esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Network Model
### Network Operation  
We assume that the network (with the installed devices according to the SBN as explained in Section 4.2) is operated in a dynamic manner. No devices can be dynamically installed in the network, but the devices can be dynamically activated or deactivated as well as reconfigured. In particular, we adopt the Energy Watermark Algorithm (EWA) [33; 34] to determine the set of active LCs in each time period \(t\in T\). EWA requires the following parameters as input: (i) the set of nodes \(V\) with \(X_{i}^{LC}\) installed LCs at each node \(i\in V\); (ii) the traffic data \(d_{a,b}(t)\) at time period \(t\in T\); (iii) the algorithm parameters \(W_{L}\), \(W_{H}\), and \(\psi\). Specifically, \(W_{L}\), \(W_{H}\) are used to trigger actions to release or to establish lightpaths, in case utilization of the last active lightpath of a logical link is below or above the thresholds \(W_{L}\) and \(W_{H}\), respectively. Moreover, the \(\psi\) parameter defines the utilization of the last active lightpath on a logical link that should not be exceeded after releasing a lightpath for energy saving. The algorithm's output for each time period \(t\in T\) is the following: (i) routing \(f_{ij}^{ab}(t)\) of traffic demand between each node pair \((a,b)\in V^{2}\) over logical links \((i,j)\in L\); (ii) capacity \(y_{i,j}(t)\) of each logical link \((i,j)\in L\); and (iii) number of active LCs \(x_{i}^{LC}(t)\) at each node \(i\in V\).  
EWA works in the following way. First, a route between all node pairs with non-zero traffic demands is ensured. Second, violation of \(W_{H}\) on any logical link is checked, and actions are taken to establish lightpaths in order to stop these violations. Finally, the lightpaths where \(W_{L}\) is violated are attempted to be released minding that utilization on any lightpath in the network does not exceed \(\psi\). We refer to [33; 34] for implementation details of EWA.
# Build Orientation Optimization Based on Weighted Analysis of Local Surface Region Curvature
## 3 Methodology  
This section presents a detailed explanation of our method to implement the proposed optimization of build orientation. Firstly, a new method curvature shift is proposed to effectively classify the surface regions of the CAD model. The method divides the original model into different clusters and each cluster represents those points and facets that have similar geometry features. Secondly, the curvature of the point is defined and estimated based on the NURBS(Non-Uniform Rational B-Splines) surface reconstructed by the point as well as its 1-ring neighbor points. The point with the big curvature is distributed a big weight factor, which is determined by the number points of the CAD model and the different ratios of each point's curvature. Thirdly, the volume error function with weight factor determined in the second step is built by calculating cusp height [28]. The orientation optimization is converted into a linear regression problem with the least absolute deviation. The least absolute deviation norm is appropriated by the least squares norm and PCA method to process eigenvalue decomposition on the scatter matrix of weighted norm vector. The optimal build orientation is finally determined from the eigenvectors which minimizes the volume error function.
# Data Quality Analysis and Improvement: A Case Study of a Bus Transportation System
## 6 Experimental Results and Analysis
### Results of Cleaning Redundant, Conflicting, and Erroneous Data
#### 6.1.3 Results of Cleaning Erroneous Data  
In the scenario of a bus transportation system, erroneous data are mainly reflected in irregular date formats. This problem is common in several different tables. The date format in the dynamic table contains only two types, while the date format in the static table is diversified. Specifically, there are several different date formats in the static data tables, including timestamp format, pseudo-standard format data \"MM/dd/yyyy HH:mmss:ms\", and multi-type format data such as \"yyyyMM.dd\", \"yyyy/MM/dd\", \"MM.dd\", and \"MM-dd\", and so on.  
To solve the date format problem, we implemented a User-Defined Function (UDF) to convert all dates into a standard format of \"yyyy-MM-dd HH:mmss\". Data standardization can not only reduce errors and complete the cleaning of data errors, but also help to make data management easier and more effective. It is also very important to improve data quality.
