esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Integrated Single-Framework Algorithm for the Multi-Level School Bus Network Problem
## 6 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Routing Status** & **School** & **Time Window** & \begin{tabular}{c} **Total** \\ **Student Traveling Time (h)** \\ \end{tabular} & \begin{tabular}{c} **Total Bus Traveling** \\ **Distance (km)** \\ \end{tabular} & \begin{tabular}{c} **Total** \\ **Students Traveling Cost (USD)** \\ \end{tabular} & \begin{tabular}{c} **Total Bus Operating** \\ **Cost (USD)** \\ \end{tabular} & \begin{tabular}{c} **Total Cost** \\ **(USD)** \\ \end{tabular} \\ \hline \multirow{5}{*}{\begin{tabular}{c} Different routing for morning and afternoon (integrated \\ framework) \\ \end{tabular} } & HS & M & 12.74 & 62.61 & 127.35 & 187.82 \\ \cline{2-6}  & MS & M & 12.29 & 61.6 & 122.86 & 184.81 \\ \cline{2-6}  & ES & M & 11.18 & 60.81 & 111.82 & 182.44 \\ \cline{2-6}  & HS & A & 12.84 & 59.57 & 128.4 & 178.71 \\ \cline{2-6}  & MS & A & 11.7 & 63.61 & 117.01 & 190.83 \\ \cline{2-6}  & ES & A & 11.96 & 54.76 & 119.63 & 164.27 \\ \hline \multirow{5}{*}{\begin{tabular}{c} Identically reversed \\ same routing \\ framework) \\ \end{tabular} } & HS & M & 14.61 & 60.22 & 146.1 & 180.65 \\ \cline{2-6}  & MS & M & 13.98 & 61.5 & 139.82 & 184.51 \\ \cline{2-6}  & ES & M & 12.42 & 56.67 & 124.16 & 170.02 \\ \cline{2-6}  & HS & A & 15.71 & 60.22 & 157.09 & 180.65 \\ \cline{2-6}  & MS & A & 17.3 & 61.5 & 172.95 & 184.51 \\ \cline{2-6}  & ES & A & 16.94 & 56.67 & 169.41 & 170.02 \\ \hline \multirow{5}{*}{\
\begin{tabular}{c} Different routing for morning and afternoon (separated \\ framework) \\ \end{tabular} } & HS & M & 14.59 & 64.11 & 145.90 & 192.33 \\ \cline{2-6}  & MS & M & 13.91 & 63.21 & 139.10 & 189.63 \\ \cline{2-6}  & ES & M & 12.34 & 62.48 & 123.40 & 187.44 \\ \cline{2-6}  & HS & A & 15.78 & 60.54 & 157.80 & 181.62 \\ \cline{2-6}  & MS & A & 16.24 & 63.14 & 162.40 & 189.42 \\ \cline{2-6}  & ES & A & 17.03 & 54.1 & 170.30 & 162.30 \\ \hline \multirow{5}{*}{
\begin{tabular}{c} Identically reversed \\ same routing (separated \\ framework) \\ \end{tabular} } & HS & M & 15.15 & 64.61 & 151.5 & 193.83 \\ \cline{2-6}  & MS & M & 14.56 & 63.88 & 145.56 & 191.65 \\ \cline{2-6}  & ES & M & 12.9 & 63.37 & 129 & 190.1 \\ \cline{2-6}  & HS & A & 16.34 & 61.83 & 163.37 & 185.5 \\ \cline{2-6}  & MS & A & 17.9 & 65.77 & 179.01 & 197.32 \\ \cline{2-6}  & ES & A & 17.55 & 56.57 & 175.51 & 169.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summarized results of the model.
## 4 Experimental Results and Analysis
### Analysis of Experimental Results
#### 4.3.2 Overhead Time  
In the space target tracking missions, the overall efficiency was determined by the bandwidth of tracking systems. Therefore, in this section, the time overhead of the model was analyzed.  
Figure 8: Comparison of RMSE and R\({}^{2}\) for models under different superpixel scales.  
We tested the impact of different segmentation scales and epoch settings on the time cost of the model, as shown in Table 3. It was observed that higher segmentation precision led to increased time cost, with a significant rise when the superpixel scale reached 1024. In contrast, under the same segmentation scale, the variation in the number of epochs did not result in significant changes in time consumption.  
