esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Results of Sentinel-2 Satellite Data
The result expands the application scenarios of NDCSI and it is possible for satellite images with RE-related bands to calculate the fractional illuminated vegetation cover and extract the bright enough portion. Figure 10: Scatterplot of FIVC and measured brightness values for typical vegetation samples with different illumination/shadow proportions. Figure 9: Result (**b**) of the illuminated/shaded vegetation fractional cover unmixing based on the NDCSI and BSI for the Sentinel-2A image, comparing to the processed standard reflectivity image of Sentinel-2A (**a**).
## 3 FR Capacity of EV Cluster  
### Grouping Strategy of EV  
As a kind of mobile energy storage device, EV has the advantage of fast response [18]. EVs can participate in system FR through controlling charging and discharging power [19]. The EV aggregator (EVA) collects the declared information of EVs connected to the grid in real time, including the residence time \(T_{i}\) in the grid, the state of charge (SOC), and the credibility \(\rho_{i}\). Based on residence time and credibility, EVs connected to the grid are divided into FR service group and energy demand group. In FR service group, according to the initial SOC when EV becomes connected to the grid, EVs are divided into charging group and discharging group. EVA reports the FR capacity of FR service group to control center in real time. According to the system frequency deviation and the FR capacity of DFIG, the grid control center allocates FR task to EVA; then the EVA instructs EVs in the FR service group to charge or discharge to provide frequency support.  
Only when the driving demand of vehicle owner is satisfied, EV will participate in FR; otherwise, the EV will be included in the energy demand group, charging to meet the driving demand. The FR service group \(Se_{j}\) at time \(j\) is shown in (7):  
\[Se_{j}=\left\{\dot{a}|T_{i}>T^{*}\cup\rho_{i}\geq\rho_{down}\cup j<d_{i}-T_{i} ^{D}\right\} \tag{7}\]  
where \(T^{*}\) is the threshold of residence time in the grid for FR; \(\rho_{down}\) is the lower threshold of credibility; \(d_{i}\) is the planned departure time of the \(i\)-th EV; \(T_{i}^{D}\) is the necessary charging duration for the \(i\)-th EV to meet driving demand.  
After being included in the FR service group, the EV begins to accept the FR instructions from EVA. In order to protect EV battery, frequent conversions between the charging and discharging state, as well as excessive charging/discharging behaviors, should be avoided [20; 21]. Therefore, during the FR process, EVs are divided into discharging group \(DC\) and charging group \(C\) based on the rules shown in Table 3, where \(SOC_{i,j}\) is the state of charge of the \(i\)-th EV at time \(j\); \(SOC_{\text{max}}\) and \(SOC_{\text{min}}\) are the upper and lower limits of SOC, respectively.  
Figure 6: Principle block diagram of rotor speed protection.  
Therefore, the discharging and charging groups at time \(j\) are shown in (8) and (9), respectively:  
\[DC_{j}=\left\{i\big{|}SOC_{i,j}\geq SOC_{\text{min}},P_{i,j}\geq 0,i\in Se_{j}\right\} \tag{8}\]  
\[C_{j}=\left\{i\big{|}SOC_{i,j}\leq SOC_{\text{max}},P_{i,j}\leq 0,i\in Se_{j}\right\} \tag{9}\]  
where \(P_{i,j}\) is the charging/discharging power for FR of the \(i\)-th EV at time \(j\).  
The EV quantity in the discharging and charging groups at time \(j\) are shown in (10) and (11), respectively:  
\[N_{j}^{dc}=\|DC_{j}\| \tag{10}\]  
\[N_{j}^{c}=\|C_{j}\| \tag{11}\]
# Named Entity Recognition in Online Medical Consultation Using Deep Learning
## 5 Conclusions and Future Work  
### Conclusions  
In this research, we proposed an innovative deep named entity recognition approach for the medical field based on a fusion context mechanism for online medical consultations  
Figure 7: The effect of the number of epochs on the F1 score during a single experimental cycle. M13 represents our proposed approach.  
