esa-sceva/satcom-chunk-collection huggingface dataset:
# Refined Prior Guided Category-Level 6D Pose Estimation and Its Application on Robotic Grasping
## 2 Related Works  
### Manipulator Grasping  
As 6D pose estimation is a crucial component of robotic arm grasping, numerous studies have focused on enhancing the accuracy of pose estimation to improve the performance of robotic grasping. Ref. [5] proposed a method for semantic grasping and pose estimation of household objects through deep learning. The core of this approach involves predicting key points from RGB images using a deep learning network, which are then matched with key points on the 3D model using the PnP algorithm, directly regressing the 6D pose of the object. This algorithm demonstrates strong robustness even in cluttered environments. Ref. [4] introduced depth images as input, proposing an end-to-end 6D pose detection algorithm and applying it to robotic grasping. DenseFusion first extracts features from RGB and depth images as input, using a pointwise approach to fuse the color and depth features of each point. The model then estimates a local 6D pose candidate for each pixel, and these per-point pose candidates are subsequently aggregated to generate a global, accurate object pose estimation.
## 2 Experimental Section
### Xyl-O1 Purification
The absence of other isoforms or isoenzymes of xylanase in the ultrafiltrate portion, which act synergistically in the hydrolysis of the substrate, may have underestimated the purification and yield values during the final fractionation step [14]. However, our fractionation method, beginning with protein concentration by lyophilization, resulted in higher purification and yield compared to other fractionation methods that are initiated by precipitation with (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) (results not shown). However, the total xylanase activity of the ultrafiltrate (29.400 IU) was significantly reduced to 1.647 IU after the lyophilization step. This finding suggests the presence of xylanase inhibitors that were produced as a result of PDCR degradation and that were also concentrated during the lyophilization step. According to [PERSON] and [PERSON] [37], the lignin derivatives (furans, phenols and low-molecular-weight acids) that are found in the hydrolysate of pretreated wheat straw inactivated both a mixture of commercial enzymes and a crude enzyme sample from _Penicillium brasilianum_ IBT 20888 during the enzymatic digestion of filter paper and xylan. The purification procedures provided an apparently homogeneous preparation of xylanase activity from _A. oryzae_. An increase in the specific activity from 9.15 IU\(\cdot\)mg\({}^{-1}\) in the FCS to 248 IU\(\cdot\)mg\({}^{-1}\) at the end of chromatography was observed. This is an approximately 27-fold increase and confirms the efficiency of the Xyl-O1 purification. The apparent purity of the enzyme was demonstrated by SDS-PAGE because under denaturing conditions, the gel showed a single band (Figure 2). The molecular weight of the purified Xyl-O1 was 21.5 kDa, as estimated by the relative migration of the standard markers. A low-molecular-weight xylanase can be useful in pulp bleaching, since smaller enzymes can penetrate further the fiber wall structure and alter more efficiently pulp properties. The molecular weight of Xyl-O1 was also estimated by gel filtration chromatography on  
Figure 2: SDS-PAGE (12%) of purified Xyl-O1. The gel was stained with (**A**) silver nitrate or (**B**) 0.1% Congo red. MW—molecular weight marker (phosphorylase b (97 kDa), albumin (66 kDa), ovalbumin (45 kDa), carbonic anhydrase (30 kDa), trypsin inhibitor (20.1 kDa), and \(\alpha\)-lactalbumin (14.4 kDa)); Lane 1—FCS from _Apergillus oryzae_ (10 \(\upmu\)g); Lane 2—Purified Xyl-O1 (5.0 \(\upmu\)g); Lane 3—FCS from _A. oryzae_; Lane 4—Purified Xyl-O1. Sephadex G-50; however, the value was less than 10 kDa (result not shown), suggesting that the enzyme interacted with the matrix. The same phenomenon has been described for \(\beta\)-xylosidase from _Trichoderma harzianum_ strains [17] and for a low-molecular-weight xylanase from _A. fumigatus_ Fresenius [34]. According to [PERSON] [35], the reason for the lower apparent molecular weight that is obtained by gel filtration could be the retarded migration of the enzyme in the gel matrix.