The proposed I-GCN model directly processed the graph-structured data generated from superpixel segmentation, resulting in very low computational cost. According to measurements of floating point operations per second (FLOPS), the model required only 1.59 MFLOPS. However, it should be noted that the superpixel segmentation process required for graph construction introduced some computational overhead.  
In practical applications, the superpixel segmentation process could be integrated into CMOS imaging modules equipped with FPGA hardware, enabling direct output of graph-structured data through hardware acceleration and avoiding the loss of computational resources. Additionally, the visualization and feature fusion processes were primarily intended for experimental analysis. In real-world deployment, the spatial target features could be directly fed back to the tracking system as compensation, without consuming bandwidth.  
Experimental results demonstrated that the proposed I-GCN-based target prediction and tracking method achieved both high computational efficiency and accuracy. Nonetheless, although increasing segmentation precision improved prediction accuracy, it also significantly increased the time cost. Therefore, in practical applications such as prediction-tracking-compensation systems, different prediction models should be selected based on actual requirements and resource constraints.
# The Necessity of LED to Ambient Light Ratio Optimization for Vehicular Optical Camera Communication
## 4 Simulation
### Effect of Communication Distance
#### 4.4.3 Optimal L2A Ratio at Different LED Intervals  
This section provides simulation results to illustrate the effect of distance and LED interval on the optimal L2A ratio. The LED panel images were replicated with the L2A ratio varying from 0.2 to 4, communication distance varying from 30 to 70 m, LED interval varying from 0.5 to 2. The ambient light is set randomly in the range from 50 to 16,000 lux. The LED luminance is set based on the ambient light and the L2A ratio. The LED surface reflectance is set randomly in the range from 0.3 to 0.5. The ISO is set based on the ambient light. The remaining parameters are set to their fixed values given in Table 1.  
Figure 19: Distribution of LED features at different communication distances. (**a**) Distance = 30 m. (**b**) Distance = 50 m. (**c**) Distance = 70 m.  
Figure 20: Variances at different distances. (**a**) Bvar and Wvar at different distances. (**b**) B2W variance ratio at different distances.  
Figure 18: LED panel frames at different distances.  
Since the Bvar and Wvar are affected by the LED interval and communication distance, the optimal L2A ratio is subject to change when these two parameters change. Figure 21a shows the optimal L2A at different LED intervals and communication distances. It can be seen that the optimal value of the L2A ratio tends to be higher at short communication distances and large LED intervals. This is because at these conditions, the LED interference is low and thus the positive impact of the L2A ratio on LED extinction ratio is only outweighed by its negative impact on LED interference at a high L2A ratio.  
Since the optimal L2A ratio varies across different distances as shown in Figure 21a, each LED interval has a range of the optimal L2A ratio as shown in Figure 21b. For each LED interval, the average value in the corresponding range is determined as the optimal L2A ratio to be applied for a running VOCC system.
## 4 Summary of AM and Conventionally Manufactured Titanium Alloy  
The AM Ti6 Al4V parts need post-processing due to inconsistency in surface hardness, residual stresses, and roughness, which is carried out by machining. Although the post-processing of AM titanium is still very efficient compared to conventionally processing parts (casting, molding, forging, etc.), it should be noted that since titanium machining is also challenging, it needs to be thoroughly investigated and optimized for minimum energy consumption. Even though the machining of conventional titanium alloy has been extensively studied, the machining of AM titanium is significantly different, with the cutting forces observed to be about 55-70% higher compared to those for conventional titanium parts [6; 74]. Higher cutting forces lead to increased energy consumption during the machining of Ti6 Al4V parts produced through additive manufacturing (AM). The machinability of these parts presents unique challenges, including high hardness and strength, as well as residual stresses. These factors contribute to elevated energy consumption, a higher tool wear rate, and increased surface roughness in the machining process.
