esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Vacuum Freeze-Drying Process and Quality Evaluation of _Stropharia rugosanmulata_
## 3 Results and Discussion
### Effects of Other Factors on the Freeze-drying Quality of Stropharia rugosanmulata  
During the vacuum freeze-drying process, as vacuum pressure increases, the drying rate gradually decreases. The rehydration ratio first increases and then decreases with increasing vacuum pressure [26]. Freeze-drying technology has the disadvantages of expensive equipment and high energy consumption. Various preprocessing methods can effectively solve problems such as long drying times and unstable drying quality during the drying process, and slicing is a crucial step in preprocessing [9]. The shape of the material affects the shrinkage of the dried food, the quality of the product, and the energy requirements. Studies have found that different slicing thicknesses significantly affect the rehydration characteristics of freeze-dried products. If the freeze-drying time is too short, the crystalline water in the _Stropharia rugosanmulata_ samples will not completely sublimate. If the samples are removed from the vacuum environment, the dried parts will absorb moisture, causing the sample surface to collapse and significantly affecting the quality and taste of the freeze-dried product. If the freeze-drying time is too long, it will cause a waste of industrial resources. Freeze-drying is a very energy-intensive operation with a long processing cycle. Selecting an appropriate freeze-drying time can save energy and produce the best-quality _Stropharia rugosanmulata_ products.
## 6 Experiments
### Benchmarking on Scale Estimation in Registration  
We benchmarked ICOS for scale estimation alone, compared with the Adaptive Voting in TEASER/TEASER++ and the 1-point RANSAC in [44]. Following the setup in Section 6.2, we further downsampled the \"bunny\" to only 100 points because the solver Adaptive Voting would run in tens of minutes with 1000 correspondences. In each run, the scale was randomly generated within \((1,5)\) m. Figure 7 shows the scale errors and the runtime w.r.t. an increasing outlier ratio. We can see that both Adaptive Voting and 1-point RANSAC broke at 90% outliers, while ICOS could stably tolerate 90% outliers and was faster than the other two competitors. In fact, ICOS could be robust against up to 99% outliers, as shown in Section 6.2.  
Figure 5: Benchmarking of point cloud registration over the “Armadillo” point cloud [52]. Top left: Examples of a known-scale and an unknown-scale point cloud registration problem with 98% outliers. (**a**) Known-scale registration results w.r.t. an increasing outlier ratio (0–99%). (**b**) Unknown-scale registration results w.r.t. an increasing outlier ratio (0–99%).
# A Power Based Analysis for a Transonic Transport Aircraft Configuration through 3D RANS Simulations
## 2 Methodology
### Definition of Power Imbalance Error  
Power-based analysis is based on the conservation of mechanical energy. This study elaborates on the power imbalance error, \(e\), as given in Equation (12). \(e\) is the difference between the power input and the power output of a domain. This provides a figure to describe how well the mechanical conservation is satisfied in this domain. In CFD simulations, the error of mechanical power imbalance can be attributed to numerical errors, which can be further broken down into modeling error, discretization error, and convergence error. In principle, numerical errors lead to the imperfection of momentum conservation, causing an imbalance in mechanical power.  
\[e=\mathrm{P_{in}}-\mathrm{P_{out}} \tag{12}\]  
For the entire computational domain, the power imbalance error is denoted by \(e_{domain}\). The value is obtained by subtracting the \(\mathrm{P_{out}}\) from \(\mathrm{P_{in}}\) of the entire domain. The input and output terms are listed in Table 1. Once these power terms are assessed, the value of \(e_{domain}\) can be evaluated according to Equation (13). It is noted that the shaft power, \(\mathrm{P_{s}}\), and thrust power, \(\mathrm{TV_{\infty}}\), are zero for the wing body configuration. For convenience purposes, the relative error is used in the following discussions. Errors are normalized with a reference power, \(\mathrm{P_{ref}}\). This study uses the total power input of the entire domain as \(\mathrm{P_{ref}}\): \(\mathrm{DV_{\infty}}\) for the wing body configuration and \(\mathrm{P_{s}}\) for BLI configuration.  
