esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Multi-DAG Satellite Network Task Scheduling Algorithm Based on Cache-Composite Priority
## 5 Conclusions  
In satellite edge computing, due to the limited computing capacity and energy consumption of MEC servers, individual MEC nodes may struggle to handle the computational load for certain tasks. To address this issue, this paper split large tasks into DAG tasks and proposed a multi-DAG scheduling algorithm with composite priority. This algorithm effectively resolves the challenge of deploying large tasks to remote clouds, thereby improving user response latency and reducing system energy consumption. At the same time, in the satellite edge computing system, the concept of public subtasks was introduced to reduce system overhead from repetitive tasks. A multi-DAG task scheduling algorithm based on cache-composite priority was proposed, and compared to the LB-HEFT algorithm, it demonstrated improvements of nearly 20% in completion time performance and around 25% in energy consumption performance. Additionally, the Cache-Composite priority algorithm reduced the unfairness by 42.1% and significantly enhanced the task completion degree. However, the performance of the Cache-Composite priority algorithm in load balancing was not ideal and requires further research and optimization.  
Conceptualization, Z.L. and L.Z.; methodology, Z.L. and L.Z.; software, L.Z. and L.W.; validation, L.Z. and X.D.; investigation, L.Z., L.W. and J.R.; writing--original draft preparation, Z.L.; writing--review and editing, Z.L., L.Z., L.W., X.D. and J.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The processed data required to reproduce these findings cannot be shared as they also form part of an ongoing study.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 4 Problem Formulation
### Appliance Energy Consumption  
In the proposed system model, the consumer appliances were classified as: deferrable appliances \(A_{d}\), base load appliances \(A_{b}\), and non-deferrable appliances \(A_{nd}\). The total number of user appliances is represented by \(A_{n}\). According to the user requirements and preferences, all user appliances are scheduled in 24-h time horizon. The total energy consumed by consumer appliances can be mathematically represented as:  
\[E_{a}(h)=(E_{b}(h)+E_{d}(h)+E_{nd}(h)), \tag{13}\]  
\[E_{a}(h)=\sum_{h=1}^{H}\left(\sum_{a_{b}\in A_{b}}(P_{b}*S_{b}(h))+\sum_{a_{d }\in A_{d}}(P_{d}*S_{d}(h))+\sum_{a_{nd}\in A_{nd}}(P_{nd}*S_{nd}(h))\right), \tag{14}\]  
where \(E_{b}(h)\), \(E_{d}(h)\), and \(E_{nd}(h)\) are the energy utilized by base load appliances, deferrable appliances, and non-deferrable appliances, respectively, in each time interval. \(A_{b}\), \(A_{d}\), and \(A_{nd}\) represent the set of base load appliances, deferrable appliances, and non-deferrable appliances respectively. \(P_{b}\), \(P_{d}\), and \(P_{nd}\) denote the power rating of appliances belonging to the base load, deferrable, and non-deferrable appliances, respectively. \(S_{b}\), \(S_{d}\), and \(S_{nd}\) represent the on/off status of the appliance belonging to the baseload, deferrable, and non-deferrable appliances, respectively, during any hour \(h\) of a day. ON status is represented by 1 while a 0 value denotes an OFF status of an appliance. An additional constraint, represented by Equation (15), should also be added for non-deferrable appliances to ensure the completion of their entire operating cycle after the beginning of their operation in the predefined interval:  
\[\sum_{h=t+1}^{t+k_{a}}S_{nd}(h)\geq\ \gamma_{a}\cdot[S_{nd}(t+1)-S_{nd}(t)],\ \ t\in \left[\hat{\alpha}_{a}-1,\ \hat{\beta}_{a}-k_{a}\right], \tag{15}\]  
where \(k_{a}\) represents the required time for an appliance to complete its operation or task and \(\gamma_{a}\) demonstrates the required number of time intervals for each appliance to complete its operation. Therefore, if any non-deferrable appliance starts its operation at time \(t+1\), then it will complete its task in at least \(k_{a}\) hours. \(\hat{\alpha}_{a}\) shows the earlier starting time of any appliance and \(\hat{\beta}_{a}\) represents its least finishing time.
# Prediction of Air Quality Combining Wavelet Transform, DCCA Correlation Analysis and LSTM Model
## 3 Results
### DCCA Coefficients on Each Scale and Their Relationship
#### 3.2.4 AQI Correlation with Wind  
The blue line in Figure 3 is the inter-relationship number change curve between the AQI and the wind speed. In the graph, the inter-relation number between the AQI and the wind speed was predominantly negative, showing a negative link between AQI and wind speed, and the inter-relation number was stable at \(-0.68\) around \(n=16{,}500\).
