esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 General Scheme of the Detection System
### Measurement Principle  
The measurement steps of the proposed method are shown in Figure 4. First, the weld shape is collected using the measurement system, and the camera is calibrated using the coplanar linear camera calibration method, as shown in step (A). Next, 18 feature points of the \"measurement unit\" are defined. A fusion line corresponds to five feature points to build the key point detection data set and train the key point detection network HRnet to realize the feature point localization of the \"measurement unit,\" as shown in steps (B), (C), and (D). Then, the coordinates of the key points are transformed to the weld coordinate system, and the five key points are fitted with the least squares method to obtain the  
Figure 4: Flow chart of weld cross-section shape detection.  
Figure 3: Illustration of weld image and geometric dimensions to be measured: (**a**) cross-sectional image of a welded specimen; (**b**) a “measurement unit” image; (**c**) geometric dimension diagram.  
geometric model of the fusion line, as shown in steps (E) and (F). Finally, the fitted curves are placed in the same coordinate system, and the coordinates of the key measurement points are derived using geometric relationships to obtain the dimensions of the shape parameters, as shown in step (G).
## 2 Results and Discussion
### Results
#### 2.1.8 Identification of Candidate Genes Involved in Ginsenoside Transport  
Interestingly, MeJA may enhance transporter synthesis to assist metabolite transport in a temporal and spatial manner in plant organs [45]. Moreover, as compared to ginsenoside biosynthesis, little is known about ginsenoside transport and regulation in cells. Therefore, we have also focused on detecting a number of genes related to the transport of secondary metabolites through plant PDR transporters belonging to the ABCG subfamily of ATP-binding cassette (ABC) transporters. Amongseveral full length ABC subfamilies, the best-characterized three subfamilies are PDR, multidrug resistance (MDR) and multidrug resistance-associated protein (MRP) involved in the transport of various molecules across membranes [46, 47]. In the present study, many ABC members such as _MDR_, _MRP_ and _PDR_ genes along with their expression were screened from our transcriptome dataset. Among the unigene hits, a total of 595 unigenes for ABC (from ABCA to ABCG), 229 for MDR, 76 for MRP and 125 for PDR were found (Tables S7-S10). Further evidence demonstrates that PDR transporters take part in exporting a wide range of substrates across membranes in different environmental conditions [46]. In our previous study, three _PgPDR_ genes (_PgPDR1_, _PgPDR2_ and _PgPDR3_) were isolated from _P. ginseng_, where _PgPDR3_ was induced by MeJA and its expression was highly related to ginseng accumulation. Our findings show that _PgPDR3_ participates in the export of ginensosides from the cells [19, 25]. Of the identified 125 _PDR_ unigenes in the current study, 70 were up-regulated by MeJA, such as the three _PgPDRs_ (mentioned above). Homology and similarity analysis shown in Figure 9 and Table S6 provided 12 promising _PDR_ candidates out of 125 _PDR_ unigenes. Of those, in particular three unigenes, \"CL1490.Contig1\", \"CL1490.Contig4\" and \"Unigene12975\" showed a much higher degree of similarity to the three _PgPDR_ genes, indicating that they were most likely to be involved in the export of ginensosides. Likewise, we found three contigs of \"CL6349\" and six of \"CL10813\" from our dataset closely related to _FvPDR3_ of _Fragaria vesca_ subsp. _Vesca_ and _VvPDR3_ of _V. vinifera_ respectively. With respect to these points of view, our MeJA-treated sequencing results provided a multitude potential genes associated with the transport of a wide range of substrates including ginensosides and other secondary metabolites.