## 5 Results
### Decoding Algorithm Improvement Experiment
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Decoding Algorithm**} & \multicolumn{5}{c}{**WER(\%)**} \\ \cline{3-6}  & & **Dev-Clean** & **Dev-Other** & **Test-Clean** & **Test-Other** \\ \hline \multirow{4}{*}{Conformer} & attention & 7.00\% & 16.26\% & 7.18\% & 16.72\% \\  & ctc\_gregrek\_search & 8.83\% & 18.41\% & 9.16\% & 18.74\% \\  & ctc\_prefix\_beam\_search & 8.69\% & 18.35\% & 9.06\% & 18.69\% \\  & adaptive attention fusion rescoring & 5.17\% & 12.35\% & 5.43\% & 12.36\% \\ \hline \multirow{4}{*}{Unified\_Conformer} & attention & 4.56\% & 10.12\% & 4.93\% & 10.32\% \\  & ctc\_greedy\_search & 4.67\% & 12.05\% & 4.87\% & 12.17\% \\  & ctc\_prefix\_beam\_search & 4.63\% & 12.02\% & 4.83\% & 12.10\% \\  & adaptive attention fusion rescoring & 4.35\% & 10.02\% & 4.73\% & 10.13\% \\ \hline \multirow{4}{*}{U2++\_Conformer} & attention & 4.39\% & 10.54\% & 4.81\% & 10.79\% \\  & ctc\_greedy\_search & 5.05\% & 12.37\% & 5.27\% & 12.60\% \\ \cline{1-1}  & ctc\_prefix\_beam\_search & 5.01\% & 12.32\% & 5.20\% & 12.47\% \\ \cline{1-1}  & adaptive attention fusion rescoring & 4.14\% & 10.20\% & 4.42\% & 10.08\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of decoding algorithms. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Decoding Algorithm**} & \multicolumn{5}{c}{**WER(\%)**} \\ \cline{3-6}  & & **Dev-Clean** & **Dev-Other** & **Test-Clean** & **Test-Other** \\ \hline Conformer & adaptive attention fusion rescoring & 5.17\% & 12.35\% & 5.43\% & 12.36\% \\ \hline Unified\_Conformer & adaptive attention fusion rescoring & 4.35\% & 10.02\% & 4.73\% & 10.13\% \\ \hline U2++\_Conformer & adaptive attention fusion rescoring & 4.14\% & 10.20\% & 4.42\% & 10.08\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Decoding algorithm innovation test results. ment of speech recognition technology and demonstrating its substantial effectiveness in practical applications.
# A New Low-Cost Compact Antenna for the 2.45 and 5.8 GHz ISM Bands
## 2 Dual-Band Antenna Design
### Antenna Design Methodology
(**a**) Step 1: initial configuration with a partial ground plane; (**b**) step 2: configuration with slots insertion on the radiating element and feed line; (**c**) step 3: structure with two circular rings on the radiating element and slots partially in the upper part of the ground plane width; (**d**) step 4: final structure with slots in the central ring and a ground plane with a crenellated shape at the top.
## 3 Experimental Setup and Results  
Figure 3 shows the experimental set-up and the schematic. Figure 3i illustrates the experimental setup inside a sports hall. Figure 3 ii illustrates the arrangement of sensors where four out of the nine positions of the source used are shown as examples. Due to space constraints sensors were kept at 9 m distance from each other. Sensors nodes communicate via the central hub using the wireless highway addressable remote transducer (wirelessHART) protocol.  
Figure 2: Algorithm flowchart.  
To evaluate the performance of the algorithm, an offline PD signal was generated by using a commercial high voltage partial discharge (HVPD) calibrator (from HVPD Ltd., Salford, UK). The HVPD calibrator was selected due to its suitability for IEC 60270 standard measurements. It can provide a wide range of calibration pulses ranging from 1 pC to 100 nC and the pulse repetition rate is selectable at 100, 200 and 400 Hz.  
The calibrator was used to generate a 10 nC charge with a repetition rate of 100 Hz and it was connected to a biconical Aaronia Bicolog 20100 E radiating antenna (from Aaronia AG, Strickscheid, Germany). This (on the left) together with the RF radiometric sensor used (on the right) are shown in Figure 4.  
Specialized PD signal emulators in [33] producing random PD signals have also been tested during this field-trial with very similar results as with the calibrator when proper averaging was applied to the received signal.  
Figure 4: Biconical antenna with PD calibrator and the radiometric sensor used.  
Figure 3: (i) Test space image. (ii) Sensors arrangement schematic.  
The biconical antenna has dimensions 0.54 m \(\times\) 0.225 m \(\times\) 0.225 m, the frequency range from 20 MHz to 1 GHz and the input impedance of 50 \(\Omega\). The plot of frequency versus gain and the antenna factor of the antenna is illustrated in Figure 5 below:  
To measure PD signal, eight receiving nodes were used. There were nine different measurements taken in a free space environment (indoors). Measurement sensors were placed over an 18 \(\times\) 18 m grid. Measurements were performed in an unshielded sports hall environment. A range of services operate at the desired frequency band such as FM broadcast and digital video broadcasting (DVB-T), etc.  
