esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent End-Edge Computation Offloading Based on Lyapunov-Guided Deep Reinforcement Learning
## 6 Conclusions  
In this paper, we proposed an intelligent computation offloading algorithm based on Lyapunov optimization and DRL. With full consideration of the offloading time, data causality, local CPU frequency, energy consumption, transmit power, and data queue stability, the long-term network computation rate maximization problem was formulated. To solve this problem, we designed the data queue and virtual energy queue backlog, derived the upper bound of the Lyapunov drift based on the Lyapunov optimization theory, and decoupled the complex MINLP problem into deterministic sub-problems. Then, we reformulated the transformed problem into MDP, and approximated the optimal solution using the proposed LyA3C algorithm. Experimental results showed that compared with two benchmark algorithms, including A2C-based and TD3-based algorithms, the proposed  
Figure 8: Effect of different energy consumption thresholds on network computation rate.  
Figure 9: Relationship between energy consumption thresholds and data queue length for different algorithms.  
LyA3C algorithm could converge stably and effectively improve the long-term network computation rate by 2.8% and 5.7% while satisfying various constraints.  
In the future, in addition to the binary offloading adopted in the paper, we will consider dividing the computation task into multiple independent sub-tasks for multi-destination computation offloading. Moreover, different from the fact that all industrial base stations are homogeneous providing the same computation resource, i.e., CPU, we will consider the case that, different industrial base stations provide different kinds of computation resources including CPU, GPU and NPU.  
Conceptualization, [PERSON] ([PERSON]), J.J. and X.F.; Formal analysis, [PERSON] ([PERSON]) and X.F.; Methodology, [PERSON] ([PERSON]) and X.F.; Project administration, [PERSON] ([PERSON]), [PERSON]([PERSON]) and X.J.; Resources, [PERSON] ([PERSON]), [PERSON]([PERSON]) and X.J.; Validation, X.F.; Writing--original draft preparation, X.F.; Writing--review and editing, [PERSON] ([PERSON]) and X.F. 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 92267108, Grant 62173322, Grant 62133014, Grant 61821005 and Grant 92267205; in part by the Science and Technology Program of Liaoning Province under Grant 2023 JH3/10200004, Grant 2022 JH25/10100005, and Grant 2023 JH3/1020006; in part by the Youth Innovation Promotion Association CAS under Grant Y2021062, and Grant 2020207; in part by the Independent Subject of the State Key Laboratory of Robotics under Grant 2024-Z12.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# Research on an Equivalent Algorithm for Predicting Gas Content in Deep Coal Seams
## 3 The Gas Content of the Equivalent Layer
### Discussion on Algorithm for Calculating the Basic Gas Content of Equivalent Coal Seams
Since the surrounding rock in the clay zone has zero gas content and is solely influenced by the thickness of the clay layer, the fundamental gas emission and its influencing factors of clay rock and thick mudstone continue to be utilized for the calculations. The fundamental gas content of coal seams across different lithologies can be calculated using the same methodology, which is:  
\[Q_{mj}\approx q_{dj}+q_{mc} \tag{16}\]  
Formula (16) serves as a fundamental indicator of the correlation between coal seam gas content and the equivalent layer gas emission. This relationship constitutes a pivotal foundation upon which an equivalent algorithm for predicting deep coal seam gas content can be formulated.
## 3 Network Architecture and System Model  
In this section, we elaborate on the SAGIN architecture and channel models. Based on the compute, caching, and communication (3C) requirements, we developed an optimization model for making computing offload decisions with the objective of minimizing costs.
