esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Result Analysis
### Materials and Methods
#### 3.1.1 Data preprocessing  
The experimental datasets in this paper were derived from the real-time detection data results, as shown in Figure 9, for the AUV field activity process. The cleaned data were visualized as position trajectories and are shown in Figure 10.  
Figure 8: AWOA-DBiLSTM neural network model.  
More than twenty thousand data points were chosen for this experiment, and they went through cleaning and feature selection procedures. Underwater vehicles may experience interference throughout their operations that causes illogical values to appear in real-time data transmitted through acoustic communication technology. Abnormal values were eliminated to prevent any negative effects on data training and testing. First, the essential data properties were chosen, and the required data format was modified. Duplicate and missing values were then examined. Three or more occurrences of a duplicate were eliminated, and nearby averaging was used to interpolate missing data. The final experimental dataset consisted of about twenty thousand data points after data filtering. Of these, twenty percent were set aside for testing, and eightty percent were used for training.  
\[X_{t}=\frac{X_{t-2}+X_{t-1}+X_{t+1}+X_{t+2}}{4} \tag{30}\]  
Figure 10: AUV motion trajectory.  
Figure 9: AUVCTD field campaign.  
After cleaning them, we normalized and de-normalized the data to lessen the dimensional effect between various qualities. Following the process of data normalization, every indicator was located in the decimal range of [0, 1]. The calculation formula is as follows:  
\[x^{\prime}=\frac{x-min}{max-min} \tag{31}\]  
\[x=x^{\prime}(max-min)+min \tag{32}\]  
where \(min\) is the minimum value of the column where the data are located, and \(max\) is the maximum value of the column where the data are located. Anti-normalization was employed to revert the model's prediction outcomes to their true values during the evaluation of the subsequent prediction results.
# Research on Part Tolerances for RV-40E Reducer Based on Virtual Prototype Technology
## 2 Materials and Methods
### Analysis of Factors Affecting Transmission Accuracy and Manufacturing Costs
Hence, this study primarily investigates the influence of the tolerance values of the eight self-manufactured parts of the RV-40E reducer on output transmission accuracy. Table 5 shows the tolerance design values of 8 self-made parts of the RV-40E reducer currently provided by a manufacturer. Subsequently, the Latin hypercube sampling method and virtual prototype simulation calculation will be applied within the tolerance range of each part, and single-factor experiments and orthogonal experiments will be conducted to analyze the sensitivity of 10 tolerances of 8 parts to the output transmission accuracy and the reasonability of the design of 10 tolerance values. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Part Name** & **Char. Param.** & **Tolerance** \\ \hline
1 & input shaft & pitch circle runout \(\Delta r_{1}\) & \(0.012\left(12^{0}_{-0.012}\right)\) \\ \hline
2 & planetary gear & pitch circle runout \(\Delta r_{2}\) & \(0.015\left(24^{0}_{-0.015}\right)\) \\ \hline
3 & crankshaft & eccentric distance \(\Delta e\) & \(0.006\left(1.25^{+0.003}_{-0.003}\right)\) \\ \hline  & & moved distance \(\Delta r_{p}\) & \(0.002\left(0.012^{+0.001}_{-0.001}\right)\) \\
4 & cycloid wheel & equidistance \(\Delta r_{rp}\) & \(0.004\left(0.020^{+0.002}_{-0.002}\right)\) \\  & & hole distance \(\Delta a_{c}\) & \(0.012\left(72^{+0.006}_{-0.006}\right)\) \\ \hline
5 & pin-wheel housing & pin-wheel ring runout \(\Delta r_{b}\) & \(0.006\left(66^{+0.003}_{-0.003}\right)\) \\ \hline
6 & pin wheel & needle radius \(\Delta r_{r}\) & \(0.002\left(3^{+0.001}_{-0.001}\right)\) \\ \hline
7 & input-end planet carrier & hole distance \(\Delta a_{k}\) & \(0.012\left(72^{+0.006}_{-0.006}\right)\) \\ \hline
8 & output-end planet carrier & hole distance \(\Delta a_{k}\) & \(0.012\left(72^{+0.006}_{-0.006}\right)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Quality characteristics and tolerances for design of experiment.
