esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment
### Objective Evaluation Index  
The typical denoising evaluation index cannot be used in this experiment since it is based on real brain tumor tissue image data that were not obtained with a clean hyperspectral image serving as the reference image. It is believed that the universal evaluation index can be utilized as a comparison for evaluating non-reference images for denoising. The objective evaluation indicators for assessing picture quality are the NR [36] and ICV [36] coefficients. To find out if an image has fringe noise, the statistical properties of the image data are examined. Thus, in order to assess the intensity of non-uniform noise, the average noise reduction coefficient (MNR) and average inverse anomaly coefficient (MICV) are utilized in this work.  
(1) MICV  
The calculation formula of MICV is shown as (10):  
\[MICV=\frac{1}{B_{i}}\sum_{i=1}^{B}\frac{R_{mi}}{R_{si}} \tag{10}\]  
Figure 9: Column mean curve.  
The specific calculation method is to select a uniform image region on the removed image, calculate the mean \(R_{mi}\) and standard deviation \(R_{si}\) of the region, respectively, and then average the calculated results in the whole band to obtain the value. The size of the image region selected in this experiment is \(10\times 10\). The larger the MICV value, the better the strip noise removal effect. The MICV calculation of the image after denoising by this method and other methods is shown in Table 2.  
(2) MNR  
The NR index reflects the strip noise removal level of the image, and the formula for calculating NR is as follows:  
\[NR=N_{0}/N_{1} \tag{11}\]  
where \(N_{0}\) represents the average power spectrum of the undenoised original image, and \(N_{1}\) represents the average power spectrum of the fringe noise. The larger the value of the MNR index, the stronger the denoising ability of the algorithm. We regard the single-band hyperspectral images as different images, calculate the NR value of each image, and then perform the full-band average to reflect the banding noise removal capability of the full-band hyperspectral images, and obtain the MNR calculation formula:  
\[MNR=\frac{1}{B}\sum_{i=1}^{B}\frac{N_{0}}{N_{1}} \tag{12}\]  
Both MICV and MNR indicators reflect the noise component of the image by analyzing the rules and characteristics of the data. Through a mathematical analysis, it can be seen that, the cleaner the image, the higher the value of MICV and MNR. The data in Table 3 show that the method proposed in this paper achieves a high score in the evaluation index without reference, which proves that the denoising method proposed in this paper can effectively remove the non-uniform noise of the spatial dimensional push sweep hyperspectral imaging system. In order to remove the non-uniform noise in the subsequent data collection, the approach will continue to be applied.
## 4 Experiment Validation  
In order to thoroughly verify the performance of the model proposed, two types of bearing datasets were used for verification. The first type of dataset is the vibration signal of the rolling bearing from Case Western Reserve University (CWRU). The second type is the accelerated life experimental data of the rolling bearing from the rotor bearing laboratory of Xi'an Jiaotong University (XJTU). The two experiments were conducted to compare the differences in fault diagnosis results between the different number of sensors, weighted and unweighted data, and other fault diagnosis methods.
# Comparative Impact Analysis of Cyclone Ana in the Mozambique Channel Using Satellite Data
## 2 Materials and Methods
### An Overview of Tropical Cyclone Ana  
Tropical cyclone Ana occurred in 2022 between the 20-25 January over Madagascar, Mozambique, and Malawi. (Figure 1).  
The track mostly impacted coastal regions of Madagascar and Mozambique. This included the areas of interest for this study, namely the Zambezia and Sofala provinces in Mozambique and the Melaky and Boeny regions in Madagascar. The storm's genesis occurred on 20 January as a tropical depression of approximately 1009 hPa and rapidly evolved into a'severe tropical storm' over the western coast of Madagascar with pressure dipping below 1000 hPa on 23 January [23]. Finally, Ana was declared a tropical cyclone on the 24 January as it hit the Zambezia province, Mozambique at noon. Maximum sustained winds were recorded at 95 km/h in Mozambique and the lowest pressure recorded was 993 [23]. It is important to note, however, that as Mozambique and Madagascar have different administrative systems, called regions and provinces respectively, these all correspond to a level 1 administrative level according to the Food and Agriculture Organization Global Administrative Unit Layers (FAO GAUL) classification [24].
