esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Domain Modeling and Analysis of Marine Steam Power System Based on Digital Twin
## 2 Mathematical Model of Marine Steam Power System Equipment
### Mathematical Model of Steam Turbine  
In order to carry out an accurate thermodynamic analysis of the marine steam turbine, the establishment of its detailed mathematical model is a necessary premise. The thermodynamic analysis of the steam turbine mainly requires analyzing and calculating the steam turbine's operation under rated and variable conditions and determining its inlet and outlet flow and output power according to the design conditions [13].  
(1) Mathematical equation of steam pressure and flow:  
\[q_{a}=q_{t}\beta_{p}\frac{p_{a}/p_{r}}{\sqrt{T_{a}/T_{r}}}f(k) \tag{1}\]  
\[\beta_{1}=\left\{\begin{array}{l}1:\left(p_{g}/p_{a}\leq\varepsilon_{cr} \right)\\ \sqrt{1-\left[\left(\varepsilon_{n}-\varepsilon_{cr}\right)/\left(1- \varepsilon_{cr}\right)\right]^{2}}:\left(p_{g}/p_{a}>\varepsilon_{cr}\right) \end{array}\right. \tag{2}\]  
where:  
\(q_{a}\), \(q_{r}\)--The actual intake of steam, the rated intake of steam, kg/s;  
\(p_{a}\), \(p_{r}\)--The actual pressure of steam, the rated pressure of steam, MPa;  
\(T_{a}\), \(T_{r}\)--The actual temperature of steam, the rated temperature of steam, K;  
\(p_{g}\)--The outlet pressure of regulating stage, MPa;  
\(\beta_{p}\)--The Pengtemen coefficient;  
\(f(k)\)--The valve characteristic curve function;  
\(\varepsilon_{cr}\)--The critical pressure ratio of governing stage.  
(2) Rotor dynamic equation  
\[J\frac{d\omega}{dt}=\tau_{in}-\tau_{out} \tag{3}\]  
where:  
\(\omega\)--The speed of rotor, rad/s;  
\(J\)--Moment of inertia, kg\(\cdot\)m\({}^{2}\);  
\(\tau_{in}\)--Input torque, kg\(\cdot\)m;  
\(\tau_{in}\)--Output torque, kg\(\cdot\)m.
## 2 Filament Extrusion for 3D Printing of Composites
### PLA Silver  
Researchers have explained a method for mixing PLA and silver nanoparticles (AgNPs) directly in the melt, which could be used widely in industry to create nano-composite filaments. At the start of making PLA/AgNP nano-composite filaments, silver ions were converted from silver nitrate (AgNO\({}_{3}\)) in the melted polymer into silver nanoparticles. Researchers achieved this transformation using either polyethylene glycol (PEG) or polyvinyl pyrrolidone (PVP), both of which are reducing macromolecular blend compounds. Researchers supplemented the PLA matrix with varying quantities of PEG and PVP. Using PLA/AgNP filaments, 3D-printed antimicrobial components were made using FDM technique. Researchers analyzed the influence of 3D-printed PLA/AgNP parts for the survival of _Staphylococcus aureus_ (_S. aureus_) and _Escherichia coli_ (_E. coli_) bacteria after 30, 60, and 120 min of contact to identify the key antimicrobial features. Figure 7 shows that the AM components tested herein imitate nature's mechanisms through bacteria. Due to their manufacturing process, their ability to interact with surfaces to prevent adhesion, and their inherent antimicrobial properties, these parts could pave the way for the upcoming development of low-cost healthcare, on-demand PPE, and nosocomial devices [60].  
Figure 6: Manufacturing process and experimental testing method [59].  
Many efforts have sought to enhance the stability of 3D printing materials or give them new, specific features. Nanomaterials offer wonderful functionality, which is why 3D printing with them is becoming increasingly popular. Additive manufacturing process for making antibacterial filament from PLA and silver nanoparticles was dealt in some papers [61]. After making the polymer dope of silver salts in PLA using both solution-phase and solvent-less methods, the next step was to extrude the mixture into filaments. Silver salt decomposed in situ during the filament's heat extrusion, creating silver nanoparticles. An XRD and TEM analysis verified the nanoparticles' production and distribution throughout the polymer framework [62]. This study proposed a method using the liquid form to prevent problems caused by how nanoparticles spread during melt mixing. Researchers prefabricated specimens using the filament and subjected them to mechanical and microbiological testing [63]. Things that could be printed out showed promise as antimicrobials when tested against common bacteria, including _Pseudomonas aeruginosa_, _E. coli_, and _S. aureus_. The study found that adding silver nanoparticles to PLA (in amounts from 0.01 to 5 wt. %) does not change its overall properties, but makes the material fight against germs. Production of personalized antimicrobial items on demand could potentially utilize the filament [64].
