esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Collaborative Distributed Scheduling Approach  
### Overview of CDSAs  
There are two separate stages for the family of CDSAs. These stages are: data delivery (DD) stage and scheduling update (SU) stage. At the DD stage, the main tasks of nodes are elaborated as follows:  
1. All nodes have the same awakening time at a fixed time interval. We focus on a series of WSN applications collecting information periodically and simultaneously. Therefore, all nodes have the same awaking time at a fixed time interval, and transmit the data to a sink node through multi-hop communication afterwards.
2. All nodes simultaneously generate their original fix-sized data packet at a regular interval.  
Figure 1: Work cycle of a node.  
Figure 2: System running process.  
3. Each node decides independently whether it should transmit its data packet according to its scheduling probability. If permitted, it will send out its data packets to the next-hop. Otherwise, it will change to Idle state. If there is no other data packets received when the node is in Idle state, the node will begin a new scheduling process after a while.
4. If the sender does not receive the ACK frame in a specific time period, it must re-transmit the data packet according to its scheduling probability.
5. After a node has sent out all data packets in its FIFO queue, it will decide whether it goes to sleep or not according to its scheduling probability.
6. The nodes, being in Sleep state, will wake up at the next wake-up time. For the nodes that are still in active phase will continue its operation when the next work cycle starts.
7. The MAC protocol implements the transmission and reception processes underneath.
8. Each node sums up the number of data packets that have been successfully transmitted, the number of packets transmitted, the total transmission time and the total active time. These four parameters are exchanged at the SU stage.  
At the SU stage, some of the recorded statistical variables are exchanged among adjacent nodes to help determine the scheduling matrix (SM) of each node. Then the SM is used to schedule node states at the next DD stage. The DD stage and the SU stage compose a round of the CDSAs. And the system will be running rounds of CDSAs. According to the different scheduling update mechanism, we put forward the following two CDSAs to collaboratively calculate the SM.
## 4 Experiments
### Measurement of Wavenumber Compensation Factor k\({}^{\prime}\)  
The ultrasonic velocity was measured for each specimen to determine the wavenumber of the longitudinal, transverse, and surface acoustic waves required to calculate the wavenumber compensation factor. The velocities of the longitudinal and transverse waves were obtained by measuring the time-of-flight (TOF) between the back-wall echo signals.  
Pulser-receiver (Olympus 5077 PR, Tokyo, Japan) and PZT transducers with main-resonance frequencies of 5.0 MHz for longitudinal waves and 2.25 MHz for transverse waves were used in the experiment. Figure 4 shows the experimental setup for wave velocity measurement. The TOF between the echo signals was measured using the auto-correlation of the received signal [21]. By using the measured TOF and thickness of the specimen, the velocities of the longitudinal and transverse waves were calculated for each specimen.  
The surface wave velocity was measured using the same apparatus shown in Figure 2. Velocity of the surface acoustic wave is measured by non-contact method using a laser. The velocity was obtained by multiplying the fundamental frequency of the received signal by the wavelength given in the line array interval [17].  
The measured longitudinal, transverse, and surface acoustic wave velocities are shown in Table 2. The \(F\) values of each material were calculated using Equation (2), and the wavenumber compensation factor \(k^{\prime}\) was obtained using Equation (7).
# DTN Trustworthiness for Permafrost Telemetry IoT Network
## 3 Remote Sensor Network Architecture
Figure 4: Network architecture of the remote WSN providing the IoT telemetry service. Figure 5: Antarctic IoT network protocol stack. In the access network, LoRa uses a reduced protocol stack, thus avoiding layers 3 to 6 of the OSI model. The application data is directly encapsulated into the LoRa data link layer. Once data arrives at the NVIS node, the protocol stack introduces all the OSI model layers and adds the DTN layer below the application layer. The DTN layer needs a convergence layer to adapt to the transport protocol below. Figure 4 shows the EAATP as the transport protocol in the backbone network, although we test diverse transport protocols in our simulations, as discussed in Section 5. Finally, when the data arrives at the last NVIS node and must be forwarded through the Internet, the DTN and convergence layers are removed. The common, well-known TCP/IP model is used, given that end-to-end connectivity at this zone is assumed.
