esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Bladder tumors are among the most fatal urological cancers worldwide, with 2 million recorded deaths in 2018 [1]. Most bladder tumors are composed of transitional cell carcinomas comprising non-muscle-invasive bladder cancer (NMIBC) or muscle-invasive bladder cancer (MIBC) [2]. The major approaches to treating NMIBC are straightforward transurethral resection and chemotherapy; however, chemotherapy, hormone therapy, radiotherapy, and surgery are used to treat MIBC, although their therapeutic effect and safety remain undetermined, and MIBC frequently resultsin high metastasis, poor prognosis, and death [3; 4; 5]. Thus, it is important to develop effective therapeutic agents with minimal adverse reactions to treat bladder cancer.  
The abnormal progression of bladder cancer cells emerging from urothelial tumor tissues is linked to multiple cascade signaling pathways involving phosphorylated signaling molecules, including phosphoinositide 3-kinase/AKT (AKT) and mitogen-activated protein kinases (MAPKs), which include extracellular-signal-regulated kinase (ERK)1/2, p38 mitogen-activated protein kinase (p38 MAPK), and c-Jun N-terminal kinase (JNK) [6; 7; 8]. Dysregulation of tumor cell proliferation results from a progression of the cell cycle at the G1, S, and G2/M phases, with transition from G1 to S phase controlled by cell cycle regulators [9; 10]. These regulators include cyclins, cyclin-dependent kinases (CDKs), and p21 WAF1, which have recently received attention as primary targets for antitumor molecules [11; 12]. Additionally, tumor-associated matrix metalloproteinases (MMPs) that promote the migratory potential and invasiveness of tumor cells are another attractive target [13; 14]. MMP-9 activity reportedly induces invasive and migratory potential that correlated with the progression and occurrence of bladder cancer [6; 14; 15]. Moreover, during bladder-tumor-derived invasion and migration, MMP-9-related regulatory mechanisms are closely involved in the binding ability of the transcription factors SP1, nuclear factor-kappaB (NF-\(\kappa\)B), and activator protein (AP)-1 [16; 17; 18].  
Resveratrol is a well-known polyphenol compound obtained from grape and peanut sprouts [19; 20], and resveratrol-enriched peanut sprout extracts acquired by hydroponic technology have attracted attention for their anti-obesity, anti-oxidative, and anti-inflammatory effects in humans [21; 22; 23; 24]. We recently reported that peanut sprout extracts cultivated with fermented sawdust medium (PSEFS) contained higher levels of resveratrol than those obtained with hydroponic technology [25; 26]. Additionally, we demonstrated the in vitro and in vivo inhibitory effects of PSEFS in benign prostatic hyperplasia (BPH) [26]. In the present study, we examined for the first time the antitumor efficacy of PSEFS against bladder cancer both in vitro and in vivo. Thus, these results may provide critical information for developing preventive or therapeutic resources for bladder cancer.
## 4 IETF 6 LoWPAN Working Group (IPv6)
### Leveraging upon 6 LoWPAN to Realize the IoT
#### 4.3.4 Other Efforts  
Management of (6 LoWPAN) networks is another important aspect of 6 LoWPAN that is being investigated. In line with this, 6 LoWPAN MIB (draft-schoenw-6 lowpan-mib) [33] is being proposed. The draft demonstrates a JSON format using a version of the Management Information Base database for management purposes along with the normal SMIv2 format.  
From the previous discussion, it has become clear that addressing is also a key aspect when extending IPv6 to the constrained world through the use of 6 LoWPAN. In this area, other IETF working groups are also conducting research activities in the area. The IETF Working group AUTOCONF (_Ad hoc_ Network Autoconfiguration) is also working on Neighbor Discovery and stateless address autoconfiguration. Finally, as 6 LoWPAN is meant as an enabler to bring IPv6 functionality to constrained networks, many higher layer protocols, such as routing, will run on top of 6 LoWPAN networks. During the design of these solutions, the relation and interaction with 6 LoWPAN is considered. For example, the routing protocols and mechanisms being developed by ROLL working group can also be seen as other 6 LoWPAN related efforts [22]. The ROLL working group is discussed in the next section.