## 5 Experimental Setups and Procedures
### Setup II Experiment Procedure  
The OVS switch can monitor the incoming packets to each receiving node and record the packet arrival rate, packet size, and transmission duration. By collecting this information, the switch can measure the throughput value of each receiving node and communicate it to the SDN controller. Once the SDN controller receives the throughput measurements from the OVS switch, it utilizes this information to make an informed decision about the interface to be used to transmit the packets.  
Consequently, the controller manages the data flows by dynamically changing the flow table rules in the switch. For instance, if the data are required to be received only over the WiFi interface of a receiving node, the SDN controller commands the OVS switch to stop forwarding the data of the LTE flow. Similarly, the OVS switch can allow only the LTE flow by dropping the WiFi route from the flow table. The flows through both interfaces are also provided in the flow table of the OVS switch. Ultimately, the entirety of setup II, as shown in Figure 6, automatically provisions the appropriate usage of the interfaces of each node that achieves the least energy consumption by running the algorithms presented in Section 6.  
Figure 5: Energy consumption with the number of nodes for different MPTCP congestion control algorithms.
## 2. **Breadboard Design and Implementation**
### Visual Magnitude of Debris  
The visual magnitude of debris relates to its capability to reflect solar light, which depends on size, phase angle, reflectivity, and the distance between the observer and the debris, known as range. The most common method to estimate the magnitude of an object/debris whose brightness depends only on the light it reflects from the Sun is [11; 21; 22]:  
\[M=m_{sun}-2.5\log\left(\frac{pAF(\phi)}{R^{2}}\right) \tag{10}\]  
In (10), \(m_{sun}\) is the Sun's visual magnitude, \(p\) is the geometric albedo (a specifically quantified form of reflectivity), \(A\) is the object cross-section area, \(R\) is the distance between the object and the observer/camera (range), and \(F\) is the solar-phase angle function, being \(\phi\) the solar-phase angle (i.e., the Sun-object-observer angle as seen in Figure 6). The latest estimation for the average albedo of debris is 0.175 [11]. As a first approximation, the observed object is considered a sphere.  
In Figure 6, \(\phi\) is the solar-phase angle, \(R\) is the range and \(\theta\) the Sun-debris angle seen by the observer. The solar-phase angle is related to the illumination condition of an object in space and is defined as the angle between the Sun and the observer seen from the object (in our case, a piece of space debris). Moreover, most of the light reflected by debris is diffuse and not specular. Then, considering an object of unknown shape as an ideal diffusing  
Figure 6: Schematic illustration of the Sun-object-observer geometry.  
reflector is a reasonable approximation [21; 23; 24]. Therefore, the diffuse-phase function for a spherical body is given by:  
\[F_{diff}(\phi)=\frac{2}{3\pi^{2}}[(\pi-\phi)\cos\ \phi\ +\sin\ \phi\ ] \tag{11}\]  
Finally, the solar-phase angle can be calculated by looking at the problem's geometry. The Sun, the debris, and the observer locations form a triangle in the celestial sphere (see Figure 6). Considering the Horizontal Coordinates of the observer, the Sun-debris angle seen by the observer can be determined by the haversine formula [25], which calculates the angle between two coordinates:  
\[\theta=2\sin^{-1}\left(\sqrt{\sin^{2}\!\left(\frac{\alpha_{sun}-\alpha_{ debris}}{2}\right)+\cos\alpha_{sun}\cos\alpha_{debris}\sin^{2}\!\left(\frac{A_{sun}-A_{ debris}}{2}\right)}\right) \tag{12}\]  
In (12), \(\alpha_{sun}\) and \(\alpha_{debris}\) are the elevation of the Sun and the debris, respectively, and \(A_{sun}\) and \(A_{debris}\), the respective azimuths. Looking again at the triangle Sun-debris-observer, the distance between Sun and debris or Sun and observer is much larger than the range of the debris. Thus, the angle at the corner of the Sun in the triangle is approximately zero. Therefore, the solar phase angle is given by:  
\[\phi=\pi-\theta \tag{13}\]  
Theoretically, the phase angle ranges from 0 deg, representing the best scenario, to 180 deg, the worst illuminated case. However, in practice, the phase angle depends on the observational window, providing a minimal angle higher than 0 deg and a maximal smaller than 180 deg.
