esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Molybdenum disulfide (MoS2) has been actively explored as a direct bandgap semiconductor in the monolayer (ML) limit for various applications due to its prominent physical properties and stability. In order to broaden its application range further, diverse treatments have been developed to modulate the properties of ML-MoS2. The native point defects, such as S vacancies, are known to activate surface charge transfer doping in ML-MoS2. Unlike conventional semiconductors, ML-MoS2 shows distinct excitonic transitions that can be exploited for controlling its optical, optoelectronic, and electric characteristics via coupling with defect-driven doping. Here, the ambient photoluminescence (PL) of ML-MoS2 could be increased by \(\sim\)1500% at the center of focused-laser irradiation (FLI). Expectedly, the PL intensity varied spatially along with exciton-trion transitions across the irradiation spot due to the Gaussian profile of laser intensity. Then, nano-Auger electron spectroscopy (n-AES) revealed that the spectral fraction of exciton PL increased by \(\sim\)69.2% while that of trion PL decreased by \(\sim\)49.9% with increasing S deficiency up to \(\sim\)13.4 \(\pm\) 3.5%. Cryogenic PL and field-effect transistor experiments were also performed to understand the defect-related phenomena comprehensively. This novel experimental combination of FLI with an n-AES probe provides a facile, effective, and cost-efficient approach for exploring defect effects in two-dimensional structures.  
two-dimensional semiconductors; molybdenum disulfide; Auger electron spectroscopy; focused-laser irradiation; defects; photoluminescence; field-effect-transistor; doping effects Article
# Parameter Estimation and Error Calibration for Multi-Channel Beam-Steering SAR Systems
## 4 Experiments and Discussion
### Point Targets Simulation Experiments
Since the spectrum is reconstructed by segment (see \(U_{a}(f+k\cdot f_{prf};r)\) in (14)), the phase inconsistency errors will disturb the weights of \(S_{a}{}^{(k)}\left(f,r\right)\) in each segment. Therefore, the spectrum with phase inconsistency errors becomes discontinuous and unsteady. The effects of these errors on Doppler spectrum in multi-channel sliding spotlight SAR are similar and are omitted here. The imaging results, azimuth profiles and quality indicators of the point targets located at the scene edge are shown in Figure 11 and Table 3. It can be seen that azimuth resolution with DRR error degrades seriously because part of the spectrum is outside the effective bandwidth. The amplitude of the target also decreases due to imperfect azimuth antenna pattern correction and spectrum loss. From Figure 11f, the first null points of azimuth profile are about \(-25\) dB, which cannot meet the quality of application in practice (\(-40\) dB). The CDC error seriously affects the image (Figure 11g) because the range cell migration is not corrected completely. In addition, both the Peak Sidelobe Ratio (PSLR) and Integrated Sidelobe Ratio (ISLR) decrease with phase inconsistency errors and the amplitude decreases as well. Overall, the effects of these errors on multi-channel sliding spotlight is smaller than multi-channel TOPS. Since the azimuth swath in multi-channel sliding spotlight is smaller, which leads the effect of DRR error smaller. CDC error also has much less impact on multi-channel sliding spotlight. However, the intensity loss is much seriously in multi-channel sliding spotlight than multi-channel TOPS.
# Prediction of the Moisture Content in Corn Straw Compost Based on Their Dielectric Properties
## 2 Materials and Methods
### Methods of the Prediction Model  
#### 2.5.1 Principal Component Analysis  
Since a specific correlation exists between the full-frequency variables of the parameters, the variables at a certain frequency point can be jointly explained by the variables at other frequency points Therefore, these variables are redundant information. This redundant information increases the complexity of the model and reduces its accuracy. Therefore, feature selection is critical [26]. Principal component analysis (PCA) is an effective mathematical method for reducing the dimensionality of multivariate data [27]. In PCA, the original indicators are combined into a set of independent, new, and integrated indicators that contain most of the original information [28]. Therefore, PCA has been used for the selection of characteristic variables for sample dielectric parameters.  
Figure 2: Testing system of straw compost electrical property. (1) Computer; (2) LCR meter; (3) LCR digital bridge test line; (4) acrylic box; (5) sample; (6) aluminum plate.