## 2 Preliminaries
### Lattice
#### 2.4.3 Lattice Trapdoors  
Lattices with trapdoors function have certain \"trapdoors\" that allow efficient solutions to hard lattice problems. A trapdoor lattice consists of the following two algorithms:  
1. \(\mathbf{T}\mathbf{P}\mathbf{G}(1^{n},1^{m},q)\mapsto(\mathbf{A},T_{\mathbf{A}})\) : The lattice generation algorithm is a randomized algorithm. It takes as input the dimensions \(n\),\(m\) and modulus \(q\) of the matrix and output a matrix \(\mathbf{A}\in\mathbb{Z}_{q}^{n\times m}\) together with a lattice trapdoors function \(T_{\mathbf{A}}\).
2. \(\mathbf{Sample}\mathbf{P}\mathbf{F}(\mathbf{A},T_{\mathbf{A}},\sigma,\mathbf{u})\mapsto\mathbf{s}\) : The pre-sampling algorithm takes a matrix \(\mathbf{A}\), the lattice trapdoors function \(T_{\mathbf{A}}\), a vector \(\mathbf{u}\in\mathbb{Z}_{q}^{n}\), and a parameter \(\sigma\in\mathbb{R}\) as the inputs. And it outputs the vector \(\mathbf{s}\in\mathbb{Z}_{q}^{m}\), which the vector \(\mathbf{s}\) satisfies \(\mathbf{A}\cdot\mathbf{s}^{\top}=\mathbf{u}^{\top}\left\|\mathbf{s}\right\|\leq\sqrt{m}\cdot\sigma\).
## 1 Introduction
An online robust identification method, combining robust statistics and confidence interval, was established and applied to identify anomaly uplift pressure of the Tongiezi Gravity Dam by [PERSON] et al. [24]. [PERSON] et al. [25] detected abnormal values early by an enhanced regression tree, applying to a 100 m high arch dam. [PERSON] et al. [26] used the density-based spatial clustering of applications with noise (DBSCAN) to identify and filter abnormal values of monitoring equipment and reduce random errors. [PERSON] et al. [27] identified abnormal uplift pressure data of the gravity dam by the kernel density estimation method. Furthermore, [PERSON] et al. [28], [PERSON] et al. [29], and [PERSON] et al. [30] constructed intelligent monitoring models to quickly and accurately predict the uplift pressure of the dam, realizing the early warning and forecast of uplift pressure. These abnormal identification models of uplift pressure are mostly extended from the dam displacement model. Therefore, it fails to conduct comprehensive and systematic research on the occurrence, application, and influence of abnormal uplift pressure. The studies mentioned above, on the influence mechanism and influence degree of uplift pressure, are primarily based on design parameters. Aiming at the shortages existing in the above research, this study will focus on the abnormal uplift pressure of concrete dams during the operation period. A series of studies on the safety state of concrete dams is carried out from anomaly identification, uplift pressure application method, structural numerical simulation, and structural diagnosis. This study has essential scientific research value and practical engineering significance for the safe operation of concrete structures.
## 2 Methodology
### A Protocol for the Assessment of Gait of Parkinson's Disease Patients Using Wearable Sensors
#### 2.2.2 The Modified Timed Up and Go Test  
The typical form of TUG test requires participants to rise from a sitting position, walk a 3 m distance, turn around and walk back to sit on the chair, while they are rated with a completion time score [38]. The modified Timed Up and Go test (mTUG), shown in Figure 2b, is configured exactly as the MDS-UPDRS item 3.9-Arising from a chair, for its initial phase, where participants cross their arms across their chest and then stand up. Once participants have managed to stand up, they are requested to walk a 10-m route, turn around, walk back, and return to their sitting position. The test is performed twice, at a normal pace of gait.
