esa-sceva/satcom-chunk-collection huggingface dataset:
# Virtual Belt Algorithm for the Management of Isolated Autonomous Intersection
## 4 Collision Checking of Virtual Belts
### Collision Checking of Virtual Grid and Virtual Belt  
Given a set of VGs (denoted as \(S\), the VGs in \(S\) are represented by \(G_{v1}\), \(G_{v2}\), \(\cdots\)), the CVGR of a VG (denoted as \(G_{v}\) ) in these VGs can be obtained through Algorithm 2.  
```
0: two VGs (\(G_{v}\) and \(G_{v2}\))
0: whether two VGs will collide with each other in the future
1: Initial the positions of \(G_{v}\) and \(G_{v}^{\prime}\)
2: Divide the time circle (\(t_{init}\), \(t_{init}+t_{c}\)) into small enough segments: \(\textbf{t}=(t_{init},t_{init}+t_{\Delta},t_{init}+2t_{\Delta},\cdots,t_{init} +t_{c})\)
3:for\(t\) in t do
4: Calculate the positions of \(G_{v}\) and \(G_{v}^{\prime}\) at the time of \(t\);
5:if\(G_{v}\) and \(G_{v}^{\prime}\) collide with each other then
6:return true
7:endif
8:endfor
9:return false
```  
**Algorithm 2** Collision checking of two VGs.  
Consider two VGs (which are labeled as \(G_{v1}\) and \(G_{v2}\), respectively) and a VB (labeled as \(B_{v}\)) as shown in Figure 10. The VPs of these VGs and VB are plotted with solid red line in that figure. The coordinates of the start and end points of these VPs are shown in that figure as well. The unit of the coordinate is meter. The lengths of the three VPs are 72 m. There are twelve VGs on \(B_{v}\), which are represented by \(G_{v1,i=1,2,\cdots,12}^{B}\). All of these VGs are uniform. \(l_{g}=6\) m, \(l_{w}=4\) m, \(v_{g}=2\) m/s. \(t_{c}=\frac{l_{p}}{v_{g}}=36\) s.  
```
0: a set of VGs (\(S\)) and a VG (\(G_{v}\))
0: the CVGR of \(G_{v}\)
1: Initial an empty set \(S_{Gv}\)
2:for\(G_{v1}\) in \(S\)do
3: call Algorithm 1. Collision checking of \(G_{vi}\) and \(G_{v}\)
4:if\(G_{vi}\) collides with \(G_{v}\)then
5: append \(G_{vi}\) to \(S_{Gv}\)
6:endif
7:endfor
8:return\(S_{Gv}\)
```  
**Algorithm 2** Calculation of the CVGR of A VG.  
Several camera shots of \(B_{v}\), \(G_{v1}\) and \(G_{v2}\) between 28 s and 33 s are plotted in Figure 11. Using Algorithm 2, it can be found that \(G_{v1}\) collides with \(G_{v1}^{B}\), \(G_{v12}^{B}\) and \(G_{v11}^{B}\). \(G_{v2}\) collides with \(G_{v2}^{B}\), \(G_{v1}^{B}\) and \(G_{v12}^{B}\). The CVGR of \(G_{v1}\) can be organized as {\(G_{v1}^{B}\), \(G_{v12}^{B}\), \(G_{v11}^{B}\)}. The CVGR of \(G_{v2}\) is {\(G_{v2}^{B}\), \(G_{v1}^{B}\), \(G_{v12}^{B}\)}. Similarly, one can obtain that the CVGR of \(G_{v5}^{B}\) is empty.  
Figure 10: Collision checking of a VB and two VGs.
# Parallel-Coupled-Line Bandpass Filter with Notch for Ultra-Wideband (UWB) Applications
## 2 UWB Filter Design and Analysis
However, increasing the value of the shorted stub impedance reduced the insertion loss level at the out-of-band as can be seen in Figure 3.8 An additional advantage of the proposed filter is that the transmission zero at the upper rejection band can be also controlled by changing the shorted stub length (\(L_{s}\)) as displayed in Figure 3.9 Therefore, different bandwidths can be obtained by only changing the short-circuited stub length (\(L_{s}\)), where reducing (\(L_{s}\)) shifts the upper transmission zero to a higher frequency, providing a larger bandwidth and vice versa. Those two advantages of the proposed structure provide more flexibility to tune and optimize the design for specific requirements. \[I_{1}=I_{2}=I_{6}=0 \tag{4}\]0 Simulated insertion loss (S21) with various values of the characteristic impedance. \[I_{1}=I_{2}=I_{6}=0 \tag{4}\]1 Simulated performance of the filter without notched band: insertion (S21) and return (S11) losses.
