esa-sceva/satcom-chunk-collection huggingface dataset:
# Cross-Domain Fusion Constellation Design of Communication, Navigation and Remote Sensing
## 4 Experimental Analysis
### Constellation Performance  
The coverage multiplicity results of constellations in each functional domain and CDFC with latitude are shown in Figure 5. The space simulation software [19] is used to calculate the constellation performance. The constellation and satellite load parameters are the design results of the previous section. The time is 24 h and the time slot is 1 min. The reference [17] in the simulation considers the perturbation of the satellite and other factors, and the setting of the simulation time is to consider that the period has met multiple periods of the satellite within the orbit range of 2000 km. It is worth emphasizing that the CDFC is a hybrid constellation as a whole, and the communication domain is the polar orbit constellation, and the navigation domain is the walker constellation. It can be seen that the coverage multiplicity of the single functional domain constellations is only above 2, and the coverage multiplicity of navigation constellations is higher, all of which meet the requirements of more than 4. Although the single domain coverage multiplicity of communication and remote sensing is small, the CDFC can achieve the lowest coverage  
Figure 4: Pareto front end diagram of three functional domains.  
Figure 3: Convergence process diagram of decision variable optimization.  
multiplicity of 12, with an average coverage multiplicity of 15. Although the coverage weight of the combined constellation is slightly smaller than that of the simple combination constellation, in general, the coverage rate of CDFC with optimized layout and number is not only higher, also relatively average between high and low orbits.  
GDOP is for the sake of navigation accuracy, as shown in Figure 6. The global GDOP value of CDFC is within the range of zeros to three, meeting the design index of less than 5. The difference between the navigation payload and remote sensing and communication payload is that the geometric features of navigation payload have an impact on service performance. In order to ensure the geometric configuration, the number of its payload is reduced to less than the other two payloads during the optimization process.
## 1 _Article_
###### Abstract  
Aim: To construct neural models to predict the progression of glaucoma in patients with sleep apnea. Materials and Methods: Modeling the use of neural networks was performed using the Neurosolutions commercial simulator. The built databases gather information on a group of patients with primitive open-angle glaucoma and normal-tension glaucoma, who have been associated with sleep apnea syndrome and various stages of disease severity. The data within the database were divided as follows: 65 were used in the neural network training stage and 8 were kept for the validation stage. In total, 21 parameters were selected as input parameters for neural models including: age of patients, BMI (body mass index), systolic and diastolic blood pressure, intraocular pressure, central corneal thickness, corneal biomechanical parameters (IOPcc, HC, CRF), AHI, desaturation index, nocturnal oxygen saturation, remaining AHI, type of apnea, and associated general conditions (diabetes, hypertension, obesity, COPD). The selected output parameters are: c/d ratio, modified visual field parameters (MD, PSD), ganglion cell layer thickness. Forward-propagation neural networks (multilayer perceptron) were constructed with a layer of hidden neurons. The constructed neural models generated the output values for these data. The obtained results were then compared with the experimental values. Results: The best results were obtained during the training stage with the ANN network (21:35:4). If we consider a 25% confidence interval, we find that very good results are obtained during the validation stage, except for the average GCL thickness, for which the errors are slightly higher. Conclusions: Excellent results were obtained during the validation stage, which support the results obtained in other studies in the literature that strengthen the connection between sleep apnea syndrome and glaucoma changes.  
Keywords:artificial neural networks; glaucoma; sleep apnea syndrome; AHI; BMI +
Footnote †: journal: Applied Sciences
## 3 EMS Selection and Description
### EMSs for FCEVs: Brief State of the Art
#### 3.1.1 Rule-Based Strategies  
Rule-based strategies are among the most straightforward approaches to energy management in FCEVs, relying on predefined rules to manage the power split between the fuel cell and the battery.  
Heuristic methods use experience-based rules derived from human expertise. The simplicity of heuristic control makes it a practical choice for initial implementations and basic EMS applications. However, its performance can be suboptimal compared to more  
Figure 1: Flowchart of the EMS selection methodology.  
sophisticated approaches [9]. An example of one of these methods is the Power Follower Control Strategy (PFCS).  
Finite State Machine (FSM)  
FSMs manage power flows based on different operational states of the vehicle, such as acceleration, cruising, and braking [10; 11]. By defining distinct states and transitions, FSMs can simplify the design and execution of control strategies, ensuring reliable performance in well-defined conditions.  
Thermostat Control  
This strategy aims to maintain the state of charge (SOC) of the battery within a specific range. When the SOC falls below a threshold, the fuel cell charges the battery; when it exceeds a limit, only the battery powers the vehicle. This approach mimics the operation of a thermostat [12].  
