esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Experimental Validation of the Low-Cost Radiometric Calibration  
An additional procedure to validate the low-cost radiometric calibration of the thermal camera is by comparing the thermal conductivity derived from the calibrated thermal images against the thermal conductivity resulting from the borehole heat exchanger geothermal model, which can be considered as the reference value.  
The temperature monitoring approach proposed by comparing thermal data from (i) the set of sensors introduced in the matrix of the borehole physical model at a depth of 0.5 m, and (ii) from the thermal images (Figure 10) has allowed:  
* The characterization of thermal models by comparing internal and surface temperature data.
* The determination of the spatial distribution of radial temperature attenuation from a central heat source.
* The validation of the low-cost radiometric calibration based on thermal conductivity data.  
The 3.5-day monitoring test for both internal and surface temperature can be seen in Figure 11, in which the change of transient to stationary thermal regime has been established after 46.7 h of experiment. In addition, a greater difference temperature  
temperature from the surface. In this case, a representative surface area for the depth location of each of the 3 temperature sensors (S2, S3 and S4), within the analyzed sector (Figure 12), was evaluated.  
With the register of the thermal infrared camera, the thermal conductivity of the sand at the surface can be calculated and compared with that obtained for the section at 0.50 m using Equation (16):  
\[k=\frac{q\ln\left(\frac{r_{2}}{r_{1}}\right)}{2\pi\Delta T} \tag{16}\]  
The thermal conductivities obtained (using Equation (16) and data from previous sections) can be compared to get an idea of the consistency of the results obtained. Table 6 shows the different results obtained.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Section** & **Thermal Conductivity (W/m-K)** \\ \hline Surface & 0.52 * \\ Sensor depth (0.5 m) & 0.46 \\ \hline \hline \end{tabular}  
* Mean of conductivities obtained from the data in Table 2.  
\end{table}
Table 6: Thermal conductivities obtained.  
Figure 11: Monitoring of the temperature reached in each inner sensor (S2, S3 and S4) and registered by the thermal camera (TC) at the position of each sensor, as shown in Figure 11 (S2 fromTC, S3 fromTC and S4 fromTC).  
Figure 12: Pixels considered for the measurement of the temperature from the TC for its comparison with each sensor.  
The slight decrease can be explained by a lower compaction of the material at the surface in comparison with the compaction at 0.5 m depth (where the sensors are), as well as a reduction of moisture content in the pores.
# Current Filaments in Asymmetric Surface Dielectric Barrier Discharge
## 5 Experimental Results
### Rogowski Coil
#### 5.1.4 Current Analysis  
The burst current distributions, \(P(I)\), display an exponential shape similar to \(P(Q)\). For BS, the current distributions are shown in Figure 14. To describe the current distributions for BS, we assume an exponential shape for \(P(I)\), as we did for \(P(Q)\), Equation (3), \(P(I)=(1/I_{0})\exp(-I/I_{0})\).  
Figure 12: Burst time duration distribution, \(P(\tau)\) vs. \(\tau\), for different HV. (**a**) Forward stroke. (**b**) Backward stroke. Note the different time scales in (**a,b**).  
Figure 13: Moments of burst time duration vs. HV. (**a**) Mean value \(\left<\tau\right>\). (**b**) Standard deviation \(\sigma_{\tau}\). (**c**) Skewness (\(S_{\tau}\)). (**d**) Flatness (\(F_{\tau}\)).  
The values of the characteristic currents \(I_{0}\) are reported in Figure 15a. This behaviour can be compared with a similar HV dependence found for \(Q_{0}\) in Figure 11. A plateau above about \(5.4\,\mathrm{kV}\) is also observed here for the characteristic current \(I_{0}\). Finally, the mean values of the current, \(\left\langle I\right\rangle\), are displayed in Figure 15b, where one can see that for BS, the mean currents are much larger than for FS. For example, at HV = \(5.5\,\mathrm{kV}\), \(\left\langle I\right\rangle\sim 35\,\mathrm{mA}\) for BS, while \(\left\langle I\right\rangle\sim(-)20\,\mathrm{mA}\) for FS.  
Analogously to the moments shown in Figure 13, we report in Figure 16 the corresponding ones for the burst current. They definitively show that the associated distributions are also non-Gaussian.  
Figure 14: The BS current distribution, \(P(I)\) [mA\({}^{-1}\)] \(I\) [mA], for different HV, displaying a shape which is approximately exponential at the tails.  
Figure 15: (a) Characteristic current \(I_{0}\) [mA] and (b) mean current \(\left\langle I\right\rangle\) [mA] vs. HV [kV].
