esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Experimental Results
#### 4.1.2 Zero Watermark Construction  
The proposed watermarking scheme was performed using Python 3.10.4, Intel(R) Core(TM) i9-10900 CPU @ 2.80 GHz, memory 32 GB. Vector maps of buildings (Figure 5a), roads (Figure 5b), and water bodies (Figure 5c) were chosen as experimental data to test the dependability of the proposed watermarking scheme. Table 1 displays the detailed data information. The buildings, road, and water body maps each have 2708, 5115, and 844 elements, as well as 61,772, 86,367, and 422,764 vertices. The three maps generate 15,381, 21,237, and 15,294 feature points after Douglas-Peucker compression (all compression thresholds are set to 50 m), with compression ratios of 75.1 percent, 75.4 percent, and 96.4 percent, respectively. Point constraint blocking is performed on this basis. The initial block area parameter set is 1/4 of the entire area of the original data, and the coordinate point threshold is 5000. Finally, the building data yields 23 sub-blocks and zero watermarks, the road data yields 31 sub-blocks and zero watermarks, and the water map yields 22 sub-blocks and zero watermarks.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data Set** & **Number of Features** & **Number of Points** & **Number of Feature Points** & **Number of Blocks** & **Number of Zero Watermarks** \\ \hline Buildings & 2708 & 61,772 & 15,381 & 23 & 23 \\ Roads & 5115 & 86,367 & 21,237 & 31 & 31 \\ Rivers & 844 & 422,764 & 15,294 & 22 & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information of original and preprocessed experimental data.  
Figure 4: Watermark image scrambling.  
Figure 5: Original vector maps.
# Full-Field Mode Shape Identification Based on Subpixel Edge Detection and Tracking
###### Abstract  
Most research on computer vision (CV)-based vibration measurement is limited to the determination of discrete or coarse mode shapes of the structure. The continuous edge of the structure in images has rich optical features, and thus, by identifying and tracking the movement of the structure's edge, it is possible to determine high-resolution full-field mode shapes of the structure without a preset target. The present study proposes a CV-based method of full-field mode shape identification using the subpixel edge detection and tracking techniques. Firstly, the Canny operator is applied on each frame of the structure vibration video to extract the pixel-level edges, and the improved Zernike orthogonal moment (ZOM) subpixel edge detection technique is adopted to relocate the precise structure edges. Then, all the detected edge points are tracked to obtain the full-field dense displacement time history that is subsequently used to determine the structure frequencies and compute full-field mode shapes by combining the covariance driven stochastic subspace identification (SSI-COV) with the hierarchical cluster analysis. Finally, the proposed method is verified on the aluminum cantilever beam in the laboratory and the Humen Bridge in the field. The results show that the proposed method is able to detect more precise structure edges and identify the full-field displacement and mode shapes of structures without the need for installing artificial targets on the structure in advance, which provides valuable information for the structural condition assessment, especially for structures with small-amplitude vibrations.  
Keywords:modal identification; full-field mode shape; subpixel edge detection; computer vision; stochastic subspace identification +
Footnote †: journal: Applied Sciences
## 4 Experimental Results and Discussion
### Robustness
#### 4.2.2 Optimized ISF  
ISF optimization using GA, ABC and FO is suggested for improved performance of the proposed scheme. The robustness performance of the proposed scheme using optimized ISF is also evaluated. NC and BER under zero attacks for different test cover images are shown in Table 4. NC and BER values are equal to ideal values for all grayscale images under zero attacks as can be studied from Table 4. For color images, from Table 4, it can be observed that the average NC value has increased from 0.9940 (with adaptive ISF) to 0.9998 (with optimized ISF) and BER decreased from 0.0115 to 0 using GA. By using ABC, NC increased from 0.9940 to 0.9995 and BER reduced from 0.0115 to 0. With FO, NC improved from 0.9940 to 0.9991 and BER reduced from 0.0115 to ideal value 0. After optimization of ISF, for the images \"Penguins\" and \"Tulips\", NC increased from 0.9992, 0.0919 to 1. For the images \"Koala\", \"Flash\" and \"Skin\", BER reduced from 0.0022, 0.229, 0.0014 to ideal value 0. From this discussion, it can be observed that, for color images, robustness increased significantly after optimization. Further robustness of the proposed scheme is evaluated for \"Lena\" image under different attacks using adaptive and optimized ISF and compared in Table 5. From Table 5, it can be seen that NC values for the majority of attacks are higher for ISF optimized by using GA, hence making the watermark more robust against common attacks. Thus, optimization can be used to improve the robustness of the proposed scheme. In addition, ISF optimization using GA is more pertinent for the proposed scheme.
