esa-sceva/satcom-chunk-collection huggingface dataset:
# A Semantic Segmentation Algorithm Using FCN with Combination of BSLIC
## 2 Related Work
### Bslic  
BSLIC comes from SLIC, which is a method of to generate superpixel. Essentially, BSLIC is a local K-means clustering algorithm. Assuming that the number of pixels in the given image is \(N\), the number of superpixels to be generated is \(k\), the coordinates of the pixels in the image are \(x\) and \(y\) and the components of CIELAB color space are \(l\), \(a\), and \(b\), the SLIC algorithm is proposed as follows:  
* _Step 1:_ The clustering center \(C_{i}=[l_{i}\,a_{i}\,b_{i}\,\,\,x_{i}\,y_{i}]^{T}\) is uniformly initialized in horizontal and vertical directions separately. The horizontal interval \(S_{h}\) and vertical interval \(S_{v}\) are as follows: \[S_{h}=\sqrt{\frac{N}{\left(\frac{\sqrt{3}}{2}\right)\times k}},\] (1) \[S_{v}=\sqrt{\frac{\sqrt{3}}{2}\times\frac{N}{k}},\] (2)
* _Step 2:_ In order to avoid selecting noise points in the image, all of the clustering centers are updated to points that have the smallest gradient value in their neighborhood.
* _Step 3:_ In the \(2S_{0}\times 2S_{0}\) neighborhood of each image pixel \(i\), the clustering center with the smallest distance to the pixel \(i\) is searched to achieve the class tag of the pixel \(i\).  
* _Step 4:_ The clustering center is updated to the mean vector of all the pixel eigenvectors in its category.
* _Step 5:_ Repeat the third and fourth steps until the last two iterations of the cluster center error is no more than 5%.  
BSLIC has an improvement in three aspects: initializing cluster centers in hexagon, rather than square distribution, additionally choosing some specific edge pixels as cluster centers, incorporating boundary term into the distance measurement during \(k\)-means clustering.  
Figure 2 shows the segmentation results of BSLIC algorithm in different input parameters \(k\) and \(m\), where \(k\) is the number of superpixels to be generated, and \(m\) is the weighting factor of color euclidean distance and the space euclidean distance. Through evaluating the segmentation results, we can draw the following conclusions: (1) larger \(m\) achieves better regularity for superpixels, and smaller m achieves better adherence to image boundaries; and, (2) the larger \(k\) aligns to more detailed boundaries.
## 3 Simulation Analysis on Failure Characteristics of the Crossing-Seam Roadway  
### Establishment of the Numerical Model  
To study the deformation failure characteristics of the surrounding rock in different crossing-seam roadways, we established a numerical model (46 m \(\times\) 45 m \(\times\) 31.5 m) for the return air subinclined shaft using FLAC 3D software, as shown in Figure 4. The physical and mechanical parameters of each rock stratum are presented in Table 1. This model is a constitutive one and is in conformity with the Mohr-Coulomb yield criterion. The  
Figure 3: The effect of roadway maintenance under original support: (**a**) location I; (**b**) location II.  
simulation model was meshed using the griddle plug-in in Rhino 6.0 software, and the mesh shape was selected as triangular and uniformly distributed. The mesh was automatically generated by the Rhino system, where the mesh width was set to 1.0 m. The model base plate and both sides are displacement boundary conditions. The model limits horizontal displacement on the side, vertical movement on the bottom, and stress boundaries on the upper surface. The top part of the model is the stress boundary. A vertical stress of 21.25 MPa was applied on the top (average burial depth: 850 m) to simulate a load of strata. With the side-pressure coefficient controlled at 1.5, the maximum horizontal stress that was set at 31.88 MPa formed an angle of about 30\({}^{\circ}\) with the axis of the roadway to simulate the tectonic stress of strata.  