and developed an experimental dataset comprising as many as 26 different medical entity types. The OOV problem was effectively alleviated by introducing ChineseBERT; the local contextual semantic representation of the data samples was effectively learned by the design of the stacked CNN with a local context mechanism component; the global contextual semantic representation of the data samples was effectively learned by designing a BiLSTM with a global context mechanism component; the local and global contextual semantic representations were effectively fused using a deep factorization machine with a fusion context mechanism component; and the occasional generation of illegal tag sequences was effectively prevented by introducing the MCRF component. Each of these can effectively improve the entity recognition performance, resulting in the proposed method outperforming previous SOTA research by 0.99% in terms of the average F1 score. This study also provides important data support for the main step of knowledge extraction in constructing domain-specific knowledge graphs. Additionally, the proposed approach in this study is based on Chinese characters, making it easily applicable to other character-level Chinese NER datasets in various domains, such as OntoNotes 4.0 [39] in the news domain and Weibo [40] in the social media domain. However, when applying this approach to NER datasets in other languages, the ChineseBERT framework needs to be replaced with a BERT model or its variants pretrained in the target language.
# Blind Image Deblurring Based on Local Edges Selection
## 2 Method
### Estimation of Clear Image  
In the estimation of the clear image, we aim to solve the function as follows:  
\[\underset{x}{\text{argmin}}\Big{\|}y-H\otimes x\Big{\|}_{2}^{2}+\Big{\|}\hat{y}- H\otimes\hat{x}\Big{\|}_{2}^{2}+\alpha Q_{TV}(x) \tag{14}\]  
\[Q_{TV}(x)=\sum_{i=1}^{4}\
abla_{i}x^{2} \tag{15}\]  
where \(\
abla_{i}\) is the image gradients filter [32] in the directions of \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\), and \(135^{\circ}\); different from the method in [32], the operators are obtained by bilinear interpolation of the basic gradients operator [21]. In fact, when \(i=2\), \(\
abla_{i}\) comprises the image gradient operators in the directions of \(0^{\circ}\) and \(90^{\circ}\), then \(Q_{TV}(x)\) represents the classic total variation [33]. We add two more directions based on the total variation term to reduce ringing artifacts.  
The dark channel and bright channel are obtained by a non-linear operation. In the method proposed by [PERSON] et al. [31], dark channel is equivalently transformed into the multiplication of the image and a linear operator, then the clear image can be achieved by FFT method. However, in [PERSON]'s method, the linear operator is obtained by using gray value image rather than color image, so it is not the best way to solve the problem by introducing a linear operator. In the proposed method, the clear image is obtained within the alternating direction method of multipliers (ADMM) framework [32], where the clear image can be achieved as follows:  
\[x=\mathcal{F}^{-1}\Bigg{(}\frac{\mathcal{F}^{T}(H)(a_{k}+b_{k})}{\mathcal{F}( H)\mathcal{F}^{T}(H)}+\sum_{i=1}^{4}\frac{\mathcal{F}^{T}(\
abla_{i})(c_{k}+d_{ k})}{\mathcal{F}(\
abla_{i})\mathcal{F}^{T}(\
abla_{i})}\Bigg{)} \tag{16}\]  
where \(k\) is the inner iteration time. With \(a_{k}\), \(b_{k}\), \(c_{k}\) and \(d_{k}\) as intermediate parameters, the process of obtaining clear image is as follows:  
In Algorithm 2, \(a_{k}\) and \(c_{k}\) are obtained by the gradient descent method [14]. In each iteration, \(a_{k}\), \(b_{k}\), \(c_{k}\) and \(d_{k}\) are obtained first, then intermediate image \(x_{k}\) are obtained by Equation (16). When iteration time reaches 20, the algorithm can converge to a high accuracy, so we empirically set \(k=20\). The detailed convergence analysis is introduced in Section 3.3.  
