esa-sceva/satcom-chunk-collection huggingface dataset:
# Modelling and Stability Analysis of Articulated Vehicles
## 3 Stability Analysis Model for Articulated Vehicles
### Effects of Torsional Stiffness  
Engineering hydraulic oil modulus of elasticity can assume a fixed value, but leakage from hydraulic steering systems, a certain amount of air in the fluid, hydraulic pipes and other influencing factors, the hydraulic oil composite modulus of elasticity of the articulated vehicle steering system will be substantially reduced compared with the ideal situation. The stability of articulated vehicles under different elastic moduli of 0.1 \(K_{C}\), 0.2 \(K_{C}\), 0.5 \(K_{C}\), 1 \(K_{C}\), and the eigenvalues and swing angles are shown in Figure 7. The eigenvalues \(\lambda_{1}\), \(\lambda_{2}\) are negative, while the two conditions with the lower modulus of elasticity at higher velocity of the eigenvalues \(\lambda_{3}\), \(\lambda_{4}\) are positive. The analysis of the results indicate that the higher the hydraulic oil composite modulus of elasticity, the greater the rigidity of the hydraulic steering system, the more stable the vehicles and the faster the convergence of the swing angle. However, the amplitude of the swing angle is not linearly related to the elastic modulus, i.e., the higher the elastic modulus, the higher the oscillation frequency.  
Figure 6: Variation of the real parts of the eigenvalues and swing angle with the center of mass: (**a**) real parts of \(\lambda_{1}\) and \(\lambda_{2}\), (**b**) real parts of \(\lambda_{3}\) and \(\lambda_{4}\), and (**c**) swing angle of the vehicle.  
Kinetic simulations of two lower elastic moduli at the respective critical velocities are performed separately, and the respective swing angles are in a steady state. When the  
Figure 7: Variation of the real parts of the eigenvalues, swing angles and lateral velocity with the torsional stiffness: (**a**) real parts of \(\lambda_{1}\) and \(\lambda_{2}\), (**b**) real parts of \(\lambda_{3}\) and \(\lambda_{4}\), (**c**) swing angle of the vehicle at 5 m/s, (**d**) swing angle of the vehicle at 15 m/s, (**e**) swing angle of the vehicle at critical velocity, and (**f**) lateral velocity.  
velocity exceeds the critical speed of a certain range, the vehicle bending angle does not oscillate with equal amplitude in the centre of line zero, but gradually deviates from the state of the centre line of the oscillation with a type of amplitude gradually decreasing, and the lateral velocities of the vehicle gradually increase. At the time the vehicle is in a relatively deviating stage with snaking, the smaller the modulus of elasticity, the faster the deviation. The stability analysis model differs from vehicle nonlinear dynamics model owing to linearisation. According to the analytical curves, the critical velocity obtained by the linearised model is relatively lower than the critical velocity obtained by the nonlinear dynamics model.
## 1 Introduction
To address these key issues, [PERSON] et al. [29] from the China Institute of Water Resources and Hydropower Research conducted finite element numerical simulations based on experimental studies. Using a cohesive zone model to simulate the bonding interaction between the anti-seepage coating and hydraulic structures, they analyzed the coating's deformation and puncture behavior under adverse conditions such as substrate cracking, water pressure extrusion, and reverse hydraulic peeling. Their findings revealed that coating thickness significantly influences both the peeling distance under reverse water pressure and the ability to restrict lateral displacement propagation of adhesive body cracks. While polyurea coatings effectively prevent internal water leakage during crack propagation, they may fail at fracture interfaces or reverse-pressure peeling zones due to tensile-shear stress concentration. Additionally, [PERSON] et al. [30] employed finite element methods to investigate the mechanical and deformation behavior of circular composite linings with anti-seepage coatings under high internal water pressure and surrounding rock pressure. A high internal water pressure of more than 2 MPa will break the concrete lining, and the most important issue is the crack resistance of the modified polydimethylsiloxane coating under such high pressure. Mechanical tests and numerical simulations are needed to evaluate the performance of each impermeable material before it is put into practical engineering application. In this paper, the numerical simulation constitutive of modified polydimethylsiloxane is studied according to the tensile test, and the criterion for judging whether the material has tensile failure is proposed. Combined with the crack opening and depth of the tunnel lining under different internal water pressures, the numerical simulation method was used to analyze whether the anti-seepage coating using the material would be damaged. Finally, the recommended thickness of the anti-seepage coating in the project was proposed. Furthermore, the crack resistance of modified polydimethylsiloxane coating under the influences of the lining's crack and internal pressure is analyzed. Finally, the relative thickness of the modified polydimethylsiloxane coating is suggested to guide the engineering application.
