esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
Consequently, this mismatch may have lowered the intention to use for Agent B compared to Agents A and C.  
This study also revealed a divergence between intimacy and intention to use. While Agent C scored highest in intimacy, it did not significantly differ from Agent A in terms of intention to use. This discrepancy can be understood by recognizing that intimacy and intention to use have complex relationships in terms of UX dimensions. Intimacy relates to the emotional connection and personal rapport that users feel with the agent and can affect the intention to use [56]. However, the intention to use is not solely affected by intimacy and can be more influenced by practical considerations, such as efficiency and reliability, depending on the context of use [57]. The findings indicate that users might value a human-like, intimate interaction, yet can prefer the mechanical voice in the context of autonomous driving. This divergence highlights the need for future research to delve deeper into the distinct factors that drive these different aspects of UX and how they can be optimized for various applications. The fact that adding human-like speech strategies to a human voice did not show a significant difference in terms of the intention to use from a mechanical voice without such strategies suggests that the effort may not always yield the desired results. Nevertheless, these human-like elements have shown positive effects in terms of intimacy, suggesting potential long-term benefits for user engagement. The sense of intimacy could lead to more proactive and participatory interactions over time. However, a mismatch between human-like speech strategies and voice characteristics, as seen in this study, can potentially lead to uncomfortable experiences, similar to the uncanny valley effect observed in visual contexts. This highlights the importance of a harmonious application of anthropomorphic elements and suggests that future research should focus not only on the levels of anthropomorphism but also on the coherence and consistency of its application. The current study opens several avenues for future research. One important direction is to investigate other potential factors that could influence trust and perceived safety beyond anthropomorphism. For example, exploring the impact of voice agent transparency, reliability of the information provided, and user control over interactions could provide deeper insights. In addition, understanding the contexts in which different levels of anthropomorphism are most beneficial in the context of autonomous driving will be critical. Finally, further exploration of the balance between human-like attributes and functional efficiency could inform the design of voice agents that meet both emotional and practical user needs.
# Investigation of Potential of GNSS-R Polarization: Theoretical Simulations
## 3 Theoretical Simulations
### Bare Soil DDM Output at Various Polarizations  
Using the model described in Section 2.1, we present the bare soil simulations at various polarizations [28]. Given that GNSS-R is a type of bistatic radar, the observation geometry will vary considerably and these variations will result in the scattering difference.  
Figure 2: Flowchart of the LAGRS model.  
Figure 3 illustrates the scattering simulations. The panels of first line to the fifth line represent the scattering properties of the LR, VR, HR+45\({}^{\circ}\), and R\(-\)45\({}^{\circ}\) R polarizations, respectively, where LR, VR, HR+45\({}^{\circ}\), and R\(-\)45\({}^{\circ}\) R refer to received polarizations with LHCP, and V, H+45\({}^{\circ}\), and \(-\)45\({}^{\circ}\) refer to transmitted polarizations with RHCP. The volumetric soil moisture content from the first column to the third column is 0.1, 0.3, and 0.6, respectively. From the \(x\) axis and the \(y\) axis, it can be seen that the observation geometry covers almost all possible azimuth and zenith angles. The simulations show that the scattering properties vary considerably and that these variations relate to the observation geometry, polarization, and soil moisture content. However, extraction of soil moisture information from these influencing factors represents important work that should be undertaken urgently.  
To obtain spaceborne information, we employed LAGRS to simulate the final DDM output. During our simulations, the integration areas were set as defined in Table 2, where X_range and Y_range are the spatial integral range in the \(x\) direction and \(y\) direction, respectively, and X_interval and Y_range are the spatial integral interval in the \(x\) direction and \(y\) direction, respectively.  
Our objective was to obtain the polarization effects; therefore, we kept other influencing factors constant. The volumetric soil moisture content was set 0.3.  
Figure 3: Bistatic scattering properties at various polarizations. vms is the volumetric moisture content of the soil. The x-axis and y-axis indicate the different scattering geometries.  
