esa-sceva/satcom-chunk-collection huggingface dataset:
# A Dual-Enhanced Hierarchical Alignment Framework for Multimodal Named Entity Recognition
## 3 Methodology
### Multimodal Feature Encoding Layer  
#### 3.1.1 Text Feature Encoding  
Extraction of textual features is fundamental to the named entity recognition task. To mitigate the sparsity problem inherent in social media texts, we enhance text features via similar word expansion, as illustrated in Figure 2.  
Specifically, we employ the BERT-large model [3] to encode each entity word \(x_{i}\) into a semantic vector \(c_{i}\) and then retrieve the top-\(m\) candidate words with the highest cosine similarity to \(x_{i}\) from the social media corpus, denoted \(A_{i}=\{a_{i,1},a_{i,2},\ldots,a_{i,m}\}\).  
However, not every word in this similar set contributes beneficially to the prediction of entity outcomes; thus, it is necessary to differentiate the contributions of individual words within their context. By employing an attention mechanism for weighted fusion, we compute similarity word weights \(P_{i,j}\) and subsequently obtain an augmented semantic vector \(d_{i}\) through a weighted summation of the candidate word vectors. This is defined as  
\[P_{i,j}=\frac{\exp(c_{i}\cdot e_{i,j})}{\sum_{j=1}^{m}\exp(c_{i}\cdot e_{i,j})}, \tag{1}\]  
\[d_{i}=\sum_{j=1}^{m}P_{i,j}\cdot e_{i,j}, \tag{2}\]  
where \(e_{i,j}\) denotes the word vector representation derived from the mapped similar word.  
Finally, we integrate the original text features with the enhanced semantic features using a gating mechanism to yield the final vector representation \(t_{i}\) for each word. The gating mechanism is formulated as  
\[\text{gate}=\sigma(W_{1}\cdot c_{i}+W_{2}\cdot d_{i}+b_{g}), \tag{3}\]  
\[t_{i}=[\text{gate}\odot c_{i}]\oplus[(1-\text{gate})\odot d_{i}], \tag{4}\]  
where gate represents the control gate parameter, \(W_{1}\) and \(W_{2}\) are weight matrices, and \(b_{g}\) is the corresponding bias term.  
Figure 2: Dual-enhanced hierarchical alignment (DEHA) framework. (1) Multimodal feature encoding using BERT-large for text and a three-level visual pyramid (ResNet-101, Faster R-CNN, Dilated Convolutional Network) for images; (2) DEHA alignment with Semantic-Augmented Global Contrast (SAGC) for global alignment and Multi-scale Spatial Local Contrast (MS-SLC) for local refinement; (3) cross-modal fusion and visual-constrained CRF for entity prediction, improving robustness against noise and misalignment.
## 4 Conclusions  
In this paper, we conducted a series of studies on how edge PLCs can efficiently allocate memory under memory-limited conditions. We have achieved the following research results:  
* We proposed an improved fixed-size allocation algorithm for memory allocation of real-time tasks in edge PLCs. The results of comparative experiments showed that, compared to the default method, the improved method reduced the maximum response latency by 41.2% and the average response latency by 13.7%.
* We proposed a dynamic programming-based memory allocation strategy for memory allocation of non-real-time tasks in edge PLCs. The results of comparative experiments showed that, compared to three open-source memory allocators, the allocation strategy improved task processing efficiency by at least 8.1% and up to 17.0%.  
It is undeniable that this research still has some limitations. Due to the curse of dimensionality in dynamic programming, the effectiveness of memory allocation in the non-real-time zone will significantly decrease when more than three memory requests are considered simultaneously. In addition, for environments with relatively large available memory space, the memory allocation strategies proposed in this paper do not perform well.  
There are also possible directions for improvement of this research. For example, by sharing a memory pool between the real-time and non-real-time zones and dynamically adjusting the size that each can use according to memory pressure, memory utilization can be improved. We will explore the possibility of improvement in subsequent research and hope that our research can provide inspiration to other researchers.  