Signals for such services could easily superimpose on the desired PD signal and could become a source of noise to the desired signal. To evaluate such background interferences, a spectral analysis was performed inside the sports hall before measurements were conducted by using a high specification spectrum analyzer as shown in Figure 6 below.  
Figure 5: Antenna gain versus frequency and antenna factor plot.  
Figure 6: Spectral analysis inside the sports hall.  
As illustrated in Figure 6, the frequency span was chosen from 50 MHz to 1 GHz covering the whole desired band. As it is evident from Figure 6, interference from FM radio, TV, LTE-4G, GSM and other communication signals were observed. To overcome such effects, bandpass RF filters have been used in the front-end part of the measurement sensor receiver as in Figure 7.
# Ambient Environmental Parameter Estimation for Reliable Diffusive Molecular Communications
## 2 System Model of Diffusive Molecular Communications
### Effect of Temperature Change on CIR  
Figures 3 and 4 show system models for homogeneous and heterogeneous temperature distribution, respectively. In the heterogeneous environment (Figure 4), the temperatures in the left and right regions differ. The diffusion coefficient and reaction rate coefficient are affected by changes in the temperature. Based on the Stokes-Einstein relation, the diffusion coefficient is obtained as  
\[D=\frac{C_{B}T}{6\pi\eta R_{\text{A}}} \tag{9}\]  
where \(C_{B}\) denotes the Boltzmann constant, \(T\) is the absolute temperature, \(\eta\) is the viscosity of the fluid environment, and \(R_{A}\) is the Brownian particle radius. Conversely, the reaction rate coefficient is expressed by the Arrhenius equation as follows:  
\[k=A_{k}\exp\left(-\frac{E_{a}}{RT}\right) \tag{10}\]  
Here, \(A_{k}\) denotes a frequency factor and corresponds to the collision frequency of the chemical species at temperature \(T\). Furthermore, \(E_{a}\) denotes the activation energy, which corresponds to the threshold energy of the reaction, and \(R\) denotes the gas constant. It is noted that the collision frequency \(A_{k}\) can be estimated by the environment parameters, such as temperature and activation entropy.
## 4 Distributed Hotspot Network Architecture
### Distributed and Decentralized Hotspot Network Layer  
This layer is the most important layer of this architecture. The idea behind this layer is related to the commonly known People Powered Network (PPN), which is similar to that which the Helium Society has developed. The goal of this layer is to enable secure IoT data transmission over the distributed hotspot network access mechanism [95,96]. The term \"people\" is used here because the hotspots can be used by any person who wants to provide IoT data transmissions in their hotspot coverage area. Typically, the range of IEEE 802.11n (2.4 GHz) Wi-Fi is about 50 m. However, some long-range Wi-Fi devices are becoming available, which can operate on higher GHz bands (3.6/4.9/5/5.9/60 GHz) and multiple channels. For example, Wi-Fi 6 (IEEE 802.11 ax) has 600-9608 Mbps and a longer range. LoraWAN can also be used as an alternative to Wi-Fi when data transmission over very long distances at minimum data rates is required. LoraWAN is a software communication protocol, and it is architecture that is based on the Lora-based Chirp Spread Spectrum (CSS) modulation scheme. Its range can be easily extended to 10-15 km, which is far more than Wi-Fi with a data capacity of 0.3-50 kbps per channel. LoraWAN has an additional advantage over cellular or other LPWAN techniques: it offers AES protection with end-to-end encrypted messaging with lower power consumption and thus a better battery life. LoraWAN operates in a bandwidth of 415/868/915 MHz in a license-free spectrum.
