esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The density-based clustering algorithm assumes that the clustering structure can be determined by the tightness of the sample distribution. In general, the density-based clustering algorithm examines the connectivity between samples and gives the connectable samples an expanding cluster until obtain the final clustering results. Several density-based clustering have been put forward, like DBSCAN, ordering points to identify the clustering structure (OPTICS), and clustering by fast search and find of density peaks (DP). With prior information of the cluster number, the DP method generates a decision map containing the density information of each sample point and the minimum distance between the sample point and any other point with higher density. The cluster centers are recognized as points for which the value of decision map is anomalously large [31; 32; 33]. The DP algorithm needs prior information of the cluster number in advance, and limits the application of MFI. The OPTICS method performs clustering by computing an augmented cluster-ordering of the database objects [34; 35; 36; 37]. It can perform clustering of different-densities clusters. In principle, the OPTICS clustering method can be used to perform MFI. However, it cannot extract change of cluster density in different OSNR and cannot be used to estimate OSNR. DBSCAN is a well-known clustering algorithm that maps the density of samples [38; 39; 40; 41; 42]. With the ability of unsupervised-learning, DBSCAN can directly extract clustering information of data which can be utilized in identifying some widely used MFs such as PDM-QPSK, PDM-8 PSK, PDM-16 QAM, PDM-32 QAM, and PDM-64 QAM without training data. Moreover, by fitting the proportion of core points in the sample set, the DBSCAN-based method can realize OSNR estimation for five MFs mentioned above with low mean absolute error even when the testing data is not trained. Thus, we propose a joint algorithm for MFI and OSNR estimation based on DBSCAN in this work. The experimental results demonstrate successful identification of QPSK & 16 QAM signals and low OSNR estimation error. In Section 2, we introduce how DBSCAN is applied to the MFI and OSNR estimation. In Section 3, we verify the correctness of the proposed method by numerical simulation and proof experiments. In Section 4, we conclude that the proposed method in this article is feasible in numerical simulated and practical optical fiber communication systems and has the potential to be used in BER calculation, linear or nonlinear impairments monitoring. In Abbreviations, we present an acronym list for conveniently searching the acronym. In Appendix A, we present the basic notion, pseudocode, and framework of DBSCAN algorithm.
## 3 Assessing the Effectiveness of the Proposed IDA Procedure
### Dispersion of the IDA Curves' Results  
To quantify the dispersion in the IDA curves, the coefficient of variation (COV) relevant to the collapse values of the \(DM\) and \(IM\) parameters will be considered. Since COV = 0 reflect a lack of statistical variation, the COV decrease as the degree of dispersion decreases and as the efficiency of the \(IM\) indicator increases. According to [PERSON] and [PERSON] [19], the collapse threshold of each IDA curve was determined by evaluating the interstory drift ratio value at which the curve's slope (\(IM\)/\(DM\)) drops under 20% of the initial slope. This value typically varies from curve to curve, although a smaller variation indicates less dispersion and, thus, higher effectiveness. The values of \(DM=ID_{max}\) and \(IM=S_{a}(T_{1},5\%)\), both of which are taken at the collapse point, will be referred to as the collapse interstory drift ratio and collapse spectral acceleration, respectively.  
Figure 13: Histograms of the 16 th, 50 th, and 84 th percentile of the collapse interstory drift ratio in the (**a**) \(x\)-direction and (**b**) \(y\)-direction.  
On the other hand, Table 6 shows that lower COV values of the collapse spectrum acceleration are found in the \(x\)-direction for the inelastic period, regardless of whether the period is conventionally estimated or rigorously calculated. In contrast, a much more remarkable reduction in the COV is attained when the smoothed peak period \(T_{inSP}\) is considered in place of the peak period \(T_{inP}\). A reduction in the COV of approximately two-thirds of the COV in the \(x\)-direction with respect to the elastic period is attained with \(T_{inSP}\), in contrast to a reduction of less than one-half with \(T_{inSP}\).  
In the \(y\)-direction, the dispersion detected when considering the Kadas or Di Sarno and Amiri values of the inelastic period (second and third rows of Table 7) is even higher than that considering the elastic period (first row of Table 6). Therefore, the use of the conventional inelastic periods given by Equations (1) and (2) does not lead to a reduction in the dispersion of the collapse spectral acceleration results in the \(y\)-direction. The COV of the collapse spectral acceleration decreases only when \(T_{inP}\) and \(T_{inSP}\) are considered (fourth and fifth rows in Table 7). Likewise, the value of COV relevant to \(T_{inSP}\) is markedly lower than that obtained for \(T_{inP}\), which means that Equation (4) provides a better choice than Equation (3) for the \(IM\) indicator.
