esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Description
### Formula of Turning Angle, Transportation Distance, and Transportation Lead Time  
The turning angle is not limited for driving on \(\mathrm{Rd_{g}}\); however, the turning angle is limited for driving on \(\mathrm{Rd_{h}}\). Here, the 2D multistate flow angle network proposed by [PERSON] [19] is applied, and thus, the turning angle can be referred to the cosine trigonometric function as Equation (1), and the Euclidean distance formula is shown in Equation (2). Moreover, the law of the turning angle is proposed as shown in Lemma 1 and Property 1.  
\[\cos\theta=\left(a^{2}+b^{2}-c^{2}\right)/2 ab \tag{1}\]  
where \(\theta\) is the angle between both sides of \(a\) and \(b\) and the diagonal sides of \(c\).  
\[d(x_{(i,j)},y_{(|,k)})=\sqrt{\left(x_{i}-y_{t}\right)^{2}+\left(x_{j}-y_{k} \right)^{2}} \tag{2}\]  
where \(x_{(i,j)}\) and \(y_{(|,k)}\) is the coordinate of two points on the \((x,y)\) plane.  
**Lemma 1**.: _Let the turning angle be an exterior angle. The cosine value of the turning angle equals the reverse side of the cosine value of the interior angle where the interior angle plus the exterior angle equals 180 deg, i.e., cos \(\theta=-cos\left(180^{\circ}-\theta\right)\) for the turning angle equals \(\theta\)._  
**Property 1**.: _For the turning angle \(\theta\in\left(0,\,\pi\right)\), cos \(\theta\) shows a decrease when \(\theta\) increases._  
The speed is slow when the truck is driving on a slow road (\(\mathrm{Rd_{g}}\)); thus, it is assumed that the transportation lead time requires 1.5 h for one unit of transport distance. In contrast, the speed is fast when the truck is driving on the highway (\(\mathrm{Rd_{h}}\)); thus, it is assumed that the transportation lead time is only 0.5 h for one unit of transport distance. Therefore,the transportation lead time of a single trip for two kinds of roads can be expressed by Equations (3) and (4), respectively.  
\[\text{T}_{\text{g}} =1.5\ (\text{L}) \tag{3}\] \[\text{T}_{\text{h}} =0.5\ (\text{L}) \tag{4}\]  
where \(\text{T}_{\text{g}}\) represents the transportation lead time while driving on \(\text{Rd}_{\text{g}}\) (hours), \(\text{T}_{\text{h}}\) is the transportation lead time while driving on \(\text{Rd}_{\text{h}}\) (hours), and \(\text{L}\) is the transport distance.
## 3 Results
### Influence of Drilling Parameters on Bit Force
#### 3.2.3 Relationship between Torque and Thrust Force  
As drilling speed increases, the thrust force gradually rises, and torque correspondingly increases. Conversely, with an increase in rotational speed, both thrust force and torque gradually decrease. The relationship between torque and thrust force during the drilling process is illustrated in Figure 9. From the graph, it becomes evident that whether at the same drilling speed with different rotational speeds or at the same rotational speed with different drilling speeds, torque exhibits a linear increase with thrust force. Furthermore, the slope of the torque-pressure relationship curve remains almost constant under the same rotational speed with varying drilling speeds. Thus, it can be observed that for the same drill bit at the same rotational speed, both thrust force and torque exhibit a linear relationship.  
From both Equations (11) and (12), we can obtain the relationship expression between torque and thrust force as:  
\[\left\{\begin{array}{l}T=\frac{\zeta_{2}}{\delta_{1}}(F-F_{0})+T_{0}\\ T=\frac{\eta_{2}}{\eta_{1}}(F-F^{\prime}_{0})+T^{\prime}_{0}\end{array}\right. \tag{13}\]  
From Equation (13), it is apparent that the relationship expressions between thrust force, torque, and drilling speed, as well as the relationship expressions between thrust force, torque, and rotational speed, yield identical forms for the relationship between thrust force and torque. Therefore, the relationship expression between torque and thrust force can be simplified as  
\[T=\kappa F+T_{0} \tag{14}\]  
where \(F\) is thrust force, \(T\) is torque, \(\alpha\) a is the slope of the relationship curve between torque and thrust force, and \(\beta\) is the torque constant.