# Development of a Robotic Arm Based Hydrogel Additive Manufacturing System for In-Situ Printing
###### Abstract  
In-situ printing is a promising injury repair technique that can be directly applied during surgical operations. This paper features a potential in-situ printing platform based on a small-scale robotic arm with a micro-sized dispenser valve. A double-light-source curing method was applied to print poly(ethylene glycol) diacrylate (PEGDA) with a 20% (weight/volume) ratio and the entire process was controlled automatically by a computer interface where droplet diameter, curing time, mechanical properties were measured and essential printing parameters (e.g., nozzle velocity, nozzle frequency) were determined. Three different two-dimensional (2D) plane models (namely, square, circular, and heart-shaped) were printed during initial printing trials. The feasibility study of in-situ printing on curved surfaces was tested using a three-dimensional (3D) printed defect model. The defect was successfully filled using both parallel and ring printing paths. In conclusion, the robotic arm printing platform and its forming method can achieve a rapid curing of PEGDA hydrogel on a curved surface and has the potential to be applied to in-situ printing.  
Keywords:in-situ printing; robotic arm; double-light-sources curing; inkjet printing; poly(ethylene glycol) diacrylate (PEGDA) hydrogel +
Footnote †: journal: Applied Sciences
## 4 Oscar: Tsch Optimized Cell Allocation Algorithm
### Design  
#### 4.1.1 Rpl Rank  
The concept of rank introduced by the Routing Protocol for Low Power and Lossy Networks (RPL) [24] is used as the basis of OSCAR to determine the distance from the root. Usage of the rank within the OSCAR algorithms will be described in later subsections.  
RPL is a proactive remote vector routing protocol for IPv6-based LLNs. The RPL topology is organized in a Destination Oriented Direct Acyclic Graph (DODAG) structure. Besides the peripheral memory, the number of nodes in a DODAG is not limited. Typically, the DODAG root corresponds to the IPv6 edge router which connects the nodes to the outside world (i.e., Internet or any other network) and from which it can receive commands for the management of the collected data. Each node is associated with a rank that is calculated using the Minimum Rank with Hysteresis Objective Function (MRHOF). The concept is that the closer the node is to the root, the smaller the rank. A node sends a packet to the root by forwarding it to a neighbor node of lower rank. Each node within the network has an assigned rank, which increases as the node move away from the root.  
The establishment and maintenance of the DODAG are ensured by DODAG Information Object (DIO) control messages broadcast using a hold timer. DIO packets contain information such as metrics used to calculate the cost of the path. Initially, only the DODAG root is part of the active RPL topology. Subsequently, DIO messages periodically broadcast the configuration parameters like the DODAG-ID and its rank within its neighborhood. As soon as a node receives multiple DIOs from different sources, it selects one of them as its preferred parent (according to rank) which also acts as the next hop to reach the DODAG root. RPL also defines the Destination Advertisement Object (DAO), which is an ascending message allowing a node to announce its prefix for the establishment of downward routes. Therefore, for example, when changing the preferred parent due to link variation, a node sends a DAO upstream to the new preferred parent to configure a new downlink route. To ensure that the message has been received, a DAO-Ack is sent by the parent to the child node.
# Optimal Model for Carsharing Station Location Based on Multi-Factor Constraints
## 5 Conclusions and Prospects  
The rapid development of the electric carsharing industry has made the way to locate and design electric carsharing stations a critical issue. This study analyzes the development status and existing problems of electric carsharing. An electric carsharing station location model is established in consideration of multiple influencing factors. The main factors for the demand of electric carsharing are summarized by studying the application scenarios and business models of electric carsharing. On the basis of these factors, a quantitative method that allocates cars according to travel demand is proposed. A model of station location is established on the basis of a two-dimensional spatial analysis. A genetic algorithm is designed to solve the problem given the characteristics of the model. The feasibility of the model and solution method is verified using a case study of Lanzhou.  
This research can be used for carsharing station location optimization in a new city. A comprehensive station location optimization model is constructed by considering factors, such as the total number of people in the city, the proportion of travel modes, station construction costs, and construction budget. This method can also be used to study the locations of shared bicycles, electric vehicle charging piles, and logistics distribution points. Therefore, this method can be extended.  