## 3 Results and Discussion
### Nuclear Position and Movement  
Cell migration, mitosis, and cell differentiation are some of the basic cellular processes that depend on the position and translation of the nucleus in a living cell [15; 16; 20; 21]. The shape of a nucleus and its positioning in a cell depend on various cytoskeletal elements (actin, microtubules, and intermediate filaments) and the motor proteins that generate active forces and are transmitted directly to the nuclear surface through the LINC complex proteins [22; 23]. It is well known that the cytoskeleton is disrupted and disorganized in cells exposed to microgravity, which provides a strong basis to investigate how the positioning of the nucleus changes in cells over time. The live images captured over time in SMG allow us to trace the nuclear shape and therefore track its position over time by tracking its centroid, as illustrated in Figure 3D.  
In addition to the nuclear position relative to a cell under SMG conditions, which can be analyzed using this module, substructures of a nucleus, such as two nucleoli in a living fibroblasts, can be also studied (Figure 3E). The ability to track the nucleoli gives us the opportunity to measure the angle at which the nucleus is rotating over time in SMG by using the mathematical derivation previously published by [PERSON] et al. [17] that measures the slope of the line joining the two nucleoli. The rotation of the nucleus in a living cell may seem random, but it is a crucial process required for normal cell function. For example, during the development of an oocyte to produce a functional egg, the sequential differentiation of the cell depends on the rotation of the nucleus in the cells [24]. In addition, the rotation of the nucleus depends on the dynein motor proteins which walk on the microtubules [25]. As it is well-established that the microtubule network is disrupted in cells exposed to microgravity [26], it is possible that the nuclear rotation function may also be disrupted in SMG.
## 2 An Overview of Recent Advances in Design Optimization of Electromagnetic Devices
### Optimization Methods
#### 2.3.6 Fuzzy Optimization  
The optimization effectiveness of deterministic and robust models depends on the precise quantifications of design parameters and uncertainties. However, these quantifications are not always possible. Fuzzy optimization is good at handling this kind of uncertainty. In this case, the performance of electromagnetic devices can be described as qualitative objectives, such as high, medium, and low. Fuzzy membership functions can be used to quantify them and can be included in quantitative optimization models. There are two main types of fuzzy optimization problems in terms of the consideration of constraints. Fuzzy programming has been developed and widely used to handle optimization problemswith fuzzy parameters and constraints [117, 118]. Fuzzy optimization has been employed to design electromagnetic devices, including different types of motors [119, 120, 121]. In addition, fuzzy method has been combined with Taguchi method to address multi-objective optimization of electromagnetic devices [122, 123, 124, 125, 126, 127, 128]. Taguchi method has a drawback of handling robust multi-objective optimization of electromagnetic devices. Fuzzy method can be employed to solve this kind of problem.
# Reverse-Net: Few-Shot Learning with Reverse Teaching for Deformable Medical Image Registration
## 3 Materials and Methods
### Coarse Registration Network  
To reduce the challenge of large differences in multimodal images, we designed a coarse registration network. Specifically, we pre-align the T1 and T2 images using the same U-net as the pixel alignment network. In this way, images with fewer differences are transmitted to the pixel alignment network, which reduces the learning difficulty of the pixel alignment network.
## 1 Introduction
### Contribution  
In this paper, we utilize a power aware RTS/CTS scheme in the IEEE 802.15.6 CSMA/CA based networks in order to increase channel utilization when nodes are hidden in a network. The key contributions of this paper are:  
* We employ RTS/CTS scheme to solve hidden node problems in IEEE 802.15.6 CSMA/CA based networks.
* we propose a power aware method that adjusts transmission power levels of nodes to avoid loss of RTS or CTS packets.
* We propose a mathematical model to validate the proposed approach.
* We perform extensive simulations to show effectiveness of the proposed approach over the conventional IEEE 802.15.6 CSMA/CA protocol in terms of throughput and energy consumption.  
Under the assumptions of a finite number of nodes, we consider a lossy channel that may distort transmitting packets. When the RTS or CTS packets are lost, we adjust the transmission power levels of the nodes. This adjustment of transmission power levels is effective when the nodes intend to send high priority or life-critical data and emergency event reports to the hub. We analyze the performance of the proposed scheme using both analytical approximations and simulations. The results are derived for different priority classes as proposed in the IEEE 802.15.6 standard in terms of throughput and energy consumption. The results obtained in this paper may help us to understand different IEEE 802.15.6 network scenarios before deployment.  