# Hom Antenna on Chip Operating at 180 GHz Using the SiGe CMOS Process
## 4 Conclusions  
The operating frequency of the SiGe CMOS Horn antenna designed for the antenna is 180 GHz. Using SiGe CMOS can have low cost and reduce dimension. At the same time, the maximum bandwidth is found by analyzing the feed and the presence or absence of the uppermost layer and conducting measurement and verification. The return loss measurement results are in the range of 177.4-183 GHz, and the measurement bandwidth has a broadband advantage compared to other documents. The antenna used measures 0.85 mm \(\times\) 0.85 mm, which is small and better than most designs. In addition, there are many areas worthy of development and research in this design in the future, including higher frequencies and wideband operation multifunctionality and multiband intelligent and adaptive technologies.  
Conceptualization, M.-A.C.; methodology, M.-A.C.; software, M.-A.C.; validation, M.-A.C., Z.-Y.H. and Y.-H.C.; formal analysis, M.-A.C.; investigation, M.-A.C.; resources, M.-A.C.; data curation, M.-A.C.; writing--original draft preparation, M.-A.C.; writing--review and editing, M.-A.C. and Z.-Y.H.; visualization, M.-A.C.; supervision, M.-A.C.; project administration, M.-A.C.; funding acquisition, M.-A.C. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **Process** & \
\begin{tabular}{c} **Frequency** \\ **(GHz)** \\ \end{tabular} & *** FBW (\%)** &
\begin{tabular}{c} **Dimension** \\ **(mm2)** \\ \end{tabular} \\ \hline
[15] & Wire bonding & 93.7 & 3.8\% & \(2.1\times 2.1\) \\
[16] & IPD & 94.5 & 2\% & \(1.9\times 1.9\) \\
[17] & CMOS & 94.5 & 10.5\% & \(2.4\times 2.4\) \\
[18] & 65 nm CMOS & 280 & *N/A & \(2.8\times 2.8\) \\
[19] & TSV & 275 & 3.6\% & *N/A \\
[20] & 130 nm CMOS & 165 & 5.2\% & \(1.88\times 1.88\) \\ Proposed & SiGe CMOS & 180 & 4.2\% & \(0.85\times 0.85\) \\ \hline \hline \end{tabular}  
* FBW = Fractional Bandwidth. *N/ A = Not Available.  
\end{table}
Table 4: The proposed antenna is compared with chip antennas using different processes.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Data is contained within the article. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
**Acknowledgments:** This chip is manufactured by Taiwan Semiconductor Manufacturing Company (TSMC) through the National Chip Implementation Center (CIC) in Hsinchu, Taiwan, and using the measuring equipment and measuring fields provided by Taiwan Semiconductor Research Institute (TSRI).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 6 Circuit Design
### Charging Circuit  
The solar/wind charging circuit is shown in Figure 14. The three-phase input of wind turbine converts AC to DC through a three-phase rectifier bridge, and mixes with the input of the PV array. In this circuit, freewheeling diodes are replaced to reduce rectification losses and the current sharing effect is significantly enhanced. The electric energy generated by wind and solar is very unstable. The electric energy is converted into a stable and usable DC through the interleaved Buck circuit. The choice of MOS tube considers the following elements: (1) Withstand voltage greater than or equal to 3 times DC bus voltage; (2) The current value is less than 1/4 of the rated current; (3) Low on resistance.  
As shown in Figure 14, MOSFET RU190N08 (Ruichips Semiconductor, Shenzhen, Guangdong, China) is selected as the power device of the interleaved Buck circuit, which has a withstand voltage of 80 V and a withstand current of 190 A. Relevant parameters of the power supply circuit can be seen in Table 11.  
