esa-sceva/satcom-chunk-collection huggingface dataset:
# A Bi-Directional LSTM-CNN Model with Attention for Aspect-Level Text Classification
## 3 Experiment
### The Use of Bi-LSTM  
Bi-LSTM network is used instead of standard LSTM network in our model to optimize the sentence with reversed expression. We do an experiment to find out the improvement Bi-LSTM has made to the model. Our model is marked as Bi-LSTM-* and the model with LSTM network as LSTM-*. The LSTM-* model was trained and tested with the same set with the one used in the baseline experiment. The result is shown in Table 3.  
In Table 3, we can find that Bi-LSTM-* has an average 0.3% better performance than LSTM-* with both accuracy and F1 score on restaurants and camera datasets. We go deep into the data, and find that Bi-LSTM-* has almost the same accuracy as LSTM-* on most review sentences. However, the model with Bi-LSTM has about 3% higher performance than LSTM-* on long sentences where the target is in front of its description and the distance between them is more than two words. For example, in the review sentence in restaurants \"We took advantage of the half price sushi deal on Saturday so it was well worth it\", the target \"half price sushi deal\" and its description \"well worth\" are four words away, which causes the LSTM-* to fail to recognize the aspect \"QUALITY\" and mark the sentence as \"RESTAURANT#GENERAL\". As shown in Table 4, the sentences of this kind occupy about 10-15% in the test set, which leads to only 0.3% in the overall experiment result.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Special**} & \multicolumn{2}{c}{**Total**} \\ \cline{2-5}  & **Train** & **Test** & **Train** & **Test** \\ \hline Restaurants & 358 & 100 & 2507 & 859 \\ Cameras & 167 & 69 & 1259 & 481 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The Summary statistics of inverted sentences on Restaurants and Cameras datasets. _Special_ stands for the number of sentences where the target is in front of its description and the distance between them is more than two words.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Restaurants**} & \multicolumn{2}{c}{**Cameras**} \\ \cline{2-5}  & **Accuracy** & **F1** & **Accuracy** & **F1** \\ \hline Bi-LSTM-* & **79.04** & **84.00** & **86.75** & **89.80** \\ LSTM-* & 78.67 & 83.65 & 86.40 & 89.52 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Contrast experiment data of Bi-LSTM-* and LSTM-* on Restaurants and Cameras datasets. Evaluation metrics are accuracy and F1 measure. Best scores are in bold.  
The result proves the effect of Bi-LSTM cell in the model. However, Bi-LSTM-* model costs much more time than LSTM-* model, which is the main reason for the slow pace of model training.  
Additionally, the test score of LSTM-* exceeds the best baseline method RCNN-LSTM, which demonstrates that the architecture of our model fully exploits each module in the ABSA tasks.
## 2 Optimization Design Process and Modeling Method
### Numerical Method
#### 2.3.2 Mesh Sensitivity Analysis  
The calculation domains should be discretized by meshes before simulation. Due to the strong adaptability to complex geometry, tetrahedral unstructured grids generated by software Ansys ICEM14.1 are adopt in this study. The meshes of all computational flow domains are displayed in Figure 4.  
Figure 3: Model of computational domains.  
The number of mesh elements can significantly influence the simulation results. Seven sets of meshes are used for grid independence analysis, with elements rising from \(34\times 10^{4}\) to \(251\times 10^{4}\). Grid convergence study is done at design flowrate using the Reynolds-averaged Navier-Stokes equations (RANS) method and boundary conditions described below. The Reynolds number value is about \(6.2\times 10^{5}\) based on the formula \(R_{\mathrm{e}}=u_{2}\cdot D_{2}/2v\). \(u_{2}\) represents the tangential velocity of the flow at the blade tip clearance under rated rotational speed 5400 r/min. \(v\) represents the kinematic viscosity of the fluid. So, it is reasonable to perform the following simulations in the framework of RANS. Seen from Figure 5, the overall difference of pump head is within 1% when the number of mesh elements exceed 1.68 million, which means the grid number has little effect on the calculation results. In order to balance the computational accuracy and the total calculation time, meshes with 168 million elements are employed for the following investigation.
