esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Chinese Named Entity Recognition Based on Lexical Information and Spatial Features
###### Abstract
The Bi-LSTM model is chosen for two reasons: on the one hand, the number of matching words in each sentence is different; on the other hand, the global bi-directional semantic and sequential temporal information of the matching words is obtained. After obtaining the matched words' global bidirectional semantic and sequential temporal information, they are fused with character features for predicting entity labels. By calculating the loss value and feedback function between the predicted label and the actual label, the conflict-matching vocabulary for obtaining global bidirectional semantic and sequential temporal information is iteratively optimized multiple times, gradually reducing the weight of the conflict-matching vocabulary to promote Chinese-named entity recognition. Secondly, preceding research within this study has already extracted the semantic information of characters and matched vocabulary, leading to the derivation of a seq*seq attention score matrix using a self-attention mechanism. In order to encapsulate the spatial relationships among characters, local attention is introduced; the local spatial relationship between the characters of unfused matching vocabulary and those of fused matching vocabulary is mainly captured by the convolutional layer; this strategy additionally fosters entity segmentation, thereby augmenting the effectiveness of named entity recognition. The principal contributions of this paper can be summarized as follows:  
* This article introduces Bi-LSTM to obtain global bidirectional semantic information and sequential temporal information of matching vocabulary. Reduce the weight of conflict-matching vocabulary to alleviate the impact of matching vocabulary conflicts and character fusion on entity participle. * The local attention method extracts the local spatial relationship between characters with unfused matching vocabulary and characters with fused matching vocabulary, further promoting the entity participle. * The efficacy of the SISF approach introduced in this paper, in comparison to both the baseline model and an enhanced variant derived from the baseline model, is validated through experiments conducted on four openly accessible Chinese-named entity recognition datasets. The framework of this article is as follows: Firstly, in Section 1, we explore the challenges and solutions faced by vocabulary enhancement in the field of Chinese named entity recognition; Section 2 provides an overview of relevant research on Chinese named entity recognition; Section 3 provides a detailed analysis of existing methods; in Section 4, we introduce the dataset on which our proposed method relies for training and evaluation, as well as the baseline model used for comparison; Section 5 conducts extensive experiments on four publicly available Chinese datasets and analyzes the experimental results in depth to validate the effectiveness of our proposed SISF method; and finally, at the end of the article, we summarize the results of this study and look forward to possible directions for future research.
## 2 Numerical Simulation and Parameter Selection
### The Soil Material Model  
The analysis assumed the soil to be a medium clay deposit, which was modeled as a perfectly elastic-plastic Mohr-Coulomb model utilizing a 15-node element. The properties and shear strength parameters employed for the medium clay are listed in Table 1.
# A New BDS-2 Satellite Clock Bias Prediction Algorithm with an Improved Exponential Smoothing Method
## 4 Results and Discussion  
Five modeling strategies are used to predict SCBs for short-term prediction first, and the forecasting performance of these models is analyzed in detail. Then, the medium-term prediction of SCBs is performed using seven modeling strategies, and the prediction performance of these models is analyzed.
# A New Approach to Defining Uncertainties for MODIS Land Surface Temperature
## 3 Validation
### Uncertainty Validation
Larger differences are primarily associated with either higher uncertainties due to cloud contamination or higher values of \(\sigma_{space}\). The former is related to how well the operational cloud detection algorithm correctly identifies cloud types over the different surfaces where the validation station is located, the latter is a function of the heterogeneity of the \(5\times 5\) pixel region of interest surrounding the station. Surface types where cloud contamination is more likely, due to the challenge of distinguishing cloud features from surface features, could lead to an underestimate of the uncertainty leading to larger biases in the matchup pair. For landscapes with higher heterogeneity, the standard deviation of the \(5\times 5\) pixel region of interest may not accurately represent the difference between the in situ measurement and the mean of the \(5\times 5\) pixel satellite pixels if the station is measuring an unrepresentative surface in the context of the wider landscape. For the validation matchups, the reduced chi-squared test can be employed to assess the goodness-of-fit between the observed uncertainty and the estimated uncertainty. The chi-squared values for each site are given in Figure 5. An accurate estimation of the uncertainty would result in a chi-squared value of 1.0. Values \(<\)1.0 imply that the estimated uncertainties are on average overestimating the observed uncertainties; and values \(>\)1.0 imply that the estimated uncertainties are on average under-estimating the observed uncertainties. The values for the seven sites are spread between 0.6 and 1.28 with the most homogeneous sites showing chi-squared values close to 1.0. Figure 5: Validation of the satellite uncertainty estimates over seven SURFAD validation sites: (**a**) Table Mountain, (**b**) Penn State University, (**c**) Bondville, (**d**) Desert Rock, (**e**) Goodwin Creek, (**f**) Sioux Falls, (**g**) Fort Peck. The standard deviation of satellite LST minus ground-based LST is plotted against bands of the total uncertainty budget for the matchup. The cone shape is distorted near zero since the in situ instruments have a calibration uncertainty. Chi-squared goodness-of-fit values are given for each site. Overall the uncertainty model is providing a reasonable representation of the observed uncertainties. The principle explanatory factor for site-to-site differences in the uncertainty validation is a result of surface heterogeneity around each site.
