for this study because they assess not individual characters but word-level expressions that often involve figurative meanings. Moreover, actual questions from each of these types in the official test were used as prompts in the experiment.  
The dataset was constructed as follows:  
* Question Type 1 (four-character idioms): Each entry consists of a four-character idiom, its constituent Chinese characters, and the Korean definition.
* Question Type 2 (synonyms) and Question Type 4 (homophones): Each entry includes a two-syllable Sino-Korean word presented as the question, its corresponding Hanja representation, and its Korean definition.
* Question Type 3 (antonyms): Each entry consists of a two-syllable Sino-Korean word as the base term, its opposing two-syllable Sino-Korean word, and the Korean definition of the antonym.  
For each question type, the dataset includes 200 samples, ensuring a balanced distribution across all categories. In addition, a supplementary dataset comprising 3829 individual Hanja entries was constructed for the purpose of generating answer choices. This dataset includes information on each character's Hanja form, Korean meaning, and pronunciation. The dataset is publicly available at [[https://github.com/es133](https://github.com/es133) lolo/Knowing-the-Words-Missing-the-Meaning]([https://github.com/es133](https://github.com/es133) lolo/Knowing-the-Words-Missing-the-Meaning) (accessed on 14 May 2025).
## 8 Use Case Output
### Use Case Layer 2: Performance Index per Device Model  
Layer 2 breaks down the analysis per device model complementing Layer 1 analysis. The analysis allows the user to pinpoint the device model performing well or performing poorly based on the defined KPIs. Table 4 presents the device model ranked by a descendent instantaneous throughput value. Three classes of devices appear in the analytics report: smartphones, dongles, and tablets.  
Table 5 presents the device model ranked by descendent response delay. It is not ideal for sensitive systems such as health monitoring or HD-video streaming devices.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Class** & **Time Delay (sec)** & **Packet\_Loss (\%)** & **Throughput (Mbps)** \\ \hline O***1 & Smartphone & 6.39 & 0.51 & 3.053 \\ H***2 & Smartphone & 38.61 & 0.51 & 2.897 \\ M***3 & Dongle & 0.5 & 0.5 & 2.248 \\ LT***4 & Smartphone & 0.25 & 0.09 & 1.876 \\ G***5 & Smartphone & 0.25 & 0.06 & 1.846 \\ G***Plus & Smartphone & 0.66 & 0.25 & 1.689 \\ M***6 & Dongle & 3.29 & 1 & 1.655 \\ G***6 & Tablet & 0.44 & 0.16 & 1.654 \\ X***3 & Smartphone & 29.37 & 0.04 & 1.374 \\ G***6 & Smartphone & 2.46 & 0.07 & 1.276 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance index per device model based on throughput: Top 10 devices.  
Figure 6: The top 10 device manufacturers with high video throughput, further grouped by technology.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Class** & **Time Delay (sec)** & **Packet\_Loss (\%)** & **Throughput (Mbps)** \\ \hline E***1 & Smartphone & 42.37 & 0.15 & 0.446 \\ Mobile*** & Dongle & 40.27 & 0.51 & 0.11 \\ L***300 HC & Tablet & 23.71 & 1 & 0.307 \\ G***3 & Smartphone & 18.77 & 0.39 & 0.44 \\ STL100-1 & Smartphone & 12.71 & 0.18 & 0.404 \\ X***3 & Smartphone & 10.34 & 0.22 & 0.856 \\ X800 & Feature Phone & 9.78 & 0.18 & 0.052 \\ G***6 & Smartphone & 9.72 & 0.5 & 0.653 \\ A***5 & Feature Phone & 9.21 & 0.62 & 0.636 \\ E3***1 & Dongle & 6.44 & 0.07 & 0.181 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Worst 10 device models with high application delay.
## 5 Results
### RQ2. Results of the Text-Code Experiment  
Second, we conducted the experiment that compared the Text Only model with the multimodal model which combined text and code data of issue reports. Table 4 showed  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Project** & **Model** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{2}{*}{VS Code} & Text Only & 68.45 & 68.27 & 68.36 \\  & MulDIC\({}_{TI}\) & **79.60** & **77.92** & **78.75** \\ \hline \multirow{2}{*}{Kubernetes} & Text Only & 66.91 & 66.10 & 66.50 \\  & MulDIC\({}_{TI}\) & **68.35** & **68.33** & **68.34** \\ \hline \multirow{2}{*}{Flutter} & Text Only & 67.70 & 67.66 & 67.68 \\  & MulDIC\({}_{TI}\) & **69.79** & **69.41** & **69.60** \\ \hline \multirow{2}{*}{Roslyn} & Text Only & 62.92 & 62.86 & 62.89 \\  & MulDIC\({}_{TI}\) & **66.13** & **66.04** & **66.08** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results that compared MulDIC\({}_{TI}\) with the Text Only model.  
the result. The second row of Table 4 per each project corresponds to the results of the Text-Code experiment.  