# Privacy and Security in Cognitive Cities: A Systematic Review
## 2 Methodology
### Topic Conceptualization  
Vom [PERSON] et al. advise that a review must begin _\"with a broad conception of what is known about the topic and potential areas where knowledge may be needed\"_. To this end, a working definition of the key terms should be provided [38] (p. 8). The two key topics addressed in this review are cognitive cities, on the one hand, and information security, on the other. Regarding the meaning of the concept _cognitive city_, we refer the readers to the introductory section of this study. Regarding the meaning of the concept _information security_, a concise introduction of the term is provided next.  
Information security is frequently referred to as the preservation of confidentiality, integrity, and availability of information, which is known as the _CIA triad_. This is in agreement with the international standard ISO/IEC 27000:2018, which additionally states that _\"other properties, such as authenticity, accountability, non-repudiation and reliability can also be involved.\"_[41] (p. 4). Other definitions have been proposed and, despite the great interest of the topic, there is still no commonly agreed definition encompassing the broad scope of the subject, from a professional and academic perspective [42]. To address a broad approach to the topic and its involved features, we use the definition of information security proposed by [PERSON] and [PERSON], which states that:  
_\"Information Security is a multidisciplinary area of study and professional activity which is concerned with the development and implementation of security mechanisms of all available types (technical, organizational, human-oriented and legal) to keep information in all its locations (within and outside the organization's perimeter) and, consequently, information systems, where information is created, processed, stored, transmitted and destructed, free from threats. Threats to information and information systems may be categorised and a corresponding security goal may be defined for each category of threats. A set of security goals, identified as a result of a threat analysis, should be revised periodically to ensure its adequacy and conformance with the evolving environment. The currently relevant set of security goals may include: confidentiality, integrity, availability, privacy, authenticity & trustworthiness, non-repudiation, accountability and auditability.\"_[43] (p. 191).  
Following this definition, we include as information security-related topics the following dimensions: confidentiality, integrity, availability, privacy, authenticity, trustworthiness, non-repudiation, accountability, and auditability. It is worth noting that, according to this definition, privacy is considered as a component of information security and, therefore, _embedded_ in the information security concept. However, being privacy distinguished as a human right by the European Court of Human Rights [44], we consider privacy as a sufficiently important topic to justify a distinctive analysis throughout this research.
# Reducing PAPR with Low Complexity Filtered NOMA Using Novel Algorithm
## 3 Simulation Results
### PAPR Performance of 16-QAM  
Figure 2 investigated the efficiency of the suggested SLM for 16-QAM NOMA. At the CCDF of \(10^{-3}\), the PAPR of conventional NOMA and F-NOMA is 9.4 dB and 8.2 dB, respectively. The SLM mode is applied to F-NOMA to lower the PAPR. It is shown that the PAPR is reduced to 2.4 dB, 4 dB, 4.8 dB, and 6.6 dB for SLM (S = 4 \(p\) = 4), (S = 2 \(p\) = 2), (S = 2 \(p\) = 2), and (S = 2 \(p\) = 4), respectively. Hence, it is concluded that SLM (S = 4 mt) outperforms conventional SLM. The PAPR analysis of 64-QAM for the proposed SLM algorithm is given in Figure 3. At the CCDF of \(10^{-3}\), PAPR is lowered to 5.2 dB, 5.9 dB, 7 dB, and 8.2 dB for SLM (S = 4 \(p\) = 4, 5 = 4 \(p\) = 2, S = 2 \(p\) = 2, and S = 2 \(p\) = 4). Therefore, it is shown that SLM attained a 5.8 dB better performance as equated with the conventional NOMA framework. In Figure 4, the PAPR of 256-QAM for the F-NOMA waveform is shown. At \(10^{-3}\) CCDF, SLM (S = 4 \(p\) = 4) obtained a gain of 0.89, 1.8, and 4 dB, correspondingly.  
