esa-sceva/satcom-chunk-collection huggingface dataset:
# GPU@SAT DevKit: Empowering Edge Computing Development Onboard Satellites in the Space-IoT Era
## 3 GPU@SAT DevKit
### Application Development Workflow  
Figure 3 illustrates a comparison between the application development process before and after the introduction of the DevKit.  
In both scenarios, the workflow begins with writing an OpenCL kernel and compiling it to generate the binary code that aligns with the OpenCL properties of the kernel itself. Before the introduction of the DevKit, users had to manually rewrite the C firmware of the embedded processor to add commands for loading kernel information onto the accelerator. This process involved compiling the firmware, uploading it to the DevBoard, and then executing it. Any errors required restarting the entire process from the firmware rewrite, assuming the kernel itself was error-free.  
The DevKit streamlines these operations significantly. Now, the user simply edits the configuration and scheduled XML files and runs the client application, which translates the files into commands for the server. This approach removes the need to manually interact with the embedded processor firmware, greatly reducing the overall application development time.
## 4 Conclusions  
In this present study, in vitro cytotoxicity in hFOB 1.19 cells in response to various concentrations (1 mM, 5 mM, 10 mM, and 50 mM) of MgO/Mg(OH)\({}_{2}\) NPs was studied. The determination of cytotoxicity in hFOB 1.19 cells provides a foundational knowledge for an allowable concentration of MgO/Mg(OH)\({}_{2}\) NPs that might be released from biodegradable Mg-based alloys in their application in orthopedic implants in future in vivo studies. The immersion test result showed that MgO/Mg(OH)\({}_{2}\) NPs degraded at a faster rate with the release of increased OH\({}^{-}\) ions at higher concentrations (50 mM and 100 mM), which was determined by the change in the pH values from days 1 to 7. However, there was no significant difference observed between the change in the pH values of 50-mM and 100-mM concentrations; therefore, we selected 100-mM concentrations of MgO/Mg(OH)\({}_{2}\) to conduct SEM-EDX mapping to understand the element depositions on MgO/Mg(OH)\({}_{2}\) NPs after the immersion test. SEM-EDX mapping showed the presence of Mg, O, Na, Ca, P, CI, and K in both MgO and Mg(OH)\({}_{2}\) NPs.  
Further, an in vitro cytotoxicity study in hFOB 1.19 cells in response to the exposure to various concentrations (1 mM, 5 mM, 10 mM, and 50 mM) of MgO/Mg(OH)\({}_{2}\) NPs (observed by live/dead assay) showed cell proliferation and viability at lower concentrations (5 mM or less). However, at higher concentrations (10 mM and 50 mM) of NPs, an agglomeration of the cells was observed, which indicated the formation of complex compounds that might be the reason for cell death in hFOB 1.19 cells.  
The morphological changes studied by SEM analysis showed comparable results to the live/dead assay for cell adhesion, proliferation, and cell death. The results from this in vitro cytotoxicity study suggest that less than or equal to 5-mM concentrations of MgO/Mg(OH)\({}_{2}\) NPs are tolerable concentrations for hFOB 1.19 cells. On the basis of this foundational study, in the future, innovative biodegradable Mg-based alloys can be developed for their application in orthopedics. We also suggest that future research must be done to understand the cytotoxicity of corrosion products through a real-time in vitro sensing system and in vivo testing.  
Conceptualization, Y.Y. and J.W.; methodology, Y.K.; software, M.P., validation, M.P. and Y.Y.; formal analysis M.P. and Y.Y., Investigation, M.P.; Resources, J.S.; data curation, M.P.; writing--original draft preparation, M.P.; writing--review and editing, Y.Y., M.P. and J.W.; visualization, M.P.; supervision, Y.Y. and J.S.; project administration, J.W. and J.S.; funding acquisition, Y.Y. and J.S.  
This research was funded the National Science Foundation Engineering Research Center for Revolutionizing Metallic Biomaterials, an NIH grant (1 SC3 GM113728), and an NSF grant (1649243).  
The authors declare no conflicts of interest.
# Few-Shot Steel Defect Detection Based on a Fine-Tuned Network with Serial Multi-Scale Attention
## 4 Experimental Results and Analysis
### Performance Evaluation of Enhanced Network on Single Defect Detection  
Single defect detection refers to the detection of a specific type of defect among the four defects (pit, crack, scratch, and oxide scale) present in the dataset, representing an individual defect category. This research focused on optimizing defect detection, and we validated the efficacy through an experiment employing a 7-shot scenario, where each new class contained seven samples. The results are demonstrated in Figure 5 and Table 6.  
