esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Key Information Extraction
#### 2.1.2 Modern Key Information Extraction Methods  
With the rapid development of deep learning, various deep neural networks such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), graph neural networks (GNNs), and Transformer have been used for document image information extraction, and these algorithms are mainly concerned with the extraction and fusion of multimodal features. Grid-based methods represent images as 2D grids with word embeddings, preserving the layout features in 2D feature maps and adding encoded textual information. Chargrid [10] utilizes 2D grid representations of rich visual information to predict the classes of entities in the grid, transforming the visual information extraction task into an instance-level semantic segmentation task. However, such methods only simplify text and structure embedding, and the fusion effect is ineffective. The GNN-based approach [11; 19] views the document image as a graph structure consisting of text slices, using a neural network model to learn the relationships between the text slices. [PERSON] et al. [11] propose using GNN to learn the semantic and layout information of the text slices. Although it takes advantage of the textual and structural information, the image information is not well utilized. The end-to-end-based approach directly uses the original image as input to obtain the key information content of the document. TRIE [13] combines the text detection and recognition system with the information extraction module into an end-toend joint optimization system. This method can reduce the accumulation of OCR errors to a certain extent, but it has many parameters and still has a gap with the Transformer-based model. The Transformer-based method uses the attention mechanism in the Transformer to fuse multimodal features [12; 20; 21; 22; 23]. BERT [20] proposes a pre-training paradigm for large-scale datasets based on the Transformer structure. LayoutLM [12] introduces additional positional embedding features based on BERT and introduces a pre-trained model to the key information extraction task. BROS [21] focuses on the logical relationships and semantic associations between each text segment, encoding the relative positional relationships between each word. LayoutLMv2 [22], LayoutXLM [23], LayoutLMv3 [24], StrucTexT [25], and StrucTexTv2 [26] further implement cross-modal interactions of image, text, and layout information in the pre-training phase. LayoutLMv2 [22] proposes a spatial-aware self-attention mechanism to encode the relative relationship between text boxes to provide a broader perspective for contextual spatial modeling. LayoutXLM [23] extends the multilingual visual richness model by utilizing the initialization weights of the pre-trained multilingual InfoXLM [27] based on the LayoutLMv2 framework. XYLayoutLM [28] proposes an augmented XYCut module to generate a reasonable reading order, further improving the model's performance. VI-LayoutXLM [29] reduces the cost of the inference time by removing the visual feature part based on the LayoutXLM structure.
## 4 Structural Models  
After the damage occurrence is confirmed and for a further detection, the model-based methods are always introduced for localizing and quantifying the damage based on the measured responses.  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Data Domain** & **Representative Methods** & **Advantages and Disadvantages** \\ \hline \multirow{7}{*}{Time domain} & Ibrahim method & Advantages: \\  & Random decrement & straightforward to use the time domain responses \\  & ERA & eliminate the need to perform frequency transformation \\  & ARMA family & no associated errors such as leakage and truncation \\  & Wavelet analysis & preserved nonlinear behavior \\  & EMD and HHT & Disadvantages: \\  & RSSI, SSI-DATA, SSI-COV & significant effects of noise \\  & KDE & difficult to interpret signal information \\ \hline \multirow{7}{*}{Frequency domain} & FRF shapes & Advantages: \\  & FRF curvature & abundant information on structure dynamic behavior \\  & Transmissibility & contains frequency information in a wide range \\  & Peak picking & disadvantages: \\  & FDD & some requires the input information \\  & & the features are too abstract \\ \hline \multirow{7}{*}{Modal domain} & Natural frequency & Advantages: \\  & Mode shapes and curvatures & modal properties are physically meaningful \\  & Modal strain energy & easy to be interpreted or interrogated \\ \cline{1-1}  & Strain mode shapes & from output-only data and not require artificial forces \\ \cline{1-1}  & Dynamically flexibility & Disadvantages: \\ \cline{1-1}  & High-order derivatives & lose much of the information \\ \cline{1-1}  & Signal processing based & numerical errors caused by inaccurate curve fitting \\ \hline \hline \end{tabular}
\end{table}
Table 1: Guideline of Response-based Methods.  