## 3 Results and Discussion
### Chemical Characterization by FT-IR Analysis
As a result of the FT-IR analysis of the PE aggregate, for nonirradiated PE, because the side chains in polymer structure can move freely, the C-C stretching peak at 1462 cm\({}^{-1}\) is wide, whereas for irradiated samples it changes to a sharp peak. In addition, the C=O stretching peak at the 1700 cm\({}^{-1}\) region increases with an increase in the gamma irradiation dose. The increase in these C=O stretching peaks is due to crosslinking by gamma irradiation. As a result of the FT-IR analysis of the PC aggregate, when gamma-irradiated, the peak at 1768 cm\({}^{-1}\) is suppressed by gamma-ray irradiation, and a new peak is formed at 1768 cm\({}^{-1}\), which originates from C=O stretching vibration. This causes the C-O single bonds to be easily broken, compared with C=O double bonds, because the bond energy of C-O is lower than that of C=O. In summary, the mechanism of the surface hardness enhancement for gamma-irradiated PC is closely related to the formation of C=O stretching vibration around 1768 cm\({}^{-1}\) and aromatic hydrocarbons. Figure 16: FT-IR spectra of polycarbonate (PC) aggregate with different irradiation doses. Figure 17: FT-IR spectra of acrylonitrile butadiene styrene (ABS) aggregate with different irradiation doses. As a result of the FT-IR analysis of the ABS aggregate, it has been confirmed that with the increase in the gamma irradiation dose, there was a decrease in the intensity of the C=O band at around 1768 cm-1 and C-O band at around 1186 cm-1, which is indicative of the chain scission of the surface of PC. As a result of FT-IR analysis of PET aggregates, it was confirmed that the peaks of the oxygen functional group C=O at 1713, 1241, and 1094 cm-1 decreased with increasing gamma irradiation dose. Upon gamma irradiation of PET samples, free radicals, ions, and neutral atoms are generated. As a result, it induces a change in PET surface polarity. This is due to the formation of OH and COOH groups during oxidation by the atmosphere. Oxygen in the air reacts with gamma-ray-induced radicals to form C=O bonds. Oxygen released in the H-C=O band can oxidize the surface and improve wettability. In addition, free radicals -C-O-, which improve polarity, can be released. Therefore, gamma irradiation on PET can change the chemical structure of the surface and increase the number of polar groups on the surface, thereby increasing polarity and wettability. The results of the comprehensive analysis of the FT-IR spectra results for each plastic aggregate are as follows: (a) Depending on the gamma irradiation, the intensity of the identification peak increases or decreases, or a new functional group is formed. Through this, it was confirmed that the chemical structure of the plastic aggregates was changed by gamma irradiation. (b) The functional group on the surface of the aggregate is one of the most important factors for improving the interfacial bonding and mechanical properties between the aggregate and cement paste, and additional analysis and investigation are needed to determine how the functional group improves the structural stability.
# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 4 Experimental Results and Discussion
### Datasets
#### 4.1.1 RSOD Dataset  
The RSOD dataset, developed by Wuhan University in 2015 for object detection in remote sensing imagery, includes four distinct object categories: airplanes, oil tanks, overpasses, and playgrounds. Specifically, the airplane category includes 446 images with 4993 annotated instances; the oil tank category contains 165 images with 1586 annotatedobjects; the overpass category covers 176 images with 180 annotated instances; and the playground category comprises 189 images with 191 annotated instances. To assess the performance of our approach, we randomly divide the dataset into a training set and a validation set with a 75% to 25% ratio.
## 1 Introduction
In this approach, the probability of failure is assessed considering the inherent uncertainties in soil properties, hydrostatic groundwater conditions and seepage behavior, resulting in a more comprehensive assessment of the safety and performance of the wall. In the design of embedded retaining walls, RBD enables engineers to systematically quantify the effects of uncertainties in groundwater levels, flow patterns and soil-water interactions [19; 20]. It uses statistical methods to model these variables as random parameters, reflecting their natural variability and lack of predictability in practice. By integrating these uncertainties into the design process, RBD provides a clearer understanding of the range of possible outcomes and identifies the likelihood of different failure modes, such as instability due to fluctuating groundwater pressure or unexpected seepage flows [21; 22; 23; 24]. The rotational and structural stability of cantilever retaining walls was evaluated by [PERSON] and [PERSON] [19] using a reliability analysis that also considers the effects of the variability of key design parameters on the reliability of the wall. The optimization of embedded retaining walls in the presence of groundwater and seepage requires a comprehensive approach that combines advanced optimization methods. By considering the uncertainties and complexities associated with the effects of groundwater, engineers can produce designs that are both reliable and capable of maintaining long-term stability. To achieve this, the optimization model for retaining walls (ORW) was developed. The optimization model includes several failure mechanisms such as rotational failure and piping. The case study suggests that reliability-based design can provide a better understanding of optimal design in terms of safety and has been shown to be more sensitive to capture the effects of seepage based on the case study. Therefore, RBD can offer greater certainty regarding the retaining wall's capacity to withstand uncertain conditions, including hydrostatic pressures and seepage (groundwater-flow), ultimately leading to more optimized structures. This paper also emphasizes the advantages of using a probabilistic framework as a design method over a deterministic approach based on partial safety factors. The proposed approach allows a direct comparison between the two design methodologies as it includes an optimization-based evaluation.
# Remote Sensing for Irrigation of Horticultural Crops
## 4 Approaches to Precision Irrigation
### Infrared Thermography
#### 4.2.1 Cwsi
[184] compared and evaluated the accuracy of several water-stress coefficient methods for estimating crop \(\mathrm{ET_{c}}\) under different levels of deficit irrigation. Four canopy temperature based methods were used to calculate Ks: CWSI, Canopy Temperature Ratio (\(\mathrm{T_{c}}\) ratio), Degrees Above Non-Stressed Temperature (DANS), Degrees Above Canopy Threshold (DACT). Methods using only canopy temperature measurements (DANS, DACT, and Tc ratio) performed with comparable error to more data intensive methods such as CWSI and demonstrated their potential for irrigation scheduling. [PERSON] et al. [185] carried out an experiment on apple trees, to compare two empirical indexes, a CWSId (averaged over daylight hours) and a CWSIm (midday CWSI). Since CWSI was very sensitive to mild variations in the soil water content, the authors suggested using CWSId and CWSIm for estimating soil water availability. These indices were also stable under transitional weather conditions. [PERSON] et al. [186] considered the time changes of IR for monitoring corn water stress at high spatial and temporal resolution for managing irrigation water within large agricultural production systems. In the 80-day study, they observed a statistical decrease in soil moisture \"_only when the CWSI value increased past 0.6 with 82% of soil moisture variation explained by the CWSI\"_. The CWSI was negatively correlated to soil moisture, demonstrating the potentiality of CWSI (i) as an alternate irrigation scheduling method; and (ii) for quantifying spatial soil moisture by IR canopy temperatures.
