esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study proposes a novel FTDO-based sliding mode control scheme for a vehicle platoon affected by matched and mismatched disturbances to accomplish control goals. The novel FTDO is designed to estimate the two types of disturbances directly without the need to know the precise upper bounds of the disturbances. To mitigate the adverse impact of disturbances, a feedforward compensation term derived from the disturbance estimation is incorporated into the FTDO-based sliding mode control scheme to address the control performance degradation caused by disturbances and to ensure the vehicle platoon's stability. The example results validate the efficacy of the proposed FTDO-based sliding mode control scheme. Note that we study the control problem of vehicle platoons with mismatched disturbances under the assumption of ideal communication conditions, while neglecting the impact of time-varying communication delays. In future work, it will be of value to investigate control strategies for vehicle platoons with communication delays.  
Conceptualization, Y.W. ([PERSON]) and Y.J.; methodology, X.Z.; software, X.Z. and X.L.; validation, X.Z. and Y.W. ([PERSON]); formal analysis, Y.J.; investigation, Y.W. ([PERSON]) and X.Z.; resources, X.T.; data curation, X.Z.; writing--original draft preparation, X.Z.; writing--review and editing, Y.J.; visualization, X.Z.; supervision, X.L.; project administration, X.T. and X.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Fund of Guangxi Science and Technology Key Research and Development Program under Grant 2024 AB33219, in part by the Fund of Guilin Science Research and Technology Development Program under Grant 20230105-3 and Grant 20230110-4, and in part by the Fund of Innovation Project of Guangxi Graduate Education under Grant JGY2023151.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **FTDO-Based Sliding Mode Controller** & **DO-Based Sliding Mode Controller** \\ \hline \hline Overshoot of \(e_{i}\) & 0.0732 & 0.1717 \\ \(ITAE=\sum_{i=1}^{6}\int_{0}^{t}t|e_{i}(\tau)|d\tau\) & 0.0047 & 0.0076 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative results under different controllers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **FTDO** & **DO** \\ \hline \hline \(ITAE=\sum_{i=1}^{6}\int_{0}^{t}t|\hat{\partial}_{1}(\tau)|d\tau\) & 0.0176 & 0.0260 \\ \(ITAE=\sum_{i=1}^{6}\int_{0}^{t}t|\hat{\partial}_{12}(\tau)|d\tau\) & 0.1156 & 0.1398 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative results under different DOs.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Design of Tunable Broadband Graphene-Based Metasurface with Amplitude-Phase Modulation
###### Abstract  
Due to the growing scarcity of spectrum resources in the low-frequency band, the requirement of beam-reconfigurable antennas in the millimeter wave band is urgent. In this paper, a W-band graphene-based metasurface working in a broad bandwidth is proposed with reflective amplitude coding. Here, graphene sheets play a dual role in radiating and regulating electromagnetic waves. By adjusting the Fermi levels of graphene, the reflective amplitude and phase of the metasurface can be modulated simultaneously, enabling multi-beam switching and beam deflection in far-field. The proposed metasurface achieves amplitude-phase modulation within a significantly wide bandwidth which covers 75-91.5 GHz and 99.3-115 GHz. By optimizing the coding patterns, the proposed graphene-based metasurfaces are able to not only realize 2-D beam steering, but also achieve beam switching from single beam to four beams at 87 GHz. The proposed design provides a novel solution for the flexible manipulation of millimeter waves, which can be applied to various fields such as vehicle radar, satellite communication, 6G wireless communication, and beyond.  
graphene; metasurface; reflection amplitude phase; code +
Footnote †: journal: materials
# Soft Error-Tolerant and Highly Stable Low-Power SRAM for Satellite Applications
## 4 Conclusions  
In this paper, we propose a radiation-hardened SRAM cell, named RHSP16T, designed for space applications to achieve immunity against soft errors while demonstrating optimized performance in terms of stability and power consumption. The proposed RHSP16T cell designates two out of its four nodes as sensitive nodes, which can recover from all SEUs induced at these sensitive nodes. Furthermore, the RHSP16T cell incorporates layout-level techniques to avoid charge-sharing effects at sensitive node pairs, thereby ensuring immunity to SEMNUs. Simulation results indicate that the proposed RHSP16T cell outperforms other comparative cells across various design metrics fundamental to SRAM operations, including write access time, write stability, read stability, and power efficiency. Additionally, the EQM evaluation further verified the overall superior performance of the RHSP16T cell. However, the proposed design has certain limitations, including a slightly increased read access time and a minor increase in area. Therefore, future research should focus on optimizing the read access time and area while preserving the cell's stability and power efficiency. In summary, the proposed RHSP16T cell provides immunity to soft errors, high read and write stability, and enhanced data reliability, making it suitable for the space industry, where reliable data processing over extended periods with relatively low power consumption is essential.  