To this end, it is necessary to have a structural model to represent the damage and the key characteristics of the structure system. Generally, two models are required, namely, a well-correlated model of the structure in its initial state, which is used as a reference model, and an updated model that reproduces the measured data under the damaged state through modifying the structural model parameters such as mass, stiffness, damping, etc. Comparisons of those two models provide the damage location and its severity. The structure models usually used for the model-based damage identification are as follows.
# Line Segment Matching Fusing Local Gradient Order and Non-Local Structure Information
## 5 Conclusions  
The present paper proposes a line segment matching method fusing intensity histogram adaptive partitioning, local gradient order information, and non-local structure information, in an attempt to match line features in various cases. The experiment shows that the designed method succeeded in improving the effectiveness of line segments matching in various scenes. The proposed method achieved higher scores in precision, recall, and F1-Measure than MSLD and LPI, especially in the cases of rotation and illumination changes. Our matching performance is slightly lower than that of IJL, but IJL's time cost is significantly higher than our method. In addition, compared with the latest methods (reference [11] (2020) and reference [22] (2021)), our method also has better matching performance in most scenes. Therefore, the proposed method not only ensures certain real-time performance, but also ensures excellent matching performance.  
Conceptualization, W.C. and K.L.; methodology, W.C. and J.C.; software, W.C. and J.C.; validation, W.C., J.C. and J.D.; formal analysis, J.C.; investigation, W.C.; resources, K.L.; data curation, J.D.; writing--original draft preparation, W.C. and J.C.; writing--review and editing, K.L., J.Z. and H.X.; visualization, Y.Z.; supervision, K.L.; project administration, K.L.; funding acquisition, K.L. and H.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Science Foundation of China (NSFC)--Guangdong Big Data Science Center Project under Grant U1911401, in part by the Science and Technology Planning Project of Guangdong Province under Grant 2015A030401086, and in part by the South China Normal University National Undergraduate Innovation and Entrepreneurship Training Program under Grant 202010574050 and 202110574048.  
The datasets used in this article and the code for MSLD, LPI, and IJL are available from this website: [[http://kailigo.github.io/projects/LineMatchingBenchmark](http://kailigo.github.io/projects/LineMatchingBenchmark)]([http://kailigo.github.io/projects/LineMatchingBenchmark](http://kailigo.github.io/projects/LineMatchingBenchmark)) (accessed on 11 December 2021).  
The authors declare no conflict of interest.
# Extended Reality Application Framework for a Digital-Twin-Based Smart Crane
## 2 Materials and Methods
### Hardware Setup  
Oculus is leading the customer-based market of the VR headset given its affordable price, portability, and sufficient capability [62]. Oculus Quest 2, released in 2021, is equipped with four infrared cameras that enable location tracking, hand tracking, and pass-through. Its per-eye resolution is 1832 \(\times\) 1920; the refresh rate is 90 Hz, and the FOV (Field of View) is around 120\({}^{\circ}\). The connection mode of the controller is inside-out tracking.  
HoloLens 2 is the latest MR device developed by Microsoft in 2019. It adapts multiple sensors, advanced optics, and holographic processes to blend seamlessly with the environment, sense the spatial orientation of the surrounding unit, track the obstacles, and generate holograms to display information and simulate a virtual world. An Optical see-through display of HoloLens 2 enables the user to view the natural world in full resolution. The FOV of HoloLens 2 is 43\({}^{\circ}\)\(\times\) 29\({}^{\circ}\); the refresh rate is 60 Hz, and the resolution is 2 K. Compared to HoloLens 1, HoloLens 2 supports OpenXR.  
In the professional business-based market, high-resolution and high performance are extensively required by the business customer. Varjo technology released Varjo XR-1 Developer Edition in 2019, which allows engineering, design, and simulation professionals to blend the real world with the virtual world. As the first industrial photorealistic mixed reality headset, it is equipped with two screens, with a per-eye resolution of 1920 \(\times\) 1080 (micro-OLED) and 1440 \(\times\) 1600 (AMOLED), 90 Hz refresh rate, and 87\({}^{\circ}\) FOV for VR. Additionally, the video see-through display of Varjo XR-1 supports AR function with FOV 82\({}^{\circ}\)\(\times\) 82\({}^{\circ}\). The tracking mode of Varjo XR-1 is outside-in tracking from two extra infrared base stations. Consequently, it has no hand-tracking feature. An additional computer with a thunderbolt card installation is needed to set up Varjo XR-1. Table 2 shows the comparison of the three XR devices used in the research.