# Turn-to-Turn Fault Diagnosis on Three-Phase Power Transformer Using Hybrid Detection Algorithm
## 2 Hybrid Algorithm in TTF Diagnosis System
### Epua  
The third sub algorithm is the EPVA, which is based on a space vector transformation. A space vector transformation describes the three-phase systems' behavior and was applied to the PT's differential protection. The EPVA is based on the spectral analysis of the differential current Park's vector modulus AC level.  
The three-phase quantities are changed into a synchronous rotating reference frame using the ABC-DQ transformation [23]. The direct and quadrature current from Park's vector components (I\({}_{\mathrm{D}}\), I\({}_{\mathrm{Q}}\)) are obtained by the Park transformation using Equation (5) [10], where i\({}_{\mathrm{da}}\), i\({}_{\mathrm{db}}\), and i\({}_{\mathrm{dc}}\) denote the differential current for each phase. The current is used as inputs for calculating I\({}_{\mathrm{D}}\) and I\({}_{\mathrm{Q}}\).  
\[\left[\begin{array}{c}\mathrm{i}_{\mathrm{dA}}\\ \mathrm{i}_{\mathrm{dB}}\\ \mathrm{i}_{\mathrm{dc}}\end{array}\right] = \left[\begin{array}{c}\mathrm{i}_{\mathrm{LA}}\\ \mathrm{i}_{\mathrm{LB}}\\ \mathrm{i}_{\mathrm{LC}}\end{array}\right] +\frac{\mathrm{N}_{\mathrm{s}}}{\mathrm{N}_{\mathrm{p}}}\left[\begin{array} []{ccc}1&0&-1\\ -1&1&0\\ 0&-1&1\end{array}\right]\left[\begin{array}{c}\mathrm{i}_{\mathrm{LA}}\\ \mathrm{i}_{\mathrm{LD}}\\ \mathrm{i}_{\mathrm{LC}}\end{array}\right] \tag{4}\]  
\[\left[\begin{array}{c}\mathrm{I}_{\mathrm{D}}\\ \mathrm{I}_{\mathrm{Q}}\end{array}\right] = \left[\begin{array}{ccc}\sqrt{\frac{2}{3}}&-\sqrt{\frac{1}{6}}&- \sqrt{\frac{1}{6}}\\ 0&\sqrt{\frac{1}{2}}&-\sqrt{\frac{1}{2}}\end{array}\right]\ \left[\begin{array}{c}\mathrm{i}_{\mathrm{dA}}\\ \mathrm{i}_{\mathrm{dB}}\\ \mathrm{i}_{\mathrm{dC}}\end{array}\right] \tag{5}\]  
\[\mathrm{i}_{\mathrm{EPVA}} = \left|\mathrm{i}_{\mathrm{D}}+\mathrm{i}_{\mathrm{Q}}\right|= \sqrt{\mathrm{i}_{\mathrm{D}}^{2}+\mathrm{i}_{\mathrm{Q}}^{2}} \tag{6}\]  
The DC level i\({}_{\mathrm{EPVA}}\) is obtained using Equation (6). The EPVA current is proportional to the balanced three-phase current system (conditions A and B in Figure 3). An incipient  
Figure 2: The negative sequence directional degree classification.  
winding fault leads to an increase in the magnitude of the differential current of the affected phase compared with a healthy situation, which results in an unbalanced three-phase currents system (condition C in Figure 3). Under these conditions, the differential current Park's vector modulus contains a DC level and an AC level at twice the supply frequency (2f) [24; 25]. The subalgorithm EPVA technique in this research is used to distinguish between internal fault and normal condition.
