esa-sceva/satcom-chunk-collection huggingface dataset:
# Boosting the Learning for Ranking Patterns
## 4 AHP-Based Learning Approach for Ranking Patterns
### Problem Statement: Learning Pattern Rankings  
In this section, we demonstrate that learning a ranking function that incorporates a set of interestingness measures to approximate user-specific preferences can be formulated as a multi-criteria decision-making problem. In this context, the criteria correspond to the interestingness measures.  
The objective of this task is to learn a ranking function for patterns based on a sample of ordered patterns. To accomplish this, we adopt the approach of ordered feedback, where users are asked to provide a total order over a small set of patterns based on their subjective interestingness. This approach is inspired by previous research [32].  
Let \(\triangleright\) be a binary preference relation between patterns. A ranking function based on \(\triangleright\), denoted by \(r_{\triangleright}(.)\), takes as input a subset of patterns \(\mathcal{P}\subseteq\mathcal{L}\) and returns as output a permutation \(S=\langle P_{r_{1}},P_{r_{2}},\ldots,P_{r_{n}}\rangle\) of \(\mathcal{P}\), such that \(|\mathcal{P}|=|S|\) and \(\forall i,j\in[1,n],i<j\), \(P_{r_{i}}\triangleright P_{r_{j}}\).  
We aim to learn the user ranking function denoted by \(r_{\triangleright_{u}}\), which is based on the user preference relation \(\triangleright_{u}\). To achieve this, we use a set of interestingness measures \(\mathcal{M}=\{M_{1},\ldots M_{m}\}\) and their corresponding ranking functions \(r_{r_{M_{1}}},\ldots,r_{r_{M_{m}}}\). The rank of pattern \(P_{j}\in\mathcal{P}\) with respect to the interestingness measure \(M_{i}\) is denoted by \(rank_{M_{i}}(P_{j})\). Here, \((P_{k}\triangleright_{M_{i}}P_{l})\) means that \(M_{i}(P_{k})\geq M_{i}(P_{l})\).  
To frame the ranking problem as a learning problem, we need to find the weights \(w_{i}\) of a linear aggregation function over the measures from a set of ranked patterns that maximizes the correlation with the user ranking function \(r_{\triangleright_{u}}\). Specifically, we aim to find the coefficients of the following function by solving a multi-criteria optimization problem:  
\[f_{w}(P)=\sum_{i=1}^{m}w_{i}\cdot M_{i}(P) \tag{4}\]  
Our approach leverages user feedback to perform pair-wise comparisons between measures and estimate their relative importance based on individual measure scores. The pair-wise comparison results are then aggregated into a matrix, which is used by the analytic hierarchy process (AHP) to compute the weights \(w_{i}\) for each measure.
## 2 Materials and Methods
### Adsorption Experiment  
The adsorption performances of red mud and the prepared adsorbents were investigated using Zn\({}^{2+}\) as target. Stock solutions with Zn\({}^{2+}\) concentrations in the range of 0-120 mg/L were prepared, and adjusted to pH 4 by adding 5% NaOH and 5% HCl. 20 mL stock solution was mixed with 0.01 g adsorbent in a series of triangular flasks. The flasks were placed in an incubator (THZ-98C, Shanghai Yiheng) and then shaken at shaken at 150 rpm for 12 h. After adsorption, the obtained magnetic adsorbents P-200 and P-270, respectively, were magnetically separated, while red mud and P-120 were centrifuged at 5500 rpm for 5 min. The concentration of Zn\({}^{2+}\) in the supernatant was measured by an inductively coupled plasma emission spectrometer (ICP-OES, Avio 200, PerkinElmer, USA).  
The adsorbed amount (\(q_{\mathrm{e1}}\), mg/g) of Zn\({}^{2+}\) was calculated via Equation (1):  
\[q_{\mathrm{e1}}=\frac{(C_{\mathrm{1}}-C_{\mathrm{e1}})\times V}{m_{\mathrm{1}}} \tag{1}\]  
where \(C_{\mathrm{1}}\) and \(C_{\mathrm{e1}}\) represent the initial Zn\({}^{2+}\) concentration and the equilibrium concentration (mg/L) after adsorption, \(V\) represents the solution volume (L), and \(m_{\mathrm{1}}\) represents the adsorbent weight (g).  
