esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Research Directions
### 6G Security in Quantum Computing  
6G BigCom is emerging as the global central backbone of tertiary communication, including satellites, UAVs, aviation, etc. Therefore, 6G communication must adapt to all emerging sister technologies such as quantum computing. It is envisaged that quantum processors will crack widely used crypto algorithms, the Rivest-Shamir-Adleman (RSA), until the 2030s [170]. Similarly, 6G communication is also considered a 2030s technology. Therefore, 6G communication requires extensive research in several related areas including post-quantum cryptography, quantum-resistant network hardware, quantum fog computing, quantum cloud computing, quantum key distribution and quantum cyber attacks.
## 1 Introduction
### Problem Statement  
For most industrial operations, there is no standard framework to accurately establish and assess the consequences of odd behavior, failure, or unscheduled stoppages, and therefore, decisions related to asset replacements or maintenance are made based on experience and inaccurate frameworks that usually focus on the overall process as a whole and not as part of the business process, although failure propagation has been studied in order to assess reliability [16,17], the criticality of the elements embedded in the system has not been studied. These issues push the necessity to define key performance indicators (KPI) for production systems [18] accountable for dependency and hierarchy, measuring the effectiveness of the KPI impact on every singular element of an industrial process. For this reason, one of the most important tools, in order to achieve the objectives of the KPI and address high impact consequences, is to acknowledge the real and effective impact of failure and stoppages underlaying in the equipment.  
In this regard, the OEI analysis helps identify opportunities for improvement in asset management. One of the most important aspects when measuring industrial performance is the operational effect linked to each element of the system, which is measured as a loss of production capacity or quality. When focusing on the impact of a single element, it must not be observed as a static parameter but as a dynamic and ever-changing parameter because of its dependency with other elements on the system, as well as its dependency on its own individual reliable and sustainable performance.  
Hence, for OEI analysis to accurately identify the above-mentioned improvement opportunities and resiliency factors, the following questions must be addressed. Which and where are the bottlenecks of the system? What causes and situations explain production loss? What is the availability of the system? Where are the most impactful opportunities for improvement?  
The objectives of this article can be summarized as follows. First and foremost, to propose a novel methodology to develop a framework that accurately analyzes the reliability and sustainability of a plant regarding its operational effectiveness and accurately identify and quantify the importance of the main assets relative to the whole system while improving the decision-making related to asset management. Secondly, to apply the proposed methodology in a wastewater treatment plant located in Chile using real data.