# Research on High-Performance Fourier Transform Algorithms Based on the NPU
## 3 Background
### The Acceleration Technique in the FFT Based on the Matrix Divide-and-Conquer Method  
We were able to derive a matrix form of the Cooley-Tukey algorithm, presented in Formula (3), by incorporating the divide-and-conquer technique [31]. With that formula, we can control the granularity of matrix factorization within the feasible range of the NPU.  
\[\begin{bmatrix}X_{1}\\ X_{2}\end{bmatrix}_{N\times 1}=\begin{bmatrix}I&D\\ I&D\end{bmatrix}\times\begin{bmatrix}A\\ B\end{bmatrix}_{N\times 1} \tag{3}\]  
where \(A\) and \(B\) are the result of multiplying the even and odd sequences of input data \(X\) by a DFT matrix of order \(N/2\), respectively; \(I=\begin{bmatrix}1&0&\cdots&0\\ 0&1&\cdots&0\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&\cdots&1\end{bmatrix}_{\frac{N}{2}\times\frac{N}{2}}\), and \(D\) is the rotation matrix, where \(D=\begin{bmatrix}1&0&\cdots&0\\ 0&\omega_{N}^{1}&\cdots&0\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&\cdots&\omega_{N}^{\frac{N}{2}-1}\end{bmatrix}_{\frac{N}{2}\times\frac{N} {2}}\), \(X_{1}=\begin{bmatrix}X(0)\\ X(1)\\ \vdots\\ X(\frac{N}{2}-1)\end{bmatrix}_{\frac{N}{2}\times 1}\),  
Furthermore, we obtained Formula (4), which is used in the merging process:  
\[\begin{bmatrix}X_{1}\\ X_{2}\end{bmatrix}=\begin{bmatrix}A+DB\\ A-DB\end{bmatrix} \tag{4}\]  
During the solving of A and B, we recursively repeat this process of decomposition and merging. In this way, the DFT transformation of a higher-dimensional vector is transformed into that of multiple lower-dimensional vectors. This algorithm has the same asymptotic time complexity as the Cooley-Tukey algorithm, but its calculation amount depends on the order of the vector yields by the last decomposition.  
It can be observed from Formula (4) that this algorithm consists of matrix addition, subtraction, and multiplication operations and is appropriate for acceleration utilizing the cube units and vector units of NPUs in combination.
# Hierarchical Visual Place Recognition Based on Semantic-Aggregation
###### Abstract
Aiming at the bucolic environments such as natural scenes with low texture and little semantic contents, but obvious appearance changes, [PERSON] et al. [31] proposed a global descriptor based on image topological and semantic information to achieve place recognition by matching semantic edges between two images. These works have shown that it is possible and efficient to apply image semantic information to VPR. [PERSON] et al. [32] studied a strong robust hierarchical localization method, and realized a coarse-to-fine hierarchical localization and autonomous navigation system for mobile robot based on pure vision. [PERSON] et al. [33] proposed an appearance-based method for topological mapping based on hierarchical decomposition of environment, and proved that the hierarchical method could reduce search space in identifying place while improve mapping accuracy in creating a map. [PERSON] and [PERSON] [34] developed a new stacked hierarchical localization framework, which concatenated localization hypotheses from techniques with complementary characteristics at each layer, performing well on two challenging datasets. These works have proved that hierarchical strategy is useful and effective in reducing search space and localization. Motivated by the works above, this paper believes that achieving efficient image-understanding-based VPR across appearance and viewpoint variations requires semantic understanding of the environment. Moreover, hierarchical strategy contributes to maintaining computational efficiency. We combine visual semantics and hierarchy and propose hierarchical place recognition based on semantic aggregation, to minimize the influences of appearance and viewpoint variations.