The inductance of interleaved Buck circuit can be obtained as follows.  
\[L_{Buck}=V_{0}(1-D)/(rl_{L}f_{s}) \tag{25}\]  
where \(L_{Buck}\) is the inductance of interleaved Buck circuit; \(V_{0}\) is DC bus voltage (24 V); \(D\) is the minimum duty cycle of the PWM control method (0.57); \(r\) is inductor current ripple peak-to-peak factor (0.4); \(I_{L}\) is inductor rated current (40 A); \(f_{s}\) is switching frequency of PWM control signal (20 KHz). Thus the inductance of interleaved Buck circuit \(L_{Buck}\) is 32 \(\mu\)H.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline \(C_{6}\) & 100 & \(\mu\)F \\ \(C_{7}\) & 0.1 & \(\mu\)F \\ \(C_{8}\) & 0.1 & \(\mu\)F \\ \(C_{9}\) & 100 & \(\mu\)F \\ \(C_{10}\) & 0.1 & \(\mu\)F \\ \(C_{11}\) & 0.1 & \(\mu\)F \\ \(C_{12}\) & 0.47 & \(\mu\)F \\ \hline \hline \end{tabular}
\end{table}
Table 10: Relevant parameters of the power supply circuit.  
Figure 14: The solar/wind charging circuit.
# An Optimization Routing Algorithm for Green Communication in Underground Mines
## 5 GA-Based Network Clustering Model  
In the GCUM, because of the heterogeneity of initial energies, their functions, and the energy consumption levels of three types of nodes, a GA can be used to optimize the clustering results.
## 1 Introduction
This system allows constant forward speed and better accuracy and, therefore, more regular trajectories. In the semiautomatic guidance system, during travel road, the driver normally operates the steering wheel. During a crop operation, on the other hand, an electrical wheel is able to move the steering wheel. This system allows the driver to pay attention to the working quality of the agricultural implement and to increase the forward speed by 10-15% for some crop operations (e.g., soil tillage, hoeing, sowing). In the automatic guidance system, during a crop operation, a control unit manages the steering wheel, the gearbox, the hydraulic lift and the connected agricultural machine or implement. The aided guidance system is offered by the manufacturer of agricultural machines or is sold by the manufacturer of GNSS receivers or specialized companies (e.g., AvMap, AgLeader) as part of the tractor or to be mounted on the tractor owned by the farmer. In many cases, it is an all-in-one system, including GNSS receiver and monitor, while it is sometimes a more complex system consisting of a GNSS receiver, monitor, actuator, controller for spatially variable rate crop input application, etc. [19; 20; 21; 22]. A low-cost solution available online for building up a homemade automatic guidance system is offered by AgOpenGPS project, which provides the outlines and designs of the hardware to be developed, as well as the software that is distributed together with GNU (General Public) license. The aim of this work is to compare the positioning accuracy of four GNSS receivers having different technical features and working modes, from a dual-frequency (L1/L2) survey-grade RTK-capable receiver to a low-cost single frequency (L1) GPS receiver.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & **Town,** & **Average** & **PV Prod.** & **Diesel Fuel** & **Heating** & **Cooling** & **Diesel** & **Reduction of** \\  & **TRNSYS** & **Air** & **el, kWh** & **Cons., 1** & **Time, h** & **Time, h** & **Generator** & **Diesel Fuel** \\  & **T \({}^{\circ}\)C** & **T \({}^{\circ}\)C** & **T \({}^{\circ}\)C** & **T \({}^{\circ}\)C** & **T \({}^{\circ}\)C** & **T \({}^{\circ}\)C** & **T \({}^{\circ}\)C** \\ \hline
0 & Garissa & 29.2 & 0 & 2402 & 0 & 5426 & 6011 & \\
1 & (Kenya) & 29.2 & 4732 & 1033 & 0 & 5426 & 2577 & 57 \\ \hline
0 & Jerusalem & 16.1 & 0 & 1247 & 185 & 1153 & 3211 & \\
