esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cooperative Control Method for Excavation Support Robot with Desired Position/Posture
## 1 Introduction
Considering the inclined staggered posture of the fully mechanized mining hydraulic support, a theoretical model of inclination deviation adjustment is established, and an independent inclination deviation adjustment control method is proposed. In the initial support stage, the balance cylinder and cylindrical cylinder are used to adjust the posture of the top beam and the position of the deflection base, which can achieve precise support position/posture coordinated control [26; 27; 28]. To sum up, the current research mainly focuses on the fully mechanized mining face robot, vehicle-mounted equipment, and single hydraulic support. The research on ESR position/posture adjustment control method and collaborative strategy in fully mechanized mining and excavation is still in the preliminary exploration stage and needs to be revealed in depth. The execution of ESR action mainly relies on the centralized liquid supply of the hydraulic servo system and the manual control and adjustment of the hydraulic valve. Restricted by factors such as complex roadway environment and subjective judgment of personnel, ESR position deviation/posture bias is a thorny problem that always exists. This topic is a more in-depth discussion based on the technical accumulation of ESR position/posture monitoring research results. Based on the ESR position/posture test data, ESR position deviation correction and posture offset adjustment are guided. Furthermore, an ESR desired position/posture coordinated adjustment control method based on real-time position/posture data and sliding mode control algorithm is proposed. The action stroke of the non-longitudinal hydraulic cylinder is controlled and coordinated according to the expected position/posture adjustment value, and the real-time deviation position and posture are quickly adjusted to ensure that the ESR reaches the optimal state of the expected position/posture coordination. The subject is organized as follows: the multiple models of ESR, including structure, hydraulic servo system and kinematic models in Section 2, are presented. Section 3 focuses on the description of the proposed controlled structure of the position/posture adjustment system, and proposes a coordinated adjustment strategy for ESR position deviation, posture bias, and optimized controller design. In Section 4, the numerical analysis of the coordinated position/posture adjustment is presented., The field experiments are verified in Section 5, where the results are discussed and the corresponding conclusions are drawn, thus validating the cooperative control method proposed in this study.
## 4 Results and Discussions
### Effect of Constraint Conditions at the Pile Tip  
#### 4.3.1 Pile Deflection and Bending Moment  
Figure 18 gives the non-dimensional depth versus non-dimensional pile deflection curves and non-dimensional bending moment profiles of the negative battered piles (\(\beta=-20^{\circ}\)) under different constraint conditions at the pile tip (free-tip and fixed-tip). The non-dimensional pile deflections and non-dimensional bending moments for cases of a fixed tip are significantly less than those of a free tip with the same surcharge load.  
The reductions of the maximum non-dimensional bending moments due to fixed tip boundary conditions under different surcharge loads are 86.7%, 53.0%, 59.4%, and 48.5%, respectively. Moreover, the distribution pattern of the bending moment profiles under free tip boundary conditions is quite different from that under fixed tip boundary conditions. The former increases first and then decreases, and the maximum bending moment appears approximately at \(z_{a}=1\); the latter is M-shaped with two extreme positive bending moments at \(z_{a}=0.5\) and 1.25. Furthermore, considerable negative bending moments would happen at the bottom of fixed tip piles, and their values are significantly greater than the positive bending moments.  
Figure 17: Non-dimensional subgrade reaction profiles under different surcharge magnitudes: (a) \(\beta=-20^{\circ}\); (b) \(\beta=-10^{\circ}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pile** & **Surcharge** & \multicolumn{4}{c}{**Maximum of Responses**} \\
**Inclination** & **Magnitudes** & \(y_{a,\text{max}}\) & \(M_{a,\text{max}}\) (\(10^{-3}\)) & \(p_{a,\text{max}}\) & \(p_{p,\text{a,max}}\) \\ \hline  & \(p=34.375\) kPa & 0.004 & 0.229 & 0.014 & 0.010 \\ \(\beta=-20^{\circ}\) & \(p=84.375\) kPa & 0.006 & 0.798 & 0.046 & 0.037 \\  & \(p=134.375\) kPa & 0.031 & 1.970 & 0.113 & 0.076 \\  & \(p=184.375\) kPa & 0.070 & 2.865 & 0.168 & 0.124 \\  & \(p=34.375\) kPa & 0.005 & 0.401 & 0.044 & 0.030 \\ \(\beta=-10^{\circ}\) & \(p=84.375\) kPa & 0.022 & 0.984 & 0.174 & 0.076 \\  & \(p=134.375\) kPa & 0.099 & 2.466 & 0.334 & 0.118 \\  & \(p=184.375\) kPa & 0.169 & 4.410 & 0.453 & 0.193 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Maximum of responses under different surcharge magnitudes.