Conceptualization, G.C. and R.S.; methodology, Z.W.; software, Z.W.; validation, W.D.; formal analysis, Z.W.; investigation, G.C.; resources, G.C.; data curation, R.S.; writing--original draft preparation, Z.W.; writing--review and editing, W.D.; visualization, Z.W.; supervision, R.S.; project administration, G.C.; funding acquisition, G.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Science and Technology SMEs Innovation Capacity Enhancement Project in Shandong Province grant number 2021 TSGC1089, Science and Technology SMEs Innovation Capacity Enhancement Project in Shandong Province grant number 2023 TSGC0587, \"20 New Colleges and Universities\" Funding Project in Jinan grant number 2021 GXRC074, and Major Science and Technology Innovation Project of Shandong Province grant number 2018 CXGC0601.  
Not applicable.  
The data presented in this study are available upon request through email to the corresponding author.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Elapsed Time** & **Default System Method** & **EPmalloc** \\ \hline
200 & 7026 & 8522 \\
500 & 18,653 & 21,230 \\
1000 & 38,264 & 42,488 \\
2000 & 70,012 & 84,272 \\
4000 & 151,739 & 168,422 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The number of tasks completed increases with time (unit: s).
## 4 Results and Discussion
### The First High-Speed Cruise Stage of Flying to the Destination
Pressure on the airfoil surface is the main source of aerodynamic force, and the variation of lift-drag characteristics is related to the change in pressure distribution to a great extent. In the transonic flow field around supercritical airfoil, the flow area above the upper surface of airfoil will form shock waves and cause shock-wave drag. Figure 6 shows the pressure coefficient distribution of morphing airfoil and BA airfoil when \(L/W_{0}=0.95\) and 0.86 during the first cruise stage. Firstly, the shock-wave position is analyzed. When the trailing edge deflects downward, the shock-wave position of VCa airfoil moves backward, while the VCaCh airfoil moves forward or changes unobviously. In terms of shock-wave intensity, both upward and downward deflections of VCaCh airfoil's trailing edge can produce shock waves in different degrees. Instead, VCa airfoil upward deflection enhances shock waves or changes unobviously and downward deflection changes unobviously. Under the condition of \(L/W_{0}=0.95\), the BA airfoil has a strong shock wave, and the VCaCh_0.8 airfoil with the lowest total drag has the weakest shock wave. In the results of drag decomposition, the friction drag of VCaCh_0.8 is the largest, but the pressure and total drag are the smallest. This phenomenon can be explained in that, if the wave drag caused by a shock wave of VCaCh_0.8 airfoil is significantly reduced, then pressure drag reduces a lot, which indicates that the VCaCh morphing method mainly depends on wakening shock waves to improve aerodynamic characteristics under a transonic state. In order to see the change of the shock-wave region in more detail, the algorithm proposed by [PERSON] et al. [67] was used to separate the shock-wave region, the formula is:  
\[F_{\text{wave}}=\frac{V\cdot\
abla p}{a\cdot|\
abla p|} \tag{8}\]  
\(V\) is the incoming air flow velocity, and \(a\) is the local sound speed, and the region where \(F_{\text{wave}}\geq 0.99\) is supposed to be the shock-wave region. Shock-wave region diagrams of BA airfoil, VCaCh_0.8, and VCa_0.8 airfoil at \(L/W_{0}=0.95\) during the first cruise stage are shown in Figure 7. It can be seen that the shock wave's position of VCaCh_0.8 airfoil is slightly different from that of BA airfoil, as the region is roughly in the range of \(x/c_{0}=0.49\)-0.53. The maximum value of _Fwave_ of VCaCh_0.8 airfoil, 1.15029, is also significantly lower than the 1.18437 of BA airfoil, which means the shock wave weakens greatly. On the other hand, the VCa_0.8 airfoil's shock-wave position, which is in the range of \(x/c_{0}=0.52\)-0.56, moves backward more obviously than that of BA airfoil, and the difference of the maximum value of _Fwave_ between them is small. Figure 6: Pressure coefficient distributions of morphing airfoil at different \(L/W_{0}\). (a) \(L/W0=0.95\); (b) \(L/W0=0.86\). Figure 7: Shock-wave region for different morphing airfoils at \(Ma=0.734\), \(L/\)W\({}_{0}=0.95\). (**a**) BA airfoil; (**b**) VCaCh\({}_{0.8}\) airfoil; (**c**) VCa\({}_{0.8}\) airfoil.
