esa-sceva/satcom-chunk-collection huggingface dataset:
# Recognition of Hand Gesture Sequences by Accelerometers and Gyroscopes
## 3 The Proposed Gesture Recognition Algorithms and Systems
### PairNet with Inception  
Another technique for the improvement of the PairNet technique is the incorporation of an inception module. As shown in Figure 7, the inception module is located at layer 5 of the network. The architecture of the inception module is revealed in Figure 8, which contains four paths. Each path is associated with convolution modules, average pooling module, and/or a zero-padding module. The convolution modules at different paths have different kernel sizes. In this way, features of hand gestures with slow or fast movements can be effectively captured by the modules.  
An additional advantage of the inception module is that it has lower size of weights. Consequently, its computation complexity is lower than that of the other feedforward counterparts. The superior computation efficiency can be observed from Figure 8 that each convolution module in the inception module carries out the dimension reduction operations. That is, each convolution modules has lower number of output channels as compared with its number of input channels. In this way, the number of weights of the network may be effectively lowered. This may be beneficial for reducing the number of addition and multiplications.  
Figure 7: The parameters of the PairNet with Inception. The input \(X_{t}\) has the same dimension and number of channels as those of the input of PairNet. The network contains seven layers, where layers 1–4 are convolution layers. The inception module is located at layer 5. The architecture of inception module is revealed in Figure 8. Layer 6 is the average pooling layer. The number of gestures to be classified is \(Q=11\). Layer 7 is a fully connected layer producing 11 outputs.
# MLSL-Spell: Chinese Spelling Check Based on Multi-Label Annotation
## 4 Experiments
### Main Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & \multicolumn{3}{c}{**Detection-Level**} & \multicolumn{3}{c}{**Correction-Level**} \\ \hline SIGHAN & & & & & & \\
2013 & D-P & D-R & D-F & C-P & C-R & C-F \\ \hline LMC [8] & 79.8 & 50.0 & 61.5 & 77.6 & 22.7 & 35.1 \\ SL [4] & 54.0 & 69.3 & 60.7 & (-) & (-) & 52.1 \\ PN [7] & 56.8 & **91.4** & 70.1 & 79.7 & 59.4 & 68.1 \\ FASpell [16] & 76.2 & 63.2 & 69.1 & 73.1 & 60.5 & 66.2 \\ BERT [5] & 57.7 & 56.5 & 57.1 & 57.3 & 56.1 & 56.7 \\ MLSL-Spell & **89.2** & 87.7 & **88.4** & **79.7** & **78.3** & **79.0** \\ \hline SIGHAN & & & & & & \\
2014 & D-P & D-R & D-F & C-P & C-R & C-F \\ \hline LMC [8] & 56.4 & 34.8 & 43.0 & 71.1 & 50.2 & 58.8 \\ SL [4] & 51.9 & 66.2 & 58.2 & (-) & (-) & 56.1 \\ PN [7] & 63.2 & **82.5** & 71.6 & **79.3** & **68.9** & **73.7** \\ FASpell [16] & 61.0 & 53.5 & 57.0 & 59.4 & 52.0 & 55.4 \\ BERT [5] & 55.6 & 63.6 & 59.3 & 54.9 & 62.8 & 58.6 \\ MLSL-Spell & **80.7** & 75.1 & **77.8** & 66.4 & 61.8 & 64.0 \\ \hline SIGHAN & & & & & & \\
2015 & D-P & D-R & D-F & C-P & C-R & C-F \\ \hline LMC [8] & 83.8 & 26.2 & 40.0 & 67.6 & 31.8 & 43.2 \\ SL [4] & 56.6 & 69.4 & 62.3 & (-) & (-) & 57.1 \\ PN [7] & 66.8 & 73.1 & 69.8 & 71.5 & 59.5 & 64.9 \\ FASpell [16] & 67.6 & 60.0 & 63.5 & 66.6 & 59.1 & 62.6 \\ BERT [5] & 71.1 & 78.3 & 74.5 & 69.2 & **76.3** & **72.6** \\ MLSL-Spell & **88.6** & **82.6** & **85.5** & **74.2** & 69.3 & 71.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of MLSL-Spell model and baseline models (%). D, C represent the detection, correction, respectively. The best results for each test set are in bold. The subtask of spelling detection is extremely crucial in the CSC task. Since detecting the position of the misspelled character is a prerequisite for spelling correction, the performance of the spelling detection module directly affects the spelling correction system. In this connection, we use the fusion vectors and adopt the sequence labeling-based method for spelling detection. The experimental results prove that MLSL-Spell model outperforms the other three benchmark models in terms of spelling detection. To be specific, the maximum differences of F1 on the SIGHAN2013, 2014 and 2015 test sets are 27.7%, 34.8%, and 45.5%, respectively. For the subtask of spelling correction, we use MLM model to generate Top 100 candidates based on the context and then perform corresponding screenings based on the error type to generate the final candidate set. Following that, we screen out the candidate characters with XGBoost and take them as the spelling correction result. Experiments show that MLSL-Spell model achieves a good performance in spelling correction, and on the SIGHAN 2013, 2014 and 2015 test sets, the maximum differences of F1 are 43.9%, 7.9%, and 28.5%, respectively.
