esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
It was included in the study to investigate the possibility of using it with ERA5 hourly fractional cloud cover data [43] in estimating clear sky irradiance. The McClear clear sky model was used as a reference because there were no observed GHI data to be used to validate the clear sky models in the study area. Among alternative data sets, the McClear clear sky model was chosen because it performed very well when compared to GHI observation data, for example [11; 38; 44; 45], and it was already preconfigured. ERA5 hourly fractional cloud cover data [43] were used to identify clear-sky days in the study area because a recent study by [PERSON] et al. [45] in South Africa confirmed that it can identify cloudy and clear-sky days. Comparing hourly model outputs to the McClear clear sky model outputs related to GHI would therefore establish the overall best method. To assess the capabilities of the clear sky models, quantitative statistical measures, namely relative mean bias error (rMBE), relative root mean square error (rRMSE), relative mean absolute error (rMAE), and the coefficient of determination (R\({}^{2}\)) were used.
# An FPGA Based Energy Efficient DS-SLAM Accelerator for Mobile Robots in Dynamic Environment
## 4 Proposed Method
### Accelerator Architecture
The input and output memory are used alternately and the output memory of the current operation will become the input memory of the next operation. The data flow is shown in Figure 5b. The 10 mode pipeline is a combination of convolution and unpooling, shown in Figure 5c. For the unpooling operation, the input not only has the calculation results of the previous kernel but also has the pooling indices with location information. Pooling indices need to be read from the off-chip DRAM to on-chip local buffer, which will cause unnecessary delay. Therefore, we prepare the required pooling indices in advance through data pre-reading. When the convolution results are transmitted to the unpooling kernel with the help of the channel, the operation can be executed directly without waiting time. This design method is beneficial for the encoder-decoder network, with a connection between the front and back layers. As shown in Figure 5d, the 11 mode pipeline performs all of the kernels, including convolution, pooling, and unpooling operations. The pooling indices required by unpooling are directly obtained from the last kernel through the channel without accessing off-chip DRAM. Figure 4: The architecture of the SegNet accelerator. Figure 5: Four pipeline modes with configurable functions. (**a**) Mode 00: convolution; (**b**) Mode 01: convolution + pooling; (**c**) Mode 10: convolution + unpooling; (**d**) Mode 11: convolution + pooling + unpooling. Each pipeline mode completes an iteration of \"data reading in, computing, and writing back.\" Then multiple iterations are combined in an orderly way to realize the whole network. For example, to implement SegNet, the combination is in sequence of \"00-01-00-00-01-00-00-01-00-00-01-00-00-11-00-00-10-00-00-10-00-00-10-00-10-00-00\". The benefit of this design approach is that using multiple pipeline loops in different patterns allows us to implement different network frameworks flexibly. This kind of function configurable pipeline architecture has a certain expansibility. By designing and replacing different kernels, we can flexibly implement other convolutional neural networks. In the process of implementing other neural networks, we should evaluate the boards' resources in advance and focus on the use of local memory. We must pay attention to the kernel design and follow the coding rules of OpenCL, so as to improve the efficiency of the hardware implementation.
