esa-sceva/satcom-chunk-collection huggingface dataset:
# Progress in Construction Robot Path-Planning Algorithms: Review
## 4 Local Path-Planning Algorithms
### Classic Algorithms
#### 4.2.2 Dynamic Window Approach (DWA)  
The Dynamic Window Approach (DWA) is a widely used technique in mobile robot path planning, which performs well in real-time dynamic environments, especially when dealing with dynamic obstacles. The core idea of the algorithm is that it acquires the current state of the robot, such as direction, speed, acceleration, etc., and generates a dynamic velocity window to predict all possible movement trajectories of the robot at the next moment. Then, it selects the path that best suits the mobile robot [62]. This algorithm has good application in construction scenarios with heavy personnel traffic, such as material handling robots, bricklaying robots, interior decoration robots, etc. However, DWA is prone to local optimal paths and requires significant computational resources during actual operation, especially in dynamic environments.  
[PERSON] combined DWA with D*Lite, making the algorithm's performance in local path-planning superior to general algorithms in intricate scenarios [63]. [PERSON] introduced an innovative density-related evaluation factor within the path evaluation function. This new factor significantly enhances the robot's ability to perceive and assess the distribution of densely located objects ahead of time. Through experimental studies, it has been demonstrated that this enhancement has led to a notable increase in the efficiency of the improved Dynamic Window Approach (DWA), with an impressive rise of 25%. This advancement suggests that incorporating density awareness into robotic navigation can greatly improve performance in complex environments [64].  
[PERSON] introduced a novel way that mixes the modified A* with an enhanced DWA. This technique involves the incorporation of trajectory estimation function into the evaluation process of the algorithm. Additionally, the resulting paths are further refined and optimized through the application of the B-spline curve method, allowing for smoother and more efficient trajectories [65]. [PERSON] proposed an algorithm that combines the improvedA* algorithm with DWA, and the hybrid algorithm successfully integrates the advantages of both, enabling robots to dynamically avoid obstacles while navigating globally [66].  
[PERSON] introduced an innovative way that integrates three algorithms. In this enhanced approach, the traditional obstacle distance evaluation function of the dynamic window algorithm was substituted with an artificial repulsive field function, effectively improving the algorithm's response to nearby obstacles. Additionally, the introduction of an end distance evaluation function further refines the path optimization process. Furthermore, the improved dynamic window method was synergistically combined with a gradient descent-smoothed version of the A* algorithm. This integration effectively tackles the limitations associated with poor global planning found in conventional algorithms, ultimately resulting in paths that are not only safer but also smoother [67].
# Sustainable Plant-Based Diets and Food Allergies: A Scoping Review Inspired by EAT-Lancet
## 2 Materials and Methods
### Eligibility Criteria  
Studies were included if they met the following criteria:  
* Investigated the effects of plant-based, sustainable, or EAT-Lancet-inspired diets on food allergy prevention or symptom modulation;
* Explored outcomes related to microbiota composition, immune tolerance, allergen exposure, barrier function, or inflammatory regulation;
* Included human subjects (adults, children, and pregnant women), animal models, or in vitro experiments;
* Employed study designs such as randomized controlled trials (RCTs), cohort and cross-sectional studies, systematic/narrative reviews, or experimental studies. Exclusion criteria:
* Studies not available in full-text or published in languages other than English;
* Articles that addressed sustainability or plant-based diets solely from ethical or environmental perspectives, without any connection to allergic outcomes;
* Editorials, commentaries, and non-peer-reviewed sources;
* Primary studies already included within eligible reviews: in such cases, the review article was retained to avoid data duplication.  
To minimize ambiguity in the definition of dietary models, we included only those studies that explicitly referred to \"sustainable plant-based diets\" or dietary patterns aligned with the EAT-Lancet Commission recommendations. Studies that generically mentioned vegetarian or vegan diets without specifying a sustainability or EAT-Lancet framework were excluded. This criterion was adopted to ensure conceptual consistency with the focus of this review.
## 1 Introduction  
During the running wheel of history, several theories have been used to model the behavior of light, with respect to obstacles [1; 2; 3]. First, geometrical optics was used to analyze the behavior of light. Light is seen as rays traveling in straight lines [2]. Geometrical optics is the least rigorous theory modeling the behavior of light (Figure 1). Wave optics, or undulatory optics, is a more rigorous theory (Figure 1) [4]. This theory considers light as a propagating wave. It allows us to give an interpretation of the interference phenomenon [5; 6]. The scalar theory of diffraction is more rigorous (Figure 1) [7; 8; 9]. It allows us to handle diffraction and the effects of the boundaries. The electromagnetic theory is more rigorous (Figure 1) [7; 10]. Propagation is seen as interaction between the interaction of the electric and magnetic fields in the medium as vector, and not scalar, entities. Quantum physics is more rigorous (Figure 1) [11].  
