esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Granularity User Anomalous Behavior Detection
## 4 Methodology
### Data Fusion and Preprocessing
#### 4.2.3 Behavior Data Partition  
As shown in the \"Data fusion and partition\" module in Figure 2, in order to detect coarse-grained and fine-grained behavior anomalies, the encoded user behavior data need to be divided into two ways: by time and by user.  
Dividing data by time refers to the processing of data in the \"extrapolation\" testing mode. We extract time characteristics such as the date and week of the behavior based on the \"Date\" field of each behavior sample, and set a ratio for partitioning based on the overall time range of the dataset. For example, if the overall time range covers n weeks, we extract the behavior data from the first m weeks as the training set and the remaining (n-m) weeks as the testing set. The reason for processing the data in this way is that we have observed many studies using \"interpolation\" to randomly partition the data without considering the order in which behavioral actions occur. This will result in the model using behavior data from the future for training, while testing on behavior data from the past, which is inconsistent with the requirements of actual scenarios. Therefore, we optimize and use the \"extrapolation\" mode for model construction and testing, that is, training with historical data and testing with future data.  
Dividing data by user refers to dividing user behavior data into subsets based on user identity. For example, if there is a user named [PERSON], we extract all of their behavior records from the whole dataset. Furthermore, of course, the behavior data of each user also need to be divided according to the time range. The purpose of constructing a separate behavior dataset for each user is to consider the personalized behavioral characteristics of the user and perform fine-grained anomaly detection.
# Significance of Camera Pixel Error in the Calibration Process of a Robotic Vision System
## 3 Results and Discussion
### Error Sensitivity Analysis of Theoretical Setup
Note that \(\delta_{B}=1\) resembles an area of \(3\times 3\) pixel block, which is coming out to be 194.6 \(\upmu\)m\({}^{2}\), where each pixel size is 4.65 \(\upmu\)m (refer Table 1). Hence, it can be asserted that introducing angular errors in the robot joints impacted the hand-eye calibration process, but the magnitude of error is not significantly large. On the other hand, inducing errors in image pixel estimation (such as in the case of low resolution camera setup) has significantly affected the hand-eye calibration process. Nevertheless, such error can be significantly reduced by increasing the number of image captures at different robot positions, as evidenced by the differences between the mesh plot for \(\eta=4\) and the solid surface for \(\eta=5\) in Figure 4 for all three methods. Note that when the errors in both matrices \(A\) and \(B\) are zero, then all the three hand-eye calibration algorithms are able to estimate the correct value of \(X\). \[P_{j}=(P_{i}X^{\prime}Q_{i})(X^{\prime}Q_{j})^{-1}. \tag{23}\]0 Error norms associated with rotation matrix and translation vector estimated using: (**a,b**) [PERSON]–Dornaika method; (**c,d**) [PERSON]–Lenz method; (**e,f**) Andreff et al. method. The mesh plot represents error values for \(\eta=4\), while solid surface plot represents error values for \(\eta=5\).
# A Review on the Use of Microalgae for Sustainable Aquaculture
## 3 Microalgae-Assisted Aquaculture
### Microalgae-Based Wastewater Remediation
#### 3.2.2 Properties of Aquaculture Wastewater  
[PERSON] (1985) reported that the production of one kilogram of live catfish releases about 51 g of nitrogen, 7.2 g of phosphorus and 1100 g of COD into the water body as organic wastes, which accumulates in the form of soluble nutrients or insoluble sludge [4]. According to the data in Table 1, aquaculture wastewater should be a good medium for microalgae cultivation and value-added biomass production. First, aquaculture wastewater is rich in essential macro-elements, such as nitrogen, phosphorus, and carbon, for microalgae growth. Since nutrients of aquaculture wastes are impacted by stocking density, water replacement frequency, feed addition, and some other factors, different nutrients profiles were observed (Table 1). In most cases, supernatant of aquaculture wastewater contains lower concentrations of nutrients than sludge obtained from the bottom of aquaculture system. Second, different from industrial effluent, municipal waste stream and mining wastewater, aquaculture wastewater contain much less toxic components, such as heavy metals [39]. On one hand, the stream without toxic components may create a good environment for microalgae growth. On the other hand, harvested microalgae without contamination by toxic components have the potential to be used for aquaculture feed production. Third, concentrations of NH\({}_{3}\)-N and COD in aquaculture wastewater are not too high to threaten the survival of microalgae. As shown in Table 1, the highest concentration of NH\({}_{3}\)-N was about 100 mg/L while ammonium toxicity for most Chlorophyceae occurs when the concentration of NH\({}_{3}\)-N reaches 300 mg/L [40].
