esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Open Differential Design on the Mass Reduction Function
## 1 Introduction
### Mass Reduction Impact on Improving Energy Efficiency
Papers [29; 30], using genetic algorithms, designed the minimum mass of the optimal combination of spur gears. By applying multi-objective optimization methods [31], two parameters, volume and center distance, are optimized and the Pareto frontier is obtained. Authors in this research used the teaching-learning-based optimization (TLBO) method. By applying RSM, authors in [32] analyze, through the response surface diagrams, the influence of individual design parameters on the set objective functions. TOM deals with the problem of optimal material distribution [33]. Their application is particularly significant in aerospace structure design, dynamic responses design, shape preserving design and smart structure design [34]. By applying these methods, the objective function refers to the saving of materials by reducing the mass of the design, taking care to preserve the functionality of the design. In this paper, an ODT with hypoid and planetary bevel gears was designed using the ISO 23509:2006 standard [35]. Dimensioning and geometry shaping of hypoid and planetary bevel gears was carried out using the CAD system for parametric design. An analytical calculation of the stress on the tooth root and tooth flank face was made. The calculation was then analyzed numerically, with the FEM, for validation purposes. The numerical calculation via FEM static analysis was performed according to a global-local FEM approach [36]. In order to reduce fuel consumption and contribute to the improvement of the vehicle's energy efficiency, the mass reduction of the designed ODT was performed. By applying the TOM and the RSM, the pinion hypoid bevel gear, ring hypoid bevel gear and drive shaft were optimized. Using the TOM, three general design parameters were determined on the ring hypoid bevel gear submodel. One additional design parameter is determined on the drive shaft (one parameter on the pinion hypoid bevel gear which is designed from one piece together with the drive shaft). Finally, these four parameters were optimized using the TOM. Through regression analysis of the previously obtained four design parameters, a mathematical model was obtained that describes the influence of each design parameter on partial masses and overall mass of differential transmission. In this paper, the mathematical objective function of the overall mass reduction of the ODT-optimized design elements was obtained. Two partial mathematical mass reduction functions were also obtained and presented: the ring gear mass reduction function and the drive shaft mass reduction function. The results were compared between the predictions of the mass reduction functions of the mathematical model and the numerical model. A control numerical calculation of the optimized design elements was also made, using the global-local FEM static analysis approach, in order to additionally control the accuracy of the obtained mathematical model and also the reliability of the optimized design elements.
## 3 Results and Discussion
### Energy Balance Evaluation  
The energy balance analysis was completed to estimate the potential energy gains that might be obtained as a result of using this technology. As can seen in Figure 3, in all cases, the thermal energy calculated on the basis of experimental data will entirely cover the digester demand used for heating the feedstock and covering the heat losses through the walls of the tank. However, the most beneficial results were achieved in IPW in two- and three-component AD. Therein, as compared to MSS mono-digestion (S1), the energy profit was enhanced by 54 and 62% in S3 and S4, respectively. A similar trend was observed in multi-component co-digestion in the presence of grease trap sludge; in this case, an increase of 125% in energy profit was achieved [46]. The generated energy surplus can be used within WWTPs or sold to an external recipient. The presented results indicate that the proposed technology might be a profitable solution for WWTPs.
## 7 MT-SOTA Optimization Techniques
### Time Execution Improvement--Multi Root Merkle Trees  
The results of our study show that the execution time increases with more software clusters used. The time required to update a software cluster can be reduced if the number of nodes used is reduced in the Merkle tree. Thus, having multiple smaller Merkle trees instead of a single, big tree can achieve a faster execution time for updating a software cluster. Given that certain software clusters can be updated more often than the others, it is efficient to reduce the updates path for more frequent software clusters. Thus, we can divide the Merkle tree into coupled multi-root trees. This allows faster execution and enables the separation of nodes into different trees because not all software clusters are updated equally and frequently. In Figure 23, we show an example use case of a 16-node Merkle tree that can be divided into two 8-node Merkle trees or four 4-node Merkle trees. The roots of the multiple trees are used as the leaf nodes of an auxiliary Merkle tree whose root will be signed with the secret key to generate the signature. As an alternative, the roots of the multiple trees can be simply hashed together and the output hash value is signed by the secret key to generated the signature. Figure 24 shows the timing improvement achieved when we divide the single Merkle tree in the ECU into multiple trees with 8 or 16 nodes per tree. The more nodes exist, the better the timing improvement is. A Merkle tree of 8 nodes is 50% better when the number of nodes is more than 40, while a Merkle tree of 16 nodes is at least 45% better when the number of nodes is more than 72. An advantage of this optimization is its ability to add more nodes than anticipated for the ECU. Due to  
Figure 22: Unbalanced Merkle tree with arrayed leaf nodes.  