In the present work, we intend to use the scalar theory of diffraction, with the aim of proposing a powerful tool to easily treat and design optical components and systems. Such analysis and design become easier and more illustrative if all elements of the system, the human eye, for example, are combined into one compound system and handled as one entity. To give an example of the usefulness of such a demarch, the design of diffractive optical systems, fulfilling preestablished constraints, becomes straightforward and doable through such a compound system, within a much rigorous theory than geometrical optics, namely the scalar theory of diffraction (Figure 1). Another interesting system is the second order dispersive medium, such as the single-mode fiber [12; 13]. The compound system presents a powerful tool in the design of fiber components compensating for chromatic dispersion. It is worth noting that these components are key elements in broadband communication.  
The contributions of this paper may be summarized as follows:  
* With the proposed formalism, we can analyze higher diffraction orders, which is not straightforwardly feasible with conventional geometrical optics.
* The new formalism allows for easily calculating the (1) PSF (point spread function), (2) DTF (optical transfer function), and (3) MTF (modulation transfer function). These three metrics are very meaningful in measuring the optical behavior of various systems. Special interest goes to the optical system of the human eye.
* The system of the human eye is easily covered by our approach.
* Optical performance criteria, as well as image quality criteria, including the optical transfer (OTE), are easily treated by our diffractive approach.
* We can easily treat optical information processing and invariance issues [14].  
The remainder of the paper is organized as follows. Section 2 provides an overview on related works. Section 3 presents the mathematical formulation of the proposed compound system. Three systems are handled as examples in Section 4. Section 5 is devoted to the discussion and concluding remarks.
## 7 Conclusion  
In this paper, we propose an efficient data collection method based on LRMA for WSNs. The contribution of the work lies in two folds. On one hand, a proposed novel ST-LRMA based data collection method introduces the spatial and temporal correlation of the sensing data into the conventional LRMA model. The experiment results indicate that the proposed method has better data recovery accuracy compared with the conventional interpolation methods and the state-of-the-art LRMA based methods. In addition, the proposed method avoids the optimization problem involving empty columns or rows, and could fill the empty columns or rows stably. On the other hand, an optimized nodes sampling method is proposed and integrated with the ST-LRMA based data collection method. In the proposed nodes sampling model, Gini index is adopted to measure the uniformity of the selected nodes and the distribution of the network energy. The data collection experiments were conducted on the simulated and real wireless networks. The results show the proposed method has good performance on network energy efficiency without losing much data recovery accuracy.  
Our experiments are implemented without considering some network properties, such as the complex routing, the network time duty circle, and the sensing data attributes. The future work is to integrate these network properties into our ST-LRMA based data collection method and apply it on real large scale wireless sensor networks. Another possible future work is to consider the multi-modality sensing data together and to formulate the sensing data as tensors to be recovered by low rank alike constraints.
## 3 RF and Bayesian MLP-Based RUL Prediction Framework  
Step 1: Data preprocessing.  
An aero-engine's monitoring parameters contain a large amount of historical degradation data. To enhance the prediction performance of the training model, it is important to select monitoring parameters that strongly correlate with the engine's life cycle. We employed a random forest algorithm to generate an importance ranking value for each monitoring parameter, and subsequently eliminated parameters with low importance. The SES algorithm was used to smooth the extracted features and reduce the influence of noise, allowing samples to retain the maximum amount of degradation information. As monitoring parameters mostly consist of different types of data, we employed the MinMax scaling method to normalize the monitoring features and reduce the influence of data magnitude.  
Step 2: Training the multi-layer perceptron model.  
Our training framework is based on artificial neural networks. Firstly, we filtered the parameters that require updating and determined the range of the search, with the root-mean-square error (RMSE) as our search target. We obtained the best possible parameters, which were then employed as the final combinations for the MLP engine RUL prediction model.  
Step 3: RUL prediction.  
We began by pre-processing the test set, which was then input into the [PERSON] prediction model for training. The prediction evaluation indexes RMSE and \(R^{2}\) values were output, and we compared and analyzed the prediction results with the actual values. Next, we calculated the expectation and standard deviation of the RUL prediction values in order to obtain the PDF of the engine's RUL. A diagram of the overall RUL prediction process framework for aero engines can be found in Figure 4.  
Figure 4: Flowchart for RUL prediction of aircraft engines.
