esa-sceva/satcom-chunk-collection huggingface dataset:
# Translation-Based Embeddings with Octonion for Knowledge Graph Completion
## 2 Related Work  
With the large-scale application of KG technology, the tasks of KGC have attracted widespread attention. KGC is accomplished by steps such as knowledge graph embedding and link prediction, whose core is the embedding process. Over the last several years, various embedding methods have been proposed. In the early research, by modeling KGs in high-dimensional vector space, the features and expressiveness could be increased to a certain extent. However, this leads to large memory consumption and increases the additional cost of model training.  
In 2013, TransE [9] proposed a translation-based method for embedding entities and relations of KGs in low-dimensional continuous space, and made substantial improvements. Since then, several categories of KGC methods based on translation [9; 21; 22; 23; 24; 25; 26], semantics [10; 27; 28; 29; 30; 31; 32], and neural networks [11; 12; 33; 34; 35] have been developed successively. These methods aim to improve the feature interaction capabilities among triple elements by different forms of innovation, each with its own advantages and disadvantages.
## 5 Methodology
### Fuzzy Logic Based Pricing Strategy  
We propose a fuzzy logic-based pricing strategy to help vehicles to determine the price they can ask for lending their resources to complete the computation task together. The determination of the price that an SV can ask is tricky. A good price should be smartly decided for each vehicle, which not only depicts the multiple important attributes of a vehicle to serve as an SV, but also helps the vehicle to win in the competition for becoming the final SVs.  
The base price of a vehicle for calculating the unit size data of a task is defined as \(C\), to reflect the instant capability of a vehicle \(sv_{j}\) to help with the task computation, as well as the utility of selecting \(sv_{j}\) as an SV, the price that \(sv_{j}\) will charge \(u_{0}\) for helping it to calculate a unit size data can be expressed as:  
\[p_{j}=C\cdot(1+\alpha_{j}), \tag{11}\]  
where \(\alpha_{j}\) is the dynamic adjusting parameter of \(sv_{j}\) to determine its price. It should be noted that in order to make profits, the final price of \(sv_{j}\) is higher than the basic price \(C\), and \(\alpha_{j}\in[0.2,1]\). We employ a fuzzy information system to determine the appropriate value of \(\alpha\) for each vehicle, according to multiple metrics that describe the capabilities and the intentions of vehicles to provide cooperation. In view of the highly dynamic characteristic of IoVs and the randomness of fast-moving vehicles, multiple attributes that represent the mobility and communications impact on the cooperation performance are analyzed and incorporated into the pricing model. Meanwhile, social attributes that reflect the willingness of vehicles to participate in the cooperation are also valued.
## 2 Concept of the ISAMD  
The main idea of the ISAMD is that an active joint between a structure and a mass damper can be locked or unlocked to form a series, namely a structure-spring-active joint-mass damper, as shown in Figure 1.  
The components of the proposed ISAMD are as follows: (A) control mass block; (B) active component unit (comprising (B1) switching spring and (B2) active joint); (C) vibration sensing unit (comprising (C1) structural vibration sensors and (C2) vibration sensors for the control mass block); (D) controller; and (E) fixed component unit (comprising (E1) fixed rebound device and (E2) energy dissipation device). The workflow of the proposed ISAMD is as follows:1. Motion of the structural floor and the control mass block is detected by motion sensors (accelerometers, C1 and C2). The signal is actively retrieved by the controller.
2. The controller converts the acceleration signal to velocity and displacement signals.
3. The controller determines whether to \"Unlock\" or \"Lock\" the active joint according to the control law and outputs the control signal to the active joint.
4. The active joint (B2) releases or captures the switching spring (B1) according to the control signal from the controller.