Fuzzy Logic Control (FLC)  
FLC handles uncertainties and imprecise data, making it suitable for dynamic and unpredictable driving environments [9; 13]. FLC uses linguistic variables and membership functions to define control rules, providing a flexible approach to EMS. This flexibility allows FLC to adapt to a wide range of driving conditions and driver behaviors, thereby improving overall vehicle performance and efficiency.  
A comparison of different rule-based strategies is carried out in [14], where a multi-layer decoupling control shows slightly better performance than PFCS and FLC. Rule-based strategies offer several advantages, including simplicity, ease of implementation, and low computational requirements. They are particularly suitable for real-time applications, where quick decision-making is essential. However, they also have limitations, such as a lack of adaptability to varying conditions and potentially suboptimal performance compared to more sophisticated methods. Despite these drawbacks, rule-based strategies remain a practical and reliable choice for many FCEV applications.
# Enhanced Solar Photovoltaic System Management and Integration: The Digital Twin Concept
## 4 Challenges of PV-DT
### Data Acquisition, Inacuracies and Inconsistency  
The quality of data acquired from the physical asset is core to the effectiveness and reliability of any DT [30]. Currently, PV systems rely on wired sensors both for energy and environmental conditions. In a typical PV system, all sensors collect data from environmental sources and the PV modules are often wired sensors. The use of wireless sensors is extremely limited to ensure the data collected is not compromised during transfer. Thewired sensors transmit the data to a main data logger or energy meter via long cable lengths that are specifically designed for the process. Wireless transmission only occurs between the main data logger/energy meter and the cloud. This model of data collection severely limits the data collection process around a solar farm and can be time-consuming during installation of maintenance. With a larger system size, effective monitoring using a DT would require more sensor locations, which would be more realistic using wireless sensors.  
Despite the use of wired sensors, there are still several sources of data inconsistencies, such as communication issues, power supply issues to dataloggers, electronic malfunction of dataloggers, sensors, energy meters, and more. Also, data inaccuracy can be prevalent where sensor cables are partly damaged or the sensors are not regularly calibrated as per the manufacturer's specifications. Failure to adhere to this specification leads to a wide error margin in measured data which propagates along the overall system analytics. This could lead to a wrong prediction for a downline system action based on error sensing and may be catastrophic depending on the nature of the automated action.
# Hashcash Tree, a Data Structure to Mitigate Denial-of-Service Attacks
## 2 Background
### Hash Functions  
The term _string_ is used to refer binary strings, that is, elements in \(\{0,1\}^{*}\); strings are also seen as sequences of bits. The _empty string_ is denoted by \(\epsilon\). The _prefix_ of length \(n\in\mathbb{N}\) of a string \(s\) is the string consisting of the first \(n\) bits of \(s\) and is denoted by \(\textit{prefix}(s,n)\). The _concatenation_ of two strings \(s_{s}\)' is denoted by \(s||s^{\prime}\). For a string \(s\) and a non-negative integer \(x\), the notation \(s||x\) is abused to denote the concatenation of \(s\) with the binary string representation of \(x\). The concatenation of a string \(s\) with itself \(n\in\mathbb{N}\) times is denoted by \(s^{n}\) and defined inductively as follows: \(s^{0}=\epsilon\); \(s^{n+1}=s^{n}||s\) for \(n\geq 1\). The \(i\)-th element of a string \(s\), and in general of a sequence or tuple, is denoted by \(s|_{i}\).  
**Example 1**.: _The concatenation \(10101000||237\), assuming an 8-bit representation of the integer \(237\), is \(1010100011101101\). The string \((101)^{3}\) is \(101101101\)._  
A _hash function_ is any function mapping strings of arbitrary length to strings of fixed length. If \(m\in\mathbb{N}^{+}\) is the fixed length, the signature of the hash function is  
\[h:\{0,1\}^{*}\rightarrow\{0,1\}^{m}. \tag{1}\]  
For a string \(s\in\{0,1\}^{*}\), the value \(h(s)\) returned by the hash function \(h\) is called the _hash value_ of \(s\); the string \(s\) is called _message_. Cryptographic hash functions additionally ensure the following properties:  
1. The probability of a particular hash value for a message is \(2^{-m}\);
2. Finding a message that matches a given hash value is unfeasible (_preimage resistance_);
3. Finding a second message that matches the hash value of a given message is unfeasible (_second-preimage resistance_);
4. Finding two different messages that yield the same hash value is unfeasible (_collision resistance_).  
**Example 2**.: _SHA-256 is a popular cryptographic hash function. It produces hash values of length 256. In Unix systems, the hexadecimal representation of the SHA-256 hash value of the string foo can be obtained as shown in Figure 5. Note that the produced hash value starts with 0z2c = 00111100, that is, has an all-zero prefix of length 2._  
Figure 5: Computing the SHA-256 hash value of the string “foo” in Unix-like systems.