# RNA Modification Related Diseases and Sensing Methods
## 2 RNA Modification  
### 2.1.5-Methylcytosine  
RNA modifications such as 5-methylcytosine (5 mC) are present in rRNA, tRNA, and mRNA (Figure 1A). This modification is abundant in the 3\({}^{\prime}\) untranslated region of mRNA, close to the ergonuate (AGO) binding site [13]. Eight 5 mC writers have been identified so far for methylated NOP2/Sun RNA Methyltransferase 1-7 (NSUN1-7) and DNA Methyltransferase 2 (DNMT2) mRNAs [14; 15; 16; 17]. NSUN1, 2, 5, and DNMT2 are present in all eukaryotes, while other NSUN members are only found in multicellular organisms, particularly during brain development, and their expression is differential [18; 19]. Studies have linked several cranial nerve system diseases, such as Dubowitz-like syndrome [20], autosomal recessive intellectual disability [21], and neurogenic and brain developmental problems, to altered 5 mC. Mutations in the NSUN2 gene have been linked to these disorders [16; 22]. NSUN7, which is mainly expressed in the testes, is critical for male germination [23].  
Figure 1: **Major RNA modifications and their modification enzymes.** (**A**) Cytosine is methylated by NDUN1-7 or DNMT7 to 5-methylcytosine (5 mC). (**B**) Adenosine is methylated by TRMT10,  
TRMT61A, or TRMT61B to N1-methyladenosine (m1A). M1A is demethylated by ALKBH1 or ALKBH3 to adenosine. (**C**) Adenosine is methylated by METTL3, METTL5, or METTL16 to N6-methyladenosine (m6A). M6A is demethylated by ALKBH5 or FTO and returned to adenosine. (**D**) RNA ribose is methylated to 2\({}^{\prime}\)-O-Methyl-Ribose regardless of the base. FBL and FTSJ are known to be methyltransferases.
# Fast 3D Analytical Affine Transformation for Polygon-Based Computer-Generated Holograms
## Appendix C Derivation of the Analytical Spectrum Expression of Tilted Triangle on the Hologram  
According to affine transformation, \(\overset{\rightarrow}{r_{g}}^{t}=\overset{\rightarrow}{R_{ra}}^{t}+\overset{ \rightarrow}{C}\). Therefore, the coordinate relationships between the global coordinate system \((x,y,z)\) and affine coordinate system \((s,t,p)\) are:  
\[\begin{array}{l}x=r_{11}s+r_{12}t+r_{13}p+c_{1},\\ y=r_{21}s+r_{22}t+r_{23}p+c_{2},\\ z=r_{31}s+r_{32}t+r_{33}p+c_{3}.\end{array} \tag{12}\]  
Additionally, the relationships between the spatial frequencies of the global coordinate system \((u,v,w)\) and the affine coordinate system \(\left(u_{s},v_{t},w_{p}\right)\) are \(\left(u_{s},v_{t},w_{p}\right)=R^{-1}(u,v,w)\). We can write as follows:  
\[\begin{array}{l}u_{s}=r_{11}u+r_{21}v+r_{31}w,\\ v_{t}=r_{12}u+r_{22}v+r_{32}w,\\ w_{p}=r_{13}u+r_{23}v+r_{33}w.\end{array} \tag{13}\]  
Note, that we have chosen \(p=0\), by using Equations (12) and (13), the exponential term in Equation (11) can be rewritten as:  
\[\begin{array}{l}e^{-j2\pi(ux+vy+uz)}\\ =e^{-j2\pi[u(r_{11}s+r_{12}t+r_{13}p+c_{1})+v(r_{21}s+r_{22}t+r_{23}p+c_{2})+w( r_{31}s+r_{32}t+r_{33}p+c_{3})]}\\ =e^{-j2\pi(uv_{1}+vc_{2}+uv_{3})}.e^{-j2\pi[u(r_{11}s+r_{12}t)+v(r_{21}s+r_{22}t )+w(r_{31}s+r_{32}t)]}\\ =e^{-j2\pi(uv_{1}+vc_{2}+uv_{3})}.e^{-j2\pi[s(r_{11}u+r_{21}p+c_{31}u)+v(r_{12} u+r_{22}p+r_{23}w)]}\\ =e^{-j2\pi(uv_{1}+vy_{1}+uz)}.e^{-j2\pi(su_{k}+tv_{1})}.\\ \end{array} \tag{14}\]  
In Equation (14), we have used the elements in the affine matrix \(T\) already solved in Equation (9), i.e., \(c_{1}=x_{1},c_{2}=y_{1},c_{3}=z_{1}\). Then, according to Equations (11) and (14), the analytical spectrum of the tilted triangle can be rewritten as:  
