esa-sceva/satcom-chunk-collection huggingface dataset:
# A Photo Identification Framework to Prevent Copyright Infringement with Manipulations
## 5 Experimental Results
### Image Verification
#### 5.3.1 Image Alignment  
We verify whether AKAZE [46] is precise enough to perform the image alignment, and the results are shown in Table 4. After the image alignment, the significantly high accuracies are shown with small deviations. These average IoUs are not visually distinctive, and AKAZE is sufficient to utilize for _Image Verification_.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Binary**} & \multicolumn{4}{c}{**IoU**} \\  & **Descriptor Size** & \(\mathbf{\sim}\)**0.7** & \(\mathbf{\sim}\)**0.8** & \(\mathbf{\sim}\)**0.9** & \(\mathbf{\sim}\)**1.0** \\ \hline \multirow{3}{*}{HoG [11]} & 128 & 4.5\% & 36.8\% & 72.4\% & 88.5\% \\  & 256 & 5.4\% & 40.5\% & 78.0\% & 92.5\% \\  & 512 & 7.8\% & 45.6\% & 83.5\% & 94.1\% \\ \hline \multirow{3}{*}{BinaryGAN [10]} & 128 & 15.5\% & 47.6\% & 78.5\% & 90.6\% \\  & 256 & 22.6\% & 55.1\% & 82.9\% & 95.2\% \\  & 512 & 25.7\% & 56.3\% & 85.7\% & 95.9\% \\ \hline \multirow{3}{*}{Proposed image hashing} & 128 & 18.7\% & 50.6\% & 80.0\% & 91.0\% \\  & 256 & 26.8\% & 59.2\% & 83.7\% & 95.5\% \\ \cline{1-1}  & 512 & 30.1\% & 62.4\% & 86.8\% & 96.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Identification results on the copyright image dataset by applying the HoG, the BinaryGAN and the proposed method with geometric manipulation and binary descriptor sizes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Binary**} & \multicolumn{2}{c}{**Color**} & \multicolumn{1}{c}{**Brightness**} & \multicolumn{1}{c}{**Gaussian**} \\  & **Descriptor Size** & **Distortion** & **Change** & **Blur** \\ \hline \multirow{3}{*}{HoG [11]} & 128 & 77.6\% & 93.2\% & 89.2\% \\  & 256 & 80.9\% & 94.4\% & 90.9\% \\  & 512 & 82.1\% & 95.6\% & 92.6\% \\ \hline \multirow{3}{*}{BinaryGAN [10]} & 128 & 76.8\% & 92.9\% & 89.0\% \\  & 256 & 78.0\% & 94.0\% & 90.6\% \\  & 512 & 80.9\% & 95.4\% & 92.1\% \\ \hline \multirow{3}{*}{Proposed image hashing} & 128 & 77.4\% & 93.4\% & 90.2\% \\  & 256 & 78.9\% & 94.7\% & 91.7\% \\ \cline{1-1}  & 512 & 81.2\% & 95.8\% & 93.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Identification results on the copyright image dataset by applying the HoG, the BinaryGAN and the proposed method with various color manipulations and descriptor sizes.
## 5 Experiments and Discussion
### Ablation Study  
To answer **EI2**, we investigated how effective the key components were on TRSA by designing five variations: (1) **TRSA\(\backslash\)T** removes the trust-aware evidence reasoning module. (2) **TRSA\(\backslash\)Sp** removes the spatial characteristics of information propagation. (3) **TRSA\(\backslash\)Tm** removes the temporal characteristics of information propagation. (4) **TRSA\(\backslash\)Sp\(\&\)Tm** removes the spatiotemporal characteristics of information propagation. (5) **TRSA\(\backslash\)EA** replaces evidence aggregation with a max-pooling layer and a fully connected layer.  
