esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of sEMG for Robot Control: Techniques and Applications
## 4 Application of sEMG Interface in Robot Control
### Robot Control Methods Using sEMG Interfaces  
In the research mentioned in the previous section, the methods for translating sEMG signals into robot control commands can be divided into the following two categories:  
Human Model-Based Method (M1): M1 involves solving muscle activation levels from sEMG data, estimating human motion based on musculoskeletal models and human dynamics, and commanding robots for human-robot collaboration activities or exoskeleton devices to follow human motion.  
Machine-Learning-Based Method (M2): M2 considers the physical system of muscle-driven bone movement in the human body as a black box and uses machine-learning techniques to directly convert sEMG signals to the joint torque and joint angle. Then, the position, velocity, and force control are implemented based on the kinematics of the robot. Interestingly, [PERSON] starts with processed sEMG pictures and uses the YOLO algorithm to identify joint movement and detect joint angles [79].  
Figure 3 shows the relationship between control methods and control strategies. When using muscle signals from limb movement (arms or legs), most researchers employ the M1 or M2 approaches. Generally, more studies adopt the M2 than the M1. The reason for this, in the author's opinion, is that human modeling based on physiology and biomechanics is highly complex, and it is difficult to guarantee its accuracy. In addition, this form of the model is easily affected by individual variances, leading to low generalizability. Hand gesture movements involve so many muscles and bones that it is difficult to discuss their dynamics. Hence, hand gesture-related research uses the machine-learning method M2 to make the process easier.
## 3 Test Results and Analysis
\[G_{\max}=31.9\text{OCR}^{0.255}\times e^{-0.463\text{OCR}+1.594}\times({\sigma_{ 3c}}^{\prime}/P_{\text{a}})^{-0.043\text{OCR}+0.597} \tag{8}\]  
Based on this prediction equation, the values of \(G_{\max}\) of the Yangtze River overconsolidated floodplain's soft soils are predicted by the following step:  
Step 1: Determining the effective stress, \({\sigma_{3c}}^{\prime}\), based on the depth of the soil extracted in situ. Step 2: Identifying the void ratio, \(e\), from laboratory experiments. Step 3: Calculating the value of the per-consolidation pressure on the compression curve (\(e-p\) curve) using the Casagrande method and computing the over-consolidation ratio, OCR. Step 4: Based on the modified \({\sigma_{3c}}^{\prime}\), \(e\), OCR, estimating \(G_{\max}\) using Equation (8). Figure 8 compares the \(G_{\max}\) tested and predicted values from Equation (8). For Yangtze River floodplain soft soils with various \(e\), \({\sigma_{3c}}^{\prime}\), and OCR, the difference of \(G_{\max}\) predicted values using the proposed evaluation model is less than 10%. [PERSON] et al. [33] developed a \(G_{\max}\) prediction model for sand with different values of relative densities, \(D_{\text{r}}\), and the deviation between the predicted and the measured values of \(G_{\max}\) was within a range of 20%. [PERSON] et al. [34] established a \(G_{\max}\) prediction model for marine soils, and the deviation between predicted and measured values of \(G_{\max}\) was within a range of 15%. Therefore, it is encouraging that the theoretically estimated \(G_{\max}\) of Yangtze River floodplain soft soils (Equation (8)) coincide well with the experimentally measured \(G_{\max}\) values. The significant implication of this study is that the predicted \(G_{\max}\) of Yangtze River floodplain soft soils serve as a valuable reference for the site seismic response analysis in the Yangtze delta region. Figure 7: The relationship between parameters d, A, n, and OCR: (**a**) d versus OCR, (**b**) A versus OCR, (**c**) n versus OCR.
# AR-Assisted Guidance for Assembly and Maintenance of Avionics Equipment
## 3 Materials and Methods
### AR-Assisted Guidance
#### 3.4.3 Auxiliary Maintenance Guidance  
Different from assembly tasks, which are carried out in a specific order based on design drawings and process guidelines, aircraft maintenance tasks often involve troubleshooting systems or components, replacing damaged parts, or performing routine maintenance. Maintenance personnel may need to make decisions based on real-time failure information and may encounter unfamiliar failure situations. While the order of assembly tasks aims to minimize blind areas in the assembly process design, maintenance tasks typically involve disassembly and replacement of individual components, which are prone to visual blind areas. We can use AR to overlay markers on damaged parts, provide workers with intuitive disassembly and assembly guidance, and display necessary maintenance data to simplify operations and reduce error rates.  