## 3 Results
### Synthesis and Resynthesis: The Statements and Their Dimensions, Constructs and Factors
#### 3.3.4 The Conceptual Model  
The CM provides an interpretative approach to the current state of the art described in the literature, where the systematic literature review plays a key role in establishing knowledge. The iterative and repetitive synthesis process resulted in the following dimensions, constructs, indicators and indicator definitions (see Tables 1-3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dimension** & **Construct** & **Indicators** & **Indicator Definitions** & **Source** \\ \hline IoT Technology & IoT expectations & \(\bullet\) & Company leadership’s view of IoT. & \(\bullet\) & The viewpoint of top managers and managers in relation to IoT technology perspectives, which influences their purpose of IoT implementation. & [19] \\  & & \(\bullet\) & Level of technological sophistication. & \(\bullet\) & The company’s potential to innovate regarding IoT technologies within its operations and in its supply chain. & [17; 18; 20] \\  & & \(\bullet\) & Level of data management. & \(\bullet\) & The company’s potential to innovate regarding IoT technologies within its operations and in its supply chain. & [17; 18; 20] \\  & & \(\bullet\) & Interrelationship among the company’s intelligent constituent elements. & \(\bullet\) & The company’s intelligence regarding the interconnection of the IoT technologies within its operations and in its supply chain, which enables the identification, selection, analysis and management of relevant information on potential events or problems with real-time responses. & [18; 20; 21] \\  & & \(\bullet\) & Level of IoT contribution to process management & \(\bullet\) & The company’s purpose of IoT implementation within its operations and in its supply chain. & [17] \\ \hline \hline \end{tabular}
\end{table}
Table 1: IoT Technology.  
Figure 3: The synthesis of the statements and their dimensions, constructs and factors.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Dimension** & **Construct** & **Indicators** & **Indicator Definitions** & **Source** \\ \hline \multirow{8}{*}{Environmental Approach} & \multirow{8}{*}{Company engagement} & \multirow{8}{*}{\(\bullet\)} & Development of environmental sustainability and digital transformation integration strategy. & The company commitment to environmental sustainability and digital transformation relationships in its own operation and in its supply chain. \\  & &
## 6 Proposed Joint Mode Selection and Resource Allocation Scheme (SD-Scheme)
### Mode Selection and Channel Assignment Scheme (C-Scheme)
Consequently, the gNB admits the D2D pairs with the highest priority while refusing the remaining pairs. For further clarification, if the D2D pair \(n\) gives a proposal to pick sub-channel \(k\) based on its greatest utility in \(\ell^{p}\), then sub-channel \(k\) is subsequently assigned to the exact D2D pair \(n\) according to the least interference impact utility function in \(\ell^{s}\). Moreover, the matching process continues till all devices in the network are paired to enhance the system performance. To provide a more detailed explanation of the resource allocation process, we now describe the matching game-based sub-channel assignment in greater depth. Matching Process Execution:  
* Each D2D pair initially proposes to the sub-channel that provides the highest data rate based on its preference list. * The gNB evaluates all proposals and initially assigns sub-channels to D2D pairs while ensuring that the total number of assigned pairs does not exceed the predefined threshold \(\ell\). * If a sub-channel receives multiple proposals, the gNB selects the D2D pairs that maximize SE and rejects lower priority requests. * Rejected D2D pairs then propose to their next preferred sub-channel, and this process iterates until a stable matching is achieved, meaning no further changes can improve the overall network performance. This iterative matching ensures an efficient and interference-aware sub-channel allocation strategy that enhances both spectral efficiency and system stability. The complete mode selection and channel assignment approach is detailed in Algorithm 1. ```
Input: \(M\), \(N\), \(K\) Output: \(\Pi_{n,k}\)
1: Initialization \(\Pi_{n,k}=\text{zeros}(N,3\text{K})\)
2: for 1 to \(M\)
3: determine \(\text{Y}_{m,k}(t)\)
4: determine \(R_{m,k}(t)\)
5: end for
6: for 1 to \(N\)
7: calculate \(\text{Y}_{n,k}(t)\)
8: calculate \(R_{n,k}(t)\), \(SE_{n,k}(t)\), \(EE_{n,k}(t)\)
9: end for
10: find \(\mathcal{M}_{D2 Dm}\) based on maximum \(\text{Y}_{n,k}(t)\)
11: find \(Q\) from the matrix \(\mathcal{M}_{D2 Dn}\)
12: for 1 to \(N\)
13: for 1 to \(K\)
14: calculate \(R^{D}_{n,k}(t)\), \(R^{R}_{n,k}(t)\), \(R^{L}_{n,k}(t)\) with regards to \(\mathcal{M}_{D2 Dn}\)
15: calculate the interference impact of CUs
16: end for
```  
**Algorithm 1** C-scheme algorithm
## 4 Discussion
### Qualitative Comparison of the Extraction Methods
Furthermore, technical considerations can play a decisive role when screening larger sample collections. The presented protocol of resin extraction appears to be rather impracticable for smaller culture volumes (>5 mL), but on the other hand, it can be more convenient when processing larger numbers of fermentations at an intermediate scale. Since we expect many of the microbial secondary metabolites to be excreted to the media in order to exert their biological effect [4], extraction of the media seems to be reasonable. However, extraction of a 10 L culture using EtOAc would demand an equal volume of the organic solvent, while resin extraction would require just gram amounts of resin, but at the expense of extracting more polar molecules. It was important for us to document that the extraction methods were qualitatively comparable for the tested compounds; therefore, other technical considerations can be prioritized when deciding on one of the protocols.