## 4 Analytical Model: Signal-to-Crosstalk Ratio
### Signal-to-Crosstalk Ratio Model
The wanted-signal is weighted in the expression by \(l_{MZI}{}^{num_{MZI}}|_{X}{}^{num_{X}}\), which corresponds to the worst path, while the cumulative crosstalk powers are weighted by \(l_{MZI}{}^{num_{MZI}}|_{X}{}^{M_{X}}\). \(M_{X}\) is the average of the number of waveguide crossings in the worst and the best paths. We add a hat operator on SCR to highlight an approximation. \(N\) is the size of the topology. The equation can be simplified as follows:  
\[S\hat{\mathcal{C}}R=\ 10 log\Bigg{(}\frac{l_{X}{}^{num_{X}-M_{X}}}{K_{MZI}(N,m) \ +K_{X}(N,x)}\Bigg{)} \tag{8}\]  
The loss factor \(l_{MZI}{}^{num_{MZI}}\) is canceled out as a common factor in the numerator and denominator. In the example of \(4\times 4\)-Benes: \(K_{MZI}(4,m)=3m+3m^{2}\), \(K_{X}(4,x)=2x\) and \(M_{X}=\frac{num_{X}}{2}=1\) because the number of waveguide crossings equals two in the worst path and zero in the best path, hence,  
\[S\hat{\mathcal{C}}R=\ 10 log\Bigg{(}\frac{l_{X}}{3m\ +3m^{2}+2x}\Bigg{)} \tag{9}\]  
It is important to note that Equation (9) is equivalent to Equation (6), considering that the factor \(l_{X}\) is very close to 1 (waveguide crossing losses varying between 0.03 dB and 0.21 dB in the literature). We should keep in mind that our model is based on two main assumptions. The first one is that all crosstalk powers pass through the same number of MZIs, which is always the case in the multi-stage architectures studied. The second one is that all crosstalk powers also pass through the same number of waveguide crossings (=\(M_{X}\)), which enables us to keep the calculus simple. The latter assumption could decrease the accuracy of our model, especially when crossing losses are significant. However, we will see in Appendix A that our model remains valid even with relatively high crossing losses, 0.2 dB. Now, our general analytical model is completely defined by Equation (8), we can then apply it to Benes, dilated Benes, switch and select, and double-layer network. To do so, the \(num_{X}-M_{X}\) exponent that we need for each architecture can be easily calculated from Table 1. All that remains is to compute the terms \(K_{MZI}(N,m)\) and \(K_{X}(N,x)\), what we are going to do in the next subsection. For simplification purposes, the terms \(K_{MZI}(N,m)\) and \(K_{X}(N,x)\) will be simply called cumulative crosstalk powers generated by MZIs and waveguide crossings, respectively.
## 4 Experimental Results and Discussion  
This section describes the experiments using three public datasets: Cora [18], Citeseer [18] (Section 4.2), and PubMed [19]. The Cora [18] dataset is composed of 2708 scientific publications, and its citation network consists of 5429 links. Each publication in the dataset is represented by a binary word vector with 0/1 values, indicating the absence/presence of the corresponding word in a dictionary. The dictionary consists of 1433 unique words, meaning that each publication is characterized by 1433 features, each represented by a 0/1 value. The Citeseer [18] dataset includes 3312 scientific publications. The citation network is composed of 4732 links. Similar to the Cora dataset, each publication in Citeseer is described by a 0/1 word vector value, indicating the absence or presence of a word in a dictionary. The dictionary for Citeseer contains 3703 unique words, and thus each publication is characterized by 3703 features, each represented by a 0/1 value. The PubMed [19] dataset consists of 19,717 scientific publications on diabetes. The citation network is made up of 44,338 links. Each publication in the dataset is described by a TF/IDF-weighted word vector with 500 unique words in the dictionary. Therefore, each publication in the PubMed dataset is characterized by 500 features, each represented by a TF/IDF-weighted value. In these datasets, the class distribution is relatively balanced, so we used a simulated imbalanced setting: randomly selecting three classes as the minority classes and downsampling them. All majority classes have a training set containing 20 nodes. For each minority class, the quantity is 20\(\times\) imbalance_ratio. All experiments are conducted on a 64-bit machine usinga Nvidia GPU (Tesla V100, 1246 MHz, 7 GB of memory). The ESA-GCN model is evaluated for solving imbalanced classification using two metrics: the average AUC-ROC score and the F1-Macro score, to assess the advantages of the ESA-GCN. The effectiveness is then demonstrated through experiments. For all methods, the learning rate is initialized as 0.001, and the weight decay is set to \(5\times 10^{-4}\). \(\Lambda\) is set as \(1\times 10^{-6}\). If not specified, the imbalance ratio is set to 0.5, and the undersampling neighbor number K is set to 3. Additionally, all models are trained until convergence. For the three datasets, the top 4, 3, and 2 classes are selected as the majority classes, and the rest as the minority class, sorted by the number of nodes. In the ablation experiment, an additional runtime indicator is included to analyze the training speed of the models.