## 4 Renewable Carbon Sources for the Biosynthesis of 3-HP
### Microbial Production of 3-HP from Glucose or Xylose
#### 4.1.1 Biosynthesis of 3-HP via the Glycerol Pathway  
[PERSON]'s team developed a production strategy for 3-HP synthesis via the glycerol pathway by co-utilizing glucose and xylose as mixed carbon sources in _E. coli_. First, the conversion of glucose and xylose to glycerol was enhanced by an overexpression of _S. cerevisiae_-derived glycerol-3-phosphate dehydrogenase (GPD1) and glycerol 3-phosphatase (GPP2). Then, the catabolite repression effect was relieved by knocking down the glucose-specific transporter protein (encoded by _ptsG_), after which xylose utilization was enhanced by overexpression of the transcriptional enhancer _xylR_. Finally, the engineered _E. coli_ with an introduced glycerol pathway could efficiently and synergistically utilize xylose and glucose to produce 29.4 g/L of 3-HP and 95 g/L of glycerol in fed-batch fermentation [68]. To address the problem of glycerol accumulation, the team proposed two strategies that attenuate glycerol production and enhance glycerol utilization. The strategy of downregulating glycerol production through the modulation of the glycerol synthesis pathway enhanced 3-HP production to 37.6 g/L [69]. The same team also enhanced glycerol utilization by overexpressing endogenous aldehyde dehydrogenase (PuuC), which boosted the 3-HP titer to 53.7 g/L [70]. Similar metabolic engineering strategies were used to construct engineered _C. glutamicum_ that produced 3-HP via the glycerol pathway from glucose and xylose, which resulted in a 3-HP titer of 54.8 g/L in fed-batch fermentation [46]. A recent study reported that _K. pneumoniae_ can produce 3-HP via the glycerol pathway utilizing glucose as substrate, but it only achieved a low 3-HP titer of 1.77 g/L in fed-batch fermentation [71]. The key to obtaining high titers of 3-HP from sugar via the glycerol pathway appears to be efficient sugar transport and reducing glycerol accumulation.
## 2 Experiment Setups
### Wind Tunnel Test  
The wind tunnel tests were conducted in the ZD-1 boundary layer wind tunnel at Zhejiang University, which had a test section of 4 m (width) by 3 m (height). The pressure scanners in association with a digital service module (DSM4000, Scanivalve Corporation, Washington, DC, USA) were utilized to record the wind pressures on the model. The fluctuating wind pressures were recorded at a sampling frequency of 312.5 Hz with a duration of 64 s. The wind tunnel tests were carried out in a uniform flow with a wind speed of 11.20 m/s and a turbulence intensity of 0.4%. To eliminate the influence of the near floor viscous layer on the test result, the test models were placed on a large plate that was 30 cm above the ground. The blockage ratios in the tests were less than 3.5%, which ensures that the blockage-caused distortion effects on the flow are negligible [17]. Considering the symmetry of the model, wind incidence angles of 0\({}^{\circ}\)-45\({}^{\circ}\) with an interval of 5\({}^{\circ}\) were tested in the experiments as shown in Figure 3.
# Rotation Invariant Graph Neural Network for 3D Point Clouds
## 3 Theoretical Background  
### Background for Graph Neural Networks  
A graph \(G\) is described as a collection of vertices \(V\) with \(n\) elements having correspondences. The existence of a relationship between two vertices is an edge of the set \(E\). The corresponding values for each relationship between vertices are given by the weighted adjacency matrix \(A\in\mathbb{R}^{n\times n}\), thus \(G=(V,E,A)\). To create a graph, the set of vertices must be established, then determining which vertices have a relationship, and finally computing the weighted adjacency matrix for the entire graph. A typical method for computing the adjacency matrix \(A\) is found in (1).  
\[A_{i,j}=e^{-\frac{\|p_{i}-p_{j}\|^{2}}{\sigma^{2}}} \tag{1}\]  
where \(\|p_{i}-p_{j}\|^{2}\) represents the Euclidean distance, while \(\sigma>0\) is given in [6; 34] as a variable parameter. The degree matrix \(D\) can be computed from the weighted adjacency matrix by summing the elements on each line and creating from it the diagonal matrix. The first iteration of the Laplacian \(L\) is \(L=D-A\). To normalize the Laplacian graph, we divide by the degree matrix as \(L_{norm}=D^{-1}(D-A)\). To ensure that the Laplacian graph matrix is symmetric, the division by \(D\) is carried out according to (2).  
\[L_{symm}=D^{-1/2}(D-A)D^{-1/2} \tag{2}\]  
Graph convolution can be seen as an extension of classical convolutional networks to graph data. Traditional convolutional networks use inputs in a fixed grid structure, whereas graph data do not have a spatially localized structure and must rely on the relationships. All traditional convolutional networks require a smaller fixed-size filtering kernel that is moved sequentially through the grid structure. In graph neural networks, vertices that have a relationship are considered neighbors and the number of neighbors of one vertex can vary greatly in number. Depending on the graph, some vertices can have many more neighbors than others.  
Figure 2: Visual comparison between Bounding Box and PCA preprocessing.