The Na\({}^{+}\) concentration in the supernatant was measured using an ion chromatograph (883 Basic, Metrohm, Switzerland) coupled with a C4 cation column (Metrohm, Switzerland). The released amount (\(q_{\mathrm{e2}}\), mg/g) of Na\({}^{+}\) was calculated via Equation (2):  
\[q_{\mathrm{e2}}=\frac{(C_{\mathrm{e2}}-C_{\mathrm{2}})\times V}{m_{\mathrm{1}}} \tag{2}\]  
where \(C_{\mathrm{2}}\) and \(C_{\mathrm{e2}}\) represent the initial concentration of Na\({}^{+}\) in the solution and the equilibrium concentration after adsorption (mg/L).
## 3 Results
Due to peculiarities of the ZnS deposition technique, at large thicknesses of ZnS, the inhomogeneity is higher, so the discrepancy with the theory can be more noticeable. Figure 4: **(a)** Real part of the SPP refractive index \(n_{\rm s}\) (without unity) and (b) imaginary part (SPP absorption index) \(\kappa_{\rm s}\) vs. thickness \(d\) of the ZnS layer on the gold surface: experiment (blue circles), approximation curves (red line), numerical solution of Equation (5) for \(\varepsilon_{\rm m}=-7000+i\):3000 (green squares), and calculation by the Drude model for \(\varepsilon_{\rm m}=-104,700+i\):317,180) (black line). Determination of the Permittivity of the Metal Surface from the SPP Characteristics on a Set of Samples with a Dielectric Layer of Various Thicknesses  
In this section, we consider an approximation method for finding the metal permittivity, which uses the \(\widetilde{n}_{\mathrm{s}}\) values found for a set of samples with ZnS coatings of various thicknesses. To determine the effective permittivity \(\varepsilon_{\mathrm{m}}\) of a metal surface guiding the SPPs and containing a thin-layer coating of thickness \(d\) with permittivity \(\varepsilon_{\mathrm{d}}\) from the found complex refractive index \(\widetilde{n}_{\mathrm{s}}\) of the SPPs, it is necessary to solve (with respect to \(\varepsilon_{\mathrm{m}}\)) the SPP dispersion equation for the three-layer structure \"metal-dielectric layer-environment\" [54]:  
\[\tanh\left(k_{0}\cdot d\cdot\sqrt{\widetilde{n}_{\mathrm{s}}{}^{2}-\varepsilon_ {\mathrm{d}}}\right)=-\frac{\sqrt{\widetilde{n}_{\mathrm{s}}{}^{2}-\varepsilon _{\mathrm{d}}}\times\left(\frac{\sqrt{\widetilde{n}_{\mathrm{s}}{}^{2}- \varepsilon_{\mathrm{d}}}}{\varepsilon_{\mathrm{d}}}+\frac{\sqrt{\widetilde{n }_{\mathrm{s}}{}^{2}-\varepsilon_{\mathrm{m}}}}{\varepsilon_{\mathrm{m}}} \right)}{\varepsilon_{\mathrm{d}}\cdot\left(\frac{\sqrt{\widetilde{n}_{ \mathrm{s}}{}^{2}-\varepsilon_{\mathrm{d}}}\cdot\sqrt{\widetilde{n}_{\mathrm{ s}}{}^{2}-\varepsilon_{\mathrm{m}}}}{\varepsilon_{\mathrm{d}}\cdot\left(\frac{ \sqrt{\widetilde{n}_{\mathrm{s}}{}^{2}-\varepsilon_{\mathrm{d}}}}{\varepsilon _{\mathrm{d}}{}^{2}}\right)}+\frac{\widetilde{n}_{\mathrm{s}}{}^{2}-\varepsilon _{\mathrm{d}}}{\varepsilon_{\mathrm{d}}{}^{2}}\right)} \tag{5}\]  
where \(\varepsilon_{\mathrm{a}}\) is the permittivity of the environment. Transcendental Equation (5) can be solved numerically, although the solution is very sensitive to small variations in the \(\widetilde{n}_{\mathrm{s}}\) within the measurement error, which can result in a large error in the determination of the metal permittivity \(\varepsilon_{\mathrm{m}}\). Therefore, we have developed an \(\varepsilon_{\mathrm{m}}\) determination method based on the approximation of the \((n_{\mathrm{s}}-1)(d)\) and \(\kappa_{\mathrm{s}}(d)\) dependences measured for a series of samples with different ZnS coating thicknesses. In the THz range \(\varepsilon_{\mathrm{m}}>>\varepsilon_{\mathrm{d}}\), \(\varepsilon_{\mathrm{a}}\), the \(k_{0}d<<1/\sqrt{\varepsilon_{\mathrm{m}}}\) condition is met at \(d<<\lambda\).
