esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Rumor Detection
In this end-to-end network, joint features of text and social context were extracted using an LSTM network and image features using a VGG19 network. Moreover, the multimodal features were fused to generate a reliable fusion classification and perform an effective rumor detection. [PERSON] et al. [4] proposed an end-to-end framework called Event Adversarial Neural Network (EANN) to identify fake news in emerging events. It could derive event invariant features for the fake news detection of unseen events. It consisted of three main components: a multimodal feature extractor, a fake news detector, and an event identifier. They were used to extract features, detect rumors, remove event-specific features, and maintain shared features between events, respectively. [PERSON] et al. [5] used a multimodal variational autoencoder (MVAE) to explore the correlation of different modalities and obtain a direct joint representation of the modalities.Moreover, [PERSON] et al. [6] proposed a similarity-aware rumor detection method SAFE (Similarity-Aware FakE news detection), which investigated the relationship between features extracted across modalities, jointly learned the feature representation and the interrelationship between news text information and visual information, and identified news articles as rumor information based on the text, image, or \"mismatch\" between the two. [PERSON] et al. [7] proposed a model for multimodal rumor detection based on cross-modal attention residuals and multichannel convolutional neural networks, CARMN (Attention Residual and Multichannel convolutional neural Networks). The model performed a selective extraction of information related to the target modality and could extract text features from both the original text and the fused text simultaneously; based on this method, the authors reduced the noise impact of the modality fusion process. [PERSON] et al. [8] proposed a multimodal rumor detection method based on ambiguity perception, called CAFE. The method included a cross-modal alignment module, a cross-modal ambiguity learning module, and a cross-modal fusion module. CAFE could adaptively aggregate the single-mode and cross-mode correlation features, that is, it relied on the single-mode feature when the cross-mode fuzziness was weak and referred to the cross-mode correlation when the cross-mode fuzziness was strong, so as to improve the accuracy of the rumor detection. In summary, it can be found that the exploration of feature information within different modalities has not been fully explored, and multimodal rumor detection by feature splicing only in the fusion stage loses valid information and leads to a redundancy of modal information.
## 3 Results and Discussion
### Mechanical Property Investigation Results
#### 3.4.2 Tensile Test Results  
To be able to observe the mechanical property enhancements of the workpieces after Vo-CAP processes, a tensile test was conducted, and the obtained stress-strain diagram is illustrated in Figure 19. As mentioned before, the comparison was carried out between annealed and multipass ECAP-processed workpieces. According to the literature, the ultimate tensile strength (UTS) value for annealed AA6082 workpieces is about 135 MPa and was obtained by [59; 62]. In this study, the UTS value was 143,94 MPa, which is very similar to the reference studies. After the multipass ECAP process, a UTS value of about 350 MPa was obtained, which is much better than annealed workpieces [59]. In return for  
Figure 18: Hardness values of annealed and Vo-CAP-processed samples.  
this novel study, after the Vo-CAP process, the obtained UTS value was 420 MPa, which is better than both the annealed and multipass-processed workpieces. So, it is easy to say that the mechanical property of AA6082 workpieces was enhanced after the Vo-CAP process.  
Moreover, the fracture surface of the tensile test specimen exhibits a 45-degree shear failure mode, as indicated by the red dashed line in Figure 20.  
A similar enhancement was acquired after the Vo-CAP process in terms of both the yield strength and toughness values. The obtained results are given in Table 4. The yield strength value increased from 92 MPa to 248 MPa after the Vo-CAP process compared to annealed material. The result is a further improvement in the material's elasticity properties. This conclusion can also be reached clearly when the elongation-at-break values are analyzed. While this value was 22.43% for annealed material, this value was reduced to 8.12% as a result of the Vo-CAP process as illustrated in Table 4.  