# An Entity-Relation Extraction Method Based on the Mixture-of-Experts Model and Dependency Parsing
###### Abstract  
Entity-relation extraction (ERE) aims to identify entity types and the relationships between them from unstructured texts and is one of the key technologies for constructing knowledge graphs. However, ERE tasks face challenges such as insufficient semantic representations and the complexity of relationship types, which lead to the difficulty of triplet extraction. To address these issues, we propose an entity-relation extraction model that incorporates dependency parsing and a mixture-of-experts architecture. Specifically, we use BERT as a character encoder, while integrating dependency syntax information as a separate encoding path. We apply additive attention to fuse the two pathways of encoding, assigning different weights to each vector in the encoding layer output through a learned weighting process. This enables the model to flexibly adjust the attention given to different features, allowing for a more accurate identification and utilization of syntactic dependencies within a sentence. In the relation classification layer, we employ a mixture-of-experts architecture, allowing each expert to focus on learning different relationship labels, thereby enhancing the model's ability to accurately identify and capture specific entity relationships. The proposed model achieves superior results to the baseline models on two public ERE datasets, providing a novel and effective solution for entity-relation extraction tasks.  
Keywords:entity-relation extraction; dependency parsing; additive attention; mixture-of-experts +
Footnote †: journal: Applied sciences
## 2 Structure of the Control Scheme  
### Kinematic Scheme of the CKCM  
The structures of the 2 DOF CKCM manipulator and the frame assignment are shown in Figures 1 and 2, respectively. Figure 1 shows a two DOF CKCM manipulator, which is a special case of the n-DOF CKCM manipulator. It consists of an end-effector platform and a fixed upper platform interconnected by two links. All links act in a parallel manner and share the same payload.  
Figure 2 depicts the frame assignment for the two DOF planar CKCM manipulator with a two-dimensional coordinate system \((x,y)\). From this figure, we obtain the following:  
\[q_{1}^{2}=x^{2}+y^{2} \tag{1}\]  
\[q_{2}^{2}=(d-x)^{2}+y^{2} \tag{2}\]  
where \(d\) is the distance between the pin joints hanging the two actuators, \((x,y)\) represents the Cartesian position of the end-effector, and \(q_{2}\) and \(q_{2}\) are the length of the first and second legs, respectively.  
We see that (1) and (2) represent a closed-form solution for the inverse kinematics in the sense that they can be used to determine the leg lengths \(q_{1}\) and \(q_{2}\) that yield a desired Cartesian position \((x,y)\).  
Moreover, from (1) and (2), the Cartesian variables \(x\) and \(y\) can be obtained as follows.  
\[x=\frac{q_{1}^{2}-q_{2}^{2}+d^{2}}{2d} \tag{3}\]  
\[y=\frac{-\sqrt{4d^{2}q_{1}^{2}-\left(q_{1}^{2}-q_{2}^{2}+d^{2}\right)}}{2d} \tag{4}\]  
We see that (3) and (4) represent a closed-form solution for the forward kinematics in the sense that a Cartesian position can be determined based the actual leg lengths \(q_{1}\) and \(q_{2}\). We note that, due to the small number of DOFs of this manipulator, it has closed-form solutions for both its forward and inverse kinematics.
# Design and Simulation of On-Orbit Assembly System Based on Insect-Inspired Transportation
## 4 Dynamic Analysis and Simulation of Patrol Process
During the loaded patrol movement simulation, finite element analysis transformed the rigid body into a flexible body, and the robot's grip caused a degree of oscillation, as shown in Figure 4d. By tracking the trajectory of the flexible body's center points, as shown in Figure 4e, the degree of oscillation can be represented. The data show that, after a period of measurement, the X-coordinate changed from \(-4556.158\) to \(-4016.946\), with a difference of \(539.212\) mm, and the Y-coordinate changed from \(-2784.092\) to \(22.0768\), with a difference of \(2806.169\) mm. Meanwhile, the Z-coordinate changed from \(-9452.606\) to \(-8943.167\), with a difference of \(509.439\) mm. These changes are significant because they reflect the challenges the robot may face during the assembly task. Figure 4: Simulation of the robot patrol process: (**a**) unloaded movement; (**b**) loaded movement; (**c**) vibration mode of the assembly unit’s flexible body under 15-order mode; (**d**) assembly unit with flexible deformation; and (**e**) oscillation amplitude of the assembly unit’s center of mass. It is worth noting that the assembly units described in this paper are standard regular hexagonal units with a side length of 10,000 mm. The dimensions of the robot body are a radius of 500 mm. The center body of the robot comprises a rectangular prism and a cylinder with a radius of 100 mm, both connected to the thigh at a length of 150 mm. The thigh measures 425 mm in length, while the lower leg measures 350 mm. This implies that in the spatial realm of precise assembly tasks, the area of the assembly unit is more than 500 times larger than that of the assembly robot. Under special weightless conditions, only then can the system plan be potentially realized. It is therefore crucial to control flexible vibrations, as failure to do so will significantly impact the assembly accuracy and system stability. To guarantee the assembly accuracy and stability of the system, effective control and stabilization measures must be taken for the robot, particularly in controlling flexible vibrations. With the implementation of appropriate control strategies and professional operational skills, errors can be significantly reduced, thereby ensuring the precision and stability of the robot during task execution.