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Vegetable Oil to Motor Oil**} & \multirow{2}{*}{\begin{tabular}{c} **Effusivity** \\ **(W-s\({}^{12}\)/m\({}^{2}\)-K)\(\times\)10\({}^{2}\)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Diffusivity** \\ **(m\({}^{2}\)/s)\(\times\)10\({}^{-8}\)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Conductivity** \\ **(Calculated)** \\ **(m\({}^{-}\)s\({}^{-1}\).K\({}^{-1}\))\(\times\)10\({}^{-1}\)** \\ \end{tabular} } \\ \hline \multicolumn{5}{c}{VO\%} & MO\% \\ \hline Motor Oil & - & 100 & 4.80 \(\pm\) 0.04 & 8.88 \(\pm\) 0.04 & 1.43 \(\pm\) 0.01 \\  & 5 & 95 & 5.20 \(\pm\) 0.04 & 7.14 \(\pm\) 0.01 & 1.39 \(\pm\) 0.01 \\  & 10 & 90 & 4.63 \(\pm\) 0.01 & 7.32 \(\pm\) 0.02 & 1.25 \(\pm\) 0.01 \\ Sunflower & 15 & 85 & 4.77 \(\pm\) 0.02 & 7.04 \(\pm\) 0.02 & 1.26 \(\pm\) 0.01 \\  & 20 & 80 & 4.81 \(\pm\) 0.02 & 7.02 \(\pm\) 0.02 & 1.27 \(\pm\) 0.01 \\  & 100 & - & 5.75 \(\pm\) 0.07 & 5.75 \(\pm\) 0.05 & 1.38 \(\pm\) 0.02 \\ \hline \multirow{5}{*}{Almonds} & 5 & 95 & 5.04 \(\pm\) 0.08 & 7.81 \(\pm\) 0.02 & 1.41 \(\pm\) 0.02 \\  & 10 & 90 & 5.05 \(\pm\) 0.08 & 7.50 \(\pm\) 0.02 & 1.38 \(\pm\) 0.02 \\  & 15 & 85 & 5.00 \(\pm\) 0.09 & 7.50 \(\pm\) 0.01 & 1.37 \(\pm\) 0.02 \\  & 20 & 80 & 4.86 \(\pm\) 0.09 & 7.08 \(\pm\) 0.02 & 1.29 \(\pm\) 0.02 \\  & 100 & - & 5.72 \(\pm\) 0.03 & 8.86 \(\pm\) 0.02 & 1.70 \(\pm\) 0.02 \\ \hline \multirow{5}{*}{Rapeseed} & 5 & 95 & 4.93 \(\pm\) 0.10 & 7.27 \(\pm\) 0.03 & 1.33 \(\pm\) 0.02 \\  & 10 & 90 & 4.95 \(\pm\) 0.07 & 7.42 \(\pm\) 0.02 & 1.35 \(\pm\) 0.02 \\ \cline{1-1}  & 15 & 85 & 4.92 \(\pm\) 0.09 & 7.57 \(\pm\) 0.01 & 1.35 \(\pm\) 0.02 \\ \cline{1-1}  & 20 & 80 & 4.55 \(\pm\) 0.02 & 7.72 \(\pm\) 0.03 & 1.26 \(\pm\) 0.01 \\ \cline{1-1}  & 100 & - & 5.58 \(\pm\) 0.03 & 8.54 \(\pm\) 0.02 & 1.63 \(\pm\) 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Obtained values for thermal effusivity and thermal diffusivity. Figure 4: Thermal diffusivity values for sunflower (squares), almond (triangles), and rapeseed (stars); solid lines represent linear fitting in the 5% to 20% range for the corresponding samples. Previous studies have shown that absolute thermal conductivity variations lower than 10% can result in smaller wear scars [7]; therefore, the addition of sunflower (5%, almost (5%,10%, and 15%) and rapeseed (5%, 10%, 15%) can result in wear scar reduction without disrupting the thermal stability of the hydrocarbons.
## 2 Materials and Methods
### Physical Characterization
#### 2.2.2 Thermal Conductivity  
The thermal conductivity of the prototypes was measured using the KD2 Pro instrument manufactured by Decagon Devices Inc., Pullman, WA, USA, which has been utilized in other studies analyzing fiber-based thermal insulation materials for construction [11; 30; 35]. The device uses an interchangeable sensor that is inserted into the material, providing a thermal conductivity value. The measurement process is based on the transient line heat source method, enabling the quick analysis of homogeneous materials, with measurement intervals of 1 s during a 90 s heating and cooling cycle. The KD2 Pro complies with IEEE 442-1981 [36] specifications and ASTM D5334-00 standards [37], with an accuracy of \(\pm\)0.001 W/mK. To ensure the stabilization of the sensor, a 10 min gap was maintained between analyses. Variations in the results may have occurred depending on fiber properties such as length, diameter, density, moisture content, and arrangement.
# CarrDet: Attention-Symmetry-Based Microscopic Carrier Detection for Wastewater Treatment
## 2 Related Work
### Object Detection in Microscopic Images  
Object detection in microscopic images is a common task in various biological and medical applications. Common methods include two categories, fine-tuned general object detection and domain-specific cell detection.  
General object detection methods can be broadly categorized into one-stage and two-stage detectors, each focusing on different aspects. One-stage detectors such as single-shot detection (SSD) [7] and you only look once (YOLO) prioritize inference speed, while two-stage detectors such as the regional convolutional neural network (R-CNN) family, including R-CNN [8], Fast R-CNN [17], Faster R-CNN [18], and Mask R-CNN [19], prioritize detection accuracy.  