## 3 Results and Discussion
### Thermal Properties  
A thermal analysis technique was employed to examine the starch structural changes that occur during the thermal transformation of distinct starch particles. The thermal parameters of the 16 BRS samples re summarized in Table 4. The BRS samples from the ABB, AAB, and AAA genome groups had different gelatinization temperatures. The gelatinization temperatures of T\({}_{\mathrm{o}}\), T\({}_{\mathrm{p}}\), and T\({}_{\mathrm{c}}\) ranged from 48.1 to 68.6 \({}^{\circ}\)C, from 71.7 to 82.4 \({}^{\circ}\)C,  
Figure 6: Iodine absorption spectra from BRS of 16 BRS cultivars.  
and from 76.7 to 87.0 \({}^{\circ}\)C among the 16 banana cultivars, respectively. The gelatinization \(\Delta\)T varied from 11.9 to 33.6 \({}^{\circ}\)C. Different T\({}_{\text{o}}\) values were observed in the same genome group. Kazirakwe (AAA), with a low OD620 value, exhibited the highest gelatinization T\({}_{\text{o}}\), whereas Yuejiao 2 (AAA), with a high OD620 value with more amylose, had the lowest gelatinization T\({}_{\text{o}}\). This phenomenon could be ascribed to the BRS samples from the same genome group varying in amylose content. The lower amylose content granules displayed a higher relative crystallinity, causing a higher temperature to disrupt them [43]. All the BRS samples of the 16 banana cultivars had a T\({}_{\text{p}}\) greater than 70 \({}^{\circ}\)C. Though both GN 60A (AAA) and Saba (ABB) had the same crystallinity for B-type, GN 60A had the largest T\({}_{\text{p}}\), while Saba was noted to be the lowest. As shown in Table 4, we found that Tigua (AAB), with the lowest T\({}_{\text{o}}\) (high amylose), had the highest \(\Delta\)T value, marking a more stable crystal pattern during the gelatinization. The \(\Delta\)T value reflected the uniformity and stability of the BRS. According to [PERSON] et al. [44], wide gelatinization ranges are mainly due to a higher heterogeneity and the differences between the arrangement of starch components in the BRS particles. Therefore, the thermal properties of the BRS were speculated to connect with the amylose content, genome group, and growing conditions.
## 4 Extraction Methods for Coriander Bioactive Compounds
### Non-Conventional Extraction Methods
#### 4.2.2 Ultrasound-Assisted Extraction
[PERSON] and [PERSON] [129] studied this non-conventional method for the extraction of coriander seed oil by studying the influence of four parameters, namely, sample solvent ratio, amplitude level, temperature, and time, providing results on the influence of these parameters on the yield of oil extraction and their antioxidant activity. Based on their findings, the best extraction conditions for maximum oil yield and antioxidant activity (30.74-72.05%), respectively, were attributed to the sample-solvent ratio of 1:13 (g/mL), the amplitude level of 82 (%), under a temperature of 45 (\({}^{\circ}\)C) and the extraction time of 9 (min). In addition, the study conducted by [PERSON], [PERSON] [130] aimed to optimize an ultrasound-assisted extraction method for phenolic compounds in coriander, focusing on caffeic acid as the primary target. Using a Box-Behnken Design (BBD) with response surface methodology (RSM), the researchers identified methanol concentration as the most crucial factor for recovery. The optimal extraction conditions were determined to be 10 min, 70 \({}^{\circ}\)C, 50% methanol in water, and a 6.51 mL/g solvent-to-sample ratio. The method showed high repeatability and reproducibility, suggesting its potential use in quality control analyses of coriander. On the other hand, [PERSON], [PERSON] [131], by comparing the efficiency of UAE to the MAE of bioactive metabolites (notably coriander polyphenols), reported that no significant difference between the two methods was found. In closing, ultrasound-assisted extraction (UAE) appears to be a promising extraction method for bioactive compounds, with significant advantages in terms of yield and the preservation of extract properties. However, its effectiveness is highly dependent on specific extraction conditions, requiring careful optimization. Although UAE sometimes outperforms conventional methods in terms of yield, it is not always the best option for all applications, and its use should be judiciously considered according to the specific extraction objectives.