To reflect the failure characteristics of the surrounding rock in different crossing-seam roadways, we simulated the distribution laws of vertical stress, horizontal stress, and plastic zone in the following four cases (hereinafter referred to as Case 1, Case 2, Case 3, and Case 4). 1 The roadway is completely located in hard rock formations, and the roof is composed of hard rocks; 2 the roadway is completely located in hard rock formations, and the roof is composed of soft rocks; 3 the roadway is completely located in soft rock formations, and the floor is composed of hard rocks; and 4 the roadway is completely located in soft rock formations, and both the roof and floor are composed of soft rocks. The simulation results are shown in Figure 5.
## 3 Macro-Element Application
### Non-Linear Range
#### 3.2.2 Uni-Directional Cyclic Loading  
In classical plasticity theory, three key features are of utmost importance: the yield criterion, the flow rule, and the hardening/softening rule [85]. As referred, a Mohr-Coulomb yield failure envelope and a non-associated flow rule have been assumed. For the hardening rule, the model depends on the definition of cracking strains associated with given effective stresses. It is the purpose of the uni-directional cyclic test to verify if the latter is well reproduced for a cyclic type of loading. Towards the latter, the same case study of Figure 7 has been used. The process of strength deterioration is itself the pure representation of the softening rule evolution [85]. Figure 10 shows that the obtained skeleton curve using the CDP model fits the expected quasi-static curve.  
Furthermore, a damage model is coupled with the plasticity one. The softening rule is interpreted through an isotropic damage variable, d, which is, by itself, a function of the equivalent plastic strains and, therefore, its value never decreases. Stiffness degradation of the material after cracking should be accounted. Figure 10 shows a comparison between the present CDP model and a pure plasticity model with an absence of stiffness degradation. On one hand, both phenomenological representations allow to achieve permanent material plastic deformations, but these are higher if a total plasticity model with initial unloading stiffness is adopted (known to be too conservative). On the other hand, the appearance of plastic strains is a manifestation of inelastic behaviour and, as Figure 10 proves, its evolution follows the expected path (see Table 2) reaching a constant value after the ultimate strain threshold limit, correspondent to a cracking strain of 6.09 \(\times\) 10\({}^{-4}\), in which the residual strength is defined and thus damage remains constant.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cracking Strain (-)** & **Stress (MPa)** & **Damage Scalar D (-)** \\ \hline
0.00 & 9.61 & 0.00 \\
5.06 \(\times\) 10\({}^{-5}\) & 9.41 & 0.20 \\
2.11 \(\times\) 10\({}^{-4}\) & 5.57 & 0.42 \\
3.49 \(\times\) 10\({}^{-4}\) & 4.03 & 0.58 \\
6.09 \(\times\) 10\({}^{-4}\) & 3.36 & 0.65 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Input stress–strain (\(\sigma-\epsilon\) ) curves for the FE trusses in the FE software (CDP model in ABAQUS).  
Figure 9: Quasi–static test: expected vs. obtained bending moment capacity curve.
# Multi-View Network Representation Learning Algorithm Research
## 3 Our Method
### Feature Weighting for Different \(k\)-Step Networks
The certainty degrees of existing edges and the link probabilities of non-existing edges are calculated by the MFI matrix of 1-step network. The specific calculation process is shown in the Equation (8) and Algorithm 1. For each \(k\)-step network, we reconstruct the \(k\)-step weight matrix based on matrix \(S\) and matrix \(A^{(k)}\). We only retain the link weights of existing edges and neglect the weights of non-existing edges between vertices. The weight matrix factor is not only to balance \(k\)-step network representation but also it can be regarded as network weight features, which can be integrated into network representations. In the \(k\)-step network, the edge relationship has been established between vertices, where the edge relationship does not exist in the original network. Weight matrix of \(k\)-step network can not only balance network representation vectors of different \(k\)-step networks but also can be regarded as network weight features of the network, which can be integrated into network representation framework. Specifically, we construct the edge weight matrix of \(k\)-step network and the weight matrix of \(k\)-step network is different. The weights of the edges of the original network are larger. The edge relationship of the 2-step network is the reachability relationship within 2 step between two vertices in the original network, which can be set as 1 and 0. By MFI similarity calculation, the edge weights of 2-step network are the similarity values between the current vertex and the neighbor's neighboring vertices in the original network. Therefore, the weights in the 2-step network is less than that in the 1-step network. So, the higher the order of the network, the smaller the edge weight in weight matrix is. By using the hierarchical weight matrix, we can combine the weight matrix with the network structure feature matrix for joint network representation learning. Thus, different weight factors are assigned to different \(k\)-step network representation vectors by weight matrix.