The Precision values of the MulDIC\({}_{TC}\) model were 68.11% for VS Code, 79.97% for Kubernetes, 68.68% for Flutter, and 66.06% for Roslyn. The MulDIC\({}_{TC}\) model demonstrated a Precision improvement of 13.06%, 0.98%, and 3.14% for Kubernetes, Flutter, and Roslyn, compared to the Text Only model. However, when it comes to the VS Code project, the Precision value of the Text Only model is 0.34% higher than that of the MulDIC\({}_{TC}\) model.  
The Recall values of MulDIC\({}_{TC}\) were 67.77% for VS Code, 78.33% for Kubernetes, 68.42% for Flutter, and 66.04% for Roslyn. The MulDIC\({}_{TC}\) model made improvements in Recall of 12.23%, 0.76%, and 3.18% for Kubernetes, Flutter, and Roslyn, respectively, when compared to the Text Only model. However, in VS Code, the performance of MulDIC\({}_{TC}\) is 0.50% lower than the Text Only model.  
The F1-score values MulDIC\({}_{TC}\) were 67.94% for VS Code, 79.14% for Kubernetes, 68.55% for Flutter, and 66.05% for Roslyn. For Kubernetes, Flutter, and Roslyn, the MulDIC\({}_{TC}\) model showed an improvement of 12.64%, 0.87%, and 3.16% in the F1-score compared to the Text Only model. However, in the case of VS Code, The F1-score of MulDIC\({}_{TC}\) is \(-\)0.42% lower than that of the Text Only model.  
We also compared the performance of MulDIC\({}_{TC}\) with that of MulDIC\({}_{TI}\). In terms of Precision, MulDIC\({}_{TC}\) outperformed MulDIC\({}_{TI}\) in Kubernetes with 11.62% difference, while MulDIC\({}_{TI}\) outperformed MulDIC\({}_{TC}\) over the other three projects, VS Code, Flutter, and Roslyn with 11.49%, 1.11%, and 0.07% differences. MulDIC\({}_{TC}\) and MulDIC\({}_{TI}\) followed the same trend in terms of Recall and F1-score.  
These results indicate that the code data in the issue reports can contribute to the issue classification task. This implies that code also provides valuable information for classifying issue reports. Consequently, integrating the text and code data of an issue report in the model enhances its comprehension of the issue report.
## 6 Simulation Results  
The simulation was run on a PC with Core i3-3220 CPU, 4G memory, and Win7 OS. The simulation is implemented in the ns-2 simulator [30]. In order to make the comparison fair, the simulation parameters we used which are listed in Table 3 are similar to those of the DAA [9] and SDA-PH [15] schemes. Some nodes are designed as aggregators. The BS is located in the central area. Data are assumed to be generated mainly by the nodes located at the edges of the network, although any node is allowed to sense events and generate data. To show the benefit of our scheme, we evaluate the computational and communication overhead of FESA, DAA and SDA-PH schemes.
## 4 Results and Discussion  
### Verification Tests  
Prior to the detailed investigation of the predicted unsteady cavitation flows around the hydrofoil using the above viscous flux models, the averaged properties of the predicted flow field, using current numerical methods, are compared to the numerical results obtained from the large eddy simulation (LES) and experiments, to confirm the validity of the current numerical methods. Figure 2 compares the distribution of pressure coefficients over the hydrofoil surface with that of [PERSON] et al. (2010), and Figure 3 compares the normal directional distributions of x-velocity from hydrofoil surface with that of [PERSON] et al. (2011). As can be seen in those figures, there are good correlations between the three different approaches, as well as between the current results and the referred data. This result could imply that the viscous models do not have a significant effect on the averaged flow field, especially between the thin layer and the full viscous models.  
Figure 3: \(Cont.\)  
Figure 2: Comparison of predicted pressure coefficient distribution with large eddy simulation (LES) data by [PERSON] et al. [40].