esa-sceva/satcom-chunk-collection huggingface dataset:
# Principles and Application of Polyimide Fiber Bragg Gratings for Surface Strain Measurement
## 5 Conclusions  
A new method is proposed with polyimide FBGs for surface strain measurement. Several conclusions were drawn and are given as follows:  
(1) When mounted on the surface of the substrate, polyimide FBGs should be bonded to the substrate by adhesive first, and then covered by epoxy resin. Solidified epoxy can provide enough protection for polyimide FBGs.  
(2) When mounted to measure strain, polyimide FBGs can produce an average strain transfer ratio of over 95%. It means that polyimide FBGs can indicate the substrate strain variation accurately.  
(3) The primary factors that influence the average strain transfer ratio of polyimide FBGs include the shear modulus of the bonding layer and the adhesive thickness between the fiber and substrate. Length bonded to the substrate has little influence when the length is over 3 cm.  
(4) Polyimide FBGs can be used in some strict measurements, such as explosion measurement and impact measurement.  
(5) Fewer strain transfer layers can increase dynamic response frequency and coupling between FBG and substrate.  
**Acknowledgments:** Support for this paper has been received from the following sources: the National Natural Science Foundation of China (51608528), the Jiangsu Province Natural Science Foundation (BK20161467), and the Central Military Committee National Defence Science and Technology Funds (3602052).  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON], and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Atmospheric Stability
#### 2.4.1 Wind Shear Exponent  
The power law describes a functional relationship between two numbers that theoretically express the wind speed at one height in exponential form of the wind speed at another height. When the atmospheric stability is close to near-neutral, the vertical wind speed distribution in which the wind speed constantly increases with height appears in the lower atmospheric boundary layer, so that the power law can reasonably be applied. In general, in this case, the vertical wind shear exponent (\(\alpha\)) or power law exponent represents a value of 1/7 (\(\approx\)0.143). Based on the approximate value of the vertical wind shear exponent of 0.143, the region showing a high value is a stable atmosphere, and an exponent lower than this means that an unstable atmosphere is dominant. The vertical shear exponent can be obtained through Equation (5):  
\[\frac{V}{V_{r}}=\left(\frac{H}{H_{r}}\right)^{\alpha} \tag{5}\]  
where \(H\) [m] is the target height, \(H_{r}\) [m] is the reference height, \(V\) [ms\({}^{-1}\)] is the wind speed at the target height, and \(V_{r}\) [ms\({}^{-1}\)] is the wind speed at the reference height. Wind shear exponents can be obtained if wind speed measurements for at least two heights are available by fitting wind speed data with Equation (6):  
\[\alpha=\frac{\ln(V)-\ln(V_{r})}{\ln(H)-\ln(H_{r})} \tag{6}\]
## 3 Experimental Setup
### Evaluation Metrics  
For quantitative performance evaluation, four very popular evaluation indices were employed: Jaccard similarity (JS) [62], DICE coefficient (DC) [63], F1 score [64], and pixel accuracy:  
\[Jaccard\ Similarity=\frac{J\cap\
mid}{J\cup\
mid} \tag{7}\]  
\[Dice\ coefficient=2\left(\frac{J\cap\
mid}{J+\
mid}\right) \tag{8}\]  
\[F1\ Score=\frac{2 TP}{2 TP+FP+FN} \tag{9}\]  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FP} \tag{10}\]  
where \(J\) is the predicted segmentation output from the method to be evaluated, and \(J\) is the expert suggested truth mask. The \(J\) contours are references for further segmentation analysis [65]. \(TP\), \(TN\), \(FP\) and \(FN\) are the truly positive, true negative, false positive, and false negative, respectively. True positive denotes the pixels present in both the truth mask and predicted segmented region. True negative denotes the pixels present in neither segmented truth mask nor predicted segmented region. False positive signifies the pixels present only in the predicted segmented region and false negative signifies the pixels present in the suggested truth mask only.