# Can Biochar Improve the Sustainability of Animal Production?
## 3 Animal Feed
### Production Parameters  
[PERSON] et al. [79] evaluated the effect of two types of wood based biochar at a 2% inclusion rate in growing pig (3-13 weeks) diets. This study was looking particularly at nutrient digestibility and the following production parameters: average daily feed intake (ADFI), average daily weight gain (ADWG), and feed efficiency (G:F). Statistical analysis also utilized the parameter apparent total tract digestibility (ATTD), which compares the nutrient concentrations in the feed versus feces.  
Neither biochar additive resulted in significant differences in ADWG, ADFI, or G:F. However, results from another finishing pig study by [PERSON] et al. [80] using 0.3% and 0.6% bamboo biochar led to 4.5% and 8.2% improved ADWG and 14.9% and 11.7% improved G:F, respectively. It seems feedstock was the greatest contributor to these differences. The bamboo biochar had a smaller micropore structure compared with the wood-based biochar.  
Several studies have also evaluated similar impacts on broiler diets, including [PERSON] et al. [75]. In addition to evaluating the biochar impacts on microbial communities, this study tested birds over their full growth period, broken up into two diets: starter (1-21 days) and grow-to-finish (21-42 days). The wood chip biochar was added at 30 g kg\({}^{-1}\) feed to a typical meat chicken diet. Biochar in the starter diet led to a 2% decrease in ADWG and a 3.5% increase in FCR (both \(p<0.05\)) vs. control.  
The finisher diet with biochar addition, however, led to an 8% increase in ADWG and 6.7% decrease in FCR (both \(p<0.001\)). These results are consistent with [PERSON] et al. [81] who also observed a decrease in performance in young birds due to biochar addition to feed. There is a need to understand the optimal addition rates, and whether improvements in production parameters are based on the growth period diet or the age and biology of the animal. Additionally, testing these trends for swine and cattle would be valuable.
###### Abstract  
Carotenoid biosynthesis in _Crynebacterium glutamic_ is controlled by the MarR-type regulator CrtR, which represses transcription of the promoter of the _crt_ operon (_PcrtE_) and of its own gene (_PcrtR_). Geranylgeranyl pyrophosphate (GGPP), and to a lesser extent other isoprenoid pyrophosphates, interfere with the binding of CrtR to its target DNA in vitro, suggesting they act as inducers of carotenoid biosynthesis. CrtR homologs are encoded in the genomes of many other actinobacteria. In order to determine if and to what extent the function of CrtR, as a metabolite-dependent transcriptional repressor of carotenoid biosynthesis genes responding to GGPP, is conserved among _actinobacteria_, five CrtR orthologs were characterized in more detail. EMSA assays showed that the CrtR orthologs from _Corynebacterium calluvae_, _Acidipropionibacterium jensenii_, _Penaarthrobacter nicotinovans_, _Microoccus luteus_ and _Pseudarthrobacter chlorophenolicus_ bound to the intergenic region between their own gene and the divergently oriented gene, and that GGPP inhibited these interactions. In turn, the CrtR protein from _C. glutamic_ bound to DNA regions upstream of the orthologous _crtR_ genes that contained a 15 bp DNA sequence motif conserved between the tested bacteria. Moreover, the CrtR orthologs functioned in _C. glutamic_ in vivo at least partially, as they complemented the defects in the pigmentation and expression of a _Pcrt_gftv_ transcriptional fusion that were observed in a _crtR_ deletion mutant to varying degrees. Subsequently, the utility of the _PcrtE_gftv_ transcriptional fusion and chromosomally encoded CrtR from _C. glutamic_ as genetically encoded biosensor for GGPP was studied. Combined FACS and LC-MS analysis demonstrated a correlation between the sensor fluorescent signal and the intracellular GGPP concentration, and allowed us to monitor intracellular GGPP concentrations during growth and differentiate between strains engineered to accumulate GGPP at different concentrations.  
