esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
These methods assess the contribution of individual features in a specific prediction by generating perturbation samples of a given data instance and monitoring the effect of these perturbation samples on DL model output. In this paper, we utilize Recurrent Neural Networks (RNNs) for DDoS attack classification. RNNs are strong and robust neural networks that are among the most effective algorithms as they have internal memory. They utilize their internal memory to recall important characteristics of the input they receive. For this reason, RNNs are the preferred models for dealing with sequential data such as time series [16; 17; 18; 19]. The LSTM network is a modified version of the recurrent neural network. LSTM is well suited for processing, classifying, and predicting time series data with unknown time spans. We then investigate the explanation of the LSTM predictions using LIME, SHAP, Anchor, and LORE explanation methods. We adopt the above explanation methods to interpret LSTM classification model predictions for two main reasons. First, these explanation methods have local interpretability, meaning they concentrate on interpreting individual predictions for a particular black-box classifier. This property is appropriate to interpret a decision taken and why a certain instance is considered an attack. Second, unlike the other explanation approaches that are designed to interpret specific models, the above four explanation methods are model-agnostic approaches that can be utilized by any classification model. In this paper, we investigate the use of the long short-term memory (LSTM) model in classifying DDoS attacks while focusing on the explanation of the DL model's predictions using LIME, SHAP, Anchor, and LORE methods. We can summarize our main contributions as follows:* Assess the LSTM performance in detecting different classes of DDoS attacks by utilizing commonly used and publicly available CIC datasets;
* Investigate the explanation of LSTM predictions on DDoS attacks in CIC datasets using four different explanation methods;
* Utilize the output of explanation methods to demonstrate intrinsic classification features and improve LSTM classification performance, particularly on the CICDDoS2019 dataset;
* Compare and evaluate the explanation methods in the domain of DDoS attack classification using two evaluation metrics. The remainder of the paper is organized as follows: in Section 2, we present the background of three explanation methods that we implement in this work. Section 3 shows recent work on the classification of DDoS attacks using the DL models and explanation methods that are used to interpret the DL models. Section 4 describes the methodology we adopt to classify the DDoS attacks using LSTM classifier and the explanation methods to interpret the LSTM predictions. This section, in particular, includes dataset preprocessing, classification model design, and the explanation methods used. The classification of the DDoS attacks using the LSTM model, the evaluation of the predictions, and their explanation are presented in Section 5. We show our conclusions and future work in Section 6.
## 2 Results
### Monitoring algal Concentration during Rotifer-Grazing and Freeze-Thaw Experiments  
#### 2.1.1 Rotifer-Grazing Experiments  
Algal cell concentrations for cultures of _M. gaditana_ in the presence or absence of grazing _B. plicatilis_ were measured at several timepoints for _M. gaditana_ alone (abbreviated Algae or A), _M. gaditana_ and _B. plicatilis_ (abbreviated Algae + Rotifer or A + R), and ESAW media blanks (abbreviated media blanks or MB), as a negative control (Figure 1A-C). After _B. plicatilis_ was added to three _M. gaditana_ cultures (Figure 1A-C, Cultures C4-C6),time-dependent decreases in algal density were observed relative to the _M. gaditana_ only controls (Figure 1A-C, Cultures C1-C3). For all three A + R experiments, rotifer grazing slowly depleted algal concentration over a period of several days (Figure 1A-C, Cultures C4-C6). Resulting A + R cultures had roughly 20-50% fewer intact algal cells present compared to corresponding time-matched A cultures. A + R cultures between the three experiments displayed different rates of algal biomass loss, where we attribute variation in rates of biomass loss to differences in the commercially supplied rotifer lots.  
Figure 1: (**A–C**) Algae concentrations as determined by direct enumeration for three healthy algae cultures (C1-C3) and three rotifer grazing algae cultures (C4-C6) for the three A + R experiments. Rotifer addition marked by “+R” with an arrow at the relevant point in time. (**D–F**) Fluorescence measurements for three healthy algae cultures (C1–C3) and three freeze–thawed algae cultures (C4–C6) for the three Freeze–Thaw Algae (FTA) experiments. The error bars represent standard deviation calculated from technical replicates for each sample (\(n=2\)). The starting concentrations of the FTA experiments were equivalent at the time of algal inoculation (t = 0 h), and thus the standard deviation for these measurements is zero. No timepoints for1 h and 6 h in FTA Experiment 1 were taken, as denoted by “NT”. SPME samples were collected at timepoints in experiments marked by an asterisk (*).
## 3 LADRC Controller Design  
In addressing the challenges posed by uncertainties in ocean current disturbances, process noise, and hydrodynamic damping coefficients, the Linear Active Disturbance Rejection Control (LADRC) method is utilized. LADRC comprises a Linear Extended State Observer (LESO) and Linear State Error Feedback Control Law (LSEFC). Of particular significance is the development of the LESO, which is implemented to estimate and counteract external disturbances and uncertainties in model parameters. The LSEFC determines the virtual control signal \(u_{0}\) by evaluating the system's state error. The control block diagram is illustrated in Figure 2.  
Figure 2: Structure diagram of linear active disturbance rejection controller.
## 3 Trilateration and PSO Algorithms
### Iterations  
Once the particles are obtained, the iteration of the PSO algorithm proceeds. First, the velocity of each particle in iteration \(t\), denoted by \(v_{t}\), is updated by using the following expression [46]:  
\[v_{t+1}=w\times v_{t}+r_{1}\times c_{1}\times(pbest_{p}-POS_{p})+r_{2}\times c_{2 }\times(gbest-POS_{p}). \tag{15}\]  
Equation (15) incorporates the inertia weight \(w\); random numbers \(r_{1}\) and \(r_{2}\) uniformly distributed within the range \([0,1]\); and acceleration coefficients \(c_{1}\) and \(c_{2}\), which determine the importance given to the particle's personal best (\(pbest_{p}\)) and the global best (\(gbest\)), respectively. Parameters \(c_{1}\) and \(c_{2}\) are commonly referred to as the cognition of the particle and the social influence of the swarm, in the given order [46].  
