esa-sceva/satcom-chunk-collection huggingface dataset:
# Model-Based Slippage Estimation to Enhance Planetary Rover Localization with Wheel Odometry
###### Abstract  
The exploration of planetary surfaces with unmanned wheeled vehicles will require sophisticated software for guidance, navigation and control. Future missions will be designed to study harsh environments that are characterized by rough terrains and extreme conditions. An accurate knowledge of the trajectory of planetary rovers is fundamental to accomplish the scientific goals of these missions. This paper presents a method to improve rover localization through the processing of wheel odometry (WO) and inertial measurement unit (IMU) data only. By accurately defining the dynamic model of both a rover's wheels and the terrain, we provide a model-based estimate of the wheel slippage to correct the WO measurements. Numerical simulations are carried out to better understand the evolution of the rover's trajectory across different terrain types and to determine the benefits of the proposed WO correction method.  
planetary rover localization; wheel slippage; wheel odometry correction +
Footnote †: journal: Applied Sciences  
**Citation**: [PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON] Model-Based Slippage Estimation to Enhance Planetary Rover Localization with Wheel Odometry. _Appl. Sci._**2021**, _11_, 5490. [[https://doi.org/10.3309/app11125490](https://doi.org/10.3309/app11125490)]([https://doi.org/10.3309/app11125490](https://doi.org/10.3309/app11125490))  
Academic Editor: [PERSON]  
Received: 30 April 2021  
Accepted: 9 June 2021  
Published: 13 June 2021  
**Publicher's Note**: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  
**Cognition**: 0221 by the authors. Licence MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
**Subject**: **The evolution of planetary surfaces with unmanned wheeled vehicles will require sophisticated software for guidance, navigation and control. Future missions will be designed to study harsh environments that are characterized by rough terrains and extreme conditions. An accurate knowledge of the trajectory of planetary rovers is fundamental to accomplish the scientific goals of these missions. This paper presents a method to improve rover localization through the processing of wheel odometry (WO) and inertial measurement unit (IMU) data only. By accurately defining the dynamic model of both a rover's wheels and the terrain, we provide a model-based estimate of the wheel slippage to correct the WO measurements. Numerical simulations are carried out to better understand the evolution of the rover's trajectory across different terrain types and to determine the benefits of the proposed WO correction method.
## 3 Design of the Proposed HRES beyond Economic Optimization  
### Optimization Problem  
To design a highly efficient HRES, priority should be given to sizing the system components. An optimization model is developed here to obtain the optimum size or the optimal configuration of the proposed HRES (Figure 1). The optimum sizing method can help to guarantee the lowest investment with full use of the HRES, so that it can work at the optimum conditions in terms of investment and system reliability. The decision variables in the optimization process are the capacity of each power generator and the storage component of the HRES. The optimization criterion can be defined on the basis of the minimization of the total cost of the system or maximization of the total revenue achieved from the generated electricity, subject to satisfying the demand load requirements. On the basis of the mode of power generation from the HRES, the optimization problems listed below were introduced in this research.
