esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Electrical Characterization
### Temperature Testing
#### 5.4.2 Bit Error Rate  
To evaluate the BER under different temperatures (0 \({}^{\circ}\)C, 23 \({}^{\circ}\)C, 80 \({}^{\circ}\)C), we conducted key generation experiments using the bottom and top 25 th percentiles of responses. At each temperature, we generated a total of 1024 keys, each consisting of 1024 bits, which resulted in over one million bits being generated for each temperature. Additionally, for each temperature, a key was generated using the enrollments obtained at each temperature.  
During the key generation process, we exclusively utilized state 1, which exhibited greater stability when subjected to lower voltage-pair differences. Moreover, a 20-microsecond settling time was used as it showed a good balance of speed and accuracy.  
When using the bottom and top 25 th percentiles, the minimum voltage pairs depended on the specific MRAM devices used. For the specific chips used for this test, we obtained voltage pairs with a voltage difference of 11 millivolts or greater.  
The results of the BER at different temperatures are presented in Table 4.  
Upon analyzing the results, it became evident that temperature had a slight impact on the BER. As the temperature of the key generation deviated from the temperature at which enrollments were taken, an increase in the BER was noted.  
However, this effect can be mitigated by conducting enrollments at various temperatures and selecting the enrollment that is closest to the operating conditions of the client device. By doing so, the impact of temperature on the BER can be minimized.
# Differential Proteomic Analysis of _Listeria monocytogenes_ during High-Pressure Processing
## 4 Discussion
### Translational Regulation
GaTC/A/B were downregulated; in particular, GaTC significantly decreased by 2.39 and 2.18 fold under 200 and 400 MPa, respectively (Table S3). It implied that biosynthesis of Gln and Asn was inhibited under HPP treatments. On the other hand, PheRS, GlyRS, HisRS, ValRS, TrpRS, SerRS and LeuRS were all upregulated,although only PheRS was significantly upregulated by 1.05 log\({}_{2}\) fold in H2/C (Table S3). Furthermore, this study showed that elongation factors (EFs) including EF-Tu, EF-4, EF-P and EF-G were all reduced after the HPP 200 and 400 MPa treatments, although only the results for EF-P in all groups were significant (Table 1 and Table S3). These lines of evidence indicated that the HPP 200 and 400 MPa treatments may diminish peptide chain elongation of protein synthesis. During termination, release factors (RFs) promote polypeptide release from 70S ribosomes. This study showed that both RF-1 and RF-3 decreased but RF-2 increased after the HPP 200 and 400 MPa treatments but without statistical significance (data not shown). Finally, 70S ribosomes are dissociated into free 30S and 50S subunits through the cooperative action of three translation factors consisting of ribosome recycling factor (RRF), EF-G and IF-3 during the recycling of free ribosomal subunits. IF-3 is an anti-association factor, whereas RRF is a dissociation factor. Collaboratively, RRF, EF-G and GTP transiently dissociate 70S ribosomes into subunits, and IF-3 releases the deacylated tRNA from the 30S subunit, thus preventing re-association back into 70S ribosomes while running out of GTP [25]. The stable dissociation complex for 70S ribosomes into 30S/50S subunits is dependent on the formation of RRF-EF-G-IF-3. When the ribosome cycle finishes, the 30S/50S subunits are recycled for subsequent translation. This study showed that both RRF and IF-3 were elevated but EF-G decreased under HPP treatments, although only the results for IF-3 in all groups and RRF in H4/C were significant (Tables S3 and 1). HPP treatments might hinder the recycling of free ribosomal subunits due to EF-G shortage. Therefore, HPP seems to promote initiation but hinders the subsequent elongation, termination, and recycling steps during protein translation.
# Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder
## 1 Introduction
[14] introduced a deep neural network to estimate transmission maps using haze features effectively. To estimate transmission maps, they proposed a neural network consisting of a layer of large filter sizes and a layer of small filter sizes that fine-tune the results. [PERSON] et al. [15] introduced a network called AOD-NET based on a haze model. Instead of estimating the transmission maps and atmospheric light, they combined parameters to create a new model with faster performance than other networks. In most situations haze and rain occur simultaneously. However, previous methods focused on removing either rain or haze. In other words, these methods are specified with  
Figure 1: Object detection results on haze and rainy road images: (**a**) hazy and rainy input images, (**b**) YOLOv3 [1] object detection results. The images are obtained from Google. rain or haze. [PERSON] [11] added a rain region which does not concern about haze models. On the other hand, [PERSON] et al. [14] considers the haze related feature, transmission map. In this paper, we propose a novel framework for removing rain and haze simultaneously in a single road image. The frequency-based image decomposition is used prior to training neural network models to consider rain and haze separately and present a deep convolution double encoder-decoder neural network model.