# Sino-InSpace: A Digital Simulation Platform for Virtual Space Environments
## 3 Key Technologies
### Environment Data Organization  
#### 3.2.1 Organization of Geographical Environment Data  
Geographical environment data is the basis of human activities on the surface of a planet, which includes all kinds of data collected by probes, primarily remote images of the planet surface, DEMs, vectors, and toponymic data, among others. To improve the efficiency of data loading and rendering, the strategy of the multiresolution pyramid model is generally adopted by most digital planet systems [57; 58] depending on a structure known as a discrete global grid system (DGGS) [59]. The traditional approach discretizing a solid planet is to use the latitude/longitude coordinate system, which was also adopted in the early stages of the platform development (see Figure 3).  
For example, considering the Mars images and DEMs, the size of the pyramid tile was set to \(512\times 512\) pixels, the first layer was divided into \(5\times 10\), and the row increased from \(90^{\circ}\) S to \(90^{\circ}\) N and the column increased from \(180^{\circ}\) W to \(180^{\circ}\) E. Then, each tile was segmented layer-by-layer, and the row and column number (Row, Col) at the coordinate (Lon, Lat) of the Ln level could be calculated using the formula specified in Figure 3. The tile used three-level storage with the naming convention, 'Ln\(\backslash\)Row\(\backslash\)Col', which was convenient for querying and loading. For feature data, the point region (PR) quadtree index [60] was created based on the location coordinates and recorded using the eXtensible Markup Language (XML) file.
## 4 Discussion and Conclusions
Although the determination of realistic values of the parameters representing inertia and memory depth is still an open question, experimental estimates of \(\alpha\) and \(\beta\) are reported in a few studies on day-to-day route choice dynamics [12; 36]. Another noteworthy result of the work described in this paper is that the choice of the specific form of the link cost functions should not be considered a marginal detail, as it may significantly affect the size of the fixed point stability region. The findings of this study seem to support the conclusion that the potential impacts of contrarian behavior on the day-to-day dynamics of route choice are worthy of further in-depth investigation. The main limitation of this research relates to the simplicity of the network considered in the analysis, so that the principal aim of future research efforts should be the extension to general networks consisting of several origin-destination pairs connected by multiple routes. A key issue to be addressed in view of the implementation of the proposed model on real-world networks is the determination of empirically founded values of parameter \(\varphi\). Estimating the extent to which contrarian behavior occurs in actual trip planning is surely a challenging task, that would require extensive collection of observations through controlled experiments or stated preference surveys. The \"natural\" target of such studies should consist of travelers actually experiencing repeated route choice situations, like individuals commuting daily from home to work. Another simplifying assumption that needs to be removed in view of a generalization of the proposed model relates to the treatment of \(\varphi\) as an exogenously fixed parameter. In a realistic day-to-day route choice setting, the fraction of travelers adopting contrarian behavior is likely to be contingent upon the outcome of previous travel experiences, thus causing \(\varphi\) to become an endogenous, state-dependent parameter. This research received no external funding. Not applicable. Not data were used in this study. The author declares no conflict of interest.