5. Steps (1)-(5) are repeated.  
The control can perform on/off switching by using a quick-reaction electromagnetic server or solenoid valve. In the \"Unlock\" status of the active joint, there may be no interaction between the control mass block and the structure, or a weak spring (E1) and energy dissipation device (E2) may maintain a weak interaction force to reduce the stroke of the control mass block. In the \"Lock\" status of the active joint, the control mass block and the main structure have a strong connection. The interaction force induced by the deformation of the strong spring is applied to change the movement behaviors of the main structure and the control mass block. The timing of the switching of the active joint can be achieved with positive/negative symbols of the work done by the control mass block on the main structure, as follows:  
1. Lock to Unlock: When the inner force of the switching spring (B1) begins to do positive work on the structure, the ISAMD switches to the \"Unlock\" status and the structure and the control mass become separated.
2. Unlock to Lock: When the ISAMD switches to the \"Lock\" status, the switching spring (B1) performs negative work on the structure. Then, the controller (D) sends a \"Lock\" command to the active joint (B2) to reconnect the main structure and control mass with the switching spring.
# Generating Datasets for Real-Time Scheduling on 5G New Radio
## 7 Conclusions  
The 5G NR scheduling is the key to improving the 5G performance. However, there is still a gap between the solution obtained by scheduling algorithms and the optimal solution. Recently, related work has introduced supervised learning to real-time multiprocessor systems and indicated that supervised learning can fill the performance gap. However, for the 5G NR scheduling problem, due to the lack of training datasets, supervised learning cannot be used. Therefore, first, we formulate the 5G NR scheduling problem into an OMT problem, which uses fewer variables than existing work. Although the OMT problem can be solved by solver Z3, the solution time is unacceptable. Then, to reduce the solution time, we transform the OMT problem into an SMT problem and propose three theorems and an algorithm to reduce the search space. Finally, we evaluate our methods. The evaluation results indicate that, compared to existing work, our proposed method reduces the solution time by 74.7%, and the supervised learning model trained on the generated dataset is effective to schedule packets on 5G NR. In the future, we will design a new neural architecture to improve the performance of 5G NR scheduling.  
Conceptualization, X.J.; methodology, X.J. and H.C.; software X.J. and H.C.; validation, H.C.; formal analysis, C.X. ([PERSON]); writing--original draft preparation, X.J. and H.C.; writing--review and editing, C.X. ([PERSON]); funding acquisition, X.J., C.X. ([PERSON]) and C.X. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by National Natural Science Foundation of China (62133014, 61972389, 62022088, 62173322, 92267108, 92067205, U1908212), Youth Innovation Promotion Association of the Chinese Academy of Sciences (Y2021062, 2020207), Science and Technology Program of Liaoning Province (2023)H3/10200006, 2023 JH3/10200004, 2022 JH25/10100005).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 19: Comparison of objective values.  
Figure 20: Comparison of satisfiable ratios.