# A High-Resolution Magnetic Field Imaging System Based on the Unpackaged Hall Element Array
## 4 Discussions  
In recent years, arrays of packaged magnetic sensing sensors have been commonly used to image magnetic fields. However, the package enclosure increases the size of the sensor, which makes it difficult to reduce the spacing between adjacent sensors, resulting in low spatial resolution. The minimum detection distance between the packaged magnetic sensing sensor and the sample surface is also affected by the thickness of the packaged enclosure, making it more difficult to detect the magnetic field on the sample surface at close range. In addition, the position of the packaged sensor may be shifted during soldering to the PCB, resulting in a positional error between the actual and ideal detection points for the magnetic field. In order to overcome the above problems, this paper utilizes the unpackaged Hall element array to design a magnetic field imaging system. The Hall elements are not limited by the package enclosure, which enables the spacing of adjacent  
Figure 16: Imaging a pair of magnetized metal scissors: (**a**) this metal scissor was magnetized by the excitation field generated in the Helmholtz coil; (**b**) image of the metal scissors after detection using the high-resolution magnetic field imaging system.  
Figure 15: Detecting and imaging the magnet array from a non-genuine wireless charging coil: (**a**) a magnet array; (**b**) magnetic field image of this magnet array; (**c**) images generated after special movement via X–Y axis motorized rail.  
Hall elements up to 255 \(\upmu\)m. The size of the Hall elements can be further decreased by the photolithography process, allowing for smaller spacing between adjacent Hall elements.  
The magnetic field imaging system designed in this paper is capable of imaging samples of magnetic materials, which helps us to study the magnetic characteristics of the samples. This system also has the ability to measure the wireless charging coils inside an Apple phone, which makes it potentially valuable for quality inspection applications. In addition, the magnetic field imaging system also provides non-destructive testing and identification of metallic materials, which has potential value in several fields such as metal identification, biomedicine, mark detection, and security.
# Research on Visual Positioning of a Roadheader and Construction of an Environment Map
## 5 Conclusions  
In this paper, according to the development requirements of intelligent and robotization of fully mechanized excavation faces, the method of estimating roadheader poses and constructing navigation maps by using vision and laser SLAM technology is proposed. The main conclusions are as follows:  
1. The positioning method based on visual SLAM technology is proposed, the environment data are collected by an airborne RGB-D camera, and the RANSAC+ICP model is constructed to solve and realize the autonomous measurement of pose by establishing and solving the optimization model of pose graph with closed-loop constraints. The global consistent pose of the roadheader can be obtained, which can effectively solve the difficult positioning and orientation problem of roadheaders in a restricted space.
2. A series of open-source algorithms, such as Gmapping, Cartographer, Karto, Hector, and RTAB-MAP, are applied to construct simulated roadway maps, including 2D occupied grid, 3D point cloud, and octree, based on a static map created by SLAM. The cost map for roadheader navigation is further constructed by setting parameters, such as expansion radius of obstacles and the updating frequency of map. Furthermore, obstacle avoidance tests based on the Dijskra+TEB algorithm show that a combination of octree map and cost map can support global path planning and local dynamic obstacle avoidance.
3. Based on the open-source library RTAB-MAP framework, by fusing laser and vision data, a composite map composed of a dense 3D point cloud and a 2D occupancy grid is constructed, which could, not only be used for plane navigation of a roadheader, but also provide support for 3D reconstruction of an environment.  
