esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Chinese Domain Named Entity Recognition:
## 4 Experiments
### Results and Analysis
#### 4.2.2 Comparative with Various Entity Types  
To comprehensively evaluate our model's recognition performance on various entities in our domain, we collected the performance of our model in identifying different entities on the PPOST dataset, as shown in Figure 8. The experimental results indicate that the recognition accuracy of various entities is mainly positively correlated with the number of labels in the dataset. This may be due to the scarcity of samples for certain types, leading to the model's inability to learn richer features of these type entities. Specifically, since we define the Feature type mostly for the attributes of things, such as the volume of fossils or the appearance of ancient organisms, and with diverse characteristics, the recognition performance for this part of entities is relatively lower. Therefore, it is more appropriate to consider conducting attribute extraction tasks for these entities.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**SubDuIE**} & \multicolumn{3}{c}{**PPOST**} \\ \cline{2-7}  & **P(\%)** & **R(\%)** & **F1(\%)** & **P(\%)** & **R(\%)** & **F1(\%)** \\ \hline BiLSTM-CRF & 72.37 & 58.71 & 64.83 & 61.14 & 47.37 & 53.38 \\ BERT & 79.48 & 55.49 & 65.35 & 66.79 & 46.88 & 55.09 \\ BERT-CRF & 78.60 & 57.66 & 66.52 & 68.82 & 46.40 & 55.43 \\ BERT-BLSTM-CRF & 76.65 & 56.71 & 65.19 & 67.78 & 46.22 & 54.96 \\ Lattice LSTM & 79.00 & 62.68 & 69.90 & 71.07 & 50.38 & 58.96 \\ LEBERT & 83.31 & 61.58 & 70.81 & 79.48 & 53.13 & 63.69 \\ BERT-GlobalPointer & 83.20 & 61.84 & 70.95 & 80.40 & 67.98 & 73.67 \\ BERT-Pointer & 83.67 & 61.81 & **71.10** & 81.50 & 71.60 & **76.23** \\ BERT-LPN & 84.47 & 63.30 & **72.37** & 83.50 & 74.07 & **78.51** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Precision, recall, and F1-score statistics on the SubDuIE and PPOST datasets.  
Figure 8: Precision, recall, and F1-score of 13 types statistics on the PPOST.  
To evaluate the recognition performance of our model for long entities and nested entities, we sampled 324 instances of long entities with an entity length exceeding 8 characters and an average length of 10 characters and 213 instances of nested entities from the PPOST dataset. The relevant experimental results are shown in Table 5. The results indicate that compared to models based on CRF, models based on pointer labeling can effectively enhance the recognition performance of long and nested entities. Moreover, our BERT-LPN model achieves the best performance among the models mentioned.
## 3 Comparison and Analysis of Acceleration Response Results
### Comparison of Flat Ground with Adjusted Elastic Modulus  
As can be observed from the results in Figure 7, using the elastic modulus for the flat ground does not offer ideal results for the Spectrum acceleration reflection obtained from the analysis with ABAQUS. In order to improve the analysis with ABAQUS, an adjusted modulus of elasticity is introduced, and the following acceleration- time history and spectrum acceleration curves are obtained.  
In general, the soil stiffness or elastic modulus depends on the density of the soil. Typical values of elastic modulus of soil are given in Table 2. This data has been recognized by many researchers, and can be used as a guideline. As mentioned above, the classification of the soil is SW-SM. Therefore, according to Table 2, the selected values are between \(1.2\times 10^{7}\) Pa and \(3\times 10^{7}\) Paso the adjusted elastic modulus of 2 \(\times\)\(10^{7}\) Pa is used in our analysis.
# Scan Matching by Cross-Correlation and Differential Evolution
## 3 Background  
This section provides the background of the presented work. It outlines the simulation-based design strategy it adopts, relevant scan matching methods such as the iterative closest point algorithm, the fundamentals of the cross-correlation principle and the optimization process implemented by the differential evolution.
# An Innovative Approach to Anomaly Detection in Communication Networks Using Multifractal Analysis
Statistical data analysis enables quantification of network behavior, and thus a description of network operation anomalies. However, the most common methods of Hurst estimation include R/S statistics as a function of time scale, variance of the scaled process as a function of time scale, and absolute value method. These methods are qualitative and are used to determine the similarity of the examined stream of events. The article presents issues related to the construction and operation of the SOC. It proposes the implementation of selected tools for the security of the IT system and assesses the effectiveness of their service in detecting threats. For the validation of the anomaly detection process, using multifractal analysis, selected attacks on virtual systems located in the network were carried out. The study of responses to a given security incident helped to create a multifractal spectra of network traffic before and during the attack based on data collected from monitoring and security devices. The collected information could verify this thesis and confirm the effectiveness of multifractal methods in detecting anomalies in the operation of any Information and Communication Technology (ICT) network, including IoT infrastructure [4].
