esa-sceva/satcom-chunk-collection huggingface dataset:
# ISO 50001 Data Driven Methods for Energy Efficiency Analysis of Thermal Power Plants
## 5 Conclusions  
The enhancement of the energy monitoring process according to the ISO 50001 standard using computational intelligence techniques represents a key point of the PDCA cycle because it allows more accurate analysis of the performances of the process analyzed.  
Large energy companies, by adopting more accurate monitoring systems, can keep better maintenance schedules, reduce over-consumptions and ultimately save money from the avoided energy waste. The model based on the ANN, properly trained and tuned with energy engineering field operation knowledge, can monitor the overall consumptions and even suggest the optimal number of active fans by estimating the overall power losses and best operating areas.  
The same model includes in the analysis a higher number of variables affecting the output variables that cannot be included by using the previously discussed tools. This higher number of variables increases the accuracy of the model as well as of the monitoring performances.  
Based on the reported numerical simulations, improved baselines have been reached up to a 5% threshold for different plant sub-units, thus representing a relevant overall saving in terms of alert threshold definition and related control efficiency: a potential saving of about 140 MWh throughout the considered three-year dataset was obtained taking into account a cooling tower sub-unit, representing a considerable economic benefit.  
A possible future development of this work could be an energy baseline specifically defined on seasonal datasets since the environmental conditions crucially affect the performances of the analyzed consumption units; however, in our paper, we used a comprehensive approach using two and a half years of time-continuous data.  
Finally, the AI approach could be extended to all the consumption units monitored in the thermal power plants as well as applying its use to other power plants that are globally active. Scaling the use of AI to improve energy management requires further investigations, however, as well as studies that would improve the current lacking literature with respect to the international standards.  
Conceptualization, F.G., M.M. and G.D.; Methodology, A.N.; Software, A.N. and G.D.; Formal analysis, F.G. and M.M.; Data curation, A.N. and G.D.; Writing-original draft, G.D.; Writing-review & editing, F.G., A.N. and M.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
*Restrictions apply to the availability of these data.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **MAE** & **MSE** & **MAPE** & \(\mathbf{R^{2}}\) \\ \hline Training & 80.31 & 10059 & 7.09 & 0.9551 \\ Validation & 142.59 & 28625 & 12.22 & 0.9386 \\ Test & 129.18 & 24025 & 11.62 & 0.9423 \\ \hline \end{tabular}
\end{table}
Table 2: Accuracy of the selected neural model.
## 4 Implementation
### Exploration Algorithms
#### 4.1.3 Farthest Cell Exploration  
Similarly, this algorithm input is given by the cost to reach each unvisited cell but accessible from the agent initial cell; however, this algorithm finds the cell with the maximum cost. Even though it seems contradictory to use the maximum cost, the farthest cell use actually improves area coverage. It is important to note that this technique is only valid in known environments (see Figure 4).
# Design and Analysis of Systematic Batched Network Codes
## Appendix C Pseudocodes for BATS Outer Encoding and Decoding  
Algorithm 1 is the pseudocode for the encoding of the BATS outer code, and Algorithm 2 is the pseudocode for the two-step decoding of the BATS outer code.  
```
1:Input:
* B: all the input packets.
* range: a range of index of batches.
2:Output:
* X: an array of the generated batches.
3:procedureENC(B, range)
4:\(\mathbf{B}_{p}\leftarrow\) Solve the precode constraint \(\big{[}\mathbf{B}\quad\mathbf{B}_{p}\big{]}\mathbf{P}=\mathbf{0}\).
5:\(\mathbf{B}^{\prime}\leftarrow\big{[}\mathbf{B}\quad\mathbf{B}_{p}\big{]}\)
6:\(\mathbf{B}_{A},\mathbf{B}_{I}\leftarrow\) Split \(\mathbf{B}^{\prime}\) into active packets and inactive packets.
7:\(\mathbf{X}\leftarrow[]\)\(\triangleright\) Initialize an array for the generated batches.
8:for\(i\) in range do\(\triangleright\) Line 7 to line 13 generate batches with key \(i\).
9: Use \(i\) as the seed for the pseudo random number generator.
10:\(d_{i}^{A}\leftarrow\) Sample a degree from the degree distribution \(\mathbf{\
abla}\).
11:\(d_{i}^{B}\leftarrow\) Randomly choose an inactive degree.
12:\(\mathbf{B}_{i}\leftarrow\) Randomly choose \(d_{i}^{A}\) packets from \(\mathbf{B}_{A}\) and \(d_{i}^{B}\) packets from \(\mathbf{B}_{I}\).