## 5 Conclusions  
The proton RBE is studied for a clinically relevant energy range of 1-250 MeV at three different dose levels (2 Gy, 5 Gy, 7 Gy) and for five cell types (of different \((\alpha/\beta)_{R}\) ratios) using nine empirical proton models and two established biophysical models (TDRA and MKM). Empirical models express the RBE as a function of the macroscopic quantity \(\text{LET}_{\text{D}}\) while biophysical models commonly use the microdosimetric quantity \(y_{\text{D}}\). For the evaluation of those parameters (\(\text{LET}_{\text{D}}\) and \(y_{\text{D}}\)), we compare an established analytic microdosimetry model with the MCDS code. The results showed that, for conventional dose fractions (\(\sim\)2 Gy) and at proton energies that correspond to the proximal and central regions of the SOBP, a constant value of RBE = 1.1 is reasonable at the \(\pm\)10% accuracy level. For low-energy protons distal to the Bragg peak, published RBE models predict significant deviations of up to \(\sim\)20-40% from RBE = 1.1, especially for cells with low \((\alpha/\beta)_{R}\). For hypoffractionated dose fractions (5-7 Gy), deviations from the fixed value are less pronounced but still sizeable (up to \(\sim\)5-20%). However, the comparable discrepancies observed among the different models make the selection of a variable RBE across the SOBP uncertain.  
Conceptualization, D.D., A.P., I.K., R.D.S., P.K. and D.E.; methodology, D.D., A.P. and D.E.; software, D.D. and A.P.; validation, D.D. and A.P.; formal analysis, D.D., A.P., I.K. and D.E.; investigation, D.D., A.P., I.K., R.D.S., P.K. and D.E.; resources, D.D., A.P., I.K. and D.E.; data curation, D.D. and A.P.; writing--original draft preparation, D.D. and A.P.; writing--review and editing, D.D., A.P., I.K., R.D.S., P.K. and D.E.; supervision, A.P., I.K. and D.E.; project administration, I.K. and D.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The raw data supporting the conclusions of this article will be made available by the authors on request.
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 6 Real Shipping Movement Model
### Shipping Model Results Summary  
Consideration of both the normalized routing loads and packet delivery ratios for the realistic shipping model shows that DSR provides a lower NRL, while AODV provides a higher PDR. However, the difference in PDR between the two protocols is not sufficiently large to make it relevant when deciding which protocol to use. Thus, the conclusion would be to recommend DSR in this case. In both metrics, DSDV performs significantly worse than the other two protocols in the realistic model and therefore, cannot be recommended for this scenario. Using the random waypoint model to compare the protocols would result in different protocol selection depending on the radio range being tested. In all cases, the performance for the random model is worse than for the realistic model. This research has demonstrated that a valid model can be built using trace data from real shipping scenarios at sea and that useful results can be obtained using this model to evaluate MANET protocols.  
Figure 17: Fixed Base Station Random Waypoint Shipping Model with Normalized Routing Load.
# Biomass and Cellulose Dissolution--The Important Issue in Renewable Materials Treatment
## 2 General Characteristics of Lignocellulosic Biomass
### Cellulose  
Cellulose is a homopolymer consisting of D-glucose monomers, linked by \(\beta\)-1,4-glycosidic bonds (Figure 4).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Type of Biomass**} & \multicolumn{4}{c}{**Content [\%]**} & \multirow{2}{*}{**References**} \\ \cline{2-2} \cline{4-5}  & **Cellulose** & **Hemicelluloses** & & **Lignin** \\ \hline Grass & 28.8–38.0 & 18.4–30.0 & 4.0–17.5 & [10; 11] \\ Sawmill wood chips & 35.0–50.0 & 15.0–39.0 & 20.0–34.0 & [6; 12] \\ Walnut shells & 25.6 & 22.7 & 29.9–52.3 & [6; 13] \\ Hazelnut shells & 25.9 & 29.9 & 42.5 & [14] \\ Walnut tree wood & 40.8–49.8 & 33.4 & 21.8–29.1 & [15] \\ Olive tree wood & 31.5–31.9 & 11.3–15.5 & 32.5 & [16] \\ Softwood & 35.0–45.2 & 25.0–31.3 & 21.7–30.0 & [14; 17] \\ Hardwood & 45.0–50.0 & 20.0–25.0 & 20.0–28.0 & [14; 17] \\ Poplar & 42.7 & 21.7 & 26.9 & [18] \\ Willow & 44.3 & 22.6 & 25.1 & [18] \\ Pine sawmill chips & 44.0 & 26.0 & 26.0 & [19] \\ Pine bark & 19.0–21.9 & 18.3–25.0 & 38.0–40.7 & [18; 20] \\ Spruce bark & 29.7 & 13.9 & 45.1 & [18] \\ Olive tree leaves & 5.7–8.5 & 3.8–5.4 & 39.6 & [16] \\ Wheat straw & 28.8–40.0 & 20.0–39.1 & 15.0–20.5 & [14; 17; 18] \\ Switchgrass millet & 30.0–50.0 & 10.0–40.0 & 5.0–20.0 & [17] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Content of cellulose, hemicelluloses, and lignin in different types of lignocellulosic biomass.  