the nature of traditional microcontrollers, we used a fixed number of levels for the Merkle tree based on the maximum number of software clusters or blocks anticipated for the ECU. With multi-root Merkle trees, a new Merkle tree can be dynamically added as long as the auxiliary Merkle tree can accommodate new leaf nodes or the roots are directly hashed.
## 4 Conclusions  
In the present study, a variety of silicon nitride-based ceramic parts with complex architecture were manufactured for the first time using the lithography-based ceramic manufacturing (LCM) method, achieving material characteristics comparable to materials obtained by conventional ceramic manufacturing methods (Figure 15). After additive manufacturing using a newly developed photopolymer-containing suspension, SiAlON ceramic bodies with a relative density of 99.8% were obtained, exhibiting a biaxial strength of 764 MPa and a hardness of 1500 (HV\({}_{10}\)). The advantages of LCM technology, such as the ability to fabricate highly precise and small complex ceramic  
Figure 14: XRD analysis of the sintered silicon nitride-based ceramics.  
Figure 13: SEM micrographs of printed & sintered samples (**a**) before and (**b**) after thermal shock resistance test (water-quenching from 800 \({}^{\circ}\)C).  
parts, have the potential to entirely innovate the shaping of nonoxide ceramic materials in general and silicon nitride-based materials in particular. This process enables the fabrication of highly complex components (such as the ones shown in Figure 15, Figure 16), which cannot be obtained by conventional processing techniques, such as insulators, springs, impeller, microturbines and cutting tools. Additionally, due to the superb biocompatibility and good osseointegration and anti-infective (bactericidal) properties, silicon nitride makes a perfect candidate for dental (Figure 16b), orthopedic (Figure 16e) and craniosulfacial implants.  
Printing, A.A.A. and T.P.; characterization, A.A.A., T.P. and T.K.; thermal post-processing, T.K.; writing--original draft preparation, A.A.A.; writing--review and editing, A.A.A., T.P., T.K. and M.S.; supervision, M.S.; project administration, M.S. All authors have read and agree to the published version of the manuscript.  
Figure 16: Printed and sintered silicon nitride-based ceramic demonstrators: (**a**) gyroids, (**b**) impeller and (**c**) spinal implant (posterior lumbar interbody fusion (PLIF) cage), (**d**) cutting tools, (**e**) dental two-piece implants with M1.6 inner thread and (**f**) [PERSON] nozzle.  
Figure 15: Printed and sintered silicon nitride-based ceramic demonstrators: insulator (**a**) and (**e**), dummy design (**b**), screw (**c**), spring (**d**), cellular designs (**f**) and (**g**).  
Funding: This work was supported by the Austrian Research Promotion Agency (FFG) under the project number 859827 (AddiZwerk).  
**Acknowledgments:** The authors thank [PERSON] for the conduction of X-Ray diffraction analyses.  
**Conflicts of Interest:** A.A.A., T.P. and M.S. are employees of the company Lithoz GmbH, supplier of the used LithaNit suspension and the CeraFab 3D printer. The authors declare no conflict of interest.
## 5 Conclusions  
This paper has presented robust RLS-based adaptive algorithms for impulsive ANC systems. Two variants of the proposed algorithm have been described. The first version is based on an FSS and signifies the importance of robustness of the objective function and further (ad hoc) modifications suggested in comparison with the classical FxRLS algorithm. In the second variant, the CCSS strategy has been incorporated to address the trade-off situation offered by the FSS-based one. Since theoretical analysis for non-Gaussian stable processes is extremely difficult, if not impossible, extensive simulations have been carried out. It is observed that the proposed CCSS-MFxRRLS algorithm achieves very fast convergence even for severely impulsive sources. Furthermore, it demonstrates robust performance for the non-stationary noise sources with time-varying characteristics from the viewpoint of impulsiveness.  