```
1. Initialization: \(x_{0}\), \(H\) (Intermediate results in the previous iteration); \(a_{0}\), \(b_{0}\), \(c_{0}\), \(d_{0}\)
2. for \(k\) = 1:20
3. Calculate \[a_{k}=\underset{x_{k}}{\text{argmin}}\Big{(}\Big{\|}H*\|x_{k}+\hat{x}_{k}\|- \Big{\|}y+\hat{y}\Big{\|}-\|x_{k-1}+\hat{x}_{k-1}\|-b_{k-1}\|_{2}^{2}\Big{)}\] \[b_{k}=a_{k}+b_{k-1}-\Big{\|}H*x_{k}-y\Big{\|}_{2}^{2}\] \[c_{k}=\underset{x_{k}}{\text{argmin}}\Bigg{(}\sum_{i=1}^{4}\| \
abla_{i}*x_{k}-d_{k-1}-x_{k-1}\|_{2}^{2}+\|x_{k}\|_{2}^{2}\Bigg{)}\] \[d_{k}=d_{k-1}+c_{k}-\sum_{i=1}^{4}\|\
abla_{i}*x_{k}\|_{2}^{2}\]
4. Obtain \(x_{k}\) by Equation (16)
5. end
6.\(x=x_{20}\)
```  
**Algorithm 2** The calculation of clear image
## 3 AIS Sensor Data Acquisition and Preprocessing  
### AIS Sensor Navigation Trajectory Data Extraction and Separation  
In contrast to actual maritime navigation, the Automatic Identification System (AIS) trajectory data of a target ship can be directly obtained as sample data. In the simulation experimental stage, the route data from within the massive AIS sensor data from a certain sea area must be extracted to screen out ship trajectories that meets the model requirements. The shore-based AIS has navigational data for all ships passing through the sea and a ship may travel back and forth several times between the starting and end points of a certain area. Thus, the route data are numerous and complicated and it is difficult to directly extract ship routes to create a sample set for model training. The trajectories of different ships are distinguished according to their unique maritime mobile service identification (MMSI) numbers. According to reference [43], the time intervals between two adjacent data points of the same ship were preliminarily calculated and the route trajectories were identified by the speed and time interval. Data points with long time intervals and instantaneous velocities of 0 or close to 0 were selected as the starting points of the route. Considering the abnormal recording of time data, the starting point of the route was determined by combining the speed and change in ship position based on a time difference judgment. An AIS sequence is denoted as \(T=\{(t_{1},v_{1},\lambda_{1},...),(t_{2},v_{2},\lambda_{2},...),...,(t_{n},v_{ n},\lambda_{n},...)\}\), where \(t_{n}\) denotes the time interval from the next
# Design of Cislunar Navigation Constellation via Orbits with a Resonant Period
## 2 Fundamental Theory
### Navigation Performance Modeling
Their relationships are given by the following:  
\[D_{p}=\sqrt{\sigma_{x}^{2}+\sigma_{y}^{2}+\sigma_{z}^{2}} \tag{10}\]  
\[D_{g}=\sqrt{\sigma_{x}^{2}+\sigma_{y}^{2}+\sigma_{z}^{2}+\sigma_{t}^{2}} \tag{11}\]  
This study focuses on Position PDOP, where a smaller PDOP value indicates reduced positioning errors and a superior constellation performance.
# Minimizing Interference-to-Signal Ratios in Multi-Cell Telecommunication Networks
## 3 Dot-Product Clustering Algorithm for Total Interference Minimization Problem  
In this section, we describe our new and simple heuristic algorithm for the total interference minimization problem. As we already mentioned, our algorithm runs in three phases. In the first, a similarity function is introduced. This phase contains the novelty of our approach. Our similarity measure depends on the relations between the base stations and the users.  
In the second phase, the base stations are clustered on the basis of the similarity measure. Here we have significant freedom to choose our clustering algorithm. The simplest possible method is arguably a hierarchical clustering method. For simplicity we use such a method in this paper, but this choice may badly affect the stability of the solutions. It may provide unbalanced cluster sizes, and it can also introduce too much engineering complication. It is possible that some back-step or averaging approach can amend the variance in the quality of the solutions.  
Finally, the third phase assigns users to the base station clusters. By design, the output of our algorithm is always an IF-cluster system. However, it would be beneficial to study methods to balance the number of users in the cluster classes.
# Experimental Study on Dynamics of Wooden House Wall Panels with Different Thermal Isolation
## 2 Static Material Tests  
Firstly, static compression and tension tests were carried out for the polyurethane foam (because of the mechanical parameters of mineral wool and the problem with fixing the samples, tests for this material were not possible). The tests were conducted using a two-column Zwick/Roell Z020 machine with a maximum force range of 20 kN.  
Polyurethane foam static compression tests were carried out on a number of cylindrical samples with a cross-section area of 18 mm\({}^{2}\) and a height of 20 mm (Figure 1). Material samples were taken directly from the wall panels of the wooden frame building, which were previously tested experimentally (see Section 4). The representative results of the static compression tests, in the form of relation between stress and strain, are shown in Figure 2. It should be added that nearly identical curves were obtained for all of the tested samples and the scatter of the results was negligibly small.  
It can be seen from Figure 2 that the foam behaves in a highly nonlinear way during compression. In the range of elongation from 0 to about 0.1, stresses increase up to about 250 kPa. Then, at the deformation above about 0.1, stabilization occurs, and above this range of strains, the stresses remain almost constant.  
