esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Solar Plant Modeling  
#### 2.2.1 Solar Collector Modeling  
The parabolic collectors LS-2 were selected to be used in the present installation. The module aperture was about 39 m\({}^{2}\), while the concentration ratio was around 23 suns [27]. Other geometrical and optical parameters are included in Ref. [27]. These collectors utilize direct beam irradiation (\(G_{bm}\)), thus, the available solar energy (\(Q_{solar}\)) in the installed aperture (\(A_{a}\)), is determined as [27]:  
\[Q_{solar}=A_{a}\cdot G_{bm} \tag{1}\]  
The useful heat absorbed by the thermal oil is described as [27]:  
\[Q_{u}=\dot{m}_{col}\cdot c_{p,oil}\cdot(T_{col,out}-T_{col,in}) \tag{2}\]  
The mass flow rate of the heat transfer fluid is estimated below [28]:  
\[\dot{m}_{col}=0.02\cdot A_{a} \tag{3}\]  
The PTC thermal efficiency is calculated as the fraction of useful energy to the available solar energy as below [27]:  
\[\eta_{th,col}=\frac{Q_{u}}{Q_{solar}} \tag{4}\]  
The incident angle modifier (K(\(\theta\))) of these collectors is defined by the following expression [27]:  
\[K(\theta)=\cos(\theta)+0.000884\cdot\theta-0.00005369\cdot\theta^{2} \tag{5}\]  
In Equation (5), the incident angle (\(\theta\)) is in degrees. Apart from the aforementioned basic mathematical equations, the collector performance can be extensively determined by a series of additional expressions that describe the heat convection inside the tube, as well as the thermal losses to the ambient. This procedure can be found in Ref. [29]. Thus,  
Figure 1: The proposed solar-fed energy system for electricity and hydrogen production.  
the thermal performance of the collector can be described by an equation based on the following structure [30]:  
\[\eta_{th,col}=a\cdot K(\theta)+b\cdot\frac{T_{col,in}-T_{amb}}{G_{bn}}+c\cdot \frac{\left(T_{col,in}-T_{amb}\right)^{2}}{G_{bn}}+d\cdot\frac{\left(T_{col,in}-T _{amb}\right)^{3}}{G_{bn}} \tag{6}\]  
The coefficients \"\(a\)\", \"\(b\)\", \"\(c\)\", and \"\(d\)\" are defined by considering the results of the detailed thermal analysis.
The paper then proposes a novel framework to address existing interoperability challenges, especially those related to data exchange, privacy, and security. The framework was designed using a bottom-up approach, which consisted of examining existing interoperability frameworks to identify their inadequacies to determine the requirements for a novel and improved framework. The novel framework aims to achieve improved efficiency compared to past frameworks and is proposed to be implemented within a single legal jurisdiction; therefore, legal interoperability is not considered in this work.
## 3 Materials and Methods
### Definition of Relevant Use Cases
#### 3.2.4 Maneuvers-Based Approach  
This approach, similar to the situation-based approach discussed in Section 3.2.3, applies only to the scenarios involving eHMI. Specifically, the maneuvers-based method was utilized to compile all potential driving actions that an SAV can perform. Driving actions were classified into longitudinal and lateral maneuvers. Longitudinal maneuvers involve maintaining a constant speed and reducing and increasing speed while the vehicle is in motion. Lateral maneuvers include driving straight, making turns (left, right), and changing lanes (left, right). As the analysis of traffic conflict cases in Section 3.2.3 already covered lateral maneuvers, all such maneuvers are filtered in this section.  
Although objects interacting with AVs can infer the vehicle's anticipated behavior through subtle implicit information such as the vehicle's speed, turn signals, and brake lights, indicating actions like maintaining a constant speed, accelerating, or decelerating, eHMI can help other road users anticipate the driving intentions of autonomous vehicles through explicit signals. Therefore, this study's five longitudinal driving maneuvers include maintaining a constant speed while driving, accelerating, decelerating, and starting or stopping.
# Differential Analysis of a Cryptographic Hashing Algorithm HBC-256
## 3 Materials and Methods of Research
### Method of Differential Cryptanalysis  
The differential cryptanalysis method was first proposed by [PERSON] and [PERSON] to analyze the DES encryption standard [20,21].  