## 2 MOFs
### Most Useful Types of MOF for Wastewater
#### 2.1.2 Zif  
The next class of MOFs is the second most commonly used. They are considered topologically isomorphic to zeolites (Figure 1) [37]. Thus, their structures are generated according to tetrahedral units where every metal ion (M) attaches to four organic imidazolate (Im) linkers (M-Im-M) [37; 38]. According to the HSAB principle, this structure can be made stable when assembled by soft azolate ligands, such as imidazolates, pyrazolates or triazolates and soft divalent metal ions, in this case, Zn, Co, Cu and Cd [18]. Due to this organisation, they can be characterized by their high surface area, regular pore structure, substantial biocompatibility, scalable synthesis and outstanding thermostability [39]. Moreover, the properties of these materials make them increasingly useful in applications such as gas separation and catalysis [40; 41]. In Table 2, some examples demonstrating the use of ZIF in wastewater to remove organic pollutants are shown. The feasibility of its use in this application has been proven due to its upward trend in the number of articles  
\begin{table}
\begin{tabular}{c c c c} \hline
**MIL** & **Pollutant** & **Processes** & **Reference** \\ \hline AgCl/MIL-100(Fe) & Sulfamethazine (SMT) & Photocatalytic & [28] \\ BC@FexC & Norfloxacin & Catalytic activation of & [29] \\  & & peroxydisilate (PDS) & \\ NCQDs/MIL-101(Fe) & Tetracycline (TC) & Photo-Fenton and Photocatalytic & [30] \\ MIL-101(Cr)@AC & Sulfacetamide & Adsorption & [31] \\ MIL-125(Ti) & TC & Photocatalytic & [32] \\ MIL-125(Ti)/BiOI & Rhodamine B and TC & Photocatalytic & [33] \\ MIL-53(Al)@SiO\({}_{2}\) & bisphenol A & Photocatalytic & [34] \\ MIL-53(Al)-NDC & Tinidazole & Adsorption & [35] \\ Mn-MIL-53(Fe) & TC & Catalytic activation of PMS & [36] \\ (PDI)/MIL-101(Cr) & Iohexol & Photocatalytic activation of PDS & [36] \\ \hline \end{tabular}
\end{table}
Table 1: Applications of MIL–MOF in wastewater treatment.  
Figure 2: Evolution of the number of published articles on wastewater treatment for each type of MOF in the last five years.  
published from 2018 to 2022 (Figure 2) [42; 43]. This type of MOF stands out in adsorption and photocatalysis processes due to the potential that it presents [42].  
Many ZIFs have been synthesized to date, but ZIF-8 is among the most widely used since ZIF-8 has the advantage of being synthesized by different routes, regardless of the method selected and can achieve a high purity [44]. However, more environmentally friendly and room temperature syntheses are used nowadays [45].
###### Abstract  
We derived the formula for the detection probability, signal-to-noise ratio (SNR), and average bit error rate (BER) for the signal orbital angular momentum (OAM) state carried via non-uniformly correlated high-order Bessel-Gaussian beam propagation in a turbulent atmosphere. The wavelength, receiver aperture, beam width, strength of the turbulent atmosphere, and topological charge effect on detection probability, SNR, and average BER of the signal OAM state were demonstrated numerically. The results show that the signal OAM state with low topological charge, a small receiver aperture, a narrow beam width, and a long wavelength can improve the performance of optical communications systems under conditions of weak atmospheric turbulence. Our results will be useful in long-distance free space optical (FSO) communications.  
free space optical communications; turbulent atmosphere; detection probability; signal-to-noise ratio; bit error rate +
Footnote †: journal: _Photonics_  
**Circation:** [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Performance of Orbital Angular Momentum Communication for a Non-Uniformly Correlated High-Order Bessel-Gaussian Beam in a Turbulent Atmosphere. _Photonics_**2024, 11**, 131. [[https://doi.org/10.3300/photonics11202131](https://doi.org/10.3300/photonics11202131)]([https://doi.org/10.3300/photonics11202131](https://doi.org/10.3300/photonics11202131))  
Received: 27 December 2023  
Received: 21 January 2024  
Accepted: 29 January 2024  
Published: 30 January 2024
# Blockchain-Based Evidence Trustworthiness System in Certification
## 5 Validation and Results
### Applicability to Other Domains  
Although the evidence trustworthiness system has been implemented for cloud services cybersecurity certification, it can also be adapted to other certification domains that require the integrity and transparency of information to make it more reliable and compliant with specific standards [25]. Certifications are performed in a variety of domains such as health [54], education [55], information quality [56], and environment [57]. All of these domains require an evidence trustworthiness system to help auditors rely on evidence and speed up and facilitate certification processes. The proposed evidence trustworthiness system has been designed in such a way that adaptations to other domains require simply to update the considered data model in two ways:  
* Decide whether the evidence to be secured is sensitive information and only the hash of the information can be recorded on the Blockchain as explained in Section 4.1. In contrast, information can be directly recorded on the trustworthiness system, making verification even more user-friendly. For example, health-related evidence will almost always be considered sensitive, as it often involves personal data that must be protected according to the GDPR. In this case, the use of hashes as proof of integrity is recommended. However, there may be cases where the evidence relates to anonymized patient information, aggregated disease information, or even public information about drugs or treatments that do not require confidentiality. In these cases, the evidence can be recorded directly in the trustworthiness system, avoiding subsequent calculation and verification of hashes.