In Figure 5, one can see that all variations performed less well than the complete TRSA model on both datasets. Specifically, when removing the spatiotemporal characteristics of information propagation, the F1 dropped by 5.5% on the PHEME dataset and 6.7% on the CED dataset. This indicates the necessity of the temporal and spatial propagation structure information to improve model performance. Furthermore, the results demonstrate that removing spatial structure caused a larger decrease in model performance compared to removing the temporal structure. This indicates that the spatial structure was more effective than the temporal structure. When removing the trust-aware evidence reasoning module, the decrease in terms of F1 on PHEME was 3.6% and that on CED was 2.9%. This demonstrates that the impact of low-quality comments on the performance of the model could be mitigated by the evidence credibility index. The replacement of the evidence aggregation module led to a decrease in F1 of 4.8% on PHEME and 3.8% on CED. This demonstrates the necessity of aggregating evidence semantics to achieve better performance.  
Figure 5: _Cont._
## 3 Experiments and Results
### Performance Evaluation Metrics of the Network Model
#### 3.2.4 mAP (Mean Average Precision)  
The mean Average Precision (mAP) is a standard metric for evaluating object detection models, reflecting the overall accuracy across all object classes. In this study, mAP is reported in two variants:  
[EMAIL_ADDRESS]: The average AP value for all categories when the Intersection over Union (IoU) threshold is set to 0.5.  
[EMAIL_ADDRESS]:0.95: The average mAP under the COCO evaluation standard, where IoU is evaluated from 0.5 to 0.95 with a 0.05 step, reflecting the model's detection performance at varying levels of localization accuracy.
# Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades
## 2 Experiment  
In this section, the sensor system and the test setup are presented. Since our approach aims to measure vibrations at any position along the blade, sensors were mounted on the outside of the blade by using self-adhesive erosion protection tape (see Figure 2). Thereby, placement restrictions on the inside of the blade could be avoided. Vibrations were measured by integrating accelerometers into a robust sensor solution by eologix [25], originally developed for ice detection on the blade. Those sensors are flexible with a thickness less than 2 mm to minimise additional aerodynamic noise. Any cabling on the outside of the blade is avoided by using solar energy harvesting and a rechargeable energy storage. In addition, wireless communication is used and measured data are transferred to a base station in the tower or nacelle of the turbine.  
The wireless prototype sensor used triaxial MEMS accelerometers with a measurement range of \(\pm\)16 g and a sensitivity lower than 0.49 mg, with 1 g = 9.81 m s\({}^{-2}\) corresponding to acceleration due to gravity. Acceleration was measured in measurement campaigns at a sampling rate of 400 Hz (Prototype 1) and 833 Hz (Prototype 2), respectively, for a duration of 10 s each with cyclic transmission to the base station. For the proposed analysis method, temporal synchronisation of measurements across sensors was not required but is considered beneficial in future monitoring studies.  
Vibration measurements were collected in two test setups: In a 2.5-month test period, Sensors S1 and S2 of Prototype 1 were tested on a 63 m rotor blade in operation of the turbine (see Figure 3, left). For this turbine, 1640 and 1100 measurements were available during uniform rotation of the turbine for Sensors S1 and S2, respectively. Measurements during standstill or steering of the turbine, i.e., non-uniform rotational movement, were not taken into account. These measurements were conducted during a large range of environmental and operational conditions and were used to develop our proposed fingerprint method as presented in Section 3.  
Figure 2: (**Left**) Exemplary sensor mounting on the outside of the blade during construction of a wind turbine. (**Right**) Demonstration of flexible sensor layout.  
In addition, an operational 2 MW turbine with a 70 m rotor diameter was equipped with Sensors S3-S32 of Prototype 2 in the eastern part of Lower Austria (see Figure 3, right). For those sensors, a maximum of 170 measurements were available from a 2-month test period. Characteristic fingerprints were extracted, and the results are presented in Section 4. For readability, sensor positions are also displayed with results in Section 4.
# Using Virtual Soundwalk Approach for Assessing Sound Art Soundscape Interventions in Public Spaces
## Materials and Methods  
### Selected Urban Open Spaces  
Three case study sites were chosen to reflect a variety of approaches to interpretation of soundscape interventions with musical features. The musical features investigated include traditional harmonies (location A: Obala Petra Kresimira IV seaside promenade in Zadar, Croatia), a synthesized cluster chord (location B: Kunsthaus Graz plaza in Graz, Austria;) and an architecturally designed echo effect (location C: Maksimir Park in Zagreb, Croatia). The soundscape intervention at locations A and C are fully dependent on the environmental conditions (A) and users (C) and driven by them so they do not require electrical power to work, while the sound art installation at the location B functions much more independently in relation to the context but requires a constant power supply.  