## 2 Related Works
### Other Methods for Anomaly Detection  
Techniques beyond supervised learning and time series analysis have also been used. For example, a study explored the usage of k-means clustering algorithms in the detection of anomalous and potentially malicious host connections in a network [19]. Specifically, a clustering engine was used to create clusters based on extracted feature sets, which include source bytes per packet, producer consumer ratio, and number of peers and unique source ports. An elbow method selected the number of predefined clusters, and connections were scored. Then, anomaly alerts were raised based on Euclidean, Minkowski, or Manhattan distance measures. A similar study avoided direct packet analysis, noting the disadvantages in terms of computational cost and privacy and focused on anomaly detection using tensor rank decomposition (PARAFAC) [20]. The study collected upload/download byte and packet counter data from home routers of an ISP and processed that data into tensors, which could then be decomposed. The Random Forest classifier was then used for anomaly detection on the extracted residuals from the tensor rank decomposition.
# Iterative Learning for K-Approval Votes in Crowdsourcing Systems
## 6 Experiments
### Error Performance with \(q\) and \(l\)  
To show the competitiveness of our algorithm, we ran our _K-approval iterative algorithm_, majority voting, and the oracle estimator for 2000 tasks and 2000 workers with a fixed \((D,K)\) pair (Figure 6). Here, the oracle estimator can give each worker an appropriate weight since we assume the oracle estimator knows the quality of each worker. The error performance of the oracle estimator is presented as a lower bound. In Figure 6 (top), we can see that the probability of error decays exponentially as \(l\) increases, and is lower than that of the majority voting above the phase transition point \(T=1\). In addition, Figure 6 (bottom) presents the probabilities of error decays as \(q\) increases.  
As detailed in Section 5, we expect a phase transition at \(T=\frac{32}{25}\big{(}\frac{D-1}{D-K}\big{)}^{2}q^{2}\hat{l}\hat{p}=1\) or \(l=\frac{4\sqrt{2}}{5}\big{(}\frac{D-K}{D-1}\big{)}\cdot\frac{1}{q}\). If we follow the result, we can expect transitions to happen around \(l=4.52\) for \((3,1)\) and \((4,1)\), \(l=3.39\) for \((5,2)\) and \(l=3.62\) for \((6,2)\). From the experiments in Figure 6 (top), we find that the iterative algorithm starts to perform better than majority voting around \(l=5,\;6,\;3,\;4\) for each \((D,K)\) pair. Note that these values are very similar to the above theoretical values. It follows from the fact that the error of our method increases with \(k\) when \(T<1\) as stated in Section 5. As can clearly be seen from the simulation results, we can be sure that the \(l\) values required for achieving \(T>1\) are not large.
## 5 Discussion
### Third Party Services  
The burden of multiple consent requirements for users of third-party services as mentioned in Case Study 4.2 may impact the successful market penetration of infotainment packages and services. Advertising based on a user's geolocation data or product  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Application of** & **Limitation** & **Suggestion** & **Benefit** & **Cost** \\ \hline \multirow{8}{*}{Insurance} & Data Access and Privacy & Privacy-by-Design & Privacy and & \(-\)2\% Revenue \\  & & Blockchain Privacy & Data Access & Fine \\  & Geolocation & Homomorphic & Driver Behavior, & Deloitte \\  & & Encryption & & \\  & Consolidation & OEM Consolidation & Standardization & \\  & & Third Party & & \\ \hline \multirow{2}{*}{Third Party Services} & \multirow{2}{*}{Data Access and Privacy} & Privacy-by-Design & Privacy and Data & \multirow{2}{*}{\(-\)2\% Revenue} \\  & & & Access & \\ \hline Energy Reduction & \multirow{2}{*}{Battery Monitoring} & Naturalistic Driving & Intermediate & \multirow{2}{*}{Battery Life \textgreater{} 5 years} \\ Schemes & & Data & Alternative & \\ \hline Communication and & \multirow{2}{*}{Latency and Reliability} & 5G, Edge Caching, & Increased Transmission & \multirow{2}{*}{1 ms Latency} \\ Latency & & Vehicle Fog & Speed and Reliability & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of Suggestions and Benefits.  
preferences will be limited by privacy and data restrictions. Depending on the service, customers can create profiles with customised privacy and user settings. User profiles can be connected with in-vehicle infotainment systems and other third-party services within the vehicle. Additional consent requests and privacy requirements will be necessary for interconnected vehicle and user profile services. Third party services will also need to make explicit their intentions for the usage of their customer's personal and geolocation data. The option for easy removal or deletion of profile data is also a requirement in EU regulation. For users not to be discouraged by the increased volume of data access and consent requests, third parties should consider uniform or simplified privacy settings that apply in-vehicle and out.  