\[\begin{array}{l}F_{HOLO}(u,v)=\iint_{\Delta}f(\overset{\rightarrow}{R_{ra} }^{\rightarrow}+\overset{\rightarrow}{C})\cdot\mathrm{e}^{-j2\pi(ux+vy+uz)} \cdot J\cdot d\mathit{sdt}\\ =J\cdot\mathrm{e}^{-j2\pi(ux_{1}+vy_{1}+wz_{1})}.\iint_{\Delta}1\cdot e^{-j2 \pi(su_{k}+tv_{1})}dsdt\\ =J\cdot\mathrm{e}^{-j2\pi(ux_{1}+vy_{1}+wz_{1})}.F_{\Delta}(u_{s},v_{t}),\\ \end{array} \tag{15}\]  
which is presented in Equation (12).
## 3 The Mediator-Wrapper Architecture
The first mediator layer can be used to mediate between wrappers over paradigmatically similar data sources or data sources that have an overlapping or connected domain. The second and upper layers of mediators can be used to raise the level of abstraction. The mediators of the upper layers are used to mediate between mediators of the lower  
Figure 1: Relationship of mediator and wrapper components [27]. layers, thus possibly encompassing multiple different data sources. Such a layering strategy is used by [31] in a form of special and central mediators to organize and distribute processing load, which can also be a beneficial effect if components are run on different machines. On the other hand, [PERSON] et al. [4] used layering to enable localized logical management of data sources. This layering strategy was also proposed by [28]. There are also proposed and implemented systems with one monolithic mediator [3; 32; 33; 34; 35; 36]. This can be found to be an ample, quick, and expedient solution if the number of connected data sources is not large or expected to rise. If the number of connected data sources rises, then the processing load on that single mediator is increased and this can easily lead to increased latency when querying any of the connected data sources through the integration system. These types of systems can be covariantly classified as multidatabase integration systems [37], taking note that the data source component translation is distributed (assigned to the wrappers). It should also be noted that in these cases, the mediator component is not really a component but rather a software module. Recently, Ref. [38] also focused on creating and maintaining a concern-oriented architecture system. Their workers each have a Data Source API in similarity with wrappers. The mediator's functionalities are assigned to a single coordinator acting as the query entry point and planner, and another worker to join the query results (akin to a reducer node in MapReduce).
## 4 Analysis of PC Member Quality Inspection Process and Major Inspection Items
### Analysis of Major Quality Inspection Items and Defect Types of PC Members  
Based on PC construction standard specifications and interviews with experts, the major quality inspection items of PC members are classified into crack, damage, deformation, reinforcement and accessories condition, and exterior surface condition, which are visually inspected, as summarized in Table 1. In addition, the major defect types of PC members are classified as discoloration, crack, damage, and bubble, as shown in Figure 4.  
Figure 3: As-is on-site quality inspection process and related problems.  
Figure 2: PC member manufacturing and quality inspection procedure.  
In order to classify the major items to be checked when inspecting the quality of PC members, companies specializing in PC were consulted to collect and analyze the PC member quality inspection checklist, as shown in Figure 5. For PC members produced at the factory, quality inspection is performed based on the primary inspection items on the quality inspection checklist. After the inspection is completed, the members clear of defects are brought to the construction site by means of a heavy transport loader with the inspection checklist attached.  