## 3 Results
### Situational Awareness
#### 3.3.2 SA Scale Data  
The measurement index of the situational awareness scale included two aspects, the objective measurement of the SAGAT scale and the subjective measurement of the 3D-SART scale. The descriptive results and analysis of variance are shown in Figure 6. Both the SAGAT and SART scores in the training group were improved before and after training [SAGAT: M\({}_{1}\) = 6.00, SD\({}_{1}\) = 1.10, M\({}_{2}\) = 8.10, SD\({}_{2}\) = 1.10; SART: M\({}_{1}\) = 17.60, SD\({}_{1}\) = 5.98; M\({}_{2}\) = 23.70, SD\({}_{2}\) = 4.56], while there was no significant change in the control group before and after training [SAGAT: M\({}_{1}\) = 5.06, SD\({}_{1}\) = 1.59, M\({}_{2}\) = 5.36, SD\({}_{2}\) = 2.01; SART: M\({}_{1}\) = 14.09, SD\({}_{1}\) = 8.99, M\({}_{2}\) = 16.09, SD\({}_{2}\) = 5.45].  
Before training, the independent-sample \(t\)-test found that there was no statistical difference in the objective and subjective situational awareness scales between the training group and the control group before training [SAGAT: T (19) = 1.558, \(p\) = 0.136; SART: T (19) = 1.042, \(p\) = 0.311]. A 2 (pre-test, post-test) \(\times\) 2 (training group, control group) repeated-measures ANOVA was performed on the situational awareness scale scores of the two groups, and the main effect of the group was significant [SAGAT: F(1, 19) = 10.254, \(p\) = 0.005 \(<\) 0.01, \(\eta^{2}\) = 0.351; SART: F(1, 19) = 5.222, \(p\) = 0.034 \(<\) 0.05, \(\eta^{2}\) = 0.216], the main effect of the measurement time was significant [SAGAT: F(1, 19) = 13.028, \(p\) = 0.002 \(<\) 0.01, \(\eta^{2}\) = 0.407; SART: F(1, 19) = 7.515, \(p\) = 0.013 \(<\) 0.05, \(\eta^{2}\) = 0.283], the SAGAT score group had a significant interaction with the measurement time [F(1, 19) = 7.285, \(p\) = 0.014 \(<\) 0.05, \(\eta^{2}\) = 0.277] and the SART group had no significant interaction with the measurement time [\(p>0.05\)]. Further simple effects analysis of the SAGAT scale data showed that the training group exhibited a significant improvement in both context awareness scale scores after  
Figure 5: Comparison of task performance differences before and after training. (**a**) Accuracy; (**b**) reaction time.  
training compared to before training [F(1, 19) = 18.995, \(p\)\(\leq\) 0.001, \(\eta^{2}\) = 0.500], while there was no significant difference in the control group before and after training [F(1, 19) = 0.435, \(p\) = 0.517, \(\eta^{2}\) = 0.022]. Bidimensional N-back working memory training can effectively improve the SAGAT and SART scores within the situational awareness scale.