## 3 Numerical Calculation and Simulation Analysis
### Single-Model Simulation Analysis  
3.1.1. Subsidy-Free Model Simulation Analysis  
Due to the proportionality between the cost-sharing contract coefficient \(\delta\) and the supplier's investment ratio \(r\), \(r\) is used as the simulation variable instead of \(\delta\), which can be numerically simpler and more intuitive. Hence, the results of each decision variable can be calculated when \(r\) varies with a step size of 0.1 within the range of 0 to 1, reflecting the impact of \(\delta\) on \(w\), \(p\), and \(\theta\) and the utilities of the supply chain as in Figure 2.  
Figure 2: The impact of the cost-sharing contract coefficient on supply chain pricing and utility. **(a)**The wholesale price, market price, and ecological level. **(b)** The profit of the supplier, distributor, and supply chain.  
The simulation results are consistent with Proposition 1, which shows that: (1) the fundamental motivation lies in both the supplier and distributor seeking the maximum benefits, thus they will generate risk aversion and fairness preference in consideration of sharing the ecological protection cost, and choose to invest less at the expense of the ecological level of the supply chain, making the wholesale price and market price drop accordingly; (2) there has been a small decrease in the supplier's utility while the distributor's utility is constantly on the rise with the increasing cost-sharing contract coefficient; (3) the impact of an increased sales volume is greater than that of lower prices, presented with a rising trend of the utility of the supply chain. Significantly, all the simulated curves change dramatically when \(r\) varies between 0 and 0.3; that is, with the distributor paying for most of the ecological protection cost. Consequently, the eco-products' distributor dominates the supply chain in the subsidy-free situation.
## 4 Conclusions
(i) B\({}_{\text{scat}}\) as a function of [NOx]/[\(\Delta\)HC] for the H2 SO4 seeded and non-seeded experiments at (a) 80%, (b) 65%, and (c) 20% RH. (ii) MSC as a function of [NOx]/[\(\Delta\)HC] for the H2 SO4 seeded and non-seeded experiments at (d) 80%, (e) 65%, and (f) 20% RH. Solid lines in (a), (b), and (c) are eye guides showing trends in B\({}_{\text{scat}}\) for NP and NR SOA in the non-seeded case, while the dotted lines show trends for NP and NR SOA in the H2 SO4-seeded SOA experiments. In (d-f) the solid lines are eye guides for the NP and NR SOA cases in the H2 SO4 seeded experiments while the dotted lines are eye guides for NP and NR SOA in the non-seeded experiments. Figure S12: (a,b): Effects of water vapor and acidity on the MSC of toluene SOA. (a) MSC of NP SOA for the H2 SO4 seeded and non-seeded experiments at three RHs; (b) MSC of NR SOA for the H2 SO4 seeded and non-seeded experiments at the three RHs. Figure S13: An example time series of experimental results. **Author Contributions:** Conceptualization, R.K.P. ; methodology, R.K.P., K.M., X.P. and H.R.M. ; software, K.M. and R.K.P. ; validation, K.M., H.R.M. and R.K.P. ; formal analysis, K.M., H.R.M. and R.K.P. ; investigation, K.M., X.P. and R.K.P. ; resources, R.K.P. and K.M. ; data curation, K.M., H.R.M. and R.K.P. ; writing--original draft preparation, K.M. and R.K.P. ; writing--review and editing, K.M., H.R.M., X.P. and R.K.P. ; visualization, K.M. and R.K.P. ; supervision, R.K.P. ; project administration, R.K.P. and K.M. ; funding acquisition, R.K.P. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by Vetenskapsradet (Swedish Research Council, VR) under grant 2015-04123 and The RKP was funded by Vetenskapsradet, Sweden. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data reported in this study are available within the manuscript and in Supplementary Materials. We will also publish at least one follow-up companion article with \(m/z\) values in this journal, along with that we will put all associated raw data to our university's open access server. **Acknowledgments:** We acknowledge support from [PERSON] for providing the CIMS machine and [PERSON] for operating the CIMS machine. **Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Results
### Runoff Simulation  
The Ashida River basin experienced its most intense precipitation in July and October 2017, with the peak hourly flow rate reaching 800.5 m\({}^{3}\)/s in that year at the Shanshou Station. The selected study timeframe spans from 0:00 a.m. on 1 January to 23:00 p.m. on 31 December 2017. The calibration period encompasses from 0:00 a.m. on 1 January to 23:00 p.m. on 31 July, while the validation period spans from 0:00 a.m. on 1 August to 23:00 p.m. on 31 December. The hourly runoff simulations conducted throughout the study period are illustrated in Figure 6. Notably, these simulations effectively capture the  
Figure 4: Pearson correlation coefficients between the 14 parameters.  
variations in runoff characteristics during both flood and non-flood periods at the two designated stations. Evaluating the simulation results using corresponding performance indices R\({}^{2}\) and NSE, as presented in Table 3, both indices surpass 0.55 overall. Furthermore, during the calibration period, both R\({}^{2}\) and NSE surpass 0.6, showcasing the model's favorable performance during this specific interval.  