Figure 8: Multiblock structured mesh (**top left:** block topology; **top right:** general view of mesh; **middle:** global view of mesh; **bottom:** body and wing).  
\[e_{domian}=\frac{\rm P_{s}+DV_{\infty}+\rm\dot{E}_{w,in}-\dot{E}_{w,out}-TV_{ \infty}-\varnothing}{P_{ref}} \tag{13}\]  
\[e_{sub}=\left(\frac{\rm P_{s}+DV_{\infty}+\dot{E}_{w,in}-\dot{E}_{w,out}-TV_{ \infty}-\varnothing}{P_{ref}}\right)_{subdomain} \tag{14}\]  
\[e_{domain}=\sum e_{sub} \tag{15}\]  
The entire computational domain contains 14 segregated subdomains. Mechanical energy conservation is satisfied for these subdomains. Therefore, it is feasible to evaluate the error of subdomains (denoted by \(e_{sub}\)) once all the input and output terms of the individual subdomains are obtained, as given in Equation (14). In principle, the power imbalance error of the entire computational domain, \(e_{domain}\), equals to the summation of the error of all of the subdomains, \(e_{sub}\), as expressed by Equation (15).
###### Abstract  
Although malpositioning of the cochlear implant (CI) electrode array is rare in patients with normal anatomy, when occurring it may result in reduced hearing outcome. In addition to intraoperative electrophysiologic tests, imaging is an important modality to assess correct electrode array placement. The purpose of this report was to assess the incidence and describe cases in which intraoperative plain radiographs detected a malpositioned array. Intraoperative anti-Stenver's view plain X-rays are conducted routinely in all CI surgeries in our tertiary center before awakening the patient and breaking the sterile field. Data of patients undergoing 399 CI surgeries were retrospectively analyzed. A total of 355 had normal inner ear and temporal bone anatomy. Patients with intra or extracorporeal malpositioned electrode arrays demonstrated in the intraoperative X-ray were described. There were four cases of electrode array malposition out of 355 implantations with normal anatomy (1.1%): two tip fold-overs, one extracochlear placement and one partial insertion. All electrodes were reinserted immediately; repeated radiographs were normal and the patients achieved good hearing function. Intraoperative plain anti-Stenver's view X-rays are valuable to confirm electrode array location, allowing correction before the conclusion of surgery. These radiographs are cheaper, faster, and emit much less radiation than other imaging options, making them a viable cost-effective tool in patients with normal anatomy.  
intraoperative plain X-ray; anti-Stenver's view; cochlear implantation; electrode array malposition; tip fold-over +
Footnote †: journal: Medicalanti-Stenver's (cochlear) view radiograph offers a quick, cheap method to diagnose and reinsert a displaced electrode array before the termination of anesthesia. Other methods to evaluate the array position are intra and postoperative computed tomography (CT), cone beam CT or fluoroscopy. In comparison to plain radiographs, those methods are more expensive and time consuming [6]. Moreover, they carry higher exposure to radiation for the patient and personnel in the operating room. Nevertheless, in patients with anatomic malformations, fluoroscopy has an important role [7; 8; 9].  
The purpose of this report was to assess the incidence and describe cases with normal anatomy in which electrode misplacement was diagnosed by intraoperative plain radiographs and corrected before the termination of anesthesia, demonstrating the efficacy of this tool.
# Estimation of Live Load Distribution Factor for a PSC I Grider Bridge in an Ambient Vibration Test
## 3 Estimation of Live Load Distribution Factor Using the Vehicle Loading Test  
### Experimental Setup  
In this study, the vehicle loading test was conducted at Geumdang Bridge as shown in Figure 3. The bridge is in Yeoju, Gyeonggi-do, South Korea, and the test sought to verify the method of estimating the LLDF of a bridge using traveling vehicles. The bridge is specifically located on the Jungbunaeryuk Expressway and consists of 7-span PSC I girder and box girder bridges with a width of 12.6 m, a skew of 15 degrees, and a total length of 273 m. The vehicle loading test was conducted in the single-span 30 m section with two lanes composed of four PSC I girders as shown in Figure 3. The concrete of the PSC I girder had a strength of 70 MPa, while the slab had a strength of 35 MPa.  
In the vehicle loading test, the displacement response was measured using linear variable differential transformers (LVDTs) that were installed on four girders (G1 to G4) at the center of the L/2 point as shown in Figure 4. Since the first natural frequency of Geumdang Bridge was 3.50 Hz, the data acquisition speed of the LVDTs was set at 100 Hz to collect more than 20 units of data in one period.  
Figure 5 shows the specifications of the vehicles used in the vehicle loading test. Two 15-ton dump trucks loaded with stones were used. Table 1 shows the weight of the vehicles. Vehicle 1 weighed 27.33 tons, while vehicle 2 weighed 27.91 tons.  
Figure 3: PSC I girder bridge.