Figure 4: 256-QAM PAPR performance.  
Figure 3: 64-QAM PAPR performance.  
Figure 2: 16-QAM PAPR performance.
## 4 Discussion
### Machine Learning Based Models  
For the models obtained by the application of artificial intelligence, the data set was firstly pre-processed through the 1 st derivative and then by the autoscale method. In accordance with the PLS-DA models, for each individual botanical differentiation, a first SVM classifier was conducted based on the entire FTIR fingerprint range. Subsequently, with the aim of improving the classification accuracy, another SVM model was developed using only the significant markers obtained from model-based feature selection.  
The SVM model for chestnut honey differentiation conducted to the correct assessment of 97% of the samples in the cross-validation evaluation technique when the entire spectral range was used as input data. The performance of the SVM classifier increased to a 98% accuracy when only the significant markers were considered for the development of the prediction model.  
Reliable classification results were also obtained in the case of the SVM models built for fir honey differentiation. Therefore, 89% of the samples were correctly attributed when the entire fingerprint was taken into account. Once again, the prediction ability of the SVM model increased when the input data corresponded only to the significant attributes (i.e., spectral points), namely a 90% accuracy was obtained during the cross-validation technique.  
The SVM model capable to differentiate the coriander honey from the rest gave a correct prediction of 92%. When the input data was reduced to the significant spectral points, no improvement was observed.  
When the honeybee botanical source aimed to be discriminated from the rest of the classes using the entire fingerprint range, an accuracy of 65% was obtained through the investigated machine learning technique. The SVM models constructed on the markers that have a higher discrimination power, and 66% of the samples were correctly assigned to their botanical group.  
The differentiation model for raspberry honey that used all the spectral data led to an accuracy of 85%. By applying the SVM algorithm on the data illustrating only the identified meaningful markers, the model's accuracy raised to 92%.  
Lastly, for the sunflower honey discrimination, the SVM model that was developed on the entire fingerprint region conducted to a 92% accuracy. When the input space was reduced to the most relevant features, 93% of the samples were correctly classified.  
The SVM models previously described correspond to the most efficient classifiers, as resulted from the grid search approach. Their configurations are presented in Table 1.
# A Thermal Discomfort Index for Demand Response Control in Residential Water Heaters
## 6 Application of the Proposed Model: TDI Calculation
### Scenario #1: Evaluation of a Simple Case Study with Two Real Water Heaters  
One of the main challenges related to the definition of TDI is modeling the differences of temperature (\(\Delta T\), a quantitative factor) between performing an interruption in different moments. A relevant case is analyzing the \(\Delta T\) values situations in the interruption affects the most to comfort.  
As a relevant sample study, the comparison of the TDI for two different values of \(\rho\) and two particular electric water heaters (\(EWH_{1}\) and \(EWH_{2}\)) is presented. The considered electric water heaters model two different utilization patterns from two different users. On weekdays, \(EWH_{1}\) has two consecutive utilizations, and \(EWH_{2}\) is only used once. The TDI associated with a 20 min interruption between 20:10 and 20:30 was considered for this case. Figure 8 presents the empirical distribution of uses (\(P(u)\)) from 19:00 to 22:00 for \(EWH_{1}\)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **1 st Utilization** & **2 st Utilization** & **3 st Utilization** \\ \hline measured temperature & 59.09 \({}^{\circ}\)C & 53.03 \({}^{\circ}\)C & 58.34 \({}^{\circ}\)C \\ linear temperature & 59.88 \({}^{\circ}\)C & 55.12 \({}^{\circ}\)C & 59.41 \({}^{\circ}\)C \\ \hline difference & 0.79 \({}^{\circ}\)C & 2.09 \({}^{\circ}\)C & 1.07 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accuracy of the water temperature model.  
(left) and \(EWH_{2}\) (right). For each case, the interruption period is represented by a green band.  
