esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Empirical Results and Discussion
#### 4.1.1 Exploratory Data Analysis  
Data covering the period 4 January 2020 to 31 October 2020 were used in this study. The data were obtained from Vuwani radiometric station. We can see from Figure 2 and Table 3 that the GHI values are right-skewed and platykurtic.  
In this work, a non-linear trend was extracted by fitting the cubic smoothing spline function provided by the equation:  
\[\pi(t)=\sum_{t=0}^{n}(y_{t}-f(t))^{2}+\lambda\int\{f^{\prime\prime}(t)\}^{2}dt, \tag{20}\]  
where \(\lambda\) is the smoothing parameter that is predicted using the generalised cross-validation (GCV) criterion. Figure 3 illustrates the cubic smoothing spline and non-linear trend fitted with the projected lambda value. The non-linear trend values extracted, used to model solar irradiance.  
Figure 1: Picture showing the location of the Vuwani radiometric station (USAid Venda). Source: [[https://sauran.ac.za/](https://sauran.ac.za/)]([https://sauran.ac.za/](https://sauran.ac.za/)), accessed 3 May 2020.  
Figure 3: Plot of global horizontal irradiance from 04/01/2020 to 31/10/2020 (green) superimposed with a fitted cubic smoothing spline trend (red).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Min** & **Max** & **Median** & **Mean** & **St.Div** & **Skewness** & **Kurtosis** \\ \hline
0.0032 & 1481.6300 & 307.6562 & 388.0439 & 324.1666 & 0.6130 & \(-\)0.7568 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The descriptive statistics of the GHI measured in W/m\({}^{2}\).  
Figure 2: Time series plot of GHI, Density plot, Quantile-Quantile(qq) plot, and Box and whisker plot.
# Research on Multi-DAG Satellite Network Task Scheduling Algorithm Based on Cache-Composite Priority
###### Abstract  
The problem of multiple DAGs sharing satellite constellation resources has gradually attracted widespread attention. Due to the limited computing resources and energy consumption of satellite networks, it is necessary to formulate a reasonable multi-DAG task scheduling scheme to ensure the fairness of each workflow under the premise of considering latency and energy consumption. Therefore, in this paper, we propose a multi-DAG satellite network task scheduling algorithm based on cache-composite priority under the Software-Defined Networking satellite network architecture. The basic idea of this algorithm lies in the DAG selection phase, where not only are task priorities computed but also the concept of fair scheduling is introduced, so as to prevent the excessively delayed scheduling of low-priority DAG tasks. In addition, the concept of public subtasks is introduced to reduce the system overhead caused by repetitive tasks. The experimental results show that the hybrid scheduling strategy proposed in this paper can meet the demand of DAG scheduling and improve the degree of task completion while effectively reducing the task latency and energy consumption.  
Software-Defined Networking; satellite network; multi-DAG scheduling; fairness Article Research on Multi-DAG Satellite Network Task Scheduling Algorithm Based on Cache-Composite Priority  
[PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Research on Multi-DAG Satellite Network Task Scheduling Algorithm Based on Cache-Composite Priority  
202413763390/electronics13040763
## 3 Numerical Results and Discussion
### Distribution of the Phase Noise Between the Transmitter and Receiver Lasers  
The results presented in Sections 3.1-3.3 had the same amount of phase noise in the transmitter and LO, which corresponds to the value of the \(X\)-axis in the plots and it is the typical case in FMF transmission experimental demonstrations. In this section we investigate the effect of varying the amount of phase noise between the transmitter and the LO when using model B for QPSK and 16-QAM modulation formats and the results are shown in Figures 9 and 10, respectively. Both figures are plotted for ratios \(\rho\in\{0,0.25,0.6,1.5,4,\infty\}\) where \(\rho\) stands for the ratio \(\rho=\text{TX}_{\text{pn}}/\text{RX}_{\text{pn}}\), where \(\text{TX}_{\text{pn}}\) is the phase noise of the transmitter and \(\text{RX}_{\text{pn}}\) is the phase noise of the receiver, and \(\rho=\infty\) means all the phase noise in the system is contributed by the transmitter and the receiver is an ideal noiseless laser.  
