esa-sceva/satcom-chunk-collection huggingface dataset:
# Temporal Continuity Expression for Network Topology of Space Information Systems
## 5 Discussion
### Analysis of the Relative Angular Velocity Factor of the Node  
The relative angular velocity factor of the node is linear within the effective range of angular velocity. However, the quality of the link may not be greatly affected within the allowable range of angular velocity, especially when the phased array antenna is used for link construction. This is because the antenna beam sweep rate can perfectly match the change rate of angular velocity. Thus, the value of the relative angular velocity factor of the node may be degraded to 0 or 1.  
When the Fourier series is used to fit the relative angular velocity factor of the node, the fitting effect of the extreme part is sometimes not satisfactory, as shown in Figure 4a,b. It can be solved by increasing the fitting order of the Fourier series. However, for practical applications, the fitting accuracy of the Fourier series in the extreme region may be not necessary combining the constraints of the minimum chain distance and the maximum relative angular velocity.
# Heart Failure Prediction Based on Bootstrap Sampling and Weighted Fusion LightGBM Model
## 2 Related Work
Cardiac magnetic resonance imaging can clearly show the structure of the heart, and accurately evaluate the pressure inside the heart, thus predicting whether the patient will have heart failure. Various cardiac imaging tests have been used to help manage patients with heart failure (HF). This article reviewed current and future HF applications for the major noninvasive imaging modalities: transthoracic echocardiography (TTE), single-photon emission computed tomography (SPECT), positron emission tomography (PET), cardiovascular magnetic resonance (CMR), and computed tomography (CT) [26]. Some researchers use MRI images to predict cardiac arrest [27]. At the same time, relevant researchers use echocardiography [28] to help detect early asymptomatic heart failure. This is achieved by measuring the structural and functional parameters of the heart, such as the left ventricular ejection fraction and the thickness of ventricular wall. This method is also significant in evaluating the severity and prognosis of heart failure. Gene detection and gene research provide a new angle and direction for the prediction of heart failure. Genetic testing is an established nursing method in contemporary cardiology practice [29]. On the one hand, it focuses on the relationship between gene polymorphism and heart failure, and most of the genetic association research focuses on ACE I/d polymorphism [30]. On the other hand, the exploration of genes related to hereditary cardiomyopathy is also deepening [31, 32, 33], which is helpful to identify high-risk groupswith genetic predisposition and provide a basis for the early prevention of and intervention in heart failure. With the development of big data and computer technology, machine learning and big data analysis are widely used in the field of heart failure prediction [34, 35, 36, 37]. By mining and analyzing a large number of clinical data, biomarker data, and image data, we can identify the high-risk population of heart failure more accurately, and we can significantly enhance the accuracy and practicability of the prediction model. For example, the prediction model of heart failure based on logistic regression, decision trees, neural networks, random forest, XG Boost, and LightGBM [38, 39, 40] provides strong support for the accurate prediction and individualized treatment of heart failure.
###### Abstract
Examples of plant _trans_-factors undergoing the dimerization process are SEPALLATA3, LBD16 and 18, and auxin-response factors (ARFs) [34; 35; 36]. The strength of transcription response and the efficient dimerization of ARFs are mediated by closely spaced (5-7 bp) cis-active elements represented by TGTCCG inverted repeats and TGTCTC or TGTCCG direct repeats [34]. The significance of the spacer length between two TGTCNN ARF _cis_-active motifs was studied in _A. thaliana_. Distribution of direct (DR), inverted (IR), or everted repeats (ER) of these sequences with spacing ranging from 0 to 10 nt was studied in the 1500 nt long promoter region of each _A. thaliana_ gene. Comparison with RNAseq and microarray studies showed that only IR8 and DR5 were associated with responsiveness to auxin treatment [34]. Usually, clusters of _cis_-active elements enabling homo-, hetero-, or oligomerization of _trans_-factors are not distributed statistically within gene promoters. Instead, they are concentrated in promoter sections known as modules [37; 38]. The dimerization of fly CAMTA is mediated by the region within the DNA-binding CG-1 domain [39; 40; 41]. In the case of fly CAMTA, dimerization is required for proper nuclear localization and is not critical for binding with the gene promoter [39]. However, the dimerization of CAMTA _trans_-factors in mice requires palindromic repeats of the core CAMTA-binding sequence CGCANTGCG [42]. Previous research indicated that two directly repeated RSREs in the 5'UTR of the _SmMECPS_ gene are separated by only 6 bp, suggesting their putative dimerization [43]. Such an observation suggests that the potential retrograde auto-regulation of nuclear _SmMECPS_ gene transcription rate by MECPD is produced by plastitial MECPS enzyme. An increased concentration of MECPD could activate CAMTA3, thereby enabling its dimerization with CAMTA2 or CAMTA4 on repeated RSREs. Established CAMTA3/CAMTA2/4 complexes could stimulate _MECPS_ gene expression [29]. The presented study aims to find out if the presence of two closely spaced, directly repeated RSREs in the 5'UTR of the _SmMECPS_ gene is specific to the medicinal plant _S. militorhizia_ or is a more common property of _MECPS_ genes in other plants. Furthermore,such closely spaced, directly repeated RSREs were searched in the regulatory regions of other MEP pathway genes in model plants to evaluate the potential regulation of different MEP pathway genes by MECPD and CAMTA _trans_-factors.
