esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of the KINEROS 2 Model to Natural Basin for Estimation of Erosion
## 3 Erosion Estimation Models Used
### Brief Introduction tokineros 2  
KINEROS 2 is a dynamic, physically based, spatially distributed model that simulates a basin using a network of flat surfaces connected by channels. KINEROS 2 is open-code software that achieves a simulation of the hydrology and sedimentology that takes into account infiltration, interception, surface runoff, and erosion processes, principally for small urban or agricultural basins. KINEROS 2 calculates excess rainfall by adding amountsresulting from infiltration and interception processes to the amount of input rainfall. In addition, KINEROS 2 works in GIS base giving the user GIS tools to work with all raster information data needed: Digital Elevation Model, surface coverage map, etc., [4; 18; 23; 24; 25].  
Infiltration is estimated using the Parlange 3-parameter model. Runoff is estimated using dynamic transport of the rainfall excess. KINEROS 2 uses the Kinematic Wave model for superficial flow transport in flat elements connected with channels [18].  
The erosion/deposition rate is the combination of splash erosion and hydraulic erosion. Splash erosion is only considered in flat elements. Erosion by surface runoff is estimated taking into account the capacity of transport, calculated using the Engelund-Hansen Equation (8), developed in 1967 [4; 18].  
\[f^{\prime}\phi=0,1\cdot\theta^{2/5} \tag{8}\]  
With:  
\[f^{\prime}=\frac{2 gSD}{v^{2}} \tag{9}\]  
\[\phi=q_{t}\cdot[\gamma_{s}\cdot\left(\frac{\gamma_{s}-\gamma}{\gamma}\right) \cdot g\cdot d^{3}]^{-1/2} \tag{10}\]  
\[\theta=\frac{\tau}{(\gamma_{s}-\gamma)\cdot d} \tag{11}\]  
where:  
\(g\): Gravity.  
\(S\): Energy slope.  
\(D\): Waterdepth  
\(v\): Average flow velocity.  
\(q_{t}\): Total sediment discharge per width unit.  
\(\gamma_{s}\): Specific weight of the sediment.  
\(\gamma\): Specific weight of water.  
\(d\): Average diameter of sediment particles.  
\(\tau\): Tangential force along the riverbed.  
Finally, sediment transport is calculated with the non-permanent flow continuity unidimensional equation.
## Abbreviations  
The following abbreviations are used in this manuscript:  
DS/FH direct sequence/frequency hopping DSSS direct sequence spread spectrum FHSS frequency hopping spread spectrum TT&C telemetry, tracking, and command SNR signal-to-noise ratio DS/FFH direct sequence/fast frequency hopping VLL velocity locked loop FLL frequency locked loop PLL phase locked loop HD-VLL high dynamic VLL HD-VLL-NCO HD-VLL based on multi-carrier NCO DLL delay locked loop VLL-SA-PLL VLL serially assisted PLL VLL-PA-PLL VLL parallelly assisted PLL DDS direct digital synthesis HD-VLL-SA-PLL HD-VLL serially assisted PLL carrier-to-noise ratio
## 1 Introduction
Over the years, many signal processing and analysis methods for vibration signals have been developed and matured. Even though it has been reported that AE signals have certain advantages over the vibration signals in early gear fault diagnosis, advanced signal processing and analysis methods for AE signals have not been well developed. Recent development in deep learning provides an excellent opportunity to integrate the AE signals and vibration signals for gear pitting fault diagnosis. In this paper, AE signals are introduced in addition to vibrational signals for gear pitting fault diagnosis. Normally, for the conversion of the time domain signals into the frequency domain signals, additional preprocessing steps are needed. The advantage of deep learning is its capability in dealing directly with the raw signals. In this paper, one-dimensional CNN is integrated with GRU network to process of AE and vibration signals for gear pitting fault diagnosis. The combination of CNN and GRU can effectively utilize their respective advantages and obtain better results for gear pitting fault diagnosis. The method presented in this paper can effectively suppress the over-fitting in gear pitting fault diagnosis. The main contributions of this paper can be summarized as follows:  
(1) The method presented in this paper directly uses the raw vibrational and the AE signals to diagnose the gear pitting faults without additional feature extraction processes. (2) This method integrates CNN with GRU to make full use of their advantages. (3) The method combines two different kinds of sensor data, the vibration signals and the AE signals, and makes full use of different sensor signal features for gear pitting fault diagnosis. (4) The method presented in this paper uses less training data to make an accurate diagnosis of gear pitting faults with efficient training time. The rest of this paper is organized as follows. Section 2 describes the gear pitting fault diagnosis method presented in this paper. In Section 3, a description of the experiment setup and the data collected for the validation of the proposed method is provided. Section 4 analyzes and discusses the results. Finally, Section 5 concludes the paper.