###### Abstract  
The number of intense tropical cyclones is expected to increase in the future, causing severe damage to forest ecosystems. Remote sensing plays an important role in detecting changes in land cover caused by these tropical storms. Remote sensing techniques have been widely used in different phases of disaster risk management because they can deliver information rapidly to the concerned parties. Although remote sensing technology is already available, an examination of appropriate methods based on the type of disaster is still missing. Our goal is to compare the suitability of three different conventional classification methods for fast and easy change detection analysis using high-spatial-resolution and high-temporal-resolution remote sensing imagery to identify areas with windthrow and landslides caused by typhoons. In August 2016, four typhoons hit Hokkaido, the northern island of Japan, creating large areas of windthrow and landslides. We compared the normalized difference vegetation index (NDVI) filtering method, the spectral angle mapper (SAM) method, and the support vector machine (SVM) method to identify windthrow and landslides in two different study areas in southwestern Hokkaido. These methodologies were evaluated using PlanetScope data with a resolution of 3 m/px and validated with reference data based on Worldview2 data with a very high resolution of 0.46 m/px. The results showed that all three methods, when applied to high-spatial-resolution imagery, can deliver sufficient results for windthrow and landslide detection. In particular, the SAM method performed better at windthrow detection, and the NDVI filtering method performed better at landslide detection.  
Keywords:windthrow; landslide; remote sensing; change detection; NDVI filtering; SAM; SVM; planestcope +
Footnote †: journal: Remote Sensing, 2020, 12, 3242; doi:10.3390/rs12193242
# Formal Modeling and Improvement in the Random Path Routing Network Scheme Using Colored Petri Nets
## 4 Experimental Results
### Verification and Validation
#### 4.1.3 Live Transition Instances  
Our proposed model displays live transition instances as none, which indicates the optimal efficiency of the model.
# Frost Resistance of Coal Gangue Aggregate Concrete Modified by Steel Fiber and Slag Powder
## 3 Results and Discussion
### Effect of Steel Fiber on Frost Resistance
#### 3.1.2 Splitting Tensile Strength Loss  
The splitting tensile strength loss of CGAC with different types and content of steel fibers after F-T is presented in Figure 6. Similar to the compressive strength loss, the splitting tensile strength loss of steel-fiber-reinforced CGAC and the reference group R0 were approximately the same after 250 F-T cycles, ranging from 9% to 13%. After 500 F-T cycles, when the steel fiber content was lower  
Figure 5: (**a**) Compressive strength and (**b**) compressive strength loss of CGAC reinforced by different types and content of steel fiber after freezing/thawing (F-T) cycles.  
than 1 vol.%, the splitting tensile strength loss of CGAC decreased with the increase of steel fiber content; and it reached the lowest value of 16.4% when the steel fiber content was 1 vol.%. When the fiber content exceeded 1 vol.%, the splitting tensile strength loss of CGAC increased accordingly, and it reached 24.4% when the fiber content was 2 vol.%. This is because when the steel fiber content is lower than 1 vol.%, most of the fibers can bond well with the cement matrix. When the specimens were subjected to splitting tension, frost damage occurs inside the concrete, but incorporating an appropriate amount of steel fibers has a positive effect on dispersing the stress concentration at the trailing edge of cracks, withstanding tension, and slowing down the cracking development inside the CGAC specimens [42]. Similar to the compressive strength loss test, with more fibers added, it was difficult for them to be mixed evenly, and even agglomeration appeared, which caused a worse bonding performance between concrete and steel fibers and increased the splitting tensile strength loss of CGAC.  
For 1 vol.% steel-fiber-reinforced CGAC with different steel fiber types, after 500 F-T cycles, the splitting tensile strength loss of USF reinforced CGAC was 14.7%, followed by HESF reinforced CGAC with a splitting strength loss of 16.4%. However, the splitting tensile strength loss of CPSF-reinforced CGAC was larger than that of the reference group. The reason may be that the 1 vol.% CPSF became agglomerations during mixing process, which made the fibers difficult to disperse uniformly and so they could not play an effective role in tensile reinforcement. This was consistent with the experimental phenomenon of poor workability of CPSF-reinforced concrete. Besides, there was not enough cement gel filling in the gap between the agglomerated steel fibers, which resulted in larger pores inside the concrete and increased the damage caused by F-T.