# Calibration of Magnetometers with GNSS Receivers and Magnetometer-Aided GNSS Ambiguity Fixing
## 3 Calibration of Magnetic Flux Sensors
### Attitude Determination with Three GNSS Receivers
#### 3.2.2 Joint Estimation of Baselines, Pseudorange Multipaths and Ambiguities  
The double difference carrier phase and pseudorange measurements from both baselines are stacked in a common measurement vector:  
\[z_{n}:=\left(\begin{array}{c}\lambda\varphi_{12}(t_{n})\\ \lambda\varphi_{13}(t_{n})\\ \rho_{12}(t_{n})\\ \rho_{13}(t_{n})\end{array}\right) = \underbrace{\left(\begin{array}{ccccc}H&0&\lambda\cdot 1&0&0&0\\ 0&H&0&\lambda\cdot 1&0&0\\ H&0&0&0&1&0\\ 0&H&0&0&0&1\end{array}\right)}_{H_{n}}\left(\begin{array}{c}\vec{b}_{12}^{ \mathrm{n}}(t_{n})\\ \vec{b}_{13}^{\mathrm{n}}(t_{n})\\ N_{12}\\ N_{13}\\ \Delta\rho_{\mathrm{MP}_{12}}(t_{n})\\ \Delta\rho_{\mathrm{MP}_{13}}(t_{n})\end{array}\right) \tag{10}\] \[+\left(\begin{array}{ccccc}1&-1&0&0&0&0\\ 1&0&-1&0&0&0\\ 0&0&0&1&-1&0\\ 0&0&0&1&0&-1\end{array}\right)\left(\begin{array}{c}\varepsilon_{\lambda \varphi_{1}}(t_{n})\\ \varepsilon_{\lambda\varphi_{2}}(t_{n})\\ \varepsilon_{\lambda\varphi_{3}}(t_{n})\\ \eta_{\rho_{1}}(t_{n})\\ \eta_{\rho_{2}}(t_{n})\\ \eta_{\rho_{3}}(t_{n})\end{array}\right),\]  
with  
\[\lambda\varphi_{1r}=\left(\begin{array}{c}\lambda\varphi_{1r}^{1l}\\ \vdots\\ \lambda\varphi_{1r}^{Kl}\end{array}\right),\quad\rho_{1r}=\left(\begin{array} []{c}\rho_{1r}^{1l}\\ \vdots\\ \rho_{1r}^{Kl}\end{array}\right)\quad\text{and}\quad H=\left(\begin{array}{c }(\vec{e}^{1l})^{\mathrm{T}}\\ \vdots\\ (\vec{e}^{Kl})^{\mathrm{T}}\end{array}\right)\cdot R_{\mathrm{n}}^{\mathrm{e} }(\lambda_{1},\varphi_{1}). \tag{11}\]  
The number of unknowns of Equation (10) exceeds the number of measurements of one epoch, but the ambiguities are constant over time. Thus, measurements from multiple epochs are required to estimate the baselines, ambiguities and pseudorange multipaths. We introduce a state space/movement model to describe the temporal behavior of the state parameters, i.e.,  
\[x_{n} = \left((\vec{b}_{12}^{\mathrm{n}}(t_{n}))^{\mathrm{T}},(\vec{b}_{ 13}^{\mathrm{n}}(t_{n}))^{\mathrm{T}},N_{12}^{\mathrm{T}},N_{13}^{\mathrm{T}},(\Delta\rho_{\mathrm{MP}_{12}}(t_{n}))^{\mathrm{T}},(\Delta\rho_{\mathrm{MP}_ {13}}(t_{n}))^{\mathrm{T}}\right)^{\mathrm{T}} \tag{12}\] \[= \Phi_{n}x_{n-1}+\eta_{x_{n}},\]  
with state transition matrix \(\Phi_{n}\) and process noise \(\eta_{x_{n}}\). A Kalman filter (see [PERSON] and [PERSON] [8]) is used to estimate the state vector. We first disregard the integer property of ambiguities to obtain a float solution. The Kalman filter includes the state prediction  
\[\hat{x}_{n}^{-} = \[PERSON]_{n}\hat{x}_{n-1}^{+}\] \[\Sigma_{\hat{x}_{n}^{-}} = \[PERSON]_{n}\Sigma_{\hat{x}_{n-1}^{+}}(\Phi_{n})^{\mathrm{T}}+\Sigma_ {x_{n}}, \tag{13}\]and the subsequent state update  