Differential cryptanalysis is based on the analysis of differences between encrypted values at various stages of encryption. As a difference, the operation of bitwise addition modulo 2 is used, including the analysis of the difference modulo 2\({}^{n}\). Differential analysis is a method of cryptanalysis of symmetric block ciphers and other cryptographic primitives, in particular, hash functions and stream ciphers [22]. To apply this type of analysis, it is necessary to build tables of differential properties for all non-linear elements of the algorithm-S substitution boxes, addition modulo 2\({}^{n}\), and others. A detailed algorithm for differential analysis of nonlinear elements can be found in [23,24].  
In the general case the application of the method of differential cryptanalysis is reduced to the following steps:  
1. Analysis of non-linear elements and determination of the most probable differences for them;
2. Construction of a multi-round characteristic (type input difference-output difference) from simple to complex, that is, from one round to n rounds. Determining the  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \(x\) & **0** & **1** & **2** & **3** & **4** & **5**  
probability of the appearance of the constructed characteristic. Search for correct pairs of texts, i.e., such texts for which the sum of the input values is the same as the value of the input difference, and the sum of the output values is the same as the output difference.  
The task of differential cryptanalysis is reduced to constructing such pairs of input-output difference, the probability of which is less than the exhaustive search of all possible values. To construct pairs of texts, the analysis of non-linear elements of a cryptographic primitive (for example, S-boxes) is carried out. After that, the transformations from round to round are compared and the possibility of transition from one state to another is determined.  
A pair of texts that corresponds to a given difference is called a correct pair of texts. In the case of symmetric encryption, the correct pair of texts allows to determine the secret encryption key or its fragments. In the case of hash function analysis, the correct pair of texts determines the collision.  
The purpose of this research is to observe the differential properties of the new HBC hashing algorithm. To apply differential cryptanalysis, it is necessary to check whether there is a way to transform differences from one operation to another and what is the probability of such transformation. The probability will determine the complexity of the analysis and the value of the difference will set the initial search parameters.
## 3 ATON Framework
### Presentation Layer
#### 3.6.6 User Interface Blueprints and Spatial UI  
Regarding _user interface_, ATON offers several built-in UI elements (HTML5) to boost the creation or prototyping of web-applications. They consist of buttons (e.g., home viewpoint, enter VR mode, etc.), toolbars, modal popups, etc.--with fully responsive support, guaranteeing a smooth, automatic adaptation to different screen sizes. They can be easily themed with custom CSS (cascading style sheets), while developers can  
Figure 10: Sample BVH trees in ATON (green) to accelerate 3D queries.  
attach custom routines to them. Furthermore, built-in elements (like buttons) work in combination with the profiler, enabling the creation of consistent user interfaces across multiple devices. A few examples are the immersive VR mode (only showing on secure connections and supported devices) or device-orientation navigation mode (only showing on mobile devices supporting this feature). The web-application can thus use these UI elements, or create their own using common HTML5 and javascript/ES6 functionalities.  
The framework provides a built-in _spatial UI_ (user interface elements living in the 3D space) specifically targeting immersive AR/VR sessions. These components were designed on top of existing guidelines [73] and design patterns related to 3D user interfaces [1], immersive VR principles targeting education [74] and immersive UIs for virtual musums [75]. The spatial UI module provides developers with buttons, 3D toolbars, labels, panels, and dynamic 3D text rendering features that allows web-applications to arrange interactive elements inside the scene (see Figure 12).  
Figure 11: **Top row**: basic (spherical) annotations interactively added using current 3D selector location and radius, with multiple shapes under the same semantic node ID (e.g., “eyes”, top right). **Bottom row**: free-form semantic annotations interactively created at runtime using multiple surface points at different scales.  
Figure 12: A few applications of spatial UI elements. **Top row** (from left to right): 3D toolbars in the virtual space with custom events, multiple measurements, 3D floating labels. **Bottom row** (from left to right): immersive VR hands, semantic labels (VR), wrist interfaces and immersive VR measurements.  