# Energy Efficient Power Allocation for the Uplink of Distributed Massive MIMO Systems
###### Abstract  
In this paper, an energy efficient power allocation scheme is proposed for a distributed massive multiple-input multiple-output (MIMO) system with a circular antenna array. Single-antenna users simultaneously transmit signal to the base station (BS) with a large number of distributed antennas. The tight approximation of the energy efficiency (EE) is derived in closed form expressions. Through jointly optimizing the power allocation and the antenna number of BS, an NP-hard problem is formulated to maximize the EE. The equal power allocation is proved to be optimal given the total transmit power and the number of antennas. Finally, the optimal antenna number is determined by one dimension search. It is noteworthy that the NP-hard problem is solved by one dimension search. Simulation results validate the accuracy and the low-complexity of our proposed scheme.  
distributed antenna systems; energy efficiency; lambert-W function; massive MIMO; power allocation; spectrum efficiency +
Footnote †: journal: Future Internet
# Global Path Planning for Differential Drive Mobile Robots Based on Improved BSGA* Algorithm
## 5 Conclusions  
In this paper, an improved BSGA* algorithm is proposed based on the traditional A* algorithm and the kinematic analysis of DDMRs. By introducing a Gaussian function to dynamically weigh the heuristic function and optimizing it using the BS structure, the algorithm performance in the face of different types of obstacles is effectively improved. In addition, a multi-layer turning point filtering strategy is proposed to reduce the redundant nodes and improve the smoothness of the path. The simulation results show that the improved BSGA* algorithm has a better performance than the ACO algorithm, D* late algorithm, and GA in different maps. Finally, the improved BSGA* algorithm is applied to the LEO ROS mobile robot, and two different environments are built. The experimental results show that the improved BSGA* algorithm is more suitable to provide global path guidance for DDMRs than the Dijkstra algorithm, A* algorithm, ACO algorithm, D* algorithm, and GA. As a static planning method, the improved BSGA* cannot deal with dynamic obstacles, so it should be combined with a local obstacle avoidance algorithm to ensure the safety of driving in future work.  
Conceptualization, M.Y.; Methodology, M.Y.; Software, M.Y.; Investigation, M.Y.; Writing--original draft, M.Y.; Writing--review and editing, M.Y., H.D. and X.F.; Funding acquisition, H.D.; Resources, X.F.; Project administration, X.F.; Form analysis, P.L.; Data curation, Y.L.; Supervision, H.L.; Visualization, H.L.; Validation, H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Technology Research and Development Program of Shandong, grant number 2022 CXGC010101 and the Key Technology Research and Development Program of Shandong, grant number 2019 JZZY010443.  
Not applicable.  
The authors declare that they have no known competing financial interest or personal relationship that could have appeared to influence the work reported in this paper.
# Reactive Molecular Dynamics Simulations of Polystyrene Pyrolysis
## 2 Results and Discussions
### Effects of Target Temperature on the Pyrolysis Products  
To test the effects of target temperatures on the equilibrium pyrolysis products, we first heated the system to 2500 K, 2750 K, 3000 K, 3250 K, 3500 K, and 3750 K. As described in the previous section, the heating rate hardly affects the equilibrium pyrolysis products, so the 50 K/ps heating rate was implied for every simulated system. Subsequently, a 6 ns isothermal simulation was followed at the respective target temperature, and all simulated systems reached equilibrium, as proved in Figure S2 in Supplementary Materials.  
The evolution of pyrolysis tissues and products at the isothermal simulation process is shown in Figure 3. According to the changes in microscopic morphology and pyrolysis products, these simulated systems can be classified into a low-temperature system (LTs, 2500 K), proper-temperature system (PIs, 2750 K, 3000 K, 3250 K), high-temperature system (HTs, 3500 K), and ultra-high-temperature system (UHTs, 3750 K).  
Figure 3: Tissue and species changes during isothermal simulations at different target temperatures.  
