esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Oxygen reduction reaction (ORR) at the cathode side of a fuel cell is slow. Its kinetics can be improved with high amounts of Pt-based catalysts but these catalysts increase a fuel cell's cost. For this reason, new and reliable nonprecious metal ORR catalysts with a performance comparable with that of Pt-based catalysts should be explored. For instance, nitrogen-doped carbon catalysts, which contain iron (Fe), cobalt (Co) and manganese (Mn), are considered the most encouraging Pt/C substitute because of the amplifying and synergistic effects between nitrogen, carbon support and transition metals [1; 2; 3]. Fe can bond with multiple nitrogen (N) atoms, which change the configuration and characteristics of Fe species, thereby improving the overall catalyst behavior [4]. The efficiency of Fe-based catalysts is strongly affected by the synthesis procedures and the nature of existing heteroatoms. The most prominent synthesis of Fe-based catalysts is pyrolysis, which improves the durability of carbon-based nonprecious catalysts [5; 6]. A carbon structure assumes a critical function in deciding the catalytic activity. Hence, appropriate carbon, metal and nitrogen antecedents should be chosen to build an exceptionally alluring catalyst for ORR [7; 8]. Graphene as a catalyst support helps increase catalytic activity and durability because of its high explicit surface area (2630 m\({}^{2}\) g\({}^{-1}\)) and high electrical conductivity [9; 10; 11]. Several graphene-type materials, such as graphene oxide (GO), reduced graphene oxide (rGO), carbon nanotube and carbon nanofiber, are used as catalyst supports. Among these catalyst supports, rGO has been the most widely explored; its thermal stability is better than that of GO [12; 13]. rGO is derived from GO, which can be produced via chemical and thermal exfoliation, chemical vapor deposition or arc discharge [10; 14]. However, these methods are not suitable for producing GO in large quantities. As such, modified Hummer's method is widely applied to produce GO and hydrazine hydrate is used as a reducing agent to subsequently reduce GO to rGO [15; 16].  
Gamma irradiation has been used to produce different metal, metallic compound and metal oxide nanoparticles [17]. It is an attractive, easy and environment-friendly method of producing graphene-based nanomaterials [18; 19]. It also helps reduce metal ions and modifies carbon properties and chemical reactions on surfaces [20; 21].  
In this study, rGO was synthesized using modified Hummer's method and reduced with hydrazine hydrate. rGO, urea and iron catalyst at different loadings were irradiated with gamma rays to produce N-doped rGO-based Fe catalysts.
## 2 Preliminaries
### SCL-Flip Decoder, SCL-SP Decoder, and ADOSPL Decoder  
To further improve the performance of the CA-SCL decoder, Ref. [18] proposed the first list flip decoder, named SCL-BF decoder. This decoder brings the flip operation idea to the CA-SCL decoder for the first time, thus allowing it to have better performance than the CA-SCL decoder with the same list size through more decoding attempts with flip operations. Obviously, the performance gain is closely related to the list flip operation itself and the decision with regard to list flip positions. To further improve the performance of the SCL-BF decoder, Ref. [21] proposed the SCL-SP decoder to optimize the list flip operation, and [20] designed the SCL-Flip decoder to optimize the decision of flip positions.  
The SCL-SP decoder uses a new list flip operation called the shift-pruning operation to replace the original flip operation; it achieves better performance than the SCL-BF decoder with the same list size. The new list flip operation refers to \(\rfloor_{best}^{(i)}\) and can be achieved by selecting \(L\) paths with a bigger \(PM\) from \(\rfloor^{(i)}\) when the \(i\)th bit channel is a list flip position. Furthermore, to improve the performance of the SCL-SP decoder, [PERSON]. [22] optimized the flip order of the SCL-SP decoder, introduced the adaptive list, and proposed a new list flip decoder called the adaptive-ordered shifted-pruning list (ADOSPL) decoder. The flowchart of the ADOSPL decoder is shown in Figure 1. When \(L_{cur}<L_{max}\), the ADOSPL decoder performs a CA-SCL decoding operation with the list size \(L_{cur}\) to obtain the estimated code word. When \(L_{cur}=L_{max}\), the ADOSPL decoder executes the OSPL decoder (a list flip decoder proposed by [22]) with a list size of \(L_{max}\) to obtain the estimated code word. Since the OSPL decoder has little correlation with this paper, we will not provide more details about this algorithm. The termination condition of the ADOSPL decoder is: either a candidate path passing the CRC is found in a decoding attempt, or \(T+1+Log_{2}(L)\) decoding attempts have been performed. \(T\) is the maximum number of additional decoding attempts with list flip operations.  