## 3 Results
### CO\({}_{2}\) Loading Relative to Standard MEA  
Table 4 and Figure 9 illustrate the percentage CO\({}_{2}\) loading increase relative to MEA for the ZIF-8 samples. It clearly demonstrates that the addition of ZS1, ZS2, and ZS3 in 5 molar MEA solution leads to enhanced CO\({}_{2}\) loading [30; 31]. Incorporating 0.25 g of ZS1 into a 5 molar MEA solution leads to an approximate 8.74% increase in CO\({}_{2}\) loading compared with the standard MEA solution. Conversely, the addition of 1.0 g of ZS3 to 5 molar MEA solutions results in an approximately 41% increase in CO\({}_{2}\) loading compared with the standard MEA solution. Previous studies in the literature have investigated the impact of ZIF-8 particle size on CO\({}_{2}\) adsorption [32]. However, in our specific case, we did not observe a significant difference in CO\({}_{2}\) loading with varying particle sizes of ZIF-8 (as the particle size of ZIF-8 did not have any big difference). Figure 9: Percentage increase in CO\({}_{2}\) adsorption with varying proportions (0.25 gm, 0.5 gm, 0.75 gm, and 1 gm) of ZS1, ZS2, and ZS3 samples in a 5M MEA solution. The following are key interpretations from Figure 9 and Table 4.  
Figure 9 shows a clear trend, where CO\({}_{2}\) loading efficiency increases with the addition of ZIF-8 (ZS1, ZS2, and ZS3) to the MEA solution. This indicates that ZIF-8 effectively enhances the CO\({}_{2}\) capture ability of MEA, potentially due to the synergistic effect between the high surface area of ZIF-8 and the chemical affinity of MEA for CO\({}_{2}\). Despite the expectation that smaller ZIF-8 particles (like ZS1) might provide higher CO\({}_{2}\) loading due to increased surface area, the figure suggests that all three particle sizes contribute similarly to enhanced CO\({}_{2}\) loading. This could imply that factors other than just surface area, such as pore accessibility or the intrinsic adsorption characteristics of ZIF-8, play a significant role. The plateau in CO\({}_{2}\) loading at higher ZIF-8 concentrations suggests that there is a saturation point beyond which additional ZIF-8 does not significantly increase CO\({}_{2}\) adsorption. This indicates that the available adsorption sites in the MEA solution become fully occupied, and further enhancements might require increasing the overall adsorption capacity of the system rather than just adding more ZIF-8. The incremental increases in CO\({}_{2}\) loading with different ZIF-8 samples suggest that ZS3 (with the largest particle size) achieves the highest percentage increase in CO\({}_{2}\) loading. This might be due to a balance between adsorption capacity and the rate of CO\({}_{2}\) diffusion through the ZIF-8 structure, where slightly larger pores in ZS3 facilitate faster diffusion and capture of CO\({}_{2}\) molecules. The observed improvements in CO\({}_{2}\) loading with ZIF-8 addition suggest that the adsorption process might be kinetically controlled, where the
# Object Detection Related to Irregular Behaviors of Substation Personnel Based on Improved YOLOv4
## 2 Irregular Behavior Detection Dataset of Substation Personnel  
### Dataset Construction and Annotation  
The IBD behaviors detection dataset of substation personnel used in this paper was partly derived from video data captured by the video surveillance system in the substation, which was converted into images for a total of 494 images. Another 2491 images were collected from photographs shot by the substation workers when they were working. The IBD dataset of substation personnel included three categories, namely, safety protective equipment, safety operating equipment, and safety personnel, specifically including safety helmets, insulating gloves, ordinary operators, safety personnel, insulated operating rods, and electroscopes. Detection of the objects that substation personnel should wear and carry when working can reflect whether substation personnel are engaging in irregular behaviors. If the control center detects these irregular behaviors, it can stop them in time to avoid personal safety accidents.  
The IBD dataset of substation personnel needs to be annotated before training the object detection model. In this paper, the LabelImg labeling tool [16] was used to annotate the location and category of an object using a rectangular bounding box. Regardless of their status as an ordinary operator or as safety personnel, their location and category were labeled as \"person\". For safety personnel, the location and category of the badge worn by safety personnel were both labeled as \"badge\". For safety helmets, insulating gloves, insulated operating rods, and electroscopes, the location and category of the objects were labeled as \"helmet\", \"glove\", \"operatingbar\", and \"powerchecker\", respectively. The annotation format used in this paper is Pascal VOC [17], a common format in the field of computer vision, and target location information can be acquired from the upper left vertex coordinates (\(x_{min}\), \(y_{max}\)) and lower right vertex coordinates (\(x_{max}\), \(y_{min}\)) of the rectangular bounding boxes.
## 4 Overview of the Proactive Transmission of Replicated Frames (PTRF) Mechanism
### PTRF Operation
#### 4.2.2 Approach B: End-to-End Estimation, Link-Based Replication  
The main characteristic of this approach is that both talkers and bridges replicate frames, whereas both bridges and listeners eliminate surplus replicas. In this sense bridges are not COTS devices, i.e., they also have to implement PTRF. Like in approach A, the number of replicas that the devices must create is calculated to achieve a specific end-to-end reliability. i.e., a given reliability for the largest path from the talker to the listeners considering the unreliability of each one of the devices and links of that path.  