## 2 Materials and Methods  
### Deposition of Vertically Arranged ZnO NRs AR on c-Si Wafer  
A P-type commercial c-Si wafer (40 \(\times\) 40 mm\({}^{2}\)) with a thickness of 120 um and sheet resistance of 1-3 \(\Omega\)cm was used as a substrate. The textured Si wafer was thoroughly cleaned by sonication, using acetone and deionized (DI) water for 15 min each. For seed layer deposition, a solution was prepared by mixing 0.9174 g (0.1 M) of zinc acetate hydrate in 50 mL of ethanol. The ZnO seed layer was deposited on the surface of a pre-cleaned Si wafer and a textured Si wafer by spin coating at 2000 rpm for 30 s, and it was annealed in a furnace at 100 \({}^{\circ}\)C for 1 h. For the growth of ZnO NRs on the surface of the Si wafer, a solution was prepared by mixing of 0.1 M of zinc nitrate hydrate (Zn(NO\({}_{3}\))\({}_{2}\)) and 0.1 M hexamethylenetetramine (C\({}_{6}\)H\({}_{12}\)N\({}_{4}\)) with 50 mL of DI water. The ZnO-seeded Si substrate was dipped vertically in an airtight vessel and kept in an oven at 75 \({}^{\circ}\)C for 3, 4, and 5 h. After a specific time, the Si wafer was taken out gently and washed with ethanol and DI water several times. Finally, it was annealed in a furnace at 250 \({}^{\circ}\)C for 1 h, as described in Figure 1. For optimization of the morphology of ZnO NRs on Si substrates, both bare and textured Si wafers were used to grow ZnO NRs. The morphologies and optical properties of the grown ZnO NRs were investigated, analyzed, and characterized by a variety of analytical tools like field emission scanning electron microscopy (FESEM), ultraviolet-diffuse reflectance spectroscopy (UV-DRS) spectroscopy, photoluminescence (PL) spectroscopy, Raman analysis, and PC1D simulation tools.
## 3 Development of a Laboratory Setup
### Development of the Spectrometer  
The spectrometer is one of the central components of the SD-LCI. In the spectrometer, the superimposed signals from the measuring and reference arms are broken down into their spectral components and evaluated. It consists of an optical grating to diffract the radiation, a lens package to focus the diffracted spectrum and the camera on whose line the focus is set. Simulations were used to determine the lens package in the spectrometer. The aim of the simulation is to image the measurement spectrum on the camera line with the smallest possible spot sizes, taking into account all pixels of the camera as well as the entire wavelength range of the light source.  
Figure 1: Transmission curve of the beam splitter with drawn-in spectra of the process laser and the selected light source.  
To avoid a loss of intensity, the beam diameter used to illuminate the grating was chosen to be 10 mm. This ensures that the light does not come into contact with the non-optical components of the spectrometer. Initially, some lens configurations were simulated with an automated simulation; for cost reasons, only standard lenses were used.  
The three best configurations were then further optimized manually. The parameters considered here were the size of the Airy-disc, the diffraction limit and the RMS of the spot radii. In a further step, the behaviour of the systems was compared with the MTF to achieve an optimal contrast ratio. Figure 2 shows a schematic sketch of the optical components of the optimized system.  
Based on the simulation with Zemax, a system for beam shaping within the spectrometer could be identified and already optically designed. Thereby the boundary conditions for the mechanical construction of the spectrometer are determined, so that a following construction can be performed.