* Identify the metadata or contextual information to be considered for each domain, depending on its particularities. Following with the example in the health certification domain, the following contextual information are examples that could be considered to characterize health evidence:
* Date and time of evidence collection.
* Data source, identifying the source of the data, such as clinical records, research studies, or medical devices.
* Collection method, defining how evidence was obtained, ensuring reproducibility and validity.
* Environmental conditions, including location or specific conditions, if applicable.
## 2 System Model
### Hardware Impairments  
In the presence of hardware impairments, the received signal of the transmission \(X\to Y\) link can be expressed as  
\[y_{\mathrm{XY}}=\sqrt{P_{\mathrm{X}}}h_{\mathrm{XY}}(s+\eta_{\mathrm{XY}})+ \mu_{\mathrm{XY}}+\
u_{\mathrm{XY}}, \tag{3}\]  
where \(P_{X}\) denotes the transmit power of the transmitter \(\mathrm{X}\), \(h_{\mathrm{XY}}\) is the channel coefficient of the \(X\to Y\) link, \(\eta_{\mathrm{XY}}\) and \(\mu_{\mathrm{XY}}\) denotes noises caused by the hardware impairments at the transmitter \(\mathrm{X}\) and the receiver \(\mathrm{Y}\), respectively, and \(\
u_{\mathrm{XY}}\) are the additive white Gaussian noises models as Gaussian random variables with zero mean and variance \(N_{0}\).  
**Remark 1**.: _Similar to [48; 49; 50], we can model the distortion noises \(\eta_{\mathrm{XY}}\) and \(\mu_{\mathrm{XY}}\) as circularly-symmetric complex Gaussian distribution with zero-mean and variance \(\tau_{\mathrm{X}}^{2}P_{X}\) and \(\tau_{\mathrm{Y}}^{2}P_{X}\gamma_{\mathrm{XY}}\), respectively._  
Let us consider the communication between the transmitter \(\mathrm{X}\) and the receiver \(\mathrm{Y}\), and the obtained instantaneous SNR of the \(\mathrm{X}\)-\(\mathrm{Y}\) link can be formulated by (see [48; 49; 50])  
\[\Psi=\frac{P_{\mathrm{X}}\gamma_{\mathrm{XY}}}{\left(\tau_{\mathrm{X}}^{2}+ \tau_{\mathrm{X}}^{2}\right)P_{\mathrm{X}}\gamma_{\mathrm{XY}}+N_{0}}=\frac{P_{ \mathrm{X}}\gamma_{\mathrm{XY}}}{\tau_{\mathrm{XY}}^{2}P_{\mathrm{X}}\gamma_{ \mathrm{XY}}+N_{0}}, \tag{4}\]  
where \(\tau_{\mathrm{X}}^{2}\) and \(\tau_{\mathrm{Y}}^{2}\) present the levels of the hardware impairments at the transmitter \(\mathrm{X}\) and the receiver \(\mathrm{Y}\), respectively, \(\tau_{\mathrm{XY}}^{2}=\tau_{\mathrm{X}}^{2}+\tau_{\mathrm{Y}}^{2}\) is defined as the total hardware impairment level of the \(\mathrm{X}\)-\(\mathrm{Y}\) link, and \(N_{0}\) is the variance of Gaussian noise at \(\mathrm{Y}\).  
For ease of presentation and analysis, the impairment levels of the data links and interference links are assumed that \(\tau_{\mathrm{SR}_{m}}^{2}=\tau_{\mathrm{R}_{m}\mathrm{D}}^{2}=\tau_{\mathrm{D}}^ {2}\) for all \(m\), and \(\tau_{\mathrm{SP}_{n}}^{2}=\tau_{\mathrm{R}_{m}\mathrm{P}_{n}}^{2}=\tau_{ \mathrm{I}}^{2}\) for all \(m\) and \(n\).