Comparing the four sets of simulations, we found that the time required for pyrolysis to reach equilibrium is proportional to the target temperature, i.e., the higher the target temperature, the shorter the time for pyrolysis to reach equilibrium. However, the process of pyrolytic evolution and the final equilibrium products are quite different. In the LTs, the equilibrium pyrolysis products are predominantly organized in cross-linked networks with discrete layers of graphite flakes of small molecules. In the PTs, the pyrolysis products show large graphite lamellar tissues and further evolve into randomly folded carbon networks when the pyrolysis equilibrium is reached. In the HTs, large graphite lamellar tissues appear more significantly during the isothermal simulation and evolve into a regular and hierarchical graphitized carbon network at the end of the simulation. However, if the temperature is extremely high, no graphitized tissues are formed, and cracked small molecules are haphazardly dispersed throughout the system at the end of the simulation, as Figure 3 shows. Thus, excessively low and high target temperatures are not conducive to generating large, graphitized tissues.
# Benefits of Auditory Training with an Open-Set Sentences-in-Babble-Noise
## 3 Results
### Training and Conservation Effects: Subjective Tools  
A three-way repeated-measures ANOVA was carried out with scores on the subjective speech perception questionnaire as a dependent variable and test sessions (T1 to T5), background conditions (quiet and noise), and the conversation partner (partner and stranger) as between-subjects variables (Figure 3). The main effects for subjective speech perception were observed for background conditions (F(1,13) = 109.04, \(p<0.001\), \(\eta^{2}p=0.89\)) and conversation partner (F(1,13) = 15.67, \(p=0.002\), \(\eta^{2}p=0.55\))). As expected, a conversation in noisy conditions was rated worse than a conversation in quiet, and a conversation with a stranger was rated worse than a conversation with a familiar partner. No main effect was observed for the test session (F(4,10) = 1.73, \(p=0.220\), \(\eta^{2}p=0.41\))), and no interactions for background conditions X conversation partner (F(1,13) = 2.34, \(p=0.150\), \(\eta^{2}p=0.15\))), background conditions X session (F(4,10) = 0.39, \(p=0.812\), \(\eta^{2}p=0.13\))), conversation partner X session (F(4,10) = 0.39, \(p=0.809\), \(\eta^{2}p=0.14\))), or background conditions X conversation partner X session (F(4,10) = 0.58, \(p=0.683\), \(\eta^{2}p=0.19\)).  
Figure 2: SRTn thresholds (SNR for 50% identification rate) for HeBio and DIN and accuracy scores (percent correct) for CVC words of the averaged pre- and post-training scores and conservation score for the speech perception measures: (**a**) sentences comprehension (HeBio) with SSN; (**b**) sentences comprehension (HeBio) with 4 TBN; (**c**) words comprehension (CVC) in quiet; and (**d**) digits identification (DIN).
## Appendix B SOI Generation  
Algorithm A1 provides the complete process for generating the SOI, covering data filtering, embedding generation, clustering, and the similarity-based selection of relevant evidence and claims.  
```
0: Dataset \(D\), Selected Claim ID \(c_{\text{selected}}\), Similarity Threshold \(\delta\), Number of Clusters \(k\)
0: SOI (Subset of Interest) for the selected claim
1: Data Preparation
2: Load dataset \(D\)
3: Filter dataset to obtain thematic subset \(D_{T}\) based on the selected theme \(T\)
4:for each claim \(c_{i}\) in \(D_{T}\)do
5: Extract associated evidence items \(E_{i}=\{e_{i,1},e_{i,2},\dots,e_{i,m}\}\)
6:endfor
7: Embedding Generation
8:for each claim \(c_{i}\) in \(D_{T}\)do
9: Generate embedding \(\text{emb}_{c_{i}}=\text{Transformer}(c_{i})\)
10:endfor
11:for each evidence item \(e_{i,j}\in E_{i}\) for each claim \(c_{i}\) in \(D_{T}\)do
12: Generate embedding \(\text{emb}_{c_{i,j}}=\text{Transformer}(e_{i,j})\)
13:endfor
```  
**Algorithm A1** SOI generation with thematic clustering and filtering
## 5 Security Analysis, Performance Evaluation, and Discussion
### Key Strength Analysis of Proposed Key/Subkey Generation Technique
#### 5.1.1 Frequency Test  
To find out the randomness in subkeys generated by the proposed KSA, we used the frequency test. This test is establishes the occurrence of the number of ones and zeroes in a random set of data. The KSA is said to pass the test if the proportion of zeroes and ones is close to 50%. Further testing is not required if the algorithm fails to clear the randomness (frequency) test.  