## 1 Introduction
### Damage Models
#### 1.1.2 Johnson-Cook Damage Model  
[PERSON] and [PERSON] (2010) developed the following relation for failure strain:  
\[\varepsilon_{f}=\left(D_{1}+D_{2}\exp\left(D_{3}\left(\frac{p}{\sigma_{y}} \right)\right)\right)\left(1+D_{4}Ln\dot{\varepsilon}\right)\left(1+D_{5}T^{*} \right), \tag{9}\]  
In which \(\varepsilon_{f}\) is the strain failure, \(\frac{p}{\sigma_{y}}\) is the stress triaxiality parameter, \(\dot{\varepsilon}\) is strain rate and \(T^{*}\) is the dimensionless temperature calculated by Equation (10). \(D_{1}\) to \(D_{5}\) are material constants.  
\[T^{*}=\frac{T-T_{room}}{T_{melt}-T_{room}}, \tag{10}\]  
where \(T_{room}\) is the room temperature and \(T_{melt}\) is the material melting point. In the Johnson-Cook model, parameter \(D\) defined by Equation (11), represents the voids' growth and is used as the fracture criterion.  
\[D=\sum\frac{\Delta\varepsilon}{\varepsilon_{f}}, \tag{11}\]  
This parameter is generally a function of strain rate and the stress triaxiality parameter. In this relation \(D\) is the damage coefficient and \(\Delta\varepsilon\) is the plastic strain increment in each iteration. In the finite element numerical simulation the value \(D\) is calculated in every loading increment for each element. When the value of \(D\) reaches unity in an element, \(D=1\), fracture occurs in that element and the element is eliminated from the computations.
## 5 Discussion
### Comparison of Spectral Indices for Burned Area Mapping
Even though each index contributes to the spectral discrimination of burned areas while introducing some spectral confusion with other surface classes [59], one can say that the BAIS2 and BAIS2\(\alpha\) spectral indices consider vegetation more than the NBR index does. Nevertheless, this new feature has been demonstrated to be suitable for the Mediterranean ecosystem and should be verified for different biomes. Comparison of further spectral indices for burned area detection was not performed in this study and requires further investigations. The design of an algorithm to combine multiple spectral indices and information is beyond the scope of this study, but this is a recommended subject for future research studies, following previous attempts for the soft integration of different spectral indices [11; 59] and the combination with SAR data [24; 58; 60].
# Data-Based Stakeholder Identification in Technical Change Management
## 6 Industrial Application and Evaluation
### Expert Knowledge  
As described in the developed methodology, the trained models were further improved by integrating expert knowledge. Therefore, 14 employees from different areas of the assembly business unit were asked to specify relevant words and word combinations which indicate the relevance of their business unit. The workshops lasted about one hour. A total of 667 words were named by the experts, which were then grouped into the five superordinate categories \"modification\", \"product component\", \"assembly\", \"assembly part\", and \"plant\" (cf. Section 5.5), which unify words with synonymous or similar meanings. From this initial collection, 7930 possible word combinations were derived, which were then automatically evaluated with regard to their potential for increasing the model quality and, at the same time, reducing the number of FN cases. The evaluation resulted in a model-specific list of 630 relevant word combinations whose integration had a positive effect on the recall without reducing the F1 score. Figure 5 shows the impact of integrating expert knowledge, with all results of the text-classification model being considered \"relevant to the business area\" if one or more of the 630 relevant word combinations occur. The results in Figure 6 show that the two-stage model, which uses the derived word combinations as well as additional data attributes, brings a further improvement in model quality. The word combinations changed the classification results for 135 of 9278 changes. As a result, 81 TN became FP, and 54 FN became TP. Accordingly, a 2.32% improvement in recall was achieved, with a 0.33% reduction in Precision and a 1.16% reduction in Specificity. Concurrently, the Balanced Accuracy value increased by 0.58% and the F1 score by 0.35%.
