esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Analysis of the Test Results
#### 3.2.3 Temperature of the Steel Bars  
Figure 15 shows the temperature-time relationship curve of the top and bottom steel bars of the specimen. ST6-1 is the top steel bar, and ST6-2 is the bottom steel bar. Due to the influence of crystal water in the concrete, the top steel bar experiences an obvious temperature plateau at 100 \({}^{\circ}\)C for approximately 30 min. The temperature difference between the bottom layer and the top layer of the specimen increases continuously. When there is no fire exposure, the temperature difference between the bottom and top steel bars of slabs CS1 and CS2 reaches 406 \({}^{\circ}\)C and 447 \({}^{\circ}\)C, respectively. Finally, according to the steel failure criterion [44; 45], that is, the condition steel temperature exceeds 593 \({}^{\circ}\)C, the fire resistance of the two concrete slabs based on the reinforcement temperature limits can be determined as shown in Table 3.  
Figure 14: Temperature–time curves across the full depth of each slab at T5.  
Figure 15: Temperature–time curves of the top and bottom steel bars in the specimen.  
The curves of the temperature of the steel bars versus the furnace temperature are shown in Figure 16. During the heating stage, the temperature inside the furnace is lower than 600 \({}^{\circ}\)C, the temperature of the upper and lower layers of steel bars increases slowly, and the speed of the lower layer is slightly higher than the speed of the upper layer. When the furnace temperature is between 600 \({}^{\circ}\)C and 1000 \({}^{\circ}\)C, the temperature of the lower layer of the steel bars increases rapidly, but the temperature of the upper layer increases slowly. When the furnace temperature exceeds 1000 \({}^{\circ}\)C, the steel bar temperature increases rapidly, and the temperature of the upper and lower steel bars increases at almost the same rate.
## 4 Proposed QLHH-II Algorithm
### QLHH-II Detailed Steps
#### 4.2.1 Evaluation  
Each UAV uses the \(Q\) function from QL to evaluate a vertex, i.e., an action, at the current solution, i.e., the state. Therefore, the function \(Q_{u}(S,v)\) gives a score for the UAV \(u\) to fly to vertex \(v\) at solution \(S\). The choice of action for the UAV \(u\), therefore, consists of choosing the vertex that maximises \(Q_{u}\).  
The purpose of the following embedding structure is to represent a vertex \(v\), according to the knowledge of a UAV \(u\), by a \(p\)-dimensional vector \(\mu_{uv}\).  
\[\mu_{uv}=\texttt{relu}(\theta_{1}\cdot x_{uv})\in\mathbb{R}^{p}\]  
with \(\theta_{1}\in\mathbb{R}^{p\times 3}\) and relu is the rectified linear unit; where \(x_{uv}\in\mathbb{R}^{3}\) is the vector of state variables of vertex \(v\) for UAV \(u\). Each component of that vector corresponds to one CCUS3O objective. For the coverage time, it equals \(1\) if the vertex is on the shortest path from the  
Figure 5: Overview of Reinforcement Learning (RL).  
position of the UAV \(u\) and its initial position, and 0 otherwise. For the coverage rate, it equals 1 if the vertex has been visited by any UAV and 0 otherwise. For the connectivity, it equals 1 if the vertex is in the communication range of another UAV and 0 otherwise. The formal definition is given below.  
\[x_{uv}^{(time)} =\left\{\begin{array}{ll}1&\text{if }dist(pos(u),P_{u}[1])= dist(pos(u),v)+dist(v,P_{u}[1])\\ 0&\text{otherwise}\\ x_{uv}^{(rate)}&=\left\{\begin{array}{ll}1&\text{if }v\
otin\tilde{S}\\ 0&\text{otherwise}\\ \end{array}\right.\\ x_{uv}^{(comm)}&=\left\{\begin{array}{ll}1&\text{if }\exists u^{\prime}\in U \backslash\{u\},dist(pos(u^{\prime}),v)\leq D^{comm}\\ 0&\text{otherwise}\\ \end{array}\right.\end{array}\right.\]  
Parameterisation  
The scoring function \(Q_{u}(S,v;\Theta)\) is a matrix computation that takes as input two \(p\)-dimensional vectors, one for the current solution and one for a vertex to evaluate. For a UAV \(u\), a vertex \(v\) is directly represented by \(\mu_{uv}\) while the solution is represented by the sum over every vertex, i.e., \(\sum_{v\in V}\mu_{uv}\).  
\[Q_{u}(S,v;\Theta)=\theta_{2}^{\top}\cdot\texttt{relu}\Bigg{(}\Bigg{[}\theta_{ 3}\cdot\sum_{v\in V}\mu_{uv},\theta_{4}\cdot\mu_{uv}\Bigg{]}\Bigg{)}\]  
with \(\theta_{2}\in\mathbb{R}^{2p}\) and \(\theta_{3},\theta_{4}\in\mathbb{R}^{p\times p}\); where \([\cdot,\cdot]\) is the concatenation operator.  
