esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Formulation
### 2D Array Manifold Matrix  
As the key parameter of Equation (1), the array manifold matrix A directly affects the accuracy of DOA estimation. Considering the spatial geometric relationship between the extended sources and the array shown in Figure 1, the time difference \(\tau\) between the transmissions of the \(k^{th}\) incident monopole source from the first sensor \((0,0)\) to \(X^{th}\) sensor \((m,n)\) can be defined as  
\[\tau_{X}(\theta_{k},\varphi_{k})=\frac{d}{c}[(m-1)\cos\theta_{k}\sin\varphi_{k }+(n-1)\sin\theta_{k}\sin\varphi_{k}] \tag{4}\]  
where \(c\) is the propagation velocity of sound waves in the air at standard atmospheric pressure. The \(X^{th}\) sensor's received acoustic signal can be expressed as-  
\[y(t)_{X}=\frac{1}{\sqrt{MN}}\sum_{k=1}^{K}a_{X}s(t)_{k}+n(t) \tag{5}\]  
where \(a_{X}=e^{j\omega\tau_{X}}\), \(\omega\) denotes the angular frequency of incident wave, \(\omega=2\pi f\), and \(f\) denotes the incident wave frequency.  
The direction vectors of the \(k^{th}\) monopole source along the \(X\) axis and \(Y\) axis directions can be represented by the Vandermonde matrix  
\[\mathrm{a}_{x}=[1,e^{j2\pi d\cos\theta_{k}\sin\varphi_{k}/\lambda},\cdots,e^{ j2\pi(M-1)d\cos\theta_{k}\sin\varphi_{k}/\lambda}]^{T}/\sqrt{M} \tag{6}\]  
\[\mathrm{a}_{y}=[1,e^{j2\pi d\sin\theta_{k}\sin\varphi_{k}/\lambda},\cdots,e^{ j2\pi(N-1)d\sin\theta_{k}\sin\varphi_{k}/\lambda}]^{T}/\sqrt{N} \tag{7}\]  
where \(\mathrm{a}_{x}\) and \(\mathrm{a}_{y}\) are the array steering vectors of the \(N_{s}\) incident sound sources in the \(X\) axis and \(Y\) axis directions respectively, and the array manifold matrix of the axis directions X and Y are expressed  
\[A_{x}(\theta,\varphi)=[a_{x}(\theta_{1},\varphi_{1}),a_{x}(\theta_{2},\varphi _{2}),\cdots,a_{x}(\theta_{K},\varphi_{K})] \tag{8}\]  
\[A_{y}(\theta,\varphi)=[a_{y}(\theta_{1},\varphi_{1}),a_{y}(\theta_{2},\varphi _{2}),\cdots,a_{y}(\theta_{K},\varphi_{K})] \tag{9}\]  
Since the 2D generalized direction vector can be approximately expressed as the Kronecker product of two 1D direction vectors along the \(X\) axis and \(Y\) axis directions [39], and the array physical structure determines the array manifold matrix, therefore, the 2D direction vector of a planar array can be defined as:  
\[a(\theta_{k},\varphi_{k})=a_{x}\otimes a_{y}=\begin{bmatrix}a_{x}(\theta_{1}, \varphi_{1})\cdot a_{y}(\theta_{1},\varphi_{1})\\ a_{x}(\theta_{2},\varphi_{2})\cdot a_{y}(\theta_{2},\varphi_{2})\\ \vdots\\ a_{x}(\theta_{K},\varphi_{K})\cdot a_{y}(\theta_{K},\varphi_{K})\end{bmatrix} \tag{10}\]  
Figure 1: Acoustic incidence model of extended sources in planar array.  
where \(a\) denotes the 2D directional vector and \(\otimes\) denotes the Kronecker products. The corresponding 2D array manifold matrix can be constructed as:  
\[A=[a(\theta_{1},\varphi_{1}),a(\theta_{2},\varphi_{2}),\cdots,a(\theta_{K}, \varphi_{K})] \tag{11}\]  
where \(A\) is the array manifold matrix combining with the axis directions \(X\) and \(Y\).