In our approach, an AR-based maintenance technical guide is provided for workers to consult. Once a maintenance fault point is identified, we can select specific failure equipment to carry out auxiliary maintenance guidance. After conducting necessary inspections, disassembly steps are indicated with highlighted yellow parts (see Figure 8). A 3D simulation animation is played at corresponding positions along with additional assistance, such as arrows, curves, circles, exclamation marks, and voice prompts. Technical documents are also attached alongside to provide instructions. After completing the disassembly, the other parts are further repaired, replaced, and assembled with assistance guidance until all maintenance tasks are completed.  
Figure 7: Presentation of 3D assembly guidance information and assembly processes: (**a**) overlay of virtual information on the actual equipment bay; (**b**) bolt holes to be assembled are highlighted in yellow; (**c**) bolt holes that are occluded are highlighted in yellow; (**d**) the occluded area can be observed through the virtual model.
## 3 Long-Term Transmission Rate Maximization Problem Formulation  
In this section, we aim at maximizing the long-term data transmission rate for uplink NOMA/OMA. The \(2M\) users in the CRN can be decoupled into pairs according to their channel  
Figure 3: Markov chain model of the primary channel.  
gain, as described in Algorithm 1. After user pairing, the joint power allocation and bandwidth allocation problem can be formulated as follows:  
\[\begin{array}{c}\mathbf{a^{*}}\left(t\right)=\underset{\mathbf{a}\left(t\right)}{\arg \max}\sum\limits_{k=t}^{\infty}\sum\limits_{m=1}^{M}R_{m}\left(k\right)\\ s.t.0\leq e_{im}^{tr}\leq e_{max}^{tr}\\ \sum\limits_{m=1}^{M}\mathbf{b}_{m}\left(t\right)=W\end{array}, \tag{7}\]  
where \(\mathbf{a}\left(t\right)=\left\{\mathbf{b}\left(t\right),\mathbf{\varepsilon}\left(t\right)\right\}\) represents the action that the CBS assigns to the CUs in time slot \(t\); \(\mathbf{b}\left(t\right)\) indicates a vector of the allocated bandwidth portions assigned to the corresponding sub-channel, where \(\mathbf{b}\left(t\right)=\left\{b_{1}\left(t\right),b_{2}\left(t\right),....,b_{M }\left(t\right)\right\}\sum\limits_{m=1}^{M}b_{m}\left(t\right)=W\) is the assigned bandwidth amount for \(m^{th}\) sub-channel; \(\mathbf{\varepsilon}\left(t\right)=\left[e_{11}^{tr}\left(t\right),e_{21}^{tr} \left(t\right),e_{12}^{tr}\left(t\right),e_{22}^{tr}\left(t\right),...,e_{1M}^ {tr}\left(t\right),e_{2M}^{tr}\left(t\right)\right]\) refers to a transmission energy vector of the CUs, where \(e_{im}^{tr}\left(t\right)\in\left\{0,e_{1}^{tr},e_{2}^{tr},...,e_{\max}^{tr}\right\}\) is the transmission energy value for \(CU_{im}\), and \(e_{max}^{tr}\) represents the upper-bounded value of transmission energy for each CU in time slot \(t\).
## 2 Materials and Methods  
### Study Area  
As shown in Figure 1, the Shendong mining area is located at the junction of northern Yulin City of Shaanxi Province and southeastern Ordos City of the Inner Mongolia Autonomous Region, in the transition zone of the Loess Plateau and Ordos Plateau, belonging to the Wulan Mulun River Basin of the Yellow River system [37]. The study area covers about 3539 km\({}^{2}\) and mainly includes Shenmu City, Fugu County, and Yijin Horuo Banner. It is located between 38\({}^{\circ}\)52\({}^{\prime}\) and 39\({}^{\circ}\)41\({}^{\prime}\)N, 109\({}^{\circ}\)51\({}^{\prime}\) and 110\({}^{\circ}\)46\({}^{\prime}\)E, about 916-1477 m above sea level, and belongs to the temperate semi-arid continental climate. This region is defined by an average annual temperature of 6.6 \({}^{\circ}\)C, arid conditions, and minimal rainfall, with an average annual precipitation ranging from 300 to 400 mm, predominantly occurring from June to September each year. The growing season for vegetation is brief, the dormant phase is extended, and the coverage rate is minimal. The main species include _Salix cheliophila_, _Artemisia desertorum Spreng_, and _Mongolian pine_. The main soil types are loess and chestnut soil. To the east and northeast of the mining area are the Loess Hills and mountainous areas, with gullies and ridges forming three landforms: ridges, gullies, and tablelands with active erosion and serious soil loss. Psammophytes and pioneering plant communities such as _Artemisia ordosica Krasch_ are distributed on the flowing, semi-fixed and fixed sandy land in the west and southwest and on sandy vegetation. Moisture-loving plants are distributed around the depressions, beaches, and lakes. As of 2020, the Shendong mining region has established a production framework of 10 million tons, concentrated around 14 mines including Daliuta, Halagou, Yujialiang, and Buertai, yielding a coal output of 200 million tons. This makes the Shendong mining area the largest coal production base in China and even the world [38].