Figure 1: The flowchart of the ADOSPL decoder.  
The SCL-Flip decoder employs a flip metric to optimize the decision of the SCL-BF decoder with regard to list flip positions. This flip metric satisfies the following equation:  
\[E_{\alpha}^{(i)}=ln\frac{\sum_{l=1}^{c}e^{-PM_{l}^{(i)}}}{(\sum_{l=1}^{c}e^{-PM_{ l+L}^{(i)}})^{\alpha}}, \tag{3}\]  
where \(E_{\alpha}^{(i)}\) denotes the flip metric \(E_{\alpha}\) value of the \(i\)th bit, and \(\alpha\) is a coefficient to compensate for the biased estimation due to the error propagation. A lower \(E_{\alpha}\) means that mistakes are more likely to occur. Because of this, the SCLFlip decoder prioritizes the flipping of information bits with lower \(E_{\alpha}\) value.  
Although these list flip decoders can achieve better performance than the CA-SCL decoder to some extent, they cannot correct high-order errors efficiently, which limits the performance gain brought by the list flip operations.
However, the use of DPR at run time to search for an adaptive working point trade-off between performance, energy efficiency, and fault tolerance in low-cost COTS devices is something not commonly found in the literature. In this paper, a distributed and hardware-accelerated implementation of a linear unmixing algorithm for on-board hyperspectral image processing is presented. Although hyperspectral image unmixing is an application that has been following the classical execution flow (i.e., on-board compression, and on-Earth decompression and processing), the use of a small and low-cost computing cluster with SoPC-based nodes, emulating real satellite deployment, provides a competitive alternative for distributed edge computing on CubeSats. Each node uses the ARTICo\({}^{3}\) architecture [18], a hardware-based processing architecture for high-performance embedded systems, and its DPR infrastructure to support user-driven run-time adaptation of computing performance, energy consumption and fault tolerance. In addition, the ARTICo\({}^{3}\) toolchain has been used to automate the implementation of the mixed hardware/software system, leveraging High-Level Synthesis (HLS) engines, and thus rendering low development times, a key factor in CubeSat deployments. As entry point for the design flow, a high-level description of the Fast UNmixing (FUN) algorithm [19] in C code has been modified to fully exploit data-level parallelism and enable a two-way scalable execution pattern: on the one hand, in the number of ARTICo [3] accelerators per computing node; on the other hand, in the number of SoPC-based nodes involved in the processing stage. Although the data-parallel extension of the FUN algorithm has been implemented and evaluated using the ARTICo [3] architecture, it is platform-agnostic and, thus, it can be deployed on any computing platform as long as the parallelism holds. Moreover, the proposed block-based partitioning scheme can provide enhanced fault tolerance to the final implementation, making it possible to extract nearly all endmembers from an input dataset even if the data associated to one of the blocks get corrupted (e.g., due to radiation in space). In summary, the main contributions of this paper are:  
* A data-parallel linear unmixing algorithm for on-board hyperspectral image processing based on the FUN algorithm (originally conceived for on-Earth processing). * A low-cost, networked, and hardware-accelerated implementation of that algorithm with two scalability levels and run-time adaptation capabilities in terms of computing performance and energy efficiency. The rest of this paper is organized as follows. Section 2 presents an overview of the related work. The FUN algorithm and the ARTICo [3] framework, which constitute the technological foundations of the presented work, are presented on Section 3. Section 4 gives an overview of the implementation details, including algorithmic modifications, hardware-optimization techniques, and network-distribution approaches. Section 5 shows the experimental validation and the obtained results, and Section 6 provides the conclusions and future work.
## 4 Doppler Frequency Estimation in the Presence of Noise
### Simulation Experiments for Performance Assessment  
To test the algorithms R\({}_{n}\)-3, C\({}_{n}\)-3 and LS, we performed several simulation experiments with different values of the carrier-to-noise density ratio (CNR), corresponding to a signal-to-noise ratio \(\rho\) given by:  
\[SNR=\frac{C}{N_{0}B} \tag{50}\]  
where \(B\) is the one side front-end bandwidth, assumed ideally flat over the whole digitization bandwidth.  
The parameters used in the experiments are \(f_{IF}=4\) MHz, \(f_{s}=17\) MHz, \(\tau=0.11\) ms, and \(f_{sp}\) is set as in Equation (33). We use the root-mean-square error (RMSE) computed for the different algorithms as a metric of performance comparison:  
\[f_{RMSE}=\sqrt{E\left\{(\widehat{f_{d}}-f_{d})^{2}\right\}} \tag{51}\]  
where \(\widehat{f_{d}}\) is the Doppler frequency estimate, and \(E\{\cdot\}\) is the expected value (estimated as a temporal average along the simulation runs). The metric \(f_{RMSE}\) is calculated for each algorithm and compared with the square root of CRLB.  