## 4 AHP/TOPSIS-Based Multi-Criteria Decision-Making
The best renewable energy source in varied climate zones was identified if it was ranked best by any two methods. In [30], ten high-risk activities in the mining sector were identified in order to access them in a work environment. The risks were associated with multiple activities to generate a house of safety with the assistance of AHP and fuzzy inference. [PERSON] et al. in [31] proposed a hybrid AHP and Data Envel-opment Analysis-Assurance Region (DEA-AR) model to measure the efficiency of public and private airports in Turkey. A total of five criteria were chosen, and 48 airports were ranked based on these criteria. In another research [32], nine risk criteria were chosen and five risk response alternatives were ranked in construction projects. They employed the Analytical Network Process (ANP) and Multi-Attributive Border Approximation Area Comparison (MABAC) techniques to reduce imprecision and fuzziness in the decision process. The work of [33] used a hybrid of AHP and Genetic Algorithms (GAs) to access ground water vulnerability in China. They optimized the land ratings from 1 to 10, where 10 indicates the highest potential to pollution, based on eight factors. In [34], Fuzzy AHP and Particle Swarm Optimization (PSO) techniques were used to solve the optimization model as a nonlinear system of equations. Their proposed method was scalable and was applied on various cases studies for prioritization even with an incomplete set of judgments. Table 1 compares the various hybrid MCDM approaches along with their applications, strengths, and weaknesses. This work proposes the use of an AHP/TOPSIS hybrid approach. The AHP/TOPSIS hybrid approach efficiently caters to weight computation and the problem of AHP having
## 3 Experiments and Results
### Details of Implementation  
We built the MHGSA system using the PyTorch (Menlo Park, CA, USA) framework with hardware specifications of Intel (Santa Clara, CA, USA) Core i9 CPU and Nvidia (Santa Clara, CA, USA) GTX 4090 GPU. To minimize losses, an Adam optimizer with 64 batches and an initial learning rate of \(1\times 10^{-3}\) was used. To dynamically adjust the learning rate, we defined a decay rate of 0.1, which was activated when the validation set metrics did not improve within 5 epochs. We performed several training and testing sessions using different random seeds to obtain more balanced results, which made the experiment more credible.
# Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8
## 5 Discussion
### Limitations and Future Challenges  
Although the improved YOLOv8 model has demonstrated superior detection performance in the study of old landslides, there remains three major limitations within this field.  
1. Limited sample size: Deep learning models, including YOLOv8-CW2, require extensive datasets for optimal training. However, there is a critical shortage of old landslide data in the Sichuan and Tibet regions of China. It is anticipated that the performance of the model could be further improved with the availability of more comprehensive datasets.
2. Limited data type: The complexity of detecting old landslides in the Sichuan and Tibet regions is exacerbated by diverse topography, landforms, climate variations,  
Figure 10: The effects of different Wise-IoU loss functions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **mAP (\%)** & **F1 (\%)** \\ \hline YOLOv8 & 69.2\% & 63.1\% & 62.8\% & 66.0\% \\ YOLOv8-CBAM & 77.6\% & 64.8\% & 65.8\% & 71.0\% \\ YOLOv9-SE & 74.4\% & 64.6\% & 63.7\% & 70.0\% \\ YOLOv8-SK & 72.2\% & 65.4\% & 64.6\% & 69.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: The effects of attention mechanisms.  
and soil types, as well as the high level of vegetation coverage due to minimal human activity. These factors make the use of optical image data for landslide detection somewhat reductive. Therefore, we should integrate high-precision DEM (Digital Elevation Model) data and InSAR (Interferometric Synthetic Aperture Radar) data to enable a more thorough assessment of old landslides in these areas.
3. Limited model transferability: The transferability of the improved YOLOv8 model to other regions and landslide types has not been fully validated. To improve the model's robustness and generalization capabilities, it is essential to expand the dataset with varied types of old landslide data, such as those from the Loess Plateau of China. This will allow the model to learn additional features relevant to different landslide characteristics and environments.
## 3 Results and Discussion
### Deformation Mechanism-Based True Stress Creep Model
Three power exponent regions could be obtained by linear fitting. Combining them with the material deformation mechanism maps [36], it could be seen that in the high-stress region (250-300 MPa) of 450 \({}^{\circ}\)C and the high-stress region (225-275 MPa) of 500 \({}^{\circ}\)C, the creep rate behavior had a constant power exponent, and the creep deformation was mainly caused by IDG. In the low-stress region (200-250 MPa) at 450 \({}^{\circ}\)C, the low-stress region (150-225 MPa) at 500 \({}^{\circ}\)C, and the high-stress region (175-225 MPa) at 550 \({}^{\circ}\)C, the creep behavior was dominated by GBS. In the low-stress region (100-175 MPa) at 550 \({}^{\circ}\)C, the creep of CGI was dominated by IDC. It can be seen that for each creep mechanism, the minimum creep rate of CGI exhibited a linear dependence on the stress on the logarithmic scale. Generally, according to Figure 6 and in combination with Equations (2) to (3), the parameters \(A_{0}\), \(B_{0}\), \(C_{0}\), \(Q_{A}\), \(Q_{B}\), \(Q_{C}\), \(p\), \(n\), and \(m\) can be determined by linear regression on the Arrhenius plots. However, in the region dominated by IDC, there was only the minimum creep rate corresponding to the low-stress region of 550 \({}^{\circ}\)C, so the parameters  
Figure 6: The relationship between the minimum creep rate and stress of CGI. \(C_{0}\) and \(Q_{C}\) could not be obtained by linear regression fitting in this study. Therefore, the multiobjective optimization method was considered in this paper to obtain the parameters of the creep model.