## 2 Results and Discussion
### The Anti-Inflammatory Activity of P13#1 in Cell Cultures  
Next, the LPS-scavenging activity of P13#1 was assayed in a cell culture model, i.e., Raw 264.7 murine macrophages. These cells, when stimulated with LPS, release several inflammation mediators including nitric oxide (NO) and cytokines [71]. Hence, Raw 264.7 cells were stimulated via exposure to LPS from _P. aeruginosa_ 10 and treated with increasing concentrations (0.1, 1 and 10 \(\upmu\)M) of P13#1 for 24 h. (P)GKY20 and colistin were used as positive controls. After the incubation, the nitric oxide (NO) released was measured via the Griess assay, and the pro-inflammatory cytokines interleukin-6 (IL-6) and tumor necrosis factor-\(\alpha\) (TNF\(\alpha\)) were quantified via an enzyme-linked immunosorbent assay (ELISA). It is worth noting that the biocompatibility of P13#1 was preliminarily tested on untreated Raw 264.7 cells by performing an MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide) assay, Griess assay and an ELISA. The collected results highlight that P13#1 does not exert a toxic effect against Raw 264.7 cells up to 10 \(\upmu\)M (equivalent to 22 \(\upmu\)g/mL of the chloride salt of P13#1) (Figure S6 in Supplementary Materials) and does not itself induce the release of proinflammatory factors (Figure 8). On the other hand, when administered to LPS-stimulated cells, P13#1 effectively inhibited the LPS-induced release of NO and cytokines (Figure 8). Very interestingly, at 10 \(\upmu\)M, a non-toxic concentration, P13#1 completely re-established basal levels of pro-inflammatory mediators. Moreover, similar results were obtained on THP-1 human monocytes (Figure S7 in Supplementary Materials). Overall, the observed results indicate that P13#1 is a powerful LPS scavenger with an efficacy comparable to that of the benchmark peptide, colistin.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Peptide/** & **LPS\({}_{Ec}\)1** & **K\({}_{\rm D}\)** & **[Binding Sites]** & **Stoichiometry 2** \\
**Peptoid** & **(\(\upmu\)g/mL)** & **(\(\upmu\)M)** & **(\(\upmu\)M)** & **(\(\upmu\)Peptoid:LPS)** \\ \hline
**Luc-P13\#1** & 40 & 0.121 (\(\pm\)0.032) & 4.53 (\(\pm\)0.123) & 1.13:1 \\
**Luc-P13\#1** & 5 & 0.085 (\(\pm\)0.021) & 2.21 (\(\pm\)0.107) & 4.42:1 \\ \hline
**Luc-GKY20** & 40 & 0.382 (\(\pm\)0.030) & 6.12 (\(\pm\)0.109) & 1.53:1 \\
**Luc-GKY20** & 5 & 0.109 (\(\pm\)0.022) & 2.62 (\(\pm\)0.153) & 5.24:1 \\ \hline  & **LPS\({}_{Pa}\)3** & & & \\
**(\(\upmu\)g/mL)** & & & & \\ \hline
**Luc-P13\#1** & 5 & 0.032 (\(\pm\)0.0085) & 1.91 (\(\pm\)0.049) & - \\
**Luc-GKY20** & 5 & 0.054 (\(\pm\)0.013) & 2.1 (\(\pm\)0.08) & - \\ \hline \end{tabular} \({}^{1}\) LPS\({}_{Ec}\): LPS from _Escherichia coli_ 0111:B4. 2 Stoichiometry was determined assuming an average MW = 10,000 Da for the _E. coli_ LPS. 3 LPS\({}_{Pa}\): LPS from _Pseudomonas aeruginosa_ 10.  
\end{table}
Table 3: K\({}_{\rm D}\) values and binding stoichiometries.
# Theoretical Study on Geometries of Electrodes in Laboratory Electrical Resistivity Measurement
## 2 Theoretical Background
### Electrical Resistance Measured between Two Cylindrical Electrodes  
We assume that two cylindrical electrodes, with radii \(r\) and penetration depth \(l\), are separated by distance \(L\), as seen in Figure 1. These two electrodes have the same amount of charge but opposite polarities. The electric potential of one electrode is calculated from the equipotential surface area in terms of the shortest distance between the electrode center line and an arbitrary point (\(s\)). The equipotential surface area of one cylindrical electrode is:  
\[A(s)=\underbrace{2\pi sl}_{\text{Cylinder side}}+\underbrace{2\pi s^{2}}_{ \text{Half sphere}} \tag{7}\]  
The electric potentials of two cylindrical electrodes are derived from the following equation:  
\[V_{1}=-V_{2}=\int\limits_{r}^{L-r}\frac{\rho l}{A(s)}ds=\int\limits_{r}^{L-r }\frac{\rho l}{2\pi sl+2\pi s^{2}}ds=\frac{\rho l}{2\pi l}\left[\text{ln}\left( 1+\frac{l}{r}\right)-\text{ln}\left(1+\frac{l}{L-r}\right)\right] \tag{8}\]  
\(V_{1}\) is the potential of positive polarity, \(V_{2}\) is the potential of negative polarity, \(\rho\) is the electrical resistivity of the material, \(l\) is the electric current between two electrodes, and \(L\) is the distance between two cylindrical electrodes.  