Figure 3: General structure of lignocellulosic.  
A high number of hydroxyl groups on the surface of cellulose enables the formation of hydrogen-bond networks and van der Waals linkages. They stabilize the cellulose molecule and significantly improve its dissolution resistance (Figure 5) [28]. The amphiphilic character of cellulose, derived from the axial hydroxyl groups and ring surface CH groups, dictates the selection of the appropriate solvent [29; 30]. The study has shown that the use of organic co-solvents (e.g., ethanol, acetone, \(\gamma\)-valerolactone, and tetrahydrofuran) in water can significantly reduce the number of hydrogen bonds formed between the cellulose chains and solvent [31].  
Cellulose consists of crystalline and amorphous domains. The content of highly ordered, tightly packed crystalline regions in cellulose depends primarily on the type of plant, e.g., wood biomass has many regions with high crystallinity (up to 65% crystalline regions), which makes it insoluble in water and much more chemically and mechanically resistant [22; 32]. Loosely arranged molecular chains are called amorphous regions [33]. In order to increase the reactivity, solubility, and susceptibility to the transformation of natural cellulose, the crystalline structure has to be destroyed to obtain an amorphous, or at least amorphized, biopolymer [34].
## 5 Feasibility and Evaluation
### Architectural Comparison
#### 5.5.2 Reliability  
The CBA and EDA designs ensure loose coupling between the system components, which enables component distribution and redundancy to support reliability, separation of functionalities and avoids single point failures [8, 12]. These properties as desirable design attributes can also be found in distributed systems. Other architectures, being single entities, may expose and threaten the whole enterprise architecture or asset if compromised [32].
# Experimental Study on Shear Creep Characteristics of Residual Soil with Different Stone Content
## 6 Long-Term Strength  
Long-term strength is a crucial parameter for landslide stability analysis. Various methods can be employed to determine long-term strength, including transition creep, isochronous curve, and the first inflection point method of the creep curve. For this particular test, the stress-strain isochronous curve method was selected to determine the long-term strength of the residual soil. In this method, the turning point on the curve is selected as the long-term strength of the specimen. Creep stress-strain isochronous curves were established, with parameters such as 50% stone content (Figure 11).  
Figure 10: The variation of Burgers model parameters with stone content under secondary loading.  
By analyzing the creep rate-stress isochronal curves of four specimen groups with varying stone contents, the long-term strengths of each group can be determined, as presented in Table 5. The discount factor can be defined as the ratio of the long-term strength to the instantaneous shear strength.  
From Table 5, it is evident that the average long-term strength of the four specimen groups with different stone contents is only 54% of their instantaneous strength under a consolidation pressure of 200 kPa. Furthermore, the long-term strength of specimens with stone contents ranging from 10% to 30% decreases more significantly compared to specimens with stone contents ranging from 50% to 70%. The strength discount factor for soil-stone mixtures with more than 50% stone content is considerably larger than that for specimens with less than 30% stone content. In creep tests conducted under the same consolidation pressure, the strength discount factor of the specimen increases as the stone content increases. Previous studies on landslide tests have also indicated that soil landslides with the same slope gradient are more susceptible to destabilization and damage compared to soil-stone mixtures [27].