# Selection of Grid Road Networks Based on Raster Data
## 4 Experiments and Evaluations  
### Experimental Area  
To test the proposed ARWS road selection method, as shown in Figure 5, we used road networks from Beijing in China to perform the selection experiments. The latitude and longitude ranges of the selected road networks for the experiments are \(39.90\leq Lat\leq 39.95\) and \(116.35\leq Lon\leq 116.48\). In this experimental area, the total length of the road networks is 487,520.12 m, and the total area covered by the road networks is 36.42 square kilometers. The area contains 1832 road segments with different widths. The longest and shortest road segments are 3498.94 and 2.55 m, respectively. Diverse road breadths are associated with varying levels of road hierarchy. Given that the primary road networks are represented in vector format, they were transposed to raster format via the ArcGIS (v.10.8.2) (ESRI, Inc., Redlands, CA, USA) suite of tools, comprehensive geographic information system (GIS) software designed for spatial data analysis, mapping, and geographic data management. The size of the converted raster road image is \(19,993\times 15,921\) pixels with a high output resolution of 1200 dpi, as shown in the bottom right corner of Figure 5.  
Figure 4: Elimination of redundant pixels at road network intersections. (**a**) Intersecting line calculation results. (**b**) Endpoint detection and line labeling. (**c**) The results of eliminating redundant pixels.
# A Novel 3D Topography Stitching Algorithm Based on Reflectance and Multimap
## 2 Materials and Methods
### Description of the Evaluation Criteria for Stitching Algorithms
#### 2.3.1 Visual Inspection (Qualitative Criterion)  
First, a visual inspection of the topography is performed to check if no major defects appear due to stitching, such as seams, holes, stitching chain reaction failure or major unexpected deformations of topographic maps. While this criterion is qualitative, this test is naturally performed by any stitching user, to determine if stitching has failed or not. However, a qualitative criterion is not enough for a correct assessment of stitching algorithms; therefore, four quantitative criteria are added.
## 1 Introduction  
Pure titanium (Ti) is one of the most common metallic biomaterials that has been used in artificial implants owing to its favorable mechanical properties, anti-corrosion performances, excellent biocompatibility, and so on [1; 2; 3; 4; 5]. However, there are still some drawbacks from Ti implants that should be considered for improvement, such as the native oxide on Ti being bioinert, which only physically contacts with bone tissue, and the mismatch in Young's modulus between the Ti implant (70-110 GPa) and alveolar bone tissue (3-40 GPa) [6; 7]. The mismatch in Young's modulus leads to the formation of stress shielding, and bone resorption and loss, as well as poor in vivo osseointegration. Moreover, the surface characteristics of a Ti implant will affect the resulting cell adhesion, proliferation, differentiation, and active-matrix remodeling behaviors [8; 9; 10; 11]. In order to improve these drawbacks, numerous studies have been done to create functionalized surfaces on Ti implants through different surface modification methods [9; 12; 13; 14]. It is well known that sand-blasting or plasma spraying method is applied to increase the surface roughness of Ti implants [15; 16]. An appropriate surface roughness results in an improved mechanical interlock between the Ti implant and bone tissue [17; 18]. For chemical etching, alkaline treatment, and chemical deposition methods, many reports have demonstrated that the surface morphology and chemical composition of Ti implants could be modified effectively [19; 20]. Thus, these kinds of surface modification methods were adopted to alter the physical and chemical properties of Ti surface in the present study.  
Recently, our research team has focused on developing a novel and efficient method to immobilize biomolecules on the Ti oxide (TiO) layer [14] to attract cells of the host tissue, which then produce their own extracellular matrix and actively remodel the environment. However, we also found that using a self-assembled monolayer (SAM) is a promising method that can be used to immobilize the molecular chain on materials for fabricating surfaces with highly controlled chemical properties [21; 22; 23]. SAMs have been applied in industrial fields, such as to protect metal from corrosion, promote coating adhesion, enhance lubrication, lithography resists and photonic devices, etc. Nevertheless, there is a great idea of interest in forming SAMs of phosphonate on the surface of a metal oxide layer using alkyl phosphate and phosphonic acid solutions as appropriate anchor groups for biomedical applications [24; 25; 26]. The SAM plays an important role in modifying the surface properties of Ti. Based on the improvement of Ti implants, the purpose of the present study was to fabricate the biocompatible SAMs on a TiO layer using four kinds of phosphonic acid solutions via self-assembly technology and investigate their surface properties and biomechanical behaviors. The research findings could provide new scientific information in the dentistry fields.
