esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Kinematic and Dynamic Models
### Dynamic Model  
A dynamic model is subsequently built for the wheeled robot to take dynamic constraints of the motor driving system into account. The dynamic model, as shown in Figure 2, can be explained with a body-fixed coordinate system, where the origin point is located at the CG of the robot. \(T_{L}\) and \(T_{R}\) are driving torques on wheels generated by left and right motors, respectively. \(F_{y}\) denotes constraint force along the \(y\)-axis, while \(F_{xL}\) and \(F_{xR}\) are constraint forces on the left and right wheels along the \(x\)-axis.  
In this paper, it is assumed that the robot is constrained to moving on the horizontal plane only. The dynamic equation of the system is formulated using the Lagrange method, which can be expressed as  
\[M\ddot{\mathbf{q}}=\mathbf{B}\mathbf{\tau}-\mathbf{A}^{T}(\mathbf{q})\mathbf{\lambda} \tag{10}\]  
where \(\mathbf{M}\) is the inertial matrix, \(\mathbf{B}\) is the transfer matrix, \(\mathbf{\tau}\) denotes the input vector of driving torques, and \(\mathbf{\lambda}\) is the vector of constraint forces.  
\[\left\{\begin{array}{c}\mathbf{M}=\text{diag}\{m,m,J,I_{L},J_{R}\}\\ \\ \mathbf{B}=\left[\begin{array}{cccc}0&0&0&1&0\\ 0&0&0&0&1\\ \end{array}\right]^{T}\\ \\ \mathbf{\tau}=\left[T_{L},T_{R}\right]^{T}\\ \\ \mathbf{\lambda}=\left[F_{y},F_{xL},F_{xR}\right]^{T}\\ \end{array}\right\} \tag{11}\]  
Figure 2: Dynamic model of a skid–steer wheeled robot.  
where \(m\) is the mass of the robot, \(J\) is its moment of inertia along the \(z\)-axis, \(J_{L}=\frac{mr^{2}}{4}+\frac{lr^{2}}{B^{2}}\) and \(J_{R}=\frac{mr^{2}}{4}+\frac{lr^{2}}{B^{2}}\) are the moments of inertia of the left and right driving wheels, respectively.  
The vector of constraint forces \(\mathbf{\lambda}=\left[F_{y},F_{xL},F_{xR}\right]^{T}\) can be defined as follows  
\[\left\{\begin{array}{c}F_{xL}=C_{xL}\,f_{L}\\ F_{xR}=C_{xR}\,f_{R}\\ F_{y}=C_{y}\,\beta\end{array}\right. \tag{12}\]  
where \(C_{xL}\) and \(C_{xR}\) represent the longitudinal stiffness of the left and right driving wheels, respectively; \(C_{y}\) is the cornering stiffness of the wheels; \(\beta\) is the wheel slip angle.  
The wheel slip angle \(\beta\) can be estimated by the velocity variables of the previous time, and can be expressed as follows  
\[\beta(t)=\frac{2\dot{y}(t-1)-B\,\dot{\theta}(t-1)}{2\dot{x}(t-1)} \tag{13}\]  
where \(\beta(t)\) is the value of \(\beta\) at \(t\) moment; \(\dot{\theta}(t-1)\) is the value of \(\dot{\theta}\) at \(t-1\) moment; \(\dot{x}(t-1)\) and \(\dot{y}(t-1)\) are the values of \(\dot{x}\) and \(\dot{y}\), respectively, at \(t-1\) moment.  
The dynamic equation (Equation (10)) can thus be simplified as  
\[\ddot{\mathbf{q}}=\mathbf{M}^{-1}\mathbf{B}\mathbf{\tau}-\mathbf{M}^{-1}\mathbf{A}^{T}(\mathbf{q})\mathbf{\lambda} \tag{14}\]  
The angular acceleration of the left and right driving wheels can be obtained, and it is employed to design the inner loop of the control framework in this paper.