The figure reveals that the hourly simulations exhibit greater accuracy for moderate peak flows and baseflow (ranging from 0 to 200 m\({}^{3}\)/s), aligning closely with observed runoff values. Notable instances include 18 April (74.3 m\({}^{3}\)/s simulated vs. 64.0 m\({}^{3}\)/s observed), 30 June (143.9 m\({}^{3}\)/s simulated vs. 120.0 m\({}^{3}\)/s observed), and 6 October (107.4 m\({}^{3}\)/s simulated vs. 71.6 m\({}^{3}\)/s observed). While the metrics suggest a higher level of satisfaction with the SWAT model's hourly scale simulations, it is evident that a significant portion of high peak flow (>200 m\({}^{3}\)/s) simulation results still underestimate the observed runoff peaks. For example, 5 July (343.8 m\({}^{3}\)/s simulated vs. 238.0 m\({}^{3}\)/s observed) and 23 October (427.7 m\({}^{3}\)/s simulated vs. 186.0 m\({}^{3}\)/s observed) stand as instances where underestimation is pronounced. This phenomenon can be attributed to two factors. Firstly, the two employed meteorological stations may not have effectively and comprehensively captured the extremities of high precipitation events. Secondly, the occurrence of extreme heavy rainfall events can trigger both natural and human-induced anomalies in river runoff (e.g., minor weir breaches, reservoir discharges, etc.). Given the fine temporal resolution of hourly simulations, their capacity to accommodate such aberrations is comparatively limited in comparison to daily scale simulations. Consequently, the hourly scale simulations demonstrate relatively less proficiency in accurately reproducing peak flows during instances of exceptionally high discharge.  
Figure 5: Distribution of parameters in one-at-a-time sensitivity analyses (where all other parameter values are held constant and only one parameter value is changed in simulation).
# The Application of Intraoral Scanners in Orthodontic Care for Adolescents with Disabilities
###### Abstract
In the future, digital optical impressions may eliminate the need for traditional impressions. The scanner enables the utilization of treatment techniques through computer software, whether for creating a clear aligner or for producing prosthetic restorations in a laboratory. This has streamlined access to digital technologies and enhanced the planning and monitoring of treatment progress, thereby augmenting its overall effectiveness [8, 9, 10]. The scanning technique employed in this system has been designated the \"Parallel Confocal/Telecentric\" method, rooted in the principles of confocal microscopy. This method leverages parallelism and telecentric to achieve precise and detailed imaging, enhancing the accuracy and reliability of the scanned data. Scanners provide several notable advantages, including minimized patient discomfort, enhanced time efficiency, streamlined clinical procedures, and simplified data storage. Their capacity for swift and uncomplicated electronic data transfer facilitates seamless communication, ensuring quick and easy access to information. This not only improves overall workflow but also contributes to the convenience and efficiency of orthodontic practices [11]. In orthodontics, as in all branches of dentistry, the brief duration of clinical procedures is pivotal for ensuring a successful and comfortable experience for both the dentist and the patient. Efficient and time-effective procedures not only contribute to patient satisfaction but also optimize the overall workflow in orthodontic practices. This emphasis on a swift yet thorough approach underscores the commitment to delivering effective and patient-friendly orthodontic care [12]. It is to be expected that computer-assisted treatment methods will determine the way dental surgeries and orthodontic and prosthetic laboratories operate in the future. The objective of the study was to assess the time required for intraoral scanning using a scanner in the orthodontic treatment of patients with disabilities undergoing clear aligner therapy in comparison to the time needed for healthy subjects and to determine whether it is possible to perform such a procedure in disabled patients without the need for general anesthesia.
# On-Board Wind Scatterometry
## 3 Wind Scatterometry Processing Chain and On-Board Processing Modifications
### Modifications for On-Board Products  
Traditional scatterometer products are labeled by different levels. Level 0 (L0) data are segmented measurements that are produced at ground stations with data downlinked from satellites. When the effects of antenna pattern and observing geometry are eliminated, NRCS can be derived. With auxiliary information, including observing geometry and polarization, Level 1 (L1) products can be assembled. Wind fields are Level 2 (L2) products. L2 winds are retrieved from in L1 NRCS that are grouped within WVCs [34]. Higher levels of data are also developed for further steps of research and applications [38]. Different processors are applied to generate different levels of products [39].  