## 4 Model
### Interaction Graph
#### 4.1.1 Schema Encoding  
Schema encoding is a method for identifying and classifying elements within a database schema. It involves encoding various components of the database, including tables, columns, and schema encoding relationships, according to specific rules. Schema encoding relationships refer to the connections between schema items, capturing the structural information inherent in the database schema. For example, \"PRIMARY-KEY\" denotes which column serves as the primary key, \"BELONGS-TO\" indicates which table a column is associated with, and \"FOREIGN-KEY\" signifies the relationship connecting a foreign key in one table to the primary key in another table.
## 3 Results and Discussion
### Comparative Analysis of COS and \(SO_{2}\) Antimicrobial Effects
Among the six types of bacteria, COS showed greater antibacterial effects on _Bacillus cereus, Paenibacillus, Pantoea_ sp., and _Lactococcus_ compared with SO\({}_{2}\), while SO\({}_{2}\) showed greater antibacterial effects on _Enterococcus_ and _Alkaliphilus_. This implied that antimicrobial effects of COS or SO\({}_{2}\) varied among different microorganisms, which might be due to physicochemical characteristics of each microorganism type, including hydrophilicity and negative charge distributions on the cell surface [32]. Overall, 500 mg/L COS showed a greater antifungal effect than 100 mg/L SO\({}_{2}\), but both of the additives had a comparable antibacterial effect.
# Evaluating Commercial Electrical Neuromodulation Devices with Low-Cost Neural Phantoms
## 2 Materials and Methods
### Measurement Systems  
A specialized 10-electrode electrophysiological recording system was designed to measure voltage and current. Figure 2 shows the circuit diagram used in the electrode probe measurements, including reference and ground, denoted by red-outlined yellow squares on the breadboard. A total of nine active electrode channels were supported simultaneously, one in each of the nine cubic volumes. Measurement electrodes were modified from multimeter probes, terminating in thin metal conical tips, 0.7 mm in diameter.  
As shown in Figure 2, the device used a Teensy 3.2 microcontroller (PJRC, Portland, OR, USA) to acquire the signals at a button press. With each button press, each electrode was sampled at 100 Hz. The average measurement of each electrode was exported to a computer connected via a USB cable. Each phantom was prepared in a 1280 mL (-43.3 fl oz) plastic container. Each electrode corresponded to a specific cubic volume in the container of the phantom material. As shown in Figure 3, the volumes corresponding to the pathway between stimulation electrodes were averaged together. The cubic volumes were denoted by a letter for position horizontally and number for vertical depth (e.g., A0, B2, C1, etc.). A total of nine cubic volumes were recorded in each experiment: A0, A1, A2, B0, B1, B2, C0, C1, and C2.  
If the measurement device could not be reliably used after three attempts, a redundant measure was used. A common multimeter was set to DC voltage measurement, and the probes were then inserted into locations and depths corresponding to each cubic measurement. A total of three measurements per cubic volume were conducted and then repeated with the probe positions switched. The schematic of the system is further detailed in Figure 4. The phantom material had electrode probes inserted into the sections detailed in Figure 3. The acquisition board, detailed in Figure 2, connected the probes with a power supply via USB cable and the data to an external device, such as a PC or laptop. The stimulation device was operated independently, with stimulation electrodes positioned after recording electrodes were inserted.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Current (A)** & **Voltage (V)** & **Power (W)** & **Time (min)** & **Time (s)** & **Dose (W-s)** \\ \hline am-tPCS & 0.004 & 5 & 0.02 & 15 & 900 & 18 \\ \hline tDCS & 0.002 & 9 & 0.018 & 20 & 1200 & 21.6 \\ \hline \end{tabular}
\end{table}
Table 2: Calculated dosages for tDCS and am-tPCS devices.  
Figure 2: Individual circuit design of a measurement electrode probe array circuit design. Output recorded on Teensy microcontroller.  
The average values were used similarly to the measurement system. Because the phantom materials were flexible and soft, constant shifting and movement meant that the electrodes may not have been in continuous contact with the material, potentially affecting the observed values.