## 2 Related Work
### Matrix Factorization Models  
The task of next POI recommendation began with the application of matrix factorization methods. One notable approach combines matrix factorization with Markov chains to learn personalized transition graphs [6]. Some works have recognized the importance of geographical influence [3] and have introduced a unified POI recommendation framework. To address the challenge of successive personalized POI recommendations, the FPMC-LR model [7] was developed, embedding personalized Markov chains and localized regions. Further advancements include the PRME and PRME-G models [8], which incorporate sequential information, individual preferences, and geographical influences. Additionally, some works [10] have demonstrated the importance of modeling multidimensional contextual information and incorporating social relations.
# An Overview on Deep Learning Techniques for Video Compressive Sensing
## 4 Video Compressive Sensing  
Obviously, the main function of video compressive sensing systems is to capture video data with low-dimensional detectors and then use the optimized based algorithms, as explained above in Section 2.3, to solve the ill-posed reconstruction problem. These two systems: the hardware encoder and the software recovery system enable to optimize encoders resources, especially in the transmission process. However, their long running time prevents them from being exploited in real-time applications. So, thanks to recent advances in deep learning, we expand the variety of algorithms used in the reconstruction phase. Deep learning-based approaches enable a fast end-to-end recovery of video scenes with better quality performances despite the long training time. Indeed, The basic framework of video compressive sensing is composed of two main systems: the hardware encoder and the software decoder, and a channel to transmit video data over it. This is the main digital video delivery system employed by communication systems that rely on compressive sensing to acquire, transmit and reconstruct data. In fact, the encoder uses special cameras (low-speed cameras such as single pixel cameras) to capture and process high speed videos. Then, it generates fewer compressive measurements that could be easily transmitted or stored. Finally, a reconstruction algorithm will be applied in order to reconstruct the received video at the receiver device (e.g., server). Figure 2 illustrates the basic video compressive sensing framework.  
Video CS algorithms have used various models and architectures to sample and reconstruct the signals. According to the way the video signals are sampled, we review these works in the following three categories: Temporal VCS, Spatial VCS and Spatio-temporal VCS.
## 6 Conclusions  
This study developed an ensemble learning framework for predicting the unconfined compressive strength of MICP-treated sand, combining advanced optimisation algorithms with interpretable techniques. Based on a comprehensive analysis, the following key conclusions can be drawn:  
1. The proposed LARO-EnML attained the best results (RMSE = 0.5449, MAE = 0.2853, R\({}^{2}\) = 0.9570, OI = 0.9597) on the test data, compared to individual ML methods. The integration of LARO optimisation effectively enhanced base learner performance, while the hierarchical architecture successfully mitigated overfitting issues, resulting in superior generalisation capability and providing a robust prediction tool for complex bio-geotechnical systems.
2. SHAP interpretability analysis identified calcite content as the dominant factor governing MICP treatment effectiveness, exhibiting a strong positive correlation with strength enhancement. Urease activity demonstrated optimal effectiveness at lower concentrations with diminishing returns at higher levels, while median particle size revealed nonlinear dependency with distinct thresholds for bio-cementation efficiency. These findings establish fundamental parameter hierarchies and provide quantitative guidance for MICP process optimisation in engineering applications.
3. While bacterial optical density, initial void ratio, and nutrient solution parameters demonstrated relatively weaker individual contributions, their synergistic interactionswith dominant factors proved essential for optimising MICP treatment outcomes. The analysis revealed that these secondary parameters influence bio-cementation efficiency through mechanisms affecting bacterial distribution, reaction kinetics, and precipitation uniformity, emphasising the necessity of holistic parameter optimisation strategies.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15147972/s1](https://www.mdpi.com/article/10.3390/app15147972/s1)]([https://www.mdpi.com/article/10.3390/app15147972/s1](https://www.mdpi.com/article/10.3390/app15147972/s1)), Table S1: Summary of input features and data sources for model development [5; 42; 46; 47; 48; 49; 50; 51].  
Y.Q.: methodology, investigation, writing--review and editing; S.Y.: data curation, investigation; H.Q.: software, formal analysis; M.K.: investigation, writing--review and editing, supervision; J.Z.: conceptualisation, writing--review and editing, funding acquisition, supervision. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Science Foundation of China (42177164) and the Distinguished Youth Science Foundation of Hunan Province of China (2022)J10073).  
Not applicable.  
All relevant data generated throughout this study are included in this article.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.