esa-sceva/satcom-chunk-collection huggingface dataset:
# Computationally Efficient Robust Color Image Watermarking Using Fast Walsh Hadamard Transform
## 1 Introduction
Spread spectrum (SS) watermarking is considered to be the most efficient, secure and robust watermarking scheme which yields high Signal to Noise Ratio (SNR), enhanced robustness and low distortion [1; 3]. As in DS-CDMA (Direct Sequence--CDMA), different watermarks are spread using mutually orthogonal codes or different parts of a single watermarks are spread using separate codes [2; 4]. The additive noise and signal distortions in communication systems are similar compared with the unintentional manipulations on the image and malicious attacks in watermarking. As the watermark is spread throughout the image bandwidth such that the energy of the embedded watermark in any particular frequency bin is very small, it is difficult for the eavesdropper to detect and destroy the watermark [2]. Most of the existing literatures use a single code to spread and few researchers have worked on multiple mutually orthogonal spreading codes [11; 14]. As each part of the watermark is spread throughout the cover image, the length of each spreading code can be increased providing more security. If N independent and orthogonal codes are used, the attacker has to break N codes [3]. The selection of transform for the decomposition of image and the properties of spreading codes determine the efficiency of the data spreading. This paper is organized as follows. Section 2 explains the proposed algorithm of embedding and decoding followed by the simulation results in Section 3 and finally, Section 4 concludes the paper. Mainly we have used, three different cover images and three different watermarks for analysis as shown in succeeding sections.
## 4 Experiments and Results
### Results for Redundancy, Overhead, and Delay
#### 4.5.3 End-to-End Delay Analysis  
The elevated Delay observed in baseline methods correlate closely with increased Redundancy and Overhead. High Redundancy implies more packet collisions and retransmissions, directly translating into extended Delay. Specifically, RL-PT's Delay increases because the instability introduced by frequent parameter reweighting causes sporadic bursts of broadcasts, leading to collisions, congestion, and subsequent retransmission delays. Likewise, NFRL's occasional neural approximation inaccuracies temporarily degrade the network's transmission efficiency, prolonging end-to-end Delay until the neural parameters reconverge.  
On the other hand, Proposed Method A effectively reduces Delay by employing a reinforcement learning strategy that maintains a balanced broadcast ratio tuned to minimize collision-induced Delay. This strategy directly addresses and alleviates congestion, hence minimizing retransmissions and their associated waiting times. Furthermore, Proposed Method B consistently delivers the lowest Delay due to its ability to dynamically select the optimal fuzzy profile. By activating profiles specifically optimized for critical network constraints (such as energy scarcity or high mobility), the method ensures fewer packet collisions and more efficient spatial distribution of broadcasts. Consequently, nodes quickly receive broadcast packets with minimal retransmission attempts, substantially lowering end-to-end Delay compared to single-profile methods.
# Aerodynamics and Motion Performance of the H-Type Floating Vertical Axis Wind Turbine
## 4 Results and Analysis
### Results and Analysis
#### 4.3.4 Motions of the Floating VAWT Considering the Wind and Irregular Wave Loads  
Motions of the floating VAWT were calculated considering the wind loads and irregular wave (JONSWAP sea spectrum) loads in this section. The calculation parameters are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Items**} & **Significant Wave** & **Spectrum** & \multirow{2}{*}{**\(V_{\infty}\)(m/s)**} & **Total Time (s)** \\  & **Height (m)** & & **Peak Period (s)** & \\ \hline LC4.1 & 2.1 & 9.74 & 5 & 3600 \\ LC4.2 & 3.62 & 10.29 & 15 & 3600 \\ LC4.3 & 6.02 & 11.38 & 25 & 3600 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Irregular wave and wind cases (LC4).  
Figure 11: Time histories of aerodynamic force on the blade: (**a**) Normal force; and (**b**) Tangential force.  
Figure 12: Spectra of blade aerodynamics: (**a**) Normal force spectra; and (**b**) Tangential force spectra.  
The time histories and spectra of pitch motions of LC4.2 and LC4.3 are presented in Figure 13. The max values, mean values and STDs of surge, heave and pitch responses of the floating VAWT are calculated, and the results are shown in Table 7.  
