esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Moreover, the authors of this work formulated a full-scale aircraft model with 6-DOF dynamics integrated with an MR shock absorber and evaluated the landing performance through the modified skyhook controller and the inverse model of the mechanical energy [13; 14]. However, only the major pressure loss was considered in these works. Consequently, the technical contribution of this work is to propose a novel mathematical design model for an MR shock absorber, which can be applicable to high-speed operating systems, including aircraft landing gear systems, by considering both the major and minor pressures losses. As a first step to achieve this target, a structural configuration of an MR shock absorber for the aircraft landing gear system is presented, and its working principle is explained. The governing equations of motions are then derived, considering the pressure drop and damping force at compression and rebound conditions. Subsequently, a prototype of the MR shock absorber is designed and manufactured on the basis of the governing equations of motions and the design parameters of a commercial lightweight aircraft landing gear system. To validate the proposed design model, a drop test, which can generate a high stroke velocity of 2.5 m/s, is established, and the results on the pressure drop versus the stroke/stroke velocity are experimentally measured at various impact energy conditions representing different stroke velocities. The results achieved from the design models considering the major loss only, the major and minor loss, and the experiment are compared to validate an excellent agreement of the proposed design model with the experiment.
# GPU@SAT DevKit: Empowering Edge Computing Development Onboard Satellites in the Space-IoT Era
## 3 GPU@SAT DevKit  
The GPU@SAT DevKit was created to simplify and accelerate the development of applications that leverage the computational capabilities of the GPU@SAT IP core. This comprehensive hardware and software framework functions as a client-server application and consists of two key parts, as shown in Figure 2:  
1. An evaluation board (_DevBoard_) that includes the GPU@SAT IP core and embedded software, which operates as the server.
2. A Python-based application running on a host PC, which operates as the client.  
These modules communicate via Ethernet and work together to deliver the full capabilities of the GPU@SAT IP core while significantly reducing development time and streamlining the overall workflow. The following paragraphs provide a detailed overview of these two modules and outline the updated workflow for application execution by the user.  
Figure 1: Structure of OpenCL device. Arrows show data exchange links between blocks.
# Boosting Noise Reduction Effect via Unsupervised Fine-Tuning Strategy
## 4 Experimental Results
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Methods** & **BM3D** & **DnCNN** & **FFDNet** & **DIP** & **DAGL** & **DeamNet** & **DRUNet** & **IRCNN** & **SwinIR** & **Stage1** & **Stage2** \\ \hline Cameraman & 26.19 & 27.03 & 27.03 & 24.56 & 27.42 & 27.42 & 27.80 & 27.16 & 27.79 & 27.88 & **28.56** \\ House & 29.57 & 29.91 & 30.43 & 28.42 & 31.04 & 31.16 & 31.26 & 29.91 & 31.11 & 31.56 & **32.13** \\ Peppers & 26.72 & 27.35 & 27.43 & 25.73 & 27.60 & 27.76 & 27.87 & 27.33 & 27.91 & 28.04 & **28.63** \\ Starfish & 24.90 & 25.60 & 25.77 & 24.53 & 26.46 & 26.47 & 26.49 & 25.48 & 26.55 & 26.66 & **27.12** \\ Monarch & 25.71 & 26.84 & 26.88 & 25.59 & 27.30 & 27.18 & 27.31 & 26.66 & 27.31 & 27.39 & **27.92** \\ Airplane & 25.17 & 25.82 & 25.90 & 24.41 & 26.21 & 26.07 & 26.08 & 25.78 & 26.14 & 26.17 & **26.82** \\ Eagle & 25.87 & 26.48 & 26.58 & 24.53 & 26.66 & 26.71 & 26.92 & 26.48 & 26.91 & 26.92 & **27.75** \\ Lena & 29.02 & 29.34 & 29.68 & 28.02 & 29.92 & 29.93 & 30.15 & 29.36 & 30.11 & 30.19 & **30.51** \\ Barbara & 27.21 & 26.32 & 26.48 & 23.29 & 28.26 & 27.60 & 28.16 & 26.17 & 28.41 & 28.31 & **28.74** \\ Boat & 26.73 & 27.18 & 27.32 & 25.78 & 27.56 & 27.59 & 27.66 & 27.17 & 27.70 & 27.83 & **28.08** \\ Man & 26.79 & 27.17 & 27.31 & 26.08 & 27.39 & 27.36 & 27.46 & 27.14 & 27.45 & 27.51 & **27.72** \\ Couple & 26.47 & 26.87 & 27.07 & 25.26 & 27.33 & 27.43 & 27.59 & 26.86 & 27.53 & 27.61 & **27.88** \\ Avg. & 26.70 & 27.18 & 27.32 & 25.52 & 27.75 & 27.72 & 27.90 & 27.12 & 27.91 & 28.01 & **28.48** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: PSNR values of different methods on 12 images in the Set12 dataset with a noise level at 50. The bold part represent the best PSNR result. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Methods** & \multicolumn{3}{c}{**Noise Level = 15**} & \multicolumn{3}{c}{**Noise Level = 25**} & \multicolumn{3}{c}{**Noise Level = 50**} & \multicolumn{3}{c}{**Average**} \\ \cline{2-11}  & **Set12** & **BSD68** & **Urban30** & **Set12** & **BSD68** & **Urban30** & **Set12** & **BSD68** & **Urban30** \\ \hline
**BM3D** & 32.36 & 31.07 & 33.14 & 29.96 & 28.56 & 30.45 & 26.70 & 25.62 & 26.36 & 29.67 & 28.42 & 29.98 \\
**DnCNN** & 32.67 & 31.62 & 33.30 & 30.35 & 29.17 & 30.44 & 27.18 & 26.23 & 26.47 & 30.07 & 29.01 & 30.07 \\
**FDNet** & 32.75 & 31.65 & 32.92 & 30.43 & 29.21 & 30.34 & 27.32 & 26.30 & 26.74 & 30.17 & 29.06 & 30.30 \\
**DAGL** & 33.19 & 31.87 & 34.99 & 30.87 & 29.42 & 32.88 & 27.75 & 26.49 & 28.70 & 30.60 & 29.26 & 32.19 \\
**DeamNet** & 33.13 & 31.87 & 34.29 & 30.78 & 29.42 & 31.67 & 27.72 & 26.53 & 28.09 & 30.55 & 29.27 & 31.35 \\
**DRUNET** & 33.25 & 31.92 & 34.48
# Shock Initiation of a Satellite Tank under Debris Hypervelocity Impact
## 4 Initiation of the Satellite Tank under Debris Impact
### Calculation of the Debris Impacting Satellite Tank  
The initiation criterion is also used to calculate the power density \(P_{d}\) in the satellite tank and to estimate whether the debris hypervelocity impact can trigger an explosion. The calculation process follows the flow chart in Figure 3. The parameters of the debris and satellite tank used in the calculations are shown in Table 7[24].  
The power density calculated by the initiation criterion in the satellite tank is \(P_{d}=5.2705\times 10^{6}\) kbar\({}^{2}\)\(\upmu\)s. Comparisons between the simulation results and calculation results are shown in Table 8. The calculated power density is larger than the simulated power density, the relative error \(\varepsilon\) between the calculated power density and the simulated power density is \(8.54\%\). The reasons for the error may be that a series of simplifications were made in the derivation of the initiation criterion, and the energy loss of the shock wave was not taken into account. The calculated power density and the simulated power density are both greater than the minimum power density \(P_{dmin}=3.98\times 10^{5}\) kbar\({}^{2}\)\(\upmu\)s, revealing that the shock wave generated by the hypervelocity impact is sufficient to trigger an explosion in the satellite tank. In general, the calculation results of initiation criterion agree well with simulation results. The two methods can verify each other and accurately predict the shock initiation of the satellite tank.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Material** & \(\mathbf{\rho}\) **(kg/m\({}^{3}\))** & \(\mathbf{a}\) **(km/s)** & \(\mathbf{b}\) & \(\mathbf{u_{p0}}\) **(km/s)** & \(\mathbf{r_{p}}\) **(cm)** & \(\mathbf{\alpha}\) **(mm\({}^{-1}\))** & \(\mathbf{\delta}\) **(mm)** \\ \hline Debris & 2700 & 5.33 & 1.338 & 10 & 2 & — & — \\ Satellite tank & 4510 & 5.02 & 1.536 & — & — & 0.25 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Parameters of the debris and satellite tank used in calculation.  