# A Reliability Scheduling Algorithm for the Static Segment of FlexRay on Vehicle Networks +
## 4 Conclusions  
Communication requirements for automotive systems include both data stability and speed of processing. Related vehicle applications such as powertrain systems, electronic stability control (ESC) and traction control systems (TCS) have response time constraints. This work presents a new fast reliability scheduling algorithm (FRSA) to recover the transient errors for the static segment of the FlexRay bus. The proposed method increased the reliability of the system by the re-transmission  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ParameterMethods** & **Average** \(cost\) & **Reduction (\%)** \\ \hline
**Setting 1** & 11.22 & 77.94 ([PERSON] [9]) & 85.59 \\
**Setting 2** & 36.64 & 67.50 ([PERSON] [11]) & 45.71 \\
**Setting 3** & 12.96 & 68.18 ([PERSON] [10]) & 80.98 \\ Average & & 70.76 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of average running time with related studies.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ParameterMethods** & **Average** \(cost\) & **Reduction (\%)** \\ \hline
**Setting 1** & 10.401 & 11.168 ([PERSON] [9]) & 6.86 \\
**Setting 2** & 5.393 & 6.140 ([PERSON] [11]) & 12.16 \\
**Setting 3** & 10.101 & 12.782 ([PERSON] [10]) & 20.97 \\ Average & & 13.33 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of average \(cost\) with related studies.  
Figure 7: Comparison of \(cost\) with existing methods in different parameters.  
of messages to ensure communication requirements. Experimental results show that the proposed FRSA has better performance for the running time of finding optimal solutions than the existing methods. In addition, the proposed fast and low-time-complexity lookup table method makes not only a significant improvement on execution time, but is also suitable for any periodic time-triggered communication system.  
Conceptualization, I-A.L. and J.-J.W.; Data curation, J.-J.W.; Formal analysis, I-A.L.; Funding acquisition, I-A.L.; Methodology, T.-Y.L.; Resources, J.-J.W.; Software, J.-J.W. and J.-T.T.; Supervision, T.-Y.L.; Validation, I-A.L. and J.-T.T.; Writing--Original Draft, T.-Y.L., I-A.L. and J.-J.-W.; Writing--Review & Editing, T.-Y.L.  
The authors would like to thank the Ministry of Science and Technology of the Republic of China, Taiwan, for partially supporting this research under Contract No. MOST 107-2637-E-027-006.  
The authors declare no conflict of interest.
## 6 Result and Discussion
### Simulation Result  
#### 6.2.1 Scenario 1  
The assumed UAV model simulation was used to prove the optimized configuration's ability to survive specified actuator failure while hovering at the target as shown in simulation Video S1. As shown in Figure 15, the UAV with the preliminary actuator orientation was commanded to take off to six meters and hover. While hovering, the fault was injected into actuator1 at a simulation time of 20 s, and the propellers were steered to tilt after sufficient detection time. The simulation result demonstrated that the optimum configuration compensated for the lost control after some perpetuation and stabilized towards its hovering state, as shown in Figure 16.  
Figure 14: The marginal evaluation results for designed mission profile (**a**) Trend of exclusion ratio and confidence level against cost function at a different stage of design parameter optimization is plotted at \(\zeta=1\); (**b**) Achievable marginal requirement.  
Figure 15: Simulation for hovering flight (**a**) Hovering at a given height in the nominal situation. (**b**) Right-side view of hovering at a given height in the presence of actuator failure. (**c**) Close-up view of hovering flight before actuator failure. (**d**) Close-up view of actuator’s reorientation after actuator failure at recovered hovering.
## 4 Conclusions  
An investigation of the compressive strength of manufactured sand concrete containing stone powder (SP), pulverized fuel ash (PFA), and silicon tune (SF) using the Box-Behnken experiment design in the response surface method (RSM) provides the following conclusions:  
1. A prediction model of the compressive strength of manufactured sand concrete with SP, PFA, and SF content was developed using multiple regression analysis with SP, PFA, and SF content as factors and compressive strength as the response value; the multiple correlation coefficient R\({}^{2}\) of the prediction model was 0.85. The prediction model for the compressive strength of concrete using manufactured sand was validated experimentally, and the validation results showed that the prediction model was credible, with a relative error of less than 10% between the experimental and predicted values.
2. The statistical values of the single factors were analyzed and the degree of significance of the single factors on the compressive strength showed that SP content had the greatest effect on the compressive strength of the manufactured sand concrete, with PFA having the next greatest effect, and SF having the least effect. For the interactions, SP and PFA content had the most significant effect on the compressive strength of the manufactured sand concrete, while the interactions between SP and SF and PFA and SF had the same effect on the compressive strength.  
Figure 8: Response surface and contour in SFA-SF. (**a**) Response surface. (**b**) Contour.  