## 5 Deployment
### Landmark Estimation  
The _Landmark Estimation_ functional block depends on the number of particles and matched observations. Therefore, monodimensional and bi-dimensional parallelizations are feasible. In more detail, the particles are assigned to blocks, and threads are assigned to the observations. The choice is motivated by the fact that the reinitialization of the weights depends on all the matched observations within each particle. Therefore, the external for loop iterates over the particles and an inner loop iterates over the observations. Consequently, multiple parallelization modalities are achievable with the following grid configurations:  
* Sequential configuration \((1B1T)\): one block and one thread.
* Thread-based configuration \((1 BNT)\): one block and \(min(N_{o},1024)\) threads--parallel on the observations and sequential on the particles.
* Block-based configuration \((MB1T)\): \(N_{p}\) blocks and one thread--parallel on the particles, sequential on the observations.
* Two-dimension configuration \((MBNT)\): \(N_{p}\) blocks and \(min(N_{o},1024)\) threads--parallel on the particles and the observations.  
The landmark estimation is carried out on a single kernel. However, as in the previous case, the algorithm is split into different sections for better readability. A summary of the landmark estimation step is available in Algorithm 5. Please refer to Appendix A.6 for more details.  
The main limitation in memory availability is the GPU's global memory, as the kernel uses a limited amount of shared memory.  
```
1:for\(m=blockId\) to \(N_{p}\), step = \(gridDim\)do
2:for\(n=threadId\) to \(N_{o}\), step = \(blockDim\)do
3: Retrieve observation \(z_{n}\)
4:if new landmark then
5: Landmark Initialization
6:else
7: Landmark Update
8:endif
9:endfor
10: Weight Update
11:endfor
```  
**Algorithm 5** Landmark Estimation
# The Traffic Adaptive Data Dissemination (TrAD) Protocol for both Urban and Highway Scenarios
## 3 Traffic Adaptive Data Dissemination
### Store-Carry-Foruard Mechanism
#### 3.2.1 The Selection of SCF-Agent  
To facilitate the understanding, we introduce some techniques which enable TrAD to identify the coordinator and the breaker.  
* _Coordinator_: After preloading an intersection position list of the urban map into the system; every vehicle will detect the distance between itself and all intersections when the vehicle receives a beacon message. If the distance is less than 20 m, the vehicle is defined as a coordinator. _Breaker_: The flow chart of identification of a breaker is shown in Figure 5. When a vehicle receives a data message, it will detect and eliminate the possibility of the coordinator role. Then, the vehicle determines whether its moving direction is the same as the data forwarding direction. If so, the vehicle will look up whether there is a further neighbor moving at the front. If not, the vehicle is defined as the breaker. In particular, this procedure is carried out at a continuous process until the boundary of the well-connected network is established.  
Figure 4: The sorting process for a priority list.  
Vehicles that satisfy any of the definitions of coordinator and breaker are selected as SCF-agents. This operation is compatible with both urban and highway scenarios. The coordinator is specialized for the two-dimensional topology of urban scenarios. At the intersection, a coordinator obviously has more chances to connect to neighbors than the vehicles located between intersections. Therefore, TrAD chooses coordinators to store and carry the data, which is beneficial to discover more uninformed vehicles. Given that there is no coordinator in highway scenarios, the breaker is defined for both urban and highway scenarios. It can move towards outside of the well-connected network and transmit the data message into a wider area.