## 3 Detection in Receiver
### Channel Coding  
#### 3.3.1 SVM Posterior Probabilities Estimate  
We have made a hard-decision by using SVM classification, in some cases, such as channel decoder needs a posterior probability to achieve capacity. [PERSON] has proposed that the SVM output can be transformed into posterior probabilities [24]. The method squashes the SVM soft output through a trained sigmoid function to predict posterior probabilities as follows:  
\[p(y=1\mid\mathbf{x})\approx P_{A,B}(f)=\frac{1}{1+\exp(\mathcal{A}f+B)} \tag{17}\]  
where \(f=f(\mathbf{x})\), let each \(f_{i}\) be an estimate of \(f(\mathbf{x}_{i})\). The best parameter setting \(z^{*}=(A^{*},B^{*})\) is determined by solving the following regularized maximum likelihood problem:  
\[\min_{z=(A,B)}\;\;F(z)=-\sum_{i=1}^{J}(t_{i}\log(p_{i})+\big{(}1-t_{i}\big{)} \log(1-p_{i})\big{)} \tag{18}\]  
where \(p_{i}=P_{A,B}(f_{i})\), \(t_{i}=(y_{i}+1)/2\).  
Unfortunately, log and exp could easily cause an overflow. If \(p_{i}\) is near zero or \(\mathcal{A}f_{i}+B\) is large, \(\exp(\mathcal{A}f_{i}+B)\rightarrow\infty\) and \(1\cdot p_{i}=1-\frac{1}{1+\exp(\mathcal{A}f_{i}+B)}\) is a \"catastrophic cancellation\" when \(p_{i}\) is close to one. The problem can usually be resolved by reformulation [25] and we can get the SVM PPEs as follows:  
\[p(y=1\mid\mathbf{x})=\begin{cases}\frac{1}{1+\exp(\mathcal{A}f+B)},\;\; \mathcal{A}f+B<0\\ \frac{\exp(-\mathcal{A}f-B)}{1+\exp(-\mathcal{A}f-B)},\;\;\mathcal{A}f+B\geq 0 \end{cases} \tag{19}\]  
From (19) we can see that the PPE is an approximate one. This means that the SVM does not provide PPEs and its output needs to be transformed, before it can be interpreted as posterior probabilities.
## 4 Validation Through Sea Trials
### Processing and Analysis of Sea Trial Data with Added Ocean Noise
#### 4.4.2 Extraction of Data Under Different Motion States from Sea Trials  
Due to significant variations in motion states within each data group and substantial differences between the motion states of different groups, to more clearly demonstrate the superiority of the CCF-MSS reception method, we extracted sea trial data based on different motion states.The data were extracted based on six motion states: \(V=1.4\) m/s, \(V=2.7\) m/s, \(V=3\) m/s, \(a=-0.01\) m/s\({}^{2}\), \(a=-0.036\) m/s\({}^{2}\), and \(a=1.065\) m/s\({}^{2}\), and ocean trial noise was added to these states. The BER curves are shown in Figure 18.  
Figure 18: BER curves of sea trial data with random ocean noise added under different motion states.  
Based on the processing of the sea trial data, we can draw the following conclusions: 1. Under low signal-to-noise ratio and complex motion conditions, CCF-MSS demonstrates superior noise resistance, speed resilience, and acceleration tolerance compared to other reception methods. At a speed of 3 m/s, when the bit error rate reaches \(10^{-3}\), the performance of CCF-MSS improves by approximately 1 dB and 2 dB compared to FDE-MSS and CDE-MSS, respectively. Furthermore, when the acceleration is 1.065 m/s\({}^{2}\), at the same BER level, the performance improves by approximately 4 dB compared to FDE-MSS. As the noise intensity increases to a certain level, the performance gap gradually widens. However, when the noise becomes excessively high, the performance differences begin to diminish. 2. Doppler shifts generated by motion can be compensated for through Doppler estimation; however, when acceleration is substantial, the errors generated by coarse Doppler estimation can also be significant, making it easy for the FDE-MSS reception method to yield inaccurate estimates of the frequency shift direction, which results in cumulative errors. In such cases, it is essential to utilize correlation cost factors for subsequent estimations, accurately confining the range of Doppler frequency shift variations within the search interval. 3. Inaccurate Doppler estimation can lead to a significant reduction in spreading gain, resulting in diminished noise resistance, which indicates that the noise resistance of CCF-MSS is attributed to its precise Doppler estimation capability.
## 3 Implementation and Experimental Results  
First, three SAT images captured from different wafers (in the same batch) on the semiconductor production line were prepared for the following experiments. For convenience, we named them _img01_, _img02_, and _img03_. In this section, we focus on the explanation and implementation of (1) automatic template extraction, (2) the training and testing stages of our pattern classification method, and (3) a discussion on using different networks as the backbone of the image feature extractor. To meet the computational requirements when executing a deep CNN-based model, a graphics processing unit (GPU)-accelerated computer was used to implement our proposed method. We run all the experiments on the computer with an Intel Core I7-8750H CPU @ 2.2 GHz, 16G DDR4 RAM 2400 MHz, NVIDIA GeForce GTX-1060. The operating system was Windows 10. The entire algorithm was programmed in Python and used OpenCV and TensorFlow.