To meet RT applications, L2 and lower level products are directly provided from an on-board processing routine. Data components and processor functions are the same as traditional procedures. However, to achieve RT ability, modifications are made. Since an on-board processing routine is used for RT applications and is complementary to existing wind scatterometry, generally, only an L2 on-board product is required for downlinking. L0 and L1 products, which are less application-oriented, can be obtained from the ground processing chain. For on-board L1 processing, due to the omission of a signal transferring procedure, packing auxiliary data into products is not necessary; only the labeling of them with corresponding observations is required. Obtained signals and auxiliary information are directly provided to the on-board L1 processor. Except for algorithm modifications, observations distributed in a WVC are identified and gathered as inputs to the L2 processor for wind retrieval. Wind inversion is immediately conducted when NRCS in a WVC are obtained. Though data structure and information are the same, the size of on-board L2 product is much smaller than that of the ground products. This is due to a much shorter period of observation for RT applications. The specific amount of data differs for different types of scatterometers [12; 14].
## 4 Discussion
### Fermentation, Acetate Production, and Utilization Genes  
The fermentative gene _ldhA_ (involved in lactate production from pyruvate), the acetate production from pyruvate, and acetate utilization genes _pflD_, _porB_, and _acs_ were found upregulated in PB12, as in the PB12 AGD\({}^{+}\) mutants, than in JM101 (Table 3, Figure 2), suggesting that both mutants produce fermentation products. PB12 has been reported to produce smaller amounts of lactate and acetate in the late log phase [11]. Our results led us to propose that the _atp_ mutant maintains this metabolic trait. Acetate formation from pyruvate involves the conversion of pyruvate to ACoA+ formate with PflB and the conversion of pyruvate to ACoA with AceE (pyruvate dehydrogenase). Then, ACoA is converted to acetyl-P by Pta, and the conversion of acetyl-P + ADP to acetate + ATP is performed with AckA (Figure 3). The second pathway involves the conversion of pyruvate to acetate by PoxB in a reaction coupled to the electron transport chain via ubiquinone [4]. The acetate metabolism by PoxB has been proposed to be less efficient than the pathway with AceE.  
Nevertheless, the PoxB pathway is relevant in synthesizing acetate under aerobic conditions [4; 27], and was identified as the main pathway for acetate production in the stationary phase [4; 28]. Previously, it was proposed for the PB12 strain that acetate produced by PoxB could be a possible autoinducer of the _acs_ operon and the glyoxylate shunt genes that were upregulated in both PTS\({}^{-}\) mutants analyzed [11], resulting in a strategy proposed previously to the strain PB12 to convert acetate to AcCoA with Acs and PoxB, a characteristic that maintains the PB12 AGD\({}^{+}\) mutant. Remarkably, the previous inactivation of _porB_ in PB12 did not affect growth under aerobic culture conditions, supporting the proposition that PoxB is this mutant's primary source of ACoA [11]. ACoA synthetase (AMP-forming) encoded with _acs_ is one of the two ways by which activates acetate to acetyl-CoA in a pathway working in an anabolic way, scavenging acetate in the medium.  
The expression of _porB_ and _acs_ is an RpoS (upregulated in both PTS\({}^{-}\) mutants, Table 3) dependent promoter [11; 29], and its activity is induced by growing on acetate [4; 30; 31; 32].