Detection of certain types of cells in microscopy images is of significant interest to a wide range of biology and clinical practices [20]. To effectively detect cells in microscopic images, a range of detection methods have been developed, each suited to different cells. [PERSON] et al. [21] presented an attention-based cell detection transformer for yeast cell detection. [PERSON] et al. [22] verified key points and centroid points using the unique geometric sequence of cells for mammalian cell detection. [PERSON] et al. [23] presented an optimized training algorithm for circulating tumor cell detection. [PERSON] et al. [10] integrated upsampled multiscale features from FPN into a single layer for cell detection on IHC-stained images. [PERSON] et al. [24] incorporated the embedding layer into the RPN to learn discriminative features based on similarity for histological cell detection. [PERSON] et al. [25] applied representation learning and deep clustering techniques to learn the morphological features of cells and representation of spatial context for breast cancer, colorectal cancer, and lung cancer cell detection. [PERSON] et al. [26] used depthwise separable convolution to reduce detector parameters and distance intersection over union for blood cell detection. [PERSON] et al. [9] refined the detection results using shape constraints of images for CD56 detection.  
However, the direct application of these methods to wastewater microscopic images may lead to false positives caused by defective carriers and unrecognized carriers with shallow edges, due to the morphological specificity of carriers. Therefore, it is imperative to develop an object detection method specifically designed for carriers in wastewater microscopic images.
# A Parallel Two-Stage Iteration Method for Solving Continuous Sylvester Equations
## 3 Parallel Implementation of the Two-Stage Iteration Method
### Data Storage  
For convenience, let \(p\) be the number of processors, \(p_{i}(i=1,2,\ldots,p)\) represent \(i\)th processor, and \(l\) is an integer in \(n=pl\).  
Mark  
\[A=(A_{1}^{\mathrm{T}},A_{2}^{\mathrm{T}},\cdots,A_{p}^{\mathrm{T}})^{ \mathrm{T}},\ B=(B_{1}^{\mathrm{T}},B_{2}^{\mathrm{T}},\cdots,B_{p}^{\mathrm{ T}})^{\mathrm{T}},\ F=(F_{1}^{\mathrm{T}},F_{2}^{\mathrm{T}},\cdots,F_{p}^{ \mathrm{T}})^{\mathrm{T}},\]  
\[X^{(0)}=((X_{1}^{(0)})^{\mathrm{T}},(X_{2}^{(0)})^{\mathrm{T}},\cdots,(X_{p}^{ (0)})^{\mathrm{T}})^{\mathrm{T}},\ A^{\mathrm{T}}=(\widetilde{A}_{1}^{ \mathrm{T}},\widetilde{A}_{2}^{\mathrm{T}},\cdots,\widetilde{A}_{p}^{\mathrm{ T}})^{\mathrm{T}},\]  
\[B^{\mathrm{T}}=(\widetilde{B}_{1}^{\mathrm{T}},\widetilde{B}_{2}^{\mathrm{T}}, \cdots,\widetilde{B}_{p}^{\mathrm{T}})^{\mathrm{T}},\ \widetilde{F}=(\widetilde{F}_{1}^{\mathrm{T}},\widetilde{F}_{2}^{\mathrm{T}}, \cdots,\widetilde{F}_{p}^{\mathrm{T}})^{\mathrm{T}},\]  
\[M_{1}=((M_{1,1})^{\mathrm{T}},(M_{1,2})^{\mathrm{T}},\cdots,(M_{1,p})^{ \mathrm{T}})^{\mathrm{T}},\ M_{2}=((M_{2,1})^{\mathrm{T}},(M_{2,2})^{\mathrm{ T}},\cdots,(M_{2,p})^{\mathrm{T}})^{\mathrm{T}},\]  
\[N_{1}=((N_{1,1})^{\mathrm{T}},(N_{1,2})^{\mathrm{T}},\cdots,(N_{1,p})^{ \mathrm{T}})^{\mathrm{T}},\ N_{2}=((N_{2,1})^{\mathrm{T}},(N_{2,2})^{\mathrm{ T}},\cdots,(N_{2,p})^{\mathrm{T}})^{\mathrm{T}}\]  
where \(A_{i}\), \(B_{i}\), \(F_{i}\), \(X_{i}^{(0)}\), \(\widetilde{A}_{i}\), \(\widetilde{B}_{i}\), \(\widetilde{F}_{i}\), \(M_{1,i}\), \(M_{2,i}\), \(N_{1,i}\), \(N_{2,i}\) (\(i=1,2,\cdots,p\)) are \(l\times n\) sub-block matrices. These are saved in row storage. Then, store \(A_{i}\), \(B_{i}\), \(F_{i}\), \(X_{i}^{(0)}\), \(\widetilde{A}_{i}\), \(\widetilde{B}_{i}\) on the processor \(p_{i}(i=1,2,\cdots,p)\).  