## 4 Discussion  
### Only Post-Lava Emplacement Processes Account for the Subsidence in the EFA?  
The vertical displacement (2009-2014) affecting the lava fields that emplaced between 1998 and 2007 at Piton de la Fournaise has been derived and analyzed in this study. Results show that the cumulative subsidence in lava fields outside the EFA increase with the increase of lava thickness, presenting a positive linear correlation (Figure 7). Similar correlations have been previously observed from lava fields at other volcanoes, such as the Etna, Okmok, Santiago, and Paricutin volcanoes (2; 16; 18; 19). For every meter of lava, the magnitude of subsidence increases as the lava age decrease. For certain lava fields, the subsidence rate decays in time (Figures 5 and 6a). These observed subsidence behaviors are expected to be the results of post-lava emplacement processes (i.e., the ongoing thermal contraction and compaction induced by the cooling and solidification of lava fields and/or the substrate (2)). However, the subsidence behaviors in lava fields inside the EFA differ significantly from those outside the EFA and in previous studies (Figures 6b, 7 and 8). The correlations between subsidence and thickness vary from one to another. Subsidence behaviors in the June and Oct 2000 lava fields even display negative correlations with thicknesses. The subsidence per meter of thickness does not correlate with the lava age (Figure 8). This irregularity is further illustrated in Figure 9, in which an exponential curve is fitted using values observed in lava fields outside the EFA to roughly explain the expected correlation between the subsidence (per meter of thickness) induced by post-lava emplacement processes and the lava age. The markers corresponding to lava fields inside the EFA deviate broadly (in particular the June 2000, Oct 2000, June 2001, Nov 2002 lava  
Figure 8: Vertical displacement with their standard deviations and histogram of population distribution in function with lava thickness, for five lava fields located in the Eastern Flank Area (EFA). (**a**) June 2000 lava field, (**b**) Oct 2000 lava field, (**c**) June 2001 lava field, (**d**) Nov 2002 lava field, and (**e**) Aug 2004 lava field.  
fields) in both directions from the simulated curve, which evidences that the observed subsidence cannot be entirely explained by post-lava emplacement processes. Other processes must have played a significant role in provoking the resultant subsidence behaviors. For instance, older lava fields (the June 2001 and Nov 2002 lava fields) exhibit larger subsidence (per meter of thickness) than younger ones (the Aug 2004 and Apr 2007 lava fields); signs of subsidence-thickness correlations for the June and Oct 2000 lava fields are even inversed (Figures 5, 8 and 9).
## 3 Results and Discussion
### Characterization of Monomers
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & **\(\lambda_{\text{max}}\) (nm)** & & & **Eg.opt (eV)** & \\ \cline{2-7}  & **CH\({}_{2}\)Cl\({}_{2}\)** & **CH\({}_{3}\)CN** & **Calculated** & **CH\({}_{2}\)Cl\({}_{2}\)** & **CH\({}_{3}\)CN** & **Calculated** \\ \hline
2,2’,5’,2’-terthiophene (2) & 355 & 358 & 364 & 3.10 & 3.10 & 3.41 \\
5-Br-terthiophene (3) & 359 & 359 & 374 & 3.04 & 3.04 & 3.32 \\
5-ethylynyl-terthiophene (**5-ET**) & 383 & 379 & 388 & 2.88 & 2.88 & 3.20 \\
3’-ethyl-terthiophene (**3’-ET**) & 352 & 348 & 375 & 2.90 & 2.95 & 3.31 \\ \hline \end{tabular}
\end{table} 
Table 2: Experimental \(\lambda_{\text{max}}\) and Eg values compared to calculated values. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & **PBE96** & **OLYP** & **B3 LYP5** & **B3 PW91** & **BHHLYP** & **O3 LY5** & **PBE0** \\ \hline \multirow{4}{*}{3-2 IG} & Time (min) & 85.6 & 123.8 & 94.9 & 100.2 & 91.1 & 99.1 & 82.9 \\  & \% AE (dist) & 2.65 & 2.49 & 2.31 & 2.02 & 1.91 & 1.76 & 1.85 \\  & \% AE (ang) & 3.06 & 21.24 & 3.47 & 3.63 & 4.69 & 3.46 & 1.68 \\  & \% AE (all) & 2.70 & 4.83 & 2.45 & 2.22 & 2.26 & 1.97 & 1.83 \\ \hline \multirow{4}{*}{6-3 IG} & Time (min) & 150.2 & 167.1 & 113.3 & 166.1 & 137.4 & 113.9 & 148.3 \\  & \% AE (dist) & 2.63 & 2.49 & 2.33 & 2.01 & 1.70 & 1.77 & 1.87 \\  & \% AE (ang) & 1.74 & 15.22 & 4.63 & 5.95 & 5.84 & 4.75 & 3.85 \\  & \% AE (all) & 2.52 & 4.08 & 2.61 & 2.50 & 2.22 & 2.14 & 2.12 \\ \hline \multirow{4}{*}{MC} & Time (min) & 269.4 & 392 & 360.9 & 348.2 & 255.9 & 311.9 & 320.6 \\  & \% AE (dist) & 2.54 & 2.37 & 2.23 & 1.89 & 1.70 & 1.63 & 2.13 \\  & \% AE (ang) & 4.78 & 20.95 & 8.76 & 10.09 & 9.15 & 8.56 & 6.57 \\  & \% AE (all) & 2.82 & 4.69 & 3.05 & 2.92 & 2.63 & 2.49 & 2.69 \\ \hline \multirow{4}{*}{def2-SVP} & Time (min) & 445.9 & 476.2 & 382 & 435.3 & 523.9 & 455.4 & 470.6 \\  & \% AE (dist) & 1.28 & 1.14 & 1.01 & 0.72 & 0.34 & 0.70 & 0.63 \\ \cline{1-1}  & \% AE (ang) & 5.53 & 12.93 & 5.19 & 6.67 & 7.69 & 5.41 & 4.77 \\ \cline{1-1}  & \% AE (all) & 1.81 & 2.62 & 1.54 & 1.47 & 1.26 & 1.29 & 1.15 \\ \hline \end{tabular} \% AE: percent average error.  