Figure 10: (**a**) Fitting curve of the peak pressures on the impact line; (**b**) Regular residuals between the pressure calculated by the fitting curve and the pressure obtained by simulation.
# Multiparameter Fusion Decision Routing Algorithm for Energy-Constrained Wireless Sensor Networks
## 3 Proposed Routing Optimization Algorithm  
### Principle and Implementation  
For a given WSN, to comprehensively consider the impact of the routing evaluation parameters defined in Section 2.2, we proposed a routing optimization algorithm based on a multiparameter fusion decision, termed the MPFDR algorithm. The basic principle of the algorithm is to construct a transmission link to the next hop with the maximum fusion contribution degree. In other words, if node \(i\) wants to send data, the next hop will be selected from the forward neighbors according to the defined routing optimization based on parameter fusion strategy. Simultaneously, if the sink is within the one-hop range of node \(i\), data can also be sent to it directly.  
Implementation of our MPFDR algorithm mainly includes three processes: establishing the forward neighbor information table, calculating routing evaluation parameters, and selecting the next-hop node from the forward neighbor set.
## 5 Application and Success Evaluation
The second research question RQ2 addressed the application effort. In comparison to manual pairwise analysis, generalization and automated calculation of risk factors led to a substantial reduction of application effort. Still, analysis showed mixed results. Attendees evaluated the application effort as sufficient for reoccurring application within the course of a project (for instance for each milestone meeting) and across projects in case the database can mostly be reused. For one-time application the effort was too high for the perceived benefit. The ability to create different databases for individually defined categories (for instance, product or customer segments) and reuse them in the course of time was seen as an advantage. A disadvantage of the method was that high effort was required to set up the database initially. In the development of the method, the functionality was implemented to individually assess the required accuracy of risk assessment (for instance, based on project priority and budget) and correspondingly invest effort to create the database. Interviews revealed that this promoted to invest low effort into creating the database. This might also has led to before mentioned issues in quality of results. As a result, application effort was seen as the biggest improvement potential: Especially the reduction of effort to create the initial database, but also the assignment of main features and influence areas to requirements. Since input from support evaluation was continuously integrated during the development of the software prototype, the functionality and usability is considered as appropriate. Still, for industrial application, intuitiveness of workflow and application guidance needs further improvements.
## 3 Results and Discussion
### Long-Term Stability at Room Temperature
However, to be fully independent of cooling and freezing, the storage tests were conducted at 20 \({}^{\circ}\)C only, as the need for a freezer is not desirable for resource-limited settings. The presented results fulfill the robustness requirement of the REASSURED criteria regarding shelf life at room temperature, which at the same time contributes to fulfilling the user-friendliness criterion. No special equipment such as a  
Figure 1: Time-to-positive values of real-time LAMP reactions performed after up to 24 weeks of storage at room temperature for the plaice-specific assay (**A**) and the safflower-specific assay (**B**). Orange: Spin-filtered Bst 3.0 polymerase, Green: Glycerol-free Bst Polymerase (GFP). freezer is required for storage and transportation. Dry storage can be ensured, for example, by storing with desiccant silica gel, which also sufficiently fulfills the criterion of freedom from equipment in connection with transport and storage. As already described in Section 3.1, additional steps, even if carried out under cooling, place a strain on the polymerase and can lead to a loss of activity and thus to fluctuating values and longer reaction times. In the commercial production of rapid test systems, an additional step for glycerol removal would not be practicable; instead, a glycerol-free polymerase would be used directly. For both large-scale industrial applications and pilot projects by research and development departments that want to test their assays in practice, it is desirable to carry out assay preparation in as few steps and with as few highly specialized devices as possible. Lyophilization can be a hurdle for initial applications, as it is associated with relatively high one-time costs if such a device is not already available. To circumvent this, commercially available LAMP reaction mixtures that can be air-dried can be used as an alternative. However, a precision oven (temperature control of \(\pm\)1.5 \({}^{\circ}\)C) is required and the components of the reaction mixture cannot be adjusted, except for the primer concentration [36]. GFP is used in the point-of-care experiments because of the shorter reaction time and easier handling (no spin filtering required), which probably also led to the lower variability of the measured values. Nevertheless, removal of glycerol by spin filtration is a suitable alternative for experiments on a smaller scale, as no new reaction conditions need to be established for the LAMP assay.