## 1 Introduction  
Source-Drain leakage current, known as one of the short channel effects, is successfully suppressed on FinFET transistors. The structure of FinFET looks like an emerging 3-dimensional \"I\" character with Source and Drain as the two ends and the channel in between. The applied bias on Gate poly-silicon crossing over the channel depletes the whole slim channel and builds up a barrier in between Source and Drain, which thus blocks or prevents the possibility of leakage current. The process is achieved because of the good conformality (step coverage) of chemical vapor deposition by flowing SiH\({}_{4}\) (silane) at chosen flow rate under certain pressure at an appropriate temperature in kinetic regime for a good deposition rate [1; 2; 3; 4; 5].  
Furthermore, the electrical performance of FinFET transistors may be also enhanced either by high dielectric constant gate oxide or by high mobility channel. On one hand, the capacitance of the gate capacitor is to be raised if the dielectric of the gate oxide, mainly silicon dioxide, can be replaced with Hf-mixed tantalum oxide whose dielectric constant is about 5 times of that of N-mixed silicon dioxide. For instance, 90 nm-process devices can be equivalently reduced to 14 nm-process devices, which is quite encouraging once the process is production-worthy. On the other hand, the mobility of silicon channel may be promoted by even 2.5 to 4 times as SiGe is technically and sophisticatedly introduced stack by stack. The above advanced techniques and other options are definitely promising and achievable, making FinFET continuously popular as currently [6; 7; 8; 9; 10; 11; 12].  
However, the electrical performances, mainly manifested in current-versus-voltage characteristic curves (I-V curves), are thus put to be parameter-extracted in the model, which takes advantage of sophisticated equivalent circuits for academic and industrial uses. Nevertheless, the measured I-V curves are speculated to be also fitted by the \"modified\" conventional formula [13; 14; 15; 16; 17; 18].  
In this study, the \"modified\" conventional I-V characteristic curve formula naively generates fitting data to fit as measured (\(l_{DS}\), \(V_{DS}\)) and (\(l_{DS}\), \(V_{GS}\)) data by choosing appropriate parameter values, such as the threshold voltage (\(V_{th}\)), lambda (\(\lambda\)) which is inversely proportional to the absolute value of Early Voltage (\(V_{A}\)), and \(k_{N}\) which is proportional to the total width of the channel and inversely proportional to the channel length.
## 4 Experimental Evaluation
### Ablation Experiments
#### 4.4.2 Results and Analysis  
The training loss of each experiment is shown in Figure 8. It can be seen that each experiment converged faster, and after training to 30,000 rounds, the change in the loss decreases and the curve tends to be smooth, with the least oscillation occurring in Experiment 4, i.e., the experiment with the input being the Shuishu_T dataset.  
The test results of each experiment on the test set are shown in Table 7. The results show that the mAP of Experiment 1 was not high, only 67.4%. The highest mAP was 99.4% and the lowest was zero. There were 13 categories that were not recognized, thus lowering the average recognition accuracy--indicating that the crux of the low mAP lies in the dataset rather than the model.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Experimental Group** & **Dataset Combination** & **Number of Images** & **Number of Character Samples** \\ \hline Experiment 1 & raw data & 1734 & 91,127 \\ Experiment 2 & raw data + handwritten data & 1773 & 103,778 \\ Experiment 3 & raw data + handwritten data + image & 83373 & 142,161 \\ Experiment 4 & Shuishu_T (cropped “raw + handwritten” data & 10583 & 164,789 \\  & & & \\ \hline \end{tabular}
\end{table}
Table 6: Dataset information of each experiment.  
Figure 8: Training loss of each experiment.  
In Experiment 2, manual handwriting data were added and a mAP of 68.8% was obtained, an improvement of 1.4 percentage points over Experiment 1. The number of categories in Shui manuscripts that could not be recognized was reduced to 11. The reason there were still target samples that could not be recognized is that the amount of manually handwritten data was still small. Although the improvement in mAP for Experiment 2 was small, it proved that the manual handwriting approach to data augmentation can effectively improve recognition accuracy.  
In Experiment 3, data from image synthesis were added, resulting in a further increase in the amount of data for the Shui character samples and a gradual reduction in the gap between the categories of rare characters and commonly used characters. Thus, in Experiment 3, a mAP of 78.9% was obtained, an increase of 10.1 percentage points over Experiment 2. In Experiment 3, all 111 categories of Shui characters were recognized. The highest mAP obtained for each category in Experiment 3 was 99.8%, and the lowest mAP was 18.2%. The significant increase in mAP in Experiment 3 is due to the addition of 38,383 samples of rare characters by image synthesis. In addition, the diversity of character samples added by image synthesis enhanced the generalization ability of the model, which is also one of the reasons for the improved recognition accuracy.  