# Dynamic Parameters Identification of Serial Robot Based on Dual Quaternion
## 4 Simulation and Analysis
### Simulation of Dynamics Parameter Identification of Linkage  
The position center of the rigid body can present the center of mass in the coordinate system where the rotation axis is located, and the influence of the parameters of the X-axis and Y-axis of the rigid body centroid on the output torque of the joint was considered. The offset parameters of the joint in the kinematic coordinate system were used as the parameters in the Z-axis direction of the center of mass in the calculation derivation. The set of dynamic parameters of the four-degree-of-freedom robot linkages for the default values has been set first, and the identification result has been obtained in the simulation. From the identification result of the simulation, there are certain degrees of errors in the specific values of the inertia term in the set of identified dynamic parameters. However, the errors between the force and torque calculated in the dynamic model from the preset value and the identification result are small, which indicates that the dynamic effect of the connecting linkage in the joint coordinate system is equivalent to the set of dynamic parameters for the default values. As mentioned above, in order to improve the understanding and mastery of the dynamic characteristics of the serial robot system, the linear dynamic model approach has been used to study and discuss the dynamic parameters identification method of the serial robot system. In terms of the identification strategy and based on the structural characteristics of the serial robot, by using a step-by-step approach to identify the adjacent linkage combinations, the load and linkage combinations as a whole part, the method is beneficial for the identification process, which can adjust the selection and adjustment of different linkages in the identification process flexibly, in order to improve the effectiveness of the obtained dynamic parameters. Figure 4 shows the difference in the simulation result of the dynamic parameters identification of linkages. The results shown in Figure 4 show that the values of the difference in the mass and the centroid are zero, and the remaining values of the difference of the elements of dynamic parameters are small.  
Figure 3: Difference in the identification results of the load.  
These result shows the efficiency of the dynamic parameters identification method proposed. Furthermore, a radial basis function neural network (RBFNN) was also used in the simulation to identify the dynamic parameters of the load and linkages of the serial robot, which can be seen in the reference [24], and the strategy of using RBFNN should depend on the actual simulation platform and the setup. The average elapse is recorded in Figure 5, and DQ stands for the dual quaternion-based method of dynamic parameters identification. The figure shows that the proposed method, DQ, in this paper has the advantage of a lower cost compared with the RBFNN-based method.
## 2 Methodology
### Simulation
#### 2.3.2 Automated Batch-Script  
In order to find the optimal reflector shape, multiple shapes were examined. The curvature is described using the minor and major axis of the ellips in order to be able to automate the exploration for the best shape. A parametric sweep is performed using a batch-script. The elliptical reflector can be described by using two parameters: the width and the height (minor and major axis). These two parameters are used during this parametric sweep. For each design, the radiation intensity is calculated in a predefined region and this value is compared to the best simulation design at that moment. The script generates a visualisation of the optimised shape, the ray trajectories and the corresponding intensities.  
As a result of the focal property of an ellipse, it is necessary to assemble the heat source accurately. An additional Matlab(r)-script is programmed to investigate the impact of faulty assembling the optimised heat source. Multiple placement heights of the halogen tube lamp are simulated to check the error size. This error is searched for three geometries to select the most appropriate one out of the simulated designs. Those designs are also compared to three experimental heating sources. Two of them are visible in Figure 1. Figure 4 visualises the output of the Matlab(r)-script used to investigate the impact of a faulty assembly of the heating source. This script is used with multiple placing heights to investigate the influence of bad assembly for the shown reflector shapes. Figure 5 shows the output for only one placement height. The script exposed that the third reflector shape has the least adverse effects of misplacing the tube lamp.  
Figure 4: Multiple geometries are compared to each other. The height location of the halogen tube lamp changes to find the heating difference as result of displacing the halogen tube lamp. Initial dimensions were different than those of the optimised reflector.
## 2 Method
### Time-Frequency Analysis  
Time-Frequency analysis involves decomposing the signal into its frequency components over time to detect changes in the spectral content of the signal. One popular method is the Continuous Wavelet Transform (CWT). The CWT represents the signal as a function of both time \(t\) and frequency \(\omega\), given by:  
\[CWT(a,b)=\frac{1}{\sqrt{|a|}}\int_{-\infty}^{\infty}x(t)\psi^{*}\bigg{(}\frac {t-b}{a}\bigg{)}dt \tag{3}\]  
where \(\psi(t)\) is the analyzing wavelet function, \(a\) is the scale parameter controlling the width of the wavelet, and \(b\) is the translation parameter controlling the position of the wavelet along the time axis. Changes in the signal can be detected by analyzing the magnitude of wavelet coefficients across different scales and positions.