Conceptualization, G.Z. and W.Z.; methodology, W.Z.; software, G.Z. and W.Z.; validation, W.Z., G.Z., and Z.Y.; formal analysis, G.Z., Z.Y., and T.P.; investigation, W.Z.; data curation, W.Z. and R.C.; writing--original draft preparation, W.Z.; writing--review and editing, W.Z. and G.Z.; visualization, W.Z. and R.C.; supervision, G.Z., Z.Y., and T.P.; funding acquisition, G.Z. and T.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Fundamental Research Funds for the Central Universities (Grant No. 2021 YJSJD10); the National Key Research and Development Program of China (Grant No. 2017 YFC0804307); Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (Grant No. MJUKF-IPIC201905).  
Not applicable.  
The authors declare no conflict of interest.
# Nascent Rice Husk as an Adsorbent for Removing Cationic Dyes from Textile Wastewater
## 2 Materials and Methods
### Batch Adsorption Experiments  
The RH was employed to elucidate the removal of MB and CV under batch conditions. The concentrations for MB and CV were set to be 300 mg/L and 400 mg/L, respectively, and they were made from MB and CV stock solutions (1000 mg/L). Batch adsorption tests were carried out by reacting 0.5 g of the RH with 30 mL dye solutions (MB and CV) in a 50 mL polypropylene tube using a quivering incubator with a stirring rate of 100 rpm and temperature of 25 \({}^{\circ}\)C. Kinetic experiments were conducted by differing the reaction time (0.5-24 h) under the concentrations of 300 mg/L and 400 mg/L for MB and CV, respectively. Equilibrium adsorption analyses were conducted using diverse initial concentrations (10-500 mg/L) for MB and CV, respectively; the samples were tested after 24 h. Thermodynamic analyses were carried out by mixing the RH (0.5 g) with 30 mL of 300 mg/L (MB) and 400 mg/L (CV) solutions at reaction temperatures of 15 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, and 35 \({}^{\circ}\)C for 24 h. The influence of solution pH, at different pH values, on MB and CV adsorption was explored. The pH of each solution was altered from 4 to 10 using 0.1 M HCl and 0.1 M NaOH; the adjusted pH was then measured with a pH meter (9107 BN, Thermo Scientific, USA). The influence of adsorbent dosage on MB and CV adsorption by RH was investigated by reacting various doses of the RH (0.05-0.5 g for MB and 0.1-1.0 g for CV) with 30 mL of each dye at initial concentrations of 300 mg/L (MB) and 400 mg/L (CV). The none-adsorbed concentrations of MB and CV in aqueous solution were measured with a UV-visible spectrophotometer (Optizen POP QX, Mecasys Co., Korea) at wavelengths of 668 nm and 590 nm, respectively. All the experiments were performed three times.  
Kinetics adsorption results were analyzed using pseudo-first-order (PFO), pseudo-second-order (PSO) and intra-particle diffusion (IPD) models [50; 51; 52]. Equilibrium isothermal adsorption data were modeled with the Langmuir and Freundlich models [50; 51]. Thermodynamic parameters were explored using the equations published in literatures [50; 51].