Through [PERSON]'s law, the electrical resistance between two cylindrical electrodes (\(R_{cylindrical}\)) is:  
\[R_{cylindrical}=\frac{\rho}{\pi l}\left[\text{ln}\left(1+\frac{l}{r}\right)- \text{ln}\left(1+\frac{l}{L-r}\right)\right] \tag{9}\]  
where \(\rho\) is the electrical resistivity of the material, \(l\) is the penetration depth of the electrodes, \(r\) is the radius of the electrodes, and \(L\) is the distance between two electrodes. As the penetration depth approach zero (\(l\)\(\rightarrow\)0), Equation (9) can have a mathematical formulation as  
\[\begin{split}\underset{l\to 0}{\lim}R_{cylindrical}=\underset{l \to 0}{\lim}\frac{\rho}{\pi l}\left[\text{ln}\left(1+\frac{l}{r}\right)- \text{ln}\left(1+\frac{l}{L-r}\right)\right]\\ =\frac{\rho}{\pi}\left[\frac{1}{r}\ln\underbrace{\left(\underset{ l\to 0}{\lim}\left(1+\frac{l}{r}\right)^{l}\right)}_{=e}-\frac{1}{L-r}\ln \underbrace{\left(\underset{l\xrightarrow{l}{L-r}\to 0}{\lim}\left(1+\frac{l}{L-r} \right)^{\frac{L-r}{l}}\right)}_{=e}\right]=\frac{\rho}{\pi}\left[\frac{1}{r}- \frac{1}{L-r}\right]\end{split} \tag{10}\]
# Automated Tunnel Point Cloud Segmentation and Extraction Method
## 2 Materials and Methods  
By combining the GuSAC algorithm and the TMatch algorithm, independent tunnel segment units can be automatically identified and separated under the premise of determining the designed tunnel axis. Subsequently, the ring-based plane fitting technique is used to extract tunnel cross-sections, enabling the analysis of tunnel structure deformation.
## 2 Modeling and Computational Approach
### Modeling Approach
The modeled contribution is calculated by \(L=\left\langle k\right\rangle^{3/2}/\left\langle\epsilon\right\rangle\). The brackets refer to averaging in time. The total length scale is calculated correspondingly by \(L_{tot}=k_{tot}^{3/2}/\epsilon_{tot}\). In these relations, we have \(k_{tot}=\left\langle k\right\rangle+k_{res}\), where \(k_{res}=\left(\left\langle\widetilde{U}_{l}\widetilde{U}_{l}\widetilde{U}_{l }\right\rangle-\left\langle\widetilde{U}_{l}\right\rangle\langle\widetilde{U} _{l}\right\rangle/2\). Correspondingly, \(\epsilon_{tot}\) is the sum of modeled and resolved contributions, \(\epsilon_{tot}=\left\langle\epsilon\right\rangle+\epsilon_{res}\). Here, the resolved contribution is calculated by \(\epsilon_{res}=\
u\big{(}\langle\partial\widetilde{U}_{l}/\partial x_{\beta} \partial\widetilde{U}_{l}/\partial x_{j}\rangle-\langle\partial\widetilde{U}_ {l}/\partial x_{j}\rangle\big{)}/\langle\partial\widetilde{U}_{l}/\partial x _{j}\rangle\big{)}\). The differences between the concept described here and related partially averaged Navier-Stokes (PANS) and partially integrated transport (PITM) models are discussed elsewhere [7; 31]. First of all, these differences are seen in regard to the ability of models to accomplish a desired imposed flow resolution. In particular, PANS and PITM approaches suffer from discrepancies between the imposed and actual flow resolution, which may imply significant deficiencies of the simulation performance of PANS and PITM methods. It is worth noting, however, that \(\epsilon_{+}=\epsilon/\epsilon_{tot}\) is usually found to be well-approximated by unity except very close to walls. If this approximation is applicable, it implies that \(R=L_{+}^{2}=k_{+}^{3}\). The involvement of \(R\) in the model via \(\beta^{*}=1+R(\beta-1)\) characterizes an essential difference to usually applied modeling approaches focusing on imposing a certain desired flow resolution via the model set up. Within the CES-KOS modeling approach, the model is informed about the actual flow resolution via \(R\). This enables the model to properly respond to changes in the amount of resolved motion (implied by changes in the mesh density or different \(Re\)). In particular, the model contribution can decrease (increase) if there is a significant amount of (little) resolved motion. Without implementation of this mechanism, the model cannot properly transition between LES and RANS regimes characterized by a significant amount of (no) resolved motion.