# Enhancing Robustness of Per-Packet Load-Balancing for Fat-Tree
## 4 Simulation Results  
We performed simulations for an 8-ary fat-tree and a 16-ary fat-tree using ns-2. All links have bandwidth of 1 Gbps, queue size of 250 packets, and propagation delay of 50 us. At the transport layer, we used TCP-Reno and set the retransmission timer to 20 ms. For tests with network failures, we simulated link failures, node failures, and degraded links. For link failure tests, we disconnected a link connecting an aggregation switch and a ToR switch. For node failure tests, we made an aggregation switch down. A node failure causes multiple link failures simultaneously. We measured the flow completion time, which is important in datacenter networks.  
Short-lived flows in an 8-ary fat-tree  
First, we simulated all-to-all short-lived flows of size 32 KB with load factor 0.8 repeatedly for about 3.3 s. In total, 128 flows were generated using a random permutation. Figure 5(a) shows the 99.9 th percentile of the flow completion times. Neither a link failure nor a node failure affects the flow completion time of TCP flows significantly. Since the flows are short, the queues do not build up enough to cause packet reordering or packet drops. Hence, the original packet scatter scheme, which distributes packets using more paths, performs marginally better than LBSP.
## 5 Results
### TDRS Orbit Determination Results
#### 5.1.1 Scenario #1  
To carry out this investigation, a set of initial conditions was established as documented in Table 1. The objective was to evaluate the accuracy of the TT&C-only TDRS solution. The investigation results, summarized in Table 2, compare the best-fit orbit solution to the operational solution. The operational solutions were derived from the TT&C and BRTS measurements using identical GTDS settings.  
To assess the accuracy of the obtained TDRS solutions, several key parameters were taken into consideration. These parameters included the differences in the spacecraft's definitive position and velocity, estimated Coefficient of Solar Reflectivity (Cr), and estimated TT&C measurement bias. For all TDRS satellites, the maximum changes in definitive position and velocity were well within the TDRS requirements outlined in Section 4.5. Additionally, the changes in Cr values were negligible, and the estimated TT&C bias differences were mostly less than one meter.  
Furthermore, analysis of the correlation matrix of all solutions revealed a strong correlation between the TT&C bias and the other state parameters. This suggests that the a priori TT&C bias and its covariance have a significant impact on the best-fit solution. This is discussed in greater detail in Section 5.1.3. Therefore, it is essential to have accurate knowledge of the TT&C bias and its covariance values when considering TT&C-only solutions.  
For this scenario, we used an operational value of the TT&C bias and its covariance. As such, the TDRS TT&C-only solution demonstrated satisfactory agreement with the BSTS and TT&C-based TDRS operational solutions (see Table 2).
## 4 Discussion and Conclusions
Lastly, the theoretical relationship between the experienced heading precision of a GNSS/INS sensor and the positional precision of a resulting point cloud was discussed, for which the results of this study supported the theory and provided compelling arguments that a dual-antenna GNSS/INS sensor consistently produces a point cloud with improved 3D positional precision. In addition to the precision assessments performed in this study, discussions were presented that aim to provide useful information to UAS-lidar mapping practitioners with respect to sensor placement and alignment, the importance of sensor dynamics (especially for single-antenna GNSS/INS sensors), and mission planning considerations (e.g., flying height, flying speed, and the impacts of a lidar sensor's FoV). As reported in existing literature and confirmed by this study, the precision of the heading estimates of a GNSS/INS sensor directly impacts the 3D positional precision of a point cloud produced from a UAS-lidar mapping system, notably more so than any other trajectory estimate. Although the use of dual-antenna GNSS/INS sensors can increase the cost and complexity of a UAS-lidar mapping system, investing in a system with a dual-antenna GNSS/INS sensor is recommended, as it provides a simple solution to realizing higher-precision heading estimates and as a result, more accurate and precise point clouds. Conceptualization, R.G.B. ; methodology, R.G.B., B.E.W., and A.R.B. ; software, R.G.B. ; formal analysis, R.G.B. ; writing--original draft preparation, R.G.B. ; writing--review and editing, B.E.W. and A.R.B. All authors have read and agreed to the published version of the manuscript. This study was supported by the United States Department of Commerce, National Oceanic and Atmospheric Administration (NOAA), through the University of Southern Mississippi under the terms of Agreement No. NA18 NOS400198. **Acknowledgments:** The authors thank [PERSON] for his assistance in collecting field data used within this study. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement. **Conflicts of Interest:** The authors declare no conflict of interest.