In Figure 2(b) we can see that the talker \(T_{1}\) transmits three replicas of frame \(f_{1,1}\). \(B_{1}\) receives the three replicas correctly, it uses replica \(r_{1,1}\) to create a new set of three replicas and ignores replicas \(r_{1,2}\) and \(r_{1,3}\). Now, \(r_{1,1}\) is affected by a temporary fault in link \(l_{2}\) and dropped upon reception by \(B_{2}\). \(B_{2}\) uses \(r_{1,2}\) to create a new set of three replicas and ignores replica \(r_{1,3}\). Finally, \(r_{1,3}\) is affected by a fault in link \(l_{3}\) and it is dropped by listener \(L_{1}\). Nonetheless, \(L_{1}\) correctly receives replicas \(r_{1,1}\) and \(r_{1,2}\), delivers replica \(r_{1,1}\) to the application and ignores \(r_{1,2}\)
# Comparing Winds near Tropical Oceanic Precipitation Systems with and without Lightning
## 2 Materials and Methods
### Synthesis  
The synthesized CYGNSS track dataset provided matchups with ASCAT-A and -B within approximately \(\pm\)90 min (the duration of one Metop orbit), IMERG within 30 min, and three different complementary lightning measurements within the duration of the track itself (typically several minutes). In addition, the analysis was structured relative to the fundamental grouping of CYGNSS wind measurements (a common track of specular points), and thus ensured spatial and temporal continuity.  
It is not clear how a different matchup approach could provide comparable spatial and temporal proximity, because the specular reflections provided by CYGNSS are fundamentally sparser in time and space than a swath-based measurement. For example, it is not realistic for CYGNSS to provide contiguous coverage of the winds in a mesoscale convective system (MCS) within the lifetime of said MCS (i.e., a few hours). ASCAT can provide swath coverage of winds (and thus a contiguous snapshot of the wind field), but this approach was not considered because the focus of this study was primarily on CYGNSS due to its ability to retrieve winds in heavy rain.  
Figure 2 shows an example of how a track that contained winds near precipitation with lightning appears. The portions of the track marked in magenta (Figure 2b) indicate what was considered in the \"winds with lightning\" and \"precipitation with lightning\") categories. A comparable track, but with no lightning anywhere along it, would have the raining portions included in the \"winds without lightning\" category. The matched ASCAT-A winds agree well with CYGNSS FDS winds in the west swath, but there is significant disagreement in the east, with the largest deviations occurring within the co-observed raining area (Figure 2b). The tendency for ASCAT winds to increase significantly relative to CYGNSS within raining areas was observed in many matched tracks. Additionally, at the east edge of the track, where RCG decreased below 10, CYGNSS wind speeds became noticeably noisier.
# Mining of Potential Gene Resources for Breeding Nutritionally Improved Maize
## 6 Identification of Maize Potential Gene Resources for Mineral Content Improvement
For example, GLK1 [133], AN1 [134], AN3 [135] and ANT1 [136] are positive regulators, while GmMYBR [137] is a \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Genes** & **Protein Function** & **Maize Orthologs** & **Gene ID** & **Strategy** & **References** \\ \hline \multirow{9}{*}{_ZIF1_} & & & _Zm00001 eb129050_ & & \\  & & _MFSD1_ & _Zm00001 eb196170_ & & \\  & & _MFSD2_ & _Zm0001 eb038000_ & & \\  & & _IDP8516_ & _Zm00001 eb039430_ & & \\  & & _TOM3_ & _Zm00001 eb039440_ & & \\  & & _UMC1028_ & _Zm00001 eb128730_ & overexpression & [112] \\  & & & _IDP7324_ & _Zm00001 eb133440_ & & \\  & & _YS3_ & _Zm00001 eb163460_ & & \\  & & _IDP6979_ & _Zm00001 eb129340_ & & \\  & & & _Zm00001 eb196180_ & & \\  & & & _Zm00001 eb332620_ & & \\ \hline \multirow{2}{*}{_ZIF2_} & & _PC0099415_ & _Zm00001 eb017730_ & overexpression & [113] \\  & & _GPM828_ & _Zm00001 eb017760_ & & \\ \hline _HMA2_ & & _carrier protein_ & _HMA2_ & _Zm00001 eb226870_ & overexpression & [114] \\ \hline _HMA4_ & & _carrier protein_ & _HMA3_ & _Zm00001 eb095020_ & overexpression & [115] \\ \hline _HMA7_ & & _carrier protein_ & _CSU904_ & _Zm00001 eb327860_ & overexpression & [116] \\ \hline _ZIP1_ & & _carrier protein_ & _Zm00001 eb139810_ & overexpression & [117] \\ \hline _ZIP8_ & & _carrier protein_ & _ZIP8_ & _Zm00001 eb303800_ & knockout & [118] \\ \hline \multirow{2}{*}{_MIPS_} & & _Zm00001 eb401220_ & & & \\  & & _smbase_ & _Zm00001 eb276490_ & & \\  & & & _Zm00001 eb283250_ & knockout & [124] \\  & & & _Zm00001 eb378070_ & & \\ \hline \multirow{2}{*}{_PGKI_} & & _Zm00001 eb191270_ & knockout & [125] \\  & & & _Zm00001 eb259060_ & & \\ \hline \multirow{2}{*}{_ITPK2_} & & _inositol 1,3,4-trisphosphate_ & & _Zm00001 eb399350_ & knockout & [126] \\  & & 5/6-kinase & & _Zm00001 eb405_ & \\ \hline \multirow{2}{*}{_IPKI_} & & _inositol_ & & & \\  & & _1,3,4,5,6-pentakisphosphate_ & _IDP8938_ & _Zm00001 eb067500_ & knockout & [127] \\  & & 2-kinase & & _Zm00001 eb432760_ & \\ \hline \multirow{2}{*}{_HAD1_} & & _Zm00001 eb063350_ & & & \\  & & & _Zm00001 eb342820_ & overexpression & \\  & & & _Zm00001 eb399750_ & & \\ \hline _PAPHY-A_ & & _PAP2_ & _Zm00001 eb064450_ & overexpression & [129] \\ \hline _PAP4_ & & _PAP22_ & _Zm00001 eb048820_ & overexpression & [130] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._negative regulator for anthocyanin synthesis. A double-stranded RNA binding protein, DRB3 has also been shown to inhibit anthocyanins biosynthesis [138]. Nine homologous genes involved in anthocyanin synthesis were identified from the maize genome (Table 6).
# An Improved Deep Belief Network Prediction Model Based on Knowledge Transfer
## 4 Experiments
### Popularity Prediction Based on Time Series
#### 4.2.2 Predictive Performance Evaluation  
In order to show the superiority of IDBN in the popularity time series, this paper compares the prediction performance of IDBN with the benchmark model. The comparison results are shown in Table 2, which includes RMSE and average training time. It should be pointed out that the parameters of all models in the table are selected through trial and error, so the performance is the best. Methods for comparison include self-organizing cascade neural network (SCNN) [21], new association model (NAM) [22], and other fixed structure models of DBN, such as CDBN, which is a DBN model with continuous input. The performance of auto-regressive integrated moving average (ARIMA)model is better than the support vector machine algorithm (SVR), because ARIMA is better at dealing with periodic time series as a classical time series analysis method. In the prediction problem based on time series, IDBN can continuously adjust the weight parameters in the model through independent construction and repair functions to obtain the improvement of prediction accuracy. Therefore, according to the experimental results, it can be concluded that IDBN can provide in the heat prediction experiment Better predictions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**LFM-1k**} & \multicolumn{2}{c}{**ML-20M**} \\ \cline{2-5}  & **RMSE** & **Training Time (s)** & **RMSE** & **Training Time (s)** \\ \hline SCNN & 0.3654 & 42.52 & 0.3724 & 44.25 \\ NAM & 0.3745 & - & 0.3958 & - \\ DBN & 0.6525 & 41.23 & 0.6724 & 43.68 \\ CDBN & 0.5535 & 44.95 & 0.5620 & 45.33 \\ ARIMA & 0.4247 & - & 0.4352 & - \\ SVR & 0.4967 & 39.65 & 0.4822 & 38.44 \\ IDBN & 0.3425 & 31.56 & 0.3614 & 30.71 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison results of popularity prediction based on time series.  
Figure 8: Time series prediction of ML-20M data set popularity: left: training results; right: test results.  
Figure 7: Time series prediction of popularity of LFM-1k data set: left: training results; right: test results.