# Load-Carrying Capacity of Bailey Bridge in Civil Applications
## 1 Introduction  
Temporary bridge structures were mainly developed for military purposes in the past. Very often, they also served to ensure rapid access through rural unexplored areas [1, 2]. Increasingly, originally military emergency ones are also used for civil purposes (Figure 1), where their adaptability, low weight, but especially extremely fast erection and almost immediate usability for traffic are utilized.  
Currently, a significant number of these structures are used when natural disasters destroy a part of transport infrastructure. Their application as temporary bridges on  
Figure 1: Bailey bridge (TS-12 + TD-33) over Vah river during construction (Slovakia).  
larger construction sites is also growing (Figure 2). In addition, they are used as'short-term' temporary (semi-permanent) bridges on local and rural or unpaved roads when the construction of a permanent bridge is still financially inefficient or not possible due to traffic issues (Figure 3).  
One of the more popular systems of this category is the Bailey bridge system [3]. The Bailey bridge (BB) was invented by [PERSON] as a military bridge about 80 years ago [4]. After the first decade, it was proven to be an innovative and successful bridge system that can be built efficiently and quickly [5]. Additionally, this temporary bridge system is currently used in many countries for civil purposes as mentioned above. However, the maximum load-carrying capacity for such a use is not commonly available. For instance, static verification is usually demanded for every application in civil conditions in Slovakia. In the case of longer term use, and in situ load test is also required [6].  
Nowadays, loads according to [7] are applied for military purposes. However, the aim of the presented study is to point out the actual possibilities of this very old structural temporary bridge system in some civil applications. Therefore, the paper presents a study on whether and to what extent the BB system with spans between 12 and 36 m is usable for transport considering conventional lorries with a total weight up to 22-28 tons. At the same time, isolated construction vehicles with a total weight up to 32-40 tons can also be allowed to pass the bridge.  
Figure 3: ‘Long-term’ temporary bridge Bailey bridge (DS-15 + 21 + 27 + 21) over Luznice river (Czech Republic).  
Figure 2: Bailey bridge (TD-36 + 36) placed on construction site road over Orava river (Slovakia).
## 3 Results
### Migration Modeling
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Temperature** & **Storage Time [d]** & & & **Concentration [mg/kg]** & \\  & & **3\% Acetic Acid** & **10\% Ethanol** & **20\% Ethanol** & **D\({}_{\text{P}}\) Predicted E\({}_{\text{A}}\)** & **D\({}_{\text{P}}\) Predicted** \\  & & **(E\({}_{\text{A}}\) exp)** & **(E\({}_{\text{A}}\) exp)** & **(E\({}_{\text{A}}\) exp)** & **Based Model** & **A\({}_{\text{P}}\) Model** \\ \hline
25 \({}^{\circ}\)C & 10 & 21.5 & 18.5 & 14.0 & 13.0 & 0.148  
for the acetaldehyde-scavenging additive anthranilamide [27] in PET bottles as well as for the polymerization catalyst antimony [28] confirm that 10 days at 40 \({}^{\circ}\)C are better testing conditions than 10 days at 60 \({}^{\circ}\)C.  
The current SML of 50 mg/L [15] allows bottle wall concentrations of BPA of 9620 mg/kg after storage of 365 d at 25 \({}^{\circ}\)C, 13170 mg/kg (10 d at 40 \({}^{\circ}\)C), and 2240 mg/kg (10 d at 60 \({}^{\circ}\)C), respectively. The implied migration level of 0.012 ug/L based on the EFSA TDI still allows 3.57 mg/kg (3% acetic acid), 3.06 mg/kg (10% ethanol), and 2.30 mg/kg (20% ethanol) of BPA in the bottle wall when stored for 365 days at 25 \({}^{\circ}\)C.  
The results of the activation energy-based prediction model are very close to the results for 20% ethanol. This indicates that the migration is predicted realistically by the activation energy-based E\({}_{\text{A}}\) model but that it is slightly overestimative. In contrast, as expected, the generally accepted A\({}_{\text{P}}\) model predicted the migration of BPA from PET much more conservatively, which results in significantly lower concentrations of BPA, which corresponds to a migration of 1 ug/L and 0.012 ug/L.