Figure 12 shows that the pitch motions of the floating VAWT increase with the increase of wave height and wind speed, and they are still dominated by the wave-frequency motions. Comparing Figure 12b with Figure 9b, the 2P frequency caused by the aerodynamic loads is more significant in the case LC4. Because of the effect of heave plates, the heave responses are small and the max values of heave is 1.15 m for LC4. In Table 7, we can find that the STDs of surge and pitch motions of the floating VAWT are small, which shows the floating VAWT has good motion performance in the wave and wind conditions.
# Zinc Oxide-Based Rotational-Linear Triboelectric Nanogenerator
## 3 Results and Discussion
### Connectivity of the Triboelectric Pairs  
Since the RL-TENG consists of three triboelectric pairs, a question that has been raised is how to connect them in order to obtain the maximum power from the device. One obvious approach is to connect each triboelectric pair with a bridge rectifier and then connect the outputs together either in series or in parallel. Such a solution has been proven to be an efficient way for scavenging the total amount of generated energy [17]. However, this means that we need to use three separate bridge rectifiers, increasing the complexity of the electronic components required for device operation. To avoid that, we have implemented an alternative approach. We have investigated the influence of the total stored energy for two different cams: (a) During the rotation of the first cam (disk A, Figure 7a), the tribogenerators are moving in phase as can be seen from Figure 7b, where we notice that the triboelectric signal of T1 and T3 are almost identical. (b) During the rotation of the second cam (disk B, Figure 7c), the tribogenerators are out of phase, and only one triboelectric pair is set in motion during a specific time interval, as can be seen in Figure 7d, where we notice that T1 is active when T3 is inactive. As a metric for the comparison between the two methods, we use the energy stored in a capacitor using a single bridge rectifier during charging.  
Figure 8 shows the energy stored at the capacitor as a function of time for triboelectric pairs T1 and T3 for two rotational velocities, i.e., 60 rpm (Figure 8a) and 300 rpm T3 (Figure 8b), when disk B is used. In the same figures, we see the energy stored when the triboelectric pairs are connected in parallel prior to the rectifier. We see that the energy of the combined triboelectric pairs is higher compared to each individual pair. Assuming that the total energy that should be stored in the capacitor is the sum of the energies that are stored in each triboelectric pair, from the analysis of the data, we see that for low rotational velocities, the stored energy is 67% of the total energy. However, as the rotational velocity increases, the amount of stored energy is reduced reaching 56% at 300 rpm. This energy loss is probably due to the random signal generated from the triboelectric pairs due to vibrations caused during device operation. These signals interfere in a destructive way,  
Figure 6: (a) Output voltage, (b) output current and (c) output power of the T1 triboelectric pair of the RL-TENG as a function of the external load for rotational velocities ranging from 100 to 300 rpm.  
cancelling out each other and reducing the amount of stored energy. Similar results were obtained when disk A was used.
## 2 Related Work
### Vector-Based Spatial Information Processing Technologies  
Vector-based data are essential for the precise representation and management of spatial information, requiring optimization techniques for processing large-scale data. One study improved GIS analysis accuracy and efficiency by standardizing building height data in LoD1 models [28]. However, this research primarily focused on building-centric data processing and did not address the integrated processing of various data types (e.g., roads, terrain). However, this research focused primarily on building-centric data processing and did not address the integrated processing of various data types (e.g., roads, terrain).  
Traditional raster-based GIS processing has been widely used in spatial analysis, including Digital Elevation Model (DEM) interpolation, satellite image classification, and land-use mapping [29; 30]. Raster-based methods represent spatial information as a fixed grid of pixels, making them suitable for continuous spatial phenomena such as terrain modeling and remote sensing. However, these methods suffer from high memory consumption, computational inefficiency in large-scale queries, and reduced precision in representing discrete geographic features (e.g., buildings, roads) [31; 32].  
To address memory usage issues in processing large-scale vector data, data compression techniques and parallel processing have been proposed. Notably, GPU-based vector data processing demonstrates the potential for performance scalability proportional to dataset size. These techniques, however, primarily focus on single workflow processes, limiting their application in the integrated processing of multi-source data and generating models compliant with standards such as CityGML.  