###### Abstract  
Modern aquaculture utilizes computer vision technology to analyze underwater images of fish, contributing to optimized water quality and improved production efficiency. The purpose of this study is to efficiently perform underwater fish detection and tracking using multi-object tracking (MOT) technology. To achieve this, the FairMOT model was employed to simultaneously implement pixel-level object detection and re-identification (Re-ID) functions, comparing two backbone models: FairMOT+YOLOv5s and FairMOT+DLA-34. The study constructed a dataset targeting the popular black porpy in Korean aquaculture, using underwater video data from five different environments collected from the internet. During the training process, the FairMOT+YOLOv5s model rapidly reduced train loss and demonstrated stable performance. The FairMOT+DLA-34 model showed better results in ID tracking performance, with an accuracy of 44.1%, an IDF1 of 11.0%, an MOTP of 0.393, and an IDSW of 1. In contrast, the FairMOT+YOLOv5s model recorded an accuracy of 43.8%, an IDF1 of 14.6%, an MOTP of 0.400, and an IDSW of 10. The results of this study indicate that the FairMOT+YOLOv5s model demonstrated higher IDF1 and MOTP scores compared to the FairMOT+DLA-34 model, while the FairMOT+DLA-34 model showed superior performance in ID tracking accuracy and had fewer ID switches.  
multi-object tracking (MOT); computer vision in aquaculture; FairMOT model; YOLOv5s; DLA-34
# Solar Irradiance Forecasting with Transformer Model
## 3 Results
#### Optimal Hyperparameters  
The optimization of the model hyperparameters was performed through the W&B Sweep environment. Tuning of the hyperparameters was aimed at minimizing the validation mean squared error, i.e., the most optimal model settings in terms of minimizing this metric were sought. The resulting model (see Table 2) contained large numbers of both encoder and decoder blocks due to the amount of training data producing the smallest overfit. This advantageous result could also be due to the appropriate setup and use of the dropout technique as well as residual connection [33; 34].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Paper** & **Location** & **MAPE (\%)** \\ \hline
[29] & Abha (Saudi Arabia) & 11.8 \\
[30] & Sirt (Turkey) & 6.78 \\
[31] & Mugla (Turkey) & 6.73 \\
[32] & Cyprus and USA & 4.7 \\
[9] & Mumbai (India) & 4.24 \\ This paper & Chennai Metropolitan Area (India) & **3.45** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean absolute percentage error results.  
Figure 12: Example of 0 th patch 7-day-ahead prediction.
# Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning
## 3 Results
### Agram Tree Detection Results  
Previous experiments (Section 3.1) helped reduce errors and map the organ areas as accurately as possible. Figure 9 shows the resulting maps of the organ zones, one for the year 2017/2018 and another for 2022/2023. Both maps efficiently show the presence of organ trees. The clear differences observed visually confirm that changes have occurred in the study area between the two periods. Figure 10 further validates the quality of the results, showing their strong alignment with ground truth data while also acknowledging the presence of some errors.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data** & **Life Cycle** & \begin{tabular}{c} **Overall** \\ **Accuracy** \\ \end{tabular} & \
\begin{tabular}{c} **Argan** \\ **Precision** \\ \end{tabular} &
\begin{tabular}{c} **Non-Argan** \\ **Precision** \\ \end{tabular} & **Kappa** \\ \hline Combination (a). (10-m bands) & 2017/2018 & 0.978 & 0.96 & 1.00 & 0.957 \\ \cline{2-6}  & 2022/2023 & 0.986 & 0.97 & 1.00 & 0.971 \\ \hline Combination (b). (10- and 20-m bands) & 2017/2018 & 0.978 & 0.96 & 1.00 & 0.957 \\ \cline{2-6}  & 2022/2023 & 0.986 & 0.97 & 1.00 & 0.971 \\ \hline Combination (c). (All spectral bands) & 2017/2018 & 0.957 & 0.94 & 0.97 & 0.913 \\ \cline{2-6}  & 2022/2023 & 0.978 & 0.97 & 0.99 & 0.957 \\ \hline Combination (d) & 2017/2018 & 0.971 & 0.95 & 1.00 & 0.942 \\ \cline{2-6} (B1, B2, B3, B4, B6, B8, ARVI, NDVI, PVI, and SAVI) & 2022/2023 & 0.971 & 0.96 & 0.99 & 0.942 \\ \hline
**Combination (e).** (All spectral bands and indices) & **2017/2018** & **0.986** & **0.97** & **1.00** & **0.971** \\ \cline{2-6}  & **2022/2023** & **0.993** & **0.99** & **1.00** & **0.986** \\ \hline \hline \end{tabular}
\end{table}
Table 11: Performance of multi-spectral data combinations (Test data: tabular).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **Data** & \
\begin{tabular}{c} **Overall** \\ **Accuracy** \\ \end{tabular} & **Argan Precision** &
\begin{tabular}{c} **Non-Argan** \\ **Precision** \\ \end{tabular} & **Kappa** \\ \hline a & Single-date (2022) & 0.823 & 0.43 & **0.88** & **0.759** \\ b & Single-date (2017 and 2022) & 0.813 & 0.47 & 0.86 & 0.745 \\
**c** & **Multi-temporal (2022)** & **0.848** & **0.65** & 0.87 & 0.739 \\ d & Multi-temporal (2017 and 2022) & 0.842 & 0.55 & **0.88** & 0.752 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Performance of multi-temporal data combinations (Test data: reference image).