# Delivering Extended Cellular Coverage and Capacity Using High-Altitude Platforms
## 5 Performance Evaluation  
We set up simulations to evaluate the HAP extended coverage system limits using the derived models and our enhanced beam-pointing algorithm. Simulation parameters are given in Table 2. Using the antenna profile in (1) and 1600 antenna elements [39], it is heuristically determined that the edge-of-cell subtended angle \(\rho=3.5^{\circ}\), resulting in an SPPC of approximately 2.5 km diameter. This is used in Algorithm 1 with the resulting cell centre coordinates used as boresights in the antenna system. The CNR and CINR of all users in the service area are evaluated using (4) and (8), respectively. The results are compared with other alternative cell placement schemes.
# Learning-Based Traffic Scheduling in Non-Stationary Multipath 5G Non-Terrestrial Networks
## 3 Results
### Bandwidth Utilization  
Figure 8 shows the bandwidth used by our AC agent and the optimal policy in different contexts. On average, both had a repetition factor of 1.4, corresponding to a 2.1 Mbps average throughput. In our simulation, we used 1.5 Mbps as the source rate. On the other hand, the RR system consumed the least bandwidth, with an average bandwidth of 1.5 Mbps (omitted in the figure because it always transmitted with one bit without repetitions). The numerical results are presented in Table 5.
# Privacy-Preserving Data Aggregation with Dynamic Billing in Fog-Based Smart Grid
## 3 Problem Formalization
### Threat Model  
To demonstrate the security and feasibility of PPDB, we considered threats faced by SGs in the real world. In this paper, AC and RKG are considered to be honest, and they always strictly execute the protocol. SMs, FNs and SP are considered to be honest-but-curious, which means that they do not tamper with data but attempt to infer private information about the others. We define an honest-but-curious entity as an internal PPT adversary. In addition, we also consider the threats from an external PPT adversary \(\mathcal{A}\). We assume that \(\mathcal{A}\) has the following attack capabilities:  
* \(\mathcal{A}\) can eavesdrop on reports transmitted through public communication channels.
* \(\mathcal{A}\) can intrude into the databases of \(FN_{s}\), AC and SP to steal reports.
* \(\mathcal{A}\) can also launch a number of active attacks to compromise the data authentication and integrity.
# On-Demand Waveguide-Integrated Microlaser-on-Silicon
## 3 Conclusions  
In conclusion, we demonstrated an on-demand WG-integrated microlaser-on-Si. We designed and fabricated a small Si-post structure in the longitudinal center of a long stripe-type Si WG. A top-down fabricated InGaAsP MQW microdisk laser was align-transferred on top of the Si-post and integrated with the Si WG. Optical experiments were carried out and the results exhibited a successful lasing oscillation with a threshold pump power of 378 uW and the light coupling and propagation through the WG. The measured scattered power at the left (\(I_{\text{left}}/I_{\text{disk}}\)) and right WG ends (\(I_{\text{right}}/I_{\text{disk}}\)), which were normalized to the emission power of the microlaser, was approximately 35.29% and 30.59%, respectively. The numerical simulation results show a calculated coupling efficiency (\(\eta_{c}\)) of approximately 44.52%. Despite the small discrepancy in the values, the normalized emitted power at one end of the WG (Top\({}_{\text{WG}}\)/Top\({}_{\text{disk}}\)) of approximately 38.09% exhibited good qualitative agreement with the experimental results. We believe that our on-demand WG-integrated microlaser-on-Si can be a powerful solution for high-quality light sources in high-density ultracompact photonic integrated circuits and further fulfill important functionality such as high-sensitivity optical sensing [3; 4; 39]. Furthermore, the proposed integration technique is universally applicable and can thus help the realization of integrated micro- and/or nanoscale optical devices operating at different frequencies.
# The Car-Following Model and Its Applications in the V2X Environment: A Historical Review
## 3 Research on Car-Following Behavior in the V2X Environment
### Modelling the Car-Following Behavior  
Constructing an accurate model is the basis for understanding, describing, and analyzing the car-following behavior in the V2X environment. Utilizing V2X technology, the information perception ability of the driver-vehicle units will be significantly improved, making it possible to obtain massive traffic information conveniently and in real-time. Affected by various information sources in the V2X environment, the new characteristics of car-following behavior and the model considering them have been the major research subjects.