3. Response surface and contour analyses were carried out where the SP, PFA, and SF contents were kept at moderate levels (10%, 15%, and 5% of cement mass, respectively). The results show that the compressive strength tends to increase and then decrease with increasing SF and PFA content, with a maximum value. The maximum compressive strength of the concrete was found when the SP, PFA, and SF contents were all at intermediate levels.  
Conceptualization, H.M. and G.M.; methodology, H.M.; software, Z.S.; validation, H.M. and Z.S.; formal analysis, H.M.; investigation, H.M. and Z.S.; resources, Z.S. and H.M.; writing--original draft preparation, H.M.; writing--review and editing, H.M. and G.M.; supervision, G.M.; project administration, H.M., Z.S. and G.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Major scientific and technological innovation projects of Shandong Province (Grant No. 2019 SDZY0304).  
Not applicable.  
The figures, tables and data that support the findings of this study are mentioned in the corresponding notes, with reference numbers and sources, and are publicly available in the repository.  
The authors declare no conflict of interest.
## 3 Sensorless FOC with Luenberger-Type Back-EMF Observer
### Sensorless FOC
#### 3.1.2 Speed Loop
On the contrary, the speed loop can run in a slower control loop with a lower sampling frequency. This means that the natural frequency of the current loop will be always higher than the natural frequency of the speed loop. Such an approach provides enough time to control the current between two speed samples [56]. Furthermore, the damping factor can be maintained to influence the controller gains and dynamics response. The damping factor could be set at less than one (underdamped system), greater than one (overdamped system), or equal to one (critically damped system). For the second-order systems, the damping factor is usually set up to 0.707, to provide a system attenuation \(-3\) dB at the controller cutoff frequency.
# Point-Kernel Code Development for Gamma-Ray Shielding Applications
## Appendix A  
_CADIS VR Methodology Overview_  
MAVRIC is the main shielding sequence of SCALE6.1.3 code package and is implementing CADIS approach to automate calculation and usage of deterministic VR parameters (biased source and importance map) calculated by \(S_{N}\) code Denovo for multigroup MC code Monaco [19]. The user is required, besides preparing complete input listing for a MC model, to enter an additional block of parameters typical for \(S_{N}\) solvers: structured mesh overlaying the physical geometry, \(S_{N}/P_{N}\) parameters (angular quadrature and Legendre order for cross section polynomial expansion), spatial differencing option, macromaterials for improving geometry discretization, and various control/numeric options. Denovo will then produce a quick estimate of the multigroup fluxes used for generating VR parameters that are consistent with particle weights and work in tandem. CADIS will thus allow the user to optimize MC result at one specific location (i.e., point detector) which is quantified by the FOM (Figure of Merit) factor, combining CPU time needed to achieve the MC result of desired precision. Since CADIS is already well documented, only a brief overview of fundamental equations is provided here.  
To calculate the reaction rate \(R\) at some point detector using the unit source \(q\), the scalar flux \(\phi\) is convoluted with a detector response function \(\sigma\) as the following integral over detector volume \(V_{d}\) and energy \(E\) of reaction interest:\[R=\iint\limits_{V_{d},E}\sigma_{d}(\stackrel{{\rightarrow}}{{r}},E) \phi(\stackrel{{\rightarrow}}{{r}},E)dVdE. \tag{10}\]  
Using the theory of general reciprocity [35], we can arrive _alternatively_ to \(R\) by integrating the source \(q\) with an adjoint scalar flux \(\phi^{\dagger}\) calculated from the corresponding adjoint problem (detector response function \(\sigma\) is an adjoint source \(q^{\dagger}\)):  
\[R=\iint\limits_{V_{s},E}q(\stackrel{{\rightarrow}}{{r}},E)\phi^{ \dagger}(\stackrel{{\rightarrow}}{{r}},E)dVdE. \tag{11}\]  
The quick \(S_{N}\) estimate of the adjoint flux by Denovo is allowing calculation of a biased source distribution to be used for source particle sampling  
\[\hat{q}(\stackrel{{\rightarrow}}{{r}},E)=\frac{q(\stackrel{{ \rightarrow}}{{r}},E)\phi^{\dagger}(\stackrel{{ \rightarrow}}{{r}},E)}{R}, \tag{12}\]  
which means that particle source weight is in accordance with mesh importance map (or weight windows) used for transport biasing, i.e., splitting and rouletting:  
\[\overline{w}(\stackrel{{\rightarrow}}{{r}},E)=\frac{R}{\phi^{ \dagger}(\stackrel{{\rightarrow}}{{r}},E)} \tag{13}\]  
The last two equations form consistent energy-dependent, mesh-based VR parameters that work in tandem during a final MC simulation of Monaco.