# An Accuracy-Efficiency-Power Consumption Hybrid Optimization Method for CNC Milling Process
## 1 Introduction
[11] used sensor technology to detect tool wear and faults, and predict the impact of tool wear to surface quality. [PERSON] et al. [12] used an intelligent online system to monitor tool wear. Although some researches on power consumption have been performed, they usually only focus on power consumption or processing independently, and still lack a method which can synchronously optimize the machining accuracy, production efficiency, and power saving. The objective of this research is to establish the hybrid optimization of machining accuracy, production efficiency, and power consumption. The characteristics of motor power consumption during machining were investigated. Furthermore, the relationship between machining parameters and motor power consumption was created. Moreover, the optimization algorithm of production efficiency and power saving was built. Finally, the hybrid optimization system was created by using Visual C# language. With this system, the user can import the Numerical Control (NC) program to optimize the parameters. Then the system will analyze and calculate the optimal machining time, power consumption, and recommend the machining parameters for better efficiency, lower power consumption, with the same product quality.
## 3 Challenges and the State of the Art Progresses
### Cross Channel Transactions  
Inter-blockchain transactions are difficult because a trusted third party is required to execute inter-blockchain transactions and hence the benefits of blockchain are lost. Lightning Network has been actively working on a decentralized option for inter-blockchain transactions, which does not require a third party. The technology is called atomic cross-chain swaps, which allows two parties on two different blockchains to trade directly and instantly. Atomic swaps make use of a scheme known as a hashed timelock contract (HTLC), which combines two technologies, a hashlock and a timelock. A hashlock uses a cryptographic puzzle to ensure that one party cannot release their funds without the other doing the same. A timelock acts as a safety net if nothing happens, routing funds back to the senders after a certain amount of time. Currently, the technology of atomic swaps is still in its early stage. A user is required to download both the blockchains that the transaction involves before starting transactions. New development is expected to make atomic swaps more user friendly and further decentralize the market [53; 54]. [PERSON] et al. proposed a solution to the inter-blockchain transaction problem by encapsulating information regarding the fidelity of peer-to-peer transaction facilitators in a hierarchical meta-blockchain layer [55].
## 3 NLOS Ranging Error and Correction Methods of UWB and Wi-Fi RTT Signals
### NLOS Signal and Identification  
As wireless radio signals, both UWB and Wi-Fi RTT signals are susceptible to NLOS errors, a common challenge encountered in all wireless signal transmission [46]. NLOS errors arise due to signal diffraction caused by obstructive elements in the environment, resulting in signal propagation distances that exceed the actual straight-line distance. Despite UWB signals possessing robust anti-interference capabilities, UWB positioning outcomes are still impacted by NLOS errors. These errors can lead to deviations of several tens of centimeters in UWB ranging measurements, thereby degrading the accuracy of UWB positioning to the decimeter level.  
Indoor environments are filled with obstacles, walls, people, and infrastructure, which disrupt electromagnetic wave distribution and pose challenges for wireless positioning due to the prevalence of NLOS signals. These signals arise from radio propagation phenomena such as multipath effects, obstructions, reflections, refractions, and scattering. For instance, UWB or Wi-Fi signals interacting with surfaces and objects lead to multiple signal paths, causing delays and distortions in time-of-flight measurements.  
To address NLOS challenges, it is crucial to recognize UWB and Wi-Fi tags and base stations under such conditions while implementing measures to mitigate their effects. Advanced algorithms, such as machine learning for identifying NLOS signal patterns and data fusion techniques, enhance positioning robustness [47]. Moreover, high-precision sensors and augmented reality technologies can reduce positioning errors caused by NLOS, ensuring greater accuracy and reliability.