## 4 Discussion
### Validation of Field Partition Models Using Biologically Meaningful CVI
Under such circumstances, BHI gave higher scores to partition models that were composed of one big cluster containing most observations and a second much smaller cluster that included few isolated values. All these models showed BHI values near to 1. The reason for the observed phenomenon is that, at least in great measure, original implementations of BHI (and BSI) were designed to be used with genes instead of suitability classes [56]. Gene classes make sense regardless of them forming part of big or small clusters, since they all have a specific function. In our case, however, functional classeswere represented by groups of places with similar potential to host pest populations (i.e., MZ). Since this potential is not an intrinsic attribute of the pest itself but of the geographic space occupied by it (i.e., distribution area), its gradation into functional intervals (i.e., pest levels) represents a rather subjective task with more than one equally plausible outcomes. This created scenarios where, even when the same MC algorithm was implemented with the same data sets, using different threshold values to define pest levels resulted in partition models with varying degrees of dissimilarity with ground truth samples (i.e., virtual data). Although BSI was useful as a secondary validator when different models showed the highest BHI, it did not offer any means to facilitate the validation process in cases where bad model partitions showed high BHI values. To facilitate the recognition of these suboptimal partition models, we recommend the exploration of \(\text{S}^{2}_{\text{T}}\) or \"total within-zone pest suitability variance\". \(\text{S}^{2}_{\text{T}}\) was designed to validate field partitioning essays with crop management purposes [36] and recently adapted to the needs of site-specific pest management [8]. Since higher \(\text{S}^{2}_{\text{T}}\) values indicate more heterogeneous classes (i.e., MZ), useful field partition models should show low scores for this index. This way, when a model shows extremely high BHI and an extremely low \(\text{S}^{2}_{\text{T}}\) values, it should be regarded with caution since suitability thresholds used to define pest levels might still need proper tuning.
# Evaluation of Low-Temperature Performance of Recycled
## 3 Results and Discussion
### Scanning Electron Microscope (SEM)
#### 3.2.2 Virgin Aggregate and Binder Interface  
The surface morphology of the interface between the new aggregate and asphalt in an asphalt mixture with different thermal histories is illustrated in Figure 8. The dark area represents asphalt, while the bright area represents new aggregate. The yellow lines in the figure roughly define the range of asphalt, aggregates and contact interfaces, and the yellow boxes mark the micropores on the microstructure of aged asphalt. A comparative analysis of the asphalt on the surface of the new aggregate reveals that the original asphalt in the mixture has not undergone the aging process, resulting in a relatively smooth surface. As the asphalt penetrates the aggregate, some boundaries become less distinct, indicating a close bond between the asphalt and aggregate [29]. Further comparative observations reveal that the surface of the asphalt in the THRAP mixture is relatively rougher and exhibits a more pronounced floc structure. Additionally, due to insufficient miscibility and aging, both asphalts contain a certain number of micropores and microcracks. And the THRAP mixture has a higher number of micropores and microcracks compared to the UVRAP mixture. Surface micromorphology observation at the interface between the new aggregate and asphalt indicates that the original asphalt mixture exhibits superior structural stability, whereas the thermally oxygen-aged asphalt presents the roughest surface, characterized by an increased number of micropores and microcracks. The UV-aged asphalt falls between these two extremes. The presence of micropores and microcracks on the surface of these asphalt may facilitate the cracking of the asphalt interface at low temperatures, consequently reducing the LTP of RAMs.