## 3 Reinforcement Corrosion Monitoring Techniques
### Corrosion Assessment with Electrochemical Sensors
Furthermore, embedded sensors should provide long-term performance, information about the coating over time and the surface condition of the reinforcement (e.g., active or passive) and be of low cost. Regarding the state of the steel, the variation of the active state to the passive is of great importance and, once the corrosion has begun, the corrosion rate and its variation over time present higher risks. It s necessary to take into consideration the processing and presentation of the data together with the durability of the sensor itself. Nowadays, embedded sensors are used to measure the following parameters: chloride content [75; 78; 86; 95], pH [106; 107], moisture [108; 109], \(i_{corr}\)[46; 73; 80; 81; 88], temperature [93; 108; 110], potential [89; 91], \(\rho\)[43; 111; 112; 113], oxygen transport [72; 78] and \(i_{gal}\)[82; 90].
# Mind Mapping Training's Effects on Divergent Thinking Skills: Detection Based on EEG Data
## 5 Limitations and Future Research Directions  
This study aims to explore the impact of mind mapping training on divergent thinking skills. Our primary focus was on analyzing the task-related alpha band power of participants engaged in divergent thinking tasks. The findings provide promising results for the use of mind mapping training to improve divergent thinking skills. However, certain limitations should be noted. Firstly, the small sample size limits the generalizability of the divergent thinking results. Additionally, it is important to note that the majority of participants in this study were female, despite previous research indicating no gender differences in creative performance [62]. In future studies, it is necessary to increase the sample size and balance male and female participants to control for gender and ensure the robustness of the research findings. Furthermore, all participants in this study used the EMOTIV EPOC X device for the first time. As a result, their mindset may not have been in a fully natural state during the experiment, potentially influencing the biometric data collected. Future studies should increase the number of measurement sessions to help participants become more familiar with the EEG signal collection device, allowing them to maintain a more natural mindset. Additionally, incorporating other neuroimaging techniques, such as functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG), could provide more comprehensive and accurate insights into brain activity. Finally, this study was exploratory and did not account for other relevant variables that may influence divergent thinking. Emotional experiences, for example, can impact divergent thinking [63; 64; 65]. In the future, we aim to analyze students' emotional states through their EEG signals to gain a more comprehensive and nuanced understanding of divergent thinking. This presents a promising avenue for further research.
# An Effective Adaptive Combination Strategy for Distributed Learning Network
## 3 Adaptive Combination Scheme  
### Minimum Variance Unbiased Estimation  
Consider the signal model and the ATC diffusion LMS algorithm in Section 2. Assume that for each node \(k\in\{1,\ldots,N\}\), the intermediate estimate \(\mathbf{\psi}_{k}(t)\) in the diffusion LMS algorithm satisfies  
\[E\{\mathbf{\psi}_{k}(t)\}=\mathbf{w}_{o},\quad\text{ for all }k\in\{1,\ldots,N\}, \tag{5}\]  
We define  
\[\mathbf{\Psi}(t)\triangleq[\mathbf{\psi}_{1}(t),\ldots,\mathbf{\psi}_{N}(t)],\;k=1,\cdots,N.\]  
Following [12], we have the minimum variance unbiased estimation problem for each \(k\in\{1,\ldots,N\}\),  
\[\min_{\mathbf{a}_{k}\in\mathbb{R}^{N}} \mathbf{a}_{k}^{\mathrm{T}}\Re(\mathbf{Q}_{\mathbf{\Psi}})\mathbf{a}_{k} \tag{6}\] \[\text{ s.t. }\mathbf{1}_{N}^{\mathrm{T}}\mathbf{a}_{k}=1;a_{l,k}=0,\text{ for all }l\
ot\in\mathcal{N}_{k},\]  
where \(\mathbf{Q}_{\mathbf{\Psi}}\triangleq E\{(\mathbf{\Psi}(t)-E\{\mathbf{\Psi}(t)\})^{\mathrm{H}} (\mathbf{\Psi}(t)-E\{\mathbf{\Psi}(t)\})\}\) and \(\mathbf{1}_{N}\) denotes the \(N\times 1\) vector with unit entries. Applying the convex combination strategy for all \(k\in\{1,\ldots,N\}\), we also require \(a_{l,k}\geqslant 0\).