Due to the embedding structure described above, a state and an action (respectively a solution and a node) are represented by two \(p\)-dimensional vectors \(\sum_{v\in V}\mu_{uv}\) and \(\mu_{uv}\), respectively. The policy of the high-level Q-learning algorithm can, thus, be used on different instances of different size (not the same number of nodes in the environment graph nor the same number of UAVs). This makes it possible to learn the policy on different instances and to apply the generated heuristic to unknown environments.
# Deep-Learning-Based Stream-Sensing Method for Detecting Asynchronous Multiple Signals
## 3 Preliminary  
This section describes the definitions of synchronously transmitted signals and asynchronously transmitted signals. Then, based on the limitations of UAVs, we explain why asynchronous transmission is better than synchronous transmission in the disaster situation that we considered in this work.
# Fast Loop Closure Selection Method with Spatiotemporal Consistency for Multi-Robot Map Fusion
## 4 Proposed Method  
Due to the presence of wrong constraints, not all the measurements \(\mathbf{Z}^{ab}\) are inserted into the pose graph, and only a part of the available ones can be accepted. PCM seeks to find the largest subset of inter-robot measurements \(\mathbf{Z}^{ab}\) that satisfies the consistency. Based on the PCM, the MEWC method [26] determines such a subset with a high degree of cardinality, consistency, and data similarity. However, exiting works on the consistency of measurements are usually based on spatial dimension in (3). In fact, the accurate measurements are also topologically (i.e., time) correct between two trajectories to a certain extent, while the wrong ones do not hold. Given the inter-robot loop closures, the degree of coincidence corresponding to the trajectories of two robots can be used to further assess the consistency of the measurements. This case is reflected in the time dimension and has not yet been introduced into the concept of consistency. Therefore, we propose a fast loop closure selection method that integrates the essentials of spatiotemporal consistency. Specifically, our method considers the degree of topology correctness, cardinality, and consistency of inter-robot measurements.
## 6 Evaluation Experiment of the Proposed Approach
### Running Crowd Selection and Inviting Selected Crowd to Crowdsourced Task  
Our compiled dataset was used for this experiment. We have used \(q_{1}\) mentioned in Section 5.1.2, however, instead of focusing on mobile app users only, as for \(q_{1}\), the selection process included mobile app developers. In this experiment, we are interested in selecting a crowd of both mobile app users and developers who have used or developed mobile apps similar to the app described above. The following is the query used for the crowd selection:  
\(q:\)role = users and developers  
category = Health & Fitness  
description = the prepared description about the fitness application mentioned above  
Figure 9: Evaluation method.  
The Health & Fitness category resulted in a total of 692 mobile apps' users and developers, which is the search space. The input description is compared against all 692. However, only 587 users and developers who have used or developed apps that are similar to the input mobile app description, according to the threshold of 0.30, are considered for selection.  
To invite the selected crowd in crowdsourcing requirements elicitation, we have utilized Google Forms as a tool for gathering the requirements due to its simplicity and popularity. The crowd were asked to write the requirements and features they desire in a fitness mobile app. Moreover, to maintain consistency in the formulated requirements, the crowd were given examples of the requirements with a certain style which starts with the following:  
_The app should be able to_  
This assisted in standardizing the crowdsourced requirements as much as possible. In addition, after sending invitations for participation, we ended up with a total of 44 requirements from the crowd. It was observed that the crowdsourced requirements have been written in different ways, where some have expressed their requirements using the same style provided in the example while others have written them in their own way. Figure 10 illustrates some samples of the crowdsourced requirements for fitness mobile apps from the crowd who accepted to participate.  
The biggest former 1 want is the before/after visualization over crowds crowds (months of how 1 am progressing with my workouts.