When the obtained results were compared in terms of toughness, after the Vo-CAP process, the calculated value was 30.09 MJ/m\({}^{3}\) [Table 4]. When the literature is examined, the toughness was generally reduced after the one-pass ECAP process. This is due to a reduction in the elongation of the material as a result of an insufficient number of passes through the ECAP process [63]. In comparison with the annealed material, although the toughness value decreases after the single-pass ECAP process, this value does not decrease and increases slightly as a result of this novel Vo-CAP process, indicating that the process also gives more efficient results for this parameter.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Sample** & **YS (MPa)** & **UTS (MPa)** & **Elongation at Break (\%)** & **Hardness (HV)** & **Toughness MJ/m\({}^{3}\)** \\ \hline Annealed AA6082 & 92 & 143.94 & 22.43 & 49.5 & 27.74 \\ \hline Vo-CAP AA6082 & 248 & 415.75 & 8.12 & 113.2 & 30.09 \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of mechanical properties of annealed and Vo-CAP-processed AA6082.  
Figure 19: Stress–strain curve of annealed and Vo-CAP-processed samples.  
Figure 20: Fracture surface of tensile test specimen.
###### Abstract
Results obtained from the Snellen chart can be converted to the LogMAR scale and vice versa either using conversion tables [12] or online tools [13]. [PERSON] et al. demonstrated [11] that \"similar acuity results were recorded from all three charts, suggesting a lack of a systematic bias as regards chart design. A small practice effect was observed for all charts but was greatest for Snellen and least for ETDRS\". TPRs are gaining prominence as indispensable tools for remote work. In this context, our research aims to guide educational institutions contemplating the integration of TPRs into their educational processes and assist potential users in making well-informed decisions when selecting a model that aligns with their expectations. Our motivation for conducting a comparative analysis lies in the assumption that all TPRs will be employed in identical training scenarios. Consequently, a direct comparison of their characteristics emerges as the most optimal method for deriving meaningful insights. The measurements in our study adhere to an industry-standard approach, utilizing optometric tables--a well-established method for measuring vision acuity over several decades. Extending this approach to estimate the vision properties of TPRs is logical, given the congruence of the measured characteristic. It is noteworthy that various visual cues and indicators are designed with individuals with reduced vision acuity in mind. Leveraging the optometric table allows us to make practical estimates, such as determining if a person with 20/40 acuity can read text without glasses from a certain distance, so can the operator of the TPR. This application adds a pragmatic dimension to our research findings. Thus, this study aims to evaluate the visual capabilities of several TPR models in a test environment that mitigates the effect of secondary factors not directly related to the image acquisition system of TPRs, proving high-quality internet connection and good lighting conditions. The study is guided by the following research questions:  
1. To what extent do the visual attributes of the examined TPRs facilitate their efficacy in supporting active engagement during academic activities such as school lessons and university lectures? 2. Which models of examined TPRs demonstrate better results in terms of streaming better-quality videos? The paper has the following structure. Following this introduction, the paper will outline the materials and methods used in the study, including the measurement of visual acuity and text readability on a wall projector. The results of the study will then be presented, followed by a discussion of the findings. Finally, the paper will conclude with a summary of the key findings and implications for future research.
# A Dynamic Framework for Community-Facility Siting with Inter-Community Competition
## 4 Empirical Study
### Data Sourcing and Processing  
#### 4.2.1 Demand Points  
For this study, the demand point set \(I\) comprised all residential buildings within 83 communities. We obtained data for a total of 18,359 buildings with an outline including the number of floors, as well as boundary data for 414 residential areas from Amap (lbs.amap.com), one of China's most advanced navigation maps in February 2024, and 12,081 residential buildings was extracted. Then, we supplemented the unavailable data of historic residence buildings with cross-checking by field investigation and satellite maps. Finally, a demand point set of 13,776 residential buildings was created. According to the model's assumption, we adopted the housing floor area as the service demand indicator for each building.  