## 7 Results and Discussion
### Results
#### 7.2.5 Network Lifetime  
The early identification and consideration of various factors (i.e., remaining energy, distance, next hopping node, and node mobility) in our proposed schemes result in the optimal cluster head selection for each cluster and data transmission, thereby increasing the network lifetime. It reduces clustering overhead, supporting the network nodes to stay for more time and stabilize the network. Our schemes remove extra duties from cluster heads, improve energy efficiency, and decrease the number of death nodes. Our proposed scheme network lifetime is better compared to FBFCP, TEEN and LEACH, as shown in Figure 12a,b with different mobility speeds. The average performances of the proposed schemes CRP-GR and CRP-G, with respect to network lifetime, were 84% and 71%. The existing schemes, such as FBCFP, TEEN and LEACH had average performances of 58%, 45% and 39%, accordingly.
# Hybrid Engineered Water-Polymer Flooding in Carbonates: A Review of Mechanisms and Case Studies
## 2 Polymer Flooding (PF)  
The use of polymers in EOR as mobility control agents was first investigated in 1964 [16]. Later, various laboratory studies of polymer properties were performed [17; 18; 19]. The first field applications of polymers were carried out during 1960-1970 in the United States. Many researchers have conducted detailed reviews regarding PF projects implemented worldwide [20; 21; 22; 23]. A successful PF project was implemented in the Marmul sandstone oil field in Oman, where PF helped reduce the water cut rate and improve the oil recovery rate [24]. As far as the implementation of PF in carbonate reservoirs is concerned, the literature shows a very small number of field projects [25; 26]. In a review by [PERSON] and [PERSON] [27], only 5% of field projects were conducted in carbonate formations. Some of these carbonate reservoirs included the Upper Shaunavon Unit of the Rapdan field [28], embark formations in the Byron and North Oregon basin fields [29], the Eliasville Caddo Limestone Unit [30], and the Pettit formation crane zone of the Northeast Hallsville field [31].  
Polymers are high molecular weight materials with specific properties based on their structures, such as their viscosification, toughness, and viscoelasticity. In contrast to water, polymer solutions exhibit non-Newtonian fluid behavior, i.e., either shear thinning or shear thickening as a function of the shear rate [32]. Two fundamental types of polymers used widely in EOR are synthetic polymers and biopolymers [33]. Hydrolyzed polyacrylamide (HPAM) is used extensively as a synthetic polymer due to its low cost and high molecular weight [34]. [PERSON] and [PERSON] [27] conducted a comprehensive review of the implemented EOR projects involving polymers and reported that HPAM was used in ~79% of the projects studied. HPAM is formed by copolymerization of acrylamide and acrylic acid [35; 36]; its chemical and physical configurations are shown in Figure 1. HPAM consists of long flexible chains with negatively charged carboxylic groups on backbones, which repel each other and keep the polymer chains stretched, resulting in high solution viscosity [37].