\[\hat{x}_{n}^{+} = \hat{x}_{n}^{-}+K_{n}(z_{n}-H_{n}\hat{x}_{n}^{-})\] \[\Sigma_{\hat{x}_{n}^{+}} = (1-K_{n}H_{n})\Sigma_{\hat{x}_{n}^{-}}(1-K_{n}H_{n})^{\mathrm{T}}+ K_{n}(\Sigma_{z_{n}})K_{n}^{\mathrm{T}}. \tag{14}\]
## 2 Methodology
### Window Processing  
The radar data acquired by the IBIS-S system (i.e., the ground-based interferometric radar) were the frequency domain sampling data of the radar signal echo. The frequency domain data must be transformed into the spatial domain through discrete inverse Fourier transform, that is, focusing to extract the deformation information of each resolution range [38]. Since many steel frame structures are found around the monitored building. The echo signal of these resolution units is generally strong, and their side lobes can interfere with the signal of adjacent resolution units, which may distort their deformation signals. Therefore, the Hann window function was applied to process the radar signal before focusing the radar monitoring data in the range direction to eliminate the sidelobe effect.
# Q-Pandora Unboxed: Characterizing Resilience of Quantum Error Correction Codes Under Biased Noise
## 4 Examining Noise in Quantum Computing  
### Background  
In quantum computing, 'noise' refers to unwanted disturbances that can cause errors in qubits. There are mainly two error types: bit-flip (X error) and phase-flip (Z error) [41]. Bit-flip errors invert a qubit's state, while phase-flip errors change the sign of the basis state \(|1\rangle\). For a state \(|\psi\rangle=\alpha|0\rangle+\beta|1\rangle\), a bit-flip results in \(X|\psi\rangle=\alpha|1\rangle+\beta|0\rangle\), and a phase-flip gives \(Z|\psi\rangle=\alpha|0\rangle-\beta|1\rangle\). These errors can combine, leading to complex disturbances.
# A Two-Stage Local Path Planning Algorithm Based on Sampling and Optimization Methods
## 3 Proposed Method
### Optimization Stage
#### 3.3.2 Obstacle Constraint  
In the planning coordinate system, the expression of obstacle information can be simplified. This is because the method only needs to consider whether the local path points are between the obstacles and the global path. First, all laser points between the preliminary path and the global path are retained. These laser points represent the relevant obstacles in the optimization process. Next, only the laser points with the maximum longitudinal distance under the same lateral distance are retained. These laser points can describe the obstacle information and are denoted as obstacle laser points \((Os_{i},Od_{i})\). These laser points can be used to determine whether the local path contacts obstacles. The penalty function of the obstacle constraint can be expressed as  
\[f_{obs}=\begin{cases}\lvert Od_{i}-d_{j}\rvert+\epsilon,&s_{j}-\epsilon<ob_{s _{i}}<s_{j}+\epsilon\\ 0,&else\end{cases} \tag{13}\]  
where \(\epsilon\) represents the expansion parameters of obstacles.