In the first group of experiments, we set \(T_{d}=4\) ms and we executed 1000 runs for each CNR. Then, according to Equation (51), we calculated the corresponding RMSE. The results can be seen in Figure 8. We can see that in this case the algorithm R\({}_{n}\)-3 is better than C\({}_{n}\)-3, as it achieves a lower RMSE closer to the CRLB. At the same time we can see that the algorithm R\({}_{n}\)-3 is very close to the least-square method, though the latter is slightly better. However, considering the computation complexity of the LS method, the algorithm R\({}_{n}\)-3 appears really competitive with respect to the LS. Finally, the threshold CNR (below which the RMSE rapidly worsens) is around CNR = 38 dB-Hz in all the proposed methods.  
In the second group of experiments, we changed the integration time to \(T_{d}=8\)ms. Similarly, we executed 1,000 runs for each CNR and obtained the results reported in Figure 9, where we can observe  
Figure 8: Results of the first group of experiments, with \(T_{d}=4\) ms (including the false alarm).  
that, as expected, the RMSE is lower than in Figure 8, since a longer integration time reduces the effects of noise. Again, R\({}_{n}\)-3 is the closest to the least-square method, and the threshold CNR decreases to around CNR = 35 dB-Hz in all three proposed methods.  
Comparing Figures 8 and 9 we can observe that, first, at the same CNR, the RMSE in Figure 9 is decreased by about a factor of 2.8 with respect to Figure 8. This is in agreement with the theoretical CRLB given in Equation (49). In fact when \(T_{d}=4\) ms is replaced with \(T_{d}=8\) ms, the CRLB bound decreases by a factor of \(\sqrt{8}\). Furthermore, when CNR is relatively high (like CNR\(\geq 45\)dB-Hz in Figure 8), the proposed three algorithms are very close to the CRLB and each other.
# Generative Model for Skeletal Human Movements Based on Conditional DC-GAN Applied to Pseudo-Images
## 4 Results
### Importance of the Modification of Generator Using Upsample + Convolutional Layers  
As explained in Section 3.4, we modified the deconvolution operation from the original DC-GAN in order to avoid checkerboard artifacts that arise when using simply convolutional-transpose layers. To tackle this problem, we applied the upsample + convolutional layer method to realize the deconvolution operation. In Figure 6, we compare the learning curves (_Loss_D_ and _Loss_G_) of our model (orange) with the original conditional DC-GAN model (blue) trained with the same input. Our model converges much faster than the original one, meaning that the new model is much easier to train.  
Furthermore, we visually inspect some generated pseudo-images in order to check the improvement. On Figure 7, we compare a real TSSI input pseudo-image on Figure 7a; a fake pseudo-image generated by the original conditional DC-GAN model with convolutional-transpose layers, having checkerboard patterns on the entire image on Figure 7b; and a fake pseudo-image without  
Figure 5: FID curves evaluated on (**0-7**) 8 different actions, (**avg**) their average score, and (**total**) the entire dataset.  
Figure 6: Learning curves of our model (using upsample and convolutional layers) (orange) and the original method (using convolutional-transpose layers) (blue): _Loss_D_ is discriminator loss calculated as the average of losses for the all real and all fake samples, and _Loss_G_ is the average generator loss.  
checkerboard artifacts generated with our modified DC-GAN on Figure 7c. This confirms that, thanks to our modification of upsampling in the generator, the checkerboard pattern artifact no longer appears.  
In order to check how essential it is for our approach to avoid checkerboard artifacts, we finally compare the corresponding generated actions. Figure 8 shows the action \"drinking\" generated by (a) new model-trained 69 epochs and (b) original models after 349 epochs when the learning curves of two models reach the same values. Comparing those two generated sequences of the action \"drinking\", the former is obviously more continuous and realistic. The human skeleton is closer to reality as well. The body shape deformations and action unsmoothness visible in Figure 8b are the consequences of the checkerboard artifacts in the generated pseudo-image, which translate into very bad pose sequences after reconverting from TSSI pseudo-image.  
In conclusion, our analysis confirms that, for our work, using the upsample + convolutional layer structure to realize deconvolution in the generator is efficient and essential for obtaining realistic generated sequences of skeletons.