# Volatile, Microbial, and Sensory Profiles and Consumer Acceptance of Coffee Cascara Kombuchas
## 3 Results and Discussion  
### Physicochemical Parameters  
The pH, total acidity, soluble solids, and sucrose content values in all kombucha beverages are presented in Table 1. In BT K, total acidity increased until day 9 (0.3 mEq/L), when pH was 3.4. pH and titratable acidity values are within the range observed in the literature [14; 37]. The increase in acidity is caused by the fermentation process, when bacteria and yeasts produce invertase that metabolizes sucrose into several organic acids, mainly acetic, glucuronic, lactic, and citric acids, and the monosaccharides glucose and fructose [38; 39].  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Days of Fer-** & **Titrable Acidity** & \multicolumn{2}{c}{**Soluble Solids**} & **Sucrose** \\  & **mentation** & **(mEq/L)** & & **(c\({}^{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{ \text{\text{\text{ \text{ \text{ \text{ }}}}}}}}}}}}}}\))** & **(g/100 mL)** \\ \hline \multirow{2}{*}{Black tea} & 0 & 0.1 \(\pm\  
Considering the CC K, while in CCB K acidity remained stable (0.1 mEq/L), in CCN K acidity increased slightly during fermentation (Table 1). Nevertheless, in both CC Ks, no significant differences were observed in pH from d3 to d9. The range of pH values in these kombuchas is considered safe for human consumption [25]. Values below pH 2.5 indicate hazardous concentrations of acetic acid; likewise, values higher than 4.2 may affect the beverage's microbiological safety [25].  
Results are in accordance with previous studies using novel ingredients to prepare kombucha beverages [40] and agree with the fact that kombucha's pH tends to stabilize after a few fermentation days, given the buffer effect caused by the organic acids and carbon dioxide formed in the fermentation process [38; 40]. According to [PERSON] and [PERSON] [40], the obtained aqueous solution of carbon dioxide dissociates and produces the amphiprotic hydrocarbon anion (HCO\({}_{3}\)\({}^{-}\)), which easily reacts with hydrogen ions (H+) from organic acids, preventing further changes in the (H\({}^{+}\)) concentration and contributing to a buffer character in the system.  
Fermentation caused 20% decrease, on average, in the content of sucrose during fermentation (Table 1), with a small increase in glucose (0.84-1.09 g/100 mL) and fructose (0.3-0.5 g/100 mL) concentrations. As expected, a decrease in soluble solids also occurred, accompanying the decrease in sucrose concentration in the culture media over time [41].
###### Abstract  
The number of real-time dynamic satellite observation missions has been rapidly increasing recently, while little attention has been paid to the dynamic mission-scheduling problem. It is crucial to reduce perturbations to the initial scheduling plan for the dynamic mission-scheduling as the perturbations have a significant impact on the stability of the Earth observation satellites (EOSs). In this paper, we focus on the EOS dynamic mission-scheduling problem, where the observation profit and perturbation are considered simultaneously. A multi-objective dynamic mission-scheduling mathematical model is first formulated. Then, we propose a multi-objective dynamic mission-scheduling algorithm (MODMSA) based on the improved Strength Pareto Evolutionary Algorithm (SPEA2). In the MODMSA, a novel two-stage individual representation, a minimum perturbation random initialization, multi-point crossover, and greedy mutation are designed to expand the search scope and improve the search efficiency. In addition, a profit-oriented local search algorithm is introduced into the SPEA2 to improve the convergence speed. Furthermore, an adaptive perturbation control strategy is adopted to improve the diversity of non\(-\)dominated solutions. Extensive experiments are conducted to evaluate the performance of the MODMSA. The simulation results show that the MODMSA outperforms other comparison algorithms in terms of solution quality and diversity, which demonstrates that the MODMSA is promising for practical EOS systems.  
eurm observation satellite; dynamic mission-scheduling; multi-objective scheduling; SPEA2
## 4 Practical Experiments
### Shisper Glacier
#### 4.1.3 Surge Investigation  
Figure 12 illustrates the change detection results, derived from DoD by subtracting the 2017 PSS DEM from the 2019 PSS DEM. Glacier outlines from Muchowar and Shisper glaciers, obtained from the RGI, were applied as a mask. The results closely align with the findings of [16]. The DoD and elevation profiles derived from the DEMs effectively highlight the glacier's surge event, illustrating the significant mass transfer within the glacier area. It should be noted that performing DoD calculation with higher temporal resolution, i.e., using DEMs from more time stamps, would likely yield a more comprehensive depiction of surge dynamics, capturing seasonal variations and transient behaviors not discernible from the two temporal points utilized in this study. This further demonstrates the utility of the method in capturing dynamic changes in glacial morphology. A detailed glaciological interpretation of these results lies beyond the scope of this study. However, readers seeking a deeper understanding may refer to [37].  