## 4 Results and Discussion: Statistical Approach
### Multiple-Regression Analysis
#### 4.3.3 Model Applicability and Utility  
The utility of both Equations (6) and (7) is clearly to estimate either the compressive strength or the modulus of elasticity, when the other mechanical property is known by a UPV reading. The known mechanical property can be determined in two different ways: it could be experimentally measured or it could be determined through the UPV using existing models [21]. In this last option, the compressive behavior of concrete can be completely evaluated by measuring only the UPV, so that through this NDT measurement, one mechanical property is first determined by means of models already available in the literature [21] and then the other mechanical property is calculated by applying the models developed in this article. Table 6 shows an example application of Equations (6) and (7), considering that the compressive strength of mix L0 is known, and its modulus of elasticity is to be calculated, for which reason the UPV index of this SCC mix was determined.  
It was found that the estimations of the modulus of elasticity using both models were adequate, with a difference between the experimental value and the estimated value of about 1 GPa. The modulus of elasticity determined by applying Equation (6) was slightly more accurate, since in the development of this model both compressive strength and modulus of elasticity were individually considered. The introduction of the mean value of both properties for the development of Equation (7) led to a small loss of accuracy that was reflected in the estimation of the modulus of elasticity shown in Table 6. The aspects discussed in the previous section mean that the use of the UPV index is not advisable for estimating the properties related to bending-tensile behavior.
# Experimental Study and Numerical Simulation of W/O Emulsion in Developing Heavy Oil Reservoirs
## 3 Results and Discussion
### Effect of water cut of emulsion  
Figure 16 shows the results of the particle size distribution of emulsion droplets under different water cuts. As shown in Figure 16, with the increase in water cut from 30% to 80%, the size range of emulsion droplets increased from 0.2-6.6 \(\upmu\)m to 0.7-17.3 \(\upmu\)m, and the average diameter increased from 0.92 \(\upmu\)m to 4.65 \(\upmu\)m. A lower water cut led to a smaller particle size of the emulsion and a more uniform distribution. This behavior is consistent with previous studies on the stability of the emulsion. An emulsion with large droplets has a high aggregation rate, resulting in instability of the emulsion. With the water cut of emulsion increasing, the coalescence resistance of dispersed phase droplets decreased, such that the particle size of emulsion droplets increased with the increase in water cut.
## 2 System Model and Methodology
### Genetic Algorithm for Optimization  
The GA is a robust optimization technique inspired by natural selection and genetic inheritance, commonly employed to address complex optimization problems across various domains. Its theoretical foundation lies in Darwinian evolutionary theory, where natural selection is emulated to evolve optimal or near-optimal solutions from an initial population. By iteratively refining candidate solutions, the GA effectively mimics the biological evolution process, progressively improving the quality of solutions.  
The core idea of the GA is the concept of evolving a population of potential solutions over generations. The process begins by encoding candidate solutions as individuals within a population. A fitness function is employed to evaluate each individual, determining its suitability for the given problem. The core operators--selection, crossover (recombination), and mutation--are applied to evolve the population. Selection favors individuals with higher fitness, ensuring that superior traits are propagated into subsequent generations. Crossover combines genetic material from selected individuals, facilitating the exploration of new regions within the solution space. Mutation introduces random variations, helping to maintain genetic diversity and prevent premature convergence to local optima.  
The iterative process continues until a predefined termination criterion is met, such as reaching a maximum number of generations or achieving a satisfactory fitness level. Through this process, the GA explores the solution space efficiently, balancing the trade-off between exploration (diversifying the search) and exploitation (intensifying the search for near-promising solutions). The algorithm ultimately converges to an optimal or near-optimal solution. In summary, the GA's ability to simulate biological evolution and adaptively explore complex solution spaces makes it a powerful and flexible tool for optimization, capable of finding high-quality solutions in a wide range of problem domains.  
Applying the GA to optimize PID controller parameters enables the automated search for optimal PID values, enhancing the capability of the control system. This method combines the simplicity and effectiveness of PID control with the global search capability of the GA, playing a crucial role in improving the active control of isolation systems. By leveraging the GA, the PID controller can adapt to varying conditions, ensuring better vibration reduction and overall system performance.