# Improving Re-Identification by Estimating and Utilizing Diverse Uncertainty Types for Embeddings
## 4 Experiments
### Simple Score-Level Fusion  
In light of the unexpectedly good re-identification performance of the standard deviation of the model uncertainty estimate (\(D^{\sqrt{\Sigma(M)}}\)) as a replacement for the feature vector, one could hypothesize that combining the re-identification results could yield further improvements. To validate this hypothesis, we conducted a second experiment to determine whether simple score-level fusion yields improvements. The distance scores for each pair of query and gallery images are stored in the distance matrices. In this case, the matrices are \(D^{\mu}\) for the feature-vector-based scores and \(D^{\sqrt{\Sigma(M)}}\) for the uncertainty-based scores. Therefore, the distance matrices can be combined element-wise.  
For score-level fusion, we considered an additive combination (Equation (29)) and a multiplicative combination (Equation (30)) of the distance scores, where \(\odot\) symbolizes an element-wise multiplication.  
\[D^{+M} =D^{\mu}+D^{\sqrt{\Sigma(M)}} \tag{29}\] \[D^{\odot M} =1-(1-D^{\mu})\odot\left(1-D^{\sqrt{\Sigma(M)}}\right) \tag{30}\]  
Table 3 shows the score-level fusion results. The additive combination (\(D^{+M}\)) does not result in an improved re-identification performance. Furthermore, the multiplicative combination (\(D^{\odot M}\)) offers only a marginal improvement over the sole feature-vector-based re-identification (\(D^{\mu}\)). Therefore, simple score-level fusion cannot achieve notable improvements. To this end, it is essential to identify a more effective approach to incorporate the model uncertainty estimate.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Distance Matrix** & **mAP [\%]** & **rank-1 [\%]** \\ \hline BOT baseline [66] & \(86.41\pm 0.09\) & \(94.42\pm 0.23\) \\ \(D^{\mu}\) (identical to UAL [25]) & \(86.77\pm 0.14\) & \(94.64\pm 0.21\) \\ \(D^{\sqrt{\Sigma(M)}}\) & \(73.53\pm 0.52\) & \(91.05\pm 0.35\) \\ \(D^{\sum(n)}\) & Uncertainty & \(53.50\pm 1.42\) & \(78.55\pm 1.31\) \\ \(D^{\sqrt{\Sigma(D)}}\) & vector & \(41.25\pm 1.15\) & \(70.49\pm 1.05\) \\ \(D^{\sum(n)}\) & features & \(41.17\pm 1.14\) & \(70.39\pm 1.08\) \\ \(D^{\sqrt{\Sigma(V)}}\) & vector & \(2.45\pm 0.45\) & \(2.79\pm 0.86\) \\ \(D^{\sum(n)}\) & & \(2.09\pm 0.36\) & \(2.21\pm 0.68\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Validation: Can uncertainty vectors be utilized as a replacement for the feature vectors?
###### Abstract  
This study evaluated changes over time in skeletal muscle atrophy, expressions of skeletal muscle anabolic and catabolic genes, and mitochondrial activity by skeletal muscle type in an adenine-induced chronic kidney disease (CKD) model. A CKD model was successfully established by feeding male Wistar rats a 0.75% adenine diet for 4 weeks starting at 8 weeks of age. Control and CKD groups were sacrificed at 12 and 20 weeks of age. The back muscles were analyzed histologically, and succinate dehydrogenase (SDH) staining was performed to evaluate mitochondrial activity. Gene expressions of myogenic determination gene number 1 and myogenicin as indicators of muscle anabolism, atropin-1 and muscle RING-finger protein-1 (MuRF1) as indicators of muscle catabolism, and peroxisome proliferatorator-activated receptor-\(\gamma\) coactivator-1-\(\alpha\) as a marker of mitochondrial biogenesis were assessed. Type I and type II muscle cross-sectional areas (CSAs) were decreased at 12 weeks, but type I muscle CSA was recovered at 20 weeks. SDH staining was lower in CKD than in control rats at 12 weeks, but no significant difference was observed at 20 weeks. Increased expressions of myogenin, atropin-1, and MuRF-1 were observed only at 12 weeks, but no differences were observed at 20 weeks. The adenine-induced CKD rat model appears to show changes in muscle atrophy over time.  