The proposed algorithms have been derived in the framework of classical RLS adaptive filtering which has a higher computational complexity in comparison with the LMS-based counterparts. The increased computational cost may be considered as the price paid for robust performance. It is important to mention that 'RLS-like' adaptive algorithms can be found in the literature which have reduced computational complexity [54; 55; 56]. In future, it will be interesting to consider such RLS-like algorithms to develop robust algorithms for impulsive ANC. In addition, it is very important to carry out theoretical analysis of the developed algorithm. This poses a great challenge for the filtered-x structure as well as impulsive nature of noise source and is left as a task for future work.  
This research was partially supported by the Nazarbayev University Collaborative Research Grants Program under the grant number 021220 CRP0222.  
The study did not require ethical approval.  
Not applicable.  
Author would like to thank the anonymous reviewers for their many insightful comments, which have greatly helped in improving the quality of presentation of manuscript.  
The author declares no conflict of interest.
# Optimal Confidence Regions for Weibull Parameters and Quantiles under Progressive Censoring
## 2 Weibull Models and Progressive Censoring
Hence, the confidence regions for \((\theta,\alpha)\) based on \((\widehat{\theta},\widehat{\alpha})\) do not satisfy the Sufficiency Principle. As mentioned earlier, the Weibull distribution is a log-location-scale parameter model. Specifically, the random variable \(Y=-\log(X)\) has a Gumbel \(G(\mu,\sigma)\) distribution with location and scale parameters \(\mu=-\log(\theta)\) and \(\sigma=1/\alpha\), respectively. From [PERSON] and [PERSON] [37], it follows for any level \(\varepsilon\in(0,1)\) that the conditional \(\varepsilon\)-confidence sets for Gumbel parameters and quantiles are numerically equivalent to the corresponding \(\varepsilon\)-credibility sets obtained with the improper prior density \(\pi_{0}(\mu,\sigma)\propto 1/\sigma\); see also [PERSON] ([35], p. 565, Property 2). Since the logarithmic transformation is strictly monotonic, the above results are also valid for Weibull parameters and quantiles when the prior pdf is defined by \(\pi(\theta,\alpha)\propto 1/(\theta\alpha)\) for \(\theta,\alpha>0\). Assuming this diffuse prior model, the posterior pdf of \((\theta,\alpha)\) given \((\mathbf{x},\mathbf{r})\) can be expressed as  
\[\pi^{*}(\theta,\alpha\mid\mathbf{x},\mathbf{r})=\frac{w^{\alpha-1}\theta^{-s \alpha-1}a^{s-1}}{K\exp(v_{\alpha}/\theta^{\alpha})},\quad\theta,\alpha>0, \tag{3}\]  
where  
\[K\equiv K(\mathbf{x},\mathbf{r})=\int_{0}^{\infty}\frac{(\prod_{i=1}^{s}x_{i })^{\alpha-1}a^{s-2}}{\{\Sigma_{i=1}^{s}(r_{i}+1)x_{i}^{\alpha}\}^{s}} \mathrm{d}\alpha=\int_{0}^{\infty}\frac{w^{\alpha-1}a^{s-2}}{v_{\alpha}^{s}} \mathrm{d}\alpha.\]The posterior pdf of \((\theta,\alpha)\) given \((\mathbf{x},\mathbf{r})\) is unimodal, which implies that the HPD estimate (or posterior mode) of \((\theta,\alpha)\), denoted by \((\widetilde{\theta},\widetilde{\alpha})\), is unique. The posterior pdf of \(\alpha\) given \((\mathbf{x},\mathbf{r})\) is given by  
\[\pi^{*}(\alpha\mid\mathbf{x},\mathbf{r})=\frac{w^{\alpha-1}\alpha^{s-2}}{Kv_ {\alpha}^{s}},\quad\alpha>0,\]  
whereas the posterior pdf of \(t=1/\theta^{\alpha}\) conditional on \(\alpha\) given \((\mathbf{x},\mathbf{r})\) is defined as  
\[\pi^{*}(t\mid\alpha;\mathbf{x},\mathbf{r})=\frac{v_{\alpha}^{s}\epsilon^{s-1} \exp(-v_{\alpha}t)}{(s-1)!},\quad t>0.\]  
Therefore, the posterior distribution of \(2v_{\alpha}/\theta^{\alpha}\) given \(\alpha\) and \((\mathbf{x},\mathbf{r})\) is chi-square with \(2s\) degrees of freedom, i.e., \(2v_{\alpha}/\theta^{\alpha}\mid\alpha,(\mathbf{x},\mathbf{r})\sim\chi_{2s}^{2}\). The posterior cdf of \(\alpha\) conditional to \((\mathbf{x},\mathbf{r})\) is then given by  
\[G(z)=\int_{0}^{z}\frac{w^{\alpha-1}\alpha^{s-2}}{Kv_{\alpha}^{s}}\mathrm{d} \alpha,\quad z>0.\]  
As graphical illustrations, Figure 1 shows the posterior pdf for \((\theta,\alpha)\) conditional to \((\mathbf{x},\mathbf{r})\) associated with the case to be analyzed in Section 5. The corresponding posterior pdfs for \(\theta\) and \(\alpha\) are displayed in Figures 2 and 3.