## 1 Introduction
This study aims to systematically investigate the nonlinear mechanism and spatial heterogeneity of built environment factors on the total ridership and inbound and outbound ridership of Shenzhen rail transit by integrating the multiscale geographically weighted regression (MGWR) model and the gradient boosting decision tree (GBDT) model, with a view to providing data-driven scientific basis for optimising the synergistic planning of the built environment and the rail transit and for enhancing the operational efficiency of the public transportation system. The study is aimed at providing data-driven scientific basis for optimising the synergistic planning of urban built environment and rail transit and enhancing the operational efficiency of public transportation system. The innovations of this paper are as follows: (1) This study combines the spatial heterogeneity analysis of Multiscale Geographically Weighted Regression (MGWR) with the nonlinear modelling capabilities of Gradient Boosted Decision Trees (GBDT) to investigate the influence of built environment factors on ridership. MGWR captures local spatial heterogeneity, while GBDT analyses the nonlinear threshold effects in high-dimensional data. This approach overcomes the limitations of traditional linear models in addressing complex interactions. (2) For the first time, we construct three-dimensional partial dependence plots that integrate building floor area ratio (FAR) and accessibility. These plots quantify the nonlinear effects of various built environment factors on ridership in different directions (inbound and outbound). Based on the identified thresholds of nonlinear effects, we propose policy recommendations for urban transportation planning and public transit development to promote more orderly urban growth. Following the introduction, Section 2 provides a literature review of the selection of built environment factors for rail transit ridership and the modelling approaches that have been used. Section 3 describes the research methodology used in this paper. Section 4 presents a case study of rail transit in Shenzhen. Section 5 analyses the results of the study and gives policy recommendations. Section 6 summarises the full paper.
# Numerical Analysis of Reinforcing Effect for Scissors-Type Bridge with Strut Members
## 5 Discussion
### Reinforcing Process  
Effective reinforcing procedures to reduce central displacement and bending moment are considered based on numerical results in last section. Table 2 shows optimal reinforcing steps in order to minimize the displacement and bending moment occurred in '\(\Lambda\)'-shaped members from the model without reinforced member (Case 0) to the model with the bottom horizontal members (Case 1). Where the symbol H is horizontal member, the symbol 'V' is vertical member, the values are decreasing rate of the displacement and bending moment from Case 0. According to progress of step 1 and 2 in the table, both displacement and bending moment become small and minimize in step 3. Both displacement and bending moment decrease by setting the reinforcing member as center vertical member \(\rightarrow\) side vertical member \(\rightarrow\) upper horizontal member.  
Similarly, Table 3 shows optimal reinforcing steps in order to minimize central displacement and bending moment occurred in member \(\overline{B_{2}A_{3}}\). According to progress of step 1 and 2 in the table, both displacement and bending moment become small and minimize in step 3. Both displacement and bending moment decrease by setting the reinforcing member as central upper horizontal member \(\rightarrow\) side vertical member \(\rightarrow\) side upper horizontal member.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Step** & **Additional Member** & **Displacement** & **Bending Moment** & **Configuration** \\ \hline
0 & - & 154.4\% & 156.4\% & \\ \hline
1 & Center V mem. & 5.2\% & 4.3\% & \\ \hline
2 & Side V mem. & 4.5\% & 3.8\% & \\ \hline
3 & Upper H mem. & 3.1\% & 2.9\% & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimal reinforcing procedure for two scissors unit model.  
Figure 18: Effect of the ratio of extensional rigidity \(\alpha\) in Case 3-4 with vertical and horizontal reinforcing member: (**a**) nondimensional stress; (**b**) nondimensional displacement.  
From the above results, it is understood that the displacement and bending moment can be reduced with a central vertical member in a two scissors unit model and by a central upper horizontal member in a three scissors unit model. According to the progress of the reinforcement, the displacement and bending moment are reduced less than 5% from a nonreinforced model. Especially, it is understood that first reinforcing member is quite important to improve the performance of a bridge.
# Aerostat-Based Observation of Space Objects in the Stratosphere
## 4 Observation Ability Analysis
### Observation Ability in Infrared Band
#### 4.2.2 Observable Objects Analysis  
Based on the detection ability in the infrared band analyzed above, we discuss what kind of space object can be detected during the daytime, thus demonstrating the application of the telescope located in the SOFOS. Under the diffuse sphere assumption, setting the object's albedo to 0.1, the size of the observable object is calculated with a detection ability of 7.5 mag and 9.5 mag, respectively. Note that the detectable size is related to the distance and phase angle of the object.  