# Investigating Models for the Transcription of Mathematical Formulas in Images
## 5 Results
### Vision-Language Models  
To investigate the complete pipeline from formula images to the transcriptions, we analyzed the VL models _BLIP_[34], _GIT_[35], _SWIN-GPT-2_[23; 33], and _TrOCR_[36] by comparing their resulting transcriptions with the transcriptions in the references of the _Formula2 Text_ test set.  
Figure 12 displays the TERs of the analyzed VL models. The blue bars display again the performances of the original pre-trained models. The green bars show the performances of the models that we fine-tuned using the _Formula2 Text_ validation set. We observe that the fine-tuned _TrOCR-Base_ performs best with a TER of 42.09%, followed by the fine-tuned _BLIP-Base_ (52.18%). Our fine-tuned model _SWIN-GPT_ only achieves a TER  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **TER [\%]** & **TER [\%]** & \(\Delta\) **Improvement [\%]** \\  & **Pre-Trained** & **Fine-Tuned** & **Relative** \\ \hline TS-Base & 54.44 & **34.95** & +55.77 \\ BART-Base & 57.57 & 35.44 & +62.44 \\ FLAN-T5-Base & 56.94 & 35.60 & +59.94 \\ ChatGFT & 39.95 & - & - \\ GPT-3.5 Turbo & 43.28 & - & - \\ GPT-4 & 46.01 & - & - \\ MathJAX & 56.59 & - & - \\ GPT-2-Medium & 76.69 & 63.59 & +20.60 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Impact of fine-tuning the NLP models.  
Figure 11: Results of the NLP models.  
of 72.91%. _GIT-Large_ shows the worst TERs, with 80.96% after fine-tuning and 82.29% without fine-tuning.  
Table 3 gives an overview of the impact of fine-tuning the VL models. We see that fine-tuning with our validation set results in relative improvements of between only 1.64% (_GIT-Large_) and even 104.51% (_TrOCR-Base_). We assume that fine-tuning _GIT-Large_ only had such a small impact (+1.64%) since the VL model was pre-trained with datasets that had a focus different from formula images [35] and the amount of samples in our validation set was not sufficient to have a large impact on performance. In contrast, _TrOCR-Base_ can already perform the OCR task, i.e., was pre-trained with images containing text. Since the difference between these training data and our formula images is not that big, _TrOCR-Base_ can easily learn the task and the impact of fine-tuning is very strong (+104.51%).
# Whole Organ Engineering: Approaches, Challenges, and Future Directions
## 3 History of Whole Organ Engineering
### First Engineered Beating Heart  
The first instance of whole organ engineering was in a heart model via the perfusion decellularization and then recellularization of a whole rat heart by [PERSON] et al. in 2008 [38]. [PERSON] utilized a modified [PERSON] heart perfusion apparatus, originally developed by [PERSON] in 1895, to perfuse cadaveric rat hearts with different detergent solutions [38]. The decellularized hearts maintained their ECM architecture, vascular patency, and valve geometry. Decellularized hearts were recellularized with neonatal cardiac cells or aortic endothelial cells via intramural injection and perfusion respectively, and maintained in a bioreactor, demonstrating electrical and mechanical activity and genetic expression of mature cardiac tissue 8 days after seeding [38]. Maintaining native ECM structure to generate a contractile whole heart with mature, functional cells further substantiated the vital role that the structure and composition of the ECM plays in tissue regeneration. As the first decellularized whole-organ construct, [PERSON]'s work opened up a new area of tissue engineering, with the techniques he developed laying the foundation for whole heart engineering through de-cell/re-cell technology.
# Towards a Digital Twin Warehouse through the Optimization of Internal Transport
## 2 Materials and Methods
### Simulation Model  
The simulation model is able to generate layouts of the storage area according to the number of zones in width and length defined by the user. This zone specification directly determines the number of aisles required to access the different zones and the storage capacity of each one of them.  