## 3 Evaluation with Expected Total Costs
### Cost Analysis
#### 3.2.2 Target Reliability Index Based on the Minimum Expected Total Cost  
We assume that structures are constructed to minimize the expected total cost [50,56,57]. Although this assumption has sometimes been criticized because of the lack of accuracy in the estimation of the probability of failure and cost, as well as the future change in the demand for performance [58], it is one of the few ways of quantitatively evaluating the safety margins, and is accepted in some guidelines [44,59].  
The failure cost varies widely depending on the structure type, location, utility form, evaluation of human life, etc. [PERSON] and [PERSON] [60] presented losses normalized by the building cost, considering the losses as damage to the structure, function loss, injuries, and fatalities for various building occupation types; the reported normalized losses were 0.1 to 2000. A large \(k_{f}\) is used when the structure is important, as in a nuclear power plant, or when the restoration is difficult. However, a value between 0.5 and 2.0 was assumed as \(k_{f}\) for port and harbor structures [61], and a value of 10 for pile foundations [52].  
Additionally, in the case of no CMs, the expected total cost, \(EC_{tot}\), can be calculated from Equations (1), (3), and (13), as follows:  
\[EC_{tot}=C_{b;T}\bigg{(}\frac{\mu}{\mu_{T}}\bigg{)}^{k_{b}}+\Phi(-\beta)k_{f}C _{b;T} \tag{14}\]  
After substituting Equation (4) for \(\beta\) and differentiating both sides by \(\mu\), we obtain:  
\[\frac{1}{C_{b;T}}\frac{dC_{tot}}{d\mu}=k_{p}\frac{\mu^{k_{b}-1}}{\mu_{T}^{k_{ b}}}-k_{f}f(\beta)\frac{1}{\mu\sqrt{\ln\bigg{[}\bigg{(}1+\text{COV}_{R}^{2} \bigg{)}\bigg{(}1+\text{COV}_{Q}^{2}\bigg{)}\bigg{]}}} \tag{15}\]  
where \(f\) (0) is the normal probability density function. Substitute 0 for the left side of Equation (15), \(\mu_{T}\) for \(\mu\), and \(\beta_{T}\) for \(\beta\).  
\[f(\beta_{T})=\frac{k_{b}}{k_{f}}\sqrt{\ln\Big{[}\Big{(}1+\text{COV}_{R}^{2} \Big{)}\Big{(}1+\text{COV}_{Q}^{2}\Big{)}\Big{]}} \tag{16}\]  
Figure 7 shows the relationship between the normalized failure cost, \(k_{f}\), and \(\beta\). Equation (16) clearly shows that \(\beta\) is more strongly affected by \(k_{f}\) than by \(k_{b}\). If \(\text{COV}_{\text{R}}\) = 0.35, \(\text{COV}_{\text{Q}}\) = 0.10, \(k_{b}\) = 1.0, and \(k_{f}\) = 80, \(\beta_{T}\) comes to around 3.0. As the range  
Figure 6: Relationship between safety factor (SF) and normalized building cost by that of the target one, \(C_{b}/C_{b;T}\). The conditions were described by [54,55], respectively.  
of \(\beta_{T}=0.0\)-4.3 [44] is roughly consistent with the range of \(k_{f}\) indicated by [60], the above assumption seems reasonable.