# An Inference Framework of Markov Logic Network for Link Prediction in Heterogeneous Networks
## 4 Proposed Method
### VEM-Based Inference
#### 4.2.1 Variational Distribution  
To enhance LP efficiency in MLN, we propose computing the true posterior distribution \(P(V_{u}|V_{o})\) within the EM algorithm by treating the labels of \(V_{u}\) as latent variables. Given the challenge posed by a large number of unobserved fact nodes in MLN for directly calculating \(P(V_{u}|V_{o})\), we approximate this distribution with a variational distribution\(Q(V_{u})\). Consequently, we derive \(P(V_{u}|V_{o})\) from \(Q(V_{u})\) by minimizing their KL divergence:  
\[\begin{split} KL(Q(V_{u})\parallel P(V_{u}|V_{o}))\\ =&\mathbb{E}_{Q}[\log Q(V_{u})-\log P(V_{u}|V_{o})] \\ =&\mathbb{E}_{Q}[\log Q(V_{u})-\log\frac{P(V_{u},V_{o} )}{P(V_{o})}]\\ =&\mathbb{E}_{Q}[\log Q(V_{u})-\log P(V_{u},V_{o} )]+\log P(V_{o}),\end{split} \tag{4}\]  
where \(\mathbb{E}_{Q}[\cdot]\) represents the expectation with respect to \(Q\).  
By swapping the term \(\log P(V_{o})\) in Equation (4), we have  
\[\begin{split}\log P(V_{o})=& KL(Q(V_{u})\parallel P (V_{u}|V_{o}))\\ &-\mathbb{E}_{Q}[\log Q(V_{u})-\log P(V_{u},V_{o})]\\ =& KL(Q(V_{u})\parallel P(V_{u}|V_{o}))\\ &+\mathbb{E}_{Q}[\log P(V_{u},V_{o})-\log Q(V_{u})].\end{split} \tag{5}\]  
```
0: observed nodes \(V_{o}\), unobserved nodes \(V_{u}\), MLN structure \(G=(V,E)\), number of substructures \(k\)
0: set of substructures \(G^{\prime}=\{g^{i}\mid 1\leq i\leq k\}\)
1:\(G^{\prime}\leftarrow\emptyset\)
2:\(V_{o}^{1}\gets V_{o}\)
3:for\(i=1\) to \(k\)do
4:\(V_{u}^{i}\gets V_{u}\cap\bigcup_{v\in V_{i}}\mathcal{N}_{1}(v)\)
5:for each node \(n\) in \(V_{u}^{i}\)do
6: Obtain \(\mathcal{N}^{*}(n)\) using Equation (3)
7:\(h\gets 1\)
8:while\(\mathcal{N}^{*}(n)\
ot\subset\mathcal{N}_{h}(n)\)do// expansion
9:\(h\gets h+1\)
10:\(NN\leftarrow\) neighbors of all the nodes in\(\mathcal{N}_{h-1}(n)\)
11:\(\mathcal{N}_{h}(n)\leftarrow\mathcal{N}_{h-1}(n)\cup NN\)
12:endwhile
13:\(\mathcal{E}_{h}(n)\leftarrow\) edges w.r.t\(\mathcal{N}_{h}(n)\) in \(G\)
14:\(g^{i}\leftarrow(\mathcal{N}_{h}(n)\cup\{n\},\mathcal{E}_{h}(n))\)// the \(i\)-th substructure of \(G\)
15:endfor
16:\(V_{o}^{i}\gets V_{o}^{i}\cup V_{u}^{i}\)// update observed nodes
17:\(G^{\prime}\gets G^{\prime}\cup g^{i}\)// add substructure \(g^{i}\) to \(G^{\prime}\)
18:endfor
19:return\(G^{\prime}\)
```  
**Algorithm 1** MLN Substructure Construction  
As \(KL(Q(V_{u})\parallel P(V_{u}|V_{o}))\) is non-negative, we define \(\mathbb{E}_{Q}[\log P(V_{u},V_{o})-\log Q(V_{u})]\) as the ELBO of \(\log P(V_{o})\). Since \(\log P(V_{o})\) is a constant with respect to \(V_{u}\), we transform the minimization of \(KL(Q(V_{u})\parallel P(V_{u}|V_{o}))\) into the ELBO maximization by iterative executions of variational E-steps and M-steps.
## 5 Discussion  
The number of automatically extracted parcels of the two test areas after aggregation, area filtering, confidence filtering, and other post-processing experiments is shown in Figure 12. Aggregation processing mainly serves to reduce the degree of fragmentation of parcels, and the number is reduced by about 10%. Area filtering only removed less than 5% of the parcels, mainly due to the small number of bare land parcels with an area less than 1200 m\({}^{2}\) in the experimental area. In addition, the proportion of small area parcels in the training samples is relatively low.  