# UAV Dynamic Object Tracking with Lightweight Deep Vision Reinforcement Learning
## 3 Preliminaries
### Actor-Critic Methods  
From Section 3.3, we have seen that the goal of any RL algorithm is to find the optimal policy that produces the highest return. This goal can be achieved by either a policy-based algorithm or a value-based algorithm. In value-based RL (e.g., Q-learning), the optimal policy is derived implicitly by calculating the optimal value function. The most profitable action at each state (according to the value function) is selected to generate the policy afterward. On the other hand, policy-based RL algorithms directly compute the optimal policy. While policy-based RL is effective in high dimensional and stochastic continuous action spaces, value-based RL has advantages in sample efficiency and stability.  
Figure 2: MDP framework [27]. An agent interacts with the environment via an action \(a\), which makes the environment move from state \(s\) to the next state \(s^{\prime}\), and return a corresponding reward \(r\) for the action \(a\).  
Actor-Critic methods are Temporal Difference (TD) methods that utilize both policy-based and value-based RL. It has an actor network, which is a policy-based component and is responsible for choosing actions at a given state. It also has a critic network, which is a value-based component that evaluates the actions selected by the actor. The critic uses a TD error to assess whether the actor has selected a good action via the following equation:  
\[\delta=r+\gamma V(s^{\prime})-V(s) \tag{3}\]  
where \(\delta\) is the TD error, \(V\) is the value function calculated by the critic, \(s\) is the current state, \(s^{\prime}\) is the next state, \(r\) is the reward, and \(\gamma\) is the discount factor. If this TD error is positive, it means the action \(a\) should be selected more often. Otherwise, it means action \(a\) should be selected less often. Using actor-critic methods, we have the following benefits:  
* It can work in infinite and continuous action space.
* Stochastic policy is learned explicitly. This ability is very useful in non-Markov cases (for example, in [28]).
## 3 Results and Discussion
### Chemical Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline
**Wood Samples** & **Holocellulose** & **Lignin** & **Extractive** & **Ash** \\ \hline Recently cut elm & 80.01 (\(\pm\)1.12) a  & 17.90 (\(\pm\)0.56) a  & 1.26 (\(\pm\)0.03) a  & 0.65 (\(\pm\)0.01) a  \\ Archaeological elm & 56.45 (\(\pm\)1.92) b  & 34.24 (\(\pm\)0.42) b  & 2.50 (\(\pm\)0.12) b  & 4.70 (\(\pm\)0.35) b  \\ Recently cut poplar & 81.46 (\(\pm\)2.00) a  & 14.66 (\(\pm\)0.05) a  & 1.48 (\(\pm\)0.42) a  & 0.61 (\(\pm\)0.04) a  \\ Archaeological poplar & 51.18 (\(\pm\)1.70) b  & 40.22 (\(\pm\)0.30) b  & 3.55 (\(\pm\)0.43) b  & 3.18 (\(\pm\)0.04) b  \\ \hline \end{tabular}
\end{table} 
Table 1: Chemical compositions of recently cut and archaeological elm and poplar (wt%). reported by [PERSON] et al. [31]. The bands at 1156, 1158, and 1029 cm\({}^{-1}\) correspond to the vibrations of the C-O and C-C groups, i.e., the glycoside symmetric vibrations of C-O and C-C and the asymmetrical stretch of C-O and C-C [5; 6; 50]. The stretching band at 897 cm\({}^{-1}\) was assigned to an in-plane symmetric vibration of C-H in cellulose [40; 51], which was slightly increased in both archaeological samples. The chemical composition and FTIR analyses of archaeological elm and poplar samples illustrated a strong degradation of polysaccharides, which might be due to the microbial degradations. [PERSON] et al. [40] revealed that bacterial degradation, e.g., erosion bacteria, cause significant degradation in holocelloses. Therefore, brown-rot or soft-rot fungi and/or other microbial degrading agents such as bacteria might have been involved in the degradation of the archaeological elm and poplar samples.