## 3 Governing Equations
### The Geometric Shape of the Utilized Textures  
This investigation examines how different texture geometries--including cubic, cylindrical, and semi-ellipsoidal forms--with controlled depth variations influence system behavior when selectively applied to specific regions of a noncircular two-lobe bearing's interior surface (Figure 3). The analysis focuses on both steady-state operational characteristics and dynamic stability metrics of the rotor-bearing set.  
As illustrated in Figure 4, the parameters \(r_{x}\), \(r_{y}\) and \(r_{z}\) represent the radius in the circumferential direction, depth, and longitudinal radius of cylindrical and semi-ellipsoidal dimples along the coordinate axes x, y, and z, respectively. Similarly, the spatial dimensions, \(2a_{x}\), \(a_{y}\), \(2a_{z}\) correspond to the length, height, and width of the cubic textures, respectively.  
In the cylindrical and semi-ellipsoidal textures shown in Figure 4, \(r_{x}=r_{z}=r\), and the equations defining cubic, cylindrical, and semi-ellipsoidal geometries are as follows:  
\[|x-x_{c}|=a_{x}\ \ \ \text{and}\ \ \ |z-z_{c}|=a_{z} \tag{5}\]  
\[(x-x_{c})^{2}+(z-z_{c})^{2}=r^{2} \tag{6}\]  
\[\left(\frac{x-x_{c}}{r_{x}}\right)^{2}+\left(\frac{y-y_{c}}{r_{y}}\right)^{2}+ \left(\frac{z-z_{c}}{r_{z}}\right)^{2}=1 \tag{7}\]  
Figure 4: Defined coordinate system for cubic, cylindrical, and semi-ellipsoidal surface textures.  
Figure 3: Engineered surface of the two-lobe bearing with cubic, cylindrical, and semi-ellipsoidal textures.  
where \(O_{c}\) is the center of each texture in local coordinates \((x_{c},y_{c},z_{c})\), located on the surface of the bearing without the texture, i.e., at \(y_{c}=0\). Additionally, the depth of each texture, as shown in Figure 3, is equal to:  
\[\Delta h=r_{y}\hskip 56.905512 pt\text{for \emph{Cylindrical and Semi-ellipsoidal Textures}} \tag{8}\]  
\[\Delta h=a_{y}\hskip 56.905512 pt\text{for Cubic Texture} \tag{9}\]  
In the top view plan shown in Figure 5, the method used to discretize the bearing surface around each texture is illustrated in both the longitudinal and circumferential directions. The parameters \(\mathrm{l_{x}}\) and \(\mathrm{l_{z}}\) represent the distances between the textures in the circumferential and longitudinal directions, respectively. Furthermore, the parameters \(\Delta\mathrm{x}\) and \(\Delta\mathrm{z}\) denote extremely small distances between the texture edge and the asymptotic auxiliary line. These distances are essential for precise simulations of the texture walls when meshing the bearing surface along the X and Z coordinate axes.
## 3 Results and Discussion
### Effect of Open-Cell Structure on the Microwave Absorption Performance  
Owing to multiple microstructures, the OCGA shows the flexible adjustability. Except for the graphene framework, the cell microstructures, including the number and size of cells, also have dramatic effects on microwave absorption property. The relationship between the cell morphologies and the microwave absorption performance was explored in this study.
## 2 Autonomous Driving in Adverse Weather Conditions
### Impact of Adverse Weather on Sensor Performance
#### 2.1.2 Radar Performance in Extreme Weather  
Radar (Radio Detection and Ranging) systems generally exhibit better resilience to adverse weather conditions than optical sensors. [PERSON] et al. compared HDIF LiDAR, radar, and millimeter-wave radar sensors in the rain, demonstrating that while all of them are affected by rain to some degree, radar systems are partially immune to weather-related performance degradation. [38; 39].  
[PERSON] et al. [40] investigated the performance of automotive millimeter-wave radar systems in the presence of rain, finding that while these systems are more robust than optical sensors, heavy rain can still cause attenuation and affect detection capabilities.