# Next-Generation Dual Transceiver FSO Communication System for High-Speed Trains in Neon Smart City
## 5 Performance Analysis
### Ln Ber
By dividing the total length of the 170 km line track by the determined coverage distances, we can accurately calculate the number of required BSs for each model. This approach ensures a systematic assessment of the deployment requirements, offering valuable insights into the optimal configuration of the G2T communication systems. As an example, let us consider LN RIS model as outlined in Table 2:  
At a BER of \(10^{-9}\), Figure 6 indicates a received SNR of 33 dB. Consulting Figure 4a reveals that the coverage distance at 33 dB is 1000 m. With our consideration of a 170 km line track, the number of required BSs results in 170 BSs. The results clearly demonstrate the advantages of the dual transceiver FSO system in terms of coverage distance and infrastructure efficiency in railway settings, leading to a significant reduction of up to 76% and 99% in the number of required BSs in the LN channel besides 98% and 99% in the GG channel compared to RIS-assisted FSO and relay-assisted FSO systems, respectively. The expansion of the coverage area not only minimizes handover processes but also has the potential to decrease CAPEX costs and required infrastructure, as fewer BSs are required to be deployed along the rail line in the city.
# Path Planning of Rail-Mounted Logistics Robots Based on the Improved Dijkstra Algorithm
## 2 Materials and Methods
### UWB Positioning  
In the UWB positioning part, the signal arrival time difference algorithm (TDOA) is used to complete the distance calculation, and the Chan optimization algorithm is introduced to optimize the location of TDOA positioning.
## 5 Results and Analysis
### Simulation Results
#### 5.2.2 Algorithm Performance Comparison  
Next, the experiment will verify the performance of IF-DDPG. The experimental environment consists of 4 edge servers and 4 terminal devices, where the number of available CPU cores for each terminal device is \(K=4\). To verify the performance of IF-DDPG, four algorithms are compared experimentally: DQN, AC, DDQN, and DDPG. Figure 6 shows the convergence performance of IF-DDPG compared with the four other algorithms in terms of reward values after 1500 iterations. From Figure 6, it can be observed that after the convergence of five algorithms, the average reward of IF-DDPG is notably greater than that of the DDPG, the AC, the DQN, and the DDQN. Additionally, the IF-DDPG exhibits lower fluctuations in the later stages of the curve. IF-DDPG's offloading strategy and resource allocation method exhibit clearer advantages over the four comparison algorithms. In addition, as shown in Figure 6, IF-DDPG exhibits a larger curve growth rate and faster convergence speed. The IF-DDPG allows multiple DDPGs to learn from each other through the FL. By sharing their deep learning network parameters, it achieves higher convergence performance and learning efficiency.  
After the 1500 iterations, Figure 7 demonstrates the energy consumption optimization performance of IF-DDPG in comparison to the 4 other algorithms. From the figure, it can be observed that after the convergence of the five algorithms, using IF-DDPG to optimize energy consumption results in lower average and minimum energy consumption compared with the comparison algorithms. Additionally, the convergence speed of the curve of IF-DDPG is faster. Compared with the traditional DDPG, the IF-DDPG optimized minimum energy consumption reduced by 15.2%. Compared with the DDQN, it reduced by 31.7%. Compared with the DQN, it reduced by 38.7%. Finally, compared with the AC algorithm, it reduced by 50.5%.  
Figure 6: Comparison of algorithms convergence.  
Table 3 shows the comparison between IF-DDPG and the exhaustion search method for optimizing the minimum value of the total energy consumption of the system. To reduce the complexity of the exhaustion search method, environment simulation validation was performed using two edge servers and four terminal devices. From Table 3, it can be observed that the minimum energy consumption of the exhaustive search method is less than that of IF-DDPG, but the computation time required to obtain the minimum energy consumption using the exhaustive search method is much greater than that of IF-DDPG. The enormous computational time consumption is unacceptable in practical intelligent factory environments. Therefore, although IF-DDPG is slightly inferior to the exhaustive search method in terms of performance, it is easier to implement in practical applications, and thus has greater practical value.