Since defects can occur to members delivered to the site during loading/unloading or transportation, re-inspection is made by the field workers. In the case of on-site quality inspection, the inspection is made based on the secondary quality inspection items. Specifically, workers perform inspections focusing on the construction project name, member identification number, manufacturing date, product inspection mark, cracks, damage, and deformation according to the PC construction standard specifications.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Inspection Items** & **Inspection Method** & **Inspection Criteria** \\ \hline Crack &
\begin{tabular}{c} Visual check or \\ actual measurement \\ \end{tabular} & According to construction specifications \\ \hline \multirow{4}{*}{Damage} & \multirow{4}{*}{Visual check} & Class 1: Structural Hazard \\  & & (Damage on hook part, damage over 200 mm of joint part) \\  & & Class 2: Hazardous to waterproofing \\  & & (Damage of the edge of the bottom plate, etc.) \\  & & Class 3: Damage other than Class 1 and 2 \\ \hline Deformation & Visual check & Torsion/Bending: \(\pm\)5 mm \\ \hline Reinforcement and & \multirow{4}{*}{Visual check} & The type and quantity of reinforcement and accessories must be in accordance with the member production drawing and installed in the correct position \\  & & Must be flat and normal appearance, have no defects due to \\ \cline{1-1} Exposed surface condition & Visual check & honeycomb, marks, air bubbles, etc., no signs of lack of \\ \cline{1-1}  & & rebar coating \\ \hline \hline \end{tabular}
\end{table}
Table 1: Major quality inspection items, inspection methods, and criteria.  
Figure 4: Major defect types of PC members.
# Fusing Appearance and Prior Cues for Road Detection
## 3 Experimental Results and Discussion
### Discussion  
From these experimental results, we can conclude that our proposed method provides remarkable road detection results when we select the optimal value of the threshold and combine the log-chromaticity space with the S channel in HSV color space to estimate the appearance model. Moreover, qualitative and quantitative evaluations on the KITTI road benchmark demonstrate that appearance cues are essential to obtain reliable road detection results and geometric prior cues provide relevant information to infer the location of the road surface, thus higher performance is obtained when fusing appearance and prior cues.  
It is important to mention that we obtained geometric prior models of UMM, UM, and UU road in view of the fact that images in the KITTI road benchmark are divided into three corresponding categories. Consequently, for each input image in a certain category, we use the corresponding geometric prior model. On the contrary, the other competing state-of-the-art methods mentioned above use a single algorithm for all three categories of images in the KITTI road benchmark. We have conducted experiments and the results show that for all the images in the dataset, a decrease of less than 0.3% of the MaxF score, i.e., almost the same performance of road detection, can be achieved when we use the non-corresponding geometric prior model. We argue that this happens since appearance cues are essential in our algorithm and prior cues provide statistical descriptions and information which are in principle similar along with the variety of road situations. Actually, we can aggregate and average all the ground-truth images, no matter which category, to obtain a single geometric prior model in practical application.  
Finally, we conduct an analysis of the computational cost of the proposed method. We run our experiments with non-optimized MATLAB code. The entire road detection process takes about 1800 ms on images with the resolution of 1242 \(\times\) 375 pixels. We think that it is possible to run in less than 300 ms with C++ code. In addition, there are some optimizations, such as using gSLICr [37] which is a GPU-implementation of the SLIC algorithm. Furthermore, considering that one third of the upper part of the input images are not likely to contain any road area, and we can remove it as [25] suggested. In contrast to our proposed algorithm, the computational cost of the other state-of-the-art methods mentioned above are as follows: SRF [6] takes about 0.2 s with C++ code using a CPU @ 2.5 GHz; CN [7] takes about 2 s with C++ code using a CPU @ 2.5 GHz; FCN-LC [12] takes about 0.03 s with Python code using an additional GPU Titan X; BM [24] takes about 2 s with MATLAB code using 2 CPU @ 2.5 GHz; ANN [27] takes about 3 s with C++ code using a CPU @ 3.0 GHz; LidarHisto [29] takes about 0.1 s with C++ code using a CPU @ 2.5 GHz.