# Querying on Federated Sensor Networks
## 4 Results
### The Comparison of the Layered and Flat FSN Models
#### 4.2.2 The Energy Consumption Results  
By L-FSN protocol, gateways consume energy on initialization of routing tables. After initialization of routing tables, the energy consumption on building nodes running L-FSN is very low compared to other protocols as shown in Figure 5. The layered federation by L-FSN prevents building nodes to consume additional energy for inter-island communication.  
By this experiment, we found that flooding-based packet routing causes a large energy consumption on the flat FSN model looking at the Directed Diffusion result. Due to network-wise flooding, Directed Diffusion has a higher energy consumption compared to other querying protocols. By flooding-based protocols, even though a query is resolved in the closest island, query messages are received by all island nodes. Therefore, flooding-based querying protocols devised for flat FSN model require additional mechanisms to limit the packet flooding.
## 2 Transient Representation of LTV Structures
### Modal Coordinate Decomposition of Vibration Response in N-DOF LTV Structures  
As the mass, stiffness and damping of the system are time-varying, the transient natural frequencies and modal shapes, as for a linear system there are N modes, are different at each moment. However, at time \(t\), for real modal analysis in the modal coordinate, the vibration response signals of weakly damped mechanical structures can be decomposed into the inner product of the modal shapes matrix and the modal responses matrix:  
\[\mathbf{X}(t)=\mathbf{\Phi}(t)\mathbf{Q}(t)=\sum_{k^{\prime}=1}^{N}\vec{\varphi _{k^{\prime}}}(t)\vec{q_{k^{\prime}}}(t) \tag{2}\]  
where \(\mathbf{X}(t)\) is the output displacement; \(\mathbf{\Phi}(t)\in\mathbb{R}^{N\times N}\) is the time-varying transient modal shape matrix composed of transient modal shapes \(\vec{\varphi_{k^{\prime}}}(t)\in\mathbb{R}^{N}\); and \(\mathbf{Q}(t)=[\vec{q_{1}}(t),\ \vec{q_{2}}(t),\cdots,\ \vec{q_{k^{\prime}}}(t),\cdots,\ \vec{q_{N}}(t)]^{T}\) is the matrix composed of modal responses \(\ \vec{q_{k^{\prime}}}(t)\) from each mode. Based on linear systems vibration theory, when the time-varying transient natural frequencies \(f_{k^{\prime}}(t)\) of each mode are not equal, the vectors of the time-varying transient main modes \(\vec{\varphi_{k^{\prime}}}(t)\) are orthogonal.  
\[\begin{array}{l}\mathbf{\Phi^{T}}(t)\mathbf{M}(t)\mathbf{\Phi}(t)=\mathbf{ M}_{r}(t)=diag[m_{1}(t),m_{2}(t),\cdots,m_{k^{\prime}}(t),\cdots,m_{N}(t)]\\ \mathbf{\Phi^{T}}(t)\mathbf{K}(t)\mathbf{\Phi}(t)=\mathbf{K}_{r}(t)=diag[k_ {1}(t),k_{2}(t),\cdots,k_{k^{\prime}}(t),\cdots,k_{N}(t)]\end{array} \tag{3}\]  
The modal response vectors \(\vec{q_{k^{\prime}}}(t)\) are then independent of each other, and \(\mathbf{Q}(t)\) satisfies:  
\[\mathbf{Q}(t)\mathbf{Q}(t)^{\mathrm{T}}=\Lambda_{N\times N}^{\prime\prime}(t )=\begin{bmatrix}\alpha_{1}(t)&\cdots&0&\cdots&0\\ \vdots&\ddots&\vdots&\ddots&\vdots\\ 0&\cdots&\alpha_{k^{\prime}}(t)&\cdots&0\\ \vdots&\ddots&\vdots&\ddots&\vdots\\ 0&\cdots&0&\cdots&\alpha_{N}(t)\end{bmatrix} \tag{4}\]  
where \(\Lambda_{N\times N}^{\prime\prime}(t)\) is a time-varying real positive diagonal matrix.