13:\(\mathbf{G}_{i}\leftarrow\) Create a \((d_{i}^{A}+d_{i}^{B})\times M\) matrix with independently uniform entries.
14:\(\mathbf{X}_{i}\leftarrow\mathbf{B}_{i}\mathbf{G}_{i}\)
15: Append \(\mathbf{X}_{i}\) onto the end of \(\mathbf{X}\).
16:return\(\mathbf{X}\)
```  
**Algorithm A1** The encoding process of the BATS outer code.  
```
1:Input:
* \([\mathbf{Y}_{1},\mathbf{Y}_{2},\dots,\mathbf{Y}_{n}]\): an array of \(n\) batches.
2:\([\mathbf{H}_{1},\mathbf{H}_{2},\dots,\mathbf{H}_{n}]\): an array of batch transfer matrices of the \(n\) batches.
3:Output:
* B: Recovered input packets
4:procedureDEC(\([\mathbf{Y}_{1},\mathbf{Y}_{2},\dots,\mathbf{Y}_{n}],[\mathbf{H}_{1},\mathbf{H}_{2}, \dots,\mathbf{H}_{n}]\))
5:\(\Omega\leftarrow\{1,2,\dots,n\}\)\(\triangleright\) The index set of unsolved batches
6:for\(i\) in \(1\dots n\)do
7: Use \(i\) as the seed for the pseudo number generator.
8:\(d_{i}^{A}\leftarrow\) Sample a degree from the degree distribution \(\mathbf{\
abla}\).
9:\(\mathbf{G}_{i}\leftarrow\) Create a \((d_{i}^{A})\times M\) matrix with independently uniform entries.
10:while\(\exists i\in\Omega\), such that \(d_{i}^{A}=\mathrm{rank}(\mathbf{G}_{i}\mathbf{H}_{i})\)do
11:\(\mathbf{B}_{i}\leftarrow\) Solve the system \(\mathbf{B}_{i}\mathbf{G}_{i}\mathbf{H}_{i}=\mathbf{Y}_{i}\).
12:for\(\mathbf{b}\) in \(\mathbf{B}_{i}\)do
13: Mark \(\mathbf{b}\) as decoded.
14:for\(j\) in indices of other batch such that \(\mathbf{b}\in\mathbf{B}_{j}\)do
15: Update the batch equation \(\mathbf{B}_{j}\mathbf{G}_{j}\mathbf{H}_{j}=\mathbf{Y}_{j}\) by canceling \(\mathbf{b}\) from that equation.
16: Remove \(i\) from \(\Omega\).
17:\(\big{[}\mathbf{B},\mathbf{B}_{p}\big{]}\leftarrow\) Decode the precode using the decoded packets.
18:return\(\mathbf{B}\)
```  
**Algorithm A2** The two-step decoding process of the BATS outer code.
# Detecting Composite Functional Module in miRNA Regulation and mRNA Interaction Network
## 4 Experiments and Results
### Kaplan-Meier Survival Analysis  
Based on the clinical data from The Cancer Genome Atlas (TCGA), we exhibit the association level of patient survival time with the detected CMFMs by the averaged miRNA expression levels.  
Figure 5: The functional consistency analysis of OV CMFMs. Though lots of modules detected by Mirsynergy have some GO-BP terms, their density values are zero. It shows that these mRNAs are loosely clustered into some modules, and not able to form an effective biological relationship with miRNAs.  
After retaining the samples recorded in the expression data, we obtained 376 samples for OV. For each module, we divided the samples into two groups based on their averaged miRNA expression levels which are higher or lower than the sample means. We then tested this hypothesis by Kaplan-Meier method using R package survival to compare the survival characteristics (log-rank test \(p<0.001\)) of the two groups. As a result, we found seven CMFMs with significant separation of two curves in the OV dataset. Figure 6 illustrates the Kaplan-Meier survival curves for CMFM 21 in ovarian cancer. For CMFM 21, the log-rank test gives \(p=0.00023\) and the median survival duration of the Low-group and High-group are 1054.2 days and 1345.7 days, respectively (Figure 6A).The patients in the Low-group faced greater risks.  
CMFM 21 detected by MBCFM from OV dataset contains 4 miRNAs and 38 mRNAs. In the module, it is clear that hsa-miR-575 and EGFR are the key nodes. The miRNA hsa-miR-575 regulates all mRNAs in the module and mRNA EGFR has interactions with the other five mRNAs. [PERSON] et al. [21] demonstrated that hsa-miR-575 is a once-miRNA. [PERSON] et al. [22] described that hsa-miR-3142 regulate cell growth and viability of thyroid cancer cells in part by repressing expression of USF2, RCL1 and SACS. [PERSON] et al. [23] studied the important role of EGFR in lung cancer disease. In conclusion, the CMFMs identified by our method are closely related to cancer.