# On the Impact of Multiple Access Interference in LTE-V2X and NR-V2X Sidelink Communications +
## 4 Analytical Characterization of the Sidelink Operations
### Model of Aggregate Fading Components  
To determine the system's performance in terms of packet success probability, the statistical characterization of the SINR is an essential step. The SINR associated to the tagged transmitter \(i\) and measured at the platoon head \(j\) is:  
\[SINR=\frac{P_{rx,i,j}}{\sum_{n\in\mathcal{I}}P_{rx,n,j}+\sigma_{w}^{2}}=f_{i} \Bigg{(}\sum_{n\in\mathcal{I}}\frac{P_{tx,n}d_{ij}^{\alpha}}{P_{tx,i}d_{n,j}^{ \alpha}}f_{n}\exp(y_{n}-y_{i})+\frac{\sigma_{w}^{2}d_{ij}^{\alpha}}{c_{0}P_{ tx,i}}\exp(-y_{i})\Bigg{)}^{-1}, \tag{3}\]  
where \(\sigma_{w}^{2}\) is the noise power and \(\mathcal{I}\) is the set of interfering vehicles that select the same resource (i.e., the same sub-channel and sub-frame). The composition of \(\mathcal{I}\) depends on the resource schedule provided by the SPS procedure. According to Equation (3) and given the power model of Equation (1), we can see that the PDF expression is not available in closed form when the SINR is defined as a weighted sum of log-normal random variables. As a result, we give an approximate description of the SINR data as follows:  
Firstly, let us elaborate on the random variable:  
\[\tilde{Y}_{i,j}=-\ln\Bigg{(}\sum_{n\in\mathcal{I}}B_{i,j,n}\exp(\tilde{y}_{n}) \Bigg{)}, \tag{4}\]  
Figure 2: Semi-persistent scheduling in cellular V2X.  
where \(\tilde{\mathcal{I}}=\{0;\mathcal{I}\}\),  
\[B_{i,j,n}=\begin{cases}\frac{\sigma_{\tilde{\mathcal{I}}}^{2}\tilde{\sigma}_{i,j} }{\varepsilon_{0}\tilde{P}_{\tilde{\mathcal{I}},i}}&n=0,\\ \frac{p_{\tilde{\mathcal{I}},i}\tilde{\sigma}_{i,j}^{2}}{p_{\tilde{\mathcal{I }},i}\tilde{\sigma}_{\tilde{\mathcal{I}},i}^{2}}f_{n}&n\in\mathcal{I},\end{cases} \tag{5}\]  
and  
\[\dot{y}_{n}=\begin{cases}-y_{i}&n=0,\\ y_{n}-y_{i}&n\in\mathcal{I},\end{cases} \tag{6}\]  
Then, according to the moment matching approximation (MMA) method (M  
LUTs that report the block error rate (BLER) as a function of the SNR for a given packet size, MCS, scenario (highway or urban), and a reference speed between transmitter and receiver with no re-transmissions. The LUTs are used to map the SINR to the packet error rate, assuming that the negative effect of the interference on the received signal is equivalent to additional noise. Without active interfering vehicles, the SINR reduces to the SNR. We remark that the derivation of an exact expression of \(PER(x)\) is a cumbersome task, and these LUTs are widely used in the related literature (e.g., [21]).
# Healable, Flexible Supercapacitors Based on Shape Memory Polymers
## 4 Conclusions  
In summary, we successfully combined the MnO\({}_{2}\)/CNT film with the mixture of PCL and SMPU substrate using transfer technology. We have demonstrated the fabrication of conductive MCPS films with good capacitance and thermal healability. The conductive MCP40S60 supercapacitors exhibit a good capacitance of 27.33 mF cm\({}^{-2}\) at 0.2 mA cm\({}^{-2}\) and cycle stability (1500 cycles with only 4.08% capacitance loss). The composite was capable of healing cracks under thermal stimulation once and excellent self-healing efficiency of up to 93% was obtained. Consequently, the superior performance of the MCPS electrode could contribute significantly to the design and fabrication of novel bio-mimetic supercapacitors.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/8/10/1732/s1](http://www.mdpi.com/2076-3417/8/10/1732/s1)]([http://www.mdpi.com/2076-3417/8/10/1732/s1](http://www.mdpi.com/2076-3417/8/10/1732/s1)), Figure S1: (**a**) CV curves of the composite with 20% PCL contents collected at different scan rates; (**b**) galvanostatic charge/discharge curves of MCP20S80 electrode film; (**c**) areal capacitances of MCP20S80 electrode film at different current density. Figure S2: (**a**) CV curves of the composite with 60% PCL contents collected at different scan rates; (**b**) galvanostatic charge/discharge curves of MCP60S40 electrode film; (**c**) areal capacitances of MCP60S40 electrode film at different current density. Figure S3: (**a**) CV curves of the MCP20S80 at different states collected at 5 mV s\({}^{-1}\); (**b**) galvanostatic charge/discharge curves of MCP20S80 electrode film at different states collected at 0.2 mA cm\({}^{-2}\). Figure S4: (**a**) CV curves of the MCP60S40 at different states collected at 5 mV s\({}^{-1}\); (**b**) galvanostatic charge/discharge curves of MCP60S40 electrode film at different states collected at 0.2 mA cm\({}^{-2}\).  