The spaceborne DDM output realized using the LAGRS model is presented in Figure 4, paper [8] presents more detail information. The DDM output at LR, VR, and HR polarizations is shown in Figure 4, respectively. From the simulations, it can be seen that the scattering properties at the specular point have certain variations, while the difference at the glistening scattering zone varies considerably. The final DDM output is the power integrated from different observation geometries, and the scattering variations at various observation angles are presented in Figure 3. These scattering properties result in the final DDM variations that are presented in Figure 4.
# Design and Control of an Ultra-Low-Cost Logistic Delivery Fixed-Wing UAV
###### Abstract
To achieve the transportation and precise delivery of micro-items to their destinations, for this study, we chose to design an ultra-low-cost fixed-wing logistic delivery drone that operates without human control, guided by a transportation route to stably transport items to the target location [13], accurately identify the delivery marker, and execute precise item dropping [14] and that is capable for automatically returning to the base. In this study, the transportation and dropping of micro-items are simulated using a 500 mL bottle of water. Nevertheless, the rapid design and control of such a drone remain a challenge [1]. Design considerations must balance aerodynamic performance, structural strength, and lightness, ensuring stability and safety [15]. Visual recognition needs to be sufficiently precise to accurately identify targets in various scenarios [16]. The control system requires precise navigation and stable control algorithms to achieve accurate positioning and task execution. Additionally [17], energy management and communication are key issues, including data transmission and command control between the controller and the flight control system [18], communication between the flight control system and sensors through a serial interface, and communication among the drone's internal subsystems. Moreover [19], the drone must accurately follow the preset flight path [20], overcoming factors, such as wind direction, that could affect the flight to ensure the precise delivery of items to the destination. This study explores these technical challenges, analyzing the aerodynamic performance and structural strength [21] and applying optimization algorithms to improve the design. Furthermore, it develops precise control strategies to enhance positioning accuracy and conduct in-depth studies on energy management and communication. The research findings are expected to guide the further development of fixed-wing delivery drones, promoting their application in military surveillance [22; 23], environmental monitoring, and disaster relief, among other fields.
## 3 Results  
### Oscillation Condition under Asymmetric Supply Structure  
Figure 2 presents a comparison of sound pressure oscillation between an asymmetric supply structure (case 35) and two symmetric systems (cases 8 and 9). In case 35, \(L_{1}\) is the same as the premixed chamber length in case 8, and \(L_{2}\) is the same as the premixed chamber length in case 9. Both the symmetric structures exhibit strong oscillation, with frequencies of 70.4 and 72.6 Hz, respectively. In case 35, the sound pressures also demonstrate a strong oscillation, with its frequency spectrum showing only one peak at 72.4 Hz, rather than two separate oscillations at 70.4 and 72.6 Hz.  
Figure 3 displays the oscillation characteristics of another asymmetric condition (case 32) in comparison to its corresponding symmetric systems (case 9 and 5). The frequency spectrum of case 32 exhibits a bimodal feature, similar to case 5. Additionally, an important observation is that the amplitude is lower for the asymmetric system compared to the two symmetric systems. Both Figures 2 and 3 support the notion that the sound pressure oscillation behavior of an asymmetric system does not arise from the linear superposition of the two corresponding symmetric systems, and its frequency differs from either.  
Figure 2: The pressure oscillation under asymmetric supply structure of case 35 in comparison to the two correlative symmetric supply structure (\(\varphi_{1}=\varphi_{2}=0.9\)).
## 3 Method
### Simulation of the Control Performance  
After the control policy was well trained, we tested the control policy by vibration-control simulations. Figure 8 shows the vibration-control-simulation architecture. The control policy, the parameters of which are fixed, and the simulator are used together for the vibration-control simulations.  