Polyurethane foam static tension tests were carried out on a number of samples with a cross-section of 10 mm\({}^{2}\) and a height of 80 mm (Figure 3). Material samples were taken directly from the wall panels of the wooden frame building, which were previously tested experimentally (see Section 4). The representative results of the static tension tests, in the form of relation between stress and strain, are shown in Figure 4. It should be added that nearly identical curves were obtained for all of the tested samples and the scatter of the results was negligibly small. It can be seen from Figure 4 that the foam behaves in a highly nonlinear way also during tension. In the strain range from 0 to about 0.05, the stresses increase nonlinearly up to about 43 kPa. In the strain interval above 0.05, the stresses increase almost linearly to the level of about 105 kPa.  
Figure 1: Samples made of polyurethane foam subjected to static compression.  
Figure 2: Relation between stress and strain for polyurethane foam subjected to static compression (average of tested specimens).
## 6 Simulation and Results
### Transmission Gain in Intra-Chip Communication Channel  
There is no relative motion between the cores in a multi-core chip; thus, we regard the channel being examined as time-invariant. Transmission gain refers to the cumulative decibel value of the transmit and receive antenna gains combined with the path gain. When measured, it essentially represents the scattering parameter \(|S_{21}|\), which quantifies the gain from port 1 to port 2.  
Using the Friis transmission equation [49], one can calculate the transmission gain.  
\[G_{a}=\frac{|S_{21}|}{(1-|S_{11}|^{2})(1-|S_{22}|^{2})}=G_{r}G_{t}(\frac{A}{4 \pi r})^{2}e^{-2\pi r}=\frac{P_{r}}{P_{t}} \tag{1}\]  
where \(|S_{21}|\) is the transmitter and receiver coupling, \(|S_{11}|\) and \(|S_{21}|\) correspond to the reflection coefficient at both ends, \(G_{r}\) and \(G_{t}\) denote the receiver and transmitter gain,  
Figure 10: Distribution of electric field magnitude (V/cm) on the die surface when the dipole antenna is stimulated at a frequency of 446 GHz.  
Figure 9: Distribution of electric field magnitude (V/cm) on the die surface when the zigzag antenna is stimulated at a frequency of 462.5 GHz.  
and \(P_{t}\) represent the received and transmitted power, \(\lambda\) is the operating wavelength (648 um and 672 um in the case of zigzag and dipole antenna, respectively), \(\alpha\) is the attenuation factor, and \(r\) is the separation distance between the transmitter and receiver.  
These antennas operate within a fixed environmental structure, which introduces limitations for measurement. Hence, the channel model is closely related to the antenna design and the physical structure of the chip. Thus, for the intra-chip channel modeling, we perform a series of measurements for transmission gain (G\({}_{a}\)) between antennas as a function of distance. All measurements are conducted at the resonant frequency of both antennas. The data are then post-processed using Equation (1), and the results are shown in Figure 11.  
In this case, we used two different die materials, i.e., polyimide and silicon dioxide, to observe the gain between the antennas. Due to the high resistivity of the silicon, we can observe the less gain between the antennas from Figure 11. The measurement is restricted to a distance of around 19 mm because the maximum separation of a 13 \(\times\) 13 mm\({}^{2}\) chip comes to 18.38 mm if the antennas are placed central to each core.  
The probability density function (PDF) based on the variation of \(G_{a}\) is shown in Figure 12. It is noteworthy that the path loss varies from around \(-40\) dB to \(+35\) dB for the simulated THz band. A dipole antenna under silicon dioxide has more variation. This study is helpful in determining link budget and channel gain estimation for a particular configuration based on antenna locations and positions.  
Figure 11: Measured transmission gain (G\({}_{a}\)) with Antenna A as a reference.  
Figure 12: Probability density function (PDF) of transmission gain (G\({}_{a}\)) deviation.