The rest of the paper is organized as follows. Section Two provides an overview of related work on the IEEE 802.15.6, while Section Three presents the RTS/CTS scheme in the IEEE 802.15.6 CSMA/CA protocol. Section Four presents analytical approximations and performance results, and Section Five concludes the paper.
## 2 Related Work
### Non-Software Defined Energy-Efficient Approaches  
LEACH (Low energy adaptive clustering hierarchy), SPIN (Sensor protocol for information via negotiation), threshold-sensitive energy-efficient sensor networks (TEEN), etc. are extremely important from the perspective of non-software defined energy efficiency in sensor networks. The idea of LEACH protocol is to organize the sensor nodes into clusters, where each cluster has one cluster head CH that acts as a router to the base station. However, the LEACH protocol has problems; one of those problems is the random selection of a cluster head or CHs [35; 36; 43]. This process does not consider the location of sensor nodes in the wireless sensor network, and hence, the sensor nodes may be very far from their CH, causing them to consume more energy to communicate with the CH [35; 36; 43]. The role of the cluster head is rotated in order to balance the energy consumption among multiple nodes. The optimum route chosen for communication is the shortest one computed using [PERSON]'s shortest path algorithm [36]. However, the shortest route is not always the most energy-efficient option.  
SPIN [38] is another energy-efficient routing protocol worthy of mention; which is a modification of classic flooding. In classical flooding, the information is forwarded on every outgoing link of a node. This drains out the battery of a huge number of nodes in the sensor network. SPIN was developed to overcome this drawback. It is an adaptive routing protocol that transmits the information first by negotiating. It proposes the use of metadata of actual data to be sent. Metadata contains a description of the actual message that the node wants to send. Actual data will be transmitted only if the node wishes to receive it, that is, it is similar to being keen to watch a movie after viewing its trailer. In this context, we humbly state that SPIN requires the broadcasting of metadata at least. However, an important aspect of the energy efficiency of SD-EAR arises from it not requiring broadcasting to establish routes.  
TEEN (threshold-sensitive energy-efficient sensor networks) [46] protocol was proposed for time-critical applications. Here, sensor nodes sense the medium continuously, but data transmission is done less frequently. A cluster head sensor sends its members a hard threshold (HT), which is the threshold value of sensed attribute, and a soft threshold (ST), which is a small change in the value of the sensed attribute that triggers the node to switch on its transmitter and transmit. The main drawback of this scheme is that if the thresholds are not received, the nodes will never communicate, and the user will not get any data from the network at all. Also, it has the complexity associated with forming clusters at multiple levels and the method of implementing threshold-based functions [46].
## 4 Results and Discussion
### Statistics of SAR-Derived Parameters
#### 4.1.2 SAR SSW Speed and Its Uncertainty
For example (Table 2), for our examined S-1 dataset, the use of the CMOD7 seems to provide on average a SSW speed uncertainty less than 2 m/s for all WVCs processed at 5 km, 10 km and 15 km. In detail, the number of WVCs that show an uncertainty less than 2 m/s is 6910 (of 11,902; 58.05%), 2119 (of 2490; 85.10%) and 811 (of 936; 86.65%) for 5 km, 10 km and 15 km processing, respectively. Nevertheless, whatever the chosen GMF and the fixed WVC size, the proposed method allows the selection of only SSW speed values with an uncertainty lower than a user's tolerable limit. As a consequence, the method is defined by the powerful skill able to generate a spatial distribution map of SSW speed characterised anywhere by an uncertainty lower than a threshold value of acceptance (e.g., 2 m/s). This characteristic is similar to the one of the MS