## 2 GFB-OFDM Transceiver
### Receiver
Then, we can obtain  
\[\boldsymbol{W}[m,b] =(\boldsymbol{Y}[m,p]\otimes\text{FIL}[p])e^{-|2\pi\frac{n_{ \text{ifft}}}{N_{\text{ifft}}}b} \tag{22}\] \[=\sum_{p=0}^{N_{\text{inft}}-1}\boldsymbol{Y}[m,p]\|\text{FIL}[b -p]\|e^{-|2\pi\frac{n_{\text{ifft}}}{N_{\text{ifft}}}b}\] (23) \[=\sum_{p=0}^{N_{\text{inft}}-1}\sum_{l=0}^{M-1}\boldsymbol{X}[l, \frac{p}{M}]e^{|2\pi\frac{ml}{M}}|\text{FIL}[b-p]|e^{-|2\pi\frac{n_{\text{illt }}}{N_{\text{ifft}}}b}\] (24) \[=\sum_{p=0}^{N_{\text{inft}}-1}\sum_{l=0}^{M-1}\boldsymbol{X}[l, \frac{p}{M}]|\text{FIL}[b-p]|e^{|2\pi n\frac{l-b}{M}} \tag{25}\]  
where \(b=0,1,2,\ldots,N_{\text{ifft}}-1\) and \(p=0,1,2,\ldots,N_{\text{ifft}}-1\). \"\(\otimes\)\" and \"\(|\cdot|\)\" denotes the circular convolution symbol and the module value, respectively. \(\text{FIL}[p]=0\) when \(p\geq M\), and \(\text{FIL}[p]=\frac{1}{M}\) when \(0\leq p\leq M-1\). Sum the frequency domain data of different subsymbols to obtain  
\[\boldsymbol{Z}[b] =\sum_{m=0}^{M-1}W[m,b] \tag{26}\] \[=\sum_{ \begin{subarray}{c}p=0\\ p=M\end{subarray} }^{N_{\text{inft}}-1}\sum_{l=0}^{M-1}\boldsymbol{X}[l,\frac{p }{M}]|\text{FIL}[b-p]|\sum_{m=0}^{M-1}\theta^{2\pi n\frac{l-b}{M}}\] (27) \[=M\sum_{p=0}^{N_{\text{inft}}-1}\sum_{l=0}^{M-1}\boldsymbol{X}[l, \frac{p}{M}]|\text{FIL}[b-p]|\delta[(l-b)_{M}]\] (28) \[=M\sum_{p=0}^{N_{\text{inft}}-1}\boldsymbol{X}[(b)_{M},\frac{p}{ M}]|\text{FIL}[b-p]|\] (29) \[=M\sum_{k=0}^{N-1}\boldsymbol{X}[(b)_{M},k]|\text{FIL}[b-kM]|\] (30) \[=\boldsymbol{X}[(b)_{M},\lfloor\frac{b}{M}\rfloor] \tag{31}\]where \(\lfloor\cdot\rfloor\) denotes the rounding down operation. At this time, it means that the modulation symbols are completely recovered from the data \(\mathbf{Z}[b]\). \(\square\)  
The receiver first removes the cyclic prefix of each received OFDM symbol, followed by the FFT to recover the modulated symbols. Subsequently, the symbol detection, the demodulation, and the decoding are performed to ultimately retrieve the information elements. Note that the number of repetition \(L\) affects the inference between the subsymbols and the one between the subbands. That is, as \(L\) increases, the interference between the subsymbols becomes larger, the interference between the subbands becomes smaller, and the computational complexity of Equation (7) becomes higher. To reduce the complexity and the overhead of the cyclic prefix, a small \(L\) is preferred; however, it may introduce the interference between the subcarriers. The simulation results in Section 3 show that the interference between the subbands is tolerable when \(L\geq 4\), and the interference between the subsymbols can be ignored. In this case, the receiver can receive in an almost lossless manner.
# A Brain-Inspired Goal-Oriented Robot Navigation System
## 2 Mathematical Model
### Navigation Strategy  
There are two ways to guide the robot to the goal in an unknown environment: select the direction according to the reward signal or the Q-value of the action cells. Initially, the robot randomly explores the environment and will get a reward signal after reaching the target. If there are no reward signals within the search scope, it then selects a direction according to the Q-values; otherwise, the robot follows the reward signals.  