The three urban open spaces chosen for this study were considered to be integrally designed as a \"soundscape application\" in the initial phase of the architectural and/or urban design. All three sites were considered to be attractive and well used public spaces. The analyzed projects were not conceived as a noise mitigation measure and the sites were not treated for lowering the noise level in the time of the recordings. The sites and the locations of the measurement points are shown in the Figure 1.  
Two selected urban open spaces are situated in a historical city centre (Zadar and Graz) and were designed within its revitalization (Graz) and reconstruction (Zadar) plans. One of the selected urban open spaces--Maksimir Park in Zagreb--is the cultural heritage site, a historical park located 1.9 km east to the historical center. All three locations are typologically different (a seaside promenade, an urban park, and a museum plaza) and they significantly differ in their area size, but they all feature a measurement point with clearly perceptible musical elements, as shown in Figure 2.  
Figure 1: Satellite images of the case study sites (from left to right): (**A**) Obala Petra Kresimira IV seaside promenade in Zadar (HR), (**B**) Kunsthaus Graz plaza in Graz (AT), (**C**) Maksimir Park in Zagreb (HR). Measurement points are marked with a corresponding code. Satellite images obtained using the Google Earth Pro 7.3.2.5776 application.
## 3 Results and Discussion
### Compressive Strength  
The test specimen used for the compressive strength test is cement paste. Figure 9 shows the compressive strength of the cubed UHSC specimen with a side length of 50 cm for \(3,7\), and 28 days of curing (three replicates were used for each test). It is clear that nS improved the compressive strength of UHSC, regardless of the curing age and dosage of nS. The main mechanisms behind this were the nano-filler effect [44] and the pozzolanic reaction [29]. The addition of nS can partially fill large pores and capillary pores. In addition, the addition of nS reacts with the calcium hydroxide formed by the binder hydration to produce more C-S-H gel [45].  
Compared to the control group at 28 days of age, the compressive strength increased by 26.31% with 2 wt.% added, and the compressive strength increased by 30.32 with 4 wt.% added. As the addition of nS increased, the ability of nS to enhance the compressive strength of UHSCs decreased significantly [46]. Similar experimental results have been reported in previous studies [11]. This behavior is most likely due to the fact that excess nS did not contribute significantly to the compressive strength. In addition, because of the high surface energy of nS, the pronounced tendency towards agglomeration reduced the ability to enhance the compressive strength [13].  
Figure 10 shows the relationship between compressive strength and the cumulative heat of hydration at 3 days of age for UHSCs. A linear relationship between the compressive strength and  
Figure 8: XRD patterns of UHSC casting for 28 days.  
Figure 9: Compressive strength change of UHSCs at 3, 7, and 28 days.  
cumulative heat of hydration can be clearly observed. The hydration heat came from the chemical reaction of binders, and the compressive strength came from the deposition of reaction products. The essence of hydration heat was found to be closely related to compressive strength development [47].
## 4 Cavitation Effects
### Spectral Analysis of the Stator and Turbine Torque  
The vapor volume is a convenient parameter for understanding the transient behavior of cavitation flows [31; 32]. In the stator, the evolution of the cavitation flow can be illustrated by the time history of the vapor volume. The variation of the vapor volume was periodic because of cavitation shedding from the suction side, as shown in Figure 14. The predicted cavitation shedding frequency was approximately 199 Hz. The O-type cavitation and shedding cavitation induced a variation of \(0.24\times 10^{-6}\) m\({}^{3}\) in the vapor volume in the stator domain, which degraded the hydrodynamic performance. The cavitation shedding frequency agreed well with the frequency of the stator torque, which indicates that the cavitation flow induced the vibration of the stator.  