## 2 Explanation of Variable Pitch Concept
### Aerodynamics in Traditional VP-Technology
Meanwhile, if the variation of the pitch angle follows a sinusoidal function, as shown in Figure 5d, the space for performance enhancement would be exceedingly limited, because the maximum amplitude of the AoA change is much smaller (Figure 5d). A new approach is explored next to solve this problem. Figure 4: Illustration cycloidal variable pitch. Figure 5: Local angle of attack as a function of the azimuthal angle. (**a**) AoA variation at different TSRs; (**b**) one pitching method at low TSR; (**c**) another pitching method at low TSR; (**d**) pitching method at rated TSR or higher TSR. At an optimum or larger TSR, the pitch angle should change in the direction of the increased AoA for an improved performance in terms of the increased peak power coefficient of the VAWT. Alternatively, if the variation of the pitch angle follows a sinusoidal function, as shown in Figure 5d, the space for performance enhancement would be exceedingly limited, because the maximum amplitude of the AoA change is much smaller (Figure 5d). A new approach is explored next to solve this problem.
## 6 Simulation and Analysis
### Verifying the Correctness of CHCs Injection  
#### 6.2.1 During Normal Operation, CHCs Are Artificially Injected at 0.3 s  
Under the normal operation of the distribution network with IBDGs, IBDG1 and IBDG2 artificially start injecting CHCs at 0.3 s according to the injection strategy in Section 5. The currents at the outlet of IBDG1 before and after injection are shown in Figure 8. The amplitude of the A-phase fundamental frequency current and CHC (350 Hz) are extracted using the FFT, and the distortion rate of A-phase CHC is calculated. The distortion rate of A-phase CHC at the IBDG1 outlet is shown in Figure 9. Figure 10 shows the amplitude \(Mag_{17}\) of A-phase CHC and the waveforms of characteristic harmonic (400 Hz) signals \(I_{\text{hd}}\) and \(I_{\text{hq}}\) injected d-axis and q-axis reference currents.  
From Figure 8, it can be seen that there is a harmonic component in the current at the outlet of IBDG1 after 0.3 s, and this harmonic component is analyzed by using FFT. Due to the inherent delay of the FFT operation, the amplitude of the detected characteristic harmonic current reaches stability after one cycle (0.02 s), and its stable value is approximately equal to the amplitude of characteristic harmonic signals, as shown in Figure 10. As shown in Figure 9, the distortion rate of CHC at the outlet of IBDG1 is stabilized at 4% after one cycle, which meets the harmonic injection regulation of the distribution network. The above simulation results are consistent with the theoretical analysis in Section 3 and can verify the correctness of the CHCs injection system under normal operation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Capacity of IBDG1 and IBDG2 & 1 MW \\ Capacity of L1, L2 and L3 & 2 MVA \\ Length of feeder sections B1B2 and B2B3 & 4 km \\ Positive-sequence impedance of feeder sections B1B2 and B2B3 & (0.13 + j0.402) \(\Omega\)/km \\ Negative-sequence impedance of feeder sections B1B2 and B2B3 & (2.00 + j0.400) \(\Omega\)/km \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the simulation model.  
Figure 7: Simulation model of 10 kV distribution network with IBDGs.
## 5 System Operational Simulation
### Operation of the Distributed Clustering Algorithm  
The distributed clustering algorithm test results in tic[1] are shown in Figure 9. The test dataset is the air pollution data of four pollutants NO, NO\({}_{2}\), SO\({}_{2}\), and O\({}_{3}\). Each data record has a time stamp  
Figure 8: Simulation monitor results  
between 9:00 and 10:00, a sensor ID which shows the data source, and four values of the volumes of the pollutants. Here we defined the \(k\)-means algorithm with \(k=3\). So there will be three clusters with the cluster ID of 0, 1 and 2. Result (a) is a part of the real-time mining result in tic[1]. We can see that, in each recursive processing of the algorithm, SSN tic[1] computes the cluster centers and assigns the distributed data it gathered to a corresponding cluster. In result (b), the algorithm finishes according to the converging criteria and tic[1] gets a local clustering result. The final cluster centers and total number of data points in tic[1] are also shown in the result.  