## 3 Test Results and Analysis
### Changes in Physical Properties
#### 3.1.3 Volume Change  
Different cooling methods have an influence on the change in the sample volume. In order to better study the volume change of the sample under different cooling methods, the volume change rate \(\Delta v\) is introduced, as shown in Equation (2):  
\[\Delta v=\frac{v_{0}-v_{1}}{v_{0}} \tag{2}\]  
Figure 4: Mass change after different cooling methods.
# A Method for Filtering Pages by Similarity Degree based on Dynamic Programming
## 4 Experimental Analysis
### Comparison with Other Methods
But the \(PR\) of MFPLR is lowest, only 32.9%. The \(PR\) of MFPSDDP is the highest, 85.1%. According to the above analysis, the \(PR\) of MFPSDDP is highest in the four filtering methods, and the \(RR\) of MFPSDDP is middle in the four filtering methods. In the four filtering methods, MFPLR and MFPSDDP need not to know the structures of webpages in advance, but MFPCS and MFPCTA need to know the structures of webpages in advance. In addition, we downloaded 200,000 webpages from the famous websites \"www.taobao.com\". We still set the similarity threshold to 0.2, and use the webpage \"[[https://item.taobao.com/item.htm?id=56165326554](https://item.taobao.com/item.htm?id=56165326554)]([https://item.taobao.com/item.htm?id=56165326554](https://item.taobao.com/item.htm?id=56165326554))\" as the template page. After our experiments, when the number of webpages filtered \begin{table}
\begin{tabular}{c l} \hline \hline
**Filtering Method** & **Main Configuration** \\ \hline MFPSDDP & The following conditions must be satisfied at the same time. \\  & **Condition 1.** The tags in each webpage tree must include only the tags \textless{}div\textgreater{} and \textless{}span\textgreater{}. This condition is expressed as follows: \\  & _page._onlyContainTags (_divTagAndspanTag_) \\  & **Condition 2.** The similarity between each webpage and a template webpage is \\  & calculated by CSR and Formula (1). The similarity threshold is set to 0.2. \\  & This condition is expressed as follows: \\  & _page._similarityOut (0.2) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: \(Cont.\)  
Figure 5: The experimental results of the accuracies of four filtering methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Filtering Method** & **TUF** & **\(AF\)** & **\(TAF\)** & \(RR\) & \(PR\) \\ \hline MFPCS & 57,570 & 72,643 & 52,175 & 90.6\% & 71.8\% \\ MFPCTA & 57,570 & 76,441 & 55,725 & 96.8\% & 72.9\% \\ MFPLR & 57,570 & 172,694 & 56,734 & 98.5\% & 32.9\% \\ MFPSDDP & 57,570 & 62,372 & 53,079 & 92.2\% & 85.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The RRs and PRs of four methods when the number of webpages filtered is nearly 200,000.  
is nearly 200,000, the \(RR\) of MFPSDDP is 91.6%, the \(PR\) of MFPSDDP is 85.7%. These show that MFPSDDP is suitable for large commercial websites with modular development. MFPSDDP can filter out the target webpages with lots of tag <div> and <span>.
## 3 Empirical Study
### Empirical Study on the Reliability of RPEM Economic Evaluation
#### 3.2.3 Mine Cash Flow Calculation  
According to Equation (14), the variation range of mining grade is obtained, and the economic benefits within this range are discussed. The coefficient of variation of the extracted ore grade is 0.1, and 11 sets of data are taken within the variation range of the ore grade (21.04%, 22.36%). Based on the mining and beneficiation production capacity of the mine, if other indicators remain unchanged, the corresponding concentrate output and concentrates sales revenue for the 11 sets of ore grades can be calculated.  