In the turbine, the evolution of the cavitation flow can be illustrated by the time history of the vapor volume. The vapor volume variation due to the rotor-stator interaction and turbine cavitation was periodic, as shown in Figure 15. The complicated cavitation processes may be attributed to the turbine blades, which are twisted in 3D. The cavitation frequency agreed well with the frequency of the turbine torque, which indicates that the cavitation flow induced the vibration of the turbine, as shown in Figure 15d. The turbine had the attached cavitation type, so the turbine torque was mainly attributed to the interaction between the pump and turbine. In addition, the attached cavitation shifted from the shell to the core with a non-uniform distribution, which induced instability. In general, the cavitation flow and torque demonstrated strong interdependence, and a cavitation flow induced vibration of the impeller torque.  
Figure 13: Mass flow rate and capacity constant caused by cavitation at various \(SRs\).  
Figure 14: Variations in the vapor volume and torque of the stator in the time and frequency domains (\(SR=0\)): **(a)** vapor volume, **(b)** vapor volume spectrum, **(c)** torque, and **(d)** torque spectrum.  
Figure 15: Variations in the vapor volume and torque of the turbine in the time and frequency domains (\(SR=0\)): **(a)** vapor volume, **(b)** vapor volume spectrum, **(c)** torque, and **(d)** torque spectrum.
## 3 Field Test Analysis
### Re-Survey Testing in the Tar Processing Area  
Based on the detection results of each survey line in Section 3.2, the key monitoring area (tar processing side) was re-examined after conducting a preliminary investigation of subsurface structures. Special attention was given to Survey Lines 1-3 and Densified Line 2. Repeating high-density resistivity surveys in this area helps monitor the evolution of subsurface contamination. By comparing resistivity data from different time periods, the migration and diffusion of subsurface pollutants can be identified. This provides critical insights into the activity level of pollution sources, the extent of contaminant spread, and groundwater flow directions--particularly important for pollution monitoring at industrial sites involving groundwater protection, where re-survey lines can track contamination trends and detect potential risks of spreading.  
A time-lapse resistivity analysis offers more detailed information on subsurface contamination. By conducting a temporal analysis of resistivity data from different periods, the transport and variation of pollutants in the subsurface medium are revealed. This helps determine the migration velocity, diffusion range, and pathways of contaminants, thereby providing a scientific basis for developing effective remediation strategies.  
After completing the survey, all 60 electrode points were marked with small red flags. Two rounds of testing were conducted one week apart. The absolute difference in apparent resistivity was calculated. The results are presented as numerical differences in resistivity changes between the two tests: positive values indicate an increase in resistivity, while negative values indicate a decrease. According to Figure 7a, the absolute resistivity change plot for Survey Line 1-3 shows a reduction of 10-40 \(\Omega\cdot\)m in near-surface resistivity, likely due to increased soil moisture from rainfall during the testing period. Figure 7b reveals localized resistivity increases of 5-10 \(\Omega\cdot\)m at depths of 2-14 m along Densified Line 2, possibly caused by changes in contaminant infiltration.  
Relative resistivity is calculated as follows: (difference in absolute resistivity) \(\times\)/(first apparent resistivity value + second apparent resistivity value). As shown in Figure 8, the variation patterns of the two survey lines can directly reveal locations with significant resistivity change rates. For operating industrial plants, a time-lapse resistivity analysis can also assist in evaluating environmental risks and formulating relevant emergency plans. By real-time monitoring of subsurface medium changes, it can better predict the diffusion trends of contaminants, enabling timely implementation of corresponding countermeasures to safeguard surrounding residents and ecological environment safety.  
Figure 7: Absolute resistivity variation maps: **(a)** Line 1-3; **(b)** Densified Line 2.
# Efficient Health Data Transmission Method in a Wireless Body Area Network for Rural Elderly
## 5 Experiment and Analysis
### Transmission Performance Analysis of Emergency Data  
In order to further verify the performance of this algorithm in emergency data transmission, we compared the cumulative transmission delay and the cumulative average hops of the four algorithms for emergency data. As shown in Figures 8 and 9, the cumulative delay and hop of emergency data in the EEAR and MMR_OR algorithms were kept at a low level. These Figures show the cumulative delay and the cumulative hop in the whole simulation round. In Figure 3, all sensors in the EEAR algorithm died early. Most of the nodes in MMR_OR also died in about 400 rounds. Therefore, it is reasonable that its cumulative delay and hop were small. However, too many nodes dying early would not allow the stable monitoring of human parameters and will lead to high equipment costs. It can be seen that when the number of alive nodes was in about the same range (before 380 rounds of simulation), the cumulative delay of our algorithm was small, and we could select a transmission path with fewer hops for emergency data. The studied algorithm could maintain the network stability while considering the emergency data.  