# Braking Intention Identification Strategy of Electric Loader Based on Fuzzy Control
## 3 Fuzzy Controller Design
### Braking Conditions Analysis  
The driver perceives driving speed and identifies the road working conditions with his eyes, then the driver decides the current driving demand under different combination working condition. The decision result is transmitted to electric loader through the control parts. According to the actual driving demand, the braking intention is mainly divided into follows:  
1. Sliding brake. The driver releases the accelerator pedal completely and does not step on the brake pedal. The vehicle speed is only affected by the friction force of ground and air, which is named sliding brake condition.
2. Mild braking. The driver releases the accelerator pedal completely and steps on the brake pedal gently, the braking strength demand is small. The purpose is mainly to adjust speed, avoid obstacles, or brake under a small speed.
3. Moderate braking. The driver releases the accelerator pedal completely and steps on brake pedal with a moderate speed, the brake pedal displacement continues to increase or maintain at a medium value. At this time, the braking strength increases, and the braking distance is shorter.
4. Emergency braking: When there is an emergency situation during driving, it is hoped that electric loader can brake immediately, so the driver steps on the brake pedal with the fastest speed, the brake pedal displacement reaches the maximum.  
To better analyze braking condition and select braking intention identification parameter, a 5-t pure electric loader is selected as the test object. The different braking intention experiments are carried out both on concrete field and sand field, a lot of data is obtained, which is used as the basis for fuzzy control variable division and fuzzy control parameter  
Figure 1: The vehicle speed curves of electric loader. (**a**) “T” type. (**b**) “V” type.  
setting. Through the induction and sorting of data, it is found that even if the working conditions are similar, the actual deceleration or braking intentions of the driver are not the same, so it cannot simply use the three levels of low, medium and high to describe all intentions.
## 1 Introduction
Hence, the strut walls of reticulated porous diatomite can also play an important role in increasing the overall permeability of the ceramics, unlike the walls of reticulated porous ceramics that act as a barrier to block fluent mass transfer. Although the nature of diatomite can be used to maximize the advantages of reticulated porous ceramics, overcoming their drawbacks remains challenging. For example, slurry rheology modification [13], dip coating [14], vacuum infiltration coating [15], centrifuge coating [16], multiple slurry coatings [17], and whisker-reinforcement coating [18] methods have been extensively investigated to reduce the shortcomings of reticulated porous ceramics. In addition, we had previously reported that the compressive strength of reticulated porous alumina can be enhanced by optimizing the processing conditions, including the multiple alumina coating layers and the heat treatment temperature [19]. However, the compressive strengths of reticulated porous ceramics cannot be increased to match those of porous ceramics. Therefore, a good approach to maximize the advantages of reticulated porous ceramics involves the use of a low-cost natural-based material such as diatomite. Various types of porous diatomite-kaolin composites fabricated by different preparation methods have been described in our previous studies [12; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31] and in those of other research groups [32; 33; 34; 35; 36]. However, to the best of our knowledge, only a few have focused on the detailed preparation and characterization of reticulated porous diatomite-kaolin composites, besides our previous report that discussed the feasibility [3]. It should be noted that the mechanical properties of reticulated porous diatomite are too weak to handle the material in a practical context and to withstand heavy loadings in load-bearing applications, which was confirmed by the reports mentioned above. Therefore, the interest in diatomite compounds naturally shifts to diatomite-based composites. In a previous report [3], a comparison of experimental data suggested that a reticulated porous diatomite-kaolin composite can be used as a porous dielectric barrier for plasma discharge, with a low density, good dielectric breakdown strength, and acceptable mechanical strength. Moreover, the composite can be prepared from only low-cost natural-based materials such as diatomite and kaolin, at low processing costs, without the aid of a pore former or sintering additive; this is achieved at a relatively low sintering temperature of 1200 \({}^{\circ}\)C.  
As a follow-up investigation, in this study, we discuss more optimized processing conditions such as the average particle size and the solid loading of the diatomite-kaolin composite slurry; a more detailed characterization of the reticulated porous diatomite-kaolin composites is also presented.