In China, housing is pivotal as the primary asset for residents, reflecting disparities in urban resource allocation and socioeconomic status [76]. Hence, the socioeconomic groups of residents can be defined by the types of houses. We addressed the types of residential buildings by considering housing prices and the attributes of residential areas [77]. Four data items from 378 residential areas were collected from China's largest real estate trading platform, Anjuke (shaoxing.anjuke.com), in February 2024: the average housing price of real-time transactions, building age, plot ratio, and property fees. Then, we classified all residential buildings into four groups through hierarchical cluster analysis in SPSS. We manually identified and categorized the remaining 36 residential areas with no real estate data. Specifically, Group 1 included villas, townhouses, and new high-end high-rise residences, characterized by high housing prices, a low or relatively low building age, and high property fees. Group 2 primarily comprised high-rise residential buildings of relatively high quality, which are distinguished by relatively high housing prices, high plot ratios, and relatively high property fees. Group 3 encompassed both old multi-story houses of relatively high quality and high-rise housing of lower quality, featuring median housing prices, relatively high building age, and low property fees. Group 4 mainly comprised old multi-story houses of low quality, characterized by low or relatively low housing prices, a relatively high building age, relatively low plot ratios, and low property fees. Meanwhile, those historical residential buildings with serious data missing were categorized into Group 5 separately. The distribution of five groups of residential buildings is shown in Figure 3.  
Figure 3: Distribution of residential buildings and candidate points.
## 3 Methodology
### Procedure of the Proposed Zero-Watermarking  
Based on the aforementioned methods, the specific process of the proposed vector map zero-watermarking is depicted in Figure 2, with the following steps:  
_Step_ 1: Firstly, the Douglas-Peucker algorithm is used to compress the original vector map \(M\) by setting the compression threshold as \(\rho\) and obtaining the feature points set \(V^{f}\). The point-constrained blocking method is then used to block the feature point set \(V^{f}\), with the setting of the initial blocking area as \(a\) and the threshold number of points as \(t\) to divide the data into m sub-blocks, and each sub-block can obtain a feature point set \(V^{f}_{r}\).  
_Step_ 2: Every sub-block can create a CDTN with feature points \(V^{f}_{r}\) and Equations (1) and (2) are used to calculate the angles of all triangles in the CDTNs. The maximum and minimum angles of each triangle are then selected to compose an angular sequence set \(A_{r}\).  
_Step_ 3: Equation (9) is used to build the complex sequence of angle values \(A^{p}_{r}\), and Equation (10) is used to obtain the sequence of phase values \(P^{p}_{r}\) following DFT. To create the feature sequence set \(P^{binary}_{r}\), each angle value in the sequence \(P^{p}_{r}\) is rounded down and converted to an 8-bit binary representation. \(P^{binary}_{r}\) is made equal length, with \(W_{inf}\) in accordance with the supplementary bits strategy in Equation (12).  
_Step_ 4: The binary image containing copyright content is taken as the initial watermark image \(W\), and the binary watermark sequence \(W_{inf}\) is read. The Arnold scrambling algorithm is employed to encrypt the watermark image, yielding the disordered watermark sequence \(W_{inf}^{exp}\).  
_Step_ 5: The Xor operation between \(P^{binary}_{r}\) and \(W_{inf}^{exp}\) yields one zero-watermark \(W_{r}^{zero}\) for each block and \(m\) distinct zero-watermark images in total. All zero-watermark images and the original watermark images are submitted to the intellectual property rights (IPR) center for preservation, and time stamps are added to resist interpretation attacks.
## 5 So, How Can We Take It to the Skies?
### How Do We Integrate into a AW189 Helicopter?  
In order to integrate the modified CT scanner structure into the AW189, it is important to first identify the overall dimensions of the interior of AW189, which can be seen in Figure 10. The entire CT scanner geometry was designed such that it does not impede the cabin entrance and exit any point.  
The shape of the AW189 interior consists of two sections--the main cabin and rear storage--and a proposed layout of the racks and CT scanner is as illustrated in Figure 13. The battery units and computers are repositioned from the default CereTom structure to the vacant spaces on the adjacent racks, where the LCD control panel is repositioned to the racks as well since the device is connected wirelessly. The default battery used in CereTom operates between 90 to 264 volts in an alternating current and at 1300 watts during the peak, where typical operations range between 110 to 120 volts. It can operate for up to 2 h when fully charged for typical operations [35]. For the application of Air-MSU, the battery's reserve power serves as a backup and is strictly for use during emergency events only and can be charged via the helicopter's auxiliary power unit (APU) during flight. Therefore, during normal operations, the CT scanner draws power directly from the APU.  