The analysis of both figures reveals that as \(\rho\) increases, i.e., the transmitter becomes noisier compared to the receiver, the behavior of the systems tends to the model A behavior even in the presence of strong coupling at the DE/MUXes. Conversely, as \(\rho\) decreases, the system tends to the model B behavior of the previous section. The conclusion is that the EEPN originates due to the phase noise contribution of the LO and hence it is important to keep the noise of the LO laser as low as possible in order to optimize the system performance.  
Figure 10: OSNR penalties in dB, computed at a BER of \(10^{-4}\), for 16-QAM modulation and model B for several distributions of the phase noise between the transmitter and receiver lasers.  
Figure 9: OSNR penalties in dB, computed at a BER of \(10^{-4}\), for QPSK modulation and model B for several distributions of the phase noise between the transmitter and receiver lasers.
## 1 Introduction
The structures of these operations are very complex, and the number of involved calculations is huge, and the problem of efficiency remains to be solved. Therefore, this investigation will attempt to use light-weight operations, such as the hash function and chaotic mapping technique, to increase computational efficiency. Extended chaotic mapping technique will be used to develop a blockchain-based medical data protection mechanism that complies with Health Insurance Portability and Accountability Act (HIPAA) security regulations. The proposed mechanism does not use time-consuming modular exponential calculations or elliptic curve point multiplication calculations, reducing the computational complexity of the operations that are carried out in wearable devices and improving computational efficiency. The contributions of this paper are as follows:  
* This research attempt to use light-weight operations such as hash function and chaotic mapping technology to improve computing efficiency;
* This research uses extended chaotic mapping technology to develop a blockchain medical data protection mechanism and complies with Health Insurance Portability and Accountability Act (HIPAA) security regulations;
* The proposed mechanism does not need to use time-consuming modular exponential calculations and elliptic curve point multiplication calculations, so it can reduce the computational complexity of the wearable device and improve computational efficiency. The remainder of this paper is structured as follows. Section 2 discusses the main existing research in the healthcare information system. Section 3 introduces background knowledge relevant to the paper, such as HIPAA privacy/security, blockchain, and chaotic maps. Section 4 presents the proposed scheme in detail. Section 5 the result of the paper which evaluates the proposed scheme, and Section 6 introduces the discussion. Finally, Section 7 draws conclusions and future work.
## 3 Results
### Predicted OA Composition  
The seven aging schemes proposed by [PERSON] et al. [20] were the most successful ones in reproducing the OA measurements in the Po Valley in Italy and showed robust source contributions. Similarly, for the boreal forest in Finland all seven aging schemes once again predicted similar source contributions with some differences in the contribution of biogenic SOA varying from 40 to 63% of the total OA (Figure 4), depending on the parameterization scheme. The highest bSOA concentration was predicted by the aging schemes that assume production of bSOA during the aging reactions. The highest contribution (63%) was for the scheme assuming a 2-bin shift functionalization and fragmentation with 40% probability (2-bin/bSOA/_b_ = 0.4). The rest of the cases predicted lower bSOA levels, around 40% of the total.  
SOA from anthropogenic VOC oxidation was predicted to contribute between 11 and 18% of the total OA (Figure 4). Its relatively low levels are consistent with the rural forested location of the boreal station of Hyytiala. Most of the corresponding production took place elsewhere, and the resulting SOA was transported to the site.  
SOA from the oxidation of intermediate-volatility compounds varied between 14 and 27%. The lowest contributions were predicted by the simulations that had high bSOA levels. The highest contributions (27%) were predicted by the simulations using the simple (1-bin) and faster (2-bin) functionalization scheme.  
The fresh POA was 4% of the total OA, the SOA from the oxidation of evaporated POA 4-6%, and the OA from long-range transport 5-6%.  
In general, all seven aging schemes were able to reproduce the variability and the average of the concentration of the OA.