The station location problem of electric carsharing is a multi-disciplinary problem and has many influencing factors. On the basis of the analysis and summary of the development status of electric carsharing, this study establishes a network location model and the solution methods. The proposed methods can be improved from the following aspects. First, the forecast of the overall demand for electric carsharing in the region is insufficiently accurate. This study only quantitatively predicts the demand from several macro data indicators. Relatively few influencing factors are considered, and the correlation between the factors has not been studied and verified with examples. Further research is needed. Second, some parameters use an average estimation method that may be inconsistent with actual operations. Finally, the model also ignores the effect of other operators on the regional demand. These issues will be complemented in the future research.  
Conceptualization, J.B. and J.C.; methodology, Q.S.; resources, J.B.; writing--original draft preparation, Q.S.; writing--review and editing, J.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (91846202, 71961137008).  
The authors declare no conflict of interest.
## 8 Phase Two Testing and Results
### Small-Scale Test Results  
Table 2 shows the mean completion time and the corresponding standard deviation (SD) of IVI and MOD IVI methods. The last column shows the \(p\)-value of the one tailed \(t\)-test when comparing the results. Recalling that MOD IVI was proposed to improve the IVI method for manipulating large-scale objects, e.g., swiping to move objects farther/closer, these features were not relevant for small-scale tests, and it is not surprising that there were no significant differences observed between Tests 1 and 2.  
The 0.037 \(p\)-value of Test 3 suggests that varying the selected object translucency is an effective option for working with increasingly complex virtual environments.
## 3 Results
### Experimental Setup  
The dataset was randomly split into training (80%) and test (20%) subsets (see Figure 2), and five independent training and test partitions were considered to further evaluate the performance of the model. The training subset was used for the model design, while the test subset was used to evaluate its performance (i.e., to evaluate its generalization ability). Bootstrap resampling was used to remove those features that were non-relevant and uninformative for predicting the target variable. Using this method, \(\hat{d}=14\) features were selected from the \(d=26\) initial features. Before training the classifiers, different values of the smoothing parameter \(w\) (between \([0.0,1.0]\)) were investigated to ensure proper use of the target encoding technique selected, addressing the over-fitting issue raised by target-agnostic approaches. The AUC values were used to select the best \(w\) for our dataset. Experimental results showed that lower values of \(w\) offer better AUC values, and consequently were more suitable for the binary classification scenario. In this paper, a regularization smoothing parameter of \(w=0.1\) was chosen for subsequent analyses.
## 2 Methodology
### The Bayesian Technique
As only random intercepts related to an objective hierarchy were considered, the model coefficients are similar across various barrier types, and only the intercept varies. A best fit model was identified by considering various predictors as a hierarchy and an objective hierarchy, which was identified through the FMM. For instance, subjective hierarchies which make engineering intuitive sense were considered. Those include considering barrier types or highway systems as hierarchies. Those three models' performances were compared with an objective hierarchical model assigned by the FMM model. Identifying a best fit model is important as the finalist model will be used for cost-benefit analysis. The deviance information criterion (DIC) was used as a measure for the models' performance comparisons [12]. That method is a generalization of the Akaike information criterion (AIC) in the Bayesian context. The method measures the complexity, and goodness of fit: the complexity is linked to the number of included parameters, while the fit measure is related to the deviance as:  
\[DIC=\overline{\mathbf{D(\varnothing)}}+p\text{D} \tag{18}\]  
where \(p\text{D}\) is a number of included parameters being used to penalize the model for the number of incorporated parameters. In summary, a best fit model would be identified based on a lower value of DIC. It is worth mentioning that in the analysis we considered a non-informative prior. For that we consider a wide variance, limited precision, with a mean of zero so the model is able to search for the posterior distribution across a wide range of values. Using a non-precise informative prior would result in erroneous results as the model is limited for sampling from the prior distribution to reach the posterior distribution.