The comparison of the average clustering accuracy of the centralized and distributed clustering algorithms is shown in Table 1. For the centralized clustering algorithm, we suppose tic[8] be the sink (central point for data processing) in the topology shown in Figure 7, which means every other node sends the collected data to tic[8]. And the classic \(k\)-means algorithm [34] is running in tic[8] for centralized clustering. For the accuracy measurement, let \(X^{i}\) denote the dataset at node \(i\). Let \(L^{i}_{km}(x)\) and \(L^{i}(x)\) denote the labels (cluster membership) of sample \(x\) (\(x\in X^{i}\)) at node \(i\) under \(k\)-means algorithm and our distributed clustering algorithm respectively. We define the Average Percentage Membership Match (_APMM_) as  
\[APMM=\frac{1}{n}\sum_{i=1}^{n}\frac{|\{x\in X^{i}:L^{i}(x)=L^{i}_{km}(x)\}|}{| \,X^{i}\,|}\times 100\%\]  
Where \(n\) is the total number of SSNs.  
For the distributed clustering algorithm, we vary the number of nodes in the Information Exchange Node Set (IENS) of each SSN from 1 to 10. Data are randomly assigned to each SSN. Table 1 shows the _APMM_ results.  
Figure 9: The distributed clustering algorithm test result in tic[1].  
From Table 1 we can see that, when the number of nodes in IENS is no less than 2, in other words, when each SSN exchanges data with at least two other SSNs, the _APMM_ exceeds 90%. When the number of nodes in IENS is no less than 4, the _APMM_ exceeds 93%. The results are achieved under the condition of assigning the data to each SSN randomly. In reality, if the patterns of the dataset are various in different locations, the _APMM_ maybe lower than the results in Table 1. In such situations, a good scheme of how to choose the nodes to construct the IENS would be very important.
# A New Orbiting Deployable System for Small Satellite Observations for Ecology and Earth Observation
## 4 Data Fusion Techniques Applied to Remote Sensing Data
However, since its initial conceptualization, data fusion has become an essential tool to process data acquired by small satellites, which can (i) guarantee a great ground coverage by means of constellations, and (ii) ensure a wider utilization of the final data products, because they are cheaper. An example can be provided by the SkySat constellation (in the case of SkySat 16 to 21, the resolutions of the panchromatic and the spectral bands are 0.57 m and 0.75 m, respectively). See Table 2 for more examples. Here, we present the results of pan-sharpening applied to panchromatic and multispectral images taken by Pleiades HR-1B, a large satellite that acquired images at the spatial resolutions of 0.5 m and 2 m for the panchromatic and multispectral filters, respectively, i.e., similar to those achievable with small satellites. Figure 1, showing the RGB composition of Lesbos Island (Greece), is taken as a sample image, where four pan-sharpening techniques are tested: multiplicative, simple mean, intensity hue saturation (IHS), and Brovey transformation. Figure 2a-d show the same scene of Figure 1 in RGB, each one after the application of one of the pan-sharpening techniques listed above. Because the best performing method cannot be fixed in an absolute way, but depends on the characteristics of the study area, several methods must be compared each time to select the one which provides suitable results for a defined purpose, by means of visual, spatial, and spectral analysis [303, 304]. Figure 1: RGB true color composition of a semi-urban area in Lesbos Island (Greece). Taken by Pleiades HR-1B instrumentations, which has a resolution of 0.5 m in the panchromatic and 2 m in the multispectral filters. The picture shows that the area taken by the instrument is at low resolutions, and details cannot be appreciated. Figure 2: The image of the same region in Figure 1, after being processed by pan-sharpening techniques: (**a**) multiplicative method, (**b**) simple mean method, (**c**) IHS, and (**d**) Brovey transformation.
