esa-sceva/satcom-chunk-collection huggingface dataset:
# Area-Efficient Universal Code Generator for Multi-GNSS Receivers
## 6 Conclusions  
In this paper, the structure of a universal code generator necessary for the development of multi-frequency receivers was proposed. By applying the hardwiring technique to the feedback polynomials of the existing individual code generators and the common elements of the initial values and output registers, the unnecessary use of multiplexers, XOR logics, and ROMs was reduced as much as possible. Using Xilinx FPGA, the structures of conventional individual code generators, the previous universal code generator [6], and the proposed hardware structure were implemented, and their hardware utilization were compared. Based on this experiment, the proposed LFSR-based universal code generator can save up to 37% of LUT and 78% of register compared to the conventional code generators, and 36% of LUT compared to the previous universal code generator. Therefore, the proposed hardware structure facilitates the implementation of multi-frequency receivers for high precision and high reliability.  
Conceptualization, H.Y.; methodology, M.K., J.P. and H.Y.; software, M.K.; validation, M.K. and H.Y.; formal analysis, J.P., G.J. and H.Y.; investigation, M.K. and G.J.; resources, M.K., J.P. and G.J.; data curation, M.K.; writing--original draft preparation, M.K.; writing--review and editing, J.P. and H.Y.; visualization, M.K., J.P. and G.J.; supervision, H.Y.; project administration, H.Y.; funding acquisition, H.Y. All authors have read and agreed to the published version of the manuscript.  
Figure 6: Hardware resource utilization comparison of the proposed multi-frequency code generator for each GNSS. (**a**) GPS. (**b**) BDS. (**c**) GLONASS. (**d**) Galileo.  
**Funding:** This research was funded by Navcours Co., Ltd.  
**Acknowledgments:** This work was supported by Navcours Co., Ltd. and National R&D Program through the National Research Foundation of Korea (NRF) funded by Ministry of Science and ICT (2020M3H2A1078119).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Effect of Controlled Defoliant Application on Cotton Fiber Quality
## 2 Materials and Methods
### Experimental Field, Cotton Cultivars, and Planting Schedule  
The experiment was carried out in EREC research fields at Blackville SC, USA and field trials was conducted in two fields at the research farm (Field 1: 33.347, \(-\)81.319; Field 2: 33.353, \(-\)81.310). Cotton has been planted in the experimental fields for many years. Delta pine cotton cultivars (DP 2038B3 XF and DP 2055) were planted in fields 1 and 2, respectively. In Field 1, cotton was planted in early May of 2022 in six rows for research treatment and two rows for control treatment and field was further divided into two plots. In Field 2, cotton was planted in late May of 2022. Similar to field 1, there were six rows for research treatment and two rows for control treatments, but field 2 has single plot and was smaller than Field 1. Thus, there were 16 and 8 cotton rows in Fields 1 and 2, respectively. The typical cotton row spacing in the research farm range was 97-114 cm. However, the research plot used in this research employed a skip row planting pattern to facilitate the movement of the autonomous sprayer. Thus, the actual row spacing for the research field was 193-229 cm, whereas, in the control plot, it was 97-114 cm. The management practices followed the South Carolina Cotton Grower's Guide [11].  
\begin{table}
\begin{tabular}{l c} \hline \hline  & **Parameter (UGA)** \\ \hline External dimensions (mm) & 990 \(\times\) 670 \(\times\) 390 \\ Internal dimensions (mm) & 296 \(\times\) 411 \(\times\) 155 \\ Weight (kg) & 50 \\ Max payload (kg) & 75 \\ Speed (m/s) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: UGV Parameters used in this work.  
\begin{table}
\begin{tabular}{l c} \hline \hline  & **Parameter (Spraying unit)** \\ \hline Nozzle types & Hollow conical nozzle \\ Nozzle number & six \\ Pressure (kpa) & 414 \\ Nozzle height from ground (cm) & 38, 84, and 145 \\ Flowrate & 0–100\% \\ Tank capacity (L) & 94 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Spraying unit parameters used in this work.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Product** & **Active Ingredient** & **Rate (per 38 L)** & **Remarks** \\ \hline Foke 6 EC & Tribufos 719 g/L & 454 g & Cotton defoliant \\ Free fall SC & Thidiazuron 42.4\% & 91 g & Cotton defoliant \\ Super boll & Ethephon 719 g/L & 907 g & Boll opener \\ \hline \hline \end{tabular}
\end{table}
Table 3: Information on chemicals used for the research.