**Author Contributions:** Conceptualization, H.L. and H.Z.; Writing, H.Z.; Data curation, H.W.; Investigation, H.Z., X.Z. and Y.Y.; Formal analysis H.Z. and X.Z.; Resources, W.L. and G.Y.  
**Funding:** This research received no external funding  
**Acknowledgments:** The authors thank the Natural Science Foundation of China (No. 51873042), the China Postdoctoral Science Foundation (No. 2015M580709), and the Applied Science and Technology R&D and Provincial Open Laboratory Construction Foundation of Guangdong Province (Nos. 2017B090915004, 2017B030314105) for providing financial support.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 5: SEM images of the cracked areas before and after self-healing corresponding to 20% (**a**,**d**); 40% (**b**,**e**); 60% (**c**,**f**) PCL contents of the samples, respectively.
## 2 Results
### Transcriptome Results
#### 2.4.3 Analysis of Differentially Expressed Genes  
When analyzing the DEGs among these groups, we mainly focused on C18 vs. the WT and P1 vs. the WT. First, a GO analysis (Figure 7a,b) of C18 vs. the WT revealed that DEGs were mainly enriched in the following pathways: the microtubule-based process, movement of cellular or subcellular component, carbohydrate metabolic process, etc. Under the CC (cellular component) category, these differentially expressed genes were significantly numerous in the microtubule cytoskeleton, cytoskeletal part and microtubule-associated  
Figure 6: Analysis of differentially expressed genes (DEGs). (**a**) Venn diagram. (**b**) Volcano plot for DEGs of C18 vs. WT. (**c**) Volcano plot for DEGs of P1 vs. WT. (**d**) Volcano plot for DEGs of P1 vs. C18.  
complex. Under the MF (molecular function) category, most DEGs were enriched in heme binding, tetrapyrrole binding and DNA polymerase activity. However, P1 vs. WT was quite different, and the DEGs were mainly enriched in photosynthesis, the oxidation-reduction process, and organonitrogen compound biosynthetic process under the BP (biological process) category, while DEGs were mainly enriched in the photosynthetic membrane, photosystem and thylakoid under the CC category. Under the MF category, we can clearly see the DEGs were enriched in cofactor binding, oxidoreductase activity and coenzyme binding.  
Figure 7: DEGs enrichment results via GO and KEGG. (**a**) GO functional classification for DEGs of C18 vs. WT. (**b**) GO functional classification for DEGs of P1 vs. WT. (**c**) KEGG pathway annotation of DEGs for DEGs of C18 vs. WT. (**d**) KEGG pathway annotation of DEGs for DEGs of P1 vs. WT.  
While through the KEGG-enriched bubble plot, we found more information about DEGs (Figure 7c,d). First, the KEGG enrichment data of C18 vs. WT showed that the DEGs were mainly associated with the following pathways: the pentose phosphate pathway, DNA replication, ascorbate and adarate metabolism, purine metabolism, the biosynthesis of secondary metabolites, etc. However, DEGs of P1 vs. WT were mainly enriched in the biosynthesis of secondary metabolites, photosynthesis, photosynthesis, photosynthesis-antenna proteins, biosynthesis of amino acids and ribosome, etc. Combining the KEGG enrichment results of these two groups, it became obvious that photosynthesis and secondary metabolite synthesis were the pathways commonly enriched by the DEGs of C18 and P1 vs. WT. Analysis of the DEGs revealed significant gene expression level differences between the C18 and P1 strains relative to that of the WT, presumably due to carotenoid metabolic pathway changes facilitating differences in other growth-related gene expression.