We carried out many simulations with different excitation signals. The simulation results show that the control policy can effectively reduce vibrations from 50 Hz to 60 Hz. For clarity, one simulation case was chosen to be shown.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
###### Abstract  
The underwater domain presents unique challenges and opportunities for scientific exploration, resource extraction, and environmental monitoring. Autonomous underwater vehicles (AUVs) rely on simultaneous localization and mapping (SLAM) for real-time navigation and mapping in these complex environments. However, traditional SLAM techniques face significant obstacles, including poor visibility, dynamic lighting conditions, sensor noise, and water-induced distortions, all of which degrade the accuracy and robustness of underwater navigation systems. Recent advances in deep learning (DL) have introduced powerful solutions to overcome these challenges. DL techniques enhance underwater SLAM by improving feature extraction, image denoising, distortion correction, and sensor fusion. This survey provides a comprehensive analysis of the latest developments in DL-enhanced SLAM for underwater applications, categorizing approaches based on their methodologies, sensor dependencies, and integration with deep learning models. We critically evaluate the benefits and limitations of existing techniques, highlighting key innovations and unresolved challenges. In addition, we introduce a novel classification framework for underwater SLAM based on its integration with underwater wireless sensor networks (UWSNs). UWSNs offer a collaborative framework that enhances localization, mapping, and real-time data sharing among AUVs by leveraging acoustic communication and distributed sensing. Our proposed taxonomy provides new insights into how communication-aware SLAM methodologies can improve navigation accuracy and operational efficiency in underwater environments. Furthermore, we discuss emerging research trends, including the use of transformer-based architectures, multi-modal sensor fusion, lightweight neural networks for real-time deployment, and self-supervised learning techniques. By identifying gaps in current research and outlining potential directions for future work, this survey serves as a valuable reference for researchers and engineers striving to develop robust and adaptive underwater SLAM solutions. Our findings aim to inspire further advancements in autonomous underwater exploration, supporting critical applications in marine science, deep-sea resource management, and environmental conservation.  
underwater robotics; simultaneous localization and mapping (SLAM); deep learning (DL); underwater SLAM; DL-based SLAM; underwater image enhancement +
Footnote †: journal: Sensors
## 2 Experimental Measurement of Truck Characteristics
### Measurement of Tire's Vertical Stiffness  
This experiment aimed to determine the vertical stiffness of the tire, which plays an important role in the truck. This test procedure was carried out on one of the truck's wheels. All four wheels of the truck had the same designation 13R22.5 149/146], and the characteristics of the tire are presented in Table 4.  
The experiment began with measuring the tire inflation pressure \(P\), which significantly influenced tire stiffness compared to other parameters. The test consisted of lifting the front of the truck and slowly lowering it while recording the mass values indicated on the scale and the displacement values provided by the HBM-LVDT sensor (HBK, Darmstadt, Germany), as shown in Figure 3a.  
LVDT is the abbreviation of Linear Variable Differential Transformer, which is a position/displacement sensor with a measuring range of 0 to 200 mm.  
A force-displacement curve was plotted from the experiment results, as shown in Figure 3b. It can be seen that the curve is linear, which made it possible to determine the vertical stiffness of the tire.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **The Correspondent Value** \\ \hline Tire inflation pressure \(P\) (kPa) & \(6.7\times 10^{2}\) \\ Nominal section Width \(S_{N}\) (mm) & \(13\times 25.4=330.2\) \\ Section height \(H\) (mm) & \(290\) \\ Aspect ratio \(AR\) (\%) & \(87\) \\ Rim diameter \(D_{R}\) (mm) & \(22.5\times 25.4=571.5\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Tire characteristics.  
Figure 3: Tire vertical stiffness testing: (**a**) procedure; (**b**) force–displacement curve.  
To verify the value of the vertical stiffness of the tire obtained experimentally, we compared it with the value obtained analytically, provided by [37]. This analytical expression, given in Equation (4), is based on experimental results obtained from several tires of different dimensions.  
\[K_{t}=9.81\cdot\left[0.00028\cdot P\sqrt{(-0.004\cdot AR+1.03)S_{N}\cdot\left( \frac{S_{N}\cdot AR}{50}+D_{R}\right)}+3.45\right] \tag{4}\]  
Table 5 shows the error between the measured and analytical values, which is less than 10%. The authors believe such a difference between the measured and analytical value is acceptable. Therefore, the measured value can be used for the tire characteristics. This value was then inserted into the \"Property file\" of the tire, which holds the pneumatic characteristics of the wheels. This \"Property file\" was then used to simulate the suspension system and the full truck model.