Figure 5 illustrates the changes in detection accuracy for four types of defects before and after network refinement. It is evident that there have been significant improvements across all defect categories following the network refinement. Particularly noteworthy is the substantial enhancement observed in the case of pit defects, with an increase in the accuracy of 5.34%. This can be attributed to the fact that pit defects typically own smaller scales and more pronounced shape features. Therefore, the network refinement has made the model more sensitive to these defect characteristics, resulting in a notable performance boost.  
Figure 4: Comparative results of mAPs.  
Furthermore, crack defects also exhibited a notable increase in accuracy within the refined model, with an improvement of 4.78%. This represents a significant improvement, demonstrating the effectiveness of the network refinement for small-scale yet crucial defects.  
Additionally, for larger-scale defects such as scratches and iron oxide, the network refinement similarly led to substantial improvements, with increases of 2.77% and 4.31%, respectively. This indicates the refined model's capability in capturing larger-scale defects, consequently reducing instances of false detections and misclassifications.  
In all, Figure 5 reveals that following network refinement, there was a noticeable improvement in detection accuracy for all categories of defects. Among these, the most noteworthy improvement was observed in the case of pit defects. As these defects are characterized by a smaller scale, the modified model demonstrated a marked improvement in detection accuracy compared with its original version. Furthermore, the refined model effectively reduced the instances of false detections and misclassifications for larger-scale defects, thereby further improving its detection accuracy. These results imply that the proposed model is capable of detecting small defects effectively in more complex scenarios.
## 4 Experimental Verification
### Comparison Experiments  
#### 4.2.1 Optimization Algorithm Performance Comparison  
To verify the performance of the SBOA, comparison experiments are conducted using three algorithms: GA, PSO, and SBOA. To ensure the fairness of the experiment, the individual dimension \(dim\), the number of populations \(N\), the maximum number of iterations \(T_{\text{max}}\), and the fitness value function are unified across all algorithms. The optimal parameters for each optimization algorithm are determined through multiple sets of experiments.  
Figure 11 illustrates the best fitness value curves for the four algorithms, GA, PSO, SBOA, and ISBOA, during the iterative process. The GA, PSO, SBOA, and ISBOA algorithms reach a steady state after 631, 557, 660, and 484 iterations, respectively, with ISBOA demonstrating a faster convergence speed and achieving the optimal fitness value.  
Figure 10: Prediction error vs. sample size. (**a**) Maximum prediction error. (**b**) Average prediction error.
# Recurrent Neural Network Based Short-Term Load Forecast with Spline Bases and Real-Time Adaptation
## 3 Test Results
### Comparison of Test Results Obtained from the Semi-Parametric Model and the RNN Model  
The MAE, RMSE and MAPE of the accuracies of the load forecasts for every month from 2018-2019 are provided. The forecasting accuracies for the two models based on the historical temperatures serve as a baseline for comparison. In Table 2 the corresponding \((D+1)\)-day monthly MAE, RMSE and MAPE of the two models with historical temperatures are given in details. It can be seen that with the actual temperature, both models have good accuracies on the \((D+1)\)-day forecasts and the performances of the RNN model is especially outstanding on most of the months from 2018-2019 with annual averages of MAPE at 2.03, 1.70 respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**SPM**} & \multicolumn{2}{c}{**RNN**} & \multicolumn{2}{c}{**Daily Load**} \\ \cline{2-9}
**Year** & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** & **Average** \\ \hline
2018 & 605.01 & 792.93 & 2.31 & 530.2 & 719.98 & 2.03 & 26,382.82 \\
2019 & 466.39 & 632.22 & 1.76 & 448.99 & 601.02 & 1.7 & 26,441.99 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of the Model Performances of the next \((D+1)\)-day Forecasting Accuracies of the SPM and RNN Models with Historical Temperatures.  
Figure 4: The training set used for model training.