## 2 Methodology
### NuSTAR Data Analysis  
_NuSTAR_([PERSON] et al. (2018)) is the first orbiting telescope capable of focusing very high-energy X-rays. It was launched on 13 June 2012 and operates in the 3-79 keV energy range, surpassing the 10 keV limit of earlier focusing X-ray telescopes. _NuSTAR_ comprises two co-aligned hard X-ray grazing incidence telescopes (Wolter I design) that focus X-rays onto two separate solid-state focal-plane detectors with a focal length of approximately 10 m. The two focal-plane modules (FPMA and FPMB) are designed to be identical, enabling their combined data to enhance the signal.  
The archival data from both FPMA and FPMB modules were processed using the _NuSTAR_ Data Analysis Software (NuSTARDAS v2.1.4) with calibration files from CALDB (v20240325). This software is included in the HEASoft (v6.34) package distributed by the High Energy Astrophysics Science Archive Research Center (HEASARC). The nupipeline command was employed to convert level-1 event files into level-2 event files, applying standard data-cleaning procedures. High-level products, including barycenter-corrected source event files, skymap images, light curves, and spectra, were generated using the nuproducts command. For this study, skymap images, created in the broad energy band of 3-79 keV, were utilized.
# Dual-Indicator Micro-Electro-Mechanical System Monitoring Method for Rock Instability Early Warning
## 5 Conclusions  
Through MEMS sensing technology, the vibrational characteristic differences between bedrock and unstable rock masses were captured, yielding dynamic indices that reflect slope stability and establishing a novel methodology for collapse monitoring. Guided by the generalized spring-proton model of unstable rock masses, two monitoring parameters--NF and RMS-VAR--were proposed and validated through laboratory collapse simulations. The main conclusions are as follows:  
1. The critical structural plane length governs stability thresholds, with NF and RMS-VAR serving as effective proxies for fracture evolution. The experimental data demonstrate an inverse correlation between bonding plane length and NF, while RMS-VAR exhibit positive scaling with crack propagation rates.  
2. MEMS sensing can accurately obtain the NF of unstable rock masses and the RMS-VAR, and can reflect the stable state of unstable rock masses in real time. The NF of unstable rock masses shows a 4:3 proportionality ratio to the limit equilibrium safety factor, and the change amplitude of RMS-VAR is three times that of the tilt angle.
3. Field implementation requires the integration of MEMS edge nodes, cloud-based analytics, and adaptive signal processing algorithms. This method provides a reliable technical guideline for rock mass monitoring in practical engineering and proposes a new concept for similar geological disaster monitoring and early warning.  
Conceptualization, M.X. and C.C.; methodology, C.C.; software, X.Z.; validation, C.C., M.X. and Y.D.; formal analysis, Y.D.; investigation, C.C.; resources, C.C.; data curation, Y.D.; writing--original draft preparation, Y.D.; writing--review and editing, C.C.; visualization, M.X.; supervision, Y.D. and X.Z.; project administration, X.Z.; funding acquisition, Y.D. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Project of China grant number 2023 YFC3081400, the National Natural Science Foundation of China grant numbers 42477167 and 41572274, and State Key Laboratory for GeoMechanics and Deep Underground Engineering, China University of Mining & Technology, Beijing grant number SKLGDUEK2130. The APC was funded by Fundamental Research Funds for the Central Universities grant number FRF-BD-20-01A. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.
# Numerical Study of Aircraft Wake Vortex Evolution under the Influence of Vertical Winds
## 3 Results
### Analysis of Wake Vortex Parameters
#### 3.3.1 Analysis of Wake Vortex Parameters under Different Wind Directions
At a distance of 200 m behind the wing, under vertical wind conditions of 1 m/s, 2 m/s, 3 m/s, and 4 m/s, the axial velocity at the vortex core increases by 4 m/s, 7 m/s, 8 m/s, and 8 m/s, respectively. This observation agrees with the findings in reference [34], which concluded that crosswinds significantly increase axial velocity in the wake vortex.