Figure 12: Major dimensions (**A**): front view (fully retracted); (**B**): side view (fully retracted), (**C**): (fully extended).  
Figure 13: Conceptual layout of AW189 interior with proposed CT scanner design.  
With the current design and layout, there is a head space of up to 23 cm between the CT scanner and the interior roof of AW189, providing additional room for further modifications where required, and the CT scanner is clear from the doorway of AW189. Furthermore, the central axis of the CT scanner bore is spaced at 67.5 cm above the rotorcraft floor, where this dimension will be crucial in selecting a stretcher that provides sufficient elevation. The stretcher will be attached to the helicopter floorboard via a fastening system, which also helps guide the movement of the stretcher and docking in a fixed position, similar to the Royal Melbourne Hospital road MSU setting.
## 5 Conclusions  
This study proposes a CNN-based monocular image approach for three-dimensional reconstruction of asphalt pavement macrotexture. The core innovation lies in developing a CNN model that reconstructs pavement macrotexture from two-dimensional images. To validate the model's accuracy and effectiveness, comparative experiments were conducted against state-of-the-art methods using both our proprietary dataset and public benchmark datasets. Systematic evaluation demonstrates the proposed method's superior reconstruction precision and practical value for asphalt pavement macrotexture analysis.  
1. The macroscopic texture RGB-D data set of asphalt pavement constructed in this paper can be directly used for model training. At the same time, the proposed CNN  
Figure 19: Linear relationship between the average texture depth of the 3D reconstruction model of the road surface and the value of \(FB_{20}\).  
Figure 20: The correlation between the structural depth of the 3D model of the road surface and the pendulum \(F_{B20}\).  
model shows excellent performance compared with other models on the NYU Depths V2 public data set.
2. The macroscopic texture of the pavement reconstructed by the CNN constructed in this paper can be directly used for the detection of asphalt pavement. The proposed macro-texture depth map meets the technical requirements of pavement skid resistance test in terms of resolution (208 \(\times\) 144 mm) and measurement accuracy.
3. Compared with the traditional sanding method, this method shows significant advantages in reconstruction efficiency and engineering applicability, and can be used as an effective supplement to the existing macroscopic texture detection system for asphalt pavement.
4. The current study primarily focuses on 3D macrotexture reconstruction of asphalt pavements under static conditions, while its application in dynamic scenarios (e.g., vehicle-mounted mobile measurement systems) has not yet been explored. Future research will specifically address three critical challenges in dynamic environments: (i) motion blur compensation, (ii) viewpoint variation handling, and (iii) real-time processing requirements. These will be tackled through the implementation of temporal modeling architectures (particularly recurrent neural networks) coupled with advanced motion compensation algorithms, ultimately enabling high-fidelity texture reconstruction for mobile measurement applications.  
Conceptualization, C.Y.; data curation, X.L.; methodology, X.L.; supervision, C.Y.; writing--original draft, X.L.; writing--review and editing, C.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Grant No. 51808327) and Natural Science Foundation of Shandong Province (Grant No. ZR2019; PEE016).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy and anonymity.  
The authors declare no conflicts of interest.
# A Cross-Medium Uplink Communication Model Merging Acoustic and Millimeter Waves
## 2 Model of Cross-Medium Communication Channel
### Physical Process  
#### 2.1.1 Underwater Propagation of Acoustic Signals  
During transmission, the acoustic signal emitted by speaker under water undergoes gradual attenuation. Underwater acoustic waves experience two primary types of attenuation: diffusion attenuation and absorption attenuation. Diffusion attenuation arises from the expansion of the wavefront surface with distance, leading to propagation loss that follows the inverse square law \(1/r^{2}\). Absorption attenuation stems from seawater absorption and inhomogeneity scattering, resulting in propagation loss that adheres to the exponential law \(e^{-\alpha r}\). Combined, these laws can be summarized as follows [22]:  
\[A(r,f)=\frac{A_{0}(f)e^{-\alpha r}}{r} \tag{1}\]  
Figure 1: Schematic of the signal propagation process in cross-medium communication.  