# Horizontal Loading Performance of Offshore Wind Turbine Pile Foundation Based on DPP-BOTDA
## 2 Field Experiment
### Engineering Profile  
The test platform is located on the Y14 wind turbine which belongs to the Xinghua Bay Wind Farm in Fujian, China. As shown in Figure 2, the Y14 wind turbine is a DEW-G5000 produced by Dongfang Electric Corporation, China. This wind turbine has a rated power of 5000 kW, a hub height of 90 m, a rotor diameter of 140 m, and a total weight of 733.5 tons. The foundation of a Y14 wind turbine is composed of four pile foundations. In order to obtain the bearing capacity of the pile foundation, a test pile was made next to the Y14 platform, and the design and construction process is the same as the Y14 platform pile.  
Figure 1: Experiment setup of the differential pulse pair Brillouin optical time-domain analysis (DPP-BOTDA). EOM, electro-optic modulator; MG, microwave generator; FBG, fiber Bragg grating; PS, polarization scrambler; OI, optical isolator; AFG, arbitrary function generator; EDFA, erbium-doped fiber amplifier; PD, photodetector; DAQ, data acquisition.  
The test platform is located on the Y14 wind turbine. The entire pile is a concrete-filled steel pipe composite pile. Above the elevation of \(-30.5\) m, the test pile is a steel pipe. At the elevation of \(-30.5\) m, the test pile is filled with reinforced concrete. The length of the concrete-filled steel tube section is \(14.5\) m. At the elevation below \(-45\) m, the pile is only reinforced concrete. The material of this steel pipe is Q345, and the dimensions are: length \(55.5\) m, diameter \(2.0\) m, and wall thickness \(27.0\) mm. The geological survey data are shown in Figure 3. The test platform is located at the elevation \(+8.0\) m, and the horizontal loading point is located at the elevation \(+9.2\) m.  
Figure 3: Test pile and geological survey data at different elevations.  
Figure 2: DEW-G5000 wind turbine in position Y14.
## 5 Conclusions  
Most commercially available terpenoids are obtained from higher plants, but their cost-effective and sustainable production for industrial purposes is still challenging because of their low abundance production from natural sources, extraction and purification obstacles, and degree of labor intensity. In this study, we have redesigned and codon-optimized the MYB36 TFs by using the advanced synthetic biotechnology approaches and heterologous overexpressed in _C. reinhardtii_ CC124 in order to analyze its potential role in triterpen biosynthesis for the first time. We have found that _MYB36_ TFs positively affect the MEP biosynthetic pathway encoding genes in MYB36-overexpressed lines compared to the wild type of _C. reinhardtii_ strain CC124. The first precursor of triterpenoids is squalene. Our results showed that the transcript level of _CrSQS_ involved in squalene synthesis increased, and ultimately led to an increase in the contents of squalene in MYB36-overexpressed lines of _C. reinhardtii_. So, _MYB36_ TFs also trigger the accumulation of the downstream pathway of triterpenoid biosynthesis genes. Our study provides new insights into the heterologous overexpression system of transcriptional factors in microalgae to modified and metabolic engineering in triterpen biosynthesis.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/pr12030487/s1](https://www.mdpi.com/article/10.3390/pr12030487/s1)]([https://www.mdpi.com/article/10.3390/pr12030487/s1](https://www.mdpi.com/article/10.3390/pr12030487/s1)), Figure S1. Concentration of Squalene in WT type (CC124) and MYB36 Overexpression lines (MYB36-OE1 and OE2).  
**Author Contributions:** Conceptualization, Z.H.; Methodology, M.A.; Software, M.A.; Validation, Z.H.; Formal analysis, M.A., J.W., J.L. and M.M.A.; Investigation, J.L.; Data curation, M.A.; Writing--original draft, M.A. and Z.H.; Writing--review & editing, M.A.; Project administration, Z.H.; Funding acquisition, Z.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Chinese National Key R&D Project for Synthetic Biology (2018 FA0902500). National Science Foundation of China (32273118), The Guangdong Key R&D Project (2022B1111070005). Shenzhen Special Fund for Sustainable Development (KCXFZ20211102164013021) and Shenzhen University 2035 Program for Excellent Research (2022B010) to Z.H.  