## 4 Discussion
Likewise, for distribution uniformity (DU), the highest R2 coefficients were recorded at 0.701, 0.479, and 0.826 for RF, XGB, and XGB-RF within the same context. It was noted that the hybrid XGB-RF model improved the R2 coefficient by 10-13% relative to the standalone XGB and RF models. In contrast, our study indicated that the SVR model consistently achieved a superior R2 coefficient across all contexts, surpassing both dual and triple hybrid models (Figure 9). [PERSON], [PERSON] [54] assessed the daily crop evapotranspiration (Etc) of spring maize utilizing support vector regression (SVR). To determine the optimal input variables for the SVR framework, random forest (RF) was employed as a data preprocessing method. The SVR model underwent optimization through the application of particle swarm optimization (PSO). The efficacy of the innovative hybrid RF-SVR-PSO model was evaluated against a standalone SVR model, a back-propagation neural network (BPNN) model, and an RF model by incorporating various meteorological input variables. The Pennam-Monteith equation was applied to derive the Etc values, which served as a reference standard against the values estimated by the models. The findings indicated that the hybrid RF-SVR-PSO model exhibited superior performance in estimating Etc for spring maize compared to the three independent models. The Nash-Sutcliffe efficiency coefficient (NSE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) were recorded as 0.956-0.958, 0.275-0.282 mm d\({}^{-1}\), 0.221-0.231 mm d\({}^{-1}\), and 0.957-0.961, respectively. The effectiveness of the hybrid RF-SVR-PSO model for daily Etc estimation of spring maize in semi-arid regions has been substantiated. This aligns with research outcomes indicating that the mean bias error (MBE) under RF and Sc3 was 5.14 m\({}^{3}\) ton\({}^{-1}\), the peak MBE under RF-SVR was 5.05 m\({}^{3}\) ton\({}^{-1}\), and the minimal MBE under RF-SVR and Sc1 was 0.03. The highest Mean Absolute Relative Error (MARE) values for RF and XGB with Sc3 were 59.23 and 57.82, respectively, while the lowest values were recorded as \(-\)4.05 and \(-\)3.99 for SVR with Sc2 and Sc1 (Table 5).
# Modeling the 2013 Zika Outbreak in French Polynesia: Intervention Strategies
## 4 Experiments and Results
### Intervention Strategies
#### 4.2.1 Travel Bans/Advisories  
The effect of implementing travel bans in areas where sufficient cases were reported was measured. The travel bans were effectuated by reducing the transit load parameter to (75%, 50%, 25%, 10%) from 100 % after \(\tau\) number of cases were reported. \(\tau\) was varied from {1, 10, 50, 100}. It was seen that  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Place**} & \multirow{2}{*}{**Reporting Rate [7]**} & **Calculated Cases** & **Reported Cases in [7]** \\  & & **(Reported, CI-95\%)** & **Reported Cases in [7]** \\ \hline Tahiti & 12\% & 19,098 (19,022–19,175) & 21,406 \\ Moorea & 8\% & 1156 (1150–1161) & 1235 \\ Austral Islands & 17\% & 933 (920–946) & 1208 \\ Leeward Islands & 12\% & 3517 (3473–3562) & 3912 \\ Marquesas Islands & 11\% & 750 (738–762) & 994 \\ Tuamotu Gambier Islands & 8\% & 641 (630–653) & 1211 \\ Total & & 26,095 (25,933–26,259) & 29,966 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Reported Cases Comparison, reporting rates are percent of infected cases that were reportedtravel bans had no effect on the size of the outbreak even if they were imposed after the first case was reported. The first reported case was recorded using the reporting rate data available for the major archipelagos for Zika (Table 4).  