Let \(a_{0}\) and \(a_{1}\) denote the number of zeroes and ones in an n-bit sequence and the statistics used is:  
\[Z=(a_{0}-a_{1})^{2}/n \tag{14}\]  
Results Discussion:  
Figure 5 shows the results of the frequency test carried out for the proposed method and is compared with AES-128. From the figure, we can observe that randomness in the subkeys generated by the proposed method is more, which is about 90.2813% (average value of the nine rounds).
# Dual-Branch Luminance-Chrominance Attention Network for Hydraulic Concrete Image Enhancement
## 4 Experiments and Results
### Ablation Study  
To rigorously ascertain the efficacy of the DBLCANet-HCIE approach, a series of ablation studies were meticulously designed, focusing on the discrete impacts of individual modules, branching architectures, and sub-loss functions. These studies were uniformly  
Figure 11: Visual comparisons of our method and others on the LLHCID. The red and blue boxes represent the enhanced contrast of the localized defects.  
executed within an identical training framework and environmental conditions, leveraging the LLHCID to ensure comprehensive and unbiased evaluations.  
We performed ablation experiments by sequentially excluding the LAB, CRM, CDB, and FDDEB from the comprehensive model architecture. The results of the ablation studies on sub-blocks and sub-branches are shown in Table 4. The exclusion of the LAB reduced the model's PSNR by 5.09 dB and its SSIM by 0.114. The absence of the CRB resulted in decreases of 2.96 dB and 0.0961 in the model's PSNR and SSIM, respectively. These results demonstrate the effectiveness of the luminance adjustment and chrominance restoration sub-branches. The ablation results of the CDB and FDDEB modules demonstrate their distinct contributions to chrominance restoration. Specifically, the CDB effectively suppresses chromatic noise in the Cb and Cr channels, while the FDDEB enhances fine-grained detail features, enabling more accurate and perceptually consistent chrominance reconstruction.  
As evidenced in Table 5, the outcomes of the ablation studies substantiate the efficacy of the proposed joint loss function. Initial experiments employing solely the \(\mathbf{\mathcal{L}}_{Y\text{CbCr}}\) loss yielded a PSNR of 26.21 dB and an SSIM of 0.8827. Subsequent individual integration of the \(\mathbf{\mathcal{L}}_{Y}\) and \(\mathbf{\mathcal{L}}_{\text{CbCr}}\) losses improved these metrics to 28.14 dB/0.9253 and 27.86 dB/0.9168, respectively. The synergistic combination of all three losses (\(\mathbf{\mathcal{L}}_{Y\text{CbCr}}+\mathbf{\mathcal{L}}_{Y}+\mathbf{\mathcal{L}}_{\text{ CbCr}}\)) achieved optimal performance, attaining a peak PSNR of 28.61 dB and SSIM of 0.9674. These experimental findings substantiate the efficacy of integrating holistic and branch-specific losses, thereby validating the necessity of employing a joint loss function for hydraulic concrete image enhancement.
# A Cross-Chain-Based Access Control Framework for Cloud Environment
## 4 The Framework's Functionality
### Use Case Scenario  
To test our developed AC framework, we simulated a communication scenario between doctors and a healthcare organization (laboratory). A doctor affiliated with a hospital considered a cloud client can transmit requests (IBC packets) from his end device through the healthcare application provided by his organization (the hospital).  
A laboratory that adopts our access control framework as a security solution checks the doctor's transaction validity and, if the attributes match, grants access to the cloud resources sought, such as blood tests and x-ray reports, which are provided using Google Cloud Platform (GCP). By adding IBC core modules to both the hospital and laboratory chains, we can ensure the IBC protocol sends packets from the issuing zone to the laboratory while maintaining a relay process.  
Upon receiving the doctor's access request by the laboratory in the form of a transaction enfolded in the IBC packet and the decryption of the received transaction attributes, the laboratory access control mechanism is triggered automatically. It employs semantic reasoning to verify whether the transaction attributes follow the matching rules outlined in the current laboratory access policy.