In Experiment 4, the Shuishu_T dataset with cropped images was used, and it had 22,628 more character samples than in experiment 3. In the end, in Experiment 4, a mAP of 85.8% was obtained, which is 6.9 percentage points higher than that in Experiment 3. Experiment 4 demonstrates that image cropping is also effective in improving the recognition accuracy of characters in Shui manuscripts.
## 8 Experimental Constraints  
Since the NS2 does not duplicate cumulative interference, the study considered its limitation and the receiver sensitivity (RXThresh) and capture threshold (CPThresh). When the physical carrier sense is utilized, a mobile node desiring to dispatch information should first detect the channel. Once the energy is identified on the channel (carrier sense threshold), the channel is occupied, and the node must postpone its transmission. Thus, a carrier-sense threshold suggests that even the mobile nodes quite far away from a sending node would recognize the channel as occupied and defer.
# Detection of Cavities from Dental Panoramic X-ray Images Using Nested U-Net Models
## 2 Methodology
### Models Employed
#### 2.1.2 U-Net ++  
To tackle the demand for improved segmentation of medical images, U-Net was enhanced through the creation of an architecture that incorporates nested and dense skip connections. This updated version was coined U-Net++ [22]. However, sufficient information has not been explored at full scale, and there is a need for further improvement [23].
## 5 Frequency Hopping Techniques
### Channel-Aware, Reduced-Hop-Set Techniques
#### 5.2.4 Advanced Frequency Hopping  
The Advanced Frequency Hopping (AFH) technique further improves the performance of the MFH technique by selecting more channels with higher gains [30]. The modified power metrics are given by  
\[Q_{k}=\frac{\left\lvert H_{k}\right\rvert^{2}}{(1+\alpha)max\left(\left\lvert H _{k}\right\rvert^{2}\right)-\left\lvert H_{k}\right\rvert^{2}} \tag{11}\]  
where \(\alpha\) is a small scaling factor that can be adjusted to obtain different responses. The MFH technique is then applied to the new power metric.  
Figure 8 illustrates the channel selection for the HGFH, MFH, CMFH and AFH techniques. In all the cases \(M=10\) channels have been selected. It can be seen how the MFH technique selects more dispersed channels compared to the HGFH. The CMFH and AFH techniques modify the power metric to cluster the channel selection around the highest gain channels but still preserving certain channel separation.  
Figures 9 and 10 illustrate the evolution of the channel selection in the CMFH and AFH techniques when varying the \(\xi\) and \(\alpha\) parameters. When increasing the threshold in the CMFH technique, the selected channels tend to concentrate around the highest gain channels, as more bad channels are discarded. The same happens when decreasing the \(\alpha\) parameter in the AFH technique.  
Figure 8: _Cont._  
Figure 8: Comparison of channel selection between different frequency hopping techniques: (**a**) Highest Gain Frequency Hopping (HGFH); (**b**) Matched Frequency Hopping (MFH); (**c**) Clipped Matched Frequency Hopping (CMFH) with \(\xi=0.1\); (**d**) Advanced Frequency Hopping (AFH) with \(\alpha=0.1\). The selected channels are represented as vertical black lines. HGFH directly uses the channel gain (red) to select the best channels, while MFH, CMFH and AFH use the cumulative metrics (blue) obtained from the power metrics (green) and their intersections with the equally spaced horizontal lines.  
Figure 9: Channel selection evolution in the Clipped Matched Frequency Hopping (CMFH) technique when varying the threshold parameter: (**a**) \(\xi=0.1\); (**b**) \(\xi=0.3\); (**c**) \(\xi=0.5\).