# Nanocarriers-Assisted Nose-to-Brain Delivery of Levodopa: Current Progress and Prospects
## 6 Nose-to-Brain Delivery of L-DOPA
### Polymeric Nanoparticles for Nose-to-Brain Delivery of L-DOPA
As a result, a 17-fold higher maximum plasma concentration of dopamine was established following the GCPQ-L-DOPA formulation administration in comparison to the standard L-DOPA solution. [PERSON] et al. prepared a chitosan-based polyelectrolyte complex loaded with L-DOPA and carbidopa, intended for intranasal delivery to the brain. The authors found the particles to possess a size in the range of 187.3-210.4 nm, a negative zeta potential (\(-\)2.90 to \(-\)38.1 mV), and up to 89.78% drug entrapment efficiency, thus proving the suitability of the proposed technology for the carrying of hydrophilic drugs [174]. [PERSON] et al. formulated chitosan-thioglycolic acid conjugated nanoparticles (CS-TGA) with L-DOPA and implemented them into an in situ gelling base of Poloxamer 407 [175]. In addition, the formulation was enriched with nitromusk (musk ketone) as a permeation enhancer. The synthesized CS-TGA nanoparticles had a particle size of 222.6 nm, zeta potential of +27.91 mV, and drug entrapment efficiency of 76 \(\pm\) 1.2%. In an in vivo study, the authors established a 1.4-fold enhanced brain bioavailability of dopamine when applying the novel formulation as compared to the standard L-DOPA solution. [PERSON] et al. combined L-DOPA and selegiline in chitosan nanoparticles and obtained a mean particle size of 109.9 \(\pm\) 1.30 nm, zeta potential of +26.36 \(\pm\) 2.58 mV, and entrapment efficiency of 91.03 \(\pm\) 3.02% for L-DOPA and 55.01 \(\pm\) 1.97% for selegiline [176]. The particles were incorporated into Poloxamer 407 in situ gelling nasal base. The in vivo study on Wistar rats demonstrated the therapeutic potential of the novel formulation through the evaluation of the alleviating motor symptoms associated with PD. These and other reports of polymeric nanocarriers for the nose-to-brain delivery of L-DOPA are summarized in Table 3.
# Three-Dimensional Localization of Underwater Nodes Using Airborne Visible Light Beams
## 5 Results and Discussion
### Number of Received Transmissions  
We have also investigated how the number of received transmissions affects the accuracy of 3D-LUNA for both static and mobile nodes using a fixed transmission power (10 W).  
In Figure 11, the results indicate that localization error decreases as the number of received transmissions increases for both node types. When only one transmission is received, the mobile node exhibits the highest error, whereas the static node shows a slightly lower error. With two transmissions, the localization error improves for both cases. The best accuracy is achieved when three or more transmissions are received, where the mobile node's error is around 4 m, and the static node demonstrates the least error.  
Figure 10: Localization performance comparison between 2D VLC and 3D-LUNA for static and mobile nodes. The error bars represent 95% confidence intervals.
# Design of an IMCPID Optimized Neural Network for Stepless Flow Control of Reciprocating Machinery
## 3 Controller Design  
In this section, the set-point tracking and decoupling control scheme will be proposed. The basic structure of the IMCPIDNN control system is shown in Figure 2. The design process of the IMCPIDNN controller mainly consists of four parts: relative gain array, feed-forward decoupling, IMC controller based on standard PID and IMCPIDNN controller design. In the whole control system, \(r(k)\) is set-point value, \(e(k)\) is error of subtracting the output value from the set-point value, \(y(k)\) is output value, \(u(k)\) is control signal, \(\lambda(k)\) is the only IMCPID controller parameter to be adjusted and \(d\) is external disturbance. The whole control algorithm is established based on the adaptive IMCPID controller and neural network algorithm, which is presented in Algorithm 1.  
```
Step 1: Initialize the weights of IMCPIDNN. Step 2: Input the set-point value of controlled system into the controller. Step 3: Regulate the compressor flow by IMCPIDNN controller. Step 4: Calculate error and evaluation function. Step 5: Adjust the weights of IMCPIDNN controller by back propagation algorithm. Step 6: Repeat steps 3 to 5. Step 7: Stop the algorithm until the error is small enough. If not, return to Step 3.
```  
**Algorithm 1** IMCPIDNN algorithm for MIMO nonlinear system.
## 4 Results and Discussion
### Qualitative Interpretation
#### 4.1.4 THD Anomaly Map  
The THD anomaly map was derived from the residual CBA map. Figure 6d shows a dense and conspicuous feature of steep gravity gradient in the central, northeast, and southwest parts of the study area. In general, two dominant lineaments trending NW-SE were observed at the central part of the study area. One of these lineaments was perfectly overlapped on Bok Bak Fault zone when THD and structural maps were superimposed to each other. Since one of the lineaments is perfectly matched to the Bok Bak Fault zone, the remaining lineaments close to it were also most likely associated with possible faults which have yet to be determined.  