Since they are designed as nodes, they are managed within a UI-graph, thus they can be freely reorganized or transformed for different purposes. A few examples involve arranging informative panels inside the environment, attaching floating 3D labels to semantic shapes or placing buttons (triggering custom events) within local or absolute coordinate systems. These elements are consistent with--and specifically indicated for--immersive VR visualization. They also allow the creation of wrist interfaces [73] for virtual hands with ease, attaching specific functionalities. A few examples are related to measurement in WebXR sessions, teleport to predefined locations, enabling/disabling temporal layers, and much more. The spatial UI component offers blueprints to build custom spatial interfaces, depending on the specific WebXR application requirements.
## 6 Comparative Analysis with National Standards  
### Temperature Distribution Comparison  
The axial expansion deformation, bending deformation, and generation of self-equilibrating stresses in concrete box girder structures are significantly influenced by the vertical temperature gradient resulting from a sudden decrease in temperature. Therefore, to validate the applicability of the proposed model, a comparative analysis was conducted among three codes--the General Code for Highway Bridge and Culvert Design (JTGD60-2015) [31], AASHTO LRFD bridge design specification (AASHTO) [32], and the British Code (BS5400) [33]--as illustrated in Figure 18. It can be observed that the vertically oriented temperature gradient model presented in this paper is 1.96 \({}^{\circ}\)C lower than JTGD60-2015 at the top of the box girder. However, it differs from the AASHTO and BS5400 specifications by 2.96 \({}^{\circ}\)C and 2.06 \({}^{\circ}\)C, with standard deviations of 0.873 and 0.451, respectively. At the bottom of the box girder, significant deviations are observed between the model proposed in this paper and the existing specifications. While JTGD60-2015 and AASHTO neglect temperature differentials at this location, they exhibit similar temperature distribution patterns which differ from those specified by BS5400. In contrast, the proposed model aligns closely  
Figure 17: Vertical temperature gradient mode (unit: cm).  
with BS5400 at the bottom, with a difference of 3 \({}^{\circ}\)C and a standard deviation of 0.508. Therefore, the temperature gradient model effectively incorporates the gradient models specified by the JTGD60-2015, AASHTO, and BS5400 codes, ensuring bridge engineering safety in high-altitude and cold regions.
# Study on Surface Damage Induced by High Heavy Layer Movement and Mining-Induced Earthquakes
## 1 Introduction
While the mechanisms of mining-induced earthquakes in the heavy layer are well understood, research on the surface damage induced by heavy layer-type mining-induced earthquakes remains incomplete. This study analyzes working faces overlain by heavy layers, taking the 7202 working face in the Zaozhuang mining area as a case study. In this case, an 80 m thick heavy layer exists at a vertical distance of 72 m above the coal seam. The research aims to develop a feasible engineering approach for mitigating surface damage in mining faces with similar geological conditions. Building on previous research, this study focuses on surface damage induced by the movement of heavy layers and the associated heavy layer-type mining-induced earthquakes. Through field investigations of the geological information and microseismic data from the 7202 working face, a fracture mechanism analysis of the movement of heavy layer was conducted based on the medium thick plate theory, and the results were compared with on-site conditions. Numerical simulations using UDEC were performed with data monitoring to compare scenarios with and without mining-induced earthquakes, analyzing surface subsidence caused by the movement of heavy layer and mining-induced earthquakes, and revealing the mechanisms of surface damage caused by the movement of heavy layer and mining-induced earthquakes. Industrial tests were carried out to provide a replicable engineering solution for the prevention and control of mine-induced earthquakes and surface subsidence control in mining areas with thick overburden layers. Finally, the research conclusions provide important theoretical references and practical value for surface protection, and rock strata movement research and coal mine safety.
## 3 Results  
### Identification of Bioactive Compounds in SHE  
The total phenolic content of the SHE was 19.51 \(\pm\) 0.24 mg gallic acid equivalent (GAE)/g extract, while the total flavonoid content was 54.65 \(\pm\) 15.61 mg catechchin equivalent (CE)/g extract. LC-MS analysis of the bioactive substances in SHE revealed a high concentration of fucoxanthin (MF: C\({}_{42}\)H\({}_{85}\)O\({}_{6}\), MW: 658.9, calculated MW: 659.3), similar to that in previous studies. In addition, several phenols, including salicortin (MF: C\({}_{20}\)H\({}_{24}\)O\({}_{10}\), MW: 424.4, calculated MW: 425.3), feruloylquinic acid (MF: C\({}_{17}\)H\({}_{20}\)O\({}_{9}\), MW: 368.3, calculated MW: 369.2), vitexin (MF: C\({}_{21}\)H\({}_{20}\)O\({}_{10}\), MW: 432.4, calculated MW: 433.2), and loligilide (MF: C\({}_{11}\)H\({}_{16}\)O\({}_{3}\), MW: 196.2, calculated MW: 197.1), were detected (Figure 1, Table 1). The highest concentration of SHE constituent was loligilide (16.52 ug fucoxanthin/mg extract), followed by fucoxanthin, salicortin, feruloylquinic acid, and vitexin.  