###### Abstract  
This paper presents an integrated mapping of motion and visualization scheme based on a Mixed Reality (MR) subspace approach for the intuitive and immersive telemanipulation of robotic arm-hand systems. The effectiveness of different control-feedback methods for the teleoperation system is validated and compared. The robotic arm-hand system consists of a 6 Degrees-of-Freedom (DOF) industrial manipulator and a low-cost 2-finger gripper, which can be manipulated in a natural manner by novice users physically distant from the working site. By incorporating MR technology, the user is fully immersed in a virtual operating space augmented by real-time 3D visual feedback from the robot working site. Imitation-based velocity-centric motion mapping is implemented via the MR subspace to accurately track operator hand movements for robot motion control and enables spatial velocity-based control of the robot Tool Center Point (TCP). The user control space and robot working space are overlaid through the MR subspace, and the local user and a digital twin of the remote robot share the same environment in the MR subspace. The MR-based motion and visualization mapping scheme for telerobotics is compared to conventional 2D Baseline and MR tele-control paradigms over two tabletop object manipulation experiments. A user survey of 24 participants was conducted to demonstrate the effectiveness and performance enhancements enabled by the proposed system. The MR-subspace-integrated 3D mapping of motion and visualization scheme reduced the aggregate task completion time by 48% compared to the 2D Baseline module and 29%, compared to the MR SpaceMouse module. The perceived workload decreased by 32% and 22%, compared to the 2D Baseline and MR SpaceMouse approaches.  
mixed reality; teleoperation; 3D mapping; robotic manipulation Article
## 4 Results  
We used a test dataset of 595 QR Code samples to verify the method described in this paper. The testing dataset contained 25 artificial QR codes of different sizes and rotations, 90 QR Codes from Internet images, and 480 QR Codes from a specific industrial process. Several examples of the testing samples are in Figure 16.  
In Table 1 and Figure 17 there are our results compared to competing QR Code decoding solutions (commercial and also open-source). In the table are the numbers of correctly decoded QR Codes from the total number of 595 QR Codes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Solution** & **Artificial Samples** & **Internet Samples** & **Industrial Samples** \\ \hline ZXing (open-source) [25] & 2 & 72 & 48 \\ Quire (open-source) [22] & 12 & 69 & 45 \\ LEADTOOLS QR Code SDK [26] & 9 & 72 & 147 \\ ZRar (open-source) [24] & 23 & 84 & 398 \\ Inlite Barcode Reader SDK [27] & 25 & 80 & 421 \\ DataSymbol Barcode Reader SDK [28] & 25 & 89 & 471 \\ Dynamsoft Barcode Reader SDK [29] & 25 & 88 & 478 \\
**Our solution** & 25 & 87 & 480 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of competing solutions with our proposed method.  
Figure 16: Testing samples: (**a**) artificial, (**b**) Internet, (**c**) industrial.  
Figure 17: Comparison of the recognition rate of our method against competing solutions.  
Our method successfully detected all QR Codes but failed to decode three samples. Two samples were QR Codes placed on a bottle, where perspective and cylindrical distortions were combined. How to deal with this type of combined distortion is a challenge for future research.  
As the commercial solutions have closed source code, we performed the testing using the black-box method. We have compiled our own QR Code test dataset (published together with this article under \"Supplementary Material\") to evaluate and compare our method, as a standardized universal dataset is not publicly available.  
In Table 2 the computational complexity of our algorithm is compared to competing open-source solutions (commercial solutions were tested online). Our algorithm was implemented in Free Pascal and tests were run on an i5-4590 3.3 GHz CPU (Intel Corporation, Santa Clara, CA, USA).  
We see the main contribution of our method in the way the broken Finder Pattern is dealt with (Section 3.3.2) and how the QR Code bounding box is determined (Section 3.6), especially for perspective distorted QR Codes. The presented method can still locate a QR Code if one of the three Finder Patterns is significantly damaged. Consecutive tests in the real manufacturing process showed that this situation occurs much more often than a situation where two or three opposite Finder Patterns are damaged. In order to detect a QR Code with multiple damage Finder Patterns, it will be necessary to combine Finder Pattern based localization with the region based localization.