# Production and Maintenance Planning for a Deteriorating System with Operation-Dependent Defectives
## 3 Notation and Problem Statement
### Problem Description  
The manufacturing system under study consists of a single machine producing one part type. Nonetheless, the machine is unreliable and is subject to random events such as failures and maintenances actions of random duration. The machine can produce at diverse capacities to satisfy a constant product demand. Additionally, our considered systems has two principal features, where its failure rate increases in function of its level of deterioration, and the quality of the items produced is not perfect, there is a rate of non-conforming units. Such rate of defectives depends on the productivity of the system, thus defining a productivity-dependent defectives rate. Therefore, the system deteriorates with age and its production pace. These assumptions are common in production management. The stock is a mixture of flawless and defective product and serves as protections against shortages. To cope with the effects of the deterioration process, when the machine is at failure a fundamental problem of the decision-maker is to decide between the conduction of:  
1. A minimal and inexpensive repair that serves to operate the machine for a while, but with the disadvantage that it does not restore the effect of deterioration, it leaves the machine in as-bad-as-old conditions, ABAO.
2. An expensive major maintenance, which mitigates completely the effects of deterioration, leaving the machine in as-good-as-new-conditions, AGAN.  
We intend to determine an optimal control policy that defines the appropriate production pace and the repair/major maintenance switching strategy that minimizes the average total cost comprising the inventory, backlog, defectives, production, and maintenance cost. Figure 1, illustrates the block diagram of the manufacturing system under analysis.
# A Review on the Lifecycle Strategies Enhancing Remanufacturing
## 3 Strategies Enhancing Remanufacturing
### Reverse Logistics (RL)  
According to the American Reverse Logistics Executive Council, reverse logistics is defined as \"The process of planning, implementing, and controlling the efficient, cost effective flow of raw materials, in-process inventory, finished goods and related information from the point of consumption to the point of origin for the purpose of recapturing value or proper disposal\" [84]. Due to environmental concerns, reverse logistics is now becoming an important strategy to increase customer satisfaction. RL originates from a waste management standpoint. It is complicated due to the presence of driving forces, return reasons, product types and uncertainty around the reverse flow. Also, how the material is recovered and who will execute and manage the various reverse operations are importantissues [85, 86]. Since reverse logistics includes a series of processes involving product return, repair, dismantling, refurbishing, recycling, remanufacturing and disposal of used or end-of-life products, the implementation of a reverse logistics network is a strategic decision. This decision seeks a single objective or multiple objectives of cost minimization, profit maximization, customer satisfaction and environmental benefit [46, 47, 86, 87]. It includes the determination of locations, the number and capacity of facilities and the flow quantity sent from one facility to another. It is severely complicated by many uncertain factors, as explained by [88]; therefore, several papers have focused on the design of reverse logistics networks [89, 90, 37, 48, 87, 91].  
A classification scheme for different types of reverse logistics networks has been identified by [92]. Reverse logistics networks range from simple echelons to complex echelons composed of forward and reverse supply chain networks, as explained by [71, 93, 94]. According to [PERSON] et al. [44], refurbishing and recycling of cell phones can be sustainable for the circular economy. Due to the complexity and economic effect of reverse logistics, a common mathematical model has been developed to solve the network problem [93, 94, 95, 96]. [PERSON] et al. [87] reviewed mathematical inventory models for reverse logistics from an environmental perspective. A more comprehensive survey of reverse logistics was undertaken by [71, 93, 97].  
The diverse research carried out on reverse logistics shows just how important it is in the promotion of a circular economy. It can greatly reduce the problems encountered in remanufacturing, especially in terms of the arrival, quality and quantity of returns at the EoL stage. [PERSON] [45] described an algorithm that can be used to gather information based on locations of companies, manufacturers, retailers and different elements of the supply chain, with this information then synchronized so as to ensure a smooth flow of resources. This algorithm is used in real-life recycling of bulk waste in Taoyuan City, Taiwan, which is a very big step toward the creation of more algorithms.