## 4 Problem Solution
The (bounded) transform domain Green's function can be written as  
\[\bar{G}(\kappa,\sigma,z,s)=\exp[-s\gamma(\kappa,\sigma)z]/s\gamma(\kappa, \sigma), \tag{19}\]  
for \(z>0\), where \(\gamma=\gamma(\kappa,\sigma)=(1/c_{0}^{2}-\kappa^{2}-\sigma^{2})^{1/2}\) with \(\mathrm{Re}(\gamma)\geq 0\). Equation (19) can be subsequently used in the transform domain expressions (16) and (17) to determine the testing fields on the left-hand side of the reciprocity relation (13). The latter relation is subsequently solved in the TD. To that end, we first expand the unknown pulses \(k_{x,y}(t)\) (see Equations (5) and (6)) in a piecewise linear manner:  
\[k_{x}(t) =\sum_{k=1}^{M}v_{kx}\Lambda_{k}(t), \tag{20}\] \[k_{y}(t) =\sum_{k=1}^{M}v_{ky}\Lambda_{k}(t), \tag{21}\]  
where \(v_{kx}\) and \(v_{ky}\) are (yet unknown) coefficients and \(\Lambda_{k}(t)\) represents the temporal triangular function:  
\[\Lambda_{k}(t)= \begin{cases}1+(t-t_{k})/\Delta_{t},&\text{for }\{t_{k-1}\leq t \leq t_{k}\}\\ 1-(t-t_{k})/\Delta_{t},&\text{for }\{t_{k}\leq t\leq t_{k+1}\}\\ 0,&\text{elsewhere}\end{cases}  \tag{22}\]  
along the discretized time axis \(\{t_{k}=k\Delta_{t};\Delta_{t}>0,k=1,2,\cdots,M\}\). Finally, upon substituting the transform domain images of Equations (5) and (6) with (20) and (21), (9) and (10) in the reciprocity relation (13), we end up with a system of equations in the \(s\) domain. Its constituent can be transformed to the original TD analytically via the CdH technique, which yields the following system of discrete time-convolution equations:  
\[\sum_{k=1}^{m}\big{(}\underline{\boldsymbol{\mathcal{Y}}}_{m-k+1}-2\underline{ \boldsymbol{\mathcal{Y}}}_{m-k}+\underline{\boldsymbol{\mathcal{Y}}}_{m-k-1} \big{)}\,\boldsymbol{\cdot}\,\boldsymbol{V}_{k}=\boldsymbol{I}_{m}, \tag{23}\]  
where \(\underline{\boldsymbol{\mathcal{Y}}}_{k}=\underline{\boldsymbol{\mathcal{Y}}}(t _{k})\) represents a \([2\times 2]\) TD admittance array that is specified in Appendix A. Furthermore, \(\boldsymbol{V}_{k}=[v_{kx},\,v_{ky}]^{T}\) is the \([2\times 1]\) array of the unknown coefficients (see Equations (20) and (21)), and finally, \(\boldsymbol{I}_{k}=\boldsymbol{I}(t_{k})=[l_{x}(t_{k}),\,\,\,l_{y}(t_{k})]^{T}\) denotes a TD excitation \([2\times 1]\) array, which, as a matter of fact, corresponds to the interaction term on the right-hand side of the TD reciprocity relation (4). Its specific form will be given in Section 5. Once the admittance and excitation arrays are specified, Equation (23) can be readily solved via the marching-on-in-time technique. This way leads to the following step-by-step updating scheme:  
\[\mathbf{V}_{m}=\mathbf{\underline{\chi}}_{1}^{-1}\mathbf{\cdot}\left[\mathbf{I}_{m}-\sum_{k=1}^ {m-1}\left(\mathbf{\underline{\chi}}_{m-k+1}-2\mathbf{\underline{\chi}}_{m-k}+\mathbf{ \underline{\chi}}_{m-k-1}\right)\mathbf{\cdot}\mathbf{V}_{k}\right], \tag{24}\]  
which yields the desired coefficients for all \(m=\{1,2,\cdots,M\}\). Illustrative numerical examples validating the TD solution (24) are presented in the following section.