# Energy-Saving Routing Protocols for Smart Cities  
[PERSON]  
1 Post-Graduation Program in Electrical Engineering, Department of Electrical Engineering,  
Federal University of Paraiba (UFPB), [PERSON] 5045, PB, Brazil  
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To deploy a WSN, several stages are needed, including the development of sensors, which depends on the application, number of sensors, network topology, communication technology, and routing protocol. It is worth mentioning that the routing protocol is critical in any WSN design and that its performance needs to be evaluated for different scenarios and applications.  
Computer simulations are an important design tool for evaluating routing protocols in high-density WSNs, thereby reducing costs and saving time during implementation. Simulations can support the choice of a particular routing protocol and help in evaluating new protocols, mainly in scenarios subjected to unfavorable conditions, such those in which device failure is highly probable.  
Another important stage in WSN implementation is the wireless communication technology used by the network. Many of the main wireless communication technologies adopted in IoT and WSN applications are based on Low-Power Wide-Area Networks (LPWAN), 3G/4G/5G cellular networks, or ZigBee. LPWANs have gained importance compared to the others thanks to relevant characteristics such as low power consumption and transmission over long distances. Among LPWAN technologies, LoRa (Long Range) is being widely used worldwide, as it can achieve ranges up to 15 km in urban areas with a very low power consumption [5; 6].  
In this context, the main objective of this paper is to implement and evaluate the performance of routing protocols for the establishment of LoRa-based WSN applications. To this end, we chose the Cupcarbon network simulator, which was developed specifically for Smart Cities and IoT scenario, to evaluate the performance of different routing protocols in terms of data package delivery rate, average jitter, average end-to-end delay, throughput, and load consumption of battery power. In addition, we propose a novel tool for determining node ranges using the Egli propagation model inside the Cupcarbon simulator. In this paper, we consider the widely used WSN routing protocols Ad Hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), and Distance Vector Routing (DVR). Additionally, a novel routing protocol based on radio power adjustment (RPA) is proposed as means of energy saving.  
The rest of this paper is organized as follows. In Section 2, details about LoRa technology and routing protocols for WSN are presented. The Cupcarbon simulator and the Egli propagation loss model are highlighted within the methodology considered in this work. Simulation results are presented and evaluated in Section 3, then and the conclusions and next steps in the research derived from this paper are discussed in Section 4.
## 3 Methods
### Statistical Methods
#### 3.2.2 Seasonal Autoregressive Integrated Moving Average (SARIMA)  
SARIMA extends ARIMA by incorporating seasonal components. The SARIMA model is denoted as \(ARIMA(p,d,q)(P,D,Q)_{s}\), where \(P,\ D,\ \text{and}\ Q\) are the seasonal parameters and \(s\) is the number of periods in a season [25]. The general formula is given as  
\[\Phi_{P}(B^{s})\Phi_{P}(B)\Big{(}1-B)^{d}(1-B^{s})^{D}Y_{t}=\Theta_{Q}(B^{s}) \theta_{q}(B)\epsilon_{t} \tag{7}\]  
In Equation (7), the seasonal autoregressive polynomial \(\Phi_{P}(B^{s})\) captures the influence of previous seasonal values, while the non-seasonal autoregressive polynomial \(\phi_{p}(B)\) models the effect of past values on the current value \(Y_{t}\). To make the series stationary, the non-seasonal differencing operator \(\Big{(}1-B)^{d}\) and seasonal differencing operator \((1-B^{s})^{D}\) are applied to remove trends and seasonal variations, respectively. The seasonal moving average polynomial \(\Theta_{Q}(B^{s})\) describes the relationship between seasonal errors at seasonal lags, while the nonseasonal moving average polynomial \(\theta_{q}(B)\) models how previous errors influence the current value. Finally, \(\epsilon_{t}\) represents the white noise error term, which accounts for random variations that cannot be explained by the model [26].  
By integrating these components, SARIMA is capable of modeling both short-term dependencies and long-term seasonal patterns, making it an effective tool for time series forecasting in scenarios where data exhibit seasonal behavior, such as monthly sales figures or annual energy consumption trends. This model allows for a comprehensive analysis of the time series, capturing the complex interplay between seasonal and non-seasonal factors that influence the observed values over time [27].