## 2 Modeling Methodology and Estimation of Input Parameters
### Modeling Details and Analysis Procedure  
For effectiveness, the axisymmetric model of Figure 2 was used and earthquake motion was induced in the x direction. Each earthquake motion was input at the bottom of the model in the form of an acceleration-time profile for rigid-base input motion boundary condition. Pile foundation was modeled by elastic with cylinder-type solid element. The element size of the numerical model was set satisfying Equation (6) for the maximum frequency content of input motion ([PERSON] and [PERSON], 1973) [31]. As a result, the pile was modeled by a radial cylinder-shaped solid element with about 46 elements in the vertical direction, and its material was modeled applying the elastic model. It was confirmed that the pile stress induced during analysis was much lower than the material yielding stress.  
\[\Delta Z\leq\frac{V_{s}}{10f} \tag{6}\]  
where f is the maximum input frequency, V\({}_{\rm s}\) is the shear wave velocity, and \(\Delta\)Z is the element size.  
The numerical analyses progressed following three steps: (1) simulation of geostatic equilibrium, (2) pile installation, and (3) application of dynamic loading. In the first step, the in-situ geostatic condition was achieved to satisfy stress equilibrium in soil under gravitational load. In the second step, soil elements corresponding to the pile zone were removed and the pile elements were generated with the soil-pile-interface elements. The stress equilibrium condition was again achieved against the stress increase due to pile material weight. Finally, dynamic loading, in the form of the acceleration-time history of the input earthquake motion, was applied to the nodes on the bottom boundary, and the time histories of the horizontal acceleration in the far-field zone were applied to the corresponding nodes of the left and right boundaries. Superstructure weight on pile head was properly simulated by adjusting unit weight of the corresponding elements.
# Development of an Augmented Reality Surgical Trainer for Minimally Invasive Pancreatic Surgery
###### Abstract  
Robot-assisted minimally invasive surgery offers advantages over traditional laparoscopic surgery, including precision and improved patient outcomes. However, its complexity requires extensive training, leading to the development of simulators that still face challenges such as limited feedback and lack of realism. This study presents an augmented reality-based surgical simulator tailored for minimally invasive pancreatic surgery, integrating an innovative parallel robot, real-time AI-driven force estimation, and haptic feedback. Using Unity and the HoloLens 2, the simulator offers a realistic augmented environment, enhancing spatial awareness and planning in surgical scenarios. A convolutional neural network (CNN) model predicts forces without physical sensors, achieving a mean absolute error of 0.0244 N. Tests indicate a strong correlation between applied and predicted forces, with a haptic feedback latency of 65 ms, suitable for real-time applications. Its modularity makes the simulator accessible for training and preoperative planning, addressing gaps in current robotic surgery training tools while setting the stage for future improvements and broader integration.  
surgical simulator; augmented reality; parallel robot; artificial intelligence; minimally invasive surgery; robotic-assisted surgery +
Footnote †: journal: Applied sciences
# Overview of Compressed Sensing: Sensing Model, Reconstruction Algorithm, and Its Applications
## 3 Reconstruction Algorithm
### Noniterative Reconstruction Algorithm  
The reconstruction of compressed sensing faces two significant challenges, recovery-algorithm efficiency or real time, and signal sparsity in some transform domain, especially when the signal is very large. Some researchers combined deep learning and CS for signal reconstruction, and their schemes performed better with respect to recovery time and peak signal-to-noise ratio (PSNR) [63; 64; 65; 66].  
The approximate-message-passing (AMP) algorithm updates the tentative condition at each iteration to find a feasible solution [63]. A recovery algorithm was developed with a hidden layer of the network based on AMP. It also has the same number of iterations as those of the AMP algorithm [67]. The weights of the neural network in deep learning provide the parameters for the AMP algorithm. Another novel neural network architecture, learned vector AMP (LVAMP) [68], was proposed, inspired by vector AMP (VAMP) [69]. LVAMP was developed by extending the VAMP algorithm to a deep network, and training its parameters with similar methods. The resulting LVAMP could improve robustness of the measurement-matrix deviation in independent and identically distributed Gaussian.
