esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Discussion
Table 10 presents a comparison between the routing approaches with regard to different equipment, like positioning systems and digital map. This equipment is used to calculate the location of vehicles. However, it is difficult to obtain the position of vehicles in some areas, such as tunnels. Also, the performance of a routing approach depends heavily on the positioning device. If this device cannot accurately calculate the location of vehicles, the performance of the routing method will not be desirable. Another point is that positioning systems are highly costly in terms of bandwidth consumption. Therefore, some routing methods such as PFQ-AODV and Q-LBR have attempted to design the routing process independent of this equipment. This idea is attractive and can be considered for designing routing methods in the future. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Scheme** & **RL Algorithm** & **Agent** & **State Space** & **Action Space** & **Reward Function** & **Learning Rate** & **Discount Factor** \\ \hline IV2 XQ [54] & Multidimensional & Central & Intersections & Selecting road & \(\varphi\), If reaching the destination intersection; 0, & Fixed & Related to the vehicle density and distance to the destination \\ \hline  & & & An \(K\times M\) array & & & \\  & & including distance & Selecting the next-hop node & Related to the received signal strength indication & & \\  & & and its neighbors & (neighboring & (RSSI), the transmission distance, and the collision between vehicles & & Fixed \\ \hline CEPF [56] & Q-learning & Packet & Vehicles & Selecting the next-hop node (neighboring node) & 1, If the node is a single-hop neighbor of the destination; 0, Else & Fixed & Fixed \\ \hline PRQ-AODV [57] & Fuzzy constraint & Packet & The set of single-hop and two-hop neighboring nodes & The set of single-hop nodes & 1, If the node is a single-hop neighbor of the destination; 0, Otherwise & Fixed & Fixed \\ \hline QGrid [58] & Q-learning & Virtual agent & Grids & Forwarding the message from one grid to the best next-hop grid. & 100, If the destination is inside the grid; 0, Else & Fixed & Related to the number of vehicles in each grid \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Most important features of RL-based routing methods.
# A Novel Visual SLAM Based on Multiple Deep Neural Networks
## 3 The Proposed SLAM  
The main idea of the proposed SLAM system is the combination of deep feature extractor, deep feature matcher and deep loop closure detector in the traditional visual SLAM pipeline. The deep feature extractor (SuperPoint [26]) provides stable feature points to the system. The deep feature matcher (SuperGlue [27]) provides consecutive pairs of correctly matched points required for pose estimation. The deep loop closure detector (Seq-CALC [28]) corrects the accumulated error of the system by detecting correct loop closure in the environment. The design of our system is greatly inspired by ORB-SLAM2 [10]. The system framework is illustrated in Figure 3, which consists of four main modules: Visual Odometry, Backend Optimizer, Map, and Loop Closing. It runs three independentthreads: Visual Odometry, Backend Optimizer, and Loop Closing in parallel. These three threads synchronize data through the Map module, which is the database.  
In the Visual Odometry, the extracted feature points are used to construct the initial map and calculate the initial camera pose, and then the map points in the reference frame and the temporary map points in the last frame are tracked by feature matching; then, the relative poses are calculated separately, and, finally, the two relative poses are fused and a new keyframe is constructed. The new keyframe is inserted into the Map as the current keyframe.  
In the Backend Optimizer, the keyframe sequence and the set of map points in the activation window, intercepted by the current frame, are read to perform the local bundle adjustment optimization and remove the outlier map points.  
In the Loop Closing module, a light deep loop closure detector is used to search the Map for the historical keyframes that form a loop closure with the current keyframe; then, the corrected poses of the current keyframe are calculated by feature matching, and finally the poses of the keyframes are corrected for the historical keyframes by the pose graph of the keyframes.  
Further, we introduce this system in detail, from feature extraction and matching, \(\mathfrak{se}(3)\)-based pose fusion estimation (\(\mathfrak{se}(3)\), which is the Lie algebra corresponding to the special Euclidean group [38]), backend optimization, to loop closure correction.