## 1 Introduction
The proposed ACNCL model builds on this principle, uniquely leveraging adversarial training to jointly optimize for image clarity and tumor detectability. The ACNCL model integrates image denoising with tumor detection, utilizing predictors like DnCNN, U-Net, DenseNet, DWT, and CA-AGF within a GAN architecture. By using PatchGAN, the model enhances local feature detection, which is crucial for identifying subtle tumor characteristics. This approach allows simultaneous learning of content and noise, improving both denoising and tumor detection accuracy in low-quality medical images. Experimental results on CT and MRI datasets demonstrate the improved performance of the ACNCL model over traditional methods, showcasing enhanced image quality and more accurate tumor detection. The model further connects to an online database for automated second-level validation of tumorous regions, a feature not incorporated in conventional methods. By integrating deep learning predictors with GAN-based architecture, the ACNCL model effectively balances noise reduction and content preservation, providing a robust solution for medical image denoising and tumor detection. This paper makes the following contributions to the body of knowledge:  
1. The ACNCL model uniquely connects image denoising with tumor detection, evaluating tumor detection performance before and after denoising. This integration addresses the gap in previous research [16], which focused solely on content and noise learning without considering the impact on tumor detection. 2. The incorporation of PatchGAN as a local discriminator ensures fine-grained texture restoration and the structural integrity of medical images. The ACNCL model further enhances denoising efficiency using dual discriminators, one for real pairs and another for fake pairs, striking a balance between noise suppression and content preservation. 3. Unlike traditional models that discard noise priors during testing, the ACNCL predictor model uses two predictors, content and noise, in a complementary manner. This approach allows for more effective noise removal while preserving critical anatomical details, enhancing the diagnostic quality of medical images.
## 3 Results
### NMR Analysis  
NMR enables tracking of the change in porous space occupied by fluids and provides this value both in volume (cc) and in porosity (%) units. We utilized the NMR for registering the fluid saturation by a number of selected tests: CPMG T\({}_{2}\) relaxation, 1D saturation profile, and T\({}_{1}\)-T\({}_{2}\) map for fluid separation. Samples before and after the combustion tube were analyzed according to the proposed methodology; at each stage, the series of above-mentioned test types were repeated with the same parameters (Figure 10).  
After the combustion, there was a near-zero volume of fluids left in the samples (Figure 11). At first approximation, the residual fluid saturation for target rocks depends on their initial saturation, the temperature of the zone, and the combustion tube section. We clearly observed an apparent decrease in total fluid volume for samples that were exposed to 300-369 \({}^{\circ}\)C, whereas in sections with higher temperatures, the fluid was almost completely removed. Exceptions can be explained by the example of sample #15, where a certain amount of fluid remained after exposure to 422 \({}^{\circ}\)C. This sample was located in CT section, which is near the end of the propagation front. We explain remaining fluid by the generated oil. Thus, the results of measurement mainly indicate the displacement and burning  
Figure 10: NMR analysis of sample #16: (**a**) T\({}_{2}\) relaxation before (blue) and after CT (red); (**b**) T\({}_{1}\)–T\({}_{2}\) map for as-received sample; (**c**) saturation profiles before (blue) and after CT (red); (**d**) T\({}_{1}\)–T\({}_{2}\) map for the burnt sample.  
of hydrocarbons from cylindrical samples along the combustion front, but this difference is controlled by several factors. These factors primarily include the petrophysical background of the rock samples (rock lithology, OM properties) and experiment conditions (temperature in the CT zone, location in CT, and duration of exposure).