# An Approach to Knowledge Base Completion
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Entity Embedding** & **Relation Embedding** & **Plausibility Function** \\ \hline TransE & \(\mathbf{h},\mathbf{t}\in\mathbb{R}^{d}\) & \(\mathbf{r}\in\mathbb{R}^{d}\) & \(\|\mathbf{h}+\mathbf{r}-\mathbf{t}\|_{2}^{2}\) \\ TransH & \(\mathbf{h},\mathbf{t}\in\mathbb{R}^{d}\) & \(\mathbf{r},\mathbf{w}_{r}\in\mathbb{R}^{d}\) & \(\|(\mathbf{h}-\mathbf{w}_{r}^{\intercal}\mathbf{h}\mathbf{w}_{r})+r-(\mathbf{t} -\mathbf{w}_{r}^{\intercal}\mathbf{w}_{r})\|_{2}^{2}\) \\ TransR & \(\mathbf{h},\mathbf{t}\in\mathbb{R}^{d}\) & \(\mathbf{r}\in\mathbb{R}^{k},\mathbf{M}_{r}\in\mathbb{R}^{k\times d}\) & \(\|M_{\mathbf{h}}+\mathbf{r}-\mathbf{M}_{r}\mathbf{t}\|_{2}^{2}\) \\ TransD & \(\mathbf{h},\mathbf{w}_{h},\mathbf{t},\mathbf{w}_{t}\in\mathbb{R}^{d}\) & \(\mathbf{r},\mathbf{w}_{r}\in\mathbb{R}^{k}\) & \(\|(\mathbf{w}_{r}\mathbf{w}_{h}^{\top}+\mathbf{I})\mathbf{h}+\mathbf{r}-( \mathbf{w}_{r}\mathbf{w}_{r}\mathbf{w}_{t}^{\top}+\mathbf{I})\mathbf{t}\|_{2}^{2}\) \\ RESCAL & \(\mathbf{h},\mathbf{t}\in\mathbb{R}^{d}\) & \(\mathbf{M}_{r}\in\mathbb{R}^{d\times d}\) & \(\mathbf{h}^{\top}\mathbf{M}_{r}\mathbf{t}\) \\ DistMult & \(\mathbf{h},\mathbf{t}\in\mathbb{R}^{d}\) & \(\mathbf{r}\in\mathbb{R}^{d}\) & \(\mathbf{h}^{\top}diag(\mathbf{r})\mathbf{t}\) \\ HolE & \(\mathbf{h},\mathbf{t}\in\mathbb{R}^{d}\) & \(\mathbf{r}\in\mathbb{R}^{d}\) & \(\mathbf{r}^{\top}(\mathbf{h}\otimes\mathbf{t})\) \\ ComplEx & \(\mathbf{h},\mathbf{t}\in\mathbb{C}^{d}\) & \(\mathbf{r}\in\mathbb{C}^{d}\) & \(Re(\mathbf{h}^{\top}diag(\mathbf{r})\mathbf{t})\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Entity and relation embeddings of knowledge graph embeddings. There have been a few studies in which the advantages of various embeddings are combined. [PERSON] et al. proposed an ensemble of knowledge graph embeddings for knowledge base completion [33]. Their method combines three knowledge graph embeddings of TransE, RESCAL, and the embedding proposed by [PERSON] et al. [34]. They applied a logistic regression to each embedding to normalize the plausibility by each embedding. As a result, as the number of knowledge graph embeddings increases, the number of logistic regressions to train also increases. Another problem of their method is that it ignores the relative importance of each embedding.
# Predictive Quantization and Symbolic Dynamics
## 4 Representation
### Rate Reduction  
Rate-Distortion is studied as a way of extracting useful or meaningful information from noisy signals, and is also motivated by aspects of human cognition [15]. Reduced representation was also explored in the context of deep neural network learning theory using the information bottleneck principle [5]. In deep learning, some attempts to consider predictive information through the use of a bottleneck or noisy representation in temporal models, such as RNNs have recently appeared in the literature[16; 17]. An important distinction between these works and the proposed framework is that these works are using rate limitation as part of the learning process. Here, we propose applying bit reduction to a pre-trained encoder-decoder network in order to reduce the complexity of the latent representation prior to decoding. This allows experimenting with various bit-rate regimes for a fixed embedding without retraining the representation network. For this purpose, we borrow bit allocation technique from the rate-distortion theory of lossy information processing. The reduced latent representation is subjected to information rate analysis using symbolization methods as previously explained.
## 2 Literature Review
### Scene Text Recognition  
Scene images contain abundant and precise information, especially the text in the scene images, which is helpful for people to understand the surrounding environment. Scene Text Recognition (STR) is developed as one of the research fields of computer vision to recognize the text from scene images and convert it into machine-readable information [23]. STR consists of two stages, which are text detection and text recognition. Text detection aims to determine whether there is text in a given image or video and to localize the text using bounding boxes. Text recognition aims to identify the detected text and translate the text into machine-readable information [24]. Recently, scene text recognition applications gained much popularity in many fields, such as car plate recognition, product labeling, sorting, and packaging.  
Traditional text detection and recognition algorithms often adopt handcrafted features to separate the text and non-text regions in a scene image, requiring demanding and intricate image processing steps [25]. Since traditional methods are not robust, challenging to implement, and constrained by the complexity of creating handcrafted features, they can hardly deal with intricate circumstances. Therefore, this section only discusses recent deep learning-based methods.