# Mesoporous Carbon as Galvanic-Corrosion Activator Accelerates Fe Degradation
## 3 Results and Discussion
### Mechanical Properties  
The typical tensile stress-strain curves of Fe and Fe/MC composites were presented in Figure 4a. As seen from the curves, the peak stresses of Fe/MC composites were obviously larger than that of Fe. The tensile strength and elongation derived from the stress-strain curves were displayed in Figure 4b. For Fe, the tensile strength was 442.3 \(\pm\) 11.1 MPa and elongation was 11.8% \(\pm\) 0.2%.  
Figure 3: The XRD spectrum of Fe and Fe/MC composites.  
Figure 2: Secondary electron (SE) and back-scattered (BSE) images showed the microstructure of as-built Fe and Fe/MC composites.  
With MC upper reaching 1.0 wt.%, the tensile strength was gradually enhanced to \(1032.9\pm 17.3\) MPa, while the elongation was reduced to \(6.8\%\pm 0.5\%\). However, after further increasing MC to 1.5 wt.%, the mechanical properties were decreased slightly, with tensile strength and elongation reducing to \(982.4\pm 22.6\) and \(3.6\%\pm 0.5\%\), respectively. In fact, micro defects appeared in the matrix of Fe/1.5 MC due to the agglomerates of MC, resulting in a weak interface and poor stress transfer. Thus, the tensile strength was decreased. The fracture surface morphologies of Fe and Fe/MC composites were presented in Figure 4c-f. It could be seen that Fe presented a typical ductile fracture covered with numerous dimples. With the addition of MC, the number of dimples gradually decreased, which represented a decrease in ductility. The fracture morphologies of Fe/0.5 MC and Fe/1.0 MC showed a small amount of cleavage planes, indicating a mixed ductile-brittle fracture. Besides, a few MC particles were observed on the tensile fracture surfaces of Fe/1.0 MC composites (as marked by red arrows in Figure 4f). As for Fe/1.5 MC, the fracture surface was covered by cleavage planes, indicating a typical brittle fracture. It was deduced that a high MC content prevented the large-scale plastic deformation of the matrix, thus reducing the ductility [33].
## 4 Results and Discussion
### Experiment 1: Evaluation of the Proposed Method on a Single Database  
To verify the effectiveness of the proposed method on a single database, experiments were performed using the MIT-BIH dataset. We used inter-patient recording divisions DS1 and DS2, as in [34], to evaluate the inter-patient performance. Table 2 illustrates the sample distribution of the 15 beat types in DS1 and DS2. Among these, only \(11\) common beats present in both DS1 and DS2 were utilized in this experiment.  
We utilized the DS2 dataset for the pre-training of the PI-beat classifier. To mitigate notable data imbalance in normal beats, we randomly sampled 20% of each patient's normal beats. The PI-beat classifier was trained to classify 11 types of beats and was used to generate PI-BSMs for 10 s ECG segments from DS1 and DS2. PI-BSMs were then fed into a binary rhythm classifier to distinguish AFib from normal rhythms. We took a binary classifier to alleviate the imbalance problem of rhythm labels. PI-BSMs from DS2 and DS1 were used for training and testing, respectively.  