Despite these advancements, integrated management of large-scale vector data remains inefficient due to excessive memory usage and resource consumption. Additionally, CityGML 3.0 schema validation continues to impose significant computational overhead, limiting its applicability in large-scale urban modeling. To address these issues, this study proposes an optimized approach utilizing parallel processing and efficient data validation techniques with the CityGML Validator to improve processing efficiency and scalability [33].
## 2 Results
### Overexpression of AcGGP3 (Actinidia32270) Increases AsA Accumulation in Kiwifruit  
To explore the function of _AcGGP3_ (Actinidia32270) in AsA synthesis, an overexpression transgenic assay was performed in kiwifruit. The CDS of _AcGGP3_ was driven by the 35S promoter, and the recombinant plasmids were transformed into kiwifruit calli by the _Agrobacteriumium_-mediated method. T1-generation lines were selected on medium containing 75 mg/L G418. Two independent transgenic lines (_OE-GGP3#1_ and _OE-GGP3#10_) were obtained, in which the AsA content was measured by HPLC (Figure 6A,B) and gene expression was measured by RT-qPCR (Figure 6C). The results showed that the expression of _GGP3_ increased 1.4- and 3.1-fold, and the AsA content rose 2.0- and 6.4-fold, respectively, compared to those of the wild-type strain (Figure 6), indicating that _AcGGP3_ is a key gene that promotes AsA accumulation in kiwifruit.  
Figure 6: Actinidia32270 (_AcGGP3_) promotes AsA accumulation in kiwifruit. (**A**) Relative AsA contents of _AcGGP3_-overexpressing kiwifruit calli. WT: wild type; _OE-GGP3#1_ and _OE-GGP3#10_ represent two kiwifruit transgenic lines. (**B**) Typical high-performance liquid chromatography (HPLC) chromatograms of AsA in (**A**). AsA (Rt \(\approx\) 7.5 min). (**C**) RT-qPCR analysis of _AcGGP3_ in (**A**). All of the above experimental procedures were performed with three replicates. Error bars: \(\pm\)SD. Significant differences were detected by a \(t\)-test (*, \(p\)\(<\) 0.05; ***, \(p\)\(<\) 0.001).
# A Study on the Optimization of the Coil Defect Detection Model Based on Deep Learning
## 5 Conclusions and Future Research  
In this study, a deep-learning-based coil quality inspection model was optimized. The study was conducted through three processes (\"Constructing a coil image data set using a prototype\", 'YOLOv5 model optimization (to find the optimal parameter value) with 3 techniques: structure modification, model scaling, and lightweight', 'Data addition and model training with the CLAHE technique for dataset augmentation'). Through the above process, various results were analyzed. Finally, the model with model scaling and CLAHE showed the best performance, and the performance improvement of the coil defect detection model was up to approximately 1.6% compared with the base model. Although this study focused on small objects, it is expected to have future use in various fields such as manufacturing, machinery, and shipbuilding. Moreover, image preprocessing techniques were analyzed to additionally apply techniques suitable for research subjects and for use as new datasets. Finally, we would like to proceed with additional model optimization using algorithms other than those applied to YOLOv5.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13085200/s1](https://www.mdpi.com/article/10.3390/app13085200/s1)]([https://www.mdpi.com/article/10.3390/app13085200/s1](https://www.mdpi.com/article/10.3390/app13085200/s1)), Figure S1: Structure modification examples.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Inspector** & **Basic Model** & **Our Model** \\ \hline Inspection of 1000 & & & \\ coils & Around 100–150 s & Around 63–83 s & Around 50–70 s \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of the number of inspections per second.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **YOLOv5s** & & **YOLOv5s** & \\  & **(Basic)** & **YOLOv5m** & **YOLOv5l** & **(CLAHE)** & **Our Model** \\ \hline mAP & 95.3\% & 95.1\% & 95.6\% & 95.3\% & 96.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Analysis of mAP between YOLOv5 models.  