Conceptualization, J.-Y.O. and S.-H.J.; methodology, J.-Y.O.; validation, J.-Y.O.; data curation, J.-Y.O.; writing--original draft preparation, J.-Y.O.; writing--review and editing, S.-H.J.; visualization, J.-Y.O.; supervision, S.-H.J.; project administration, S.-H.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
Figure 8: EQM values for different SRAM cells on a log scale.  
**Acknowledgments:** The EDA Tool was supported by the IC Design Education Center, Republic of Korea.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# SLNER: Chinese Few-Shot Named Entity Recognition with Enhanced Span and Label Semantics
## 5 Analysis  
### Ablation Study  
To validate the impact of enhanced span representation and label representation on SLNER, we conducted extensive ablation experiments on the Ontonotes, MSRA, Resume, and RISK datasets under different sampling settings (K = 250, 500, 1000, and 1350) as shown in Table 6. Specifically, we first removed the enhanced span representation module and observed a decrease in performance across all datasets with different sampling sizes. This demonstrates the effectiveness of the module, which we analyze in Section 5.2. Next, we removed the label representation module. It is worth mentioning that, due to the removal of the label representation module, we had to introduce a top-layer classifier, which resulted in the inability to use the source-domain-initialized model. As a result, the performance of our model significantly dropped, confirming our hypothesis that the label semantics carry rich prior knowledge. This also indicates the importance of prior knowledge from the pre-fine-tuning stage in low-resource environments. We provide a detailed analysis of this in Section 5.3.  
To further analyze the impact of label representation, we experimented with different definitions: full label name (the one used in our model), misleading label name, and indistinguishable label name. Misleading label name refers to randomly shuffling the full label name, for example, changing the label name corresponding to \"PER\" from \"A\" (person name)\" to \"B\" (organization)\". Indistinguishable label name refers to unifying all different label names into a single label name, such as using \"A\" (person name)\" for all labels. We conducted a comparative experiment on the Ontonotes dataset, as shown in Figure 10. The results indicate that a misleading label name slightly negatively affects the model, but an indistinguishable label name has a significant negative impact. This suggests that the selection of label names must be category-related and that a label name carries entity-specific prior knowledge.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Method** & **K = 250** & **K = 500** & **K = 1000** & **K = 1350** \\ \hline \multirow{3}{*}{Ontonotes} & SLNER & 77.66 & 80.46 & 81.53 & 82.23 \\  & -ESR w/SD & 75.63 & 80.02 & 81.04 & 81.91 \\  & -LR w/o SD & 72.90 & 75.62 & 77.98 & 78.09 \\ \hline \multirow{3}{*}{MSRA} & SLNER & 87.65 & 89.30 & 89.67 & 90.08 \\  & -ESR w/SD & 87.99 & 89.60 & 89.42 & 89.80 \\  & -LR w/o SD & 80.72 & 84.06 & 84.87 & 84.54 \\ \hline \multirow{3}{*}{Resume} & SLNER & 94.02 & 94.86 & 95.99 & 96.36 \\  & -ESR w/SD & 93.52 & 94.46 & 95.38 & 95.95 \\  & -LR w/o SD & 87.64 & 94.17 & 95.48 & 95.35 \\ \hline \multirow{3}{*}{RISK} & SLNER & 63.97 & 65.62 & 68.06 & N/A 1  \\  & -ESR w/SD & 63.27 & 65.26 & 67.29 & N/A \\ \cline{1-1}  & -LR w/o SD & 55.67 & 58.94 & 61.23 & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 6: The results of the ablation experiments. ESR, enhanced span representation; LR, label representation; SD, source domain dataset.