Figure 5 compares the baseline n-BSMs and PI-BSMs derived from identical 1D signals. The baseline n-BSM corresponds to our earlier work [10], where the adversarial framework  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **N** & **L** & **R** & **V** & **A** & **a** & **J** & **F** \\ \hline DS1 & 38041 & 3945 & 3776 & 3681 & 806 & 100 & 32 & 414 \\ DS2 & 36386 & 4119 & 3472 & 3217 & 1734 & 50 & 514 & 388 \\ \hline  & **j** & **E** & **Q** & \(I\) & **f** & **e** & **S** & **Record** \\ \hline DS1 & 16 & 105 & 8 & 0 & 0 & 16 & 8 & 22 \\ DS2 & 213 & 1 & 7 & 0 & 0 & 0 & 0 & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of beat samples in MIT-BIH DS1 and DS2.  
was not employed. Figure 5a showcases a normal rhythm, while (b) illustrates an AFib rhythm, each originating from different patients. In the PI-BSMs, the distinction between normal and AFib rhythms is observed more clearly than in the baseline n-BSMs. This is attributed to the exclusion of patient-specific features from both rhythms. In particular, the regular beat pattern becomes more evident for the normal rhythm, whereas the irregular pattern is emphasized in the AFib rhythm. Consequently, the PI-BSM results in a sharper contrast between the two rhythms than the baseline n-BSM.  
Figure 6a,b depict the confusion matrices when baseline n-BSM and PI-BSM are utilized as the input for rhythm classification, respectively. Table 3 compares precision, sensitivity, F1-score, and accuracy for the normal and AFib classes between baseline n-BSM and PI-BSM. These results show that PI-BSM exhibits a substantially improved performance compared to the baseline n-BSM. Specifically, there is a 4.94% increase for the normal rhythm and a 27.7% increase for AFib in the F1-score, highlighting the effectiveness of the adversarial framework when applied to n-BSM within a single database.
## 4 Discussion
### Effect of the Strain Rate  
Strain rate influenced the VN foams behavior and energy absorption capabilities: The absorbed energy was four times higher at 100 s\({}^{-1}\) than at 0.01 s\({}^{-1}\). This is not surprising as the strain rate dependency has been established for other polymer foams [5; 8; 22; 34]. The difference between VN foams behavior at 100 s\({}^{-1}\) and at 0.01 s\({}^{-1}\) is probably due to a combination of the strain rate dependency of the base material [35], and both distinctive cell deformations [36], and different contributions to the gas pressurization in the closed cell between quasi-static and impact loading [24; 32]. The first consequence of this result is that VN foams must be tested at strain rates relevant to the intended use. For instance, the transmitted force of VN-B at maximum efficiency was three times higher at 100 s\({}^{-1}\) than in quasi-static compression. Thus, candidate material selection should not be based solely on quasi-static material data. Secondly, the high strain rate dependency makes it difficult to precisely compare the results with foams from other studies. Indeed, high strain rate characterization of foams is often performed with drop tests where the impactor decelerates during the deformation of the sample [5; 22; 37; 38]. For instance, in our experiments, with the same impact speed of 4 m.s\({}^{-1}\), the strain rate at 50% strain was 170 s\({}^{-1}\) for VN-A, 120 s\({}^{-1}\) for VN-B, and only 55 s\({}^{-1}\) for VN-C (Figure 3a-c). The strain rate variation also depends on the mass and the speed of the impactor and the dimensions of the foam samples. Thus, when comparing two stress vs. strain curves (or two energy diagrams) with the impact speed only, the authors compare stress vs. strain curves with different strain rates. In this article, the method of [PERSON] et al. [8] was adapted. This method consisted of interpolating a stress vs. strain curve at constant rate between stress vs. strain curves from drop test results at several impact speed. However, instead of using linear interpolation between the experimental stress vs. strain curves, a polynomial surface fitting was used to obtain the equation of the fitted surface. The advantage of this polynomial fit is that no additional knowledge of the foam is necessary: It is a phenomenological model requiring only the displacement and the force recorded during the impact tests and accounting for over 90% of the total variation in the data. However, this method could also be implemented with other mathematical models specifically developed to represent foam behavior: Phenomenological models [39] or models accounting for foam density or foam structure [6; 40]. In future works, similar methods could be used when characterizing and comparing strain rate dependent materials using drop tests.