# Finite Element Analysis of Thermal Stress and Thermal Deformation in Typical Part during SLM
## 2 Mobile Heat Source and Thermal-Mechanical Coupling Algorithm  
### Heat Source  
The laser source was a Gaussian heat source, with the maximum heat flux density that decays exponentially far away from the center, which can be described according to [17]  
\[q=q_{0}\exp(-\frac{2r^{2}}{\omega^{2}}) \tag{1}\]  
where \(q\) is the heat flux density (J/m\({}^{2}\cdot\)s); r is the radial distance from the beam center; \(\omega\) is the radius of the beam; and \(q_{0}\) is the intensity of the beam at \(r=0\):  
\[q_{0}=\frac{2 PA}{\pi\omega^{2}} \tag{2}\]  
\[q_{(r)}=\frac{2 AP}{\pi\omega^{2}}\exp(-\frac{2r^{2}}{\omega^{2}}) \tag{3}\]  
where \(P\) is the power of laser (W) and \(A\) is the absorptivity of the powder material, which can be calculated as 1 minus the reflectivity of the material and the transmissivity of light (usually, the absorptivity is considered 0.7 for 316L stainless steel).  
Next, the heat source model was given a picture, as shown in Figure 1a. The scanning paths of the heat source are drawn in Figure 1b, which is the moving path of the laser source by layer to layer in different directions. Each layer path changed by 30 degrees, which cycled from 0 to 180 (0, 30, 60, 90, 120, 0, 30, etc.).
# Data Sensing and Processing Tensioning System Based on the Internet of Things
## 3 Intelligent Tensioning System Design
### Pushing and Receiving  
The transport of structured packets depends on data-pushing and data-receiving services. SOCKET communication technology has the characteristics of small amount of data transmission, fast transmission and security. Therefore, it is used to push data from the job site to the remote cloud server. In practical application, many tensioning devices will be distributed in different beam fields. Each tensioning operation is random, and each operation is about 12 min. Only in tensioning operation, the sender will actively establish a connection channel with the receiver and carry out data transmission. The monitoring service in the data-receiving service is in the monitoring state at any time. When the request from the data-pushing end is monitored, it is identified in accordance with the rules in the transport rule base, and the rule-matching information is returned. When a condition in the transport rule base is satisfied, a transport channel is developed between the server and users. After receiving packets, the data-receiving service selects and rejects them in line with the rules. The exception-handling service at the pushing end deals with transport errors or network anomalies.
## 8 Performance Evaluation
### Computational Offloading and FL Characteristics
#### 8.3.1 Simulation Setup  
For the UAV setup, we formed a UAV swarm network, where 5 UAVs are kept as part of each swarm network. An edge server node is set up to communicate with the UAV controller, which is equipped with an Intel Core i7 processor, operating at 2.6 GHz and 32 GB RAM capacity running Ubuntu 21.04 LTS. Table 5 presents the simulation parameters for the UAV and FL setup. The task size \(b_{k}\) is varied as [0,200] MB. The flying altitude is set to 40m, and data input follows the uniform distribution expressed as follows.  
\[F(x)=\begin{cases}1/(q-p)&q\leq x\leq p\\ 0&\text{otherwise}\end{cases}\]  
For the FL learning setup at \(E_{ser}\), we install TensorFlow Federated (TFF) and import tensor collections. Next, we load the input collected data from UAVs and create two instances, one for _client_ID_ and one for _server_ID_. We use the weighted FedAvg algorithm with stochastic gradient descent (SGD) optimizer. The FedAvg algorithm updates the state value for processing iterations and sets up the variables _SERVER_STATE_ and _TRAINING_METRICS_. The client nodes download this data and run the local model for \(k\) iterations. With each iteration, the _CLIENT_STATE_ and _GRADIENT_STATE_ are optimized, which are communicated to _SERVER_STATE_.  