# A Bacterial Form I' Rubisco Has a Smaller Carbon Isotope Fractionation than Its Form I Counterpart
## 2 Materials and Methods
### Rubisco KIE Assay Summary  
We used a substrate depletion method to measure the KIE of each rubisco as used previously in similar studies [25; 35; 36; 37]. Briefly, this method relies on measuring the time-varying \(\delta^{13}\)C value of the CO\({}_{2}\) pool as the reaction goes to completion instead of directly measuring the difference in \(\delta^{13}\)C between the initial CO\({}_{2}\) and final 3 PG pool. The KIE is then calculated from these data using a Rayleigh relationship, which considers the log-log transformation of the CO\({}_{2}\) isotope data against the fraction of substrate consumed. Linear regression of these data can then be converted to a measure of the instantaneous isotope fractionation--the empirical measure of the isotope effect associated with rubisco carboxylation. With this formulation, larger KIEs correspond to steeper slopes in a Rayleigh plot.  
The assay mix we used is based on previous similar studies. In this set-up, inorganic carbon is supplied as HCO\({}_{3}\)\({}^{-}\) which is converted to CO\({}_{2}\) by a carbonic anhydrase (CA), typically derived from bovines. CO\({}_{2}\) and RuBP is then catalyzed by rubisco to create 3 PG. Therefore, our reaction mixture contains CA, rubisco, HCO\({}_{3}\)\({}^{-}\), and RuBP to yield the full reaction, and additional reagents including: (i) MgCl\({}_{2}\) (Sigma-Aldrich, St. Louis, MO, USA) to support correct rubisco active site metalation, (ii) bicine (Sigma-Aldrich, St. Louis, MO, USA) as a buffer, and (iii) dithiothreitol (DTT) (Sigma-Aldrich, St. Louis, MO, USA) to prevent rubisco oxidation and degradation [38].  
In our experiment, instead of limiting CO\({}_{2}\), we limited RuBP. In addition, \(f\) (the proportion of CO\({}_{2}\) remaining) is typically known from an external measurement. Prior experiments have labored to constrain \(f\) by taking a separate aliquot of the assay to measure CO\({}_{2}\) concentration directly [25; 36]. In our experiment, we converted sampling time to \(f\) by fitting our data to the model y = a*EXP(\(-\)b*\(\lambda\)) + c based on the fact that the \(\delta^{13}\)C of the reactant pool will increase during the reaction and then asymptote to a fixed value as the reaction ceases (i.e., no further carbon isotope discrimination can occur because rubisco can no longer pull from the CO\({}_{2}\) pool as RuBP runs out). In essence, we are interested in the curvature of this line, similar to prior rubisco assays where the \(\delta^{13}\)C of the reaction vessel headspace was monitored continually on a membrane inlet mass spectrometer [35] instead of traditional methods where discrete aliquots are taken [25]. See below and Supplemental for further discussion.
## 4 Discussion and Conclusions  
The algorithm discussed in this manuscript allows industry to perform non-destructive inspections during manufacturing without having to adjust the translation speed of the objects to the frame rate of the camera. Nowadays, it is necessary to synchronize the translation speed of an object with the acquisition rate of the camera used. The frame rate of a camera is often fixed; consequently, the production process has to be adapted to the capture rate. With this algorithm, the production process is no longer dependent on the camera used. In other words, the number of objects produced is not determined by quality control. The results show that we are able to successfully apply the algorithm to three different cameras, each camera operating a different wavelength. We are able to achieve an accuracy of 0.1 pixels while stitching the temporal matrix. This accuracy can be further increased by changing the upscaling factor if this is necessary for the specific purpose. The proposed algorithm can measure the pixel shift in two different ways, through checkerboard detection or field-of-view calculation.  
In addition to the aforementioned advantage of this algorithm, it also allows samples to be examined without the adverse effects of a heterogeneous light source.
## 1 Introduction
### Variable Identification in the Sand Retention Test
#### 1.1.4 The Condition in Test Cell  
The test cells' conditions that consider plugging performance and sand retention efficiency are the flow rate, sand concentration, and pressure. The different flow rates used in the experiment lead to different results. The high flow rate initiated at the beginning of the experiment leads to plugging, but gradually increasing the flow rate throughout the experiment reduces the risk of plugging [5]. However, initiating the experiment with a low flow rate without increasing it leads to a higher amount of sand passing through the screen [6; 10].  