# Experimental Study of a Novel Prototype of Dual-Cycle Two-Phase Cooling System for a Data Cabinet
## 2 Description of the Dual-Cycle Two-Phase Cooling System
\begin{table}
\begin{tabular}{c c c c c} \hline
**Instrumentation** & **Brand** & **Model** & **Range** & **Precision** \\ \hline PID controller & Hongrun & E300 & \(-\)150–150 \({}^{\circ}\)C & \(\pm\)0.1 \({}^{\circ}\)C \\ Chiller & Changhong & AL-300 & 5 HP & - \\ Server & IBM & X5680 & 0–100 \({}^{\circ}\)C & \(\pm\)1.0 \({}^{\circ}\)C \\ Pump & Suofu & NP190 & 0–10 L/min & - \\ Flowmeter & Weill & WL-LWDN4 & 0.67–4.17 L/min & \(\pm\)0.2\% \\ Anemometer & Testo & 405-V1 & 0–10 m/s & \(\pm\)0.1 m/s \\ Temperature sensor & Stars sensors & CWDZ11 & 0–100 \({}^{\circ}\)C & \(\pm\)0.1 \({}^{\circ}\)C \\ Power monitor & Gcnzdes & DTS4921 & 10 kW & \(\pm\)0.2\% \\ \hline \end{tabular}
\end{table} 
Table 2: The instrument parameters used in the experiment. \begin{table}
\begin{tabular}{c c c c c} \hline
**Cooling System** & **Measurement Object** & **Symbol** & **Range (kPa)** & **Precision** \\ \hline \multirow{8}{*}{PCTCS} & Inlet of liquid phase manifold & P101 & & \\  & Middle of cold plate evaporators & P102 & 0–500 & \\  & Bottom of two-phase manifold & P103 & & \\  & Reservoir of internal loop & P105 & & \\ \cline{2-5}  & Inlet of PHE & P201 & & \\  & Outlet of PHE & P202 & & \\  & Reservoir of external loop & P203 & & \\ \hline \multirow{8}{*}{PBTCS} & Inlet of PFHE & P301 & 0–1200 & \\  & Outlet of PFHE & P302 & & \\ \cline{1-1}  & Inlet of FC & P303 & & \\ \cline{1-1}  & Inlet of PHE & P304 & & \\ \cline{1-1}  & Reservoir & P305 & & \\ \hline \end{tabular}
\end{table} 
Table 3: Pressure measuring point layout and parameter information. \[\text{COP = \frac{Q}{P},} \tag{2}\]  
\[Q\ =\ c_{p}\dot{m}\Delta T_{a}, \tag{3}\]  
where \(c_{p}\) is the specific heat at constant pressure, kJ/(kg. \({}^{\circ}\)C); \(\dot{m}\) is the mass flow rate of condenser air, kg/s; and \(\Delta T_{a}\) signifies the temperature difference of inlet and outlet air of condenser, \({}^{\circ}\)C.  
The experimental uncertainties related to temperature, cooling capacity, and pPUE can be determined based on the measurement accuracy and equations provided above. \[\delta R_{t}^{2}\ =\ \sum_{i\ =\ 1}^{N}\left(\frac{\partial R_{t}}{\partial X_{i }}\delta X_{i}\right)^{2}, \tag{4}\]  
where \(\delta Rt\) is the total uncertainty; \(R_{t}\) is the result of an experiment or calculation; and \(X_{i}\) is the various variables that affect the result, such as temperature, pressure, etc. Table 4 displays the experimental data of standard uncertainty.
# Fog-Based Two-Phase Event Monitoring and Data Gathering in Vehicular Sensor Networks
## Preliminaries  
### Network and Data Gathering  
We assume each vehicle, \(v_{i}\), monitors the road condition and surrounding environment through periodical sensing (Figure 1). It generates pieces of data and sends them to the roadside units (RSUs) through one-hop vehicle to infrastructure (V2I) or multi-hop vehicle to vehicle (V2V) transmissions. RSUs are fixed infrastructure on the roadside, which also serve as the fog nodes to provide computation, storage and networking services between the vehicular nodes and ITS (intelligent transportation system) clouds. For example, Intel's Next Unit of Computing (NUC) is a small-form-factor computer, whose motherboard measures \(4\times 4\) inches and could be integrated with the RSUs. An RSU would initiate an event-checking procedure to evaluate the received data, which might lead to the upload of more detailed data to the base station when it considers this necessary. The detailed data are uploaded within a constrained time delay to the central ITS server. The server, due to its powerful computational resources and global knowledge of the road and the network, would then make the final decisions based on further data analysis.  