Figure 1: Representative LC-MS chromatogram of _S. horneri_ extract. 1. Fucoxanthin, 2. Salicortin, 3. Feruloylquinic acid, 4. Loligilide, and 5. Vitexin.
# Location Selection Methods for Urban Terminal Co-Distribution Centers with Air-Land Collaboration
## 4 Algorithm Design
### Co-Distribution Center Site Selection Method Based on GWO
#### 4.2.4 Design of the Fitness Function  
In the process of selecting co-distribution center sites, it is necessary to consider the optimization of two key indicators, cost and delivery time. The aim is to simultaneously minimize these indicators, as specified by Equation (1). For this purpose, the following fitness function is set:  
\[fitness=\omega_{t}f_{time}+\omega_{c}f_{cost} \tag{25}\]  
where \(f_{time}\) is described in Equation (26) and \(f_{cost}\) is described in Equation (27). For both functions, smaller values are preferable.  
\[f_{time}=\frac{\text{max}Z_{time}-Z_{time}}{\text{max}Z_{time}-\text{min}Z_{time}} \tag{26}\]  
\[f_{cost}=\frac{\text{max}Z_{cost}-Z_{cost}}{\text{max}Z_{cost}-\text{min}Z_{cost}} \tag{27}\]  
In Equation (26), the maximum distribution time \(\text{max}Z_{time}\) occurs when all co-distribution centers are of ground type and each customer demand point is distributed by the farthest distribution center. The minimum distribution time is 0, satisfying \(f_{time}\in(0\,,1]\). Similarly, in Equation (27), the maximum cost \(\text{max}Z_{cost}\) occurs when all co-distribution centers are of air type and each customer demand point is distributed by the farthest distribution center. The minimum cost is 0, satisfying \(f_{cost}\in(0\,,1)\). The scaling coefficients \(\omega_{t}\) and \(\omega_{c}\) in Equation (25) ensure that the two indicators can be considered in a balanced way.
###### Abstract  
Underwater Acoustic Network (UAN) is an emerging technology with attractive applications. In such type of networks, the control-overhead, redundant inner-network transmissions management, and data-similarity are still very challenging. The cluster-based frameworks manage the control-overhead and redundant inner-network transmissions persuasively. However, the current clustering protocols consume a big part of their energy resources in data-similarity as these protocols periodically sense and forward the same information. In this paper, we introduce a novel two-level Redundant Transmission Control (RTC) approach that ensures the data-similarity using some statistical tests with an appropriate degree of confidence. Later, the Cluster Head (CH) and the Region Head (RH) remove the data-similarity from the original data before forwarding it to the next level. We also introduce a new spatiotemporal and dynamic CH role rotation technique which is capable to adjust the drifted field nodes because of water current movements. The beauty of the proposed model is that the RH controls the communications and redundant transmission between the CH and Mobile Sink (MS), while the CH controls the redundant inner-network transmissions and data-similarity between the cluster members. We conduct simulations to evaluate the performance of our designed framework under different criteria such as average end-to-end delay, the packet delivery ratio, and energy consumption of the network with respect to the recent schemes. The presented results reveal that the proposed model outperforms the current approaches in terms of the selected metrics.  
underwater sensor network; data-similarity; control-overhead management; statistical test; sleep-awake aware +
Footnote †: journal: Sensors
### Contribution  
In the aforementioned resource distribution methods, the limited utilization of spectrum resources results in constrained throughput and restricted accessibility for VUEs. In multiaccess resource allocation, a pair of VUEs can utilize various subcarriers. At the same time, a single subcarrier can also be shared among numerous pairs of VUEs. This has the potential to enhance the efficiency of VUEs by increasing throughput and access rate while maximizing the utilization of spectrum resources. The main contributions of this article are as follows:  
* We propose a multiaccess resource-allocation scheme for V2X communication that allows multiple VUEs to share the same subcarrier and one pair of VUEs to use multiple subcarriers, which can improve the spectrum utilization and data rate of V2X communication.