Figure 8: Benefits of deploying 6G-enabled UAV over conventional 4G and 5G channels for responsive 3 FFs management to tackle the fight against COVID-19 and future pandemics. **(a)** Improved UAV mobility of 6G over 5G services. **(b)** Radio loss and lifetime of 6G connections over 5G and 4G. **(c)** Impact on UE data rates of 6G over 4G and 5G.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of swarm networks & 2 \\ UAVs/Swarm & 5 \\ UAV Flying altitude & 40 m \\ Edge nodes & 2 \\ Transmission power & 0.2 W \\ Input data size & Uniform distribution \\ \(E_{cs}\) propagation delay & 25 ms \\ \(E_{cs}\) capability & 30 GHz \\ \hline \hline \end{tabular}
\end{table}
Table 5: FL and UAV parameters.
# On-Orbit Absolute Radiometric Calibration and Validation of ZY3-02 Satellite Multispectral Sensor
## 3 Methodology
### Synchronous Measurement of Surface Reflectance  
The reflectance measurements of the gray-scale permanent artificial targets and radiometric calibration tarps were collected within 30 min before and after the overpass time of the ZY3-02 satellite. The size of each gray-scale permanent artificial target is 48 m \(\times\) 48 m, and covers about 8 cross-track pixels and 8 along-track pixels of ZY3-02 imagery. The spectroradiometer was configured to average five spectra per sample, and 16 samples were collected within each single target. During the measurements, the direction of the probe is nearly straight down, and, considering the Lambertian of the target, the error could be neglected [20]. The radiometric calibration tarps were 60 m \(\times\) 60 m, covering about 10 cross-track pixels and 10 along-track pixels of ZY3-02 MUX imagery, five spectra per sample, and 25 samples were collected within each single tarp. The four gray-scale permanent artificial targets and six calibration tarps gave a total of 214 samples and more than 1000 spectra collected. It took more than 30 min to collect these data. The reflectance was measured by the SVC HR-1024i spectroradiometer (SVC Spectra Vista Corporation), which covered the wavelength range from 350 nm to 2500 nm. SVC HR-1024i is a high-resolution field portable spectroradiometer with a spectral resolution of 1.5 nm in the 350-1000 nm spectral range, 3.8 nm in the 1000-1890 nm spectral range, and 2.5 nm in the 1890-2500 nm spectral range. The output of the SVC software was interpolated at 1 nm intervals in the 350-2500 nm range. During spectra collection, five spectra were collected in each point and these five spectra were averaged to obtain the mean spectrum in this point. Figures 6 and 7 show the reflectance measurements from the targets and tarps in the range of 400-1000 nm.  
Figure 5: Flow chart of the reflectance-based radiometric calibration approach.
## 4 Results and Discussions
### Comparison of Group Effect Parameter with Design Codes  
In this section, the groups effect parameters averaged from numerically generated \(p\)-multipliers are compared and quantified with those provided in the design codes, which are American Association of State Highway and Transportation Officials AASHTO [33] and Federal Emergency Management Agency FEMA [34].  