# Integrated Precise Orbit Determination of Multi-Gnss and Large LEO Constellations
## 4 Result Analysis and Discussion
### Integrated POD with Full LEO Constellation
#### 4.1.3 Integrated POD with Different LEO Altitude  
Table 5 lists the result of the integrated POD with LEO satellites at different altitudes. The 60 polar-orbiting LEO satellites at altitudes of 600, 1000, and 1400 km are considered (see Figure 1a). It can be seen that there are almost no orbit accuracy differences among the solutions. The integrated POD with higher LEO satellites presents a slightly higher orbit quality than that with lower LEO satellites, except for the BDS GEO satellites. For BDS GEO, the best orbit accuracy is achieved by the 600 km solution. Given the motionless characteristic of GEO satellites, the LEO satellites orbiting at an altitude of 600 km can contribute more improvements to the tracking geometry for BDS GEO due to their faster motion, which may be the reason for the better performance of the 600-km solution in GEO orbit accuracy. In addition, it should be noted that the LEO satellites at low altitude usually suffer from complex SRP modelling. Also, the trajectory of low-altitude LEO is perturbed by a stronger drag force, which will increase the difficulty of drag force modeling and lead to accelerated orbital decay for LEO. The difficulty of air drag and SRP modelling may negatively affect the result of the integrated POD. Fortunately, with the proper modeling of these forces, such as estimating the drag parameter with a shorter interval, low-altitude LEO satellites can obtain a high precision orbit result, which has been evidenced in many low-altitude LEO missions, such as Swarm [38].
## 2 An Adaptive Fading-Memory Receding-Horizon Filter  
### An Optimal Receding-Horizon Filter  
Consider the linear discrete time-varying state-space model given by  
\[x_{k+1} = A_{k}x_{k}+G_{k}w_{k}, \tag{1}\] \[y_{k} = C_{k}x_{k}+v_{k}, \tag{2}\]  
where \(x_{k}\) is the state vector, \(y_{k}\) is the output vector, and the pair \((A_{k},C_{k})\) is assumed to be observable. The process noise vector \(w_{k}\) and the output noise vector \(v_{k}\) are assumed to be zero-mean white Gaussian noise and uncorrelated with each other and with the initial state \(x_{0}\). The process and measurement noise covariance matrices are denoted as \(Q_{k}\) and \(R_{k}\), respectively, and are assumed to be positive definite.  
On the finite horizon \([k-N\;\;k-1]\), a recursive optimal RH filter to determine the a priori state estimate \(\hat{x}_{k|k-1}\) can be expressed as [13]:  
\[\hat{x}_{k-N+i+1|k-1} = A_{k-N+i}\Big{\{}\hat{x}_{k-N+i|k-1}+K_{k-N+i|k-1}\big{(}y_{k-N+i} -C_{k-N+i}\hat{x}_{k-N+i|k-1}\big{)}\Big{\}}, \tag{3}\] \[P_{k-N+i+1|k-1} = A_{k-N+i}\big{(}P_{1,k-N+i|k-1}-K_{k-N+i|k-1}C_{k-N+i}P_{k-N+i|k- 1}\big{)}A_{k-N+i}^{\mathsf{T}}\] (4) \[+G_{k-N+i}Q_{k-N+i}G_{k-N+i}^{\mathsf{T}},\]  
for \(0\leq i\leq N-1\), where the gain matrix \(K_{k-N+i|k-1}\) is defined as  
\[K_{k-N+i|k-1} = P_{k-N+i|k-1}C_{k-N+i}^{\mathsf{T}}\big{(}C_{k-N+i}P_{k-N+i|k-1}C _{k-N+i}^{\mathsf{T}}+R_{k-N+i}\big{)}^{-1}. \tag{5}\]  
The initial conditions \(\hat{x}_{k-N|k-1}\) and \(P_{k-N|k-1}\) are obtained as  
\[\hat{x}_{k-N|k-1} = P_{k-N|k-1}\hat{w}_{k-N|k-1}, \tag{6}\] \[P_{k-N|k-1} = S_{k-N|k-1}^{-1}, \tag{7}\]  
where \(\hat{w}_{k-N|k-1}\) and \(S_{k-N|k-1}\) represent the a priori information state and its information matrix, respectively, which are obtained from the following recursive calculation:  
\[\hat{w}_{k-l-1|k-1} = C_{k-l}^{T}R_{k-l}^{-1}y_{k-l}+A_{k-l-1}^{T}\hat{w}_{k-l|k-1}-A_ {k-l-1}^{T}S_{k-l|k-1}G_{k-l} \tag{8}\] \[\times\Big{(}Q_{k-l}^{-1}+G_{k-l}^{T}S_{k-l|k-1}G_{k-l}\Big{)}^{- 1}G_{k-l}^{T}\hat{w}_{k-l|k-1},\] \[S_{k-l-1|k-1} = C_{k-l}^{T}R_{k-1}^{-1}C_{k-l}+A_{k-l-1}^{T}S_{k-l|k-1}A_{k-l-1} -A_{k-l-1}^{\mathsf{T}}S_{k-l|k-1}G_{k-l}\] (9) \[\times\Big{(}Q_{k-l}^{-1}+G_{k-l}^{T}S_{k-l|k-1}G_{k-l}\Big{)}^{- 1}G_{k-l}^{T}S_{k-l|k-1}A_{k-l-1},\]  
for \(1\leq l\leq N-1\), with the initial conditions \(\hat{w}_{k-1|k-1}=0\) and \(S_{k-1|k-1}=0\).