# Clustering-Based Energy-Efficient Self-Healing Strategy for WSNs under Jamming Attacks
## 3 System Model
### Channel Model  
The log-distance model is a suitable channel model for this flat scenario with small distances between nodes [44], in which the received power is calculated as  
\[P_{r}=\frac{kP_{i}}{d_{tr}^{\alpha}}, \tag{1}\]  
where \(P_{i}\) is the transmit power, \(d_{tr}\) is the distance between transmitter and receiver, \(\alpha\) is the path-loss exponent, and \(k\) accounts for other factors such as the wavelength, height, and antenna gains [44; 45]. Consequently, the signal-to-interference-plus-noise ratio (\(SINR\)) perceived by a receiver in the presence of a jammer when the \(i\)th node transmits is  
\[SINR_{i}=\frac{kP_{i}d_{tr}^{-\alpha}}{kP_{j}d_{tr}^{-\alpha}+n_{0}W}, \tag{2}\]  
where \(P_{i}\) and \(P_{j}\) are the transmit powers of the \(i\)th node and jammer, respectively; \(d_{ir}\) and \(d_{jr}\) are the distances from the \(i\)th node and the jammer to the receiver, respectively; \(n_{0}\) is the noise power spectral density, and \(W\) is the channel bandwidth. Therefore, if the transmit power and location of the jammer are estimated, it is then possible to estimate the minimum transmit power (\(P_{i}^{*}\)) required by the \(i\)th node to satisfy the \(SINR\) threshold (\(\gamma_{0}\)) at the receiver, according to a maximum transmit power (\(P_{\max}\)) constraint,  
\[P_{i}^{*}=\frac{\gamma_{0}(kP_{i}d_{p}^{-\alpha}+n_{0}W)}{kd_{ir}^{-\alpha}}, \quad\text{s.t.}\quad P_{i}^{*}\leq P_{\max}. \tag{3}\]
## 2 Technical Architecture and Solution  
The proposed technical architecture is within the current state of the art and leverages the latest advances in RAG and agent-based implementations to enhance e-government applications. At its core, the system employs the best-known and established LLMs with memory capabilities and the open-source Haystack LLM orchestration framework [25], enabling more sophisticated data processing and automation. Additionally, the system implements multiple pre-processing and post-processing methods to refine the data in indexing and after retrieval.  
Data management is based on a vectorized database, specifically Pincone (accessed on 1 July) [26], which efficiently manages multiple indexed data sources, ensuring rapid and accurate information retrieval. The primary data sources integrated into our system include Press Corner [27], the Organisation for Economic Cooperation and Development (OECD) library [28], Web Search, and in-memory user documents, all vectorized to facilitate seamless access and processing.  
The architecture is based on [PERSON]'s framework [25], a powerful toolset that supports the integration and management of LLM operations. For web search capabilities, we incorporate Serper (accessed on 1 July) [29], an advanced tool that enhances our system's ability to retrieve relevant web-based information. The next sections will detail the methodologies and techniques employed in each component, providing a comprehensive overview of our system's architecture and implementation.
## 3 Results and Discussion
### Total Viable Counts (TVC)
#### 3.1.2 Yeast  
The changes in concentration of yeast during fermentation are summarised in Table S2. Fermentation time had the most significant effect (F = 6728.24, \(p\) < 0.001) on the TVC of yeast similar to LAB growth. The growth of yeast was, however, much less, with around a 1 log increase after 96 h of incubation, compared to the 2 log increase in LAB during fermentation. Sucrose concentration also showed a significant impact (F = 961, \(p\) < 0.001) on the growth of yeast cells. Sets 3 and 9, which both contained 12 g of sucrose, showed the highest yeast TVC similar to LAB cell growth. [PERSON] and [PERSON] [40] also reported the growth of yeast cells in water kefir (fermented sugar solutions) supplemented with sucrose.
## 6 Simulation Results and Discussions
### PAPR Simulation Results  
In this sub-section, the PAPR performance of the waveforms mentioned above are compared. PAPR simulations consist of two parts. In the first part, PAPR simulation studies on the transmitter side, and in the second part, PAPR simulation studies on the receiver side at different SNR values are made. PAPR is defined in the next equation.  