1 & (Israel) & 16.1 & 4769 & 56 & 190 & 1152 & 135 & 96 \\ \hline
0 & Malakal & 28.0 & 0 & 2117 & 0 & 4367 & 5374 & \\
1 & (South Sudan) & 28.0 & 4417 & 827 & 0 & 4367 & 2092 & 61 \\ \hline
0 & Kigoma & 23.5 & 0 & 1654 & 0 & 2340 & 4439 & \\
1 & (Tanzania) & 23.5 & 3588 & 577 & 0 & 2340 & 1627 & 65 \\ \hline
0 & Addis Ababa & 16.6 & 0 & 947 & 38 & 762 & 2375 & \\
1 & (Ethiopia) & 16.6 & 4496 & 54 & 41 & 762 & 145 & 94 \\ \hline
0 & Peshawar & 22.6 & 0 & 1697 & 56 & 3125 & 4256 & \\
1 & (Pakistan) & 22.6 & 4213 & 519 & 56 & 3125 & 1293 & 69 \\ \hline \end{tabular}
\end{table} 
Table 8: Summary of an annual operation of mobile energy unit in the world’s 10 Largest Refugee Camps climate conditions. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**City**} & \multicolumn{3}{c}{**Average**} \\ \cline{2-4}  & **DNI, kWh/m\({}^{2}\)** & **PVout, kWh/kWp** \\ \hline Garissa & 4.01 & 4.3 \\ Jerusalem & 5.97 & 4.85 \\ Malakal & 4.18 & 4.42 \\ Kigoma & 4.33 & 4.46 \\ Addis Ababa & 4.82 & 4.7 \\ Peshawar & 3.97 & 4.25 \\ \hline \end{tabular}
\end{table} 
Table 9: Average Solar direct normal irradiation (DNI) and Specific photovoltaic power output (PVout).
# Recurrent Neural Network Based Short-Term Load Forecast with Spline Bases and Real-Time Adaptation
## 2 Methodology
### Spline Basis Functions  
To catch the general behaviors of the daily load patterns, we consider the class of multi-resolution basis functions proposed by [PERSON] and [PERSON] [19], which are ordered in the direction of increasing resolution detail, with the number of bases, \(K\), being chosen to be large enough to represent the general 24-h patterns. On the other hand, due to the fact that the daily load patterns may change rapidly between the peak and off-peak periods, we also include the cubic \(B\)-spline basis functions to accommodate load patterns that change substantially within relatively short periods of time. Two sets of bases functions are used:  
Figure 1: Proposed spline-bases assisted Recurrent Neural Network (RNN) for Short Term Load Forecasting (STLF) and RNN\({}_{adp}\)  
(1). the multi-resolution bases \(\{f_{1},\ldots,f_{n}\}\) defined on \(n\) control points \(\{s_{1},\ldots,s_{n}\}\) and (2). the \(B\)-spline bases of order \(d\),\(\{B_{i,d},\ i=1,\ldots,n\}\) with knots at \(\{s_{1},\ldots,s_{n}\}\). Details about the spline basis functions can be found in [19].
# Path Planning and Collision Avoidance in Unknown Environments for USVs Based on an Improved D* Lite
## 3 Improved D* Lite
### Adjacent Node Expansion and Steering Angle Limitation
#### 3.2.2 Steering Angle Limitation  
Constrained by the steering maneuverability and the fewer DOF of USVs, the steering angle of USVs is limited. There may be still some sharp turns on the planned path even if we expand to 48 adjacent nodes for path planning. To plan a more realistic path for USVs, the algorithm should consider the steering angle limitation between two adjacent nodes along its path. When a turning angle is larger than the steering angle limitation that the USV can steer each time, we should divide it into several turning operations. Therefore, the steering angle can be constrained within the steering angle limitation. The definitions of USV heading and steering angle are as follows:  
1. USV heading: the vector from the previous position to the current position of the USV. Initially, the USV heading is towards the goal position.
2. Steering angle: assume that the vector from the previous position to the current position is \(\vec{A}\), and the vector from the current position to the next position is \(\vec{B}\). We define the steering angle as the angle between \(\vec{A}\) and \(\vec{B}\), which is shown in Figure 2.  