If third parties intend on offering their services, the principals of privacy-by-design software development should be considered [67]. Developing a service with data privacy as a core requirement for the service will alleviate the effects of restrictions to data access, and its benefits are further detailed in Table 2. The change in the developing process can still be costly for the third party, as additional time and resources will be required to change their core software development model. A combination of privacy-by-design principals and engagement with OEMs on how their device processes/stores data, may significantly reduce the risks third parties experience when releasing their product.
## 2 Materials and Methods
### Data Analysis  
An \"a priori\" sample size was calculated with an 80% power and an alpha error of 0.05 to detect a 20% minimum difference in PFM tone and strength between groups. The estimated required sample size was n = 16 participants per group; this was then increased by 30% to account for non-response and lost to follow-up (total, n = 42). Data normality was verified using the Shapiro-Wilk test before using parametric tests. Between-group differences in the change pre-post in load absorption, ME 1 and 2, MVC1, and MVC2 were analyzed using independent t-tests, while paired t-tests assessed within-group changes were performed (Bonferroni correction was applied). Results are expressed as means, standard deviations, and ranges. Statistical significance was initially set at \(p\) < 0.05, and then to 0.015 to correct for multiple hypotheses. Analyses were performed using Stata v.18.0 (StataCorp LLC, College Station, TX, USA).
# Internet-of-Things and Information Fusion: Trust Perspective Survey
## 2 Fundamental Trust Model Concepts
### Reputation  
In a dynamic setting, devices need to manage risks involved in interacting with other devices. In a dynamic environment, devices are vulnerable to risks because of unknown, incomplete, or distorted information about each other. One way to address this problem is to establish trust through reputation. The reputation concept is already used in trust models. When making trust-based decisions, devices can rely on others for information pertaining to a specific device. For example, if device \(x\) wants to make a decision about whether to engage in a transaction with device \(y\), which is unknown to \(x\), \(x\) can rely on the reputation of \(y\). The definition of reputation used in this article is adopted from [18; 19]:  
_The reputation of an entity is an expectation of its behavior based on other entities' observations or the collective information about the entity's past behavior within a specific context at a given time._  
Forming the reputation of a device so that it is effective, informed, and reflects the device's \"trustworthiness\" depends on two factors: (a) the honesty of the information source (i.e., the device that sent the information) and (b) the accuracy of the information received. Therefore, the objective is to rely on devices that are honest as well as to rely on information that is accurate. Honesty and accuracy are defined next.
# Review of Physical Layer Security in Integrated Satellite-Terrestrial Networks
## 5 Future Directions
### Resource Allocation  
The use of AI-based resource allocation can be a smart choice in such complex networks for emerging security threats, user demands, and dynamic channel conditions. For adaptive resource allocation, reinforcement learning can be used to interact with the environment to learn the optimal policies. Federated learning can be utilized to develop a distributed resource allocation algorithm to protect user privacy.  
In integrated networks, dynamic spectrum sharing can be investigated to ensure secure and efficient utilization of spectrum resources. Furthermore, cognitive radio techniques can be used for spectrum sensing and access for improved spectrum utilization and security. A UAV could serve as an aerial base station for spectrum sharing and security enhancement. Joint beamforming and resource allocation can be applied to maximize secrecy capacity while minimizing energy consumption and interference. Resource allocation techniques should be robust to channel estimation errors and other uncertainties. Cross-layer solutions, including cryptographic solutions, should be attempted along with PLS for enhanced  
Figure 9: This figure illustrates potential advancements in wireless communication technologies, including dynamic resource allocation, intelligent beamforming, cooperative communication, innovative physical characteristics, and emerging techniques such as Quantum Key Distribution, dynamic spectrum access, cognitive radios, and AI. These advancements aim to improve the efficiency, secrecy capacity, and reliability of the network to enhance PLS.  
protection. The allocation schemes should be privacy-preserving, support secure data offloading between terrestrial and satellite networks, and secure edge computing. Various trade-offs, such as QoS, security, and energy efficiency, should be taken into consideration.