Table 6 lists the recommended \(p\)-multipliers provided in AASHTO [33]. This design report presents \(p\)-multipliers obtained from free-head pile group tests. It should be noted that the effects of soil shear strength and pile head condition are not accounted for in the AASHTO recommendations [34]. The \(p\)-multipliers are provided for \(S/D\) = 3 and 5. No recommendation is provided for \(S/D\) greater than 5. The effect parameter for AASHTO [33] is calculated by averaging the \(p\)-multipliers for all rows of pile group and for \(S/D\) = 4; the group effect parameters are interpolated between \(S/D\) = 3 and 5. The equations suggested by [PERSON] et al. [45] for estimation of the \(p\)-multipliers were recommended in FEMA [34]. These equations were proposed on the basis of three full-scale tests, where 3 \(\times\) 3, 3 \(\times\) 4, and 3 \(\times\) 5 pile groups with \(S/D\) = 5.65, 4.4, and 3.3 were used. Soil type was stiff clay and free-head condition of pile groups were considered in all of the tests performed. The effect of soil type was not accounted for in FEMA [34].  
Figure 10 compares group effect parameters calculated numerically with those recommended in AASHTO [33] and FEMA [34] for both free- and fixed-head conditions. For \(S/D\) = 3 and 4, the group effect parameters of AASHTO [33] are lower than those of FEMA [34], whereas for \(S/D\) = 5 the trend is reversed. The calculated parameters for the free-head pile groups match well with those presented in AASHTO [33] and FEMA [34]. The results for \(\varphi\) = 30\({}^{\circ}\) produce the upper limit of the parameters for all \(S/D\) compared with those presented in AASHTO [33] and FEMA [34]. For \(S/D\) = 3, AASHTO [33] and FEMA [34] parameters are similar to the calculated values for \(\varphi\) = 40\({}^{\circ}\) and \(\varphi\) = 35\({}^{\circ}\), respectively. At higher \(S/D\)s, \(\varphi\) = 35\({}^{\circ}\) results are similar to the mean values of the parameters presented in AASHTO [33] and FEMA [34]. Both AASHTO [33] and FEMA [34] highly overestimate the parameters for fixed-head condition, and therefore should be used with caution.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Pile Spacing**} & \multicolumn{4}{c}{\(p\)**-Multiplier**} \\ \cline{2-5}  & **Leading Row** & **1 st Trailing Row** & **2 nd Trailing Row** & **Group Effect** \\  & & **Row** & **and Higher** & **Parameter** \\ \hline
3D & 0.8 & 0.4 & 0.3 & 0.5 \\
5D & 1 & 0.85 & 0.7 & 0.85 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The \(p\)-multipliers suggested in AASHTO [33].
# A Reverse Shortest Path Tree-Based Multicast Joining Node Selection Method
## 4 Simulation
### Delay Comparison  
The delay refers to the time from when DR receives the join request to when it calculates the MJN. We assume a propagation delay of 1 ms for each link. The delay of WAVE consists of four parts. The first part is the forwarding delay of the request from the DR to the root node of the multicast tree. The second part is the delay of the request spreading from the root node to the leaf node. The third part is the forwarding delay of the control message from the node on the multicast tree to the DR, and the fourth part is the delay in obtaining MJNs from the NMRS. RSPT, Near, and SPT only have a delay in obtaining MJNs from the NMRS. In our experiment, we place the NMRS on the central node.  
Figure 3: Control message number comparison.  
In the left part of Figure 4, we conducted experiments in topologies of various sizes to compare the delay of different algorithms. It can be seen that the delay of WAVE increases with the topology size. This is because the larger the topology, the larger the multicast tree, and the longer it takes for the DR to complete the detection of all nodes on the multicast tree. In a topology of 200 nodes, the DR needs to spend at least 20 ms to process one join request on average. In a topology of 2 million points, the DR needs to spend 1600 ms. Compared to WAVE, RSPT, SPT, and Near have almost no delay, only a delay in obtaining the MJN list from the NMRS, which is less than 10 ms.  
In the right part of Figure 4, we conducted experiments in the topology with 2 million points. It can be seen that the delay of WAVE increases with the size of the multicast group. This is because the larger the multicast group, the larger the multicast tree, and the longer it takes for the DR to complete the detection of the multicast tree. However, the delay of RSPT, SPT, and Near are not affected by the group size at all. This indicates that RSPT, SPT, and Near can be applied to scenarios with extremely large group members, while WAVE cannot.  