# Potential New Treatments for Knee OA: A Prospective Review of Registered Trials
## 4 Discussion
### Drugs  
Disease modifying drugs are currently lacking. The present knowledge on OA pathophysiology is growing and has created new possibilities for potential therapeutic targets currently being investigated in randomized controlled trials [3, 7, 10, 11, 12]. Emerging solutions target matrix-degrading proteases, cartilage metabolism, bone remodeling, inflammation and Wnt signaling. Furthermore, nerve growth factor inhibitors could address OA pain [11; 12; 42].  
Inhibition of specific cytokines may slow OA progression. Anakinra inhibits IL-1 had encouraging results in animal models and is currently investigated on humans [3; 7; 10; 11; 12; 42]. Bone Morphogenetic Protein 7 is a growth factor which also has an anabolic effect on cartilage. Cathenpsin K inhibitor slows down bone degradation [3; 7; 10; 11; 12; 42]. Lorecivivint modulates Wnt signaling which induces cartilage breakdown and osteophyte formation. Preclinical development produced encouraging results of chondrocyte regeneration, cartilage protection, anti-inflammatory activity and improved joint health. It could be a first disease modifying agent for OA [11; 42]. IA Sprifermin (Human-Recombinant Fibroblast Growth Factor 18) may support chondrocyte proliferation, collagen expression and matrix production. It is currently investigated for IA injection [11; 42]. Monoclonal antibodies have improved receptor selectivity and are already being used in several diseases. Subcutaneous Tanezumab and Fasinumab have shown improvements in pain and patient reported outcomes in patients with knee OA. Gene therapy includes IA administration of adeno-associated virus carrying IL-1 receptor antagonist [3; 11; 42].
## 1 Introduction  
Polyvinyl alcohol (PVA) is the largest volume synthetic water-soluble polymer produced in the world [1]. PVA has excellent chemical resistance, a good mechanical property, a good film forming capability and an excellent biocompatibility [2]. In addition, PVA is biodegradable in the presence of suitable microorganisms. The several useful properties of PVA result in broad industrial use and make it particularly interesting to elaborate environmentally friendly materials [3; 4; 5]. However, PVA can be considered as an inert material. The introduction of chemical functionalities is necessary to improve its reactivity. Chemical modifications of PVA, which can be made through its hydroxyl groups, are reviewed in the literature. The most common PVA modification reactions are esterification, etherification or acetalization of the hydroxyl groups [6].  
Particular attention has been recently devoted to the chemical modification of natural and synthetic polymers using a green solvent due to environmental factors. It is evident that water is the most available environmentally friendly and inexpensive solvent [7]. The present paper describes the incorporation of chemical group in PVA polymer chain using water as solvent. We mainly focused on the incorporation of tosyl, azide and amine groups leading to the enhancing of the reactivity of PVA.  
The tosyl and azide groups were introduced in the PVA polymer chain using pyridine and DMF as solvent [8; 9]. Tosyl moiety is an excellent leaving group in nucleophilic substitution reactions and can, for instance, be substituted by halogens, azide, or amines. Tosylation of cellulose and of lignin in water or in eco-friendly medium was reported in the literature [10; 11; 12]. To the best of our knowledge, the tosylation of PVA in aqueous media has not yet been reported.  
Moreover, the presence of azide moiety is of considerable utility in the generation of other functionalities, e.g., amines via reduction, amines via rearrangement, triazoles and other heterocycles via cycloaddition [13; 14; 15]. Herein, we investigate the use of water as solvent during the azidation reaction. Finally, we made a reaction between the azido PVA and the porpargylamine by click reaction in order to introduce the amine function on the PVA polymer chain. The possibility to perform the click reaction in water has been reported [16]. The presence of amine moiety allows the reaction of PVA with polymer or molecules containing acid carboxylic function by amidation. For example, the formation of an amide function between modified PVA and cellulose fibers containing a high amount of carboxylic acid can be used to elaborate a homogenous biocomposite. The amidation can also be performed in water using a carbodiimide mediated pathway [17].
## 2 Literature Review
### Residual Stress in Railway Rails  
Residual stresses are the stresses that remain within a material after it has been subjected to mechanical processing or external forces. In railway rails, residual stress significantly influences fatigue life, wear resistance, and overall mechanical performance. Both tensile and compressive residual stresses can form during manufacturing or service. While tensile residual stresses increase the likelihood of crack initiation, compressive residual stresses are beneficial as they mitigate crack propagation [12; 13].  
In recent years, several researchers have focused on the residual stress profile within railway rails, using both experimental techniques and simulations to better understand how these stresses develop. For instance, [10] reviewed existing methods for measuring residual stresses in rails, highlighting that techniques like X-ray diffraction and neutron diffraction have been instrumental in identifying residual stress distributions. However, the authors also pointed out the limitations of these methods in terms of cost and accessibility, prompting the need for predictive modeling tools like [14; 15].