\[PAPR_{dB}(x[n])=10\log_{10}\left(\frac{\max_{0\leq n\leq NL-1}|x[n]|^{2}}{E \left[|x[n]|^{2}\right]}\right) \tag{30}\]  
Here, \(X[n]\) denotes the sent data of the systems mentioned above, \(NL\) is the oversampling factor, \(\max[.]\) denotes the maximum value and the operator \(E[.]\) denotes the statistical mean (expectation). The PAPR performance is evaluated using the Complementary Cumulative Distribution Function (CCDF) of PAPR. Considering the PAPR threshold \(PAPR_{0}>0\), the probability \(PAPR_{dB}\) that the PAPR is higher than the threshold is computed by CCDF and is expressed in the following equation:  
\[CCDF(PAPR_{0})=Pr\{PAPR_{dB}>PAPR_{0}\} \tag{31}\]  
Here, \(Pr\) denotes the probability. PAPR simulations were carried out using 1000 realizations of the above-mentioned waveforms. In all PAPR simulation studies, the roll-off factor of the RC pulse shaping filter was 0.1, and was carried out using \(K=64\) sub-carriers and \(M=8\) sub-symbols for GFDM, WHT-GFDM, and the proposed waveforms.
## 2 Problem Statement
Recall \(|d_{2}|\leq\phi_{2}\), \(\left|\dot{d}_{2}\right|\leq\rho_{2}\) in (2), \(k_{T}\geq T\phi_{2}\) in the first line after (7), and denote \(\beta=\rho_{2}+k_{T}+\eta\). Due to \(s=u_{n}+d_{2}\) in (9), (6\[\dot{u}_{n}=-Tu_{n}-\beta<0\]  
Similarly, if \(u_{n}<-\phi_{2}\), then \(\text{sign}(u_{n}+d_{2})=\text{sign}(u_{n})=-1\). This leads to:  
\[\dot{u}_{n}=-Tu_{n}+\beta>0\]  
Thus, the inequality \(u_{n}\dot{u}_{n}<0\) holds outside of \([-\phi_{2},\phi_{2}]\), and consequently \(u_{n}\) cannot leave \([-\phi_{2},\phi_{2}]\), i.e., \(|u_{n}|\leq\phi_{2}\) and, accordingly, \(\left|\dot{d}_{2}(t)+Tu_{n}(t)\right|\leq\rho_{2}+T\phi_{2}\) are retained for any \(t\geq 0\). Differentiating the Lyapunov function \(V(s)\) yields:  
\[ \begin{array}{ll}\dot{s}\dot{s}&=s\Big{(}\dot{d}_{2}+\dot{u}_{n}\Big{)}\\ &=s\Big{(}\dot{d}_{2}+\big{(}\dot{u}_{n}+Tu_{n}\big{)}-Tu_{n}\Big{)}\\ &=s\Big{(}\dot{d}_{2}+v-Tu_{n}\Big{)}\\ &\leq\Big{(}\dot{|d}_{2}-Tu_{n}\Big{|}-(\rho_{2}+k_{T}+\eta)\Big{)}|s|\\ &=\Big{(}\Big{|}\dot{d}_{2}-Tu_{n}\Big{|}-(\rho_{2}+T\phi_{2})-(k_{T}-T\phi_{2} )-\eta\Big{)}|s|\\ &\leq-\eta|s|\end{array}  \tag{11}\]  
The inequality (11) indicates that the system states will reach the sliding surface \(s=0\) in finite time. Once the desired sliding mode \(s=0\) is established, the dynamics of the system can be expressed as:  
\[\left\{ \begin{array}{l}\dot{x}_{1}=x_{2}+d_{1}(t)\\ \dot{x}_{2}=-c_{2}|x_{2}|^{\alpha_{2}}\text{sign}(x_{2})-c_{1}|x_{1}|^{\alpha_ {1}}\text{sign}(x_{1})\end{array} \right. \tag{12}\]  
In the case in which there are no mismatched disturbances, (12) becomes:  
\[\left\{ \begin{array}{l}\dot{x}_{1}=x_{2}\\ \dot{x}_{2}=-c_{2}|x_{2}|^{\alpha_{2}}\text{sign}(x_{2})-c_{1}|x_{1}|^{\alpha_ {1}}\text{sign}(x_{1})\end{array} \right. \tag{13}\]  
From (13), if \(c_{i}\) and \(\alpha_{i},\ i=1,2\) are chosen as below (7) and as in (8), respectively, then the system states will converge to the equilibrium in finite time [47; 49]. However, in the case in which mismatched disturbances exist, based on the equation set (12), one can obtain the below relationship:  
\[\ddot{x}_{1}+c_{2}\big{|}\dot{x}_{1}-d_{1}\big{|}^{\alpha_{2}}\text{sign}\big{(} \dot{x}_{1}-d_{1}\big{)}+c_{1}|x_{1}|^{\alpha_{1}}\text{sign}(x_{1})=\dot{d}_ {1} \tag{14}\]  
It is clear from the above equation that, despite the control effort, the mismatched disturbance still exists in the system dynamics. Thus, the resulting system state is strongly affected by the mismatched disturbance; that is, although the system state can be attracted to the sliding surface in finite time, it cannot converge to the equilibrium point due to the presence of the time-varying mismatched disturbance. As a result, the chattering-free TSMC developed in [30] can cope with matched disturbances, yet is unable to overcome the influence of mismatched disturbances.