Due to the openness of the wireless communication environment, the wireless signal is vulnerable to eavesdropping. Recently, physical-layer security (PLS) has become a research hotspot [24], which achieves perfect information security encrypted by the wireless channel characteristic. Assisted by the RIS, the authors in [25] proposed an RIS-aided secure transmission scheme considering the hardware impairments. In [26], the jamming scheme was utilized to further improve the secrecy rate for RIS-aided networks. In [27], the quantized phases were introduced in the secure transmission, where the phase shift was optimized based on the quantized phase. However, to the best of the authors' knowledge, the secure transmission for the RIS-aided UAV network has not yet been researched in sufficient detail, which will be studied in this paper.  
Motivated by the above challenges, we propose an RIS-aided UAV secure transmission scheme, where an UAV carries RIS to assist the secure transmission between the UAV and the ground user (GU). In the proposed scheme, the signals transmitted by BS are reflected to GU through an UAV equipped with RIS, and UAV flies along its optimized trajectory. For this scenario, we maximize the average secure rate by jointly optimizing the UAV trajectory, passive beamforming and phase shift under the constraints of RIS phase shift and actual UAV mobility. Different from the existing works that assumed fixed RIS, we utilize UAV carrying RIS that can utilize the flexibility of RIS and mobility of UAV to further improve the secrecy performance. Moreover, we formulate an average secrecy rate optimization problem to allocate the beamforming power, and design the phase shift and UAV's trajectory. For this non-convex problem, we decompose it into the power beamforming problem, the phase shift optimization problem, and the UAV's trajectory design problem, and proposed an efficient iterative algorithm to solve the problem. The numerical results verify the superiority of the proposed scheme, which can improve the average secure transmission rate by about 20% compared to that of the baseline method.  
The rest of this paper is organized as follows. Section 2 describes the communication system model of the proposed scheme. In Section 3, we formulate the optimization problem and optimize the transmit power, phase shift and UAV's trajectory. In Section 4, we present the extensive simulations, and Section 5 concludes the paper.
# Achieving Low Latency Communications in Smart Industrial Networks with Programmable Data Planes
## 5 Implementation and Performance Evaluation  
This section explains the performance evaluation carried out to show the suitability of the proposed solution, including the implementation of a testbed of the targeted network and the design of the validation tests.
## 2 The Method for Self-Healing Efficiency Estimation
### Adaptive Filter Preparation
#### 2.1.2 Spectral Wavefield Image Pattern Estimation  
This operation provides an image that represents a spectral wave pattern for specific material and excitation. It is prepared based on a number of wavefield images in the wavenumber-wavenumber domain by determining the mean value at each point as:  
\[W_{avg}\big{(}k_{x},k_{y}\big{)}=\frac{1}{N}\sum_{\mathbf{n}}W\big{(}k_{x},k_{y},t _{\mathbf{n}}\big{)}, \tag{3}\]  
where \(\mathbf{n}\) is a vector with a number of time frames used for creating spectral wave pattern; \(N\) is a total number of frames that were used. In this study, we arbitrary selected the first 30 wavefield images for this purpose to determine the spectral wavefield image pattern, which is presented in Figure 3.  
Figure 2: Full wavefield image captured at time moment \(t_{1}\): (**a**) in the space-space domain; (**b**) in the wavenumber-wavenumber domain.
# Bounded Attitude Control with Active Disturbance Rejection Capabilities for Multirotor UAVs
###### Abstract  
This paper addresses an attitude tracking control design applied to multirotor unmanned aerial vehicles (UAVs) based on an ADRC approach. The proposed technique groups the endogenous and exogenous disturbances into a total disturbance, and then this is estimated online via an extended state observer (ESO). Further, a quaternion-based feedback is developed, which is assisted by a feedforward term obtained via the ESO to relieve the total disturbance actively. The control law is bounded; consequently, it takes into account the maximum capabilities of the actuators to reject the disturbances. The stability is analyzed in the ISS framework, guaranteeing that the closed loop (controller-ESO-UAV) is robustly stable. The simulation results allow validation of the theoretical features.  
multirotor UAV; quaternion feedback; extended state observer; ADRC; bounded input
## 2 Data and Methodology  
### Study Area  
The study area of this paper is in Dunkuotan Village, Yuli County, Xinjiang Uygur Autonomous Region, China. Yuli County, also known as Lop Nur, is in the middle of Xinjiang, China (Figure 1). Yuli County is in the southern foothills of the Tianshan Mountains and on the northeast edge of the Tarm Basin. Dunkuotan Village is 24 km away from Yuli County, with a total area of 15,468.9 km\({}^{2}\) and an average altitude of 901 m. Dunkuotan Village is suitable as a research area of the Chinese western social economy and natural resources development because it is not only the geographical center of Xinjiang but also the intercontinental hub of the Silk Road Economic Belt.  