The sand concentration in the test cell also affects sand production. [PERSON] and [PERSON] [6] found that the sand slurry's low sand concentration leads to an increasing amount of sand produced before bridging occurs. Large grains are essential for the bridging process to start on the screen and must be large enough to fit through the slot. This finding is supported by [PERSON] and [PERSON] [10], where the lower volume fraction of formation sand in the flow stream causes a higher sand production.  
Other than that, pressure drop and pressure gradient are used to interpret the sand production and plugging performance. According to the practice of SAS selection recommended by [PERSON] et al. [9], the screen with the lowest pressure drop and the highest permeability are associated with high sand retention. [PERSON] et al. [7] investigated the correlation of a sand reaching screen with the pressure drop. It showed a steep gradient for simulated laser particle size analysis (LPSA) sand compared to simulated sieve analysis and reservoir sand. An increase in the pressure drop represents an increase in the flow resistance through the screen, due to the build-up of particles on top and inside the screen [9]. Therefore, a higher pressure drop produces a more elevated amount of sand production [10].  
Since there is no standard way to interpret the trend of the pressure gradient towards plugging, [PERSON] et al. [9] mentioned that the pattern that shows a linear pressure build-up represents the formation of the permeable sand pack on top of the screen while the exponential behavior is a sign of plugging occurring on the screen. However, the observation of pressure during the laboratory test shows that plugging does not contribute to the decrease or increase of pressure [6]. The pressure slowly falls when there is some plugging of the screen and decreases initially when some new sand is washed through the screen. In other words, the pressure gradient results come from variations in sand characteristics rather than plugging.
# Miniaturized Compact Reconfigurable Half-Mode SIW Phase Shifter with PIN Diodes
## 2 Design of the HM-SIW Phase Shifter  
The proposed HM-SIW consists of a one-layer substrate, and the background is covered with copper all around. The substrate material is Rogers RT/duroid 5880 with 2.2 relative permittivity (\(\epsilon_{\mathrm{r}}\)), 0.508 mm thickness, and 0.0009 loss tangent. The first step in a reconfigurable compact modular phase shifter design is to create a proper transmission line. This transfers signals within the frequency band with low dissipation.
# Optimal Design and Real Time Implementation of Autonomous Microgrid Including Active Load
## 5 Conclusions  
In this paper, an autonomous microgrid including active load has been modeled and analyzed. The stability of the inverter-based microgrids considering active load has been assessed. By minimizing the proposed objective function using particle swarm optimization, the control problem has been optimally designed. A weighted objective function has been used to curtail the errors in the measured active power and DC voltage based on time-domain simulations. Different AC and DC disturbances have been introduced to verify the optimal parameters effect on the microgrid stability. The proposed controllers successfully coordinate among the distributed energy resources. Results show satisfactory performance with efficient damping characteristics of the considered autonomous microgrid. Additionally, results prove the superiority of the proposed optimal controller over the controller presented in the literature. The considered microgrid including the three DGs, active loads, and their associated controllers has been developed and implemented on real time digital simulator (RTDS). The performance of three inverter-based DGs as well as the active load of the considered autonomous microgrid with the proposed controllers has been verified on RTDS. The experimental results confirm the effectiveness of the proposed controllers to enhance the stability of the proposed microgrid under different disturbances and operating conditions.  
**Author Contributions:** [PERSON] and [PERSON] initiated the idea, formulated the problem, performed the simulation, and analyzed the results. [PERSON] participated in paper revision stage, contributed in enhancing the simulation results, and shared in paper writing.  
Figure 14: (**a**) DG3 output voltage and (**b**) DG3 reference voltage due to voltage step changes.  
Figure 13: (**a**) DG1 output voltage and (**b**) DG1 reference voltage due to voltage step changes.  