# Alternative Categorization of Radio Frequency Power Amplifier for Generalized Design Insights
## 3 Alternative Categorization  
The classification technique proposed in this work is based on the analysis of three pillars: time, frequency, and space. To mathematically represent the relation between these parameters with the efficiency of an RFPA, as defined in Equation (2), one can expand it into Equation (4) [17].  
\[\eta=\frac{P_{out}}{P_{DC}}=\frac{P_{out,fo}}{P_{diss}+\sum_{h=2}^{\infty}P_{ out,hf}+P_{out,df}} \tag{4}\]  
Here, the subscript \(f_{o}\) is defined as follows: \(f_{o}=1/t_{o}\), where \(t_{o}\) is the fundamental period. \(P_{out,fo}\) is the total output power delivered to the load at that fundamental frequency. \(P_{diss}\) is the dissipated power in the device which can be related to time. \(\sum_{h=2}^{\infty}P_{out,hf}\) is the power lost into the harmonics due to the inherent non-linearity of the device, which is related to frequency, and finally \(P_{out,df}\) is the maximum power that can delivered to the load efficiently at a design frequency which is related to space in this paper.  
The foundation of this categorization draws inspiration from the field of category theory [18], which is a branch of abstract mathematics dedicated to studying mathematical structures and their intricate relationships.  
In this context, time, frequency, and space are the objects within this categorical framework, and their relations are described by morphisms. These can simply be thought of as functions subject to certain additional axioms, as their compositions describe the cascade effect of modifying, for instance, the time waveform, on the frequency domain or, indirectly, the space domain. Additionally, the concept of identity in category theory  
Figure 1: Basic RFPA block with its typical performance. (**a**) Basic amplification; (**b**) typical performance of RFPA.  
applies here, with the physical meaning of a certain unique transformation in time domain that leaves morphisms acting from time to frequency/space invariant.  
Considering one factor at a time allows for the evaluation of efficiency in isolation for each of these critical parameters. However, it is essential to recognize that the interplay and combination between all these elements is inevitable, which can be seen in any practical RFPA design. As will be seen, this categorization may suggest techniques and design approaches that address known problems encountered in RFPA design, such as non-linearity and limited bandwidth, efficiency, or gain.
## 2 Preliminary
### OPTICS Clustering Algorithm
In terms of clustering parameter selection, OPTICS offers a more reasonable approach compared to clustering algorithms like K-means and DBSCAN [17]. It generates a reachability plot, as depicted in Figure 1, which allows for visual parameter selection. This reduces the impact of randomly setting clustering parameters on the final clustering results. In Figure 1, the horizontal axis represents the sequence of output objects, and the vertical axis stands for the reachability distance of the current object with reference to the previous object, i.e., the reachable distance between object A and object C is 1.5. By setting the value of \(eps\) to 3.9, all the objects can be effectively divided into two clusters: {A,C,D,F} and {B,E}.
## 3 Results and Discussion
### Excess Pore Pressure (\(\Delta u\))
Figure 11 shows the excess pore pressure for the HS_80% model (solid line) and the HS_60% model (diamonds). Figure 10a shows the applied stress curves from 10 to 25 kPa. For both models, pore pressure values are low compared to applied stress; however, the HS_80% model shows higher values than HS_60%. In Figure 10b, HS_80% exhibits significantly higher excess pore pressure values than the 60% model, with the smallest difference of 16%. The results shown by HS_80% are similar to those of the saturated condition, displaying pore water pressure values that tend toward zero at the boundaries as if it were saturated soil. Table 4 shows that HS_80% reaches 100% effective saturation at an applied stress of 100 kPa. In the soil with 60% saturation, the \(\Delta\)u profile shows a shallower slope, indicating lower pressure accumulation throughout the model's height. In contrast, in the soil with 80% saturation, the \(\Delta\)u profiles are steeper. Figure 11: \(\Delta\)\(u\) immediately after each vertical stress is applied for HS_60% and HS_80%: **(a)** Stresses from 10 to 25 kPa; **(b)** 800–3200 kPa.