**Note:** Due to the storage method, we chose the way of matrix multiplication with block row-row matrices in parallel computing process. Detailed descriptions of parallel computing Matrix multiplication are found in References [5,23,34].
_Article_  
Adaptive Graph Convolution Using Heat Kernel for Attributed Graph Clustering  
[PERSON] 1,2, [PERSON] 1,2,*, [PERSON] 1,2 and [PERSON] 1,2  
Received: 13 January 2020; Accepted: 17 February 2020; Published: 21 February 2020  
**Featured Application: We propose a novel model to perform attributed graph clustering, which exploits heat kernel to enhance the performance of graph convolution and adopts adaptive architecture to work on different graph datasets. The model proposed in this paper can be deployed to a product recommendation system, where users with specific preferences can be classified precisely and recommended satisfactory products. It can be applied to citation networks to analyze the categories of different articles without prior knowledge. It can be deployed into business forecasting, where the proposed model can identify the operating situation of enterprises significantly by analyzing their business data and investment relationships jointly.**  
**Abstract: Attributed graphs contain a lot of node features and structural relationships, and how to utilize their inherent information sufficiently to improve graph clustering performance has attracted much attention. Although existing advanced methods exploit graph convolution to capture the global structure of an attributed graph and achieve obvious improvements for clustering results, they cannot determine the optimal neighborhood that reflects the relevant information of connected nodes in a graph. To address this limitation, we propose a novel adaptive graph convolution using a heat kernel model for attributed graph clustering (AGCHK), which exploits the similarity among nodes under heat diffusion to flexibly restrict the neighborhood of the center node and enforce the graph smoothness. Additionally, we take the Davies-Bouldin index (DBI) instead of the intra-cluster distance individually as the selection criterion to adaptively determine the order of graph convolution. The clustering results of AGCHK on three benchmark datasets--Cora, Citeseer, and Pubmed--are all more than 1% higher than the current advanced model AGC, and 12% on the Wiki dataset especially, which obtains a state-of-the-art result in the task of attributed graph clustering.**  
**Keywords: graph convolution; attributed graph clustering; heat kernel; Davies-Bouldin index**
# Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms  
[PERSON]  
The intelligent damage detection of catenary insulators is one of the key steps in maintaining the safe and stable operation of railway traction power supply systems. However, traditional deep learning algorithms need to train a large number of images with damage features, which are hard to obtain; and feature-matching algorithms have limitations in anti-complex background interference, affecting the accuracy of damage detection. The current work proposes a method that combines deep learning and Zernike moment algorithms. The Mask R-CNN algorithm is firstly used to identify the catenary insulators to realize the region proposal of the insulators. After image preprocessing, the Zernike moment algorithm is used to replace the existing Hu moment algorithm to extract more detailed insulator contour features, then the similarity value and its standard deviation are further calculated, so as to complete the damage detection of the catenary insulator. The experimental results show that the mean average precision of insulator identification can reach 96.4%, and the Zernike moment algorithm has an accuracy of 93.36% in judging the damage of insulators. Compared with the existing Hu moment algorithm, the accuracy is increased by 10.94%, which provides a new method for the automatic detection of damaged insulators in catenary and even other scenarios.
# Fault Investigation of Circulation Pumps to Detect Impeller Clogging
## 2 Materials and Methods
### Experimental Execution  
The pump described in Section 2.1 was used to investigate the fault impeller clogging. The pump was installed in the middle strand of the right hydraulic circuit (see Figure 2), with the left and right strands remaining shut off. The setup presented in Section 2.1 was used to record the values of the pump and process. The sampling frequency of the current measurement with the help of the oscilloscope is set to 10 kHz at a recording time of 30 s At each load point, a total of 30 values are recorded for the static values and averaged afterwards.  