**Acknowledgments:** The authors acknowledge the support provided by the Deanship of Scientific Research, King Fahd University of Petroleum & Minerals through project # RG171002.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Numerical Example
### Discussion  
As can be seen from Figures 11, 13 and 15, the amplitude of the magnetic flux density is high near the edges and decreases to a very low level in the center on each mesh plane. Furthermore, the amplitude of magnetic flux density is higher under positive lightning stroke than in negative stroke for the same mesh plane. This is due to the fact that current amplitude of a positive lightning stroke is larger than that of a negative lightning stroke. Although the current wavefront steepness of the positive lightning stroke is much less than that of the negative lightning stroke, the field component depending on the instantaneous current (see (10) and (11)) plays a major role in the magnetic flux density at the close range of the magnetic field [20; 24; 28]. Contrary to the magnetic flux density, the induced voltage under positive lightning stroke is evidently lower than that under negative lightning stroke in the same mesh. The reason for this is that the induced voltage is in direct proportion to the time derivative of the magnetic linkage through the mesh (\(\mathrm{d}\Phi/\mathrm{d}t\)). The time derivative depends mainly on the current wavefront steepness rather than its instantaneous value (see (15)). In addition, when the lightning currents under both polarities have the same amplitudes, the amplitude of the magnetic flux density under positive lightning stroke is slightly less than that under negative lightning stroke (see Figures 13 and 15). However, the induced voltage under positive lightning stroke is much lower than that under negative lightning stroke (see Figures 14 and 16). This is because the current wavefront steepness under negative lightning stroke is far greater than that under positive lightning stroke in the case of the same current amplitude.
###### Abstract  
Recent studies in text-to-speech synthesis have shown the benefit of using a continuous pitch estimate; one that interpolates fundamental frequency (F0) even when voicing is not present. However, continuous F0 is still sensitive to additive noise in speech signals and suffers from short-term errors (when it changes rather quickly over time). To alleviate these issues, three adaptive techniques have been developed in this article for achieving a robust and accurate F0: (1) we weight the pitch estimates with state noise covariance using adaptive Kalman-filter framework, (2) we iteratively apply a time axis warping on the input frame signal, (3) we optimize all F0 candidates using an instantaneous-frequency-based approach. Additionally, the second goal of this study is to introduce an extension of a novel continuous-based speech synthesis system (i.e., in which all parameters are continuous). We propose adding a new excitation parameter named Harmonic-to-Noise Ratio (HNR) to the voiced and unvoiced components to indicate the degree of voicing in the excitation and to reduce the influence of buzziness caused by the vocoder. Results based on objective and perceptual tests demonstrate that the voice built with the proposed framework gives state-of-the-art speech synthesis performance while outperforming the previous baseline.  
continuous F0; speech synthesis; Kalman filter; time-warping; HNR +
Footnote †: journal: Applied  
1
Footnote 1: _Appl. Sci._**2019**, 9, 2460; doi:10.3390/appr9122460
## 3 Results
### Regional Scale Simulation
#### 3.2.2 Analysis of the Impact of Topographic Factors on NPP  
1. Altitude The average value of NPP in the study area basically fluctuated up and down with altitude, ranging from 512 to 554 g C m\({}^{-2}\) a\({}^{-1}\), with a small variation range, as shown in Figure 10a.
2. Slope The average value of NPP in the study area showed a decreasing trend with the increase of altitude. When the slope was within the range of 0-5\({}^{\circ}\), the average value of NPP was the largest, which was 560.24 g C m\({}^{-2}\) a\({}^{-1}\). When the slope was greater than 30\({}^{\circ}\), the average value of NPP was the lowest, which was 513.77 g C m\({}^{-2}\) a\({}^{-1}\), as shown in Figure 10b.
3. Aspect The average NPP values of the northeast slope, the east slope and the north slope were higher, reaching 565-575 g C m\({}^{-2}\) a\({}^{-1}\), as shown in Figure 10c. The average NPP values of  
Figure 10: Impact of topographic factors on NPP. (**a**) Altitude; (**b**) slope; (**c**) aspect.  
Figure 9: NPP distribution of the study area in 2020.  
the south slope and the southwest slope were the lowest, reaching 508 g C m\({}^{-2}\) a\({}^{-1}\). NPP was generally affected by slope aspect as follows: shady slope > sunny slope.