## 1 Introduction
At present, existing studies on grassland vegetation monitoring primarily rely on _NDVI_, which suffers from low accuracy (\(\leq\)65% in sparse vegetation areas) due to soil background interference [16]. Although _kNDVI_ has shown promise in nonlinear vegetation modeling, its application to grassland desertification restoration areas remains underexplored. Additionally, most deep learning models for remote sensing change detection (e.g., U-Net, PT-Former) neglect spectral index features like _kNDVI_, leading to suboptimal performance in capturing subtle vegetation dynamics [17]. This study focuses on long-termtime-series monitoring during the peak vegetation growing season (August) to capture the core phenological signals of ecological restoration. These gaps are addressed by integrating _kNDVI_ with a Transformer-based architecture, enabling the high-precision monitoring of ecological restoration effects.
# An Innovative Recompression Scheme for VQ Index Tables
## 2 Related Work
### Side Match Vector Quantization Algorithm  
In 1992, [PERSON] proposed SMVQ [13], which utilizes the high correlation between neighboring blocks of an image to search for indices based on edge similarity. Similar to the SOC algorithm, when processing a block based on raster scanning, it can only refer to previously processed blocks. Since the raster scan proceeds from left to right and top to bottom, the side match algorithm utilizes the upper and left neighboring blocks for matching.  
The concept of SMVQ is illustrated in Figure 5. \(X\) is the block currently being processed, while the upper neighboring block \(U\) and left neighboring block \(L\) have already been processed. All pixel values in \(U\) and \(L\) are known and remain unchanged. Therefore, we utilize the information from \(U\) and \(L\) to find a suitable codeword from the codebook in order to restore \(X\). Except for the pixel \(X_{11}\), which is adjacent to both blocks and needs to be matched with both \(U_{n1}\) and \(L_{1n}\), the other pixels that are only adjacent to one block just need to match their corresponding neighboring pixels. For example, \(\{U_{n2},U_{n3},\ldots,U_{nn}\}\) matches \(\{X_{12},X_{13},\ldots,X_{1n}\}\), and \(\{L_{1n},L_{1n},\ldots,L_{1n}\}\) matches \(\{X_{21},X_{31},\ldots X_{m1}\}\). Afterwards, their Euclidean distances are calculated and sorted in ascending order. The codeword closest to the current block is found from the codebook to restore \(X\). Through SMVQ, the bit rate can be effectively reduced. The specific algorithm and examples are explained in Section III.  
Figure 4: An example of an SOC algorithm.
###### Abstract  
The application of CFRP tendons in precast segmental concrete beams (PSCB) as internal un-bonded prestressing reinforcement is a newly developed scheme to improve structural flexural performance. The stress increment of the un-bonded tendon, depending on the whole structural deformation, is a crucial value to be predicted for flexural capacity design. Due to the discontinuity of the opening joints, the deformation modes of segmental beams differ from the monolithic ones. The existing prediction methods built for monolithic beams can not be directly used for segmental beams. In this paper, the new prediction equations of the tendon stress increment and flexural capacity were put forward for PSCB with internal un-bonded CFRP tendons (PSCB-IUCFRP). Firstly, the differences between the deformation modes of monolithic and segmental beams were compared and clarified based on the numerical model analysis. Then, a parametric analysis was conducted on 162 numerical models, and the results were employed to evaluate the applicability of existing methods for PSCB-IUCFRP. The predictions of the ACI 318-14 model and the AASHTO LRFD model were both conservative and scattering compared with numerical results. The ACI 440.4R model underestimated the tendon stress increments of beams under one-point loading but overestimated it for those under two-point loading. According to the failure mode of PSCB-IUCFRP, a simplified curvature distribution mode was assumed, and the relation between tendon elongation and structural deflection was derived. The prediction equations for PSCB-IUCFRP were proposed using the back-calculated plastic hinge length. Compared with existing methods, the proposed equations considered the deformation characteristic of segmental beams and had clear physical significance. The predictions of the proposed method were in good agreement with the numerical and experimental results. Furthermore, a balanced prestressing reinforcement ratio equation is proposed for PSCB-IUCFRP to avoid tendon rupture-controlled failure. The proposed equations provide suggestions for the flexural design of PSCB-IUCFRP and will help to popularize this new structure.  
practical concrete beam; CFRP tendon; un-bonded phenomenon; stress increment; flexural capacity +
Footnote †: journal: Applied Sciences