###### Abstract  
Introduction: The aim of this study was to determine the placement of an orthodontic mini-screw (OMS) and the length of an anterior retraction hook (ARH) with en masse retraction. Continuous maxillary tooth movement pattern was simulated by finite element analysis (FEA). Materials and methods: Extraction of the first premolar was hypothesized with a finite element model. The placement of OMS was analyzed for the following two groups: (1) a high OMS (HOMS) group with OMS placed horizontally at the mesial side of the second premolar and apically 10 mm above the arch wire, and (2) a low OMS (LOMS) group with OMS placed horizontally between the second premolar and the first molar and apically 8 mm above the arch wire. According to the height of ARH, each group was divided into three subgroups. Results: When the extraction space of the first premolar was closed, anterior teeth were intruded in the HOMS group but extruded in the LOMS group. In all cases, the first molar was intruded. According to the intrusion of the first molar and extrusion of anterior teeth, the occlusal plane rotated clockwise (CW) in the LOMS group. However, in the HOMS1 group, the occlusal plane rotated counterclockwise (CCW) due to more intrusion of anterior teeth than that of the first molar. Conclusion: By analyzing six cases of different OMS and ARH, changes of incisor and molar in en masse retraction with the extraction of the first premolar could be predicted. In addition, OMS placement and ARH length can be determined based on results of incisal showing. This study can also help esthetic orthodontic results.  
finite element analysis; continuous teeth movement; orthodontic mini-screw; anterior retraction hook; rotation of occlusal plane; en masse retraction
## 5 Conclusions  
This work investigated the feasibility and effectiveness of using the EKF algorithm to identify the structural damage in multi-DOF systems. Parameter identification and damage detection have been successfully investigated based on the numerical simulations and laboratory experiments. Firstly, the effect of different integration methods for estimating the structural stiffness and damping was studied. The results show the stiffness is not sensitive to the integration method, and the simple rectangular integral can achieve good result, while the damping is very sensitive to the integral method, which requires the fourth-order Runge-Kutta integration. Meanwhile, EKF-based identification was also studied for structures subjected to Gaussian and non-Gaussian noises. The identification results under Gaussian noises are better than those under non-Gaussian noises. Finally, experiments were conducted and structural damage indicated by the decrease of stiffness was detected effectively. It is shown that EKF is capable of being applied to damage identification and health monitoring for civil engineering structures.  
[PERSON] conceived and designed the simulation and experiments. [PERSON] finished the numerical simulation. [PERSON] and [PERSON] performed the experiments. [PERSON] and [PERSON] analyzed the data. [PERSON] and [PERSON] wrote this paper.  
This research was funded by the National Natural Science Foundation of China (Grant Numbers: 51578140, 51478356, and 51778490), the National Key Research and Development Program of China (2016 YFE0127600, and 2017 YFC0805900), Shanghai Municipal Education Commission-Gaofeng Grant Support, and the open fund of the Key laboratory of concrete and pre-stressed concrete structure of Ministry of Education of China (CPCSME2015-01).  
The authors declare no conflicts of interest.
# Stochastic Predictive Energy Management of Multi-Microgrid Systems
## 7 Experimental Results  
The test system has been implemented at the AC Microgrid Research Laboratory at Aalborg University as represented in Figure 8 [35]. A real-time platform (dSPACE 1006) is used for real-time simulation. As it can be noted, the setup includes three inverters (2.2 kW, _FC_302) fed through a stiff bidirectional DC source (0-800 VDC, 0-50 A, 64 kW), a regenerative grid simulator (45 kVA \(|\)330 V _L-N_) as well as line models, and load boxes. Due to the technical limitations, each MG is modeled by a single inverter. However, as the paper aims to show the capability of the MGs to follow reference trajectories, it will not raise any issue. It should be noted that generation/consumption profiles and the time slot have been scaled down such that 1000 W:1 W and 3600 s:20 s. Experimental tests are exemplary carried out in the time interval 18:00-20:00. Figure 9 represents the tracking performance of the local controllers. As can be seen, a good tracking performance has been achieved during the test interval. Moreover, taking into account the time scaling factor, the transient time of MGs is about 6s which approves the efficiency of the proposed approach in calculating and implementing optimal operation strategy and makes it applicable for real EMS. System frequency and voltage profile at PCC are also denoted in Figure 9. As can be seen in this figure, the system frequency has been kept within an acceptable range and a three-phase balanced output voltage regulation has been achieved.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Operation Mode** & **MG 1** & **MG 2** & **MG 3** & **MMG** \\ \hline Single & 794.01 & 589.82 & 448.17 & 1831.99 \\ Coordinated & 306.87 & 339.06 & 317.96 & 963.79 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Penalty cost of MGs in different cases [5].  