## 3 Results
### The Relationship between \(SIF_{canopy}\) and \(APAR_{chl}\) Based on the Simulated Data  
2.1 Effect of Chlorophyll Content on the Relationship between \(\mathrm{SIF}_{canopy}\) and \(\mathrm{APAR}_{chl}\)  
Simulated data representing canopies with different values of chlorophyll content but the same leaf angle distribution function (Spherical: LIDFa = \(-\)0.35, LIDPb = \(-\)0.15) was selected to investigate the relationship between \(\mathrm{SIF}_{canopy}\) and \(\mathrm{APAR}_{chl}\) at the O\({}_{2}\)-A and O\({}_{2}\)-B bands, illustrated in Figure 4. For the O\({}_{2}\)-A band (Figure 4a), \(\mathrm{SIF}_{canopy}\) shows a strong linear relationship with \(\mathrm{APAR}_{chl}\) for all the values of Cab tested, which means this linear relationship is almost independent of the chlorophyll content. It can be concluded that \(\mathrm{SIF}_{canopy}\) at the O\({}_{2}\)-A band is primarily driven by \(\mathrm{APAR}_{chl}\). For the O\({}_{2}\)-B band, when the results for different values of Cab are plotted, the relationship between \(\mathrm{SIF}_{canopy}\) and \(\mathrm{APAR}_{chl}\) exhibits a pronounced wing-like shape (Figure 4b). The slope of \(\mathrm{SIF}_{canopy}\) to \(\mathrm{APAR}_{chl}\) is sensitive to the Cab content, decreasing when Cab increases and becoming almost saturated when Cab is bigger than 40 \(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{cm}^{2}\). These results are similar to those found by [PERSON] et al. [33].  
The ratio of \(\mathrm{SIF}_{canopy}\) to \(\mathrm{APAR}_{chl}\) for different Cab levels was also calculated using the simulated dataset, as illustrated in Figure 5, which represents the response of \(\mathrm{SIF}_{canopy}\) to \(\mathrm{APAR}_{chl}\). The ratio  
Figure 4: The relationship between \(SIF_{canopy}\) and \(\mathrm{APAR}_{chl}\) at the O\({}_{2}\)-A (**a**) and O\({}_{2}\)-B (**b**) bands for different values of the Cab content. These results were obtained using the simulated dataset.  
Figure 3: The ratio of \(\mathrm{fPAR}_{chl}\) to \(\mathrm{fPAR}_{green}\) for canopies with different LAI and Cab based on the simulation carried out by the SCOPE model.  
of \(\mathrm{SIF}_{canopy}\) to APAR\({}_{chl}\) is equal to the product of the fluorescence efficiency (\(\varepsilon_{F}\)) and the fraction of SIF photons escaping the canopy (\(f_{esc}\)). \(\varepsilon_{F}\) was fixed in the simulation experiment and hence, in this experiment, this ratio was determined by \(f_{esc}\). At the O\({}_{2}\)-B band, \(f_{esc}\) decreases markedly from 0.016 to 0.002 \(\upmu\)m\({}^{-1}\) sr\({}^{-1}\) as Cab increases but, at the O\({}_{2}\)-A band, it remains almost constant for a given LAI. Therefore, the ratio of \(\mathrm{SIF}_{canopy}\) to APAR\({}_{chl}\) is less influenced by the Cab content at the O\({}_{2}\)-A band than the O\({}_{2}\)-B band, which accounts for the linear relationship between \(\mathrm{SIF}_{canopy}\) and APAR\({}_{chl}\) at the O\({}_{2}\)-A band, which exists no matter what the value of Cab is.
## 3 The Proposed Octree-Hiding Scheme
### The Extraction Process  
Three different methodologies are employed to optimize the quality and efficiency of secret data recovery: semi-blind extraction, non-blind extraction without error correction, and non-blind extraction with error correction. The semi-blind approach allows the receiver to extract hidden information by utilizing minimal parameters shared by the sender, resulting in reduced communication costs, albeit at the risk of extraction quality. In contrast, the non-blind extraction method demands comprehensive parameter transmission, ensuring higher extraction fidelity but potentially leading to increased communication overhead. Lastly, the non-blind approach with error correction seeks to balance extraction quality and communication efficiency by transmitting only error correction data, thus refining the output while minimizing the amount of information exchanged. Each of these techniques offers unique advantages and challenges, shaping the effectiveness of the data extraction process.  
Figure 3: Locating the area of the significant coefficients in each cube using the following equation: \(xyt\leq\sqrt{C_{1}C_{2}C_{3}}=C^{\prime}\).
## 3 Results and Discussion
### BET Analysis  
The isotherms obtained via the BET method on N-TiO\({}_{2}\) powder, together with the pore size distribution, are given in Figure 4. The properties of the N-TiO\({}_{2}\) powder as extracted from the BET analysis are presented in Table 1.  