## 2 Wireless Sensor Network Technology
### ZigBee Wireless Sensor Network Technologies  
Among the first proprietary standards for low-cost mesh networks used for WSNs was ZigBee [40]. ZigBee is a set of high-level communication protocols that utilize 2.4 GHz low-power radio modules based on the IEEE 802.15.4 LPWA standard [41; 42]. More recently, the ZigBee technology has been further developed into ZigBeePro [43] and JenNet [44]. Each component of a ZigBee based WSN has a radio module to enable wireless communication. The ZigBee radio modules have several software interfaces that connect the hardware devices (physical layer and peripherals) to the user application. The user has the possibility to control the sensor network and manage the communication between the devices by means of the application support layer (APS) and the application programming interface (API). The routing of data within the network and data transmission is handled by the media access control layer (MAC). This is based on the IEEE 802.15.4 standard and is located on the physical layer (PHY). The PHY layer includes the transceiver as well as the sensors and the power source [41]. Finally, the user has the possibility to realize advanced functions (e.g., logging function, sensor driver, etc.) by developing a special user software that configures the sensor network.  
One example of a ZigBee-based soil moisture WSN is SoilNet [14; 45], which was developed at the Forschungszentrum Julich using the proprietary license free protocol stack JenNet developed by Jennic Ltd., South Yorkshire, UK [44]. JenNet uses the unlicensed 2.4 GHz band and supports star, tree, and linear topologies. In the case of a tree topology, JenNet can support WSN with up to 250 nodes and in the case of a linear topology, even up to 1000 nodes. JenNet transmission distances are limited to less than 100 m in the case of underground WSN. That is why SoilNet uses a hybrid WSN method consisting of a mixture of underground terminals, each wired to multiple ground sensors and above-ground router devices. This allows significantly greater transmission ranges of up to several 100 m and enables SoilNet to cover whole catchment areas (Figure 3).
## 4 Results and Discussion
### Electrical Resistivity Tomography 2D Survey
It was not possible to gather a sample from the material featured in ERT with a green colour (1000-2000 \(\Omega\).m); therefore, we cannot define the exact material type (Figure 4). The results of the 2D study fit coherently with the texture datasets provided by the borehole logs and what is expected for the study area. In general, at the top, a detrital layer, 1.2 m thick, showed especially high ERT values. However, this did not always match the coarser grain sizes. BH3 resistivities between 4000 and 9000 \(\Omega\).m were recorded in medium gravels with a significant effective grain size (D\({}_{20}\)) (3.3 mm), due to matrix composed of clay and sand, which decreased the porosity estimates (\(\phi\) = 0.27) and hydraulic conductivity (CH \(\leq\) 6 10\({}^{-4}\) m.s\({}^{-1}\)). This high relationship between ERT and D\({}_{20}\) was also found in BH2 and BH4, where the highest resistivity (5000 to 8000 \(\Omega\).m) occurred in the subsurface deposits with greater D\({}_{20}\) grain sizes (2.5 to 3.2 mm), and lower HC (<7 \(\times\) 10\({}^{-4}\) m.s\({}^{-1}\)). In BH1, located on the right part of the channel, the upper sedimentary layer (1 m thick) showed moderate ERT values, associated with the presence of medium gravels and higher porosity (\(\phi\) = 0.35). The central part a longitudinal bar has been developed, whose recent evolution reflects a sedimentary pattern typical of several ephemeral gravel-bed streams, which are subjected to the effects of climate change: a progressive decrease in particle size with depth and strong surface armouring. Conversely, the sorting index increased towards the deeper alluvial layers.
## 3 Radiotherapy for Rectal Cancer  
### The Evolving Role of Radiotherapy for Rectal Cancer  
Locally advanced rectal cancer (LARC) surgery alone resulted in high local recurrence (LR) rates in the past, reaching 30% in the pre-Total Mesorectal Excision (TME) era, with significant morbidity. Thus, RT was used to improve local control (LC), with contemporary regimens including short-course RT (SCRT, 25 Gy in 5 fractions) and long-course chemoRT (LCCRT, 45-50 Gy in 25 fractions concurrently with radiosensitization).  
Initial randomized trials like GITSG 7175 [22] and NSABP R-02 [23] showed that postoperative LCCRT improved LC. The Swedish Rectal Cancer Trial [24] was the first to demonstrate the efficacy of preoperative SCRT in reducing LR (9% vs. 26%) and improving survival, though it lacked modern surgical techniques like TME. The Dutch TME trial [25] confirmed the LR benefit of preoperative SCRTs (2.4% vs. 8.2%) without a significant difference in overall survival (OS).  