## 1 Introduction
The study of vehicle-bridge coupling vibration, a time-varying dynamic problem, primarily relies on numerical analysis methods. The research on vehicle-bridge coupling for highways gradually developed after the study of vehicle-bridge coupling for railways [23]. Based on the process of establishing the motion equations for the vehicle-bridge coupling system, these methods can be categorized into the \"integrated method\" and the \"segregated method\" [24; 25]. [PERSON] [26] considered pavement roughness by adjusting the constant terms in the displacement constraint equations between the wheels and bridge nodes. This approach to considering pavement roughness is referred to as the displacement coupling method. When the wheels and bridge nodes coincide, this method can effectively simulate pavement roughness. However, when the wheels and bridge nodes do not coincide, interpolation with element shape functions determines the bridge node displacement. This method may lead to overly complicated shape functions in complex cases. Therefore, this study adopts the displacement contact method [27] on the basis of the displacement coupling method to address the challenge of coupling vibration analysis within elements. Furthermore, most previous studies have focused on two-dimensional pavement roughness [28; 29]. This assumption does not adequately represent the real-world pavement conditions of highway bridges. In light of this, this study proposes a three-dimensional contact constraint method. The contact constraint method integrates and improves upon the previous methods, enabling convenient vehicle-bridge coupling simulation analysis that considers the three-dimensional pavement roughness of highway bridges. This study establishes a spatial vehicle-bridge vibration coupling model. Both the vehicle and the bridge are spatial models, and three-dimensional pavement roughness is considered. Moreover, it does not require complex secondary programming work. Additionally, the calculation methods for impact factors in codes from various countries are mostly based on statistical data from traditional concrete bridges. There is no specific consideration for corrugated steel web box girder bridges [30]. Nevertheless, existing studies have shown that due to the influence of shear deformation in steel webs, corrugated steel web box girder bridges exhibit certain differences in dynamic characteristics compared to concrete box girder bridges [31]. Therefore, it is necessary to study the applicability of the impact factor calculation methods in international codes to corrugated steel web bridges. In summary, this study aims to investigate vehicle-bridge coupling vibration in corrugated steel web box girder bridges, considering the three-dimensional pavement roughness. Vehicle-Bridge Coupling Vibration Analysis Method Based on the Three-Dimensional Contact Constraint Method
# Obstacle Avoidance in a Three-Dimensional Dynamic Environment Based on Fuzzy Dynamic Windows
## 3 Proposed Dynamic Window Approach
### The Two Velocity Windows  
Due to the acceleration and velocity limitations, the agent cannot move as required. Meanwhile, the high rate iteration of the algorithm or obstacles may cause the agent speed to be extremely slow, which may cause the speed group to be ignored and run errors. Therefore, two-level velocity windows are used to deal with this problem, as shown in Figure 3.  
Figure 3a shows the two-level velocity windows. The black cube represents the agent and the blue cube represents the 'local window'. The local window is the reachable velocity by the agent in the next iteration, which is limited by the agent acceleration and maximum velocity. The light cube represents the window of the maximum speed that the agent can reach. The maximum speed window can ignore the kinematic constraints of the agent. In Figure 3b, according to the elevation and azimuth angles, the agent's motion is divided into different motion planes to form a 3D fan-shaped area. The dense part of the 3D sector represents that the agent can reach predicted positions if the agent maintains its velocity for 3 s. The dark-blue area represents the positions where the agent can reach at the maximum speed. The red rectangular prism represents the position of the agent selected by the evaluation function.  
Figure 2: The DF_DWA flowchart.  
By setting two-level velocity windows, when the agent iteration speed is extremely slow, the best trajectory between the maximum speed is first determined. Then the agent moves to near the optimal position in the next iteration. Based on this, we can further analyze how the agent determines the best local goal when it encounters a large obstacle or a U-shaped obstacle.
# Development of an Estimation Formula to Evaluate Mission Motion Suitability of Military Jackets
## 2 Methods
### Data Analysis for Developing an Estimation Formula  
Correlation and factor analyses of mission motions were conducted to identify correlations in mission motions and to extract the main mission motions. A correlation analysis between general motion and mission motion was also performed to select the independent variables used in the estimation formula. A multiple regression analysis was used to develop an estimation formula for motion suitability evaluation based on the correlation between general motion factors, which could be used instead of the mission motion suitability evaluation.  
Data analysis was performed using SPSS 28.0 ver. The mean scores (m) and standard deviation (SD) of the suitability evaluation of each motion were included in the results. As a result of Shapiro-Wilk test, the \(p\)-value is higher than 0.05 to prove a normal distribution at the level of \(p\) < 0.05; therefore, in order to determine validity, a paired \(t\)-test was conducted between the estimation formula value and the real mission motion suitability score for the tracked vehicle crew jacket and the military winter jacket. In addition, to verify the reliability of the repeated measurement of the estimation formula, the formula was applied to the general motion evaluation results of 10 study subjects who wore the tracked vehicle crew jacket, and the results were compared.