For the presented example, it is expected for the TDI value to be higher for \(EWH_{2}\) than for \(EWH_{1}\), because in the hours immediately after the interruption analyzed, the average historical utilization is higher for \(EWH_{2}\). On the other hand, as the value of \(\rho\) increases, it is expected that the gap between the TDI of both electric water heaters becomes larger. Table 2 reports the TDI values computed for each electric water heater, considering two different values of \(\rho\) (\(\rho=1\), and \(\rho=2\)).  
The results in Table 2 confirm that the proposed index correctly models discomfort. The TDI value is higher for \(EWH_{2}\), and the difference widens when considering larger penalty values (\(\rho\)). Results show that TDI properly models the differences of temperature between a scenario with an interruption and a scenario without interruption, as expected.
# Quality Assessment of Photogrammetric Methods--A Workflow for Reproducible UAS Orthomosaics
## Materials and Methods
### Assessing the Orthorectification Surface  
The standard workflow in Metashape suggests a DSM created from a dense pointcloud as the orthorectification surface (Figure 2a) [16]. In vegetation free areas, this DSM is mostly equivalent to a digital elevation model (DEM) [39] or digital terrain model (DTM) and therefore suitable for the creation of orthomosaics. In areas with vegetation, the DEM requires the classification of ground points in the dense pointcloud which is currently not viable in Metashape for structurally rich environments like forests or grasslands in the phases of maturation and flowering. Alternatively, a 2.5D mesh can be created from the sparse cloud on which the images are projected [40]. By smoothing the mesh to eliminate sharp edges, the surface can be regarded as an approximation of a DEM. This approach requires far less computational ressources since the creation of a densecloud is skipped. It is therefore more suitable for low-budget UAS setups. To demonstrate and validate its usage, the mesh surface was compared to the DSM for one of the forest scenes with respect to the reproducibility of the derived orthomosaics using the pixel-wise correlation method described above.
## 2 Power Internet and High Voltage Transmission Line Condition Monitoring Intelligent Sensor Design
### Power IoT Architecture and Component Technologies
#### 2.1.2 Intelligent Sensing Technology  
As an important means of interaction with the external environment and the main source of sensory information, intelligent sensors have become one of the core technologies for the future development of the information industry. Energy is needed by the Internet to achieve rapid, extensive and accurate information collection and to realize, via information interaction, two-way flow and optimal control of energy across different regions, types of energy producers, and consumers.  
Power sensors and sensor networks are important infrastructures for the energy Internet and will have application value in all its aspects. One of the key tasks in building the energy Internet is to make full use of new Internet technologies, by developing smart sensing technologies and industries. Smart sensors first sense the physical quantity to be measured with the help of its sensitive elements, obtain the physical quantity through conditioning circuits, and then convert it into the corresponding electrical signal.
## 9 Real-World Applications, Case Studies, and Tools
### Biomedical and Scientific Data Annotation  
The complex, specialized nature of biomedical data annotation has traditionally required scarce expert resources, but AI agents are rapidly transforming this landscape. In genomics, where functional annotation requires labeling DNA sequences with gene functions and regulatory elements, generative AI agents are accelerating the understanding of gene functions as highlighted in a 2023 Lancet commentary [131]. Clinical settings have seen the experimental deployment of specialized healthcare LLMs that automatically annotate radiology reports and clinical notes with medical codes and key observations--tasks typically performed by specialized medical coders. Studies show that LLMs fine-tuned on clinical text can approach professional-level accuracy in diagnosis and procedure code annotation. While these systems undergo rigorous validation to address regulatory and precision requirements, they demonstrate how annotation agents can dramatically reduce the documentation burden in healthcare while maintaining annotation quality comparable to expert annotators. Figure 6 shows a representative suite of medical annotation agents spanning imaging, EHR extraction, and evidence retrieval.
# A Weighted Histogram-Based Tone Mapping Algorithm for CT Images
## 3 Theory  
The main drawback of local histogram equalization methods is the often visible halos around strong edges. This issue originates from the limited size of the locality window. However, applying a large window limits the locality of the method, and gradually converges towards a global method, while it requires more computational power. Using adjacent or slightly overlapping blocks instead of sliding windows could effectively reduce the required resources, but could lead to blocking artefacts.  