## 1 Introduction
In a study by [PERSON] et al. [11], the compressive strength of high-strength concrete cylinder and cube specimens was analyzed, revealing that the size effect was more significant in cube specimens. [PERSON] et al. [12] developed a numerical model for recycled aggregate concrete (RAC) based on microscopic mechanics and investigated the impact of maximum aggregate size (MAS) on the size effect of the compressive strength of RAC; the results showed that an increase in MAS inhibited the size effect. RFC is composed of rock aggregate and SCC. The force transfer skeleton formed by rock aggregate makes RFC different from ordinary concrete in stress characteristics. For  
Figure 1: RFC cutting specimen (the red outline indicates the rock aggregate shape). rock materials, [PERSON] et al. [13] analyzed the uniaxial compressive strength of seven kinds of rocks at different sizes and fitted the strength with a negative exponential function, with good fitting results. [PERSON] et al. [14] conducted uniaxial and triaxial compression tests on slate specimens with different diameters, and found that the uniaxial and triaxial compressive strengths of slate exhibit the same size effect law, which was incorporated into the Hoek-Brown and Saeidi failure criteria. [PERSON] et al. [15] proposed a constitutive model that considers the influence of rock size on mechanical properties based on the boundary interface plasticity theory, and conducted uniaxial and triaxial compression tests on sandstone specimens with different diameters; the results showed that the model could predict the stress-strain behavior in the experiments well. At present, compared to rock and ordinary concrete, the size effect of RFC still needs to be further studied. In a study of particle reinforced composites, [PERSON] [16] found that the smaller the size of the reinforced particles, the higher the overall strength of the material in the tensile test of aluminum alloy reinforced by 15% silicon carbide particles; on this basis, [PERSON] et al. [17] established a numerical model to reproduce the above experiment and obtained the same conclusion. [PERSON] et al. [18] tested the mechanical properties of silicon carbide particle-reinforced composite materials with two particle diameters, and the results showed that the distribution of the silicon carbide particles with small particle diameters in the matrix was more uniform, and the strength of the composite materials was also higher. [PERSON] [19] studied the relationship between the tensile strength and the particle size of particle-reinforced elastic composite, and found that the tensile strength of the material decreased with the increase in particle size. For RFC with large-size aggregate as reinforcement, this paper aims to reveal the size effect law of its compressive strength and the influence of aggregate size on its strength. Based on the variation law of the compressive strength of RFC under different specimen sizes and aggregate sizes, this article provides a certain reference basis for the design of RFC dams.
## 4 eLSNN: The Optimized Embedded LSNN
### Membrane Voltage Update  
The computation involved in the membrane voltage update (\(v\)), as shown in Figure 6 in the first blue \(\oplus\) operator, is done through the following steps:  
1. Membrane voltage decay: \(v^{(\alpha)}:=\alpha v\)
2. Contribution of inputs on membrane voltage: \(v^{(I)}:=W^{I}x\)
3. Contribution of internal neuronal activity on membrane voltage: \(v^{(H)}:=W^{H}z\)
4. Reduction of membrane voltage in case of previous spike emission: \(v^{(th)}:=-v_{th}z\)
5. Update of membrane voltage: \(v\gets v+v^{(\alpha)}+v^{(I)}+v^{(H)}+v^{(th)}\)  
In Table 2 we report the operations (sum and multiply) of both the non optimized (Naive) and optimised version for each component of \(v\). Overall, the operations to be performed are: \((I+N+2)Nt_{inf}\) sum and \((I+N+2)Nt_{inf}\) multiplication.  
Figure 6: Flowchart of the LSNN update cycle. Neuron variables are represented with rectangles. Simple mathematical operations are represented with circles. Matrix multiplication operations with synaptic weights are represented by grids. Constants are represented with black circles. Data movements are represented by arrows between operators and operands.  