# Hydraulic Fracture Propagation near the Cavity in a Poroelastic Media
## 3 Numerical Test Results and Discussion
### Uncoupled Problem  
In the first numerical experiment, an uncoupled problem, i.e., \(\alpha\) = 0 is considered. In this case, the results of modeling the hydraulic fracturing correspond to the solutions obtained in the elastic model. Some aspects of such a problem already have been studied [35; 37; 50]. Figure 3 shows the paths of the hydraulic fractures at different hydrostatic stresses, \(P\), and different distances between the initial crack and the cavity. The distance between the initial crack and the cavity, \(s\), is different, i.e., \(s\) = 0.5 \(R\), \(s\) = \(R\), \(s\) = 2 \(R\). The hydrostatic stress in the medium, \(P\), is equal to 0, 1.5, and 6 MPa. In Figure 3, number 1 denotes the curve obtained at \(P\) = 0 MPa, number 2 denotes the curve at \(P\) = 1.5 MPa, and number 3 denotes the curve at \(P\) = 6 MPa. Since the computational area is under hydrostatic compression, the problem is symmetric to the axis normal to the initial crack and passing through its center. Therefore, when calculating and displaying the results, we  
Figure 2: (**a**) 2D area containing circular cavity and initial crack; (**b**) Discretization of the model used for numerical experiments.  
use only one of the symmetric planes shown in Figure 3. It can be seen that the cavity has a strong influence on the paths of the hydraulic fracturing, and they are different from straight lines. The main reason for the curvature of the cracks is associated with the stresses arising around the cavity under hydrostatic loading. As the hydrostatic stress increases, the fracture path becomes more curved. Also, when the initial crack is placed closer to the circular cavity, the influence of the stress around the cavity is enhanced.  
The paths of the cracks also are influenced by the parameters of the medium. Figure 4a,b show the paths of the cracks for different values of the critical tensile stress, \(\sigma_{c}\), and the elastic modulus \(E\). It is seen that, as \(\sigma_{c}\) and \(E\) increase, the effect of the cavity decreases, and the cracks are less curved. Changes in the other parameters of the medium, such as the critical energy release rate, \(G_{\rm LC}\), [PERSON]'s ratio, \(\upsilon\), and the dynamic viscosity of the fluid, \(\eta\), do not lead to significant changes in the directions of the development of the fracture and are not presented in the article.
## 1 Introduction
Meanwhile, most empirical models for predicting roadheader performance do not incorporate joint-related parameters [19; 20; 21; 22; 23; 24; 25; 26], except for a few that consider RQD [27; 28; 29]. [PERSON] et al. [30] pointed out that joints have a significant impact on roadheader performance and that RQD alone is insufficient to fully characterize the properties of jointed rock masses. Although studies such as the numerical analyses by [PERSON] et al. [31] and [PERSON] [32] have examined drag pick cutting in jointed rock masses, detailed investigations into the effects of joints on roadheader performance remain limited. This study employed an experimental method to examine the effects of joints on rock cutting with a conical pick. Conical picks are widely used as primary cutting tools in mechanical excavation equipment such as roadheaders and continuous miners, particularly for soft to medium-hard rock. Their self-rotating design reduces uneven wear and enhances overall cutting efficiency. A series of unrelieved cutting tests was carried out on jointed rock mass specimens to examine the effect of joints on cutting behavior, considering various distances between the joint plane and the cutting path, as well as different cutting depths. Cutting forces were analyzed based on the distance from the joint plane. In addition, groove profiles were captured using a 3D scanner, and the obtained data were used to investigate the characteristics of crack propagation.
# Multi-Dimensional Feature Fusion and Enhanced Attention Streaming Movie Prediction Algorithm
## 4 Results
### Introduction to the Dataset
#### 4.1.3 Director-Actor Impact Factor  
The core purpose of building an actor-director collaboration network graph is to provide structured collaboration features for the proposed model FFLSTMEA. This network graph extracts the director-actor network influencing factors of each movie composite by quantifying the node degree and other indexes, and transforms them into multidimensional features for input into the prediction model. Combined with feature fusion and the attention mechanism, it dynamically focuses on the key collaboration features to improve the accuracy of predicting the movie's split box office.  
The directors and lead actors of streaming movies were selected as network nodes. Considering the complexity and diversity of actors in streaming movies, the top five leading actors are selected for each movie to ensure the representativeness of the collected data and the depth of the study. An undirected weighted graph is created when two actors or directors work together on the same movie, creating an edge in the network between these two nodes. The edge's weight represents how often the two actors or directors have worked together; the higher the weight, the more often these two performers have worked together. The actor and director cooperation network is shown in Figure 5. Through network analysis, the degree, number centrality, proximity centrality, and structural hole constraint degree of each node are calculated, and then the metric values of all the participants of the movie are aggregated and then averaged to characterize the network attributes of the movie. If a movie contains multiple directors and actors, the corresponding metric values take into account the contributions of all participants to obtain a combined director-actor network influence factor for each movie. This approach ensures that the network characteristics of each movie accurately reflect the position and role of all its participants in the collaborative network.  
Figure 4: Movie genre importance.