As a reference point, the shut-off valve on the pressure side is set so that a flow of 17.9 m\({}^{3}\)/h and a head of 6.6 m at a speed of 2500 rpm was achieved. To avoid turbulence in the pump, the down shut-off valve remained completely open. Additional measurement data were recorded at speeds of 1800 and 1000 rpm. This is the reference point for the following test runs:  
* First test run: This test is only for recording the process curve at this valve position. No motor current is recorded. A process curve shows the dependence of the pressure drop in the pipes on the flow and is usually a quadratic function. It is calculated with the help of the flow measured by the pump and the head measured by the two pressure sensors in the active strand. Thereby, the speed of the pump was varied in the range of 1000 to 2500 rpm. The process curve makes it possible to calculate back on the flow by measuring the head for later fault cases. This method was valid for heads over 2 m.
* Second test run: As in the first test run the shut-off valve was left in a constant position. Now one impeller channel after the other is always clogged with polystyrene. In order not to make the measuring effort too great, only adjacent channels were clogged. First, one channel was clogged and the pump was measured, then two channels were clogged and the pump was measured etc., until seven, and thus all, channels of the impeller are clogged. Figure 4 shows the implementation of the fault with two clogged impeller channels. For each clogging degree, all relevant data were recorded for the different speeds 2500, 1800 and 1000 rpm.
* Third test run: in this test, only the healthy pump was measured. This test run was used to generate more reference points. The shut-off valve was adjusted so that the power consumption of the healthy pump corresponds to the power consumption of the individual fault conditions of the second test run. For each load point, all relevant data were recorded for the different speeds 2500, 1800 and 1000 rpm.
## 5 Materials and Methods
### System Design Rationale  
The system architecture was developed following the principles established in Section 3, with the aim to support high-frequency, protocol-diverse data streams and enable real-time knowledge modeling.  
The design integrates three core dimensions:  
Real-Time Data Acquisition: Native protocol-based communication enabled integration with both legacy (e.g., S7-300/400) and modern PLCs (e.g., S7-1200/1500). The use of IBH Link gateways ensured compatibility with older control units, while heterogeneous systems were connected directly via Ethernet and OPC UA servers.  
Edge-Based Preprocessing: A dedicated edge node (Intel i5, 16 GB RAM, 1 TB SSD) runs custom Python 3.10 services for data parsing, semantic annotation, and real-time analysis. The system employs buffered queues and event-driven logic to support data rates > 10 MB/s while maintaining latency below 50 ms (see Section 6).  
Visualization:  
* ibaQPanel for the visualization of measurement data in real time (value progression, limit value violations, signal status);
* Configuration of the panels via ibaPDA configuration editor.  
The design rationale ensures alignment between practical requirements and epistemic goals as outlined by the Knowledge Staircase. Each subsystem was validated under realistic conditions (Sections 6.1-6.3).
## 4 Joint Motion Planning Based on Modified Hermite Interpolation  
### Function Transformation between Different Intervals  
Since the variable interval of cubic Hermite interpolation is [0,1], and the variable interval of industrial robot motion planning is generally not [0,1], function transformation between different intervals is needed. There is an arbitrary function expressed as \(y=f(u),\ u\in[u_{0},u_{1}]\). Given that the function is symbolically independent, this function can also be expressed as \(y=f(x),\ x\in[x_{0},x_{1}],\ x_{0}=u_{0},\ x_{1}=u_{1}\). When the interval [\(x_{0}\), \(x_{1}\)] is not equal to the interval [\(u_{0}\), \(u_{1}\)], that is to say, the variable interval changes, the function expression should be transformed into the following formulas (9) and (10) in order to keep the function values of \(y\) unchanged.  
\[u=u_{0}+\frac{(u_{1}-u_{0})(x-x_{0})}{(x_{1}-x_{0})},u\in[u_{0},u_{1}],x\in[x_ {0},x_{1}] \tag{9}\]  
\[y=f(u)=f\bigg{[}u_{0}+(u_{1}-u_{0})\frac{x-x_{0}}{x_{1}-x_{0}}\bigg{]}=f(x),x \in[x_{0},x_{1}] \tag{10}\]  
When function interval is changed from [\(u_{0}\), \(u_{1}\)] to [\(x_{0}\), \(x_{1}\)], the transformed function \(y=f(x)\) can keep the values of the initial function \(y=f(u)\) unchanged, but the first-order and second-order derivatives of the function \(y\) with respect to \(u\) are no longer equal to that of the function \(y\) with respect to \(x\), as shown in Equations (11) and (12).  