# Secure and Privacy-Aware Blockchain Design: Requirements, Challenges and Solutions
## 5 Evaluation and Discussion
### Current Recommendations and Research Directions
#### 5.2.1 Current Recommendations
It works efficiently because it has lower data size overhead and memory cost than identity-based encryption and onion routing mechanisms. 6. **Symmetric key cryptography** is relatively fast for large amounts of data because of its low computation overhead as compared to asymmetric key cryptography. In symmetric key cryptography, keys can be developed and exchanged between two parties through cryptographic algorithms (e.g Diffie-hellman key exchange). 7. **Asymmetric key cryptography** is easy to use due to separate public and private keys, however, it has high computation overhead as compared to symmetric key cryptography. 8. **Homomorphic encryption** is considered less complex and efficient due to its low memory cost as compared to ZKPs, onion routing, and symmetric key cryptography [62]. Advances in cryptography are consistently being integrated to possibly overcome security and privacy issues. Homomorphic encryption is well-positioned to address most privacy concerns. However, the lack of practical, scalable, and audited implementations is hindering integration. 9. **Hash function** is good to fulfill identity and transaction data protection requirements without an intermediate party, because it has lower computation overhead without latency. It generates a unique signature of fixed size from variable-size data input and it is useful to assess the integrity of data during data exchange. It is very widely used for data integrity verification.
## 1 Introduction
Data were obtained using the OptiTrack motion tracking system and the onboard system with doublet input as a reference command. The obtained linear time-averaged longitudinal dynamic models for hover and forward flight up to 1 m/s showed more than 80% fit. The majority of system identification research uses high-speed camera systems with high data rates to obtain experimental flight data. High-speed camera systems are very expensive. In addition, the camera marker balls are relatively heavy compared to the small mass of the KUBeetle (only 17.7 g), which could affect the dynamics. Moreover, there exists little difference between the angle measured by a high-speed camera system and that obtained by an onboard sensor, due to misalignment, scale factor errors, and added measurement noises. So, the model obtained using the high-speed camera system should be modified to be used in a real feedback control system, while the dynamic model obtained using onboard sensors can be used directly without modification. Many system identification methods have been developed. They are available for open-loop and closed-loop system identification [78; 79; 80; 81; 82]. Because KUBeetle flight is unstable, a closed-loop system identification was chosen. Considering prior knowledge of the dynamics of the KUBeetle and the PD feedback controller, a gray box model approach with indirect methods [79; 82] was utilized. The longitudinal mode extended dynamic model of KUBeetle, categorized as a linear, time-invariant, and single-input multiple-output system with state space model structure, was refined with the help of system identification tools in MATLAB(r). Several variations of input and output data pairing and different identification methods on model refinement were investigated and compared. By investigating dynamic model parameters and time-domain responses, the most appropriate refined dynamic model was obtained. The main contributions of this paper are: (1) dynamic model parameters for the new KUBeetle using only low-cost onboard sensors outputs; and (2) stability and frequency domain analyses, which could be used for designing a closed-loop feedback controller. The remainder of this paper is organized as follows. Section 2 describes the design and longitudinal mode dynamic model of the KUBeetle. Section 3 presents experimental flight, system identification setups, and the results. Finally, Section 4 provides conclusions and indicates towards future research topics.
## 4 Discussion
### Limitations  
Firstly, one of the primary limitations of our study is the degree of data availability for the automated optimization. Access to comprehensive and accurate data on company's key performance indicators, driver skills, driver preferences, enroute change of drivers, etc. would allow for a more complex analysis and the more realistic modeling of real-life scenarios. One of our future prospects is to integrate such parameters (when available) in the training or testing of our models. Secondly, our dataset size is rather small, making us uncertain on the model's behavior on large-scale optimization problems. Thirdly, the testing of the models was carried out on internal data. Scalability and computational efficiency are yet to be ensured and evaluated on larger and external datasets, which is another future prospect of our research.
## 4 Proposed Work
### Mutual Authentication  
In Figure 4 the Phase 1 mutual authentication process in the secure IoT communication protocol involves several key steps. First, each IoT device generates an asymmetric key pair consisting of a public key and a private key. The public keys are then registered on the blockchain for authentication. Each device retrieves the other device's public key.  
Using its private key and the other device's public key, each device calculates a shared key via the Diffie-Hellman protocol. This shared key is combined with the common ID to generate a Secure Authentication System (SAS). Each device stores its SAS on the blockchain and then retrieves and compares the other device's SAS. If the SAS matches (SAS1 = SAS2), communication is authorized, ensuring secure mutual authentication between the IoT devices. This process guarantees the confidentiality, integrity, and security of the exchanged data.