chronic kidney disease; sarcopenia; animal model; muscle atrophy; mitochondria 2024, 14, 9106. [[https://doi.org/10.3390/app14199106](https://doi.org/10.3390/app14199106)]([https://doi.org/10.3390/app14199106](https://doi.org/10.3390/app14199106))
# Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization
## 4 Experiments
### Ablation Studies
#### 4.4.2 Importance of Residual Attention Module  
To demonstrate the effectiveness of the proposed residual attention module, we conducted an ablation experiment. In the control group, we replaced the residual attention module with a \(1\times 1\) convolutional layer, which was used to change the number of feature channels. The experimental results, as listed in Table 5, indicate that adding the residual attention module improved the detection accuracy by 28.68% and the segmentation accuracy by 8.94%. This demonstrates the significance of incorporating the residual attention module into the model.
## 7 Experimental Evaluation
### Results for the Full Request Satisfaction Mode
#### 7.2.3 R-DPAP Results
With a low \(minD\) value, orbit sharing can be possible, while using a high \(minD\) value may prevent such splitting, and in the extreme case R-DPAP becomes equivalent to V-DPAP, utility-wise.
###### Abstract  
This study presents a prospective study for the potential exploitation of pelagic _Sargassum_ spp. as a solid biofuel energy source. It was carried out in three stages. First we conducted a morphological, physical-chemical, and structural characterization using scanning electron microscopy (SEM), infrared spectroscopy (FTIR), and X-ray diffraction (DRX), respectively. Second we evaluated the material's functional properties as a solid biofuel based on its calorific value and the quantification of polymeric components like hemicellulose, cellulose, and lignin, as well as thermogravimetric and differential analysis to study the kinetics of its pyrolysis and determine parameters like activation energy (\(E_{a}\)), reaction order (\(n\)), and the pre-exponential factor (\(Z\)). Third we analyzed the energetic potential considering the estimated volume of pelagic _Sargassum_ spp. that was removed from beaches along the Mexican Caribbean coast in recent years. Results of the kinetic study indicate that _Sargassum_ spp. has an enormous potential for use as a complement to other bioenergy sources. Other results show the high potential for exploiting these algae as an energy source due to the huge volumes that have inundated Caribbean, West African, and northern Brazil shorelines in recent years. As a solid biofuel, _Sargassum_ spp. has a potential energy the order of 0.203 gigajoules (GJ)/m3. In the energy matrix of the residential sector in Mexico, its potential use as an energy source is comparable to the national consumption of firewood. The volume of beachcast _Sargassum_ spp. that was removed from \(\sim\)8 km of coastline around Puerto Morelos, Mexico in 2018-2019, could have generated over 40 terajoules/year of solid biofuel.  
**Keywords: bioenergy; solid biofuel; sustainability; energy renewable; macroalgae bloom**
# VR Simulation and Implementation of Robotics: A Tool for Streamlining and Optimization
## 4 Case Study
### Simulation Modelling  
Exploring various models or potential solutions to a given problem proved instrumental in identifying the optimal course of action while concurrently enhancing workplace efficiency. The pivotal decision to amend the model entailed substituting Human Operator no. 1 with a robotic arm, which you can see in Figure 9. This sophisticated robotic apparatus possesses the capability to navigate the designated area for conveying or manipulating components, thereby automating a task hitherto executed by a human operator. Nevertheless, determining the extent of the robotic arm's efficacy vis a vis a human operator remains imperative, particularly concerning components' material handling tasks in conjunction with a CNC milling machine.  
Figure 8: Current state statistics part 2—utilization.  
As observed in Figures 10 and 11, the human operator has been substituted with a robotic arm. Throughout the simulation process, it became imperative to gather data concerning the operational speed and usability of the robotic device, which are pivotal factors in assessing overall performance efficiency. The simulation results indicate that by implementing the proposed changes, the need for goods transportation would be entirely eliminated, reducing the handling time to just 2.38 s. This represents a significant efficiency improvement, with approximately 70% time savings compared to the previous handling time of 7.84 s. Additionally, the utilization of the robot would reach 100%, maximizing its efficiency.  