## 8 Simulation Setup and Results  
In the experimental setup, we have considered a scenario in which neighbor vehicles make small social groups in VIoT. To evaluate trustworthy of the target vehicle for relay/receive information within this group, we have used NS-2.35 [42] network simulator. Performance analysis in terms of throughput, latencies, packet loss, is conducted to validate our proposed TER scheme. These simulations are performed using Intel Core-i3 and 4 GB SDRAM with 2.66 GHz Lenovo machine running on Ubuntu-12.04, 64-bit operating system. The basic parameters of our simulation are listed in Table 3.
# Wideband Printed Wide-Slot Antenna with Fork-Shaped Stub
## 3 Results and Discussion
### Fabrication and Measurement  
To validate the proposed technology, the proposed antennas (antenna A and antenna B) were fabricated. Figure 8 shows the fabricated prototype of the proposed WS antennas. The reflection coefficients of the fabricated antennas were measured by a vector network analyzer and the results were compared with those obtained from the simulation, as shown in Figures 9 and 10.  
The impedance BW of antenna A, which ranged from 0.9 GHz to 5.6 GHz, reached 144% for S\({}_{11}<-8\) dB. The measured reflection coefficient of antenna B seemed to be much better. The impedance BW of antenna B ranged from 0.9 GHz to 6.1 GHz, indicating a relative BW of 148.6% or 5.78 octaves for S\({}_{11}<-10\) dB. The measured results were in accordance with those from the simulation. It is noted that, for antenna A, the measured and simulated results showed the same resonant frequency points under 5 GHz. However, the measured reflection coefficient of antenna A was higher than \(-10\) dB at some frequency points. For antenna B, the measured reflection coefficient was lower than \(-10\) dB in the whole bandwidth, but the position of the resonant frequency points was slightly offset. The  
Figure 8: Photographs of the fabricated antennas with a soldered SMA connector; (a) antenna A; (b) antenna B.  
Figure 7: Simulated current distributions of the normal WS antenna and the proposed antennas at a frequency of 1 GHz, 4 GHz, and 6 GHz.  
existence of some discrepancy between the measured and simulated results for both of the proposed antennas was mainly due to the effect of SMA connector soldering and fabrication error.  
Figure 11 shows the radiation patterns that were measured at 1 GHz, 2 GHz, 4 GHz and 5 GHz. The radiation patterns of the two proposed antennas were very similar. Both of the antennas exhibited a stable dipolar pattern on the yoz-plane (E-plane) and a stable nearly-ommidirectional pattern on the xoz-plane (H-plane). It can be seen that good agreement was achieved between the simulation and the measured results.  
Figure 12 shows the simulated and measured peak gain of the proposed antenna B. The measured peak gain of antenna B was 4.71 dB at the frequency of 1 GHz. It can be seen that the measured results were in good agreement with the simulation results.  
Figure 10: Reflection coefficients of the proposed antenna B, comparing the measured result with the simulation.  
Figure 9: Reflection coefficients of the proposed antenna A, comparing the measured result with the simulation.  
Figure 11: Radiation patterns of the proposed antennas; (**a**) 1 GHz; (**b**) 2 GHz; (**c**) 4 GHz; (**d**) 5 GHz.
## 5 Performance Evaluation of Our Routing Protocol
### Comparison of Running Time  
As mentioned earlier, for channel assignment, we use a genetic algorithm on a subgraph of the whole conflict graph to reduce convergence time. To validate this, we compare our scheme with the scheme that uses a genetic algorithm on all the vertices of the conflict graph, in an Intel Core2 Duo processor, running at 2 GHz. The result is shown in Figure 12, which shows that using backtracking on the planar subgraph and genetic algorithm on the remaining vertices of the conflict graph reduces the convergence time by upto 90%. This clearly shows the advantage of using the combination of backtracking and genetic algorithm for channel assignment, as discussed in Section 4. We can also observe that the improvement reduces with the increasing number of flows. This is because increasing in number of flows results in more vertices in the conflict graph, which makes it less planar. Thus more vertices go to the GCL which reduces the amount of improvement. The improvement also increases with the increase in number of channels.  
Figure 11: Comparison of (**a**) throughput (**b**) delivery ratio (**c**) delay (**d**) jitter with different number of channels for multi-channel WOBAN with multiple gateways.