## 5 Conclusions  
In our study, differently to previous studies, the Newmark model that considers the vertical acceleration of earthquakes is used to reveal the initiation mechanism of the DGB landslide. By analyzing the change in permanent displacement, the following conclusions are obtained:  
1. The vertical acceleration and horizontal inertia force significantly increased the permanent displacement of the DGB landslide and increased the possibility of landslide instability. The permanent displacement is 4.9 cm considering \(a_{n}\), while it is just 2.0 cm without considering \(a_{n}\).
2. Compared with the lower shear strength parameter of the sliding surface, the contributions of vertical acceleration and inertial force to the permanent displacement are more obvious when the shear strength parameter of the sliding surface is higher. When \(\phi>18^{\circ}\), the \(D/D^{*}\) is greater than 1, and the maximum \(D/D^{*}\) reaches 7. When \(\phi<22^{\circ}\), the D/D\({}^{*}\) increases in an upward convex pattern with an increasing internal friction angle. When the internal friction angle is \(22^{\circ}\)-\(28^{\circ}\), the increasing trend of \(D/D^{*}\) is not obvious. The \(D/D^{*}\) increases linearly with increasing cohesion. When the internal friction angle is certain, the change in cohesion has less influence on the \(D/D^{*}\). When the internal friction angle is greater than \(30^{\circ}\), the \(D/D^{*}\) is 2-7;
3. The contribution of vertical acceleration is significantly enlarged (87.8-90.7%) by the decreasing of the internal friction angle of the slide surface, while it is less influenced (5-27.4%) by the cohesion.
4. The fast accumulation event of permanent displacement is triggered in the concentration stage of the seismic energy release, and 50% of the energy was released within 30-50 s in the DGB landslide area. It is assumed that the DGB landslide may have been triggered at 30-50 s due to 50% of the seismic energy being released in this time span.  
Conceptualization, G.X.; Methodology, Q.Y.; Software, T.J.; Validation, S.C.; Formal analysis, L.Z.; Investigation, Y.H.; Resources, H.L. All authors have read and agreed to the published version of the manuscript. This study was financially sponsored by the Natural Science Foundation of Sichuan Province, China (Grants Nos. 2022 NSFSC1121, 2022 NSFSC1053 and 2023 NSFSC0789).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
## 3 Method
### Waveform Retracking Method
#### 3.2.2 Specular Echo Processing  
When the measurement footprint of altimeter covers a calm sea surface, the waveform is similar to a'single peak' (as shown in the left of Figure 3), which is called specular echo and needs to be handled separately. In the HY-2B satellite altimetry data processing framework, the OCOG algorithm is used to address specular echoes. In this study, because the number of specular echoes is small in the study area, we screen them out if the pulse peakness (PP) is larger than 5 and the average power of the waveform tail is smaller than 0.25. Then, we take the major peak as the main part of specular echo to fit by the MB4. The PP index is calculated as follows [31]:  
\[PP=\frac{31.5\times p_{\text{max}}}{\sum\limits_{i=5}^{64}p_{i}} \tag{3}\]  
where, the P\({}_{1}\) is the power of \(i\)th gate.