## 5 Conclusions  
PDiff Caco-2 cells could simulate in vivo tumor cells and model intestinal epithelial crypt cells in their proliferative phenotype and lack of brush borders. In this study, we show how PDiff Caco-2 cells respond to peroxid oxidized linoleic acid, 13-HPODE, in terms of differential gene expression, gene ontology, and pathway enrichment, which might reflect the response of in vivo intestinal tumor or crypt cells exposed to dietary LOOHs. Figure 7 summarizes the cellular events and processes altered by each treatment. The stress brought to PDiff cells by 13-HPODE caused the cells to upregulate genes and pathways involved in detoxification as rigorous activation of antioxidant response was required to maintain cellular function and prevent damage. Induction of peroxisomal pathway and cytochrome P450 may, on the one hand, metabolize lipids, detoxify radicals, and maintain redox homeostasis, but on the other hand, can increase ROS production and lipid peroxidation and contribute to the development or progression of disease including gastrointestinal cancer. Moreover, enhanced peroxisomal fatty acid oxidation due to either treatment might favor carcinogenesis. This could propose a mechanism by which 13-HPODE contributes to tumor progression or resistance to anticancer therapy. While induction of PPAR signaling by 13-HPODE promoted FA oxidation and lipid metabolism and might have anti-inflammatory and anti-proliferative effects on PDiff Caco-2 cells, it may maintain the tumorigenic phenotype of the cells. Moreover, upregulated glycogen metabolism by either 13-HPODE or LA and upregulated amino acid metabolism by 13-HPODE or H\({}_{2}\)O\({}_{2}\) may promote cell survival. Although our results, in part, indicate reduced proliferation, the overall changes in cellular response to 13-HPODE and H\({}_{2}\)O\({}_{2}\) may provide a carcinogenic environment. The negative impact we observed of 13-HPODE on electron transport chain and phage-lysosomal degradation pathways might link LOOHs to different intestinal pathologies including IBD and cancer, which could be initiated by intestinal crypt cells. Compared to 13-HPODE, H\({}_{2}\)O\({}_{2}\) treatment not only enhanced antioxidant adaptive response, which protects against cytotoxicity, but also upregulated steroid biosynthesis, which is a hallmark of tumor development. This study expands the knowledge of how PDiff Caco-2 cells respond to 13-HPODE, which possibly resembles the response of in vivo intestinal tumor or epithelial crypt cells exposed to dietary LOOHs. In this context, our results suggest that dietary LOOHs, such as 13-HPODE, could trigger a strong antioxidant mechanism in intestinal tumor or epithelial crypt cells, which provides a defensive response to stress. Our results also indicate that dietary LOOHs may modify metabolic processes and alter energy production and degradation processes creating an environment that renders these proliferative cells to initiate or progress disease. Hence, this study provides possible mechanisms by which dietary LOOHs might contribute to disease development or