The designed annual mine output of the underground mining project is 1300 kt, the mine ore grade is 21.04%, the production cost of concentrate per ton of ore is CNY 114.59 (excluding tax), and the annual average fixed assets investment and current asset investment is CNY 9.9686 million. The taxes and fees paid by enterprises are subject to changes in sales revenue, as changes in ore grade affect concentrate production, which in turn affects changes in sales revenue. When the ore grade is 21.04%, the annual tax paid by the enterprise is CNY 39.6797 million. Taking a mining grade of 21.04% as an example, the annual cash flow of the mine is calculated, and the specific steps are as follows:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Symbol** & **Underground Mining** \\ \hline Average geological grade of recoverable & g & 26.3\% \\ reserves (\%) & & \\ Dip angle of ore body (°) & \(\alpha\) & 62–87 \\ Thickness of ore body (m) & m & 20–40 \\ Actual mining recovery rate (\%) & n & 88 \\ Actual dilution rate in mining (\%) & \(\rho\) & 20 \\ Raw ore (ore extraction) grade (\%) & g\({}^{\prime}\) & 21.09 \\ Concentrate grade (\%) & \(\gamma\) & 65 \\ Beneficiation recovery (\%) & \(\varepsilon\) & 77.2 \\ Concentrate yield (t/t) & K = G-\(\varepsilon\) & 0.25 \\ Sales price of iron concentrate (CNY/t) & W & 744 \\ Underground mining cost (CNY/t) & Cp & 78.31 \\ Mineral processing cost (CNY/t) & Cu & 36.28 \\ Production cost of ton of ore concentrate (CNY) & Cc & 114.59 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main production technical and economic indicators.  
1. Annual cash inflows from mines \(F\): \[F=A\frac{g\cdot\varepsilon\cdot w}{\gamma} =130\times\frac{21.04\%\times 77.2\%\times 74}{65\%}\] (15) \[=241.6941(\text{million Yuan})\] (2) Annual cash outflow from mines \(P\): \[\begin{array}{ll}P&=A\cdot\frac{g\cdot\varepsilon}{\gamma}\cdot C_{c}+C_{1}+C_{2 }\\ &=130\times\frac{21.04\%\times 77.2\%}{65\%}\times 114.59+3967.97+996.86\\ &=198.6153(\text{million Yuan})\end{array}\] (16) (3) Annual net cash flow of the mine \(I\): \[\begin{array}{ll}I=F-P&=241.6941-198.6153\\ &=43.0788(\text{million Yuan})\end{array}\] (17)
# Multi Swarm Optimization Based Clustering with Tabu Search in Wireless Sensor Network
## 5 Conclusions  
To operate WSNs effectively and reliably, the most important requirement is energy-saving, and for significant technical problems and resource management, much importance is presented in energy consumption. This work's major aim is to improve the WSN CH selection through a hybrid heuristic system. Thus, an algorithm based on MSO and TS is intended to optimize the routing in WSN and CH selection. The presented method is energy efficient and in response to the network. Effective CHs are chosen by the presented technique and in response to the network. Effective CHs selected by the proposed scheme optimizes the routing and increases the network's lifespan. Based on the exhaustive comparison of the proposed MSO-Tabu approach with GA, DE, Tabu, and MSO based clustering, the following conclusions are drawn  
* In terms of the number of clusters formed, the proposed MSO-Tabu approach is on an average 14%, 5%, 11%, and 4% better than GA, DE, Tabu, and MSO based clustering, respectively.
* MSO-Tabu has lower average end-to-end delay by 11%, 2%, and 7% compared to GA, DE, and MSO based clustering, respectively. However, for the various number of nodes, the average performance of Tabu is better than MSO-Tabu by 4%.
* In terms of average packet loss rate, the proposed MSO-Tabu approach is 20%, 6%, 14%, and 6% better than GA, DE, Tabu, and MSO based clustering, respectively.
* MSO-Tabu has higher lifetime computation by 136%, 36%, 136%, and 38% compared to GA, DE, Tabu, and MSO based clustering, respectively.
* The average energy dissipated by MSO-Tabu is 22%, 16%, 51%, and 12% higher than GA, DE, Tabu, and MSO based clustering, respectively.  
The key limitation of the proposed algorithm lies in tuning parameters selection, which is not adaptive and is static in nature. Noticeably, the selection of diversity control parameters has a major role in the convergence of the algorithm in the solution space.  
Improper selection of those parameters results in poor local search capability of the MSO-Tabu algorithm. However, the proposed algorithm provides stable performance concerning changes in other parameters under consideration.  