Another NE-SW strong gradient phenomenon is observed both northeast and southwest of the study area. Structural lineament analysis was then conducted by tracing the lineaments observed in both the THD anomaly map and structural maps. Trends of both lineaments are compared with topography map lineaments analysis, which represents general surface structures. The lineaments trends deduced from the topography map and THD residual anomaly map are shown in Figure 7a,b, respectively. The generated synoptic Rose diagrams on structural trends in Figure 8 inferred the delineation of the major lineaments in the NW-SE direction followed by the NE-SW direction.  
Figure 7: (**a**) Lineament traced from topography map; (**b**) Comparison of lineament trend traced from THD residual anomaly map and topography map.  
The topographic lineament is, in general, quite similar to the result reported by [PERSON] et al. [47] stressing the presence of a dominant lineaments trend in central Kedah along NE-SW and NW-SW. The THD residual anomaly structures trend resembles the analysis of [PERSON] and [PERSON] [22] who stated that the NW and NE trending sections are along 325\({}^{\circ}\) and 37\({}^{\circ}\), respectively. In general, the lineaments of the THD residual anomaly map that represent shallow subsurface structures have mimicked the trend to the surface topography lineaments with less than 8\({}^{\circ}\) dipping angles.
## 4 Review: Slant Links Rain Fade Models
### Limitations
#### 4.1.9 [PERSON], [PERSON] and [PERSON] (YLO) Model  
This model [12] is based on \"extensive satellite link measurement data\" and uses the ITU-R P.838-3 prediction model [109]. The main area of concern for its application is the tropical region. The model has been verified up to a frequency of 30 GHz. The rain attenuation determination procedure by this model is presented in Algorithm 9.  
```
1 Compute \(PRF\leftarrow\frac{1}{\frac{1}{\sin^{2}+\zeta_{1}\cdot R_{0}\Omega\cdot(H-H_{5})-c_{3} F+\zeta_{4}}}\) /* PRF: path reduction factor */
2 if\(PRF>1\)then
3\(PRF\gets 1\)
4 else
5\(PRF\leftarrow\)PRF
6 Calculate k and \(\alpha\) /* TTU-R P.838-3 [109] */ // Here, \(H_{R}\) is determined by [33]
7 if\(0^{\circ}\leq\varphi<36^{\circ}\)then
8\(H_{R}\gets 3.0+0.028\varphi\)
9 else
10\(H_{R}\gets 4-0.075(\varphi-36^{\circ})\)
11 if\(\theta\geq 5^{\circ}\)then
12\(L_{S}\leftarrow(H_{R}-H_{S})/\sin\theta\) /* \(L_{S}\) in km */
13 else
14 Elevation angle error/* Invalid for \(\theta<5^{\circ}\) elevation angles */
15 Calculate \(A_{0.01}\gets k\cdot R_{0.01}^{\alpha}\cdot L_{S}\cdot PRF\) // Calculation of \(\beta\)
16 if\(p\geq 1\%\) or \(|\varphi|\geq 36^{\circ}\)then
17\(\beta\gets 0\)
18 if\(p<1\%\) and \(|\varphi|<36^{\circ}\) and \(\theta\geq 25^{\circ}\)then
19\(\beta\leftarrow-0.0055(|\varphi|-36^{\circ})\)
20 else
21\(\beta\leftarrow-0.0055(|\varphi|-36^{\circ})-1.7008+7.8503\sin\theta\)
22 Calculate \(A_{P}\gets A_{0.01}\cdot\left(\frac{p}{0.01}\right)^{b_{1}+b_{2}\ln p+b_{3 }\ln A_{0.01}+\beta\sin\theta}\) Return \(A_{p}\)
```  
**Algorithm 9**YLO Model [12]  
Advantages  
It has a path length adjustment factor to refine the rain attenuation.
## 4 Evaluation Metrics  
### Mobility Models' Metrics  
We are comparing mobility models and evaluate them by means of seven protocol-independent metrics. Some of these metrics have been used in the literature [3, 31] for the same purpose of comparing mobility models. We have added a few here, as we believe that they further describe the dynamics of the network, and knowing them will help explain the performance of the routing protocols later.