As shown in Figure 10, the debris with a size of 0.2 m can be observed at a 1000 km distance and a phase angle of 40\({}^{\circ}\). For the debris in geosynchronous orbit, an object of about 7.0 m can be observed at the phase angle of 40\({}^{\circ}\).  
For daytime observations for NEA, Figure 11 shows the observable object sizes in km from about one earth-moon distance to a 0.1 Astronomy Unit (AU). When the distance is 380,000 km/1.5 million km/5 million km/0.1 AU (about 15 million km) at a phase angle of 40\({}^{\circ}\), the 9.5 mag ability telescope can observe the NEA object of about 80 m, 290 m, 980 km, and 2.9 km, respectively.  
Figure 11: Observation ability for NEA by means of the SOFOS during the daytime.  
Figure 10: Observation ability for space debris by means of the SOFOS during the daytime. The contour number indicates that the object size that the unit is m. (**a**) Observation ability of 7.0 mag. (**b**) Observation ability of 9.5 mag.
# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## 1 Introduction
### Contribution of This Paper  
This paper defines and solves a new optimization problem that finds the number and position of UAVs to be deployed over an area to provide VoWiFi service to a set of ground users. Our optimal 3D placement minimizes the number of UAVs deployed over time (i.e., rate of drones per hour of service) while satisfying coverage and QoS constraints.  
The contributions of this paper can be summarized as follows:  
* We mathematically formulate a new UAV optimal deployment problem that accounts for UAVs turnover rate, coverage, and quality of service.
* We provide an analytical model that predicts the speech quality in a IEEE 802.11 WLAN for a set of VoIP traffic sources.
* We provide an analytical model that predicts UAV energy expenditure due to wireless communications. This information is used along with flight energy consumption to predict UAVs endurance.
* We solve the UAV optimal deployment problem using well-known heuristic methods such as genetic algorithm and particle swarm optimization.
## 1 Introduction
The activities undertaken and reported in this study represent a step toward the development of an acoustic emission based approach for the assessment of reinforced concrete structural systems through remote monitoring. To the authors' knowledge, this research is the first of its kind to study the suitability of acoustic emission to monitor corrosion damage in the field while comparing the results to a test block from a similar structure tested in a controlled laboratory environment. The study is divided into two main activities: (1) Remote acoustic emission monitoring and analysis of data collected at the 105-C Reactor Facility and (2) Accelerated corrosion testing to assess corrosion damage within an aged and reinforced concrete block supplied by SRNS at the University of South Carolina Structures and Materials laboratory.
## 3 Perturbed Orthogonal Matrices and Perturbation Parameters  
In the perturbation analysis of the SVD, it is convenient to find first componentwise bounds on the entries of the matrices \(\delta W_{U}:=U^{T}\delta U_{1}\) and \(\delta W_{V}:=V^{T}\delta V\), which are related to the corresponding perturbations \(\delta U_{1}\) and \(\delta V\) by orthogonal transformations. The implementation of the matrices \(\delta W_{U}\) and \(\delta W_{V}\) allows us to find bounds on  