Figure 8: Comparison of the emergency data transmission delay.  
Figure 7: Comparison of the average number of retransmissions.
# Principle and Implementation of Stokes Vector Polarization Imaging Technology
## 4 Application of Stoker Vector Polarization Imaging
A split-focal plane linear polarization camera (FLIR, BFS-U3-512P-C) was used to collect images with four different polarization directions, and then low-pass filtering was performed on the images to perform rough haze separation to reduce the adverse effects of noise. Secondly, the key parameters of the scattering model are estimated according to the filtered image, and a new transmittance image is obtained by image fusion technology to enhance the line-of-sight of the defogging image. Thirdly, these parameters are substituted into the scattering model to obtain the defogging image. Finally, in order to improve the visual effect of the image, an adaptive adjustment algorithm is used to adjust the light distribution of the image. Several fuzzy images were captured, and the conventional method and this scheme were used for defogging, and three evaluation indexes were used to quantitatively evaluate the defogging images. Experimental results show that this method can better solve the problems existing in the traditional method. This method cannot operate effectively under the condition of low DOP of background scattered light, reduce the adverse effects of haze, and ultimately enhance the visual effect and quantitative evaluation. The physical model of atmospheric scattering and Polarimetric imaging proposed in [51], as shown in Figure 10.  
Reference [8] proposed a method to suppress scattering by Stokes vector polarization, using the total radiation polarization information of the gradient image pixel level prior knowledge to build a global model, the method can solve the target image of a light polarization degree of global change, achieve the goal calculated polarization of the light with each pixel in the scene, to effectively restore the details of the underwater scene. Experimental results show that the global pixel calculation method has obvious effect on underwater scene image details restoration, the influence of backscattered light on underwater imaging is effectively suppressed, image contrast is significantly improved,  
Figure 10: The physical model of atmospheric scattering and polarimetric imaging. \(L(i,j)\): non-polarized light, \(A(i,j)\): Airlight, \(D(i,j)\): direct light, \(I(i,j)\): the irradiance [51]. and clear underwater vision is achieved. The underwater imaging model in reference [8] is quoted, as shown in Figure 11.
## 3 Methodology
### Structured Data Classification Framework
#### 3.2.3 Dictionary Augmentation Module  
In the Financial Data Hierarchical Classification Library of UP-SDCG, the detection of certain entities using regular expressions presents challenges. To address this issue, we need to construct specific dictionaries for entities such as names, app names, car brands, and others. Unlike keywords, the words within these dictionaries do not have exact semantic matches but tend to appear within similar contextual structures. Leveraging this characteristic, we propose a word2 vec-based augmentation scheme, illustrated in Figure 7, to enhance the detection capabilities.  
Figure 7: Word2 vec-based dictionary augmentation scheme.  
Word2 vec [37] is a word embedding technique introduced by Google, which aims to represent abstract words as vectors in the real number domain. The method comprises two architectures: Continuous Bag of Words (CBOW) and Skip-gram. CBOW predicts the current word based on its context, whereas Skip-gram predicts the context given the current word. To enhance training efficiency, Word2 vec introduces two training algorithms: Hierarchical Softmax and Negative Sampling. Word2 vec's ability to capture synonymy between words proves advantageous in dictionary construction. In our study, we utilized word2 vec to train a Chinese word vector library specifically tailored to the financial domain. By combining publicly available Chinese word vectors from the industry, we constructed a dictionary using word vector similarity. The process is as follows:  
1. Utilize open-source pre-trained word vectors, such as Tencent AI Lab Embedding Corpus for Chinese Words and Phrases [38], Stanford GloVe Embeddings [39], fastText word vectors [40];
2. Load the Embedding model with the selected pre-trained word vectors and fine-tune it using the financial corpus, which includes financial reports, financial news messages, etc.;
3. Subsequently, extract similar words from the fine-tuned word vectors using cosine distance to calculate the distance between words and construct the dictionary.