# Formal Analysis of Ratchet Protocols Based on Logic of Events
###### Abstract  
Ratchet protocols are a class of secure protocols based on ratcheting encryption mechanisms, widely employed in instant messaging. Against the backdrop of frequent incidents of communication privacy breaches, ratchet protocols have become a vital technology for ensuring secure end-to-end communication. This paper presents a formal analysis framework for ratchet protocols grounded in Logic of Events theory (LoET). We further extend LoET by introducing dedicated Diffie-Hellman (DH) and ratchet event classes, along with tailored axioms and inference rules, to support precise modeling of ratcheted encryption. Using the Signal protocol as a case study, we construct a bidirectional authentication model and rigorously prove that both its symmetric and asymmetric ratchet phases satisfy strong authentication properties. Compared with existing formal approaches, our method enables more expressive modeling of key update sequences and supports structured reasoning over causality and authentication flows. The proposed framework lays a theoretical foundation for analyzing the security of modern ratcheted protocols and holds potential for future automated verification.  
ratchet protocols; logic of events; formal analysis; signal protocol; strong authentication properties 2020 achemic Editor: [PERSON]
## 3 Renewal and Revocation Protocols
### Multihop Shared Key Protocol  
Our aim is to establish a shared key between any two authenticated nodes \(I\) and \(R\) of the network (not necessarily in range). We propose two protocols, called MSK\({}_{a}\) and MSK\({}_{b}\). The protocol MSK\({}_{a}\), depicted in Figure 7a, uses the secure channels created between the sink and each node to communicate the public key of \(I\) and \(R\). Notice that in our context, the sink knows all of the public keys of all nodes, and a node only knows its public key and the public key of the sink. The initiator node \(I\) builds a request containing the identity of node \(R\) and a nonce \(n_{I}\). This request is encrypted with \(K_{DH}(I,S)\) and sent to \(S\). The sink \(S\) sends:  
* to \(I\), the identity of \(R\), a nonce \(n_{S}\), the public key of \(R\) encrypted with the shared symmetric key \(K_{DH}(I,S)\),
* to \(R\), the identity of \(I\), the same nonce \(n_{S}\), the nonce \(n_{I}\) received from \(I\) and the public key of \(I\) encrypted with the shared symmetric key \(K_{DH}(S,R)\).  
Once these messages are received, the two nodes are able to compute \(K_{DH}(I,R)\) as follows:  
* Node \(I\) computes \(sk(I)\times pk(R)=sk(I)\times sk(R)\times G=K_{DH}(I,R)\).
* Node \(R\) computes \(sk(R)\times pk(I)=sk(R)\times sk(I)\times G=K_{DH}(I,R)\).  
To ensure mutual authentication of \(R\) and \(I\), node \(R\) generates a nonce \(n_{R}\), then uses \(K_{DH}(I,R)\) to encrypt its own identity, the two received nonces from \(S\) plus its nonce \(n_{R}\). This cipher is sent to \(I\), without necessarily passing by \(S\). Finally, node \(I\) verifies that the received nonce from \(R\) is the same as the one sent by the sink. Then, it confirms that it correctly received the message by sending to \(R\) its own identity and the two nonces \(n_{S}\) and \(n_{R}\), encrypted with \(K_{DH}(I,R)\).  
Notice that the computation of the new keys can be done by the sink in order to save some computations on nodes \(R\) and \(I\). This version, called MSK\({}_{b}\), is depicted in Figure 7b.  
Figure 6: Protocol RNK: renewing the network key.
# Development of a Multifunctional Unmanned Boat Platform for Aquaculture Automation
## 3 Experimental Verification
### Recognition Results of Underwater Aquaculture  
Given that the main cultured species in the test farming area are sea crabs, sea cucumbers, and scallops, among other species, this paper focuses on the training and testing of the above three cultured species based on the YOLOv5 algorithm.  