# Curcumin Innovative Delivery Forms: Paving the Yellow Brick Road' of Antitumoral Phytotherapy
## 2 Biological Actions of Curcumin
### Molecular Targets of Curcumin
#### 2.2.3 Curcumin Inhibits Inflammatory Cytokines  
Tumor necrosis factor (TNF) and interleukins (IL) are two kinds of inflammatory cytokines with an important mediating role in tumorigenesis. Curcumin was shown to have profound effects on TNF inhibition in dendritic cells, macrophages, monocytes, alveolar macrophages, and endothelial and bone marrow cells [118]. The suppression of the TNF-signalling pathway by curcumin is related to the inhibition of NF-kB phosphorylation, as detailed in 2.2.1 [119; 120].  
Interleukins (ILs) contribute to tumor invasiveness and angiogenesis by induction of the expression of metalloproteinases, adhesion molecules and STATs [146]. Curcumin inhibits the expression of IL-1 [121], IL-2 [122], IL-5 [123], IL-6 [118], IL-8 [122], IL-12 [124], and IL-18 [125], being thus a potent inhibitor of these classes of cytokines.
## 5 Discussion
### Performance Comparison  
This section is dedicated to comparing the performance of various algorithms. The HV can be utilized to estimate the performance of the multi-objective optimization algorithms, which can be obtained according to Equation (23). As WSM is not a multi-objective optimization algorithm, it is not involved in this section.  
As shown in Figure 11, our INSGA-II has larger average HVs than all the baseline methods for all tourists and has higher upper and lower bounds of HV in most cases, indicating that the non-dominated solutions obtained from INSGA-II have better convergence and distribution than the baseline methods. Moreover, INSGA-II has a narrow range of HVs across multiple independent experiments, demonstrating that INSGA-II is more robust. Generally, evolutionary algorithms (INSGA-II and NSGA-II) perform better than swarm intelligence-based algorithms (MOPSO and MOACO) in solving the proposed problem in this paper. All methods perform poorly in designing routes for tourist #4, #9, #10, and #17 with a tight time budget of no more than 9.5 h. Surprisingly, under such an extreme time budget, the INSGA-II performs significantly better than the baseline methods.
# Blockchain and Smart Contracts for Insurance: Is the Technology Nature Enough?
## 3 Blockchain Applications in Insurance: Selected Use Cases
### Pay-Per-Use/Micro-Insurance  
Smart contracts- and blockchain-based payments could enable new revenue sources, such as micro- and pay-per-use insurances. Though in the past micro-insurances were threatened by administrative costs, the exploitation of smart contracts could enable quick and cheap policy undersignment and management (even on mobile devices) [51]. Similarly, pay-per-use insurances could become a praxis, possibly in combination with IoT solutions for automatic undersignment. For instance, GPS data could be used to automatically collect, e.g., a travel premium only if the customer is abroad, a car premium only when the car is moving, etc. Pay-per-use mechanisms could be exploited in services such as Uber or Airbnb, e.g., activating the service when a customer is picked up or hosted.  
With respect to the other use cases described in the paper, from the point of view of actors and technology to be involved, this is probably one of the quickest and easiest to be realized (because of the limited number of involved actors, and because the feasibility of prototypal solutions has already been demonstrated [51]). Moreover, from the point of view of the insurance company, introducing blockchain-based pay-per-use insurances (which could be even paid by using cryptocurrencies) could bring a competitive advantage, especially attracting young, technology enthusiasts.  
Concerning architectural choices, companies aiming at addressing pay-per-use insurance could rely on a public blockchain. In this way, a smart contract could collect money from customers (e.g., Ethers or Bitcoins), keep them until a given date and transfer them to the insurance company if no damage occurs. Being on a public blockchain, everyone could inspect the smart contract code, increasing trust between parties.
## 1 Introduction
For the in-situ monitoring of the heat data, a thermocouple device was used, and through the K-nearest neighbor (KNN) technique reflecting dynamic time warping (DTW), which is an ML technique, thermal time series data were analyzed to present a method for effectively predicting and detecting warping deformation. In addition, this study explores the feasibility of early detection of warping deformation. Note that early detection of warping deformation can potentially improve the printing quality through real-time introduction of corrective measures or save both time and material cost through halting the printing process, once warping deformation is detected. Additionally, a detailed method for predicting warping deformation through time-series analyses was discussed. The paper is organized as follows. Section 2 explains the background of warping deformation and the DTW and KNN technique used in this study. Section 3 shows the experimental setup, data set, and model framework. Section 4 shows the results and discussion. Finally, Section 5 shows the conclusions, including the limitations and future works.