# SC-SA: Byte-Oriented Lightweight Stream Ciphers Based on S-Box Substitution
### Current Research Status
In [10], the authors analyzed and evaluated the scheme, showed the different factors that affect its performance, covered the scheme's efficiency from a security perspective, and introduced the experimental results of two encryption algorithms. In the same year, [PERSON] and his team [11] proposed Grain-128 AEAD, designed for encrypting and authenticating messages, which incorporates certain improvements to bolster security while complying with the NIST's standardization requirements. The image encryption technology, based on high-capacity data hiding [12], was proposed by [PERSON] et al. and applies traditional stream ciphers and compression coding encryption, thereby expanding the application of stream ciphers in the field of image encryption. In 2020, [PERSON] et al. [13] pointed out that stream ciphers exhibit significant advantages in terms of speed and scale in hardware implementation as cryptographic algorithms. However, with the increasingly complex application environments, challenges are posed to existing cryptographic algorithms, necessitating the development of new and suitable designs. Meanwhile, [PERSON] et al. [14] presented a cipher algorithm, introducing innovation into the field of stream ciphers. In the domain of quantum cryptography, [PERSON] [15] and [PERSON] [16] introduced applications of digital coherent PSK Y-00 quantum stream ciphers and Y-00 quantum-noise randomized stream ciphers. These applications highlight their potential utility in optical communications and physical layer security, underscoring the increasing interest in quantum cryptography for data protection. In the field of image data encryption, [PERSON] and colleagues [17] introduced a new efficient and configurable image encryption structure in 2020, while in 2022, [PERSON] et al. [18] proposed a stream cipher generator based on deep learning for encrypting and decrypting medical images. These developments reflect the growing demand for safeguarding sensitive data, particularly in the domain of medical imaging. In the realm of cryptographic attacks, a new algebraic attack against the Trivium stream cipher was introduced [19]. In terms of research directions, probabilistic constellation shaping for quantum-noise stream key generation was utilized [20], and researchers are exploring stream ciphers for rapid Fully Homomorphic Encryption (FHE) evaluation [21], which holds significant implications for privacy protection and secure computation.
## 2 The Proposed Multi-Objective Majority-Minority Cellular Automata Algorithm (MOMmCAA)
### Multi-Objective Majority-Minority Cellular Automata Algorithm (MOMmCAA)  
The MmCAA was devised to solve single-objective optimization problems; therefore, it needs to be modified to deal with multi-objective problems. This results in the creation of the proposed multi-objective variant, MOMmCAA. Taking as a basis the MmCAA inspired by the combination of majority and minority cellular automata and the handling of a solution repository on the Pareto front of the MOPSO algorithm, the MOMmCAA uses the following mechanisms for multi-objective optimization. **Updating of each smart-cell**: Each smart-cell solution generates a new set of neighboring solutions by taking its information or the information from one or two neighboring smart-cells (depending on the evolution rule that is randomly selected). Certain rules favor exploration by taking information from other smart-cells, while others favor exploitation by only taking information contained in the same smart-cell. From this set of neighbors, the one that is not dominated by the rest is used to update the smart-cell if it dominates it. **Repository with non-dominated solutions**: The non-dominated smart-cells are stored in a repository, which also serves as a file to take neighbors when applying the different evolution rules that define the MOMmCAA optimization process. In order for a smart-cell to enter the repository, either it must dominate another solution or it must be the case that no other solution in the repository dominates it. The repository has a limited capacity; if a new smart-cell enters the repository, then the smart-cell that is dominated or the one that is in a region of the objective space with high density is deleted. **Hypercube density management in the objective space**: Taking inspiration from the MOPSO mechanism, solutions in the PS are ranked depending on the density of the hypercube in which they are found in the solution space. If any other solution in the repository does not dominate a new solution and in turn is in a hypercube of lower density, then a solution that is in the hypercube with higher density is removed from the repository. This allows the repository to contain a better diversity of solutions. If a new solution is found in a new hypercube, then the boundaries of the solution space are expanded and the hypercube densities are recalculated. Figure 3 shows the handling of smart-cell selection in the repository using hypercubes; in (A), a new smart-cell replaces another smart-cell in a hypercube if it dominates it. In (B), if the new smart-cell falls into a higher-density hypercube and the repository is complete, then one of the smart-cells is randomly removed. In (C), if the new smart-cell falls into a less dense hypercube and the repository is complete, then one smart-cell is randomly removed from the denser hypercube. In (D), the hypercube boundaries are updated if the new smart-cell falls outside the current hypercube's boundaries. Figure 2: Majority–minority Cellular Automata Algorithm (MmCAA) [42]. The pseudocode of the MOMmCAA is presented in Algorithm 4.