# Characterisation of Coastal Sediment Properties from Spectral Reflectance Data
## 1 Introduction
A key question is how location-specific measurements can be applied to similar depositional settings elsewhere [27; 32] or how patterns of (for example) grain size, calcium carbonate (CaCO\({}_{3}\)), organic carbon or biomass content can be mapped across space using automated remote sensing techniques [21; 33; 34; 35]. Although field and laboratory hyperspectral devices have been used to derive data on coastal sediment properties [16; 28; 36], there have been hitherto no published studies using the spectral properties of sediments from coastal settings in South Africa. This study uses laboratory hyperspectral measurements of sediment samples collected from a beach-dune system on the coast of South Africa, focusing on relationships between selected properties of the field samples (including grain size and carbonate content) and their associated spectral signatures. The aims of this study are to describe the nature of beach-dune samples in terms of their spectral signatures and to examine these relationships using statistical methods. This can be considered as a first step towards developing a robust methodology for automated mapping of sediment properties across beach-dune environments applicable globally.
# Vehicle Detection with Self-Training for Adaptative Video Processing Embedded Platform
## 1 Introduction
To achieve real-time processing on an embedded platform, an energy-efficient CNN, called SqueezeDet, is employed [11]. Our idea is illustrated in Figure 1, where models trained on a dataset perform well in the case that the test data are similar to the training set. However, when deployed to a new environment, those models may fail in seemingly simpler cases (see the left column). Therefore, automatically collected labels are applied for fine-tuning the baseline model (as in the center column). After fine-tuning of the model, the vehicles are more successfully tracked for a bigger number of frames and some vehicles located at the far end are more efficiently recognised (Figure 1--right column). To summarise, in this work, the focus is on developing a system for traffic monitoring. Firstly, the proposed system is deployed to the embedded Jetson Tx2 platform. Further, adaptation methods are employed in order to improve the efficiency of the deployed system in a real-world scenario. With the ubiquitous presence of such cameras in cities around the world, there is great potential for practical implementations of solutions of this kind. Finally it is assumed that no labelled data from  
Figure 1: Given the initial imperfect detection model (left column), pseudo-labels are automatically collected (centre column) for model fine-tuning. As a result (right column), vehicles detection accuracy increases and vehicles are tracked for more frames. Red dots mark detections in consecutive frames. the target environment is available, which is a realistic scenario. The contribution of this paper is, as follows:  
* the SqueezeDet algorithm is adjusted for vehicle detection on an embedded platform, and the accuracy on UA-Detrac dataset and its performance on a Jetson TX2 under different input image resolutions is reported,
* automatic labelling is applied for one of our cameras. Data are collected for several days, and fine-tuning happens at the end of each day. This procedure results in a substantial increase in vehicle detection accuracy, as explained in the section devoted to the discussion of the results, and
* experiments on the human-in-the-loop scenario are performed, which further increases the efficiency of the detection.
# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 2 Related Works
### Motion Modeling and Multi-Modal Fusion  
Motion prediction is a key component in multi-object tracking. SORT [8] used linear Kalman filtering to predict target positions but performed poorly on non-linear motions. DeepSORT [7] combined appearance features and motion information but still used a simple linear motion model. OC-SORT [22] improved Kalman filtering through observation correction but did not consider acceleration changes, leading to inaccurate predictions for sudden motions. BoT-SORT [23] introduced camera motion compensation but lacked an adaptive parameter adjustment mechanism.  
In terms of multi-modal fusion, most methods use simple weighted averaging or cascading strategies. StrongSORT [19] linearly fused appearance and motion similarity, but the weights were fixed and could not adapt to different scenes. ByteTrack [18] and OC-SORT [22] mainly relied on a single modality (IoU or motion), lacking an effective multi-modal fusion strategy. Unlike existing methods, the DMM module proposed in this paper extends the Kalman filter framework, introduces an acceleration component, and designs a data-driven noise adjustment strategy, significantly improving the prediction accuracy for non-linear motions. Additionally, our dual-modal matching decision method adaptively balances appearance and motion information through a dynamic weight fusion function, effectively enhancing tracking performance in complex scenes.