Conceptualization, S.S., N.U., M.M. and M.R.; methodology, S.S., N.U., M.M. and M.R.; software, S.S., N.U., M.M. and M.R.; formal analysis, S.S., N.U., M.M. and M.R.; writing--original draft preparation, S.S., N.U., M.M. and M.R.; writing--review and editing, S.S., N.U., M.M. and M.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Liquidation of Shallow-Lying Post-Mining Excavations
## 2 Methods of Rock Mass Investigation in Shallow Exploitation Areas
### Electrical Resistivity Tomography Method  
The electrical resistivity tomography method, like other electrical resistivity methods, uses the differentiation of the electrical properties of the rock medium. It is a combination of electrical resistivity profiling, due to measurements carried out along the profile, and geoelectric probing due to the increasing depth range during the tests [12]. The number of measurements using the electrical resistivity tomography method corresponds to the performance of a dozen, or so, classic profilings using systems with different depth ranges and several dozen probings with different maximum lengths of the supply line [11]. A series of continuous measurements is performed based on the automatic selection of a combination of 4 electrodes, from among all those connected at equal intervals to the cable along the profile, in accordance with the specified type of system, e.g., Schlumberger or other. The measurements are performed using systems with increasingly larger spacing with the desired step along the designated profile [8]. The maximum spacing of the measuring system depends on the length of the profile and translates into the depth range of the tests. The average depth of measurement penetration is, depending on the specified measuring system, from 1/3 to 1/6 of the distance between the extreme electrodes [13]. The depth range is also influenced by the resistivity characteristics of the rock medium, which depend on humidity, porosity, salinity, and others [6]. The results of measurements using the electrical resistivity tomography method are presented in the form of apparent resistivity cross-sections. The tests can also be performed in 3D--by taking measurements along many profiles in different directions. The electrical resistivity tomography method is characterized by a wide range of applicability. It is used to recognize the geological structure, determine the depth and thickness of overburden layers and embankments, determine slip planes in landslides, and locate sinkholes and voids of anthropogenic origin [14; 15].
# Optical Design of a Miniaturized Airborne Push-Broom Spectrometer
## 1 Introduction  
The spectrometer appeared in the late 1970s and it is a breakthrough in the remote sensing field [1]. Since its birth, it has been favored for its wide application range, which includes military reconnaissance, surveys of agriculture, forestry monitoring, and mineral resource exploration [2; 3; 4; 5]. With the rapid development of unmanned aerial vehicles (UAVs) and small satellite technology, the demand for miniaturized spectrometers has become increasingly intense [6; 7; 8; 9; 10]. We have investigated some typical small commercial spectrometers suitable for UAVs. For example, the AFX10 of Specim Spectral Imaging Ltd. for visible and near-infrared (_VNIR_) bands, which has a spectral resolution of 5.5 nm, a field of view (_FOV_) of 38\({}^{\circ}\), a volume of \(131\times 152\times 202\) mm, and a weight of 2.5 kg. Hyspex's VNIR-1800 has a spectral resolution of 3.26 nm, an _FOV_ of 17\({}^{\circ}\), a volume of \(390\times 99\times 150\) mm, and a weight of 5 kg. A spectrometer with a wide _FOV_ has a larger imaging width, which could improve the efficiency of the push-broom. The spectral resolution of these commercial spectrometers is already very high, but the _FOV_ and miniaturization design still need to be improved to meet different application needs.  
The traditional spectrometer with grating or a prism has the disadvantage of spectral line curvature. The Offner spectrometers have been widely developed for their miniaturization with better image quality and spectral resolution, which consist of three spherical concentric elements (a convex grating and two concave mirrors) that simultaneously achieve aberration correction and spectral separation.  
The Offner spectrometer not only realizes a long slit length and a low \(f\)-number with a miniaturized structure, but also solves the problems of \"smile\" and \"keystone\" distortions [11; 12; 13; 14; 15; 16; 17].  
According to the application requirements, combined with the development status of the airborne push-broom spectrometers, a miniaturized Offner-type spectrometer with a wide \(FOV\), which can be integrated into a UAV platform or other aircraft, is designed in this paper. The objective lens and Offner spectrometer are designed in isolation before integration based on the calculation of the signal-to-noise ratio (SNR). A preliminary optical-mechanical design scheme is also discussed in this paper.