## 3 System Model and Problem Formulation
### System Model
#### 3.1.3 Network Topology  
Consider the network topology which is shown in Figure 1. Sixteen UAV nodes form a flying ad hoc network. Here, the distance between two UAVs, i.e., \(i\) and \(j\), is denoted by \(d_{ij}\). If the distance \(d_{ij}\) is less than the communication threshold, they can communicate with each other. A collision occurs when the distance \(d_{ij}\) is less than the collision threshold \(d_{C}+d_{I}\).  
Thus, the communication graph is defined as \(G_{C}=\{V,E_{C}\}\), where \(V\) represents the UAV set, \(V=\{v_{1},v_{2},\ldots,v_{N}\}\), and \(E_{C}\) is the edge set, i.e., \(E_{C}=\{\left(v_{i},v_{j}\right)|v_{i},v_{j}\in V\}\). Similarly, the collision graph is defined as \(G_{I}=\{V,E_{I}\}\), and \(E_{I}\) is the edge set, i.e., \(E_{I}=\left\{\left(v_{i},v_{j}\right)|d_{ij}\leq d_{C}+d_{I},v_{i},v_{j}\in V,v_ {i}\
eq v_{j}\right\}\).  
Figure 1 illustrates the interference of three nodes (UAV-1, UAV-4, and UAV-16) in every communication slot. Slot 1 is the transmitting slot belonging to UAV-1. For higher slot utilization, if UAV-4 and UAV-16 also transmit signals in slot 1, UAV-4 will interfere with UAV-1 where UAV-16 will not, and UAV-16 does not interfere with UAV-4. Similarly, if UAV-1 and UAV-4 transmit signals in slot 4, interference occurs for \(d_{1,4}\leq d_{I}\).
# Integrated System for Official Vehicles with Online Reservation and Moving Path Monitoring
## 2 System Architecture
### Set-Top-Box (STB)
#### 2.1.2 Control System
For n GPS locations per second, the system constantly compares the distance between Pn and Qn. When d is less than the preset dmin, the GPS data are stored as the movement of the vehicle is regarded as stopping and waiting. When the vehicle turns (D, E, and F), the gyroscope measures the angular velocity w of the vehicle, sets the variation of w (\(\pm\theta\)), and the GPS data are recorded in the micro-SD card. Figure 4: Schematic diagram of the distance between two different points in spherical coordinates. Figure 5: Schematic diagram of vehicle driving state determination. Figure 3: Three-axis orientation sensing of the attitude indicator of the STB. * SD card storage device  
The data format of the official vehicle's travel route is designed for time, longitude, and latitude and does not change due to time. Therefore, it is appropriate to use comma-separated values (CSV) for data storage. If the longitude is taken to the fifth decimal place, the error value is about 1.1 m. To improve the accuracy of vehicle displacement, the GPS data are taken to the 8 th decimal place. Then, the GPS data of the vehicle on each trip are stored in the serial peripheral interface (SPI) format. * Cloud server  
After the user returns the key and the STB of the vehicle to the cabinet, the stored data of the micro-SD card are automatically transmitted to the cloud server through WiFi (Figure 6). The data are uploaded to the database of the cloud server in JavaScript object notation (JSON) format (Figure 7). The JSON format has the characteristics of reading, writing, and lightweight and is easy to parse and classify in the background database. In uploading the data, the STB acts as a client and sets the URL and IP address in the background to submit the data to the backend SQL server database by POST method in the hypertext transfer protocol (HTTP). The data are displayed on the management page through the classification processing of driving data. To avoid data abnormality and loss during transmission, the server returns the number and status to define the following status codes after each data transmission. The STB then makes a warning according to the status code, as shown in Table 2, when the transmission data are abnormal.
# A Disruption Tolerant Distributed Routing Algorithm in LEO Satellite Networks
## 6 Conclusions  
A disruption tolerant distributed routing algorithm (DTDR) is proposed in this paper, which resolves the unstable intersatellite link issue to improve the performance of the data loss ratio. Considering the characteristics of the LEO satellite network, our proposed algorithm only requests that each satellite maintains limited information and performs distributed routing computing. DTDR floods the link failure information to neighboring nodes within a specified number of hops to achieve load balancing in the area of the failed link. It utilizes the mesh characteristics and periodic rules of the network topology of the space segment, and combines static routing with dynamic algorithms, which greatly improves the efficiency of the routing calculation. In addition, a loop avoidance mechanism is formulated in combination with the network topology characteristics of the space segment. Various simulations are constructed, and the results show that the proposed algorithm outperforms the existing algorithms in terms of transmission delay and packet loss ratio.  