\end{table} 
Table 3: Computing time and percent average error (AE%) at different functionals and basis sets. On these bases, the singlet excited states were calculated (Table 2) evidencing that all \(\lambda_{\text{max}}\) values are in a narrow range (360-390 nm), being ascribable to \(\pi\)-\(\pi^{\text{+}}\) transitions on the common terthiophene skeleton of the investigated species. Experimental and theoretical \(\lambda_{\text{max}}\) values are in good agreement, as well as Eg,opt values, showing a 15% maximum difference between the calculated and experimental data. The red-shift in \(\lambda_{\text{max}}\) and the corresponding lowering in Eg,opt argue a more extended conjugation in the terthiophene framework, due to the presence of an unsaturated fragment on the C-5 atom instead of on the C-3\({}^{\prime}\) position.
# RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management System
###### Abstract
Although these popular distributed storage systems can provide large scalable capacity and high I/O performance, there are some limitations for these storage systems to manage large-scale remote sensing images because they just regard remote sensing images as big unstructured binary files, ignoring their specific data structures and spatiotemporal features. HDFS, for example, was designed and optimized for large file (pigabytes to terabytes in size) batch processing. Internally, files are split into one or more data blocks of a pre-determined size (default 128 MB). However, lots of remote sensing image band files are less than 200 MB, e.g., Landsat 8 image band files are about 100 MB. Indeed, HDFS put more emphasis on high throughput rather than low latency, which is not suitable for real-time interactive data analysis. Moreover, HDFS expects that data process programs should run on the node where the data were located, which increases the burden of data nodes and limits the program development and deployment. HDFS follows a master-slave architecture, where all the metadata (data blocks locations, data nodes information, etc.) are stored in the master node (NameNode). The architecture has SPOFs (single point of failure) and limits the expansion of storage space. Besides the system architecture and internal storage structure, most of above systems provide different distinct interfaces, which requires rewriting the related programs if remote sensing data are migrated into these storage systems. Considering the specific features of remote sensing data and limitations of current storage systems, we proposed the remote sensing images management system (RSIMS), a new distributed storage system for large-scale remote sensing data storage, access and retrieval. In addition to excellent data compatibility and capacity scalability, RSIMS provides efficient spatiotemporal retrieval interfaces based on the distributed spatial index, which is built with global multi-level Hilbert grids. Test results with 14,187,101 remote sensing images show that RSIMS can retrieve millions of remote sensing images intersected with a very large geospatial region within less than 10 seconds. Besides metadata management, RSIMSprovides two GDAL-compatible interfaces, which allows popular tools and software access the remote sensing image files in RSIMS directly. RSIMS is designed for remote sensing data owners to provide data access and retrieval service for authorized users. From the perspective of users, RSIMS is a storage software deployed on multiple remote servers. Large amounts of remote sensing data are uploaded into RSIMS by administrators and then accessed by common users remotely. The remainder of this paper is structured as follows. Section 2 describes RSIMS in detail, including the system architecture, the data structures and easy-to-use interfaces design. Section 3 evaluates the performance of RSIMS. Section 4 discusses the results of test. Section 5 makes a summary about the main work of this paper and the performance of RSIMS.