LG-Mod algorithms, which is able to discard those SSW directions that are considered not reliable enough when their uncertainty is higher than a threshold value of tolerance (e.g., 20\({}^{\circ}\)). On the latter point, the presented methodology may be used in an operational context as follows. Where wind-aligned patterns (e.g., wind rows) are visible on SAR images and local SAR-derived SSW directions are considered reliable within the MS LG-Mod approach, these directions and their uncertainties may be adopted as input to the methodology for SSW speeds and uncertainties estimation. Elsewhere, NWP model wind directions and their overall uncertainty may be exploited as well. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & & **CMOD5.N** & & & **CMOD7** & \\ \hline  & **5 km** & **10 km** & **15 km** & **5 km** & **10 km** & **15 km** \\ \hline min\(\forall_{i}\) & \(\
eg\) N\_ROI(W\_ROI) (m/s) 1  & 6.8 & 7.2 & 7.5 & 6.6 & 7.1 & 7.4 \\ max\(\forall_{i}\) & \(\
eg\) N\_ROI(W\_ROI) (m/s) 1  & 31.8 & 27.7 & 23 & 30.6 & 26.9 & 22.3 \\ mean\(\forall_{i}\) & \(\
eg\) N\_ROI(W\_ROI) (m/s) 1  & 15.1 & 15.2 & 15.20 & 14.84 & 14.74 & 14.73 \\ std\(\forall_{i}\) & \(\
eg\) N\_ROI(W\_ROI) (m/s) 1  & 3.6 & 3.49 & 3.46 & 3.37 & 3.21 & 3.18 \\ min\(\forall_{i}\) & \(\
eg\) N\_ROI(E\_ROI) (m/s) 1  & 0.4 & 0.4 & 0.5 & 0.4 & 0.4 \\ max\(\forall_{i}\) & \(\
eg\) N\_ROI(E\_ROI) (m/s) 1  & 12.7 & 4.7 & 4.4 & 12.2 & 4.8 & 4.2 \\ mean\(\forall_{i}\) & \(\
eg\) N\_ROI(E\_ROI) (m/s) 1  & 2.07 & 1.39 & 1.35 & 1.97 & 1.31 & 1.26 \\ std\(\forall_{i}\) & \(\
eg\) N\_ROI(E\_ROI) (m/s) 1  & 1.12 & 0.63 & 0.58 & 1.11 & 0.60 & 0.56 \\ \hline \end{tabular}
\end{table} 
Table 2: Statistics about the SAR SSW speed (m/s) and the related uncertainty (m/s) for all ROIs, at various dimensions and for two different applied Geophysical Model Functions (GMFs).
# Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images
## 3 Results and Analysis
### Spatial Distribution of Winter Wheat in Shandong  
In this research, based on time-series Sentinel-2 data, a classification method combining pixel-based and object-oriented approaches was followed, and the spatial distribution map of winter wheat in Shandong with a resolution of 10 m was finally generated. As shown  
Figure 4: Feature importance of random forest. (**a**) refers to the importance of each type of features; (**b**) is the importance of various features of each month.  
in Figure 5, a large area of wheat cultivation is concentrated in the northwestern plain, the southwestern plains and the JiaoLai plains of Shandong. In addition, winter wheat in Shandong is more concentrated in areas where economic development is relatively lagging behind, such as Heze, Dezhou, Liaocheng, Jining and Weifang, etc., and their winter wheat planting area accounts for about 57% of the province. As a whole, the spatial distribution of winter wheat shows more in the west and less in the east, and is uniformly dense in the west and sparsely scattered in the east. The spatial distribution pattern has obvious geographical characteristics. From the perspective of the spatial distribution of the river systems, winter wheat is concentrated in the Yellow River Basin, Huaihe River Basin and Haihe River Basin, with a small amount in the Peninsula Basin. This distribution pattern can make full use of irrigation resources.
### Contribution  
* The proposed routing scheme is designed to reduce energy consumption and latency issues. Therefore, the proposed method uses the sink mobility technique to transfer data to the sink node. This will minimize energy consumption because of the direct transmission of data. When the sink is in communication range, the nodes will transfer the data directly to the sink.
* Data transferring through a single link does not ensure the reliability of data. Therefore, the proposed method uses the cooperative data forwarding scheme to reduce the end-to-end delay and increase the network's reliability.
* The proposed routing scheme performance is evaluated through the MATLAB simulation tool, which shows the improvement in energy consumption, end-to-end delay, PDR, and transmission loss.