Figure 12: Shisper glacier change detection covering the 2018 surge event. DoD derived from multi-temporal DEMs using PSS images (2017/2019) within the ROI, covering Shisper and Muchowar. The black dashed line indicates the approximated Shisper glacier centerline profile, including elevation measurements along the centerline. The histogram points to elevation changes within the ROI.
## 2 Materials and Methods: Ten-Koh Mission Description
### Challenges Faced during the Project
#### 2.7.6 COTS Design Heritage From Previous Missions  
Ten-Koh avionics are based on Shinen-2's COTS components and other Kyutech satellite missions that have been flight-proven in space. However, in certain cases, no heritage components from previous Kyutech missions were found. In these instances, literature review was conducted to find candidate COTS components. ADUM1250 and ADUM14 xx families of magnetic isolation devices are of note in this respect because the entire satellite data handling subsystem relies on these ICs, and thus choosing components likely to survive space environment was needed [16]. The ADUM1250 IC is used for I2C isolation purposes while the ADUM14 xx family is used for SPI and general-purpose digital signals with 5V-TTL compatible levels. If no components with reported flight heritage were found, device packaging, used technology, and temperature range were considered in order to select the IC from amongst the commercially-available ones.
# Enhancing Recognition of Human-Object Interaction from Visual Data Using Egocentric Wearable Camera
## 4 Experimental Results and Analysis
### Implementation Details  
As the proposed framework uses two different approaches for HOIR, i.e., ODIR and OIIR, the dataset was labeled in two different ways. The label assignment in the case of ODIR is different from OIIR. For ODIR, we assigned each video snippet an object and interaction label. In the case of object recognition, all interactions relevant to an object were assigned the same object label, whereas for interaction recognition, we combined all instances of the interactions against each object and assigned the respective interaction label. The ground truths for these labels came from the corresponding video sequences in the HOI4D dataset. In the case of OIIR, we combined the object and the relevant interaction label to generate a single HOI label for each video snippet. Thus, overall, we had 54 different HOIs for OIIR, with 16 object categories: bowl, bottle, mug, toy car, bucket, knife, kettle, chair, storage furniture, pliers, laptop, lamp, safe, trash can, scissor, and stapler. In this study, we employed a random forest (RF) classifier implemented using scikit-learn. The RF classifier was configured with a maximum depth of 25 and it comprised 100 decision trees. A personal desktop computer was used for the experiments in this study. The computer was equipped with an Intel(R) Core(TM) i5-8265U CPU that operated at 1.60 GHz (up to 1.80 GHz with turbo boost), with 8.00 GB of RAM.
## 5 Conclusions  
The present findings revealed significant enrichment of As, Hg, Cd, Pb, Ni, Cu, and Zn in construction land soil in Kunming City. The surrounding agricultural land was significantly enriched in As, Hg, Cd, Pb, Cu, and Zn. Cd was the most enriched element in construction and agricultural lands. In the construction land, seven of the heavy metal(loid)s were higher than the background value of Kunming City, except for Cr, and the contents of As, Cd, and Pb in the construction and agricultural land soil samples were all higher than the soil risk screening values of construction and agricultural lands in China.  
The PLI results indicated that the study area was heavily polluted. The Igeo values indicated slight pollution by all eight heavy metal(loid)s, with relatively more serious pollution by As, Cd, Cu, Pb, and Zn. The Nemero Integrated Pollution Index showed that the P\({}_{\text{N}}\) for construction and agricultural land exceeded 3. Construction land was more polluted by As and Pb than agricultural land. The As pollution of the construction land was heavy, the Cd and Pb pollution of the construction land was mild, and the Cd pollution of the agricultural land was moderate.  