In short, RSPT is a low-latency method.
## 4 UAV Data Acquisition and Preprocessing
### Flight Control Strategy
#### 4.1.3 Flight Velocity  
The appropriate flight velocity is also essential in UAV imagery acquisition to avoid redundancy and motion blurring. It should be determined based on the degree of overlap between neighboring images and the consistency and quality stability of the aerial images. Typically, a minimum forward overlap of 75% and a minimum side overlap of 60% are recommended. Figure 5c illustrates how the flight velocity can be calculated based on the desired overlap degree and the sampling frequency of the neighboring frames, using Formula (3):  
\[v=L*(1-r)/t \tag{3}\]  
where \(v\) is the flight velocity (m/s) and \(t\) represents the shooting interval of two adjacent images (s), typically set to 2. The overlap degree (\(r\)) is defined as the forward overlap and is commonly taken as 50-75%, since UAVs are often operated at the same speed and uniform linear motion with the forward direction. \(L\) represents the ground truth length of the road in an image (m). \(L\) can be determined based on the _GSD_ and the road width (W) covered by the UAV imagery, using the following formula.  
\[L=W/GSD=(W*f)/(\mu*H) \tag{4}\]  
In our experiment, the road width (\(W\)) was chosen as 16.0 m and the GSD was computed as 0.4125 m/pixel using Equation (2), thereby, the ground-truth road length (\(L\)) in an image was calculated as 38.78 m using Equation (4). Given that the sampling frequency \(t\) was 2 s, the forward overlap of the captured images was set to 75%. According to the Equation (3), the minimum speed \(v\) was 4.85 m/s, and finally, 5 m/s was determined as the flight velocity for this experiment.
## 3 Results
### Chemical Oxygen Demand (COD)  
Chemical oxygen demand (COD) is equivalent to the amount of oxygen required to chemically oxidize the organic matter content in a water sample. The COD of the DCF solution before and after EB irradiation with and without nanobubbling was measured (Figure 11). The DCF decomposition was markedly influenced by the dose up to 3 kGy. In comparison with the control non-treated sample (154 mg O2/L), the COD value increased after EB irradiation at 3 kGy (461 mg O2/L) and after nanobubbling and EB irradiation in the batch system (307 mg O2/L). On the other hand, the nanobubbling + EB irradiation process in the flow system markedly increased the COD value (1382 mg O2/L). These results are not in agreement with those obtained by [PERSON] et al. [21], which described a COD removal efficiency of 18% at 2 kGy, or by [PERSON] at al. [18], who reported that the COD decreased  
Figure 10: DCF degradation in aqueous solution in flow system under nanobubbling (Nano), electron-beam radiation (EB), and EB with nanobubbling (Nano + EB) ([DCF]0 = 125 mg/L).  
Figure 9: Possible bond cleavage of DCF under EB irradiation.  
by 20% at 20 kGy. The higher values of COD for these samples could be attributed to the increase in oxidable organic matter in the samples, corroborating the obtained results for TC (Figure 8). The presence of filamentous fungi detected in the samples of the nanobubbling and EB irradiation process might suggest a high biodegradability of this solution.
# Digital Twin-Based Automated Guided Vehicle Scheduling: A Solution for Its Charging Problems
###### Abstract  
Due to poor predictability of resources and difficulty in perception of task execution status, traditional Automatic Guide Vehicle (AGV) scheduling systems need a lot of extra time in the charging process. To solve this problem, a digital twin-based dynamic AGV scheduling (DTDAS) method is proposed, including four functions, namely the knowledge support system, the scheduling model, the scheduling optimization, and the scheduling simulation. With the features of virtual reality data interaction, symbiosis, and fusion from the digital twin technology, the proposed DTDAS method can solve the AGV charging problem in the AGV scheduling system, effectively improving the operating efficiency of the workshop. An AGV scheduling process in a discrete manufacturing workshop is taken as a case study to verify the effectiveness of the proposed method. The results show that, compared with the traditional AGV scheduling method, the DTDAS method proposed in this article can reduce makespan 10.7% and reduce energy consumption by 1.32%.  