# A Reference Model for Monitoring IoT WSN-Based Applications
## 4 Model Description  
This section describes the proposed model starting with a model overview, followed with a deeper description of each one of its components and concludes remarking the main features that this model offers. As shown in the literature and mentioned above, every functional monitoring platform has to deal with some issues. Our proposal identifies these problems and classifies them into three categories:  
* The first category makes reference to monitoring data. These data must be captured, analyzed and shown to the user in a meaningful way. The semantic meaning of the data is highly related to the application and the monitoring needs.  
Once obtained, data must be expressed in a convenient way to ensure the integration of data provided by heterogeneous sources. Some issues related to this deal with a common time base and capturing conditions.
* As the monitoring platform is distributed in space, all the data obtained must be centralized and stored to ensure a global comprehension of the system functioning.  
Keeping these different--and usually independent--problems in mind, the proposed model is composed by three layers, as shown in Figure 1:  
The Monitoring Layer will be located at the upper level of the model. This layer is in charge of all the issues related and specific to the WSN under observation. It must deal with the definition of what should be monitored, how this information must be acquired and the way it has to be processed and shown to the user.  
The Information Layer is located under the Monitoring Layer. It must provide the support needed to code the obtained information in a standard way. This level also deals with timing issues, which includes when the information must be captured (triggering) and stores this time value into obtained data (time stamp).  
Finally, the Interchange Layer allows the information captured in different points alongside the monitored WSN to be transferred and stored. Upper layers will retrieve this information to be analyzed and/or visualized by the Monitoring Layer.  
The model must locate the interfaces between each pair of adjacent levels, obtaining in this way layer-independency, portability and interoperability between components from different sources (researchers, manufacturers, developers, etc.). It is also necessary the definition of a set of protocols to support the communication needs inside each layer. The standardization of these protocols will open the possibility of universal reuse of off-the-shelf components. Each of these layers is detailed in the following sections.  
Figure 1: Proposed model.
# Enhancement of Yaw Moment Control for Drivers with Excessive Steering in Emergency Lane Changes
## 1 Introduction
This enhancement aims to improve the system's emergency response capabilities. Building on our previous work [22], this paper highlights several key features:  
1. Front-wheel steering saturation scheme: Specifically designed for managing crises during emergency lane changes involving driver's excessive steering. This technique intervenes only in cases of excessive steering, allowing drivers to steer normally under regular conditions. 2. Comprehensive control theory explanation: Provides a detailed explanation of the control techniques, including the following: (I) Analysis and consideration of matching and unmatching uncertainties; (II) Detailed derivations of the constraints on the required yaw moment and wheel torques for the emergency management; and (III) Detailed derivations and proofs of lemmas and theorems, presented in Appendix A. 3. Simulation section for effectiveness demonstration: Focuses on demonstrating and discussing the effectiveness of the control functionality in handling excessive steering during emergency lane changes. This article is organized as follows: The lateral control model section outlines the derivation of the safety reference states and reference steering angle essential for safe vehicle operation. In the control system configuration and yaw moment control design sections, we propose a strategy to limit front-wheel steering angles and develop an LPV-LMI-based DYM control approach under yaw moment constraints. Enhancements to this approach include the integration of a high-gain input saturation scheme. Finally, through simulations, we validate the efficacy of the control design and give the conclusions of the study.