# Applications of Probabilistic Forecasting in Smart Grids: A Review
###### Abstract  
This paper reviews the recent studies and works dealing with probabilistic forecasting models and their applications in smart grids. According to these studies, this paper tries to introduce a roadmap towards decision-making under uncertainty in a smart grid environment. In this way, it firstly discusses the common methods employed to predict the distribution of variables. Then, it reviews how the recent literature used these forecasting methods and for which uncertain parameters they wanted to obtain distributions. Unlike the existing reviews, this paper assesses several uncertain parameters for which probabilistic forecasting models have been developed. In the next stage, this paper provides an overview related to scenario generation of uncertain parameters using their distributions and how these scenarios are adopted for optimal decision-making. In this regard, this paper discusses three types of optimization problems aiming to capture uncertainties and reviews the related papers. Finally, we propose some future applications of probabilistic forecasting based on the flexibility challenges of power systems in the near future.  
probabilistic forecasting; smart grids; decision making under uncertainty; renewable generation; stochastic programming +
Footnote †: journal: Applied Sciences
## 3 Methods
### Metrics for Error Assessment  
Mean absolute percent error (MAPE), \(\%\delta_{abs}\), is a commonly used metric in assessing performance in \(r_{rs}\) match ups:  
\[\%\delta_{abs}=100*\frac{\delta_{abs}}{\overline{y}},\ \ \delta_{abs}=\frac{ \sum_{i=1}^{n}|y_{i}-\hat{y}_{i}|}{n}. \tag{29}\]  
The MAPE metric takes into account the absolute magnitude of the residuals, giving them equal weight. Other commonly used metrics include root mean square error (RMSE), a measure of accuracy and potential forecasting errors in simulating \(r_{rs}\) when the errors may be assumed to be unbiased and normally distributed [74]. RMSE gives greater weight to larger errors than \(\delta_{abs}\). Since bias errors are often expected to be more significant than random, normally distributed errors in simulations, \(\%\delta_{abs}\) is expected to be the most appropriate metric to assess match ups [29]. Other common metrics (e.g., [75]) are shown in the following plots as appropriate. Also see [PERSON] et al. [13] for a detailed discussion of performance metrics.
## 3 Results
### Hyperspectral Image Analysis--Mapping of PFTs  
The results of the mapping project indicate that the random forests supervised classification analysis was highly successful at distinguishing among cover types (Figure 10). The random forests model selection algorithm was set to separate cover into four classes based on the entire hyperspectral dataset of 360 spectral bands. The overall model had an out-of-bag error rate of 9.85% and a Cohen's kappa coefficient of 0.869. Forest and shrub classes were classified with the greatest accuracy, and graminoid fen was the least accurate (Table 7). We found several spectral bands to be consistently important in making these classifications, as estimated by both the mean decrease in accuracy as well as the mean decrease in node impurity due to variable permutation (Table 8). Nearly all these bands fell within the 1800-2500 nm range representing the short-wave infrared region of the electromagnetic spectrum.  
Figure 10: Results of supervised classification analysis of PFT distribution at APEX and the surrounding area made using a random forests modeling approach.
# Monitoring of Surgically Treated Upper Arm Fracture by Implanted Antenna at 402 MHz
###### Abstract
The specific absorption rate (SAR) was simulated, and the excitation signal was selected to satisfy the SAR standard limit and to be sure that the implanted antenna is safe for human tissues. Measurement and verification were carried out on lifeless animal models. The results of the measurements were in good agreement with the simulation. The rest of the paper is organized as follows: Section 2 introduces the proposed monitoring application. The model and monitoring method are illustrated in Section 3. The simulations are presented in Section 4. Section 5 demonstrates the measurements and validation. Finally, Section 6 concludes the paper.
# Optical Medieval Music Recognition--A Complete Pipeline for Historic Chants
## 5 Workflow
### Layout Analysis  
The objective of the layout analysis is to partition the scanned document into music regions, text regions, drop capitals and chants. This analysis takes as input the deskewed color image of the scan and the encoded staves.  