# Common Molecular Mechanisms and Biomarkers in Breast, Colon and Ovarian Cancer
## 3 Results
### Identification of Common Proteins  
The identification of common proteins among breast, colon, and ovarian cancer was performed using a Venn diagram to compare the protein lists presented in Supplementary Table S4. The results show 24 common proteins exclusively shared between breast cancer and ovarian cancer, 90 between breast cancer and colon cancer, and 37 common proteins between colon cancer and ovarian cancer. It is essential to emphasize that the diagram identified 27 proteins commonly shared among all three types of cancer (Figure 3). This finding suggests a significant relationship not only between breast and ovarian cancer, as described in the literature, but also with colon cancer. Table 2 shows the 27 common proteins identified for breast, colon, and ovarian cancer, with a brief description of their function. \begin{table}
\begin{tabular}{c c} \hline \hline
**Protein** & **Description—Functions** \\ \hline AKT1 & RAC-alpha serine/threonine-protein kinase—Metabolism; proliferation; cell survival; growth; angiogenesis \\ \hline ANXA5 & Annexin A5—Anticoagulant protein; indirect inhibitor of the thromboplastin–specific complex \\ \hline BAX & Apoptosis regulator BAX—Mitochondrial apoptotic process; activation of CASP3 \\ \hline BRCA2 & Breast cancer type 2 susceptibility protein—Double-strand break repair \\ \hline CASP3 & Caspase-3 subunit p12—Activation of caspases responsible for apoptosis \\ \hline CDH1 & Cadherin-1—Connecting cells; regulation of cell–cell adhesions; mobility and proliferation of epithelial cells; invasive suppressor role; promotes uptake of bacteria \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of common proteins identified in breast, colon, and ovarian cancer with description obtained from the STRING database (version 12.0). Figure 3: Comparison of the proteins identified in breast (483), colon (521), and ovarian (223) cancer through the Venn diagram. The diagram shows that the three types of tumors share 27 common proteins.
## 4 Results and Discussion
### Metrics Obtained from the Different Datasets Generated with Open Hardware
#### 4.1.2 Ualm2  
The UALM2 dataset (also referred to as DSUALM2) is a new high-resolution dataset designed for NILM. It was launched in February 2023 by the UAL. This dataset was generated using the OMPM device (developed by the University of Almeria, Almeria, Spain) [31], a low-cost and scalable open hardware solution. UALM2 is a multichannel dataset that includes measurements of the main aggregate consumption and five individual low-power profile household appliances: a fan, a deep fryer, an LED lamp, a laptop, and an incandescent lamp. The measurements are taken at a sampling frequency above 10 Hz, and the dataset includes variables such as the RMS voltage, RMS current, active power, frequency, and power factor. Unlike other datasets in the UALM series, UALM2 does not include measurements of the apparent or reactive power or harmonics, due to the characteristics of the PZEM-004 modules used by the OMPM. The creation and labeling process involved the development of a new converter (UALM2) specifically for this dataset, adapted from the DSUAL converter (based on the iAWE converter), to organize the files in a unified HDF5 format compatible with the NILMTK. This converter supports 13-digit timestamps, and each CSV file contains the header \"timestamp, VLN, A, W, F, PF\".  
UALM2 is a publicly accessible dataset for the research community [35], promoting transparency and reproducibility in NILM research. As it was launched in February 2023, the dataset is already available for download and use by researchers. No future timeline for access or hosting plan has been specified, as the dataset is actively available on the Zenodo platform.  
This dataset includes, in addition to the metadata, the electrical measurements (voltage, current, active power, and frequency) of several hours of aggregate consumption and of five low-consumption profile devices: a fan, fryer, LED lamp, laptop computer, incandescent lamp, and one fan, as shown in Figure 7.  
Figure 7: Schema of appliances used in UALM2 (Source: own elaboration).  
The results of applying eight algorithms are shown in Table 3. Classical models, such as the CO, Mean, and FHMM, demonstrate a solid performance in terms of accuracy, classification, and disaggregation, while maintaining a low computational time, making them suitable for applications that require speed and stability. In contrast, deep learning-based models, such as DAE, Seq2 Seq, RNN, Seq2 Point, and WindowGRU, exhibit higher complexity and longer runtimes, with mixed results. Among them, WindowGRU shows the most promising balance between performance metrics and computational cost. Therefore, while classical approaches remain reliable for general use, WindowGRU offers potential advantages in capturing complex patterns when sufficient computational resources are available.  
The OMPM's innovative design and the use of the CO algorithm demonstrate its potential for accurate energy monitoring and load disaggregation, making it a valuable tool for optimizing energy efficiency in various applications.