# Improvement of Radiant Heat Efficiency of the
## 5 Conclusions  
In this study, a method and procedure for applying AM technology to reduce the fuel consumption of a W-type RT installed on a CAL has been presented. The design variables of the honeycomb pattern printed on the RT surface were calculated by FE analysis of radiant heat transfer. The actual size prototype honeycomb pattern was printed on the RT surface by the DED. The thermal performance evaluation of the RT with honeycomb pattern printed was done at the pilot furnace and the RT was installed in the actual CAL in POSCO Gwangyang Steel Works to examine its practical applicability and soundness. Following are the main conclusions of this study:  
(1) The radiant heat efficiency of the RT with honeycomb pattern printed was increased by 10.42% and 12.31% compared to that of plain RT for each soaking time, which indicates that honeycomb pattern printed RT proposed in this study is good enough to be used in the actual CAL in steelmaking industry. (2) The DED method adopted for printing the honeycomb pattern on RT surface was appropriate for objects with large and curved surfaces. (3) Since the honeycomb pattern created by the DED has sufficient adhesion strength between the pattern and the RT surface, the DED has no problem in applying it to the RT being used in repeated heating and cooling environments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Honeycomb** & **Set-up** & **Measured** & **Gas Consumed for** & **Gas Consumed for the** \\
**Pattern** & **Temperature** & **Temperature** & **the Soaking Time** & **Whole Heat Cycle** \\ \hline Without & 780 \({}^{\circ}\)C & 782 \({}^{\circ}\)C & 48 m\({}^{3}\) & \\  & 880 \({}^{\circ}\)C & 882 \({}^{\circ}\)C & 65 m\({}^{3}\) & 118 m\({}^{3}\) \\ \hline With & 780 \({}^{\circ}\)C & 783 \({}^{\circ}\)C & 43 m\({}^{3}\) & \\  & 880 \({}^{\circ}\)C & 882 \({}^{\circ}\)C & 57 m\({}^{3}\) & 105 m\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: The average amount of gas consumed to maintain 780 \({}^{\circ}\)C and 880 \({}^{\circ}\)C for 1440 min and for an entire heat cycle (3000 min).  
**Author Contributions:** Conceptualization and investigation, W.H. and Y.L.; formal analysis and validation, Y.R. and Y.L. writing--original draft preparation, W.H.; writing--review and editing, Y.L.; visualization, Y.R.; supervision, Y.L.; project administration, J.H.; funding acquisition, J.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by POSCO, grant number 2015A036.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Cubic \(q\)-Bezier Triangular Patch for Scattered Data Interpolation and Its Algorithm
## 1 Introduction
[19] introduced rational cubic Bezier triangular methods for positivity-preserving interpolation, asserting continuous differentiability. [PERSON] and [PERSON] [20] examined range-restricted interpolation using cubic Bezier triangles with partial derivatives calculated using [PERSON]'s approach. Research has also explored quartic Bezier triangular patches for scattered data interpolation. [PERSON] et al. [21] and [PERSON] et al. [22] developed \(C^{1}\) (or \(G^{1}\)) methods using quartic Bezier triangles, optimizing inner Bezier points to maintain range and positivity. [PERSON] et al. [23] expanded this to convexity-preserving interpolation with rational quartic Bezier triangles, addressing optimization challenges for \(C^{1}\) continuity. Comprehensive surveys on scattered data interpolation are available in sources like [1; 24; 25; 26; 27]. Among the various generalizations of Bezier curves, \(q\)-Bezier curves introduce greater versatility to the classical Bezier curves through the use of the parameter \(q\). This parameter allows for more flexible and dynamic curve modeling, accommodating a wider range of shapes and applications [28; 29; 30; 31; 32; 33]. [PERSON] et al. [30] expanded the \(q\)-Bernstein basis of univariate polynomials to a triangular domain, providing a novel framework for handling scattered data. However, the evaluation algorithm they proposed does not generally involve linear convex combinations, which limits its practical utility in some applications [30]. [PERSON] [17] addressed this limitation by extending the evaluation algorithm to incorporate convex combinations, enhancing the algorithm's applicability and robustness. Inspired by these advancements, we aim to further explore and apply these extended \(q\)-Bezier curve techniques to the challenging problem of scattered data interpolation, leveraging their flexibility to achieve more accurate and efficient interpolation results.
# A Novel Covert Channel for IEEE 802.11 Networks Utilizing MAC Address Randomization
## 4 The Proposed MAC Address Randomization Covert Channel
### Covert Channel Metrics  
In a standard network transmission channel, it is imperative to establish a comprehensive set of evaluation metrics. These metrics act as performance indicators, facilitating the assessment of the transmission channel's quality, identification of parameters and factors that directly impact it, and implementation of measures to mitigate potential performance degradation caused by these parameters. When evaluating the performance of the proposed covert channel, we employ the same metrics utilized for assessing regular standard transmission channels, namely throughput, delay, and jitter, measured according to the definitions in [46]. We decided to introduce an additional metric, namely covert channel frame efficiency. This metric indicates the ratio of the actual PR frames received to the number of PR frames transmitted. Our observation revealed that many covert channels focus solely on measuring bandwidth, which represents the theoretical throughput but not the actual performance. Furthermore, when measuring throughput, they often consider only the number of frames sent, without emphasizing whether those frames are received or lost. By including covert channel frame efficiency as a metric, we aim to provide a more comprehensive evaluation of the covert channel's health and performance.