It is necessary to define the nodes and the paths that link them in order to calculate at each moment which is the optimal route (minimum distance traveled). This is supported by a network of nodes and arcs, G (\(N\), \(A\)), where N is a set of n nodes, and A is a set of m directed arcs; each arc is defined by \((i,j)\in A\), and has an associated length \(c_{ij}\) that represents the distance between nodes \(i\) and \(j\), according to notation given by [PERSON] et al. [37]. Equation (1) shows the objective function, minimizing the distance traveled, where \(x_{ij}\) is a nonnegative decision variable. Equations (2) and (3) represent the constraints of the problem.  
\[\textit{Minimize}\sum_{(i,\ j)\in A}c_{ij}\times x_{ij}, \tag{1}\]  
subject to,  
\[\sum_{(j:(i,\ j)\in A)}x_{ij}-\sum_{(j:(j,\ i)\in A)}x_{j,\ i=b(i)},\forall i \in N, \tag{2}\]  
\[d(j)\leq d(i)+c_{ij},\forall(i,\ j)\in A, \tag{3}\]  
where \(\sum_{i=1}^{n}b(i)=0\), \(b(s)=1\), \(b(t)=-1\), and \(b(i)=0\), with s being the source node and t the sink node, for each node \(i\). Moreover, \(d(j)\) is a distance label for each node \(j\in N\). Equation (3) is the optimality condition, and it indicates that the shortest path length to node \(j\), \(d(j)\) is not greater than the arc length \((i,j)\), \(c_{ij}\), plus the shortest path length to node \(i\), \(d(i)\), all for each arc \((i,j)\).  
In any given scenario, two nodes are located at fixed positions outside the warehouse. The first one corresponds to the previous storage area, and the second one to the final unloading area. At the beginning of the simulation, the algorithm generates nodes at the aisle crossings. To complete the number of nodes, it is necessary to generate a pair of nodes that are dynamically located at the chosen row entry position and at the final position within the row (loading or unloading process).  
The algorithm used dynamically links these nodes in such a way that the possible paths coincide with the defined aisles. This ensures that the optimal routes are always followed (shortest distances traveled).  
Solving is executed by FlexSim [42]. This software makes it possible to define speeds and accelerations in different situations for the forklift truck. Thus, the speeds are different for aisle travel, changes in direction, and even for positioning within the selected row. The choice and configuration of these operating parameters have been chosen based on the characteristics of the forklift truck. Figure 8 shows a scheme of the main elements of the simulation model and the relationships between them.
# Analysis and Design of a Minimalist Step Climbing Robot
## 6 Discussion  
In this section, the reasons for the failure of the robot during its ascendance and descendance on the stairs when the inclination angle of the robot is not controlled are discussed. Figure 22 illustrates two situations of the robot while it is climbing up on the stairs. Figure 22a shows the controlled situation while Figure 22b shows the uncontrolled situation. In the schematic, point \(G_{L}\) indicates the center of gravity of the load. \(\beta\) represents the angle made by a line joining the center of gravity of load and the center of front wheel (\(O\)) with the horizontal. The subscripts of angle \(\beta\), 'c' and 'wc' indicate the controlled and without control conditions. \(\tau\) is the torque of the motor at the front wheel. '\(\ell\)' and 'N' are the frictional forces and normal reactions of the front wheel on the edge of the step, respectively.  
It may be observed that the following is the case.  
\[\beta_{c}>\beta_{wc}\]  
Hence, the counterclockwise moment in the uncontrolled condition (due to the center of gravity of the load) about the point \(O\) is more than that of the controlled condition. Therefore, the following is the case.  
\[\tau_{c}<\tau_{wc}\] \[\therefore f_{c}<f_{wc}\]  
Since the frictional force on the front wheel during the uncontrolled condition is greater than that of the controlled condition, the probability of slippage during the uncontrolled condition is greater than that of the controlled condition. Similar behavior has also been observed during the experiments.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Inclination Angle (Deg)**} \\ \cline{2-3}  & **Ascending** & **Descending** \\ \hline Mean & \(-21.37\) & \(35.13\) \\ \hline Std. Dev. & \(10.95\) & \(37.89\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Statistical analysis of variation in difference of inclination angle during ascending and descending experiments on the prototype with controlled and uncontrolled conditions.  
Figure 22: Robot schematic while ascending; (**a**) with control; (**b**) without control.  
In Figure 23, the schematic of the two situations of robot is shown while the robot is descending the stairs. Figure 23a illustrates the controlled situation while Figure 23b shows the uncontrolled situation. 'M' is the moment due to the center of gravity of load with respect to the front wheel center (\(O\)). It may be observed from Figure 23 that the moment due to the center of gravity of the load (about \(O\)) is in the counterclockwise direction during the controlled condition, whereas the moment is in the clockwise direction during the uncontrolled condition. This will result in the toppling of the robot in the uncontrolled condition, which is also observed during the experiments.