## 4 Discussion  
### Advantages of the Proposed F-SFM Algorithm  
Some recent studies have been proposed focusing on the full-spectrum SIF retrieval based on the SFM algorithm [30, 31]. The reconstruction of reflectance and SIF spectra is a major problem in full-spectrum SIF retrieval. [PERSON] _et al._[30] tested the performance of different mathematical functions for the reconstruction of reflectance and SIF spectra. [PERSON] _et al._[31] used polynomials for the reconstruction of reflectance but used the SVD technique for the reconstruction of SIF spectra. However, the shape of reflectance spectra is complex and difficult to accurately determine using simple mathematical functions. This is especially true for the O\({}_{2}\)-B band, which is located near the \"red-edge\". To deal with this problem, in F-SFM, both the reflectance and SIF spectra are modeled by PCA based on a simulated training dataset. We have also introduced an iterative process to improve the accuracy of the reflectance estimations. In each iteration, the SIF spectra retrieved from the previous run are removed from the total upwelling radiance to minimize the small contribution of the SIF to the apparent reflectance outside the absorption bands. Using this iterative process, the accuracy of the estimated reflectance can be significantly improved.  
As shown by the tests using a simulated dataset, F-SFM can provide accurate estimate of full-spectrum SIF. In addition, compared to the 3 FLD and iFLD methods, F-SFM is less sensitive to the SR or SNR. For example, the widely used ASD FieldSpec spectrometer has an SR of 3 nm, which is not sufficient for SIF retrieval by the 3 FLD or iFLD methods at the O\({}_{2}\)-B band. The F-SFM algorithm, however, can provide a satisfactory SIF retrieval performance using the ASD FieldSpec spectrometer, giving an RRMSE of less than 20%.
## 2 Materials and Methods
### Immunohistochemistryistochemistry and Western Blot Analysis  
The melanoma tissue was excised from the animals of each group and fixed in formalin for the preparation of the paraffin sections. It was then deparaffinized in xylem, and then with 100%, 90%, 80%, and 70% ethanol, followed by treatment with phosphate-buffered saline (PBS). Tumor sections were stained with hematoxylin dye and counterstaineded with eosin dye (H&E). Afterwards, tumor sections were immunost overnight at 4 \({}^{\circ}\)C with the _P53_ antibody (1:300; Abcam, Cambridge, MA, USA), the _Parp_ (1:300; Abcam), the _Bcl-2_ (1:200; Abcam) antibody, _Casp8_ (1:200; Abcam) antibody, and _Akt_ (1:200; Abcam) antibody. After washing in PBS, a 1:200 dilution of biotinylated goat anti-rabbit IgG or anti-mouse IgG antibody in a blocking solution was applied to the sections. After the PBS treatment, ABC reagent was applied to the sections and incubated for 1 h. After hematoxylin counterstaining and clearing with a graded ethanol series and xylene, sections were mounted with Canada balsam. Images were photographed using an IX71 microscope (Olympus Tokyo, Japan) equipped with the DP71 digital imaging system (Olympus). For western blot analysis proteins were extracted and using SDS-PAGE, were separated and blotted into nitrocellulose membrane. Furthermore, samples were incubated with the primary antibodies overnight at 4 \({}^{\circ}\)C; anti-_Parp-1_, anti-_Casp8_, anti-_Akt_, anti-_P53_ and anti-_s-actin_. The blots were developed using horseradish peroxidase-conjugated secondary antibodies at room temperature for 2h and anticipated using chemiluminescence procedures.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Sequence (5\({}^{\circ}\)-3\({}^{\circ}\))** \\ \hline _18s RNA F_ & CAGGTCTGGTGTGCCTTAGA \\ _18s RNA R_ & GCTTATGACCGCACTACTG \\ _P53 F_ & GCCCCTCCTCAGCATCATCAT \\ _P53 R_ & AAAGCTGTTCCGTCCCAGTAG \\ _Bax F_ & AAGAAGCTGAGCTGTTCTC \\ _Bax R_ & GCTGGCAAAGTAGAAAAGGGC \\ _Casp8 F_ & CCCAAATCAACAAGAGCCTGC \\ _Casp8 R_ & TCAGACAGTATCCGGGTT \\ _Akt F_ & CAAAGAAGTGCAAAGGGGGTGC \\ _Akt R_ & TGTAGCCAATGAAGGTGCCAT \\ _Parp F_ & GGTATTGGAAGGGTAGCCG \\ _Parp R_ & TCCCCCACAGACACAACACACAAA \\ _Bcl-2 F_ & CCACACAGAAGCAGGAAC \\ _Bcl-2 R_ & GCAGGATAGCACACAGGAT \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of primers used for the study. 18s RNA was kept as the endogenous control for the q-pcr experiment. (F = forward primer; R = Reverse Primer).