The results of the spatial distribution show that, except for Cr, the distribution characteristics of the other seven elements were relatively similar, and there were noticeably high levels of pollution in the center. Correlation and principal component analyses showed that the sources of pollution were different in general. Arsenic, mercury, cadmium, and lead were primarily derived from industrial and agricultural sources. Traffic sources had a greater impact on Zn, Cd, Pb, and Cr, whereas natural sources were the primary sources of Cr, Ni, Cd, and Ni were also affected by industrial sources. Zn and Cu concentrations were also affected by agricultural sources.  
In summary, the surface soil of the study area is severely polluted by As, Cd, and Pb. Cd is a widespread pollutant and should be a priority for the management of soil heavy metal (loid) pollution.  
Conceptualization, Y.Z. and W.L.; methodology, Y.Z. and W.L.; software, W.L.; validation, Y.Z.; formal analysis, W.L. and Y.Z.; investigation, P.W., W.L. and C.S.; resources, Y.Z. and P.W.; data curation, P.W., W.L. and C.S.; writing--original draft preparation, W.L.; writing--review and editing, Y.Z. and W.L.; visualization, W.L.; supervision, Y.Z. and P.W.; project administration, Y.Z. and P.W.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ten Thousand Talent Program of Yunnan Province (grant no. YNWR-QNBI-2019-157), the projects of the YM Lab (2011), and the Innovation Team of the Yunnan Province (2008 and 2012).  
The dataset used in this study is not publicly available because of data privacy agreements with the Kunming Geological Exploration Institute of the China Metallurgical Geology Bureau. This information can be obtained from the corresponding author upon request.  
The authors declare no conflicts of interest.
# A Comparative Study of Control Methods for X3D Quadrotor Feedback Trajectory Control
## 3 X3D Quadrotor Controller Design
### Model Reference Adaptive PID Control System Based on MIT Rule
\] (35)  
The value of the adaptation gain (\(\gamma\)) has a direct relationship with the convergence rate. The simulation results show that it is correct for small values (\(\gamma\)) but impulsive for high values, indicating that the right selection of (\(\gamma\)) is critical. The quadrotor's attitude (roll \(\phi\), pitch \(\theta\), and yaw \(\psi\)) is controlled by three MARC adaptive PID controllers, indicated by \(MARC\ \phi\), \(MARC\ \theta\)_and_\(MARC\ \psi\), respectively. MARC Z oversees controlling the quadrotor's height, while MARC X and MARC Y are in charge of controlling the quadrotor's position. The architecture of the MARC adaptive PID controller with MIT rule including all six degrees of freedom (DOF) quadrotor output is shown in Figure 11. Three PID controller parameters \((K_{P}\,K_{I},\ and\ K_{d})\) can be modified using the MARC system and the MIT rule to make the nonlinear X3D Quadrotor stable and track to the appropriate reference input. Figure 11: MRAC Adaptive PID Controller with MIT for an X3D Quadrotor.
# Efficient Gamma-Based Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
## 4 Results
### Ablation Experiment
#### 4.2.1 Hyperparameter \(a\)  
From the previous analysis and Figure 4, it is evident that hyperparameter \(a\) plays a crucial role in controlling the intensity of image enhancement. When \(a\) is either too small or too large, achieving the desired enhancement effect becomes challenging. Therefore, it is necessary to investigate the appropriate value of \(a\). Table 1 presents the variation in PSNR and SSIM metrics across a combined dataset of LOLv1, LOLv2-Real, LOLv2-Synthetic, and LSRW-huawei as \(a\) ranges from 0.10 to 0.16. The results demonstrate that the value of \(a\) enables the model to achieve the best performance between 0.13 and 0.14. Specifically, the PSNR is higher when \(a\) is closer to 0.13, and the SSIM is higher when \(a\) is closer to 0.14. Considering that the enhancement of darker regions is more effective when \(a\) is set to 0.13 compared to 0.14, we select 0.13 as the value for hyperparameter \(a\). Additionally, these metrics consistently decline when \(a\) deviates from 0.13 and 0.14. The possible explanation for this trend is that, when \(a\) is smaller, the curve's enhancement capability becomes excessively strong, leading to images with increased noise or overexposure. Conversely, when \(a\) is larger, the enhancement effect on dark regions becomes less pronounced, and the curve's ability to enhance low-light conditions deteriorates, failing to adequately meet the task requirements.