If the reward signals can be detected in the step range, the value of the reward signals will increase according to Equation (12). With each step, all nodes update their reward signals. The direction with the maximum sum of reward signals will be chosen.  
\[r(t)=r(t-1)+\Delta r \tag{12}\]  
If the reward signal is zero at the present position, the robot selects the direction according to the Q-values. In our system, there are eight action directions (at \(45^{\circ}\) intervals). Each action cell receives the input from all activated place cells. According to a winner-takes-all mechanism, the direction with the maximum mean of Q-values is the optimal output direction.  
\[Q_{o}=\frac{\sum_{i}w_{i,\phi}\psi_{i}(g)}{\sum_{i}\psi_{i}(g)} \tag{13}\]  
Figure 2: (**a**) Illustration of ring attractor mechanism for path integration. The attractor model was simplified into a one-dimensional linear ring attractor model. Suppose a mouse moves in one direction along a straight path. With different distances from the apex, each attractor can be expressed by the activity of a ring attractor. (**b**) Schematic diagram of periodic oscillation obtained by interference of different frequency waveforms. \(y_{1}=\sin(14\pi x+\pi/2);y_{2}=\sin(16\pi x+3\pi/2);y_{3}=y_{1}+y_{2}\); The 0 oscillating beat interference will produce a fixed interval beat y4, and the amplitude of the beat oscillation will be identified at each attractor. (**c**) Illustration of how hexagon-like firing mode was generated. Trajectory of random movement of robot (blue) and grid-like firing mode generated by this paper’s method (red).  
where \(w_{i,o}\) expresses the weight of place cell \(i\) in action direction \(o\). The weights of the winning neuron are modified according to the following equation:  
\[\Delta w_{i,o}=\alpha(r+\beta\text{max}(Q_{o})-w_{i,o}) \tag{14}\]  
If both the Q-value and the reward signal are zero at the present position, the robot will move randomly. In this case, the robot keeps the direction with a probability of \(1-\xi\). With a probability of \(\xi\), it randomly chooses a new direction with the aim of finding other, shorter routes.
## 2 Joint Optimization Algorithm of Genetic Optimization Algorithm and Capsule Network  
The joint optimization algorithm that combines the genetic optimization algorithm and the capsule network algorithm is used to construct a multi-parameter optimization design scheme for electromagnetic characteristic fitting based on this method, as shown in Figure 1. This model mainly includes four parts: First, according to the geometric characteristics and electromagnetic characteristics of the optimization target, appropriate optimization variables are selected and the variable range is determined; second, according to the characteristics of the optimization function, the variables are sampled according to specific variable sampling rules, and the target RCS characteristics are solved to generate the basic database of the target RCS characteristics; third, the parameters are optimized through the genetic algorithm to improve the working efficiency of electromagnetic characteristic fitting; fourth, the optimal parameters and thresholds are repeatedly trained and learned through the capsule network to obtain the optimal solution of the electromagnetic characteristic fitting scheme.
## 1 Introduction  
Traffic accidents involve one or more road users, and a crash involving only one vehicle is referred to as a single vehicle crash (SVC). SVC on highways accounts for 60% of all fatal vehicle crashes [1]. SVCs can be classified into two groups, on road crashes (ORCs) and run-off road crashes (RORC), among which RORC is mostly related to a single vehicle. RORC occurs in the roadway departure of a vehicle, causing 70% of the total fatal SVC [2]. The factors that contribute to RORC can be classified into three categories--environmental factors, driver-related factors, and roadway-related factors. The environmental factors, such as weather and traffic, affect the RORC [3], and the driver-related factors, such as driver condition, inattention, and speeding, also increase roadway departures and the speed of crashes in curved sections [4, 5]. The roadway factors such as lane width and clear zone distance, side slope, fixed object density, and offset from fixed objects also have a direct impact on the road departure of the vehicle [6, 7, 8, 9, 10, 11].  
Among the three factors described above, the roadway factors play an important role in determining if the crashes are caused by human error [12, 13]. With regard to the roadway cross-section elements and road facility installation, as the shoulder width of the roadside increases, the probability of RORC decreases [6], and the crash severity decreases [7]The vertical slope [8] and radius of the curve [9; 10] affect the roadway departure of a vehicle, and the roadside fixed objects (e.g., guard rails, utility poles, piers, and trees) cause RORC [11].  
To reduce the high accident severity of RORC, an external protective fence and the shock absorber have been installed and operated. It should be installed and operated only on dangerous road sections where fatal traffic accidents can occur according to precise guidelines. However, inefficient road safety facilities are being installed because the RORC contribution factors for each road section cannot be identified. In other words, an analysis of the contributing factors and preventive measures in consideration of the characteristics of each road section has not been established.  