When the steering angle (\(\alpha\)) is larger than the steering angle limitation of the USV (\(\beta\)), we should divide the turning operation into several steps, and we only turn an angle no more than the steering angle limitation in each step. We let the distance between the current position and the next position be the radius \(R\). Because \(\alpha>\beta\), we turn the USV to the next position with a limited steering angle \(\beta\). Thus, we can obtain a new position as the next position for path planning. Figure 3 demonstrates the turning procedure between two original positions and the calculation for the new next position.  
Assume that the coordinate of the current position is \((x_{1},y_{1})\). The USV firstly calculates the current heading according to the previous and current positions. Afterwards, the original next position \((x_{2},y_{2})\) and original steering angle \(\alpha\) are calculated. If the original steering angle is larger than the USV's maximum steering angle (\(\alpha>\beta\)), then we calculate  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Adjacent Nodes** & **Number of Search Directions** & **Minimum Steering Angle** \\ \hline
8 & 8 & 45\({}^{\circ}\) \\
24 & 16 & 22.5\({}^{\circ}\) \\
48 & 32 & 11.25\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relationship between number of adjacent nodes, number of search directions and minimum steering angle.  
Figure 2: USV heading and steering angle.  
the actual next position \((x,y)\) according to Equation (7). The process is repeated until the USV's heading is turned towards the original next position.  
\[\begin{cases}x=(x_{1}-x_{2})\times\cos(\alpha-\beta)-(y_{1}-y_{2})\times\sin( \alpha-\beta)+x_{2}\\ y=(y_{1}-y_{2})\times\sin(\alpha-\beta)+(x_{1}-x_{2})\times\cos(\alpha-\beta)+y _{2}\end{cases} \tag{7}\]
## 4 Main Results
### The Proposed Decentralized Scheme Based on ADRC  
The decentralized control technique was proposed to control the nonlinear MIMO system given in Equation (2). This method utilizes the IADRC paradigm due to its high robustness against the effects of nonlinear coupling, inherent uncertainties, and exogenous disturbances, which are all estimated and canceled in an online fashion by the ESO.  
In this control scheme, the nonlinear-coupled MIMO system in Equation (1) was converted to multi single-loop linear time-invariant SISO systems by treating the coupling input as a component of the generalized disturbance. By modifying Equation (1), this gives:  
\[\left\{\begin{array}{c}\xi_{i}^{(\gamma_{i})}=f_{i}(\xi,\eta,w)+\sum\limits_ {q=1}^{p}\Big{(}g_{i,q}(t)-b_{i,q}\Big{)}u_{q}+\sum\limits_{q=1,q\
eq i}^{p}b_{ i,q}u_{q}\\ y_{i}=\xi_{i},\quad i\in\{1,2,..,p\}\end{array}\right. \tag{13}\]  
The generalized disturbance \(F_{i}^{\prime}\), including the coupling inputs, is given by:  
\[F_{i}^{\prime}=f_{i}(\xi,\eta,w)+\sum\limits_{q=1}^{p}\Big{(}g_{i,q}(t)-b_{i, q}\Big{)}u_{q}+\sum\limits_{q=1,q\
eq i}^{p}b_{i,q}u_{q} \tag{14}\]  
Finally, the nonlinear MIMO system can be expressed in the simplified model, given as:  
\[\left\{\begin{array}{c}\xi_{i}^{(\gamma_{i})}=F_{i}^{\prime}+b_{i,i}u_{i}\;, \\ y_{i}=\xi_{i},\;\;i\in\{1,2,..,p\}\end{array}\right. \tag{15a}\]  
Let \(\xi_{i,l}=\xi_{i}^{(l-1)}\) for \(i\in\{1,2,..,p\}\) and \(l\in\{1,2,..,p\}\). Additionally, let \(\xi_{i,\gamma_{i}+1}=F_{i}^{\prime}\Rightarrow\dot{\xi}_{i,\gamma_{i}+1}=\dot{ F_{i}^{\prime}}\). The subsystem (Equation (15a)) can be written as:  
Figure 3: The decoupled ADRC control scheme, where each ADRC block involves a SISO connection of a tracking differentiator (TD), nonlinear state error feedback (NLSEF), and an extended state observer (ESO), as in Figure 1[23].  