C. glutamic; regulation of carotenogenesis; GGPP; biosensor +
Footnote †: journal: Molecular Sciences
## 4 Conclusions  
The removal of H2S is of the utmost importance in order to avoid negative consequences such as exposure to humans and the overall environment. Though some of the technologies know commercial scales, they are far from being perfect; thus, there is still a wide margin for their improvement and optimization to gain further reduction in costs and increase in the separation characteristics of the technology. When H2S is present in the gas stream, in the order of a few mg/L, adsorption with metal-oxide-based adsorbents is the most used separation technology in the removal of this toxic gas, whereas this technology together with absorption and membranes is used when there is a need to separate H2S from natural gas and when H2S is present in the gas stream in the order of mol% levels. Using membrane technologies, polymeric membranes are the most used material, whereas in absorption processing alkanolamines are the preferable solvents for practical use. Future investigations may be directed to the enhancement of the separation efficiency with respect to H2S, and this goal is focused on in the development of materials and solvents with improved properties; the technologies to reach these expectations rely in better molecular design/grafting and the use of less expensive solvents and materials also fulfilling environmental laws, i.e., adsorbents produced from wastes.  
The simultaneous recovery of both H2 Sand CO2 is possible by the use of high-level technology readiness carbon-capture operations; however, the selective removal of both gases needed intensive investigations. This will help with the recovery and isolation of H2S and its further processing to different valuable products, including hydrogen and sulfur, together with sulfur-containing composites/materials used in applications in relation to the concept of the circular economy.  
Table 7 summarizes advantages and disadvantages of the different H2S removal technologies, though, in the opinion of this author, a series of potential readers of this manuscript will not agree with these and will add or void others.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technology** & **Advantages** & **Disadvantages** \\ \hline Absorption & Established technology, possibility to treat tail gas & Chemistry of alkanolamines, solvent regeneration seems difficult \\ Adsorption & Established technology, high removal capacity & Generation of toxic wastes, difficult to operate offshore, stability of the adsorbent \\ Membranes and membrane contacts & Modular configuration, large surface area per unit volume & Possible limitations due to permeability, resistance due to membrane, degradation of the membrane \\ \hline \hline \end{tabular}
\end{table}
Table 7: Advantages and disadvantages of the various H2S removal technologies.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** This work was supported by the CSIC Agency (Spain).  
**Conflicts of Interest:** The author declares no conflict of interest.
# Recent Advances in Lithiophilic Porous Framework toward Dendrite-Free Lithium Metal Anode
## 3 Non-Metal-Based Li Host  
Carbon-based materials are considered as an ideal candidate for a Li host. Employing the carbon-based Li host lowers the packing cost and increases the energy density of the battery due to its lower cost and lower density. Porous carbon materials have attracted intense attention due to their unique properties such as a high surface area with excellent mechanical strength and chemical stability, natural abundance, and are eco-friendly, which have great potential for practical energy storage applications. Using 3D porous carbon on top of the metal current collector shows the conductivity difference between them. The weak cohesion between the metal current collector and the porous carbon host provide space to deposit the Li underneath the porous carbon. The emerging 3D carbon such as hollow carbon spheres [73], carbon nanofiber (CNF) [74; 75], carbon nanotubes (CNT) [76], porous carbon [10], graphene or reduced graphene oxide (rGO) [77; 78; 79; 80; 81], and carbonized MOFs [82] have made a great success by the pre-electrodeposition of Li. However, the lack of spatial control during the electrodeposition of Li leads to uneven Li deposition and incompetent production. The thermal infusion of molten Li into the scaffold of 3D porous carbon is one of the prominent approaches to make the Li/C composite anode. Moreover, most of the carbon materials possess a relatively weak affinity and lack a bonding interaction towards Li. The poor Li wettability of the porous carbon structure leads to the increase in nucleation overpotential, increases the voltage hysteresis, increases the cell's impedance, and cannot uniformly guide the Li deposition. Thus, it is imperative to decorate or modify the 3D porous carbon with the lithiophilic layer or seed growth. The various types of lithophilic coatings and decoration of seeds for the guiding the lithium deposition will be discussed in the following section.
# Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks
## 8 Conclusions  
In this paper, a double-scale adaptive transmission mechanism has been proposed for UASNs with time-varying channels. Firstly, the historical channel state series has been decomposed into large-scale and small-scale series, which can then be predicted by a novel \(k\)-nearest neighbor search algorithm with sliding window and auto-regressive algorithm, respectively. Since only a few historical channel state series are needed for channel prediction, the proposed mechanism can ensure the prediction performance with a greater reduction of the computation complexity and the storage size. Then, an energy-efficient transmission algorithm is designed to solve the problem of long-term modulation and coding optimization, and an improved genetic algorithm is designed to accelerate the convergence speed. With the theoretical analysis for the transmission impact of buffer threshold adopted in our proposed mechanism, we have optimized the selection of buffer length. Numerical simulation results show that the proposed methods achieve good performance in terms of channel prediction and energy efficiency. The predicted large-scale channel states can reflect the channel trend in the future, and the adaptive transmission mechanism can significantly reduce the energy consumption of communication. Meanwhile, the simulation results of buffer threshold impact on average energy cost and average transmission delay are consistent with the theoretical analysis. For our future work, we will investigate the combination of the double-scale adaptive transmission mechanism and media access control protocol to further improve the overall performance of underwater acoustic sensor networks.  
Conceptualization, Y.C. and D.L.; Data curation, Y.C.; Formal analysis, Y.C.; Funding acquisition, D.L.; Investigation, Y.C.; Methodology, Y.C.; Project administration, Y.C.; Resources, Y.C.; Software, Y.C.; Supervision, Y.C.; Validation, Y.C.; Visualization, Y.C.; Writing and original draft, Y.C.; Writing, review and editing, Y.C., M.L., D.L., K.M. and H.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 61571334 and in part by China Postdoctoral Science Foundation under Grant 2020M672412.  
Data Availability StatementData sharing not applicable.  
The authors declare no conflict of interest.
## 4 Construction of Attack-Defense Game Model  
### Stochastic Game Model  
In this study, both the attacker and defender are set as agents, the single-agent model becomes insufficient for our requirements, so we use stochastic game to model the attack-defense process. Stochastic games extend the single-agent Markov decision process to multi-agent environments [29]. Within this context, we introduce a non-cooperative game environment comprising both an attacker and a defender, which is modeled as a stochastic game with two agents, expressed as \(MASG=(N,S,A,R,\gamma,p)\), as follows:  
(1) \(N=(a,d)\) represents the players of the game, \(a\) represents the attacker, and \(d\) represents the defender.  
(2) \(S\) represents the state space. Let \(S_{a}\) and \(S_{d}\) denote the position indices of the attacker and defender, respectively, then, the system state at step \(t\) is expressed as \(S_{t}=(S_{a},S_{d})\).  
(3) \(A=(A_{a},A_{d})\) represents the action space of the participant, where \(A_{a}\) represents the action space of the attacker, and \(A_{d}\) represents the action space of the defender. The action space is the set of actions that the game player can choose. At the beginning of every step, all game players need to choose an action from their own action space, all the actions form a joint action and interact with the environment, then players will get their own reward to evaluate their chosen action.  
Figure 4: Path diagram.  
(4) \(R=(R_{a},R_{d})\) represents the reward set of the participant, where \(R_{a}\) represents the reward of the attacker and \(R_{d}\) represents the reward of the defender.  
(5) \(\gamma\) is the discount factor, which describes the discount proportion of the reward in the future stages relative to the current stage. When considering decisions, the decision-maker will consider the payoffs in the future stages as one of the decision criteria, but they are not as important as the payoff in the current stage, and the discount factor is used to describe the importance of the payoffs of future stages.  
(6) \(p\) is the state transition probability, which is determined by the agent's mixed policy actions and the system state.