As we are investigating a catalyst (TiO\({}_{2}\)), surface area and adsorption-desorption phenomena are of great importance. In more detail, the smaller the particles, the more the surface area increases, leading to more active sites being available, as well as to higher photoactivity [39]. The surface area of the as-prepared N-TiO\({}_{2}\) powder was 82 m\({}^{2}\) g\({}^{-1}\), which is higher than that of the Degussa P25 commercial powder (plain TiO\({}_{2}\), 53 m\({}^{2}\) g\({}^{-1}\) surface area) [40]. The space between the adsorption and desorption isotherms in Figure 4 is linked to the porosity of the specimen and is expected for materials with pores ranging from 1.5 to 100 nm in size [41]. The estimated average pore diameter was 13 nm, and the powder was porous, with a 0.3 cm\({}^{3}\) g\({}^{-1}\) pore volume. All of the abovementioned results are in accordance with the relevant literature [40].
## 2 Site Characterization  
Groundwater was sampled near the sludge storage facility for low-level radioactive waste represented mainly by uranium and its daughter isotopes. Host rocks at the site are represented by middle Quaternary fluvialocastrine sediments and can be divided into three layers. A layer of loam with a penetrated thickness of 3-9 m is located at the bottom. It is distinguished by low water permeability (Kf = \(<\) 0.001 m/day). The overlying layer is composed of intercalations of yellowish-brown loam with sandy loam and fine-grained sand. The sandy loam and sand of this layer are the soils with the highest water permeability (Kf = 1.5 m/day), and the main groundwater flow (corresponding to the first aquifer) occurs in this area. The layer at the top consists of loess-like yellowish-brown loam with a layer thickness of 8-15 m that reaches the maximum in watershed areas (Kf = 0.01-0.1 m/day). Quartz, feldspar, calcite, magnetite, and ilmenite predominate in the mineral composition. Quartz, plagioclase, potassium feldspar, mica, chlorite, calcite with limited amphibole, and smectite are present in the fine fraction (>0.001 mm). The total content of clay minerals is below 3%.  
Three samples of groundwater and soil (sandy loam collected in boreholes) taken from a depth of 7-9 m at different distances from the pollution site (sludge pond) were used for detailed analysis. Sample 1 is the natural background of the area, located at a distance of more than 300 m upstream of the collector. Sample 3 is water from a well located at a distance of ~10 m from the collector, where the dilution of technogenic water by groundwater is minimal. Sample 2 was taken at a distance of ~150 m from the collector (Table 1). In this sample, the dilution rate of chloride and nitrate ions was approximately 5.5 times that of Sample 3.  
The unpolluted (background) groundwater is fresh (total dissolved solids--TDS is below 0.47 g/L) with a hydrocarbonate-calcium composition. The contaminated samples (Samples 2 and 3) show an evident increase in TDS compared to the background value (3.83 g/L for Sample 2 and 14.9 g/L for Sample 3) due to their main components--nitrate, sulphate, chloride ions, calcium, and sodium. The water chemistry in Sample 2 is SO4-NO3-Na-Mg, but in Sample 3, it is NO3-Cl-SO4-Na-Ca, indicating that the chemical type changes even at a distance of 150 m from the well. The content of uranyl ions in the sample from the well near the contamination zone (1.58 mg/L) is 2000 times higher than the background value. It is worth mentioning that in the sample from the contaminated well, metal concentrations in excess of the MAC (maximum allowable concentration) are observed--in particular, zinc, manganese, iron, and copper. The oxygen content in the samples varies over a wide range characteristic of both aerobic-microaerophilic and anaerobic conditions.
## 5 Prediction Modeling and System Optimization
### Testing the Performance of the ANN Models
Therefore, the proposed ANN model with PSO shows strong learning ability and good generalization performance and can be used to predict the power output of the capacity regulation system. To verify the consistency of the optimization results, the conventional ANN model and the modified ANN model with PSO were trained repeatedly (five times). After each training, the test data were tested, and it was found that in different tests, the prediction deviation of the traditional ANN model was larger at some points, while the improved ANN model was close to the real value every time. Therefore, the optimization results show a good consistency.