**Author Contributions:** Conceptualization, C.-M.N. and J.-C.L.; data curation, C.-M.N. and J.-G.J.; funding acquisition, S.-S.K.; methodology, C.-M.N. and J.-G.J.; resources, S.-S.K.; supervision, J.-C.L.; writing--original draft, C.-M.N., J.-G.J. and S.-S.K.; writing--review & editing, S.-S.L., S.-C.S. and J.-C.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the technology transfer and commercialization Program through INNOPOLIS Foundation funded by the Ministry of Science and ICT, (2022-BS-RD-0126-01-101/Development of intelligent machine vision technology for inspection of defects in appearance of small parts).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 CRKG in Massive MIMO and mmWave Systems
### Massive MIMO  
The large scale antenna array in massive MIMO systems offers high spatial dimension for the key generation. As the number of antennas increases, the signals are precoded to be concentrated toward one or more directions [42]. The narrow and directional beam increases the received SNR, as well as the secret key rate. Moreover, the narrow beam weakens the signal reception at the passive eavesdropper significantly.  
On the other hand, CSI acquisition is a challenging problem in massive MIMO systems. Orthogonal pilots are usually used to estimate the CSI and their length scales with the number of transmit antennas. When the number of antennas at the BS is large, it is impractical to estimate the downlink CSI at the user terminal side. Besides, eavesdroppers may utilize the pilot contamination attack [43], where the eavesdropper transmits the same pilot signals as the legitimate user. Then, the BS obtains the summation of the channel legitimate user and that of the eavesdropper, impacting the reciprocity of the channel observations of the BS and user terminals.  
Fortunately, the spatial sparse property of massive MIMO channels provides a potential solution [42; 44; 45]. With a massive antenna array, the BS can distinguish propagation paths from different directions. By preprocessing, the channel matrix (or vector) reveals the sparse property, where a few dominant elements represent the channel gains of different paths and contain the most channel energy [44; 45]. Then, the BS and user terminals can estimate their effective channel matrices with low dimensions, containing the dominant channel path information. For the pilot contamination attack, there exists a crucial complementary relation between the received signal strengths at the eavesdropper and the legitimate user [43]. This relation can measure the amount of information leakage to the eavesdropper and a rate-adaptation scheme is proposed.  
Figure 5 illustrates the secret key rates as the number of antennas increases. Consider 64, 128, and 256 antennas at the BS and single antenna users. When the antenna array at the BS is uniform linear array (ULA), we can employ the discrete Fourier transform (DFT) matrix to transform the channel vector into the beam domain [45]. In the beam domain, principle components or dominant beams of the channel vector contain most of the channel power. We calculate the secret key rate of the 40 strongest principal components. From the results, by extracting a few (e.g., 10-20) components, the secret key rate well approaches that of the whole channel. Thus, in the massive MIMO communications, we only need to estimate the principal components of the beam domain channel vector, significantly reducing the pilot overhead.
###### Abstract  
A sustainable information and communication flow (ICF) supports lifelines in floods, especially transport systems. A detailed insight into barriers regarding effective ICF through the implementation of information and communication technologies (ICTs) in the lifecycle of floods was given for evolved and evolving economies, i.e., York and Head-Marala, with sample sizes of 240 and 300 experts, respectively. All experts responded on an ethically approved questionnaire with further information notes that were used whilst discourning the factors. ICTs were segregated into two groups, ranging from simple to advanced technologies. KMO and Bartlett's tests confirmed high sampling adequacy with values ranging from 0.679 to 0.823 (\(\geq\)0.5) with \(p\)-values \(\leq\) 0.05. An amended version of Q-Methodology was used to identify nine factors in total. Each factor with an Eigenvalue \(\geq\)1 was retained, and all factors were highly reliable with values between 0.89 and 0.96. Factors were explained through communalities, factor loadings, pattern and structure matrices, and notes from experts. Results showed that under-evolved economies have limited technological resources and under-developed flood coping plans compared to evolved economies. Also, the unacknowledged possibility was uncovered that ICF can certainly be sustained if all possibly available ICTs are engaged through a thorough deployment plan of action. Authorities needed to make ICT engagement plans simple and efficient through effective coordination among different institutions. Though authorities were trained and equipped with modern tools, equipment, and technologies, dense and dependent populations overwhelmed the rescue capabilities. Other than VMS, social media pages, and radio, other ICTs were not tried and tested in floods for the exchange of transport-flood ICF. These findings are useful for stakeholders from all communities, transport planning institutions, and flood managers who are not fully benefiting from the extended use of ICTs to manage travel activities in floods.  