For an event-based data-gathering process, there are three kinds of tasks:  
* Continuous monitoring: the network is monitored through low-cost collection of sensing data so that ITS is informed when an event occurs. This task is represented by the event-checking procedure;
* Data gathering: more detailed data are gathered to the ITS cloud, and the data uploaded are time-limited and delay-constrained;
* Event verification: the ITS system verifies whether an event occurs and gives feedbacks to the VANETs.  
Furthermore, we assume the sensing operators equipped in vehicular nodes are roughly classified into two types/modes: the low cost sensing (LCS) mode and the high cost sensing (HCS) mode. The former requires much less sensing and calculation power than the latter. Each piece of sensed data is denoted by a tuple \(d(id,v,\alpha,ts,mode)\), where \(id\) is a unique identification of the sensing node, \(v\) is the sensed reading, \(\alpha\) indicates the confidence of the true reading, \(ts\) is the timestamp and \(mode\) is the mode of sensing, which is either LCS or HCS. The HCS mode is also called deep sensing, which would generate more accurate and detailed sensing data, e.g., through more accurate and powerful sensing devises or through a higher sensing rate.
## 6 Limitations and Future Work  
One of the limitations of this preliminary study was the limited number of participants in the two experiments. The reason for this limitation was the difficulty of recruiting participants with amputations. Moving forward, experiments with a larger number of participants should be conducted to determine the smallest detectable variation in the volume of the limb by the proposed method. A larger population could further increase the confidence level in relation to the possibility of detecting variations in the volume of the limb.  
The system proposed in this study was developed with the primary goal of providing feedback to the users for their limbs' volume adjustments to maintain a high quality of socket fit. To achieve this, a feedback system needs to be incorporated into the system in future work that alerts users when their limb volume needs to be adjusted, e.g., by adding or removing prosthetic socks. Such a feedback can be provided through a mobile application that connects to the sensory system. An added feature to this application could be the continuous calibration of the system.  
On average, the five repetitions of each test took approximately one minute for each participant. Prosthetic sockets were then doffed to adjust the number of sock plies, also releasing any pressure from FSRs between tests in the meanwhile. Since these tests were carried out in a short amount of time, we did not take the drift, creep, or the effect of temperature changes into account. The effect of these factors in prolonged use of the sensors should be characterized and accounted for to make the long-term application of the system possible.  
Conceptualization, C.A., B.P., and C.M.; methodology, C.A. and B.P.; validation, C.A. and C.M.; formal analysis, C.A.; investigation, C.A.; writing--original draft preparation, C.A.; writing--review and editing, B.P. and C.M.; visualization, C.A.; supervision, C.M.; project administration, C.M.; funding acquisition, C.M. All authors have read and agreed to the published version of the manuscript.  
Research supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), the Canadian Institutes of Health Research (CIHR), and the Canada Research Chair (CRC) program.  
The Principal Investigator, [PERSON], and members of his research team have a vested interest in commercializing the technology tested in this study, if it is proven to be successful and may benefit financially from its potential commercialization.  
The following abbreviations are used in this manuscript:  
FSR \[\text{Force Sensitive Resistor}\] \[\text{TSB}\] \[\text{Total Surface Bearing}\] \[\text{TPE}\] \[\text{Thermoplastic Elastomer}\] \[\text{PTB}\] \[\text{Patellar Tendon Bearing}\] \[\text{HMI}\] \[\text{Human--Machine Interface}\]