# Retrieval and Comparison of Multi-Satellite Polar Ozone Data from the EMI Series Instruments
## 4 Results and Discussions
### Comparison between Multi-Satellite Total Ozone Columns
As shown in Figure 4, the monthly EMI series mean relative difference of the overall means were 0.16%, 2.38%, and 2.58%, respectively. As shown in Figure 3, the average relative deviations between the EMI series TOCs and TROPOMI TOCs for the latitudes of 60-90\({}^{\circ}\)S were \(-\)1.78%, 3.38%, and 3.42%, respectively, with average standard deviations of 1.98%, 2.98%, and 3.58%, respectively. The average relative difference in the Antarctic was negative, whereas it was positive in the tropical regions. This may have occurred because of the difference between the results generated by the linearized vector discrete ordinate radiation transfer (VLIDORT) code and the scientific radiation transfer model constructed using the EMI TOCs. However, at high TOC values, the EMI-DQ01 TOCs appeared to be slightly higher than the TROPOMI TOC values. The difference between the EMI-GF5(02) and TROPOMI datasets was relatively large because the overpass time of EMI-GF5(02) was in the morning, while that of TROPOMI was in the afternoon. This demonstrates that polar ozone TOC values with different overpass times are beneficial for analyzing the intraday variation in ozone and improving the temporal resolution of satellite-based observations. The linear fits between the daily global TOCs of the EMI-GF5(01A), EMI-GF5(02), and EMI-DQ01 datasets and those of the TROPOMI dataset for 20 November 2023 are shown in Figure 5. And Figure A1 shows the daily spatial distributions of the EMI series and TROPOMI datasets for Antarctica (60\({}^{\circ}\)S=90\({}^{\circ}\)S latitude belts). The regression analysis shown in Figure 5d demonstrates that the EMI-GF5(01A) and EMI-DQ01 TOCs were highly correlated, with a Pearson's correlation coefficient (R) of 0.98. Figure 5: **(a)** Linear fit between global EMI-GF5(01A) and TROPOMI TOCs for 30 November 2023; **(b)** linear fit between global EMI-GF5(02) and TROPOMI TOCs for 30 November 2023; **(c)** linear fit between global EMI-DQ01 and TROPOMI TOCs for 30 November 2023; **(d)** fit between global EMI-GF5(01A) and EMI-DQ01 TOCs for 30 November 2023.
## 6 Evaluation
### Ciphertext Inference
#### 6.2.1 Analysis and Comparison of Ciphertext Inference Results  
In most cases, the cloud would perform privacy classification on multiple images from the user, so batch inference can be performed on the images. However, due to memory limitations on the server we use, we cannot set the batch size too large. At the same time, it cannot be too small either, as this would increase the amortized runtime. Taking these constraints into account, we set the inference batchsize for CNN-6, AlexNet, and ResNet-20 to 64, 8, and 16 respectively. Table 5 shows the specific time consumption of the three model reasoning processes using an RNS-CKKS encryption scheme. We chose HEAAN as the back-end encryption library and only encoded the model parameters without encrypting them. For time consumption, the total reasoning times of our CNN-6, ALexNet, and ResNet-20 are 142.62 s, 244.90 s, and 1027.96 s, respectively, and the corresponding amortized running times (time per image) are 2.23 s, 30.61 s, and 64.25 s, respectively. In practical applications, we can run our model on GPU to further reduce inference latency [36]. Regarding memory usage,  
the total reasoning memory usages of the three are about 23 G, 81 G, and 96 G respectively. As can be seen from the table, the AlexNet model parameter encoding stage has the largest memory occupation, which is caused by the huge number of parameters in its full connection layer. For the ResNet-20 model, the polynomial degree corresponding to CIFAR-10 image ciphertext is \(N=2^{16}\), and bootstrapping refresh noise is adopted, so the corresponding context memory is relatively large. We also evaluated the memory consumption of inferencing only one image at a time, with the three models consuming 6.91 G, 24.58 G, and 34.63 G of memory, respectively. We compared our results with existing solutions, and Table 6 presents the results of evaluating DNN using homomorphic encryption on different datasets in previous work. Considering evaluation metrics such as accuracy, inference time, and security, our solution outperforms all other solutions. For example, our MNIST CNN-6 has the highest inference accuracy and the lowest amortized time, achieving a standard 128-bit security. Our SKIN AlexNet has an amortized time of 30.61 s, which is 1.49 times faster than AlexNet using Apprx-ReLU [21], and has higher accuracy. Finally, our CIFAR10 ResNet-20 is 348.20 times faster, 181.88 times faster, and 165.01 times faster than FCryptoNets [37], CryptoDL [12], and PACN [14], respectively, with higher security. Although the amortized time of LDCN [38] is also low, it is an improvement of PACN by its authors, mainly focusing on improving the speed of convolution and bootstrapping. In contrast, we mainly focus on reducing the inference latency of the activation layer, and our ResNet-20 trained using the proposed method can also adopt the convolution and bootstrapping strategy of LDCN [38] to further improve the inference speed. We note that the results reported in Table 6 were obtained on different machines. The MNIST CNN-4 of CryptoNets [9] was run on a machine with an Intel Xeon E5-1620 CPU at