# Mechanical Properties of Desert Sand-Based Fiber Reinforced Concrete (DS-FRC)
## 3 Results and Discussion
### Microstructure  
The optical microscope and scanning electron microscope (SEM) were used to understand the microscopic morphology of DS-FRC and the interface properties between fiber and matrix. Through 80 times optical microscope (see Figure 9a), it could be clearly illustrated that the micro-crack width in DS-FRC ranged approximately from 40\(\upmu\)m-60\(\upmu\)m due to the fiber bridging effect, which was anticipated to match with excellent crack controlling properties of high ductility cementitious composites and achieve good durability. Besides, the debonding between fiber and matrix (see Figure 9b-d) and the microstructure (see Figure 9e) of DS-FRC were clearly revealed by SEM images with 500-2000 magnification. According to Figure 9b,c, a larger area of single fiber detached from the matrix, and obvious debonding traces (see Figure 9d) were observed. The surface of fiber was covered by full hydration products, leading to relatively high interfacial adhesion and the fiber bridging effect. On the other hand, the hydration product covering the surface of fibers gave a friction which facilitated mechanical interlocking during the slippage of fiber. They all were beneficial to deformation performances of DS-FRC, especially for the tensile strain hardening property and the compression ductility. Besides, the hydroxyl group of PVA fiber was extremely easy to react with water to form hydrogen bonds, which formed good bonding between fiber and matrix and promoted the post-hydration reaction of DS-FRC. The full hydration reaction was essential to the strength development of composites. As is shown in Figure 9e, the compactness of the matrix was effectively improved due to the filling effect of small particle desert sand and rich hydration products, which filled the cementitious matrix, refined internal air voids and resulted in the strength development of uniaxial tensile and compression of DS-FRC. Moreover, the Si\({}_{4}\)+ dissolved from desert sand with fine particles might promote the pozzolanic reaction which was also conducive to the development of strength.  
Figure 8: The relation between \(A_{\text{np}}\) and \(\sigma_{\text{u}}\).  
However, it might be due to the relatively low content of silica in desert sand that the slightly reduced ultimate compression strengths were observed. It was worth noting that there were few unreacted spherical fly ash granules which might have a negative effect to the mechanical properties of DS-FRC, attributed to relatively high volume of the fly ash.
## 4 Case Study
### Web Application  
The WebAPI agent offers an API REST for third party applications which can obtain the data processed in the MAS and ask for demand predictions of the stations in the system. In addition, there is a web application where users can make visualization and prediction tasks. The web application consists of two sections; in Figure 16 we can see an initial view of the visualization section. In this section, it is possible to view the trips travelled in the system by year and month.  
Figure 16: Web application served by the WebAPI agent. Visualization of the historical data section.  
Figure 15: Sequence diagram of trip data loading by multiagent system.  
The visualization feature has various sections where data can be looked up by introducing a particular station, time (year, month and day), trip (arrivals or departures) and user. These sections act as filters and thus allow to visualize a series of specific data. In Figure 17, we can see the map with departures from the selected station, within the time period indicated in the left corner of the image. The number of trips at the selected station is represented by an arrow, its colour scale and width is proportional to the number of rides made between the station of origin and the destination.  
Trips between the station of origin and the rest of stations are represented with arrows, whose colour and width is proportional to the number of rides made between a particular station and the rest. These visualizations allow the BSS administrator to analyse the history of the system and find the stations with the greatest numbers of arrivals and departures. Figure 18 shows the demand prediction section which employs the selected models to predict demand at each station.  
Figure 17: Visualization section. Where a station is selected for a period of time and the number of trips (origin-destination) is visualized.  
Figure 18: Prediction tool section.  
It is possible to select both, an arrow from the past and an arrow for the future. An arrow from the past, apart of showing the data collected by the system, will also visualize the prediction made by the model and the real data that were collected for that station, within the indicated date. These data will be useful to BSS administrators at the time of relocating the bicycles at the stations.