\[\delta U_{1}=U\delta W_{U} \tag{6}\]  
and  
\[\delta V=V\delta W_{V} \tag{7}\]  
using orthogonal transformations without increasing the norms of \(\delta W_{U}\) and \(\delta W_{V}\). This helps to determine bounds on \(\delta U_{1}\) and \(\delta V\), which are as tight as possible. First, consider the matrix  
\[\delta W_{U}=U^{T}\delta U_{1}=\left[ \begin{array}{cccc}u_{1}^{T}\delta u_{1}&u_{ 1}^{T}\delta u_{2}&\ldots&u_{1}^{T}\delta u_{n}\\ u_{2}^{T}\delta u_{1}&u_{2}^{T}\delta u_{2}&\ldots&u_{2}^{T}\delta u_{n}\\ \vdots&\vdots&\vdots&\vdots\\ u_{n}^{T}\delta u_{1}&u_{n}^{T}\delta u_{2}&\ldots&u_{n}^{T}\delta u_{n}\\ \hline u_{n+1}^{T}\delta u_{1}&u_{n+1}^{T}\delta u_{2}&\ldots&u_{n+1}^{T} \delta u_{n}\\ \vdots&\vdots&\vdots&\vdots\\ u_{m}^{T}\delta u_{1}&u_{m}^{T}\delta u_{2}&\ldots&u_{m}^{T}\delta u_{n}\\ \end{array} \right]\in\mathbb{R}^{m\times n}.\]  
Further on, we shall use the vector of the subdiagonal entries of the matrix \(\delta W_{U}\),  
\[x := [x_{1},x_{2},\ldots,x_{p}]^{T}\in\mathbb{R}^{p},\] \[= \underbrace{[u_{2}^{T}\delta u_{1},u_{3}^{T}\delta u_{1},\ldots, u_{m}^{T}\delta u_{1}}_{m-1},\underbrace{u_{3}^{T}\delta u_{2},\ldots,u_{m}^{T} \delta u_{2}}_{m-2},\ldots,\underbrace{u_{n+1}^{T}\delta u_{n},\ldots,u_{m}^{T }\delta u_{n}}_{m-n}]^{T},\]  
where  
\[p=\sum_{i=1}^{n}(m-i)=n(n-1)/2+(m-n)n=n(2m-n-1)/2.\]  
As it will become clear later on, together with the orthogonality condition  
\[[U_{1}+\delta U_{1}]^{T}[U_{1}+\delta U_{1}]=I_{n},\]  
the vector \(x\) contains the whole information necessary to find the perturbation \(\delta U_{1}\). This vector may be expressed as  
\[x=\text{vec}(\text{Low}(\delta W_{U})),\]  
or, equivalently,  
\[x=\Omega_{x}\text{vec}(\delta W_{U}),\]  
where  
\[\Omega_{x} := [\text{diag}(\omega_{1},\omega_{2},\ldots,\omega_{n})]\in\mathbb{ R}^{p\times mn},\] \[\omega_{i} := \left[0_{(m-i)\times i},I_{m-i}\right]\in\mathbb{R}^{(m-i)\times m },\ i=1,2,\ldots,n,\] \[\Omega_{x}\Omega_{x}^{T}=I_{p},\ \|\Omega_{x}\|_{2}=1\]  
is a matrix that \"pulls out\" the \(p\) elements of \(x\) from the \(m\cdot n\) elements of \(\delta W_{U}\) (we consider \(0_{0\times i}\) as an empty matrix). If, for instance, \(m=6\) and \(n=4\), then  
\[\text{Low}(\delta W_{U})=\left[ \begin{array}{cccc}\delta w_{u21}&\\ \delta w_{u31}&\delta w_{u32}&\\ \delta w_{u41}&\delta w_{u42}&\delta w_{u43}\\ \delta w_{u51}&\delta w_{u52}&\delta w_{u53}&\delta w_{u54}\\ \delta w_{u61}&\delta w_{u62}&\delta w_{u63}&\delta w_{u64}\\ \end{array}
## 4 FE Analysis Procedure
### Step 1: Machine Parameters Estimation  
Let us impose the stator \(d\) and \(q\)-axis currents and assume the \(dq\) reference frame position as in Figure 3. The rotor current, according to the inverse-\(\Gamma\) model, is set along the \(q\)-axis, and its amplitude is given by relationship (8).  
The inductances \(L_{\text{m}}\) and \(L_{\tau}\) are unknown at the beginning: a first MS FEA is necessary to derive their value, making an initial hypothesis about \(i_{\text{rq}}\) amplitude.  
Since \(L_{\text{m}}\simeq L_{\tau}\), a reasonable approximation is: \(i_{\text{rq}}=-i_{\text{sq}}\). On the other hand, the rotor \(d\)-axis current is already set to zero.  