The final confidence filtering step can significantly reduce the redundant parcels, and the final numbers were 7216 and 1109 for Yulin and Taiyuan, respectively. Compared with the initial data volume, the total number of parcels decreased by more than 50%.  
Compared with the manually labeled parcels, the false and missed detection rates of the two areas were calculated. As shown in Table 4, compared with the initial extraction results in Table 1, the FDR decreased by approximately 30%, and the MDR correspondingly increased by approximately 4-6% with a significant reduction in the total numbers of extracted parcels.  
Statistical analysis results suggested that the optimization processing method proposed in this paper markedly reduces the false detection of automatically extracted parcels, which can solve the current problems of large data volume and high-FDR associated with automatically extracted change parcels using deep learning and other methods. This study improves the feasibility of using automatic change detection parcels in actual monitoring  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experimental** & **Parcels after** & **Manually** & **Intersecting** & **FDR** & **MDR** \\
**Area** & **Post-Processing** & **Labeled Parcels** & **Parcels** & **FDR** & **MDR** \\ \hline Yulin & 7216 & 5748 & 3820 & 47.1\% & 33.5\% \\ Taiyuan & 1109 & 879 & 616 & 44.4\% & 29.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parcel accuracy after processing.  
Figure 12: The number of parcels in each process.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Examples** & **P1** & **P2** & **P3** & **P4** & **P\({}_{com}\)** \\ \hline
1 & 158 & 189 & 148 & - & P\({}_{com12}=\)0.88 & P\({}_{com13}=0.77\) \\
2 & 155 & 176 & 189 & 147 & P\({}_{com12}=0.95\) & P\({}_{com23}=0.90\) \\
3 & 166 & 163 & 184 & - & P\({}_{com12}=0.50\) & P\({}_{com13}=0.95\) \\
4 & 194 & 176 & 214 & - & P\({}_{com12}=0.95\) & P\({}_{com23}=0.75\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confidence and proximity scores of parcels.  
applications. Furthermore, the process of confidence filtering will inevitably delete some correct parcels, and it is necessary to select the optimal confidence \(p\) value through a large number of experiments to balance the FDR and MDR of the final extraction results.
# The Analysis of the Applications of Crop Seed Tape Sowing Technology and Equipment: A Review
## 5 Discussions and Prospection of Seed Tape Sowing
### Development Prospection Trend of Seed Rope Sowing Technology  
With the continuous progress of agricultural science and technology, combined with the direction of agricultural development, the development trend of seed rope planting technology is summarized as the following points:  
1. The intelligent high precision and high-speed seed tape manufacturing equipment. Agricultural production is gradually developing towards factorization, such as the SEDOS and Nippon company. Farmers who sow with seed tape could buy seeds from the factory, and the factory needs to provide farmers with seed tape reels on time. Therefore, the working efficiency of the seed tape manufacture will be greatly improved in the future. (2) The emergence of large intelligent integrated seed tape planter of \"land preparation-seeding rope system-seeding\". It would include the soil preparation system, seed tape production system, seed tape laying system, autopilot system, the GNSS system and some other intelligent control system. The soil preparation system directly ploughs the soil into the most suitable conditions. Then the seeds are formed into the seed tape reel in the integrated seed tape planter and lay down into the soil. Integrated seed tape planter integrates the seed tape making and laying which reduces the manpower input in the process of making the seed tape. (3) Development of small and medium-sized mobile (manpower) walk-behind planners. For farmers with small areas of arable land, small and medium-sized mobile walk-behind planners will be popular. This type of rope planter has the advantages of good manoeuvrability, flexibility in and out of small fields, easy transportation, and low cost. (4) Seed tape planting technology can be used for agricultural landscape planting. With the improvement of people's living quality, the agricultural landscape is more and more sought after by people, such as rice field painting and lawn painting. Most paddy fields are planted by hand, which makes them expensive to produce. If the seed tape seeding technique is used for paddy field painting seeding, the corresponding seeds can be wrapped into the seed tape in seed tape manufacture. As [PERSON] et al. proposed, the seed tape mixed weaving of rapeseed and wheat seeds could realize landscape planting in the field [73]. (5) Seed tape planting technology can be used for transplanting crops. Usually, we use the seed tape technology for crop cultivation is to pull the seeds into the seed tape, and then lay down the seed tape by the laying device which belongs to crop direct seeding. Many crops such as rice and rape are mainly produced by transplanting, Transplanting with seed tape begins by germinating the seed in the seed tape. When the seedlings grow to a certain extent, the seed tape with the seedlings is laid down in the groove by special transplanting equipment. It is consistent with the idea of seed zone transplanting proposed by [PERSON] [103].