In this study, a model for RORC severity prediction was developed by applying TAN, and the prediction power of the proposed model was analyzed through comparison with the severity of actual accidents. The sensitivity analysis was performed for each variable, and the improvement priorities and measures were presented according to the degree of impact of each RORC contributing factor, which has not been investigated in previous studies.
## 2 Theoretical Framework
### Methods Combined in the MCDM Model
Similarly to distance-based methods, such as the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) [43], Multi-Criteria Optimization and Compromise Solution (VIKOR) [44], or Comprehensive Distance-Based Ranking (COBRA) [45], ADAM focuses on distances. However, unlike these methods, ADAM does this in a multi-dimensional space, thus forming complex geometric bodies that can be displayed graphically. Because of this, the results of applying this method are very intuitive; that is, the decision maker can identify the best alternative (the largest polyhedron, that is, the polyhedron with the largest volume) by simply looking at the graphical representations of the obtained polyhedra. So, ADAM might provide a clearer picture of how alternatives compare to each other on various criteria. Compared to pairwise comparison methods, another very popular group of MCDM methods whose best-known representatives are the Analytic Hierarchy Process (AHP) [46], the Analytic Network Process (ANP) [47], Step-wise Weight Assessment Ratio Analysis (SWARA) [48], etc., the ADAM method is much more efficient because it provides results with significantly less evaluation and consumption of resources (primarily time and humans). One of the main disadvantages of the ADAM method is that it requires the direct input of criteria weights and specific values of alternatives according to criteria. However, it can be very easily combined with other MCDM methods, which has already been proven in the literature (e.g., [49]), so the first drawback is easily overcome. The second drawback can also be overcome by extending the method to the environment of intuitive or interval sets. The ADAM method has so far been extended to the fuzzy environment [50] but not to the grey environment, which is another research gap that this paper covers. Although one of the newest MCDM methods, the applicability of the ADAM method has been widely proven by solving various problems, such as the ranking of drones from the perspective of their applicability in selecting tourism accommodation facilities [51], measuring the efficiency of logistics processes [52], ranking entrepreneurial systems in various European countries [53], selecting warehouse locations [54], evaluating and selecting city logistics concepts [55], evaluating strategies to achieve circularity in the agroindustry [56], etc. Aside from the fact that the BWM and ADAM method have not been used to rank supply chain practices, they have also never been combined in a grey environment, which are also research gaps that this study covers. Grey sets, in contrast to fuzzy and rough sets, offer improved handling of partial data and the ability to merge vague or incomplete information into a single model [57]. Furthermore, grey sets can represent scenarios that cannot be captured by either fuzzy or rough sets [57].
# Label-Guided Data Augmentation for Chinese Named Entity Recognition
## 2 Related Work  
### Chinese NER  
The field of Chinese NER has witnessed substantial progress due to the integration of pre-trained language models and novel architectures. Early advancements include BERT [10], which laid the groundwork by encoding words and adding a conditional random field (CRF) layer for sequence labeling. Lattice LSTM [11] expanded on this by employing a dictionary-based lattice structure to better utilize character and sequence data in Chinese NER. To further refine Chinese character understanding, Glyce-BERT [12]incorporated pictographic features by using a grid convolutional neural network and enhanced generalization through multi-task learning, while LEBERT [13] adapted the BERT framework with a lexicon adaptation layer, converting sample characters and lexicon information into \"character-word pair\" sequences. FLAT [14] improved upon lattice structures by adopting a flat Transformer architecture, allowing for more comprehensive character and entity learning. Innovative approaches like BERT-MRC [15] redefined NER as a reading comprehension task, using entity labels as queries to improve entity identification. However, this transformation, which enlarges the sample set and extends the length of text sequences, consequently increases the computational time required for both training and inference.  
XLNet-Transformer-R [16] combines XLNet with a Transformer encoder and R-Drop to prevent overfitting, effectively handling long texts and context. MW-NER [17] enriches semantics and reduces segmentation errors by fusing multi-granularity word information with character data through a strong-weak feedback attention mechanism.  
While these methods have shown promise, they also have limitations. Many rely heavily on large annotated datasets, which can be scarce for certain languages or domains. Additionally, some methods may struggle with class distribution imbalances, leading to less accurate recognition of infrequent entities. Furthermore, the complexity of certain models can make them computationally expensive and difficult to fine-tune for specific tasks.