The study area is located in the Asia-Africa desert region, with complicated plant composition and low vegetation coverage. The grassland in the study area is composed mainly of perennial tepid and xerophytic plants. Most of them are in the form of shrub grassland, which is short and sparse. The woodland vegetation, on the other hand, is composed mainly of sparse _Populus euphratica_. The shrubbery is red willow, with rich constructive species and diverse ecological types. The river net structure is determined by the topography in the study area. In addition, the simple road, around 6 m in width, is the common type of road. The rooftops are made up of either cement/metal (for most of the town area) or wood/cement (for the rural area), according to the onsite investigations (see Figure 2).  
Figure 1: Map of the study area. The left map shows the location of Yuli County, Xinjiang, China, and the right picture is a remotely sensed image of a part of Dunkuotan Village, Yuli County.
## 4 Simulation Results and Analysis
### Simulation Experiments for Different Road Levels for a Light Vehicle  
In order to verify the control performance and robustness of the semi-active suspension system based on the PPO algorithm, simulation comparison experiments were conducted for the passive suspension system, the semi-active suspension system based on fuzzy control, and the semi-active suspension system based on the PPO algorithm under the same conditions. Specifically, a vehicle speed of 20 m/s was simulated on the C-, D-, and E-class roads, respectively, and the road power spectrum is shown in Figure 5.  
The simulation experimental results of the body acceleration, suspension deflection, and dynamic tire load on C-class and D-class roads at a vehicle speed of 20 m/s are shown in Figures 6 and 7. For the ease of comparison, the percentages of the root mean square (RMS) values for the body acceleration, suspension deflection, and tire dynamic load of the passive suspension, the semi-active suspension system based on fuzzy control, and the PPO algorithm under the same conditions are given in Table 5, and the corresponding root-mean-squared error (RMSE) values are given in Table 6.  
The simulation results showed that the body accelerations of the PPO-based semi-active suspension system and the fuzzy-based system were reduced by 59.1% and 26.3%, the suspension deflections were reduced by 45.5% and 27%, and the dynamic tire loads were reduced by 28% and 13.3%, compared with the passive suspension system under the D-class road.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Hyperparameters** & **Symbols** & **Values** \\ \hline Actor learning rate & \(\alpha_{A}\) & 0.0002 \\ Critic learning rate & \(\alpha_{C}\) & 0.001 \\ Discount factor & \(\gamma\) & 0.98 \\ Clip parameter & \(\varepsilon\) & 0.2 \\ GAE parameter & \(\lambda\) & 0.9 \\ Batch size & & 64 \\ Time steps per batch & & 600 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameters for the proximal policy optimization structure.  
Figure 5: The road grade distribution.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Road Level**} & \multicolumn{2}{c}{**Body Acceleration**} & \multicolumn{2}{c}{**Suspension Deflection**} & \multicolumn{2}{c}{**Tire Dynamic Load**} \\ \cline{2-7}  & **Fuzzy** & **PPO** & **Fuzzy** & **PPO** & **Fuzzy** & **PPO** \\ \hline C & 26.4\% & 61.8\% & 6.1\% & 47.8\% & \(-\)18\% & 28.8\% \\ D & 26.3\% & 59.1\% & 27\% & 45.5\% & 13.3\% & 28\% \\ E & 26.4\% & 59.3\% & 27.1\% & 46.5\% & 13.3\% & 28.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: The percentages of RMS values of the two semi-active suspensions for a light vehicle under the same conditions.  
Figure 6: Simulation results of fuzzy, PPO, and passive suspension systems for a light vehicle on a C-class road.  
Figure 7: Cont.