Subsequently, two randomized studies confirmed the superiority of preoperative over postoperative RT. [PERSON] et al. [26] reported reduced LR (6% vs. 13%) and toxicity with preoperative LCCRT. The MRC CR07 trial [27] found SCRT reduced LR risk and improved disease-free survival (DFS), with positive circumferential resection margins (CRM) predicting higher LR risk despite postoperative therapy.  
The optimal neoadjuvant RT regimen remains debated despite both SCRT and LCCRT showing oncological benefits. SCRT is shorter and more cost-effective but raises concerns about suboptimal tumor downstaging and sphincter preservation. The Polish I trial [28] found no significant differences in OS, DFS, or sphincter-preservation rates between SCRT and LCCRT, though SCRT outcomes may reflect staging inaccuracies and prognostic imbalances. The T-TROG 01.04 trial [29] confirmed higher pathological complete response (pCR) and downstaging with LCCRT but no differences in long-term outcomes. However, SCRT trended toward higher LR rates, especially for distal tumors. To address downstaging limitations, the Polish II trial [30] showed that adding chemotherapy after SCRT improved pCR rates.  
SCRT's shorter duration is advantageous in settings with limited RT access or during crises like the COVID-19 pandemic. However, LCCRT is recommended for cT4, node-positive, low-lying tumors, or threatened mesorectal fascia due to its superior downstaging potential. Both regimens remain valid, with patient-specific factors guiding choice.  
LCCRT is typically combined with chemotherapy agents that act as radiosensitizers to enhance the cytotoxic effect. Fluoropyrimidines remain the standard for radiosensitization, offering improved LC and reduced LR rates in rectal cancer. FFCD 9203 [31] and EORTC 22921 [32] trials demonstrated improved LR pCR rates with chemoRT (CRT) over RTalone, without significant differences in sphincter preservation, OS, or DFS. The addition of oxaliplatin [33] increased toxicities without improving key outcomes. Monoclonal antibodies [34; 35] failed to show perioperative benefits.
## 6 Conclusions  
In this study, six polygon shape indicators are selected to establish shape complexity models for data partitioning. Using vector data sets containing two different amounts of data, we perform multicore parallelization of the intersection operator and difference operator of the Vatti algorithm and analyze the results of the proposed multicore parallelization method in comparison with the results of the traditional method based on polygon number division. The research conclusions are as follows:  
1. For large data sets, when the Vatti algorithm intersection operator and difference operator are executed on multicores in a parallel environment with more than four threads, the data partitioning method based on shape complexity significantly improves the speedup and load balancing performance compared with the data partitioning method based on the number of polygons.
2. For small data sets, multicore parallelization with data division based on shape complexity has acceleration effects similar to those of the traditional multicore parallelization method of dividing data based on polygon numbers. However, the proposed data partitioning method based on shape complexity can greatly improve the load balancing among threads.  
In summary, the multicore parallelization method designed in this article based on shape complexity-based data partitioning can significantly improve the running speed of the Vatti algorithm and achieve better load balancing. When data complexity increases, higher speedups can be achieved. Relevant research results are highly important for overcoming bottlenecks such as balanced task decomposition and effectively improving the computing efficiency of parallel vector polygon overlay analysis algorithms in a big data environment.  
Conceptualization, J.F. and G.S.; Data curation, J.Z.; Formal analysis, J.Z. and Z.S.; Funding acquisition, J.F. and G.S.; Investigation, Z.S.; Methodology, J.F. and J.Z.; Resources, Z.S.  
Y.G. and Y.Z.; Software, J.Z. and Z.S.; Supervision, G.S.; Validation, J.Z.; Visualization, J.Z. and Y.G.; Writing--original draft, J.F. and J.Z.; Writing--review & editing, J.F., G.S. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 42171413), a grant from the State Key Laboratory of Resources and Environmental Information Systems, and the Natural Science Foundation of Shandong Province (No. ZR2020 MD015).  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[http://www.openstreetmap.org](http://www.openstreetmap.org)]([http://www.openstreetmap.org](http://www.openstreetmap.org)), accessed on 1 July 2023.  
We would like to thank the OpenStreetMap. We appreciate the editors and reviewers for their constructive comments and suggestions.  
The authors declare no conflicts of interest.