where \(A_{0}\) represents the initial peak value of the underwater acoustic source, \(r\) denotes the underwater transmission distance of the acoustic source signal, \(f\) corresponds to the frequency of the acoustic source signal, \(A(r_{f})\) indicates the amplitude of the acoustic source signal after traveling the underwater distance \(r\), and \(\alpha\) represents the attenuation coefficient of the acoustic source signal in the water. The definition of \(\alpha\) is presented below [26]:  
\[\alpha(r)=\left(\frac{0.109f^{2}}{1+f^{2}}+\frac{40.7f^{2}}{4100+f^{2}}+3.01 \times 10^{-4}f^{2}\right)\times\left(1-6.67\times 10^{-5}r\right)\times 10^{-6} \tag{2}\]  
The underwater acoustic source signal propagates through water as a pressure wave. For calculation, the acoustic signal amplitude can be converted into sound pressure using the following equation [27].  
\[p_{i}(f,r,t)=A(r,f)e^{if(t-\frac{r}{v})} \tag{3}\]  
where \(p_{i}\) represents the incident sound pressure, and \(v\) is the sound speed, which is affected by many underwater environmental parameters. The specific relationship is as follows [26]:  
\[v=1450+4.21 TEM-0.037 TEM^{2}+1.14(S-35)+0.175P_{w} \tag{4}\]  
where _TEM_ is the temperature, \(S\) stands for salinity, and \(P_{w}\) represents water pressure.
# Enhancing Autonomous Driving Navigation Using Soft Actor-Critic
## 4 Methodology
### State Space  
In our scenario, we process a stack of four RGB images captured by the front camera of the AV. Initially, these images have dimensions of \(800\times 600\times 3\times 4\) pixels. We resize and reduce them to \(84\times 84\times 3\times 4\) pixels, and then convert them to grayscale. This transformation yields a new state, denoted as \(\mathfrak{s}_{t}\), with dimensions of \(84\times 84\times 4\), which are fed into the input of the neural network. The architecture comprises three convolutional (Conv) layers, all featuring kernel sizes of \(5\times 5\). Each layer contains 16, 16, and 64 kernels,respectively. Strided convolutions with a stride value of 2 are applied across all three layers. Additionally, we employ average pooling with an output size of 1.  
In the diagram provided (see Figure 2), the architecture comprises three Convolutional (Conv) layers followed by average pooling. The feature maps resulting from these layers have dimensions of \(40\times 40\times 16\), \(18\times 18\times 64\), \(7\times 7\times 256\), and \(1\times 1\times 256\), successively. Finally, these features are flattened into a vector denoted as \(h_{t}\), with a dimensionality of \(1\times 1\times 256\). We can use the function \(f_{CNNs}\), depicted as follows:  
\[f_{CNNs}(s_{t})=h_{t} \tag{10}\]  
where \(s_{t}\in\mathbb{R}^{84\times 84\times 4}\) and \(h_{t}\in\mathbb{R}^{256}\).  
To ensure the AV reaches its intended destination effectively, it is crucial to integrate the current state \(h_{t}\) with the destination vector \(\text{dest}_{t}\in\mathbb{R}^{2}\). This destination vector is expressed as \(\text{dest}_{t}=[X_{dest}-X_{AV},Y_{dest}-Y_{AV}]\), where \(X_{dest}\) and \(Y_{dest}\) denote the \(x\) and \(y\) coordinates of the destination position, while \(X_{AV}\) and \(Y_{AV}\) represent the corresponding coordinates of the AV at each time step \(t\) as shown in Figure 3. Subsequently, the update function is as follows:  
\[f_{CNNs}(s_{t},\text{dest}_{t})=hd_{t},\quad hd_{t}\in\mathbb{R}^{258} \tag{11}\]  
Figure 3: The architecture of the state network employs CNNs to extract main features from images. These features are then concatenated with destination vectors to provide guidance for the AV to successfully navigate toward its intended destination.  
Figure 2: The CNN architecture consists of three convolutional layers and average pooling to extract features, followed by a fully connected layer to accommodate the data for the network model.