Our approach is built on the following ideas:  
* Local neighborhood is important in order to determine a given pixel's intensity.
* Neighborhood should not have a strong cut-off; the weighted contribution of the whole image should be taken into account.
* The contribution is a decreasing function of the distance.
* The contribution can be calculated using a Fast Fourier transform (FFT) [36].
* The intensity of the pixel is determined based on the local relative intensities in the source image.
* Locality and noise tolerance are equally important.  
The concept of using convolution to calculate the sliding window local histogram was established decades ago [11]. Initially, the rectangular window function was used; later, this was generalized to other shapes and functions. However, to the best of our knowledge, the weighting function always had a cut-off, either determined by the window size, or by the fast decreasing weights, such as Gaussian weighting.  
We argue that a power function should be used as the weighting function for two reasons. First, according to [PERSON] [37], normalized local contributions to perceived brightness follow a power-law with a negative exponent:  
\[W(r)\sim\frac{1}{r^{a}}. \tag{8}\]  
Using this function family means that our proposed algorithm uses a similar weighting approach as the human visual system.  
Second, intuitively, this function shape is required for good local contrast without creating halos: if the function does not decrease fast enough, the weighting will resemble a global histogram equalization and will not sufficiently prioritize local information. On the other hand, if the weighting function has a small effective kernel, either because of the window size, or because of the too fast decreasing weights, then the further lying pixels will not contribute enough to the local histograms which leads to halos around sharp edges. However, this effect can be controlled using contrast stretching limitations, such as in CLAHE.  
The FFT-based contribution calculation [11] and contrast-limited equalization [7] are known methods, but they are important for the proposed algorithm, and are thus briefly reviewed in the following sections.
## 2 Background of Load-Carrying Capacity Evaluations  
An evaluation of the load-carrying capacity of a bridge is part of the bridge evaluation process. Such assessments quantitatively evaluate the live-load resistance that the bridge can safely support and can be used as basic data for the planning and maintenance of repair and reinforcement work. In the United States AASHTO Bridge Evaluation Manual, it is recommended to update the rating factor (RF) to evaluate the load-carrying capacity, such that it reflects the condition of the bridge [6]. The RF of the bridge is defined as shown in Equation (1).  
\[RF=\frac{C-\gamma_{DC}DC-\gamma_{DW}DW}{\gamma_{LL}LL(1+IM)} \tag{1}\]  
where \(C\) represents the current load-carrying capacity of the bridge member; \(DC\) and \(DW\) are the dead load effect due to the structural components or wearing surface and utilities, respectively; \(LL\) is the live load; and \(IM\) is the allowance of dynamic load. \(\gamma_{DC}\), \(\gamma_{DW}\), and \(\gamma_{LL}\) represent the load factors in each load type. In order for RF to be calculated, it is very important to calculate the structural capacity (\(C\)) that reflects the current condition. \(C\) defined by Equation (2):  
\[C=\phi_{c}\phi_{s}\phi R_{n} \tag{2}\]  
where \(R_{n}\), the nominal capacity of the member, is calculated based on the design plan. \(\phi_{c}\), the condition factor, reflects the decrease in the nominal resistance due to structural deterioration. \(\phi_{s}\), which is the system factor, is a factor of the nominal resistance that reflects the redundancy level of the entire deck system. \(\phi\) is the resistance factor based on the construction materials.  
As mentioned above, the evaluation of the nominal capacity is performed by calibrating the condition, system, and material components. These calibrations are conducted via engineers' judgments based on the condition rating system, which relies on a visual inspection. On the other hand, it is argued that this calculation approach often underestimates the safe load-carrying capacities of bridges [20; 21]. Accordingly, in order to calculate the precise structural capacity, as shown in Figure 1, it is important to identify the degree of deterioration through a field test along with condition evaluations. Field tests include examining the material strength, reinforcement condition, bearing plate condition, chloride content, exposure level, and the corrosion of the reinforcing bars. The response comparison through a static load test clearly reflects the uncertainty of the information about the bridge. However, these field trials and proof-loading tests require large-scale traffic closures that frequently prevent their implementation, especially on highways or bridges. For this reason, for many bridges, it is difficult to evaluate the load capacity as reflected in the current state.