## 3 Problem Formulation
### Objective Functions
#### 3.2.1 Minimization of Total VAR Investment Cost  
One of the important objectives in the RPP is the total cost of VAR planning. In spite of allocating the optimal location and capacity for VAR sources, optimal VAR planning can handle the RPP problem from economic aspects. For this reason, the first objective function is a cost-based objective function comprising two main parts, as follows:  
1. The first part evaluates the expected cost of energy loss (\(W_{c}\)) during the generated scenarios and is expressed as follows [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26]: \[W_{c}=\pi_{s}\langle h\sum_{s\in\Omega_{s}}t_{s}P_{loss,s}\rangle\] (10) where \(P_{loss,s}\) shows the active power losses during the \(s^{th}\) scenario, \(t_{s}\) represents the duration of the \(s^{th}\) scenario, \(h\) is a constant parameter that is related to the first part cost-based objective function and identifies the per-unit energy cost, and \(\pi_{s}\) denotes the probability of the \(s^{th}\) scenario. To calculate the total active power losses, Equation (11) can be used as follows [27; 28; 29; 30]: \[P_{loss,s}=\sum_{\begin{array}{ll}l\in\Omega_{Line}\\ l=(i,j)\end{array}}G_{(l,s)}\Big{(}V^{2}_{i,s}+V^{2}_{j,s}-2V_{i,s}V_{j,s}\cos \Big{(}\theta_{i,s}-\theta_{j,s}\Big{)}\Big{)}\] (11) where \(V_{i,s}\) and \(V_{j,s}\) are the sending and receiving ends voltage magnitude of the \(l^{th}\) transmission line for the \(s^{th}\) scenario, respectively, \(\theta_{i,s}\) and \(\theta_{j,s}\) are the sending and receiving ends voltage angles of the \(l^{th}\) transmission line for the \(s^{th}\) scenario, respectively, and \(G_{(l,s)}\) is used to designate the conductance of the \(l^{th}\) transmission line for the \(s^{th}\) scenario.
2. The second part measures the expected cost of VAR investment (\(I_{c}\)) during the generated scenarios and is derived as follows [14; 15; 16; 17]: \[I_{c}=\sum_{s\in\Gamma_{s}}\pi_{s}(\sum_{i\in\Gamma_{Comp}}(e_{i}+C_{Ci}Q_{Ci,s }))\] (12) where \(e_{i}\) and \(C_{Ci}\) are the fixed and variable installation costs of VAR sources, respectively. Accordingly, the first objective function (\(f_{1}\)) can be derived as follows: \[f_{1}=F_{c}=W_{c}+I_{c}\] (13) where \(F_{c}\) shows the expected Total VAR Cost (TVC).
# Characteristics of Removal of Lead, Cadmium and Chromium from Soil Using Biosorbent and Biochar
## 2 Materials and Methods
### Preparation of Model Soil  
The preparation of model soil containing lead (Pb), cadmium (Cd), and chromium (Cr) involved several unit operations. In the first step, the moisture content of the soil intended for the study was assessed. The results obtained were used for further calculations based on the dry weight of the soil. The initial concentration of each heavy metal in the soil was set at 1000 mg/kg. Appropriate amounts of Pb(NO\({}_{3}\))\({}_{2}\), Cd(NO\({}_{3}\))\({}_{2}\).4H\({}_{2}\)O, and K\({}_{2}\)Cr\({}_{2}\)O\({}_{7}\) salts were weighed and dissolved in 100 cm\({}^{3}\) of deionized water. Then, 800 g of raw soil was weighed into three containers, and each container was treated with one of the prepared solutions. Additionally, a soil sample containing all the salt solutions was prepared. The mixture was thoroughly stirred to ensure a uniform distribution of the chemical substances in the soil material.