## 2 Relevant Techniques
### Quaternion Discrete Fourier Transform (QDFT)  
Quaternion was introduced by [PERSON] [29], and was a generalization of a complex number. Quaternion [30] was regarded as a kind of hyper-complex, which can be represented by a four-dimensional complex number with one real part and three imaginary parts, and is defined as follows:  
\[\phi=\alpha+\beta i+\gamma j+\delta k. \tag{4}\]  
where \(\alpha\), \(\beta\), \(\gamma\), and \(\delta\) are real numbers, \(i\), \(j\), and \(k\) are imaginary operators with the following properties:  
\[i^{2}=j^{2}=k^{2}=i\cdot j\cdot k=-1. \tag{5}\]  
where the '\(\cdot\)' is the cross product, \(i\cdot j=k\), \(j\cdot k=i\), \(k\cdot i=j\), \(j\cdot i=-k\), \(k\cdot j=-i\), \(i\cdot k=-j\).  
[PERSON] [30] was the first to demonstrate formulations of quaternion discrete Fourier transform (QDFT). Considering that QDFT does not satisfy the commutative law, QDFT is divided into three types, namely, left-way transform \(F_{L}\), right-way transform \(F_{R}\)[8], and hybrid transform \(F_{LR}\)[30]. The form of the left-way transform \(F_{L}(\lambda,v)\) is as follows:  
\[F_{L}(\lambda,v)=\frac{1}{\sqrt{X,Y}}\sum_{x=0}^{X-1}\sum_{y=0}^{Y-1}e^{- \theta 2\pi(\frac{x0}{X}+\frac{w}{Y})}f(x,y). \tag{6}\]  
where \(f(x,y)\) is a color image of size \(X\times Y\) represented in the quaternion form as Equation (8). The inverse \(QDFT\) (\(IQDFT\)) [8] is defined by,  
\[f(x,y)=\frac{1}{\sqrt{X,Y}}\sum_{x=0}^{X-1}\sum_{y=0}^{Y-1}e^{\theta 2\pi(\frac{x0}{X}+ \frac{w}{Y})}F_{L}(\lambda,v). \tag{7}\]  
In these definitions, the quaternion operator was generalized, and \(\theta\) is any unit of pure quaternion, where \(\theta^{2}=-1\). The operators \(i\), \(j\), and \(k\) are special cases of \(\theta  
Using the Equations (6) and (8), we can obtain \(A(\lambda,v)\) is a real component, \(C(\lambda,v)\), \(D(\lambda,v)\), and \(E(\lambda,v)\) are the three imaginary components in Equation (9).  
\[F_{L}(\lambda,v)=A(\lambda,v)+C(\lambda,v)i+D(\lambda,v)j+E(\lambda,v)k. \tag{9}\]  
the inverse \(QDFT\) (\(IQDFT\)) can be represented as follows:  
\[f(x,y)=F_{-L}=A_{IQDFT}+C_{IQDFT}+D_{IQDFT}+E_{IQDFT}. \tag{10}\]  
where \(P_{IQDFT}\) is the real inverse quaternion discrete Fourier transform of array \(P\), and \(F_{-L}\) is the \(IQDFT\).
# SatScope: A Data-Driven Simulator for Low-Earth-Orbit Satellite Internet
## 3 SatScope Model Framework
### Ground Segment Modeling  
In the ground segment, SatScope employs network measurements to achieve data-driven, real-time simulation of network architecture. Specifically, SatScope analyzes connectivity paths between ground stations and end-users using Traceroute data to obtain network topology information. Leveraging random sampling techniques, SatScope can flexibly simulate satellite network scales without altering the distribution of ground nodes.
# A Real-Time Water Level and Discharge Monitoring Station: A Case Study of the Sakarya River
## 2 Materials and Methods
### Discharge Level Monitoring and Discharge Calculation Integration into the Web System
The Manning's formula is used to calculate the discharge, and transfer from a MySQL database to a web page is facilitated through PHP code to ensure visibility This PHP code comprises a class that handles river data-related operations in a web application and a script that processes incoming HTTP requests using this class.