# Blockchain for the Healthcare Supply Chain: A Systematic Literature Review
### The Blockchain Technology for Healthcare Supply Chains  
In the healthcare sector, blockchain can potentially play a vital role in improving data security, interoperability, and the traceability of data and information.  
One major challenge facing the healthcare industry is the lack of secure and reliable methods for storing and accessing data. Blockchain technology offers a solution by allowing data to be securely stored and shared in a decentralized database, making it difficult for unauthorized individuals to access or alter the data.  
Additionally, blockchain can facilitate interoperability among different healthcare systems and organizations, allowing for more seamless communication and data exchange. Currently, many healthcare systems are siloed and use incompatible technologies, making it difficult for different providers to access and share data. By using a shared, decentralized database, healthcare providers can more easily access and share relevant information, leading to better care coordination and outcomes.  
Another potential benefit of blockchain in healthcare is the ability to trace the history and authenticity of medical products, such as prescription drugs and medical devices. Byusing blockchain to track the SCs of these products, it becomes easier to identify counterfeits and ensure that patients are receiving genuine, safe, and effective treatments.  
Therefore, blockchain has been widely implemented in healthcare [19; 20; 21], and it could also help solve SC challenges [22]. A fundamental advantage of these distributed systems is that they resolve problems of disclosure and accountability among individuals and institutions whose interests might not be aligned [23]. Therefore, blockchain technology could be effective for SCs' accountability, integrity, transparency, confidentiality, and reliability.  
Product tracing using blockchain technology enables manufacturers, distributors, and dispensers to store traceability information in a distributed ledger that automatically verifies key data. This creates a system for verifying products and, if necessary, can be used to detect and report counterfeit or unapproved drugs through notifications from both public and private actors [23].
# Probabilistic Models for the Shear Strength of RC Deep Beams
###### Abstract  
A new shear strength determination of reinforced concrete (RC) deep beams was proposed by using a statistical approach. The Bayesian-MCMC (Markov Chain Monte Carlo) method was introduced to establish a new shear prediction model and to improve seven existing deterministic models with a database of 645 experimental data. The bias correction terms of deterministic models were described by key explanatory terms identified by a systematic removal process. Considering multi-parameters, the Gibbs sampling was used to solve the high dimensional integration problem and to determine optimum and reliable model parameters with 50,000 iterations for probabilistic models. The model continuity and uncertainty for key parameters were quantified by the partial factor that was investigated by comparing test and model results. The partial factor for the proposed model was 1.25. The proposed model showed improved accuracy and continuity with the mean and coefficient of variation (CoV) of the experimental-to-predicted results ratio as 1.0357 and 0.2312, respectively.  
deep beams; shear strength; Bayesian-MCMC method; probabilistic prediction; model uncertainty
## 2 Theory and Methods  
### Uniaxial Compression Test  
The rock specimens used in this study were granite processed into standard cylindrical specimens with a height of 100 mm and a diameter of 50 mm. Generally, using rectangular specimens to study the fracture behavior of rock specimens is a typical practice in fracture mechanics, which allows for cracks to be simplified into two-dimensional planes for analysis. However, the most used method in geological and geotechnical engineering research involves drilling cylindrical rock cores to obtain specimens representative of underground conditions. This study aimed to investigate the effect of cracks on the overall mechanical properties of rock specimens. Using cylindrical specimens makes getting typical stress-strain curves and relevant mechanical parameters easier, providing valuable insights into fracture mechanics. Additionally, conducting tests with cylindrical specimens aligns with the testing standards recommended by the International Society for Rock Mechanics, contributing to ensuring the consistency and comparability of experimental results.  
The pre-existing cracks were fabricated using the ultra-high-pressure CNC waterjet cutting method. The pre-existing cracks were located at the center of the specimen, with a length of 10 mm and a width of 1 mm, inclined at a dip angle \(\alpha\) with the horizontal plane. The \(\alpha\) values were 15\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\), and 90\({}^{\circ}\), respectively. The schematic diagram of a pre-cracked specimen is shown in Figure 1. To determine the crack fracture behavior, using sharp crack tips is more in line with the recommended standard than using blunt crack tips. On the one hand, due to the strength of granite and the precision of the processingequipment, it was challenging for us to prepare granite specimens with sharp crack tips. On the other hand, the testing in this study aimed to investigate the influence of cracks on the macroscopic mechanical properties and fracture patterns of specimens without delving into the initiation and propagation behaviors of crack tips. Therefore, using blunt crack tips was suitable to achieve the objectives of this research.  
The uniaxial compression test was conducted using the MTS 815 rock mechanics testing system, as shown in Figure 2. The initial and elastic stages of the compression test were controlled through axial loading, with a loading rate of 20 kN/min. When reaching the damage stage, it was switched to a radial displacement control, with a loading rate of 0.04 mm/min until the specimen ruptured.