Because of the event-driven design of the network, it is possible to implement the computation of the \(v^{(I)}\) and \(v^{(H)}\) components more efficiently. Considering the row-column multiplication between the matrix \(W\) (with \(N\times M\) elements) and the column vector \(x\) (with \(M\times 1\) elements), the operation result will be added to the vector \(v\). When the elements of \(x\) can only assume binary values (\(x_{i}\in\{0,1\}\)) the \(Wx\) operation can be implemented using only sums. The following pseudo-code formally describes the operation:  
Let \(\vec{x}\) be a list containing only the index of the non-zero elements of \(x\). By iterating over the elements of \(\vec{x}\) we select the columns of \(W\) to be considered in the sum cycle (inner cycle). The sum cycle iterates over the rows of the selected column, and for each element accumulates its content in the result vector. The required operations will therefore be dependent on the number of non-zero elements in the vector \(x\). The number of non-zero elements in the vector \(x\) will change with each inference tick. We then identify using the symbol \(\vec{x}\) the average number of non-zero elements of \(x\) for each inference tick.  
We then reduce the sums required for the membrane voltage update from \((I+N+2)Nt_{inf}\) to \((\vec{x}+\vec{z}+3)Nt_{inf}\) and the multiplications from \((I+N+2)Nt_{inf}\) to \(Nt_{inf}\). The value of \(\vec{x}\) depends on the chosen event encoding. The value of \(\vec{z}\) depends on the behaviour of the network. When training the network, it is possible to minimise \(\vec{z}\) by introducing its value into the loss calculation.
## 3 Types of Channel Hole Defect
### Channel Hole Bowing  
Bowing is an abnormal profile resulting from the unexpected increase in the channel hole diameter in a particular vertical location. (Remember, the channel hole has a 3D cylindrical structure.) Figure 6c,d describe two types of bow defects: Soft Bowing and Hard Bowing. Bowing is generated when the non-ideal effects of the etching process are exacerbated in the specific stacked layers during channel hole formation, especially near the top layers. When the etching process starts to form channel holes, the lateral and vertical etching rates can be controlled precisely. However, as the etching process proceeds, it shows a relatively unstable state due to the complex fluid dynamics of the etchant, so the lateral etching rate near the topmost layer can unusually become higher than the vertical etching rate (i.e., the channel hole diameter of near the topmost layer is enlarged). Therefore, there is a high possibility that an unexpected shape in the channel hole can be created in the upper layers rather than the lower layers. (Note that the etching process proceeds from the topmost layer to the bottom layer.) Furthermore, an unstable etching process makes the shape of channel holes a rugged shape, unlike a uniform circle. As shown in Figures 4 and 5, since a channel hole can be seen as a physical barrier to the charge flow along WLs in h-layers, the fluctuations in the channel hole diameters can increase the parasitic resistance or capacitance, thus resulting in large variations in the error characteristics between different layers in a flash block.  
When the degree of Bowing becomes more severe, neighboring channel holes become punched-through and connected, which is called Hard Bowing. As shown in Figure 4, the major components that make up the flash cell (e.g., tunnel oxide, SiN trap layer, blocking oxide, and metal gate) should be deposited in order along the cylindrical channel hole. Once Hard Bowing occurs, flash cells in the corresponding layers cannot avoid an abnormal or incomplete structure, which prevents NAND operations from working properly.
# Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM
## 2 Algorithm Description
### MOD for Track Deviation Detection
When considering the position anomaly only, \(w_{d}=1,w_{v}=0,w_{\theta}=0,w_{\alpha}=0\). Based on the defined multi-factor distance \(mfdist(P_{\alpha},P_{b})\), the dimension of directional DTW distance will be expanded, and the multi-factor directional DTW distance between multidimensional tracks \(TR_{A}\) and \(TR_{B}\) is defined as:  
\[ \begin{split}\delta_{M}(TR_{A},TR_{B})=mfdist(TR_{Ai},TR_{Bj})+ \min\Bigl{\{}mfdist(TR_{A(i-1)},TR_{B(j-1)}),\\ mfdist(TR_{A(i-1)},TR_{Bj}),\ mfdist(TR_{Ai},TR_{B(j-1)})\Bigr{\}} \end{split}  \tag{4}\]  
Among them: \(i\in[1,n],j\in[1,m]\). \(\delta_{M}(TR_{A},TR_{B})\) represents the similarity of multidimensional tracks \(TR_{A}\) and \(TR_{B}\). Calculate MOD for multidimensional tracks \(TR_{i}\in TD\):  
(1) Calculate the multi-factor neighbor boundary distance of the track:  
\[\delta_{M\ \text{border}}(TR_{i},k)=\delta_{M}(TR_{i},NN(TR_{i},k)). \tag{5}\]  
(2) Calculate the neighboring track of the track \(TR_{i}\):  
\[\mathcal{N}(TR_{i},k)=\bigl{\{}TR_{j}\in\text{TD}\backslash TR_{i}\mid\delta_ {M}(TR_{i},TR_{j})\leq\delta_{M\ \text{border}}(TR_{i},k)\bigr{\}}. \tag{6}\]  
(3) Calculate the multi-factor reachable distance from the track \(TR_{i}\) to the track \(TR_{j}\):  
\[\delta_{M\ \text{reach}}\bigl{(}TR_{i},TR_{j},k\bigr{)}=\max\bigl{\{}\delta_{M \ \text{border}}(TR_{i},k),\delta_{M}\bigl{(}TR_{j},TR_{i}\bigr{)}\bigr{\}}. \tag{7}\]  
(4) Calculate the nearest neighbor density of the track \(TR_{i}\):  
\[\rho(TR_{i},k)=\frac{|\mathcal{N}(TR_{i},k)|}{\sum\limits_{TR_{j}\in\mathcal{ N}(TR_{i},k)}\delta_{\text{Mreach}}(TR_{i}TR_{j},k)} \tag{8}\]  
In the formula, replacing \(\delta_{M}\) with \(\delta_{M\ \text{reach}}\) can smooth the neighbor density. The larger the value \(k\), the smoother the effect becomes. (5) Calculate the Multidimensional Outlier Descriptor of the track \(TR_{i}\):  
\[MOD(TR_{i},k)=\frac{\sum\limits_{TR_{j}\in\mathcal{N}(TR_{i},k)}\rho(TR_{j},k) }{\rho(TR_{i},k)|\mathcal{N}(TR_{i},k)|} \tag{9}\]  
where: the greater the ratio of the neighbor density of the tracks \(TR_{i}\) and the neighbor of the track \(TR_{i}\), the greater the degree of anomaly of the track \(TR_{i}\) becomes.