digital twin; AGV scheduling; dynamic scheduling; charging scheduling +
Footnote †: journal:  
0000-0001-9993-3499][PERSON]  
1 State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, China; [EMAIL_ADDRESS] (W.H.); [EMAIL_ADDRESS] (B.L.); [EMAIL_ADDRESS] (Z.Z.); [EMAIL_ADDRESS] (X.M.)
## 3 Edge-Based Finite Element Analysis  
In this study, we used the edge-based FE method with quadratic elements, as discussed in the previous section. We used the edge shape function \(N_{i}\) to approximate the secondary electric field within an edge element:  
\[E_{s}=\sum_{i}^{N_{\rm edge}}N_{i}E_{s,j} \tag{9}\]  
where \(N_{\rm edge}\) denotes the total number of edges in the edge element. For the first-order edge element, the \(N_{\rm edge}=12\); for conventional serendipity and Lagrange-type second-order hexahedral elements, \(N_{\rm edge}=36\) and \(54\), respectively.  
By substituting (9) into (7) and applying the [PERSON] method, we can determine that the weak form of the original differential equation is:  
\[R_{i}=\int_{\Omega}N_{i}\big{[}\
abla\times\
abla\times E_{s}-i\omega\mu_{0} \sigma\vec{E}_{s}-i\omega\mu_{0}\Delta\sigma\vec{E}_{\rm P}\big{]}\mathrm{d}v \tag{10}\]  
where \(\Omega\) denotes the modeling area.  
Applying first vector [PERSON]'s theorem to Equation (10), the secondary electric field is given, and it is continuous across the inner boundary; the surface term in Equation (10) vanishes. Then, the discretized form of Equation (10) for each element can be expressed as follows:  
\[R_{i}^{\rm Ne}=\sum_{1}^{N_{\rm e}}\Big{[}K^{e}E_{\rm s}^{e}-i\omega\mu_{0}M_ {1}^{e}E_{\rm s}^{e}-i\omega\mu_{0}M_{2}^{e}E_{\rm P}^{e}\Big{]} \tag{11}\]  
where \(K^{e}\) and \(M_{1}^{e}\) and \(M_{2}^{e}\) are the local stiffness matrices defined as follows:  
\[K_{i,j}^{e}=\int_{\Omega_{e}}(\
abla\times N_{i}^{e})\cdot\Big{(}\
abla\times N _{j}^{e}\Big{)}\mathrm{d}v \tag{12}\]  
\[M_{1,j}^{e}=\int_{\Omega_{\rm e}}N_{i}^{e}\cdot\sigma\cdot N_{j}^{e}\mathrm{d}v \tag{13}\]  
\[M_{2,i,j}^{e}=\int_{\Omega_{\rm e}}N_{i}^{e}\cdot\Delta\sigma\cdot N_{j}^{e} \mathrm{d}v \tag{14}\]  
where \(\Omega_{\rm e}\) denotes the domain of an element.  
By assembling a global matrix with local matrices, a linear equation for the elements is obtained. The equation is typically sparse:  
\[A\vec{e}=\vec{b}, \tag{15}\]  
Here \(A\) denotes the global stiffness matrix, and \(\vec{b}\) denotes the RHSs of the equations.  
To solve Equation (14), a proper condition should be added to the equation. For simplicity, let us consider the homogeneous Dirichlet boundary condition:  
\[\left.\mathbf{e}\right|_{\Gamma}=0 \tag{16}\]  
Here, \(\Gamma\) is the boundary area.  
IDR(s) iterative solvers with an ILU preconditioner were used to solve the linear equations in Equation (15).