A straightforward algorithm is employed, leveraging the encoded staves to determine the music and text regions. Each stave's bounding box (smallest possible axis-parallel rectangle enclosing the object)  is calculated and then padded with \(d_{SL}/2\), marking it as a music region. Lyric regions are subsequently identified by marking the areas between two music regions. Notably, the lowest lyric region of each scan page requires separate calculation, as it does not lie between two music regions. To address this, the average height of a lyric region is computed and used to determine the last lyric region.  
Given the necessity to identify individual chant sections within a book, initial letters (drop capitals) must also be recognized, as they typically signify the beginning of a newchant. To accomplish this, a Mask-RCNN with a ResNet-50 encoder trained on the \"Latin 14819\" dataset [34] is employed. The raw color images serve as input for the Mask-RCNN, with a threshold of 0.5 applied to compute drop capital instances. Additionally, a post-processing step is applied to calculate the concave hull of each drop capital instance.  
Next, drop capitals are categorized as small or large, with only large drop capitals indicating the start of a new chant. A drop capital is classified as large if it overlaps at least 90 percent of the height of the stave.  
Subsequently, large drop capitals are utilized to delineate the lyric regions. If a drop capital is found within a lyric line, it is split into two regions to the left of the drop capital's bounding box, as they belong to different chants, simplifying OCR post-processing.  
Moreover, the reading order of lyrics and drop capitals is calculated from top to bottom and left to right to generate chants. A chant encompasses all text and music lines between two large drop capitals and may span multiple pages.
## 5 Conclusions  
This paper has proposed an ML-based PL model under agricultural scenarios. A new network structure named DWN-ANN has been developed to predict the PL of the LoS and NLoS paths, which can be pre-trained and further optimized by using RT simulation and measurement data, respectively. Furthermore, an RT pre-correction module has been introduced to optimize the RT simulation accuracy, and modified material electromagnetic parameters have been given. To thoroughly analyze and appraise the proposed model, extensive channel measurement campaigns have been conducted over farmland areas at 3.6 GHz. Through a comparison with the measurement data, our proposed model has been proven to be more accurate than traditional models. This suggests that our model is better suited for UAV-aided A2G communications in agricultural scenarios. In the future, it is envisaged that measurement campaigns will be conducted on diverse farmland areas with the aim of substantiating the proposed model's universality.  
Conceptualization, H.L. (Hanpeng Li) and Q.Z.; funding acquisition, Q.Z. and F.A.; investigation, K.M. and X.Y.; methodology, H.L. ([PERSON]); software, H.L. ([PERSON]) and X.Y.; data acquisition, Y.G. and H.L. ([PERSON]); supervision, Q.Z. and F.A.; validation, T.Z. and M.W.; writing--original draft, H.L. ([PERSON]) and K.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported, in part, by the National Natural Science Foundation of China under Grant No. 62271250; in part by the Natural Science Foundation of Jiangsu Province, No. BK20211182; in part by the Key Technologies R&D Program of Jiangsu (Prospective and Key Technologies for Industry) under Grants BE2022067, BE2022067-1, and BE2022067-3; and in part by the open research fund of the National Mobile Communications Research Laboratory, Southeast University, No. 2022D04.  
The datasets collected and generated in this study are available upon request to the corresponding author.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Figure 6a** & **Figure 6b** & **Figure 7a** & **Figure 7b** & **Figure 8a** & **Figure 8b** \\ \hline CI model & 2.1912 & 2.3071 & 3.4089 & 2.8562 & 3.4089 & 2.8562 \\
3 GPP model & 2.2732 & 3.1757 & 4.1951 & 4.1922 & 4.1951 & 4.1922 \\ DWN-ANN & 2.1727 & 2.2877 & 4.0231 & 3.5267 & 3.2425 & 2.7330 \\ \hline \end{tabular}
\end{table}
Table 3: The RMSE of CI model, 3 GPP model, and the proposed DWN-ANN model.