Figure 7: Unscheduled power exchange with the main grid.  
Figure 8: Experimental setup.  
Figure 9: Tracking performance of local controllers.
## 4 Discussion
Investigate the use of GNSS signal enhancement techniques--for example, GNSS reflectometry--to improve the accuracy under canopy cover;
3. Apply machine learning algorithms to GNSS data to predict the accuracy based on foliage density and tree height, and evaluate the effectiveness in real-world applications;
4. Investigate integrating the GNSS with other sensors, such as LiDAR and cameras, to improve accuracy under canopy cover;
5. Use a priori forest models/tree maps, e.g., from ALS and integrated LiDAR SLAM, to replace the need for a total station for practical long-term studies and forestry practice (1).
## 4 Discussion
Our observations were not comparable with some previous reports, which showed altered \(vtg\) gene regulation or VTG levels after exposure to DBDPE or BTBPE in certain fish species [28; 29; 30]. Nevertheless, several possible reasons could explain the present observations. One possible reason may be that the levels of E2 were not sufficiently high to induce vitellogenesis, which is in agreement with previous studies [51; 52]. Secondly, hormone metabolic breakdown or clearance can be considered [43]. In a previous report, E2 metabolites and conjugates e.g., estradiol-sulfate or glucuronide in liver and plasma, may affect binding affinity of E2 with ER in rainbow trout and Siberian sturgeon [53]. Lastly, effects of 11-KT on ovarian development and \(vtg\) are also possible. Several works showed that androgens could alter the levels of \(vtg\) gene, VTG synthesis, or binding to ERs in both in vitro and in vivo fish studies [54; 55; 56]. Our observations imply that DBDPE and BTBPE may possess weak hormone-disruption potency in male zebrafish and H295R cells, while steroidogenic gene alterations and sex hormone receptor activation could not explain the observation. This study has several limitations. First, only the genes related to the steroidogenic pathway in H295R cells and the vitellogenesis in male zebrafish were evaluated, and therefore, different modes of action that may cause sex hormone disruption, such as altered hepatic metabolisms [44], could not be tested. Secondly, our study employed only male zebrafish, thus the potential effects on female zebrafish remain to be investigated in the future. Lastly, exposure levels of the present study are higher than the levels of environmental occurrences. Therefore, studies of the consequences of long-term exposure at environmentally relevant concentration are warranted to understand the reproduction process and its detailed mechanisms in the aquatic ecosystem.
## 2 Experimental Testing
### Experimental Result
#### 2.2.4 Hoop and Axial Strain  
The gauge strain distributions in hoop and axial direction around the liner circumferences for the SSL specimens (T1-T5) are illustrated in Figure 10. Figure 11 shows the pressure-strain curves of the sample T1 under four different buckling modes. After buckling, both deformation and strain increase significantly, even if the pressure is less than the critical buckling pressure, which clearly indicates that the lining loses its structural stability after buckling. By analyzing the strain distribution, the mechanical behavior before and after buckling was also studied. Initially, the tensile strain develops uniformly around the lining. The annular strain was mostly strained before buckling. When buckling, the strain near the buckling position reaches a maximum (tension) or a minimum (compression). The annular strain distribution reveals the buckling position and buckling range of the lobe. After buckling, most strains relaxed when the vacuum was reduced, except the minimum strain near the buckling position.  