# Steel Roll Eye Pose Detection Based on Binocular Vision and Mask R-CNN
## 6 Posture Detection Test
### Visual Inspection Accuracy Experiment
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{
\begin{tabular}{c} **Actual** \\ **Deflection** \\ **Angle (\({}^{\circ}\))** \\ \end{tabular} } & \multicolumn{2}{c}{**Actual World**} & \multicolumn{2}{c}{**Detection of**} & \multicolumn{2}{c}{**Detection of**} \\  & & **Coordinates of the** & **Angle (\({}^{\circ}\))** & **Deflection** & **Center of the Circle** \\  & & **Center of the** & **Angle (\({}^{\circ}\))** & **Angle (\({}^{\circ}\))** & **in World** \\  & & **Circle (mm)** & & & **Coordinates (mm)** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{\(-5.00\)} & (0.00, \(-400.00\), & \multirow{2}{*}{0.36} & \multirow{2}{*}{\(-4.86\)} & (3.53, \(-403.27\), \\  & & \(-300.00\)) & & & \(-302.36\) \\  & & (5.00, \(-400.00\), & & & (8.42, \(-402.55\), \\  & & \(-350.00\)) & & & \(-348.37\)) \\ \multirow{2}{*}{3} & \multirow{2}{*}{0.00} & (10.00, \(-400.00\), & \multirow{2}{*}{\(-1.21\)} & \multirow{2}{*}{1.35} & (12.79, \(-398.22\), \\  & & \(-400.00\)) & & & \(-402.14\)) \\ \multirow{2}{*}{4} & \multirow{2}{*}{0.00} & (15.00, \(-400.00\), & & & (12.97, \(-398.25\), \\  & & \(-450.00\)) & & & \(-453.01\)) \\ \multirow{2}{*}{5} & \multirow{2}{*}{5} & \multirow{2}{*}{5.00} & (\(-5.00, \(-400.00\), & \multirow{2}{*}{1.29} & \multirow{2}{*}{6.77} & (\(-7.26\), \(-397.83\), \\  & & \(-500.00\)) & & & \(-497.33\)) \\ \multirow{2}{*}{6} & \multirow{2}{*}{5.00} & (\(-10.00, \(-400.00\), & & (\(-12.97\), \(-398.55\), \\  & & \(-550.00\)) & & & \(-553.01\)) \\ \multirow{2}{*}{7} & \multirow{2}{*}{10.00} & (\(-15.00, \(-400.00\), & & (\(-12.84\), \(-404.05\), \\  & & \(-600.00\)) & & & \(-603.52\)) \\ \multirow{2}{*}{8} & \multirow{2}{*}{10.00} & (\(-20.00, \(-400.00\), & & & (\(-16.61\), \(-403.15\), \\  & & \(-650.00\)) & & & \(-645.33\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Position detection results. Figure 23: Shooting Sample. for targets and lacks the capability to perform pixel-level target segmentation [28]. Mask R-CNN is able to extract the boundary of the target more finely by performing target mask prediction with additional branches, which is suitable for application scenarios requiring precise segmentation and boundary extraction, such as medical image analysis, complex obstacle recognition in autonomous driving, etc. Faster R-CNN inherits the excellent target detection capability, but Mask R-CNN resolves the problem of fine segmentation, which is not possible with simple target detection [29; 30]. This enables Mask R-CNN to excel in both instance segmentation and object detection. RetinaNet demonstrates superior performance in small object detection and operates more quickly, but it lacks the capability to provide target masks like Mask R-CNN, which are essential for fine-grained target segmentation [31; 32].
# Signaling in the Tomato Immunity against _Fusarium oxysporum_
## 4 Materials and Methods
### DNA Extraction and Fungal Quantification in Planta  
The level of fungal presence in the vascular tissues of tomato plants was evaluated by real-time quantitative PCR (qPCR) [55]. Total DNA was extracted by adding 500 \(\upmu\)L of extraction buffer to a small amount of homogenized infected tomato hypocotyls in an Eppendorf tube, and then incubated for 30 min at 65 \({}^{\circ}\)C. A volume of 450 \(\upmu\)L of chloroform-isoamyl alcohol (24:1) was added to the mix, vortexed and centrifuged at 13,000 rpm for 10 min. Once the organic phase was removed, one volume of isopropanol was added to the aqueous phase and centrifuged at 13,000 rpm for 10 min. DNA sediment was washed twice with 70% ethanol and centrifuged for 5 min at 13,000 rpm. Pellet was resuspended in 50 \(\upmu\)L distilled water containing 2% RNase and quantified using a Nanodrop ND-1000 spectrophotometer (ThermoFisher Scientific, Waltham, MA, USA).  
For fungal quantification, qPCR analysis of DNA from infected hypocotyl tissues corresponding to 2 independent plants (3 technical replicates) were performed [55] using tomato actin as an endogenous reference gene. The PCR primers used to amplify the rDNA - intergenic spacer of _Fol_ (rDNA-IGS, Genbank accession AB106019) were the following: 5\({}^{\prime}\)-GCTGGCGGATCTGACACTGT-3\({}^{\prime}\) as the forward primer (sp1-2f) and 5\({}^{\prime}\)-CCTAAACCACATATCTCGTCCAAA-3\({}^{\prime}\) as reverse primer (sp1-2r), according to [56]. For actin amplification (Genbank accession AB199316), 5\({}^{\prime}\)-CTAGGCTGGTCGAGAGATGATG-3\({}^{\prime}\) and 5\({}^{\prime}\)-GTCTTTTGACCCATACCACCACCACCACCACC-3\({}^{\prime}\) were used as the forward and the reverse primers, respectively.