# A Review of Recent Research into the Causes and Control of Noise during High-Speed Train Movement
## 4 High-Speed Train Noise Control Methods
### Traditional Noise Control Methods for High-Speed Trains
#### 4.1.3 Railroad Sound Barriers  
A sound barrier featuring easy installation and obvious noise reduction is an important method of traffic-noise management, which has been widely used and developed throughout the world [84]. Sound barriers mainly include wide top types, semi-enclosed types, fully enclosed types, etc. The diversity of sound barriers makes it possible to have different classification methods, which are based on appearance, line form, material, acoustic performance, structural form, etc. According to the differences in line form, the sound barriers can be classified into bridge sound barriers and road sound barriers; according to the differences in unit plate material, the sound barriers are classified into metal and non-metal sound barriers. As shown in Figure 6, the common structures of sound barriers are upright insert types, T types, inverted L types, Y types, multiple edge types, etc. [85]. In practical application, insert-type sound barriers are most widely used. [PERSON] et al. [86] used proportional model experiments to compare the noise reduction effects of upright, T-shaped, and Y-shaped sound barriers and proposed, for the first time, that T-shaped sound barriers provide the best noise reduction effects. [PERSON] et al. [87] simulated the noise reduction effect of T-shaped sound barriers with sound-absorbing materials using the boundary element method and undertook experimental verification thereof. [PERSON] et al. [88] improved the acoustic performance by providing slots at the top of T-shaped sound barriers and optimizing the form and disposition of the slots using genetic algorithms. [PERSON] et al. [89] investigated the factors affecting the noise reduction effect of T-shaped sound barriers using numerical calculations and found that the additional noise reduction of the top structure is related to the locations of the source and receptor of the sound and the location and height of the sound barrier. [PERSON] et al. [90] studied the roof-inclination angle of upright-type sound barriers and found that the sound barrier was most effective in reducing high-frequency sound waves when the roof inclination angle was 120\({}^{\circ}\). [PERSON] et al. [91] proposed a semi-enclosed sound barrier with slits and verified its sound insulation effect. At present, sound barriers provide good control of environmental noise along the railroad, but the noise frequency band they control is singular, and because most of them are reflective sound barriers, they will aggravate the noise inside the train instead.  
Figure 6: (a) Y-shaped sound barrier; (b) inverted L-shaped sound barrier; (c) upright type sound barriers; (d) semi-enclosed sound barrier; (e) fully enclosed sound barrier.
## 3 System Description  
The network model is composed of SNs which are deployed at random over an \(M\times M\) network area as illustrated in Figure 1. To analyze the impact of energy heterogeneity, the SNs are divided into three types, which can be adjusted based on real-world scenarios. These three types of SNs are classified as normal, medium, and superior SNs where each type has different levels of initial energies such as superior > medium > normal [38]. This classification reflects the varying battery capacities typically found in different WSN applications. In addition, a RN may be placed in the center of the network. The distance between different SNs, the BS, and the RN can be calculated by using the Global Positioning System (GPS).  
The network operates in two different modes based on the position of the nodes and the clusters as illustrated in Figure 2. In the direct mode, a SN forwards its collected data to a RN or to the BS if the RN or the BS is closer than its CH. In the clustering mode, clusters of SNs transmit data packets to their designated CHs. The CHs, in turn, forward the aggregated data to the RN if the RN is closer than the BS. The RN then transmits the data to the mobile BS which is situated at its optimal location Alternatively, the CHs can directly transmit their data to the BS. Since the BS is assumed to have significantly higher  
Figure 1: Network relay node and sensor nodes.  
energy reserves compared to the SNs, the energy consumed by the BS during its movement is considered negligible, ensuring efficient network operation.  
As demonstrated in Figure 3, there is a grid of points for the mobile BS, denoted as the virtual BS points grid, where the mobile BS's ideal location will be chosen out of all the grid's points.