Figure 10: Statistics of the first variant of simulation model part 1.  
Figure 9: First variant of simulation model.  
Nonetheless, the query arose regarding the necessity of retaining Human Operator no. 2 in light of the company's objective to achieve full automation within the production process. Additionally, it was imperative to consider the evaluation of the efficiency and usability of Human Operator no. 2, which can be seen in Figure 12, and which, based on the attained results, warranted scrutiny in assessing and recommending innovations.  
To address this dilemma, we devised a model illustrating fully automated operations, wherein we eliminated Human Operator no. 2 and substituted them with a second robotic arm. The results and parameters of the measured intervals and usability for the first robotic arm remain consistent. However, in this instance, we juxtapose the statistic of Human Operator no. 2 with a second robotic arm, as depicted in the ensuing figures. Evidently, the manipulation interval in this scenario is 2.49 s, signifying a remarkable time-saving or efficiency enhancement of 62% compared to the prior duration of 6.61 s. The results  
Figure 11: Statistics of the first variant of simulation model part 2.  
Figure 12: Second variant of simulation model.  
are shown in Figures 13 and 14. Furthermore, akin to the initial model, the usability of the robotic arm stands at 100%, while once more, material handling is entirely obviated, aligning seamlessly with the company's imperative for fully automated operations.
## 3 Results  
### Microstructure of DKM  
#### 3.1.1 Overview of DKM Microstructure  
Figure 3 shows the DKM microstructure as revealed by SEM images. Kaolin aggregates are observed in the form of layered kaolinite platelets and the kaolinite platelets are associated with each other via face-to-face, edge-to-face, and edge-to-edge interparticle contacts (Figure 3a). The diatoms are mainly intact disk-shaped ones (_Stephanodiscus_ and _Cyclotella_) and their fragments (Figure 3b): the diameters of intact _Cyclotella_ and _Stephanodiscus_ diatoms are ca. 70 \(\upmu\)m and ca. 30 \(\upmu\)m, respectively, whereas the size of a smallest diatom fragment is equal to that of a kaolin platelet; also presented in this figure are some tube-shaped diatoms (_Aulacoseira_, with a diameter of ca. 10 \(\upmu\)m). Figure 3d-f shows the typical microstructural features of DKM samples. The kaolinite platelets and diatoms gathered together to form the diatom-kaolin aggregate, which has massive pores within it. According to [PERSON] and [PERSON] [2], the pore types of DKM include inter-aggregate, intra-aggregate, skeletal, and intra-skeletal: the former two are due to kaolin aggregates, whereas the latter two are due to diatom frustules. For kaolin, the inter-aggregate and intra-aggregate pores of various sizes are identified in Figure 3a. For the diatom, the skeletal pores with a diameter of 0.1-1 \(\upmu\)m and an intra-skeletal pore can be seen in the magnified image of a single disk-shaped diatom (Figure 3c). For DKM, all types of pore are found in diatom-kaolin aggregates (Figure 3f); some skeletal pores therein are surrounded or even filled by kaolinite clay particles, which provide a connection between the intact diatoms and diatom fragments. Figure 3g-i shows SEM images of a chain-like skeleton structure of crushed diatoms, which is formed by the 15-25 \(\upmu\)m long fragments of diatom frustules. Figure 3g,h shows that a few tiny frustules with diameters of 12-25 \(\upmu\)m remain intact. Additionally, the intact diatoms have dominant intra-skeletal and skeletal pores, but these types of pores mostly disappear in the crushed diatoms because the skeletal structure of the diatom has been damaged, leaving the inter-aggregate and intra-aggregate pores to dominate.  
TEM images of the kaolin and diatoms are shown in Figure 4. The kaolinite platelets with a size of several hundred nanometers are stacked mainly via face-to-face contacts (Figure 4a,b). Also visible in these images is the tube-like halloysite formed by rolled hydrate kaolinite flakes; the halloysite occasionally appeared along with the kaolinite platelets and interspersed among them. Figure 4c shows a two-dimensional image of the internal structure of a 3.5 \(\upmu\)m diameter diatom particle; inter-connective pores are uniformly and hexagonally arrayed on the profile surface of the frustule and the TEM image with magnification of 80,000\(\times\) (Figure 4d) shows that the diameter of the pores observed in the hexagonal arrangement from Figure 4c is ca. 200 nm.