# A Novel Slicing Strategy to Print Overhangs without Support Material
## 2 A Novel Slicing Strategy with Geometrical Pre- and Post-Transformation
### Pre-Transformation of the STL File
#### 2.2.1 Refinement of Triangulation  
The STL file describes the triangulated surface by the unit normal and the vertices of the triangles in a three-dimensional Cartesian coordinate system. The following shows the definition of one triangle in an ASCII STL file:  
```
facet normal n1n3 nk outer loop vertex v1 xv1 yv1z vertex v2 xv2 yv2 vertex v3 xv3 yv3z endloop endfacet
```  
The triangles are usually as large as possible to keep the file size small. However, with this slicing approach, large triangles lead to artifacts, which decrease the surface quality of the printed object dramatically. Figure 6 shows this effect, where the triangles of the upper STL file have a length of 15 mm and the ones of the lower STL have a length of 2 mm. So, the maximal size of the triangles is decreased, until the part reaches the required surface quality. Experience shows that the maximal length of a side of a triangle should be between 1-2 mm.  
Figure 5: Workflow architecture of the slicing strategy.  
There are different ways to refine the triangles. One is to use the export settings of the CAD, where possibly the maximal triangle size can be defined. Another way is to integrate the refinement into the Python script that transforms the STL file. Therefore, the sides of each triangle are bisected, and the three newly generated midpoints are connected (Figure 7). One triangle produces four smaller (and congruent) triangles with this procedure.  
An easy solution is to repeat the refinement several times to all triangles. However, the downside of applying it to all triangles is that already small triangles are unnecessarily refined. This has a negative effect on the file size and the computational time.
# River Surface Space-Time Image Velocimetry Based on Dual-Channel Residual Network
## 4 Experiments and Discussions
### Surface Velocity Comparison Experiments
#### 4.3.4 Test 3: Cloudy Day at Hebian Station  
To test the applicability of DCResNet to other rivers, a comparative experiment was conducted at the Hebian hydrological station in Qujing City, Yunnan Province, which monitors a typical medium-sized river in mountainous areas. The river video of this test was captured at 10:50 on 30 May 2022, during the high flood period with cloudy conditions. The river cross-section is shown in Figure 20. Unlike the two tests in Panzhihua station, the river flows from right to left, and there are more waves on the river surface. A total of nine testing lines were measured, with a starting distance of 2 m to 18 m. The corresponding STIs are shown in Figure 21.  
The MOT and surface velocity results are listed in Tables 8 and 9, respectively. At testing line 1, the camera captures fewer flow features and is accompanied by obstacle interference, which manifests as feature ambiguity in the corresponding STI. The AE of FFT is 3.94\({}^{\circ}\), while that of DCResNet is only 0.37\({}^{\circ}\). The corresponding RE of DCResNet is reduced by 38.29% compared with FFT. In addition, at testing line 8, due to the significant interference of river waves, the consistency of the STI texture is weakened. The AEs of FFT and DCResNet are 2.57\({}^{\circ}\) and 2.67\({}^{\circ}\), respectively. The corresponding RE reaches as high as  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number** & **Starting Distance (m)** & **FFT** & **DCResNet** & **Label Value** & \multicolumn{2}{c}{**RE (\%)**} \\ \cline{3-7}  & **Distance (m)** & **(m/s)** & **(m/s)** & **(m/s)** & **FFT** & **DCResNet** \\ \hline
1 & 50 & 0.20 & 2.06 & 2.21 & 90.95 & 6.79 \\
2 & 60 & 3.28 & 3.04 & 3.16 & 3.80 & 3.80 \\
3 & 75 & 3.66 & 3.75 & 3.81 & 3.94 & 1.57 \\
4 & 85 & 3.69 & 3.47 & 3.37 & 9.50 & 2.97 \\
5 & 95 & 3.92 & 4.12 & 4.01 & 2.24 & 2.74 \\
6 & 105 & 3.80 & 3.93 & 3.87 & 1.81 & 1.55 \\
7 & 120 & 3.71 & 3.84 & 3.74 & 0.80 & 2.67 \\
8 & 140 & 4.75 & 2.85 & 2.56 & 85.55 & 11.33 \\
9 & 160 & 0.56 & 1.06 & 0.99 & 43.43 & 7.07 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Surface velocity results under rainy conditions.  
Figure 19: Surface velocity distribution of test 2.  
The corresponding surface velocity distribution is shown in Figure 22. Overall, the surface velocity results obtained by DCResNet are closer to the label values than those obtained by FFT. Except for the uncovered wave scenario at test line 8, DCResNet achieves an MRE of 5.53%, representing a 7.05% reduction compared to FFT. These results demonstrate the model's good applicability to the Hebian station.