## 4 Experimental Setup
### Evaluation Scheme  
The preprocessed dataset is split into 2 distinct parts: 80% ratings of each movie are used as the training set, and the 20% remaining as the testing set. To compare the performance between models, the following widely-used indicators are used:  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **\# Ratings** & **\# Users** & **\# Movies** & **Sparsity** \\ \hline Original dataset & 20,000,263 & 138,493 & 27,278 & 99.47\% \\ \hline Preprocessed dataset & 19,793,342 & 138,185 & 10,239 & 98.97\% \\ \hline \end{tabular}
\end{table}
Table 1: Summary of the original MovieLens 20 M and the preprocessed dataset.  
* RMSE (Root Mean Squared Error) for rating prediction task: evaluate the errors of the predicted ratings where smaller values provide more accurate recommendations: \[\text{RMSE}=\sqrt{\sum_{u,i\in\text{TESTSET}}(\hat{r}_{ui}-r_{ui})^{2}/|\text{TESTSET}|}\] (9) where \(|\text{TESTSET}|\) is the size of the testing set, \(\hat{r}_{ui}\) denotes the predicted rating of user \(u\) to item \(i\) estimated by the model, and the corresponding observed rating in the testing set is denoted by \(r_{ui}\).
* Precision@k (P@\(k\)) and Recall@\(k\) (R@\(k\)) for the ranking (top-k recommendation) task: Precision@\(k\) is defined as a fraction of relevant items among the top \(k\) recommended items, and Recall@\(k\) is defined as a fraction of relevant items in the top \(k\) recommended items among all relevant items. Higher P@\(k\) and R@\(k\) indicate that more relevant items are recommended to users.
* Time [s] for timing evaluation: the total duration of the model's learning process on the training set and predicting all samples in the testing set.  
All experiments in this work are conducted on a workstation consisting of an Intel Xeon Processor E5-2637 v3 3.50 GHz (2 processors) with 32 GB RAM and no GPU.
## 3 Automated Data-Generation and Labeling  
The MAVS [11] was used to generate synthetic imagery for this work. An open-source software library for simulating the sensors, environment, and vehicle dynamics of autonomous ground vehicles (AGV), MAVS was created to provide real-time, physics-based simulation capability in a modular, customizable architecture that can be integrated with a variety of other systems and software like the Robotic Operating System (ROS). The MAVS uses ray-tracing to generate realistic sensor data for Light Detection and Ranging (lidar) sensors, cameras, and global-positioning system (GPS) [26]. It features a C++ API, as well as a Python interface to the API that allows for rapid development of simulated experiments.  
MAVS is optimized for simulating AGV operating in off-road environments. Factors that influence off-road driving include soil type and strength, vegetation type and density, terrain slope and terrain roughness. The simulator includes physics-based models to account for all these off-road features. The lidar simulation accurately accounts for beam-scattering in dense vegetation, while the vehicle simulation accounts for soft-soil and surface roughness effects. In addition, MAVS can automatically create off-road scenes and generate semantically labeled lidar point clouds and camera images. It can also account for weather and environmental affects like rain, snow, dust, fog, and time-of-day, as shown in Figure 2.  
MAVS has been used for a variety of applications ranging from developing algorithms for automatically removing rain in images [1], to optimizing the placement and orientation of lidar sensors on an AGV [27], to assessing the impact of vegetation on autonomous navigation [28]. The past success of MAVS in supporting machine-learning studies make it a good candidate simulator for this research.  
Three primary simulation components are required in order to generate realistic synthetic imagery:  
1. Scene Generation-A model of the geometry, texture, and color of the surfaces in the environment;
2. Radiative Transfer-A model of how light reflects and propagates through the environment; and
3. Camera Simulation-A model of how the camera captures and processes light.  
For this project we will use physics-based simulation to develop synthetic images. In this case, physics-based refers to all three phases of the simulation. In the following sections, each of these three components will be discussed in more detail.
## 1 Introduction
Furthermore, control of switch times ensures that the number of conversions for distribution schemes corresponding to frequency domains of [6 MHz, 17 MHz] and above is limited to between 6 and 13 times. The optimization of numerical settings results in an average overall reduction of 24.3% in the offset frequency values. Section 4 shows two final offset frequency distribution schemes for the different frequency domains in the simulated environment.