## 4 The Algorithms
### Population Management Role
#### 4.2.2 Removing Agents from the Group  
Removing agents from the group is a rule working against the addition rule. Therefore, it is important for these two behaviors to be in balance. An agent may fail temporarily in real-life situations. If the problem persists for long enough, the agent is replaced by another one that is added to the group. If the failure is resolved, for example, the agent's battery is recharged from a solar panel, this re-enabled agent becomes redundant. The removal rule works to avoid congestion. If an agent \(a_{\{f,k,v\}}\), while exploring the subspace \(s_{\{k,f\}}\), records more conflicts than \(TCr_{\{f,k,v\}}\) (i.e., \(CCr_{\{f,k,v\}}\geq TCr_{\{f,k,v\}}\)) or more conflicts than \(TCr_{\{f,k,v\}}\) during the period \(tr_{\{f,k,v\}}=TRr_{\{f,k,v\}}\), then the agent transfers a pheromone mark \(Pr_{\{f,k,v\}}\) to the entrance \(e_{k_{m}}\) it has previously used to enter the subspace. The pheromone mark \(Penr_{\{k,m,f\}}(t)\) requests the removal of an agent from the group. The agent then resets its \(CCr_{\{f,k,v\}}\) and \(tr_{\{f,k,v\}}\) to zero even if \(tr_{\{f,k,v\}}==TRr_{\{f,k,v\}}\) is not satisfied.  
\[\text{if }tr_{\{f,k,v\}}==TRr_{\{f,k,v\}}\implies CCr_{\{f,k,v\}}=0),tr_{\{f,k,v \}}=0.\]  
The agent removal requesting the pheromone mark \(Penr_{\{k,m,f\}}(t)\) at the entrance \(e_{k_{m}}\) decreases (evaporates) in time:  
\[Penr_{\{k,m,f\}}(t)=\begin{cases}b*Pr_{\{f,k,v\}}&\text{if }Penr_{\{k,m,f\}}(t-1)==0\\ b*Pr_{\{f,k,v\}}+Penr_{\{k,m,f\}}(t-1)-1&\text{if}Penr_{\{k,m,f\}}(t-1)>0\\ \end{cases}, \tag{10}\]  
where \(b\) is equal to 1 if the agent placed the request at the entrance \(e_{k_{m}}\); otherwise, 0. If another agent places a removal request too, \(Penr_{\{k,m,f\}}(t)\) increases by \(Pr_{\{f,k,v\}}\) according to the formula above. An agent may be removed from the group only if it is in the process of space exploration (state 2 in Figure 2). When an agent \(a_{\{f,k,v\}}\) comes near the entrance \(e_{k_{m}}\) and the pheromone mark \(Penr_{\{k,m,f\}}(t)\) is strong enough, it leaves the subspace through the entrance. This happens if  
\[Penr_{\{k,m,f\}}(t)\geq TPr_{\{f,k,v\}}. \tag{11}\]  
The condition that an agent may be removed from the group only if it is in state 2 eliminates the possibility of an agent to be removed by itself. Several agents must place the request in succession for the mark to become strong enough.
## 3 Construction of Mathematical Model
### Transmission Loss Equation of BDS Inter-Satellite Link Signal in Cislunar Space  
The inter-satellite link transmission losses between BDS satellites located in different orbits and non-cooperative targets in cislunar space is shown below:  
\[L_{i(BD\to space)}=\left(\frac{4\pi d_{i(BD\to space)}}{\lambda_{iBD}} \right)^{2} \tag{11}\]  
Similarly, the inter-satellite link transmission losses between the BDS satellites shining onto the targets then reflected by them and the ground-based CEI receiver array is shown as:  
\[L_{i(space\to CEI)}=\left(\frac{4\pi d_{i(space\to CEI)}}{\lambda_{iBD}} \right)^{2} \tag{12}\]  
where \(L_{i(BD\to space)}\) and \(L_{i(space\to CEI)}\) are the interplanetary link transmission losses between the GNSS satellite and targets in Earth-Moon space, and between the targets and the ground-based CEI receiving array after reflection, respectively; \(d_{i(BD\to space)}\) and \(d_{i(space\to CEI)}\) are the distances between the GNSS satellite and the targets, and between the targets and the ground-based CEI receiving array, respectively; and \(\lambda_{iBD}\) is the carrier wavelength of the GNSS satellite interplanetary link.  