Conceptualization, J.J. and H.D.; methodology, J.J. and F.T.; software, J.J. and Z.Y.; validation, F.T. and G.L.; formal analysis, J.J.; investigation, J.J.; resources, F.T. and G.L.; data curation, J.J.; writing--original draft preparation, J.J.; writing--review and editing, F.T.; visualization, J.J.; supervision, G.L.; project administration, G.L.; funding acquisition, G.L. and F.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National special support plan for high-level talents (grant number WJJH19 DH01), Youth Innovation Promotion Association of Chinese Academy of Sciences (grant number 2021291) and Shanghai Sailing Program (grant number 19 YF1446400).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Microalgae: Potential for Bioeconomy in Food Systems
## 3 Nutritional Composition
### Lipids and Fatty Acids  
Microalgae accumulate lipids for growth, cell division, and as energy reservoirs under stress conditions [42]. Over the past decade, the production of microalgae oils has focused on third-generation biofuels [43]; however, a wide range of potential applications can arise from their diverse lipid composition--saturated, monounsaturated, polyunsaturated, branched fatty acids, and fatty acid amides [44]. Under optimal conditions, the total lipid production by microalgae can exceed 50% of their dry biomass. Specifically, microalgae lipid profiles are rich in polyunsaturated fatty acids, such as omega-3, omega-6, docosahexaenoic acid (DHA), and eicosapentaenoic acid (EPA) [45]. Microalgae are primary producers of EPA and DHA in aquatic systems, which enables their sub  
Figure 1: Biomolecules available in microalgae that are useful for human, plant, and animal nutrition.  
sequent bioaccumulation in higher organisms. EPA and DHA may account for a high proportion of total lipids in microalgae, as observed in _Nannochloropsis_ sp. (26-28%), _Phaeodactylum tricornutum_ (39%), _Chlorella_ sp. (39%), _Pavlova_ sp. (36-41%), _Schizochytrium limacinum_ (30-40%), _Thraustochytrium_ sp. (45%), and _Isocrysis_ sp. (28%) [15, 45, 46]. Microalgae also produce other essential fatty acids, such as alpha-linolenic acid, gamma-linolenic acid, linoleic acid, and arachidonic acid [47].
## 3 Results and Discussions
### Microstructural Analysis
The dominant hydration product, crystalline C-S-H gel, determined from the ruling elements (Ca = 24.6%, Si = 9.9%, Al = 3.9% and O = 45.8%) in SEM/EDS analysis, is responsible for its highest compressive strength amongst all the mortars. The hydrated FA-F improved the workability of the mixes, and the presence of shorter linkages in C-S-H gels in comparison to the alkali-activated gels (N-C-A-S-H/N-A-S-H and C-A-S-H) resulted in the lowest plastic viscosity, lowest yield stress, highest slump flow spread and longest setting times among all the mortar mixes (as indicated in Table 4). On the other hand, the cement-free binary and ternary alkali-activated mortars exhibited the formation of a blend of crystalline (C-A-S-H/C-S-H) and amorphous (N-C-A-S-H/N-A-S-H) products (Figure 10a,b), showing comparable fresh state, rheological and strength characteristics to that of the control mortar.
## 3 Results and Discussion
### Robust Estimation of Arrival Time of PD Pulse
Time-varying kurtosis curves were drawn for the de-noising signal in Figure 8c and the corresponding noisy signal in Figure 4b, as shown in Figure 9. The first arrival time of PD pulse in Figure 4b is picked up at the 400 sampling point manually, and the maximum value of the time-varying kurtosis curve for the de-noising partial discharge signal is 400, which is consistent with the manual pick-up result. The maximum value of the kurtosis curve is at 794 sampling point of the noisy signal, indicating that it is no longer possible to pick up the first arrival time. In summary, the proposed de-noising method in this paper combines the adaptive VMD and wavelet packet decomposition could extract PD signal effectively from complex noise in practice.