The results for the size obtained from the four reduction rates and no reduction were compared using ANOVA. Even the most radical reduction of 10 % did not change the size of the outbreak significantly in all islands. It is shown in Figure 4 that the size of the outbreak in Tahiti doesn't change in relation to reducing the transit load. However, reduction in the transit load from 100% to 25% at the start of the simulation delayed the start of the outbreak in all other islands except for Tahiti (the infection was introduced in Tahiti). This can be seen in Figure 5.  
Figure 4: The effect of introducing travel bans (reduced transit load) on the size of the outbreak in Tahiti (distribution of epidemic size).  
Figure 5: The effect of load of transit network (load) on the start of the outbreak on each island (m = 3).
## 3 Results and Discussion
### Effect of High CO\({}_{2}\) in CA on the Sensory and Flavour Attributes  
As shown in Figure 4, the CO\({}_{2}\) treatments affected the sensory attributes, as determined by 10 panellists. These panellists were asked to compare the sensorial attributes of spearmint stems with the control reference sample in each day of analysis. All samples were randomly labelled with three-digit numbers so that the judges were unable to tell which samples belonged to which treatments. The reference sample was named as REF. At  
Figure 3: Heatmap of the volatile profile of spearmint during low temperature storage in air (AIR, \(n\) = storage day) and in low CO\({}_{2}\) concentrations (5, 10 and 20%, \(n\) = storage day) (**A**). Score plot clustering of the treatments (**B**) as well as their correspondent volatile compounds (**C**) (Cr\({}_{2}\) where \(n\) is the compound number assigned in supplementary Table S1).  
day 5, the judges could tell the difference between the control sample with 5% and 20% CO\({}_{2}\) treatments in terms of aromatic odour, off-flavour, degree of browning, and development of off-odour. The lowest appearance score was found in the 20% CO\({}_{2}\) treatment at day 10, thus its quality assessment was thereafter terminated. The 5% CO\({}_{2}\) treatment seemed to improve aroma and visual quality, while browning and off-odour were noticeable in the 20% CO\({}_{2}\) treatments. At day 10, the aroma and visual quality decreased significantly, and this treatment was removed from the experiments. At day 14, browning was greater in the CO\({}_{2}\) treatments and the visual quality decreased. [PERSON] et al. [54] described that high CO\({}_{2}\) and humidity accumulated in the package are key factors that have an adverse effect on the sensorial quality of minimally processed herbs. A further explanation was that excessive CO\({}_{2}\) may lead to the acceleration of the plant metabolism, mainly through the respiration and the development of off-odour.  
Figure 4: Sensory attributes scores (viz., odour intensity, off-odour, visual quality, yellowing or depreening and browning) evaluated by 10 panellists at the sampling days 5 (**A**), 10 (**B**) and 14 (**C**), on a 7-point scale, where 7 was described as having a much higher overall appearance than the reference (CTRL). Scale bar = 1 cm.
# A 40 Mb/s VLC System Reusing an Existing Large LED Panel in an Indoor Office Environment
## 2 Experimental Setup
### The Light-Emitting Diode (LED) Panel  
In Figure 2a, the internal structure of the LED panel is depicted. Two LED strips are mounted on opposite sides of a square aluminium frame that hosts a transparent light guide plate to direct the light evenly within the panel area. On the backside of the light guide plate is a single layer of reflective paper that is used to redirect the optical power to the receiver plane. The other side of the light guide is attached to a diffuser plate to produce evenly-distributed light for illumination. The two LED strips consist of 12 serially connected blocks of 20 LEDs that are connected in parallel, see Figure 2b.  
Figure 1: (**a**) System block diagram; (**b**) experimental setup.
## 5 Parameter Study and Discussion
### Influence of the Single-Pile Foundation Parameters
#### 5.2.2 Influence of Pile Bottom-Support Stiffness  
Figure 10 shows that the resonance frequencies and amplitudes of the pile cap increase with increasing pile bottom-support stiffness. This depicts that the harder the underlying soil layer, the stronger the supporting effect on the pile bottom, and the greater the resonance frequency and amplitude of the pile foundation.