In the unit of dB, the above interstellar link transmission loss equations can be expressed as:  
\[L_{i(BD\to space)}=92.45+20\lg f_{iBD}+20\lg d_{i(BD\to space)} \tag{13}\]  
\[L_{i(space\to CEI)}=92.45+20\lg f_{iBD}+20\lg d_{i(space\to CEI)} \tag{14}\]  
where \(f_{iBD}\) is the carrier frequency of the inter-satellite link of the Beidou-3 satellite, in GHZ, and the unit of \(d_{i(BD\to space)}\) and \(d_{i(space\to CEI)}\) is km.
## 2 Methods and Materials
### Data Acquisition
#### 2.2.1 Sampling System  
The designed image sampling system mainly consists of three parts, including a rotatable mount, a high-resolution microscope camera, and a ring light source, as shown in Figure 5. As you can see from Figure 1, the aviation plug contains multiple rows of small solder joints, the smallest of which is just 1 mm. Therefore, with the aid of an auxiliary microscope lens (0.5\(\times\) magnification), an industrial camera with a pixel size of 0.01 mm and a resolution of 4000 \(\times\) 3000 pixels, capable of capturing 30 frames per second (FPS) is adopted. The rotatable mount is responsible for the angle adjustment of the shot. Due to the non-planar, layered structure of solder joints in the aviation plug, the vertical acquisition angle cannot capture the surface characteristics of the solder joints. To alleviate this problem, we use an oblique angle to take the picture as shown in Figure 5. In practice, to avoid the problem of occlusion between adjacent rows of solder joints, the angle between the optical axis of the camera and the horizontal plane is set to 60\({}^{\circ}\). However, because the angle of the shot is inclined, each row of solder joints is not at an equal distance from the lens. As a result, a camera with a given depth of field (DoF) cannot cover the entire image, resulting in only the middle region of the image can be clearly captured, while the others are slightly blurred. For this reason, a two-phase strategy is used in the detection approach. To provide sufficient and uniform light, a ring light source with a power of 5 w is mounted at the end of the camera lens. Meanwhile, the camera and light source are kept on a center line for a good shot. The sampling system is installed in a fixed position, and only moving the aviation plug via a sliding platform is needed to capture the characteristics of these small solder joints. In addition, the captured images are transferred to the computer via a USB cable for subsequent processing.
## 5 Analysis and Discussion
### Effectiveness of AGMM  
To validate the effectiveness of the proposed AGMM method, we conducted further ablation experiments comparing the AGMM module with other methods, including: (1) Traditional Hybrid Residual Connection (HRC), and (2) Adaptive Mixing Module (AMM) [51]. The experimental results are presented in Table 7.  
HRC achieved certain results by enhancing the neural network's utilization of shallow and deep information, but it did not consider the exclusion of redundant information. AMM better utilized shallow information by adaptively fusing information from downsampling and upsampling layers, but it uniformly adapted the fusion of all information without prior information selection.  
In contrast, our AGMM method efficiently uses shallow information through two steps: information selection and adaptive fusion. In the experiments, compared to AMM, our method achieved a performance gain.
# Self-Optimizing Traffic Steering for 5G mmWave Heterogeneous Networks
###### Abstract  
Driven by growing mobile traffic, millimeter wave (mmWave) communications have recently been developed to enhance wireless network capacity. Due to insufficient coverage and the lack of support for mobility, mmWave is often deployed in the ultra-dense small cells of the 5G heterogeneous network. In this article, we first summarize the characteristics of the 5G heterogeneous network from the viewpoints of devices, spectra, and networks. We then propose a triple-band network structure which incorporates licensed bands, sub-6 GHz unlicensed bands, and mmWave bands to support various types of mobile users. Based on the novel network structure, we further propose a self-optimizing traffic steering strategy which can intelligently steer traffic to specific networks and spectra according to the dynamic network and traffic environments. Several use cases are also discussed to facilitate the implementation of our proposals. Finally, we present numerical results to demonstrate that the proposed network structure and strategy can effectively enhance the system throughput and energy efficiency.  
5G heterogeneous network; mmWave communication; traffic steering; self-optimization Article  
Self-Optimizing Traffic Steering for 5G mmWave Heterogeneous Networks. _Sensors_**2022**, 22, 7112. [[https://doi.org/10.3390/s22197112](https://doi.org/10.3390/s22197112)]([https://doi.org/10.3390/s22197112](https://doi.org/10.3390/s22197112))