# A Compact Broadband Planar Inverted-F Antenna with Dual-Resonant Modes
## 3 Principle and Analysis
### Loading of Slots 1
#### 3.1.2 Improving the Radiation Pattern  
Next, the effects of Slots 1 on the radiation patterns of the two modes are also studied. As can be seen from Table 2, when Slots 1 are not loaded, the TM\({}_{1/2,2}\) mode of a conventional PIFA produces a non-broadside radiation [30], while the E-plane pattern of the TM\({}_{3/2,0}\) mode has a \(-30\)-degree tilt. Both of the radiation patterns are undesirable. When Slots 1 are introduced, there is a change in the radiation current distributions of both the two modes, thereby improving their radiation patterns. Specifically, the radiation direction of the TM\({}_{1/2,2}\) mode is transformed from non-broadside to broadside. The radiation pattern is symmetrical with a low cross-polarization level of less than \(-20\) dB (within the 3-dB  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Antenna I** & **Antenna II** \\ \hline \multicolumn{3}{c}{**Relation patterns of the TM\({}_{1/2,2}\) mode**} \\ Peak gain: 3.03 di \(@\)\(270^{\circ}\) & 7.2 di \(@\)\(0^{\circ}\) \\ \multicolumn{3}{c}{} \\ \(-\)10 & 120 \\ \(-\)10 & 150 \\ \(-\)10 & 180 \\ \(-\)10 & 150 \\ \  
beamwidth) and a peak gain of 7.2 dBi at 0\({}^{\circ}\) achieved. On the other hand, the main beam direction of the TM\({}_{3/2,0}\) mode is adjusted from \(-30^{\circ}\) to 0\({}^{\circ}\). The radiation pattern also becomes symmetrical, and the cross-polarization levels are better than \(-18\) dB while the peak gain is 6.36 dBi at 0\({}^{\circ}\). By observing the surface currents of the two modes, the working mechanism is analyzed as follows:  
For the TM\({}_{1/2,2}\) mode, the out-of-phase distribution of surface currents along the x-direction leads to the high cross polarization in the H-plane and the radiation null in the broadside direction. Slots 1, which are etched near the shorting wall, are employed to cut the y-direction current in this area. According to the antenna theory, the slots which cut the current will become the radiation source [31]. Thus, with this cutting, Slots 1, instead of the original radiation edge, become the main radiation source of the TM\({}_{1/2,2}\) mode, producing y-direction electric fields. Meanwhile, near the patch's edge, the effective components of the currents for radiation have also been transformed to be in the y-direction. Thus, a normal broadside radiation pattern with a low H-plane cross-polarization is obtained.  
For the TM\({}_{3/2,0}\) mode, the partial out-of-phase currents along the y-direction cause the incline of the E-plane radiation pattern. Slots 1 just cut near the area where the y+ and y-currents meet together. Due to the adverse effect of the slots [32], the out-of-phase currents in the top region realize a 180\({}^{\circ}\) flip, and turn to be in phase with the currents underneath. Therefore, the incline of the E-plane pattern can be improved, and a good symmetrical broadside radiation pattern can be achieved.
## 3 Results and Discussion
### SDX Transformation Products
#### 3.4.2 Hydroxylation  
Regarding this mechanism, all hydroxylated TPs that were detected and identified were mono-hydroxylated, meaning that a single hydroxyl group was attached to each parent molecule. There were four TPs involved in this pathway: TP327 (C\({}_{12}\)H\({}_{15}\)N\({}_{4}\)O\({}_{5}\)\({}^{+}\), \(m/z\) = 327.0758), TP302 (C\({}_{11}\)H\({}_{16}\)N\({}_{3}\)O\({}_{5}\)S\({}^{+}\), \(m/z\) = 302.0804), TP290 (C\({}_{11}\)H\({}_{16}\)N\({}_{3}\)O\({}_{5}\)S\({}^{+}\), \(m/z\) = 290.0438), and TP244  
Figure 5: Proposed mechanisms for the formation of SDX TPs with heterogeneous photocatalysts. The mechanisms include: (**a**): bond cleavage, (**b**): hydroxylation, and (**c**): desulfonation.  
(C\({}_{8}\)H\({}_{10}\)N\({}_{3}\)O\({}_{4}\)S\({}^{+}\), \(m/z=244.0387\)). The proposed structure of the molecule is based on the fragmentation pattern (Supplementary Materials, Table S2). However, the precise location of hydroxylation could not be determined.