## 4 Discussion: Comprehensive Analysis of the Deep-Learning-Oriented Framework
Adapting our framework to accommodate this complexity, while ensuring real-time processing capabilities, was a significant challenge. Additionally, the scarcity of comprehensive and standardized datasets specific to seaport environments posed limitations on the depth of our preliminary testing. Without access to extensive real-world data, our ability to provide concrete quantitative results was constrained. Furthermore, the integration of artificial intelligence and computer vision technologies into seaport environments presented its own set of challenges, particularly concerning model robustness and accuracy. Addressing the potential challenges and limitations in the real-world implementation of our framework is crucial for a comprehensive understanding. Scalability emerges as a concern, particularly in larger seaports, where accommodating more cameras and increased data volume could strain the computational resources, potentially leading to longer processing times and heightened costs. Data privacy is also vital, given the sensitive nature of seaport operations, necessitating compliance with data protection regulations and the implementation of robust security measures, such as encryption and access controls. Furthermore, the infrastructure requirements for real-time video analysis should not be underestimated, prompting seaports to invest in high-performance servers, GPUs, and network equipment. The regular maintenance and calibration of CCTV cameras are essential to ensure consistent performance, with environmental factors such as dust and weather conditions posing challenges. Achieving low-latency responses in the face of a large number of video feeds calls for the optimization of algorithms and hardware. Additionally, the framework's adaptability across various seaports may require customization and fine-tuning, due to the differences in layouts and operational practices, adding complexity to its implementation. The seaports must carefully consider the costs of encompassing hardware, software licenses, and the recruitment and retention of skilled personnel proficient in deep learning and computer vision. These considerations highlight the need for meticulous planning and a comprehensive approach to successfully apply our framework in dynamic seaport settings. Despite these challenges, our study has provided valuable insights and has laid the foundation for future research endeavors, where addressing these limitations will be a key focus. By acknowledging these challenges, we aim to encourage further research in overcoming these obstacles, ultimately advancing the application of AI and CV technologies in seaport logistics.
###### Abstract  
With the rapid advancement of urban underground space development, shield tunnel construction has seen a significant increase. However, at the initial launching stage of shield tunnels in shallow-buried weak strata, engineering risks such as face instability and sudden surface settlement frequently occur. At present, there are relatively few studies on the reinforcement technology of the initial section of shield tunnel in shallow soft ground and the evolution law of ground disturbance. This study takes the launching section of the Guangdong New City depot access tunnel on Guangzhou Metro Line 10 as the engineering background. By applying MIDAS/GTS numerical simulation, settlement monitoring, and theoretical analysis, the reinforcement technology at the tunnel face, the spatiotemporal evolution of ground settlement, and the mechanism of soil disturbance transmission during the launching process in muddy soil layer are revealed. The results show that: (1) the reinforcement scheme combining replacement filling, high-pressure jet grouting piles, and soil overburden counterpressure significantly improves surface settlement control. The primary influence zone is concentrated directly above the shield machine and in the forward excavation area. (2) When the shield machine reaches the junction between the reinforced and unreinforced zones, a large settlement area forms, with the maximum ground settlement reaching \(-\)26.94 mm. During excavation in the unreinforced zone, ground deformation mainly occurs beneath the rear reinforced section, with subsidence at the crown and uplift at the invert. (3) The transverse settlement trough exhibits a typical Gaussian distribution and the discrepancy between the measured maximum settlement and the numerical and theoretical values is only 3.33% and 1.76%, respectively. (4) The longitudinal settlement follows a trend of initial increase, subsequent decrease, and gradual stabilization, reaching a maximum when the excavation passes directly beneath the monitoring point. The findings can provide theoretical reference and engineering guidance for similar projects.  
shallow-buried muddy soil layer; shield tunnel; tunnel face reinforcement; ground deformation 2020 IEEE/RSJ International Conference on Intelligent