## 3 Improved Strategy for the Uncalibrated Visual Servo of a Hyper-Redundant Space Manipulator
### Online Estimation of Total Jacobian
Compare the calculated boundary layer \(\psi_{K+1}\) with the threshold \(\psi_{S}\), which is based on the specific conditions of the system, and switch the appropriate filter gain: If \(\psi_{K+1}\leq\psi_{S}\), the KF gain is switched to provide an optimal estimate: \[K_{K+1}=C^{+}\overline{E}_{K+1}\psi_{K+1}^{-1}\] (30) If \(\psi_{K+1}>\psi_{S}\), the SVSF gain is switched to provide a robust estimate: \[K_{K+1}=C^{+}diag\Big{[}E_{K+1}\circ Sal\Big{(}\frac{e_{Z,K+1|K}}{\psi}\Big{)} \Big{]}\times\left[diag\Big{(}e_{Z,K+1|K}\Big{)}\right]^{-1}\] (31) where \(\psi\in\mathbb{R}^{8\times 1}\) is the standard boundary layer in SVSF, and this can be adjusted according to the diagonal elements of the calculated smoothing boundary layer matrix \(\psi_{K+1}\). Then, we complete the subsequent total Jacobian estimation process: 6. Calculate the a posteriori state error covariance matrix: \[P_{K+1|K+1}=(I-K_{K+1}C)P_{K+1|K}(I-K_{K+1}C)^{T}+K_{K+1}R_{K+1}K_{K+1}^{T}\] (32)
7. Obtain the estimated state vector \(\hat{X}_{K+1|K+1}\): \[\hat{X}_{K+1|K+1}=\hat{X}_{K+1|K}+K_{K+1}e_{Z,K+1|K}\] (33)
8. Split the state vector \(\hat{X}_{K+1|K+1}\) into the estimated Jacobian \(J_{t}\) as: \[\left(J_{t}\right)_{K}=\left[ \begin{array}{ccc}\left(\hat{X}_{K+1|K+1} \right)_{1}&\cdots&\left(\hat{X}_{K+1|K+1}\right)_{9}\\ \vdots&\ddots&\vdots\\ \left(\hat{X}_{K+1|K+1}\right)_{64}&\cdots&\left(\hat{X}_{K+1|K+1}\right)_{72} \end{array} \right]_{8\times 9}.\] (34)  
**Remark 2**. : _When carrying out dynamic tracking tasks, the expanded total Jacobian matrix \(J_{e}=\left[J_{t},\partial h/\partial t\right]_{8\times 10}\) is estimated online, where \(\partial h/\partial t\) represents the Jacobian between the gradient of the task function and the velocity of the target and is important for the construction of a dynamic tracking controller._
# Evaluation of Novel Preformed Particle Gel System for Conformance Control in Mature Oil Reservoirs
## 2 Results and Discussion  
### Chemical Structure  
The grafting copolymerization of AM onto the CMC backbone is initiated by APS. Upon decomposition, APS generates sulfate radicals, which abstract hydrogen atoms from the hydroxyl groups of CMC, generating radicals along the CMC chains. These radicals serve as initiators for the grafting copolymerization by attacking the double bonds in AM. The resulting AM radicals initiate a chain reaction as they react with other AM molecules. Concurrently, AM molecules can also engage in reactions with MBA, a crosslinker containing two vinyl groups susceptible to radical attacks. This leads to the formation of crosslinked structures between the grafted chains, enhancing the structural integrity of the material. The grafting copolymerization reaction can be terminated through various mechanisms, including the recombination of two copolymer radicals, effectively concluding the chain growth process, as explained in Figure 1.  
The confirmation of successful CMC/PAMBA synthesis and its chemical structure was achieved by comparing its FTIR spectrum with that of CMC (Figure 2). In the FTIR spectrum of CMC, a broad band in the 3700-3000 cm\({}^{-1}\) region signified the stretching vibration of the OH group. The peak at 2914 cm\({}^{-1}\) was attributed to a C-H stretching vibration. Strong absorption bands at 1590 cm\({}^{-1}\) and 1414 cm\({}^{-1}\) corresponded to the asymmetric and symmetric stretching modes of the carboxylate anion, while the peak around 1324 cm\({}^{-1}\) resulted from an O-H bending vibration. The characteristic C-O vibrations from alcohol were observed at 1050 cm\({}^{-1}\), and 81-4 glycoside bonds between glucose units were evident at 898 cm\({}^{-1}\). In the FTIR spectrum of CMC/PAMBA, the absence of the C=C strong stretching peak between 995 and 900 cm\({}^{-1}\) indicated the successful conversion of AM and MBA. The grafting onto the CMC backbone was confirmed by new peaks related to both PAM and MBA. Bands at 3184 cm\({}^{-1}\) and 3329 cm\({}^{-1}\) were assigned to the symmetric and asymmetric stretching of the N-H bond. Vibration bands at 1646 cm\({}^{-1}\) and 1607 cm\({}^{-1}\) were attributed to C=O stretching and N-H bending, while peaks at 1420 cm\({}^{-1}\) and 1319 cm\({}^{-1}\) were related to C-H bending and C-N stretching, respectively. The shifting peak from 1050 cm\({}^{-1}\) to 1100 cm\({}^{-1}\) in CMC/PAMBA compared to CMC, typically associated with a strong C-O stretch, could be explained by the appearance of C-O-C groups resulting from the grafting reaction of hydroxyl functions with the \(\pi\)-bond of PAM and MBA. The proposed chemical structure of the prepared PPG, as depicted in Figure 3, aligns with findings from previous studies [52; 53].  
Figure 1: Proposed grafting copolymerization mechanism for the synthesis of CMC/PAMBA.  
Figure 3: Chemical structure of CMC and CMC/PAMBA.  
Figure 2: FTIR spectra of CMC and CMC/PAMBA.