\[\left\{\begin{array}{c}\dot{\xi}_{i,1}=\xi_{i,2},\\ \dot{\xi}_{i,2}=\xi_{i,3},\\ \vdots\\ \dot{\xi}_{i,\gamma_{i}}=F^{\prime}_{i}+b_{i,i}u_{i},\\ \dot{\xi}_{i,\gamma_{i}+1}=F_{i},\;i\in\{1,2,..,p\}\end{array}\right. \tag{15b}\]  
Figure 4 illustrates the system given in Equation (15a) and considers the generalized disturbance \(F^{\prime}_{i}\) of Equation (14). Figure 5 shows the decentralized ADRC scheme for controlling the nonlinear MIMO system (Equation (15)). In this case, the control signals \(u_{i},\;i\in\{1,2,..,p\}\) were produced in the same way as in Equation (11), i.e.,  
\[u_{i}=v_{i}-\frac{\dot{\xi}_{i,\gamma_{i}+1}}{b_{i,i}},\;i\in\{1,2,..,p\} \tag{16}\]
## 5 Parallel TLBO Implementation on GPU  
As explained above, applying a series of modifications to the TLBO algorithm can achieve significant results in discrete combinatorial problems, in this case, in the TSP problem. Satisfying results are achieved compared to other algorithms [35].  
The changes implemented in the original TLBO cause the different stages to be more complex and add a higher computational cost to the algorithm. As a consequence, the iterations of the DTLBO are significantly more expensive than those of the original TLBO, and therefore the computation time is penalized. With the aim of minimizing the impact of these modifications and the extra cost of computing on the performance of the algorithm, a parallel implementation of the algorithm was developed using a CUDA architecture in a GPU environment. Although parallelization of an algorithm using CUDA is not always the best solution when applying parallelization techniques, the specific features of DTLBO can be exploited to provide a remarkable improvement in performance when compared to sequential solutions using this parallel architecture. Previous research has attempted improvement of metaheuristic methods applied to graph search problems by means of parallel implementations [36].  
The initial and fundamental step when proceeding with the algorithm parallelization consists in creating an adequate design of the memory structure and the execution flow to minimize the global thread blockages. If GPU memory is mismanaged, the impact in execution time can be detrimental because transference operations between the different memory levels within the GPU imply a high latency. Therefore, these transferences should be minimized.
# Simulation of Fidelity in Entanglement-Based Networks with Repeater Chains
## 3 Entanglement Generation and Distribution
### Entanglement Swapping  
In classical communications, the message is transmitted sequentially across nodes, needing to reach repeater \(k\) before being forwarded to repeater \(k+1\). Quantum communication utilizes entanglement's advantage to overcome this constraint. However, there is no analog to a classical repeater in quantum communications. Additionally, there are no commercial quantum repeaters available, and the experimental implementation is scarce. In this work, it has been opted to assume the existence of first-generation quantum repeaters, which act as switches, and a basic entanglement swapping protocol has been added. In this quantum network, all entangled pairs are generated simultaneously and transmitted across each of the quantum channels. Subsequently, Bell State Measurements (BSM) are executed on the photon received from the preceding node and the stored one for every quantum channel. Depending on the outcome of the measurement at each node, a specific quantum gate needs to be applied to the final qubit. This value is sent classically and applied in the last node. The complete protocol is referred to as entanglement swapping and ends up with the generation of a heralded Bell state \(|\Phi^{+}\rangle=(|00\rangle+|11\rangle/\sqrt{2}\) between two nodes not directly connected. In this process, two quantum memory positions are used. In this paper, entanglement swapping is supposed to be run sequentially across adjacent links, and we assume that multiple overlapped entanglement generation requests can exist at a given time in a QR. These requests can be served under two possible policies: oldest qubit first (QQF) or newest qubit first (NQF).