## 3 The Optimal Selection Model of the Backhaul Links
### Derivation of the Optimization Model  
The introduced system elements include the candidate set \(H(t)\) and the selected set \(F(t)\). \(H(t)\) represents all link connection methods, and \(F(t)\) represents one of the selection methods. The latter is a subset of the former. When \(F(t)\) is determined, the edges of the graph are also determined. In the graph, the distance of the edge between two vertices can be represented as \(d(t)\). Due to the large-scale fading characteristic of electromagnetic waves, the receiving power decreases with the increase in the distance between the satellites and UAVs. Then, according to the Shannon channel capacity formula, the capacity decreases. This is an indicator of system effectiveness. In addition, since satellite motion is regular and predictable, the time during which the satellite stays in the visible range of the UAV can be calculated according to the UAV's location. This period is called the remaining service time (RST). Once \(F(t)\) is determined, the RST is also determined. The greater the remaining service time, the less link switching and the more stable the system is. This is an indicator of system reliability.  
In multiservice IoRT scenarios, different scenarios have different needs for both the capacity and RST. Therefore, a comprehensive system performance indicator \(Q(t)\) is proposed as a tradeoff between effectiveness and reliability. It is affected by selecting the associated satellite set \(F(t)\). Moreover, since the storage and forwarding capabilities of satellites are limited, the number of UAVs connected to each satellite should also be capped. Meanwhile, the selected set \(F(t)\) must be in the candidate set. These are two constraints in the optimization model. In summary, the important elements of the optimization model include: the decision variable is the associated satellite set \(F(t)\), the objective is to improve the quantity \(Q(t)\), and the feasibility constraints are the capped number of satellite connections and the visibility of satellites to UAVs. They are rigorously defined in next subsection.
# Three-Dimensional Dead-Reckoning Based on Lie Theory for Overcoming Approximation Errors
## 4 Experiments and Results  
This section demonstrates the improvements achieved by applying Lie theory through real-world experimental data. The proposed method is compared with the previously widely used DR. The comparison focuses on the accuracy of the prediction results when the sensor data reception cycle is varied. The prediction results refer to the calculated position, velocity, and attitude. Hereafter, the proposed method will be referred to as \"proposed\", and \"Conv-DR\" will represent the previous method, i.e., the previously widely used linear approximation-based DR.
# On the Contribution of Wind Farms in Automatic Generation Control: Review and New Control Approach
## 1 Introduction
The parameters of the proposed controller are optimized by sine-cosine algorithm (SCA) to attain efficient control performance in multi-area power systems. The main contributions of this paper are:  
1. Wind farms contribution to frequency regulation of power systems is studied. 2. Review on application of wind farms in AGC is presented. 3. The FOPID controller is applied to variable speed wind turbine. 4. Performance of FOPID controller for variable speed wind turbine-based wind farm is compared to classical controller. 5. Frequency variation effect of different penetration levels of wind farms in power systems is investigated.
## 7 Conclusions  
A tool to simulate electromagnetic and temperature fields in the SiC crystal growth furnace is developed by using finite volume method with C programing language. LU decomposition algorithm based on GPU technology is developed to accelerate solving process of radiation heat flux, and the least distance method (LDM) is proposed for S2S radiation model to check whether mesh face can see others or not. The electromagnetic and temperature fields are simulated for axisymmetric and spiral coils at \(I\) = 1250 A and \(F\) = 16 kHz. The radiation and conduction heat transfer are compared with each other quantificationally, and the uniforms of temperature and its gradient are also discussed. The following conclusions are obtained:  
Figure 10: Temperature and its gradient fields in SiC crystal. (**a**) temperature for axisymmetric coil; (**b**) temperature for spiral coil; (**c**) temperature gradient for axisymmetric coil; (**d**) temperature gradient for spiral coil.  
1. The coil structure has a great effect on temperature field of SiC crystal growth furnace, spiral coil will cause non-axisymmetric temperature field;
2. Radiation heat flux is about 2\(\sim\)3 orders of magnitude bigger than conduction heat flux in the growth chamber, and radiation is helpful to make more uniform temperature field and decrease temperature gradient between powder surface and crystal surface;
3. The non-axisymmetric temperature and its gradient in the crystal caused by the spiral coil are adverse to grow crystal with fewer defects.  
[PERSON] and [PERSON] wrote the paper; [PERSON] and [PERSON] organized figures; [PERSON] and [PERSON] contributed to the paper structure and sentences revise.  
This work was supported by Shandong Academy of Sciences Foundation (2014 QN016, 2016 QN005, and 2013 QN016).  
The authors declare no conflict of interest.