## 3 Results
### Histone Modification Other than H3K9 me3
#### 3.2.1 GSE159075  
Regions, is, with non-zero missing values among three samples were discarded in advance. PCA was applied to three samples with the same histone modification (H3K4 me3, H3K27 me3, or H3K27 ac) and \(v_{1j}\) was always associated with the independence of \(j\) (biological replicates). Next, the corresponding first PC score, \(u_{1i}\), was used for gene selection. SDs were optimized, and the SD = 0.19218492 (H3K4 me3), 0.8650455 (H3K27 me3), and 0.3911769 (H3K27 ac). The histogram of \(1-P_{i}\) seemed to obey a combination of two Gaussian distributions, and not a single Gaussian distribution (Figure 3). As a result, 34,538, 62,141, and 61,306 regions were selected, and 13,692, 5217, and 11,604 Entrez gene IDs were identified. The 13,671, 5208, and 11,590 gene symbols associated with the gene IDs were identified and uploaded to Enrichr. Table 5 shows the performance evaluation by Enrichr and demonstrates the success of the proposed method.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & & \multicolumn{2}{c}{**Histone**} & \\  & & & & & & **Modification** & \\ \hline
**GEO ID** & **Histone** & **Regions** & **Entrez** & **Gene** & **Simbols** & **All** & **Targeted** & **Species** \\  & **Modification** & **/Peaks** & **Gene** & **Symbols** & & & \\ \hline  & H3K4 me3 & 34,538 & 13,692 & 13,671 & 198 & 54 & \\ GSE159075 & H3K27 me3 & 62,141 & 5217 & 5208 & 83 & 56 & Human \\  & H3K27 ac & 61,306 & 11,604 & 11,590 & 175 & 24 & \\ \hline GSE74055 & H3K4 me1 & 61,329 & 11,890 & 11,858 & 58 * & 6 * & Mouse \\ (when PCA is replaced with TD) & 70,187 & 14,220 & 14,187 & 102 * & 10 * & \\ \hline  & H3K4 me1 & 164,466 & 14,893 & 14,866 & 3 * & 0 * & \\ GSE124690 & H3K4 me3 & 37,534 & 14,972 & 14,946 & 200 & 54 & Human \\ (CUT&Tag) & H3K27 ac & 81,249 & 13,086 & 13,061 & 139 & 24 & \\ \hline GSE188173 & H3K27 ac & 105,438 & 15,579 & 15,548 & 155 & 24 & Human \\ \hline GSE159022 & H3K27 me3 & 55,923 & 5022 & 4996 & 70 & 56 & Mouse \\ \hline GSE168971 & H3K9 ac & 58,490 & 15,460 & 15,452 & 81 * & 6 * & Mouse \\ \hline GSE159411 & H3K36 me3 & 253,326 & 12,282 & 12,270 & 201 & 32 & Human \\ \hline GSE181596 & H3K27 me3 & 36,972 & 3543 & 3545 & 72 & 56 & Human \\ \hline \hline \end{tabular}
\end{table}
Table 5: The number of regions/peaks, Entrez gene IDs, and gene symbols selected by various methods and that of associated enriched histone modification (all and targeted) in the “Epigenomics Roadmap HM ChIP-seq” category of Enrichr with adjusted \(p\)-values less than 0.05 for other profiles than GSE24850 shown in Table 3. *: ENCODE Histone Modifications 2015.
# Trajectory Tracking Algorithm Study of Coal Mine Water Detector Drilling Bar Installation
## 5 Parameter Optimization of the Controller Based on PSO
### Determination of Controller Parameters Based on PSO  
During the iteration, the particle dynamically adjusts the speed and position through individual and global extremums to constantly approach the optimal target at a certain rate [21]. In the t + 1 iteration, the speed and position of the particle update are as follows:  
\[\begin{split}\mathrm{v_{id}}(\mathrm{t}+1)&=\omega_{ \mathrm{pos}}\mathrm{v_{id}}(\mathrm{t})+c_{\mathrm{t}}\mathrm{r_{1}}(\mathrm{P _{id}}(\mathrm{t})-\mathrm{x_{id}}(\mathrm{t}))+c_{\mathrm{2}}\mathrm{r_{2}}( \mathrm{G_{id}}(\mathrm{t})-\mathrm{x_{id}}(\mathrm{t}))\\ \mathrm{x_{id}}(\mathrm{t}+1)&=\mathrm{x_{id}}( \mathrm{t})+\mathrm{v_{id}}(\mathrm{t}+1)\end{split} \tag{17}\]  
In the formula, \(\mathrm{P_{id}}(\mathrm{t})\) is the individual extremum of particle i based on its own flight experience, \(\mathrm{G_{id}}(\mathrm{t})\) is the global extremum obtained from the group of particles' flight experience, \(\omega_{\mathrm{pos}}\) is the inertia weight factor, and \(\mathrm{c_{1}}\), \(\mathrm{c_{2}}\) is the acceleration constant.