In this paper, the host running system for learning is the Windows 10 operating system, intel Core i7-6800K CPU processor with a main frequency of 3.4 GHz, GTX2080 Ti GPU processor, PyCharm test platform (2023.3.3 Community Edition), PyTorch machine learning framework (PyTorch 2.0.1). The whole model is trained through 80 epochs, batch is 32, and the initial learning rate is 0.0001. The dataset is derived from a curated selection of online images and the Open-Sea Farm 4K high-definition dataset. The Open-Sea Farm dataset consists of three categories (sea cucumbers, sea urchins, and scallops) and includes 2227 images [41].  
The model based on the trained model works better in real operations and can accurately recognize all kinds of breeding species (Figure 15). To more accurately measure and evaluate the precision, this study selects multiple metrics for assessing object detection performance for analysis.  
Average Precision (AP) is a metric used to evaluate the performance of individual categories in object detection tasks. This metric combines Precision and Recall and is obtained by calculating the area under the Precision-Recall curve.  
Mean Average Precision (mAP) is a core metric used to evaluate the overall performance of a model in object detection tasks. It calculates the mean of the Average Precision  
Figure 14: Results of the evaluation of fishing areas.  
Figure 15: Underwater object recognition result.  
(AP) across all categories, reflecting the model's comprehensive performance in multi-class detection. The higher the mAP, the better the model's detection performance.  
Intersection over Union (IoU) is a metric used in object detection tasks to measure the degree of match between a predicted bounding box and a ground truth bounding box. It calculates the ratio of the area of overlap to the area of union between the predicted box and the ground truth box. Typically, an IoU threshold (e.g., 0.5) is used to determine whether a detection is successful, making it a key indicator for evaluating the accuracy of object detection. The closer the value is to 1, the higher the overlap between the predicted box and the ground truth box.  
The mean Intersection over Union (mIoU) is the average of the Intersection over Union (IoU) across all categories. The higher the mIoU, the better the segmentation performance of the model.  
The values of each metric are shown in Table 4.
# Uncertainties in the Seismic Assessment of Historical Masonry Buildings
## 4 Results
### Lausanne Malley
#### 4.3.2 Failure Analysis  
Figure 18 shows the results of 400 IDAs for the Lausanne Malley model in terms of modal periods, fragility curve and the failure PGA correlations. At the start of each IDA, modal periods were determined to calculate the Rayleigh damping coefficients and evaluate the stiffness of the building. The material parameters \(E_{m}\), \(G_{m}\) and \(f_{cm}\) correlated positively with the PGA at failure, with the strongest correlation detected for \(E_{m}\). It is important to note that these parameters were set as correlated when the LHS sample was prepared. A correlation between the limit drift values was expected for the in-plane failures. Since the Lausanne Malley building was more prone to failing in flexure, flexural drift at collapse more significantly influenced the PGA at failure than the shear drift at collapse. It was also expected that the Rayleigh damping ratio and the failure PGA will be correlated at failure for any failure mode for non-linear dynamic analyses. This is especially true for buildings that tend to fail out-of-plane, because the damping ratio is known to influence the out-of-plane displacement significantly [16]. Here, the floor stiffness correlated with the PGA at failure. However, the floor-to-wall friction coefficient was more significantly correlated with the PGA at failure, as was expected in a building with such a significant percentage of out-of-plane failures in both directions.  
Figure 17: Lausanne Malley model Spearman correlation matrix for PGA 0.105 g.  
The part of the building that fails was also influenced by selected material and modelling parameters. The distribution of the types of failure can be seen in Figure 19. Out-of-plane is the dominant failure mode, accounting for 92% of the total failures. In-plane failures were again dominated by flexural failures, accounting for 97% of total in-plane failures. This was expected for a tall and slender building, especially for upper floors with rather low axial loads on the piers. A majority of the failures, either in-plane or out-of-plane, were localized in the fifth floor piers. Twice as many in-plane failures occurred in the piers in x-direction than in the y-direction, while this difference was smaller for the out-of-plane failure modes. Regardless, in all the other storeys, the failure location was occasionally detected in both directions. This scatter of failures emphasizes the sensitivity of numerical simulations.  
Figure 18: Lausanne Malley model: (**a**) Distribution of modal periods. (**b**) Fragility curve. (**c**) Correlations between PGA at failure and input parameters.