## 2 Motivation and Background Analysis
### Reason for Coexistence of ICN and Edge Computing
#### 2.3.5 Easier Mobility Management  
The concept of ICN is content-oriented, while the current Internet architecture is host-oriented. The host-oriented architecture needs end-to-end connection establishment after handover, but ICN does not need that. Therefore, ICN provides better mobility management than the existing mechanisms because contents can be accessed directly without establishing the end-to-end connection again.
# How Useful Are Strain Rates for Estimating the Long-Term Spatial Distribution of Earthquakes?
## 3 Results
### Hybrid Models
For the forward testing period, the hybrids again perform differently with different strain rate covariates. With BSS (Figure 3b), the hybrids including strain rates are not as dominant as in [PERSON] et al. [11], due to the revision of the non-strain-rate covariates (Appendix B). Hybrid \"13\" performs almost as well as \"15\", i.e., the IGPEs of both hybrids are similar. With GSS (Figure 4b), \"13\" performs much better than \"15\". With HWS, \"15\" outperforms \"13\" and all other simple hybrids. Moreover, all hybrid models including HWS outperform most hybrids without HWS except \"123\". Hybrids with HWS perform not quite as well as those with BSS in the fitting period (Figures 4a and 5a) but better than those with BSS in the forward testing period (Figures 4b and 5b). Larger IGPEs in the forward testing period may be due to a partial overlap between this period (2012-2015) and the time range of GNSS data used to derive HWS (1995-2013). Hybrids with GSS perform not as well in either the fitting or forward testing period as their counterparts with BSS and HWS (Figure 4a, Figure 5a, and Figure 6a, and Figure 4b, Figure 5b, and Figure 6b, respectively). Figure 5: Information gain per earthquake of hybrid models relative to SUP baseline model with the HWS strain rate covariate. The combination of covariates contributing to each hybrid is indicated by the model number, which is a composite of the numbers corresponding to the baseline model and selected covariates, as follows: 1-SUP; 2-PPI; 3-PMF; 4-FLT; 5-HWS. (**a**) Fitting period (1987–2006); (**b**) Forward testing period (2012–2015); (**c**) Reverse testing period (1951–1986). Error bars are 95% confidence intervals. Hybrids with BSS perform poorly in the reverse testing period 1951-1986 (Figure 3c) and even worse than those without BSS, including the SUP baseline (since their IGPEs are negative). For all models without BSS, IGPE is mostly lower than in the fitting and forward testing periods. The largest IGPE for the reverse testing period was 0.27, obtained by hybrid \"12\", and is only half of the corresponding IGPE for the fitting period. With GSS, the IGPE of the \"15\" hybrid is again negative in the reverse testing period, as are the IGPEs for \"12345\" and \"1345\". With HWS, the \"15\" hybrid has a small positive IGPE of 0.03 for the reverse testing period, and all the hybrids have small positive IGPE values (Figure 5c). Thus, hybrid models with HWS perform slightly better than the corresponding models with BSS or GSS in the reverse testing period (c.f. Figures 3c, 4c and 5c). However, overall, the performance of hybrids including strain rates is poor in the reverse testing period.