Figure 10: _Cont._  
According to the test results of lining mechanical properties in Section 2.1.1, the elastic limit strain of SSL was obtained as \(\epsilon=0.105\%\). Figure 10 shows the elastic strain area of the monitored cross-section during SSL buckling, and blue dots mark the location where the buckling occurred. According to the hoop and axial strain pattern of buckling mode 1, samples in groups T1, T2, T3 and T4 all enter plastic buckling state from elastic buckling. The hoop and axial strains corresponding to the buckling of T1, T3 and T4 groups exceeded the elastic limit range. However, only the axial strain exceeded the elastic limit when the T2 group's buckling occurred. The buckling of group T5 occurred within the elastic limit, which was consistent with the video record of the experiment. When the initial buckling occurred and the vacuum degree was reduced, the retraction of the buckling part was observed. In Figure 11, whether in hoop or axial direction, with the increase of vacuum degree of the system under different modes, a large tensile stress occurs at the buckling position. When the critical pressure is reached, the tensile stress decreases. For M1H (Hoop Strain of Mode 1), SSL even eventually transforms from tensile stress to compressive stress. After producing three lobes, a fourth buckling lobe was produced at a higher pressure. The strain discipline recorded by the strain gauge at the position of the fourth lobe during the vacuum degree increase was as follows. Initially, the lining material reaches the limit of elastic strain (tensile strain). Subsequently, in the course of the three buckling lobes of the first platform, the lining material changes from tensile strain to compressive strain. Finally, as the vacuum continues to increase, the pressure state changes to the tensile state again, and buckling occurs in the second platform.  
Figure 10: SSL strain in different buckling modes.
###### Abstract  
Dynamically partially reconfigurable (DPR) technology based on FPGA is applied extensively in the field of high-performance computing (HPC) because of its advantages in processing efficiency and power consumption. To make full use of the advantages of DPR in execution efficiency, we build a DPR system model that meets to the actual application requirements and the objective constraints. According to the consistency of reconfiguration order and dependencies, we propose two algorithms based on simulated annealing (SA). The algorithms partition FPGA resource to several regions and schedule tasks to the regions. In order to improve the performance of the algorithms, we exploit the module merging technology to improve the parallelism of task execution and design a new solution generation method to speed up the convergence speed. Experimental results show that the proposed algorithms have a lower time complexity than mixed-integer linear programming (MILP), iterative scheduler (IS) and Ant Colony Optimization (ACO). For applications with more tasks, the proposed algorithms show performance advantages in producing better partitioning and scheduling results in a shorter time.  
Keywords:dynamical partial reconfiguration; scheduling; partitioning; FPGA; simulated annealing +
Footnote †: journal: Electronics Electronics
# Invisible Threats in the Data: A Study on Data Poisoning Attacks in Deep Generative Models
## 4 Experiments
### Set Up
#### 4.1.4 Evaluation Metrics  
To evaluate objective 1 (the fidelity of our threat model), this study will employ three commonly used metrics from the literature. These metrics will be used to demonstrate that models poisoned by our invisible backdoor attack method can still maintain the level of fidelity. This approach will provide objective evidence of the effectiveness of our attack in preserving the model's intended functionality while introducing an invisible backdoor.  
**KID** (Kernel Inception Distance) [43] was employed to assess the difference in distribution between the image generated by the generative model and the authentic image. It should be noted that a smaller value for KID indicates a higher quality of generated images.  
**FID** (Frechet Inception Distance) [44] and **EQ_T**(equivariance integer translation) [38] are also critical parameters for evaluating fidelity. A high EQ_T/a low FID indicate a higher fidelity of the threat model.  
Furthermore, in order to measure objective 2 (stealthiness of the trigger), this study utilizes image datasets generated from both a StyleGAN3 model poisoned by our invisible backdoor attack method and a clean StyleGAN3 model. These datasets are then applied to various machine learning models (ResNet18, ResNet34, ResNet50, VGG16) for image classification. By comparing the baseline accuracy (BA) values obtained from these models, we can assess the stealthiness of the poisoned images. A significant difference in BA values between models trained on clean and poisoned datasets would indicate the effectiveness of our invisible attack in concealing the trigger.  
The **BA** is defined as the ratio between the number of successfully identified samples and the corresponding total number of samples. \(D_{\text{train}}\) represents the training dataset, \(D_{s}\) represents the data that have been successfully recognized, and \(y_{t}\) represents the target label, \(y_{t}\in Y\):  
\[\textbf{BA}=\frac{D_{s}}{D_{train}} \tag{7}\]  
\[\textbf{D}_{s}=\{(x_{t},y_{t})\mid x_{t}=\textbf{G}_{\theta}(x),(x,y)\in D_{ poison}\} \tag{8}\]