# Theory of Regions for Control Synthesis without Computing Reachability Graph
## 1 Introduction
Nevertheless, as the number of shared resources in uncontrolled systems has increased, too many subsystems should be disposed. Thereby, as the net system size becomes larger, the improvement of this divide-and-conquer method will be impeded. Our new control policy is based on the researches addressed on the use of the theory of regions. Up until now, the TR is recognized as one of the powerful method for deadlock prevention and supervisory control policies for obtaining maximally permissive PN controllers [8; 9] which bans the set of forbidden states MI; unlike a legal marking, any state that does not comply with a control constraint is a non-legal state in the graph and will be denoted a forbidden marking \(\mathrm{M}\in\mathrm{M}\). In fact, many research works sought to lower the computational burden of the theory of regions; in [17], the authors used the siphons-approach before using the TR to synthesize PN controllers. Besides, in [18], a new method is developed for deadlocks prevention to decrease the number of reachability conditions of TR in order to facilitate the supervisor calculation. Their experimental results seems to be the most effective approach in deadlock prevention policy compared with existing works of [15; 19; 20]. Unfortunately, the computation of RG and its analysis to determine the basic cycles and the legal/forbidden markings for this method are still heavy. However, the methods able to apply the theory of regions for control synthesis without computing the reachability graph have not been investigated. This paper sheds new light on TR simplification by eliminating the RG calculation and avoiding the combinatorial explosion of states which is a meaningful step for RG-based method. In addition, this work is only focused on bounded PNs. In our previous works [21; 22; 23; 24; 25], a new method of minimal cuts in RG is developed using the theory of regions. Through this approach, we have also minimized the computational cost of PN supervisor using the TR on specific areas of the graph and not on the whole RG. Nonetheless, the graph generation step and its analysis are inevitable. Accordingly, to reduce the computational burden of the TR, the RG generation step should be abandoned. In fact, based on PN properties and mathematical theories, one can speed up the synthesis process of PN controller. Consequently, the criterion of computation time of PN controllers will be a key factor in the comparison of this work with the previous ones [8; 23]. The rest of this paper is structured as follows. Section 2 depicts the background of the theory of regions and PN tools. Section 3 introduces the concept of our new methodology for control synthesis. Next, three case studies will be given in Section 4 in order to illustrate the contribution of the proposed approach. Section 5 presents the results of comparison with previous works. Finally, conclusions are drawn in the Section 6.
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
## 4 An INS Cyber-Physical Range
### Capabilities and Functionalities  
A cyber-physical range can be developed for the rapid test of hardware and software components both for INS concept and conventional bridges [27]. The performance of the components can be tested as per the requirements in maritime regulations (e.g., performance standards defined by the IMO) [34]. The range can be used for risk assessment, as well [31, 33]. It should provide a direct connectivity feature for Verification and Validation (V&V)-related equipment, such as RADAR, echo sounder, sensor of rpm of the engine, sensor of rudder angle, or actuator through sensor box [34]. The V&V can be conducted with different methods, such as requirement-based tests, fault injection tests, simulation-based, or vessel in the loop architecture [34]. The range should be possible to be used as a mobile test environment [8]. Therefore it should be possible to be used for early testing of developing maritime technologies [26]. Such a range may be also used for research and development activities, such as bridge design, traffic management, autonomous vessels, remote pilotage, and situation detection [28].
## 2 Materials and Methods
### Surface Characterization  
#### 2.3.1 Sulfonation Uniformity and Repeatability  
Sulfonation was performed on three separate days, with two specimens prepared each day to assess the repeatability of the process. These six specimens were treated as independent replicates for the study. Imaging was conducted using a Keyence VPIX digital microscope, VK-X laser microscope, and associated software (Version 1.2.0.2) (Keyence Corp., Osaka, Japan). Image optimization was performed using the software's brightness and contrast settings to analyze the images with ImageJ (Version 1.53e) to measure pore sizes. Images were converted to black and white to prevent coloring obscuring pore visualization. Pore size was quantified using the Feret diameter. The detection limit for pore size using ImageJ was approximately 15 \(\upmu\)m due to the limitation in pixel sizes; anything smaller would be excluded, as it would be considered image noise. The specimens were labeled according to their sulfonation batch as S1-A, S1-B, S2-A, S2-B, S3-A, and S3-B. Porosity distribution, size, and depth --critical factors in osseointegration--were evaluated using established measurement techniques previously validated in the literature [43; 49].