# Design and Control of an Ultra-Low-Cost Logistic Delivery Fixed-Wing UAV
## 5 Test Process and Result Analysis
### Field Experiments Compared to Those of Other Ultra-Low-Cost Drones  
#### 5.2.1 Test Environment and Conditions  
In outdoor experiments, flights were performed using small fixed-wing unmanned aerial vehicles (UAVs) available on the market within the price range of USD150-200, as well as a hand-launched fixed-wing UAV optimized to a cost of approximately USD100. To minimize the variables, all the flights were conducted under clear, windless weather conditions at low altitudes. The flight plans and waypoint diagrams were designed for a flight area of 220 by 50 m, with the flight altitudes ranging from 10 to 60 m and at a speed of 20 m/s. The waypoint diagrams for each UAV were developed accordingly. Figure 32  
Figure 31: Flight path planning for drones.  
Figure 30: Results of image recognition.  
shows a map of the waypoints of the drones on the market. Figure 33 is UAV waypoint diagram optimized in this study.  
The commercially purchased UAV has a flight time of 1 min and 6 s, while the UAV in this study has a flight time of 1 min and 32 s. Relevant flight videos are shown in Videos S1 and S2. Their respective advantages and disadvantages in various aspects are analyzed in detail in Section 5.2.2. The test conditions were as follows:  
Figure 33: UAV waypoint diagram optimized in this study.  
Figure 32: A map of the waypoints of the drones on the market.  
1. Information on the takeoff point, waypoint, landing point, and target point of the unmanned aerial vehicle, as well as flight parameters and control parameters, appear on the flight control panel. It is prohibited to use any equipment for the real-time control and autonomous flight of the model;
2. The drone carried a simulated package consisting of an unopened 500 mL bottle of water;
3. The drone's mission simulated the precise delivery of a courier package by a low-cost drone, with the model aircraft in the autonomous flight mode, while completing parcel organizer identification and parcel-dropping tasks;
4. The flight process and results of the UAV were recorded and displayed on a computer at the ground station, including data on the UAV's position, speed, attitude, control inputs, and image information, as well as images of the UAV's flight trajectory, the location of the parcel organizer, and the point at which the parcel was released.
## 2 LI Device Design for Fast Motion
### Fabricating the ODT and Verifying the Performance  
The proposed ODT fabricated as shown in Figure 9 has the following major characteristics: (1) high transmission efficiency via the novel GOPS for driving the segment belts for Y-axis motion, (2) low-weight unit segments for fast X-axis motion, (3) distributed motor actuation to reduce the motion response time and the strain on transmission parts.  
Figure 8: Low-level speed controller for motor synchronization.  
Figure 9: The developed ODT using GOP actuation on the active surface, distributed actuation, and the dynamics simulations results.  
To verify the performance of the proposed ODT, we measured the maximum acceleration and velocity of the active surface. The motion command was given as a sinusoidal signal of a magnitude of 3 m/s at 0.16 Hz for generating the maximum acceleration of 3 m/s\({}^{2}\). In addition, to verify the low-level controller, the velocity and torque transmitted from the motors were also measured.  
Figure 10 shows the results of the maximum speed and acceleration. The root mean square (RMS) value of the speed difference was 0.0087 m/s for the Y-axis, and 0.0013 m/s for the X-axis, respectively, as shown in Figure 10a,c. Thus, the speed synchronization and command-following performance are considered to be stable. In the Y-axis case, the RMS value of the torque bias was 27%, while in the X-axis case, the RMS value of torque bias was 20%, as shown Figure 10b,d. Thus, the low-level controller can overcome the nonlinearities present in the power transfer process and adequately distribute the load to the motors while executing the motion commands. It can also adequately compensate for the torque difference.  
In the maximum performance, both axes showed a torque requirement of about 1.75 times the rated motor capacity, due to the implicit friction of the real system. However, the servo motors guarantee operation at 200% of the rated torque capacity for a period of 5-10-min. Therefore, the system can safely achieve high velocities and accelerations exceeding 3 m/s and 3 m/s\({}^{2}\), respectively.  
Table 3 compares the specifications of the existing 2D treadmills and the proposed ODT. The proposed ODT has a workspace of 2.5 m \(\times\) 2.5 m, which is sufficient for safe running and various other types of locomotion, such as crawling. It can achieve the higher velocities and accelerations than the others. Also, due to the use of the distributed actuation system, the overall system height is \(<\)64 cm.  
Figure 10: Verification of the performance using a sinusoidal motion command for surface acceleration of 3 m/s\({}^{2}\). (**a**) Y-axis speed synchronous performance, (**b**) Y-axis torque synchronous performance, (**c**) X-axis speed synchronous performance, and (**d**) X-axis torque synchronous performance.