In the first simulation of the procedure, stator and rotor current vectors are:  
\[\begin{split}&\vec{i}_{\text{s}}=i_{\text{sd}}+\text{j}i_{ \text{sq}}\\ &\vec{i}_{\text{r,1}}=\text{j}i_{\text{rq}}=-\text{j}i_{\text{ sq}}\end{split} \tag{11}\]  
At this step, the conditions \(\lambda_{\text{rq}}=0\) are not verified. However, such analysis is necessary to derive stator and rotor leakage inductances \(L_{\sigma\text{s}}\) and \(L_{\sigma\tau}\), besides the mutual synchronous inductance \(L_{\text{m}}\). In fact, substituting (11) in the flux linkages equations:  
\[\begin{split}&\vec{\lambda}_{\text{s}}=L_{\text{s}}\vec{i}_{ \text{s}}+L_{\text{m}}\vec{i}_{\text{r}}=L_{\text{s}}i_{\text{sd}}+\text{j}L_ {\sigma\text{s}}i_{\text{sq}}\\ &\vec{\lambda}_{\text{r}}=L_{\text{m}}\vec{i}_{\text{s}}+L_{ \sigma\tau}\vec{i}_{\text{r}}=L_{\text{m}}i_{\text{sq}}-\text{j}L_{\sigma\text {r}}i_{\text{sq}}\end{split} \tag{12}\]  
Figure 3: In MS FEA, stator and rotor currents have to be imposed according to the inverse-\(\Gamma\) model: the condition \(\lambda_{\text{rq}}=0\) has to be verified in order to properly analyze the machine (the rotor flux linkage space vector lies along the \(d\)-axis).  
The first field solution is shown in Figure 4. In Figure 4a, it can be noticed that the flux lines within the rotor widely cross the \(d\)-axis, meaning that the \(q\)-axis rotor flux linkage is not zero (\(\vec{\lambda}_{\text{r}}\) is out of phase with respect to the \(d\)-axis).  
The flux linkages in (12) are related to the equivalent circuit in Figure 4b, where the mutual coupling is modelled with the T-form inductance network. The number of effective turns, chosen for the rotor equivalent winding, is the same as the stator. Thus, the transformation ratio \(n^{\text{T}}=(N_{\text{s}}k_{\text{ws}})/(N_{\text{r}}k_{\text{ws}})=1\).  
From the field solution reported in Figure 4a, stator and rotor flux linkages are derived by integrating the magnetic vector potential within the slots. The machine inductance are derived as:  
\[L_{\text{s}\text{s}}=\frac{\lambda_{\text{sq}}}{i_{\text{sq}}};\quad L_{\text{ cr}\text{r}}=\frac{\lambda_{\text{rq}}}{i_{\text{rq}}};\quad L_{\text{m}}= \frac{\lambda_{\text{rd}}}{i_{\text{sd}}};\quad L_{\text{r}}=L_{\text{m}}+L_{ \text{cr}\text{r}} \tag{13}\]  
These parameters are effective only in one specific working point, since, varying the motor current, or the rotor position, also the saturation map changes.
# Post-Flood UAV-Based Free Space Optics Recovery Communications with Spatial Mode Diversity
## 2 Free Space Optics for Post-Flood Recovery Communications
In [43], to reduce the outage probability, aperture diversity was realized through several transmitter apertures transmitting the same data stream on fundamental Gaussian beams whilst mode diversity was realized through multiple receiver apertures that decompose the incoming beam to several orbital angular momentum (OAM) modes, in conjunction with digital signal processing. In [44], OAM modes were used for spatial mode diversity through a 2 km FSO link to improve the link resilience and system capacity. The overall channel capacity was maximized by selecting optimal OAM mode numbers at each value of the SNR and turbulence strength. Spatial mode diversity has also been demonstrated using photonic crystal fibers [45; 46; 47; 48]. Another approach for spatial mode diversity was demonstrated using Hermite-Gaussian (HG) and Laguerre-Gaussian (LG) modes of identical size [39]. This was shown to improve the bit error rate by up to 54% without an increase in the total transmit power or radius of the receive aperture. Unmanned aerial vehicle (UAV)-based FSO strategies have been explored for improving coverage between base stations for emergency communications during natural disasters, focusing on downlink scheduling [31] and the placement of FSO transceivers [32; 33] and hybrid RF/FSO links [35; 36].