## 2 Model Description
### Atmospheric Stability Classification
#### 2.2.4 Monin-Obukhov (MO) Method  
In the Monin-Obukhov theory, the atmospheric stability is described by the Obukhov length \(L\), which is determined directly from sonic anemometer measurements of friction velocity and heat flux:  
\[L=-\frac{\left(u^{*}\right)^{3}}{\kappa\frac{g}{\overline{v^{\prime}}}\overline{ v^{\prime}T_{S}^{\prime}}} \tag{6}\]  
where \(\overline{v^{\prime}T_{S}^{\prime}}\) is the covariance of temperature and vertical wind speed fluctuations and \(g\) is the gravitational acceleration. When only the temperature gradient is available, \(L\) can be obtained by [30].  
\[\left\{\begin{array}{ll}L=\frac{z}{Rt}&(Ri<0)\\ L=\frac{z(1-5 Rt)}{Rt}&(Ri>0)\end{array}\right. \tag{7}\]  
According to the relationship between \(L\) and \(Ri\), Table 4 shows the classification of stability based on \(L\) for flows above different terrains [27].
# Review of Task-Scheduling Methods for Heterogeneous Chips
## 4 Research Status of the Task-Scheduling Algorithm
### Hybrid Scheduling Algorithm  
The hybrid scheduling algorithm uses a combination of different algorithms to determine the optimal solution. The algorithm considers not only the computational load and data dependence of the task but also the performance characteristics of the processor, the energy consumption requirements, and the load balance of the system. A hybrid scheduling method usually includes four key components: task feature analysis, system state monitoring, a task processor matching algorithm, and scheduling decision execution. Through the coordinated work of these components, the hybrid scheduling method can optimize energy consumption and improve the overall efficiency of the system [33] while ensuring the system's performance.  
[PERSON] et al. propose a multi-application scheduling algorithm [34], which combines a hybrid scheduling approach, including two stages: a neural network-based task priority scheduling and a heuristic-based task mapping scheduling for handling processor task scheduling and resource allocation problems. [PERSON] et al. proposed a two-stage hybrid task-scheduling algorithm specifically for heterogeneous chips [35]. This algorithm first decomposes the input task graph using optimization-based spectral partitioning techniques and then schedules tasks using a heuristic genetic algorithm to minimize power consumption and execution time. [PERSON] et al. combined the characteristics of heuristics (decision-making through fuzzy logic and neural networks) and dynamic scheduling algorithms [36]. The scheduler first trains a fuzzy neural network through a multi-objective evolution algorithm. Then, they use the trained network as an online scheduler to optimize the key performance indicators of the multi-core system in real time. The task is mapped to the most appropriate heterogeneous chip processing unit by fuzzy rules and dynamic voltage frequency adjustment.  
Hybrid scheduling algorithms can combine the advantages of various algorithms to effectively deal with the heterogeneity of tasks and processing units and improve the flexibility and adaptability of heterogeneous chip task scheduling. However, such algorithms usually have high computational complexity and fail to fully consider many complex factors in real scenarios, such as bus conflict and dynamic changes in task execution time.
## 2 Methodology
### LCA Framework  
All environmental impact calculations employ the ReCiPe 2016 impact assessment method, chosen for its dual emphasis on midpoint and endpoint indicators, which capture both detailed process-level environmental stressors and broader consequences for human health, ecosystem quality, and resource scarcity. ReCiPe 2016 was selected because (i) Indian normalization factors are available, (ii) its midpoint-to-endpoint linkage enables damage-oriented policy interpretation, and (iii) it is the method recommended by the UNEP/SETAC Life Cycle Initiative for emerging-economy LCAs. Following ISO 14044, only indicators that are materially influenced by at least one of the three energy systems reported in the results section were retained. The detailed indicators have been given in Appendix A.  
Together, these 18 mid-point indicators cover the three protection areas, i.e., human health, ecosystem quality, and resource availability recommended by ISO 14044, while avoiding superfluous or duplicate metrics. Endpoint damage categories (Human Health, Ecosystems, Resource Scarcity) are also reported to aid interpretation.  
By relying on ReCiPe 2016, this study offers a balanced, internationally recognized framework that clarifies the trade-offs among different impact categories, thus enabling robust comparisons and facilitating an understanding of how each energy technology performs relative to multiple sustainability metrics. All midpoint and endpoint impacts were computed as  
\[\text{Impact}_{\text{k}}=\sum_{i=1}^{n}(\text{A}_{i}\ \times\ \text{CF}_{i,k})\]  
where A\({}_{i}\) is the life cycle inventory flow per functional unit (1 kWh of useful energy), CF\({}_{i,k}\) is the corresponding ReCiPe 2016 characterization factor for impact category k (midpoint or endpoint) [23].