* We design a three-stage algorithm to solve the subcarrier-allocation problem for CUEs and VUEs. The algorithm first allocates subcarriers to CUEs to ensure their data rate requirements, then allocates the remaining subcarriers to VUEs, and finally allows VUEs to reuse the subcarriers already allocated to other users.
* We formulate the power control problem as a mixed integer nonlinear programming problem and solve it using the successive convex approximation method, which can transform the non-convex problem into a convex problem and obtain the optimal power-allocation solution.
* To ensure the effectiveness of the proposed method and algorithms, we have simulated them in MATLAB. Regarding the access rate and throughput for VUEs, the paper evaluates the suggested approach against the current literature. The research also investigates the impact of numerous characteristics on the system performance, such as the number of VUEs, the number of CUEs, and the vehicle speed. The study demonstrates that the suggested method can effectively raise the bar for both access rate and throughput for VUEs while still meeting the needs of the CUEs in terms of required data rates.  
Hence, this paper employs a multiaccess resource-allocation strategy and joint power control to optimize the system throughput of VUEs while simultaneously guaranteeing the data rate of CUEs. The implementation of multiaccess matching algorithms has the potential to enhance system performance by employing effective power control and resource-allocation techniques. Nevertheless, this technique gives rise to significant co-channel interference on cellular and vehicle networks. Hence, the challenge addressed in this article is the rational allocation of spectrum resources and mitigating interference.  
The rest of the paper is organized as follows. Section 2 describes the system model and the problem description. The proposed multiaccess resource allocation and power control algorithms are included in Section 3. In Section 4, simulation results and analysis are reported. Section 5 concludes the paper.
## 2 Model Framework and Methodology
### Hidden Markov Model (HMM)  
Lubricating oil degradation trajectory with a prespecified number of states is widely modeled using a stochastic technique called the hidden Markov model (HMM). In general, the state space for lubrication degradation is discretized based on experience [23; 26]. HMM has proved its efficiency in the field of diagnostics. HMM assumes that the states are hidden for observed values. \(Zt\)'s hidden state for observation \(Y_{t}\) at time \(t\) is assumed to evolve through \(K\) possible states. The new state, \(Z_{0}=0\), depicts the new oil and initial state of degradation. The chain rule then gives the state sequence probability [10]  
\[P(\overset{\rightarrow}{Z})=P(Z_{t},\;Z_{t-1},\ldots Z_{1};\lambda), \tag{4}\]  
\[P(\overset{\rightarrow}{Z})=P(Z_{t}|\;Z_{t-1};\lambda)\times P(Z_{t-1}|\;Z_{ t-2};\lambda)\ldots\ldots P(Z_{2}|\;Z_{1};\lambda)\times P(Z_{1}|\;Z_{0};\lambda) \tag{5}\]  
where \(\lambda\) is the state transition matrix given by  
\[\lambda=\lambda_{ij}=\left(\begin{array}{ccccc}\lambda_{11}&\lambda_{12}& \lambda_{13}&\ldots&\lambda_{1K}\\ \lambda_{21}&\lambda_{22}&\lambda_{23}&\ldots&\lambda_{2K}\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ \lambda_{K1}&\lambda_{K2}&\lambda_{K3}&\ldots&\lambda_{KK}\\ \end{array}\right) \tag{6}\]  
HMM hypothesizes that the observation of emission sequence is generated from hidden states traversing over time. In the case of lubrication degradation, the hidden state can traverse to \"any state\" with \"continuous observations\". However, HMM requires a \"discrete state space\" for hidden states that need to be \"defined and specified in advance\". Considering these inherent restrictions, HMM cannot directly provide an optimal prognostic and health management (PHM) strategy for practical real case scenarios, more so, for new systems with insufficient knowledge. An advanced variant of the HMM with an infinite-state transition matrix needs to be considered to model this. A prior over-the-transition matrix is achieved using the hierarchical Dirichlet process (HDP), which will be discussed in the next subsection.