\[\frac{dy}{du}=\frac{dy}{dx}\cdot\frac{dx}{du}=\frac{dy}{dx}\cdot\frac{1}{\frac {du}{dx}}=\frac{dy}{dx}\cdot\frac{x_{1}-x_{0}}{u_{1}-u_{0}} \tag{11}\]  
\[\frac{d^{2}y}{du^{2}}=\frac{d(\frac{dy}{du})}{du}=\frac{d(\frac{dy}{du})}{dx} \cdot\frac{dx}{du}=\frac{d(\frac{dy}{dx}\cdot\frac{x_{1}-x_{0}}{u_{1}-u_{0}} )}{dx}\cdot\frac{1}{\frac{du}{dx}}=\frac{d^{2}y}{dx^{2}}\cdot(\frac{x_{1}-x_ {0}}{u_{1}-u_{0}})^{2} \tag{12}\]  
Since the variable interval of cubic Hermite interpolation is [0,1], the following Equations below can be obtained in the interval [\(x_{0}x_{1}\)] instead of the interval [0,1] according to the above equations. These formulas can be used in the following study.  
\[u=\frac{(x-x_{0})}{(x_{1}-x_{0})},u\in[0,1],x\in[x_{0},x_{1}] \tag{13}\]  
\[y=f(\frac{x-x_{0}}{x_{1}-x_{0}}),x\in[x_{0},x_{1}] \tag{14}\]  
\[\frac{dy}{du}=\frac{dy}{dx}\cdot(x_{1}-x_{0}) \tag{15}\]  
\[\frac{d^{2}y}{du^{2}}=\frac{d^{2}y}{dx^{2}}\cdot(x_{1}-x_{0})^{2} \tag{16}\]  
Figure 5: Angular velocity fluctuation in quintic polynomial planning.
# Recent Developments in Modulation Spectroscopy for Methane Detection Based on Tunable Diode Laser
## 5 Conclusions  
The characteristics of modulation spectroscopy techniques, based on TDLs for detecting methane in the last decade, have been typically reviewed and codified, as shown in Table 2. Recent developments in semiconductor lasers and modulation spectroscopy techniques for methane detection have been described throughout this article. Moreover, some other trends are becoming visible.  
Firstly, although QCLs, as convenient mid-infrared laser sources, have not been widely used for commercial detection systems, they may find broad application in mid-infrared modulation spectroscopies in the near future. Then, other wideband-tunable room temperature mid-infrared sources and corresponding mid-infrared technology, such as DFG sources, optical fiber, and detection instruments, will be further developed for trace gas detection. Except for the development of TDLs and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Spectroscopic Technique** & \begin{tabular}{c} **Spectral** \\ **Wavelength** \\ **(nm)** \\ \end{tabular} & \
\begin{tabular}{c} **Detection** \\ **Limit/Measured** \\ **Concentration** \\ \end{tabular} &
\begin{tabular}{c} **Source Type** \\ **Co  
mid-infrared sources, the optimization of optical interference fringe and the formation of multipass cells are observable factors in improving the detection performance. Better sensitivity for methane detection system will be expected when new designs of lasers and multipass cells and methods of suppressing noise are put in operation.  
Conceptualization, F.W., and S.J.; investigation, F.W., S.J., Y.W., and Z.T.; Funding acquisition, S.J.; writing--original draft preparation, F.W. and S.J.  
This work was supported by the National Natural Science Foundation of China (NSFC) (Grant No. 51575437), the NSFC--Shanxi Provincial Government coal-based low carbon joint fund (Grant No. U1510114), the Key Science and Technology Program of Shaanxi Province of China (Grant No. 2014K07-02) and XJTU University Funding (Grant No. PY3A048).  
The authors declare no conflict of interest.