Data Availability Statement: Data will be available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 2 System Model
### Passengers Future Location Estimation  
Let the association of users \(u\) to their SCs according to the geographic locations at time instant \(k\) be \(\mathbb{U}\in M_{k}=(x_{k},y_{k})\) and the predicted cell HO tuple for each user mobility be \((\mathbb{M}_{u},\mathbb{T}_{u})\). We use this information to establish users' future cell associations in next time intervals \(k+\bar{k}\) as \(M_{k+\bar{k}}\). Taking references from [14; 23] that the nodes (passengers in our case) usually move around in such a way that daily passengers move to complete their routine tasks with fairly regular landmarks and tourists for well-visited landmarks, we utilise their mobility logs to estimate: (i) most probable landmarks of daily commuters; and (ii) visited landmarks by non-frequent travellers in each SC. By harnessing mobility information, trajectories from current location to the most predicted locations would be estimated by cell sojourn HO time \(\mathbb{T}_{HO}\) and multiple distance metrics mentioned in the section above. Let the coordinates of the most probable landmark for users \(u\) mobility in the next cell \(\mathbb{M}_{u}\) be \(\mathbb{U}\in M_{\mathbb{M}_{u}}=(x_{\mathbb{M}_{u}}^{lm},y_{\mathbb{M}_{u}}^{ lm})\) and the unit vector \(\widehat{v}\) based on current coordinates in the direction of \((x_{\mathbb{M}_{u}}^{lm},y_{\mathbb{M}_{u}}^{lm})\) is given as:  
\[\widehat{v}=\mathbb{U}\in\left(\frac{\big{[}M_{\mathbb{M}_{u}}-M_{\bar{k}} \big{]}}{||\big{(}M_{\mathbb{M}_{u}}-M_{\bar{k}}\big{)}||}\right)\,, \tag{3}\]  
where \(||.||\) is the _Mahalanobis_ norm operator. The future coordinates at time interval \(M_{\bar{k}+\bar{k}}\) can be estimated as:  
\[M_{\bar{k}+\bar{k}}=M_{k}+\int^{K}\!\left[\frac{\sqrt{\big{(}x_{\mathbb{M}_{ \mathbb{M}_{u}}}^{lm}-x_{k}\big{)}^{2}-\big{(}y_{\mathbb{M}_{u}}^{lm}-y_{k} \big{)}^{2}}}{T_{HO}}\right]*\widehat{v}\ \ d\bar{k}\, \tag{4}\]  
The pseudo-code for the users mobility prediction in terms of future location estimations is given in Algorithm 1.  
```
Input :Passengers current location parameters \(M_{k}\), \(\mathbb{M}_{u}\), \(\mathbb{T}_{u}\), \(M_{\mathbb{M}_{u}}\), \(SojournTime_{max}\) Output :Passengers next move mobility prediction \(M_{k+\bar{k}}\)
1 for\(u\in\mathbb{U}\)do
2 if\(u(Sojourn-time)\geq\ max(Sojourn-time)\ OR\ no(Training-samples)\) i.e., \(M_{\mathbb{M}_{u}}=\varnothing\)then
3 Initialise \(M_{k+\bar{k}}=M_{\bar{k}}\);
4 else
5 Execute Equation (4)
6 end if
7
8 end for
```  
**Algorithm 1**Deep channel residual learning
### Contribution and Structure of the Paper  
This paper analyzes two FFC concepts for IIDGs based on voltage characteristics for positive and negative sequence components, as required in [2]. The standard, i.e., current-based concept adjusts the magnitude and phase of the line currents injected by an IIDG. The proposed concept adjusts FFC through active and reactive power employing the same voltage characteristics, which is the first contribution of this paper. Both FFC concepts, i.e., current- and power-based, are formulated in the phasor-domain, which is the second contribution of this paper. Thus, a simple IIDG model is obtained that complies with grid codes and is suitable for protection studies in large networks; moreover, BFFC and UFFC are both possible. The gap between complicated instantaneous-based FFC calculation approaches and the simplified constant current approach is filled in this way. Furthermore, several protection studies have already reported the impact of IIDGs on protection operation in distribution networks and microgrids, focusing on overcurrent protection coordination. However, a comprehensive analysis of the operation of transmission-network protection impacted by IIDGs is missing. Analysis of IIDG responses and distance protection operation during transmission-network faults is another contribution of this paper.  
The rest of this paper is organized as follows. Section 2 proposes the phasor-based IIDG model and describes the discussed FFC concepts based on reference current and power. The test network is presented in Section 3, including relevant data, topologies, and the different SSC of an aggregated network supply. The active and reactive power of all the IIDGs during the network faults are analyzed, together with the voltages at the connection point and distance protection operation. Section 4 presents a case study, i.e., a transmission network related to the 110-kV northeastern part of the Slovenian power system, considering the expected IIDG's capacities. The power generated by IIDGs, voltage profiles, and distance protection operation are analyzed for different topologies during the faults at different locations. Section 5 raises a discussion on IIDG models for FFC injection, and indicates future work on protection studies, while Section 6 concludes the paper.