developed and developing countries; floods; information and communication flow; information and communication technologies; transport systems +
Footnote †: journal: Applied  
0000-0001-5992-2300][PERSON]. [PERSON]
## 3 Results and Discussion
### Results Analysis of Single-Factor Experiments
#### 3.1.3 Effect of Sterilisation Temperature on NFC Goji Juice Quality  
The effect of sterilisation temperature on juice quality is illustrated in Figure 4. At a sterilisation temperature of 100 \({}^{\circ}\)C, the juice achieved the highest TSS/TA ratio and sensory score, at 18 and 75 points, respectively, with a turbidity of 1294 NTU. However, as the temperature increased to 110 \({}^{\circ}\)C, juice quality declined, with a 15.56% decrease in the TSS/TA ratio and a 6.67% decrease in the sensory score compared to those at 100 \({}^{\circ}\)C. This decline is attributed to higher sterilisation temperatures causing flocculation in the juice, where the precipitate adsorbs some of the total soluble solids (TSS), resulting in decreased TSS content [36]. [PERSON]'s [36] study on the impact of heat treatment on non juice quality indicated that low-temperature heat treatment (85 \({}^{\circ}\)C for 5 min) significantly increased juice TSS (\(p<0.05\)), whereas medium and high-temperature treatments (100 \({}^{\circ}\)C and 115 \({}^{\circ}\)C for 5 min each) decreased TSS content. This suggests that low-temperature heat treatment can better maintain juice quality. However, in the current study, 100 \({}^{\circ}\)C provided the best juice quality, likely due to the short duration of high-temperature instantaneous sterilisation, which did not adversely affect the juice quality.
# Recent Advances in Artificial Intelligence-Assisted Ultrasound Scanning
## 3 Scanning Guidance with AI
### Robot-Operated Systems  
A significant amount of the published articles on ultrasound imaging scanning assistance discuss how to control the movement of a robot with an US transducer. [PERSON] et al. [62], from Philips Research, proposed a system composed of a high-end ultrasound device, a robotic arm, and a fetal US phantom used to mimic patient anatomy. They claim that initial attempts to develop control systems for robots that screen certain organs were traditionally not fully automated or sufficiently accurate [63; 64]. They obtained relatively good results in an ideal scenario with phantom data and a simple DNN architecture inside their control system.  
[PERSON] et al. [65] used a transfer learning approach using an 18-layer CNN pre-trained using the ImageNet dataset, and they modified the two latest layers to build two fully connected regressors that are trained from scratch to learn the relative position and orientation with image clues. They modified the loss function considering 6 degrees of freedom [66]. This approach has several limitations, but the system was able to indicate how to move the probe from an arbitrary position to reach a target image in most phantom locations.  
[PERSON] et al. [67] follow a similar approach to [PERSON] [58]. However, they used a robotic arm to sample the patients on a grid for scanning the lower back (instead of manually sampling the gridded surface as [PERSON] did) and, ideally, navigate on a flat surface to locate the sacrum area. Although this work also assumed an ideal simulated scenario, it showed several interesting contributions in RL and NN architecture.  
[PERSON] et al. [68] proposed a robotic device that could achieve dense 3D ultrasound scanning over a flat surface in the 2D plane. Even in ideal laboratory conditions, the study presented several weak points, such as the fact that the depth camera (a Kinect-based sensor camera) does not work properly when the acoustic gel is applied on the surface of the phantom due to the specular reflections and refractive optical properties of the acoustic conductor gel.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multicolumn{3}{c}{**Scanning Guidance Robot-Operated Systems**} & \multirow{2}{*}{**AI Model**} & \multirow{2}{*}{**Results**} \\  & & & & & \\ \cline{1-1} \cline{6-6} \cline{5-6} \  
In summary, these robot-related works show that fully automated ultrasound scanning with robots is promising but still far from being integrated into clinical practice.