Next, the algorithm updates the position of particle \(p\) for the next iteration based on  
\[POS_{t+1}=POS_{t}+v_{t+1}. \tag{16}\]  
Finally, the objective function value of each particle is updated based on Equation (14). Consequently, each particle updates the parameters \(gbest\) and \(pbest_{p}\) if they are found to be smaller than the current values.  
One conventional method of obtaining an estimated receiver position is by solving equation [44; 45]:  
\[\begin{bmatrix}x_{2}-x_{1}&y_{2}-y_{1}\\ x_{3}-x_{1}&y_{3}-y_{1}\end{bmatrix}\begin{bmatrix}x_{t}\\ y_{t}\end{bmatrix}= \tag{17}\] \[\begin{bmatrix}(R_{1}^{2}-R_{2}^{2})+(x_{2}^{2}+x_{2}^{2})-(x_{1} ^{2}+y_{1}^{2})\\ (R_{1}^{2}-R_{3}^{2})+(x_{3}^{2}+x_{3}^{2})-(x_{1}^{2}+y_{1}^{2})\end{bmatrix},\]  
where \(R_{i}\) is the result obtained from Equation (13) and can be expressed as  
\[R_{i}^{2} =(x_{i}-x_{t})^{2}+(y_{i}-y_{t})^{2} \tag{18}\] \[=\left(\frac{A_{ri}(m_{i}+1)}{2\pi}T_{si}(\psi_{i})g_{i}(\psi_{i} )P_{ti}\frac{(z_{i}-z_{t})^{m_{i}+1}}{P_{\text{LOS}i}}\right)^{\frac{2}{m_{i} +3}}-(z_{i}-z_{t})^{2}.\]  
Equation (17) arises from working with the distance equation from the receiver to each LED, which is shown in expression (13).
## 5 Simulation Analysis
### Simulation Conditions  
In this paper, we assume that the navigation system is the Beidou satellite navigation system. The initial epoch is UTC 0h0 min0s on 28 May 2020. The Beidou navigation satellite orbit simulation data is obtained from the TLE data by the North American Aerospace Defence Command. Figure 2 is an illustration of the orbits of three BEIDOU navigation systems (contains BDS-1, BDS-2, BDS-3) including 49 satellites, and these orbits are represented by different colors. An inclined geo-synchronization orbit (IGSO) satellite is the user satellite. The coordinate system used in the simulation is the J2000.0 geocentric inertial coordinate system, the time system is Coordinated Universal Time (UTC). The simulation duration is 43080 s, and the sampling time interval is 4 s. In the experiment, only the main lobe signal of the navigation satellite has been considered, and the receiver sensitivity has not been considered. Set the half-angle of the main lobe signal beam as \(21.3^{\circ}\), and the half-angle of the part occluded by the earth as \(13.35^{\circ}\). The satellite orbit dynamics model of user satellite is set as a two-body model, with a mean square error of position error of 10 m and a mean square error of velocity error of 0.1 m/s [39; 40], the mean square error of measured white noise is set to be 1 m, and the position and velocity at the initial moment are  
\[\mathbf{X}_{0}=[2.242\times 10^{7}\ \text{m}\ 3.257\times 10^{7}\ \text{m}\ 1.539\times 10^{7}\ \text{m}\ -2.139\times 10^{3}\ \text{m/s}\ 469.418\ \text{m/s}\ 2.122\times 10^{3}\ \text{m/s}],\]  
The error covariance matrix at the initial moment is  
\[\mathbf{P}_{0}=\text{diag}(\begin{bmatrix}10&10&10&0.1&0.1&0.1\end{bmatrix}).\]  
Figure 2: This is the Beidou navigation constellation map under J2000.0 geocentric inertial coordinate system.  
According to Section 3.2, due to the real-time requirements of the navigation system of high-orbit satellites, in the observability calculation matrix, the parameter \(m\) equals to 1. According to the selection criteria of the adjustment factor in Section 4, if the number of visible satellites is larger than 3, the observability mapping function is constructed as  
\[(\mathbf{\psi}_{k})_{i}=\begin{cases}\dfrac{\eta_{i}}{\min\limits_{i=1,2,3}\eta_{i} }\,i=1,2,3\\ \dfrac{\eta_{i}}{16},i=4,5,6\end{cases}\]  
If the number of visible satellites is smaller than 3,  
\[(\mathbf{\psi}_{k})_{i}=\begin{cases}1.1,i=1,2,3\\ \dfrac{\eta_{i}}{2},i=4,5,6\end{cases}\]
# Dynamic Response Analysis of Structures Using Legendre-Galerkin Matrix Method
## 3 Legendre-Galerkin Matrix Method
### Expression of the LGM Method
Using Equations (35) and (36), the matrix form for approximation of Equation (14) can be obtained as:  
\[(\mathbf{M}\mathbf{D}^{2}+\mathbf{C}\mathbf{D}+\mathbf{K}\mathbf{A})\Phi_{N}( t)=\mathbf{P}\Phi_{N}(t), \tag{38}\]  
or  
\[\mathbf{U}\Phi_{N}(t)=\mathbf{P}\Phi_{N}(t), \tag{39}\]  
where,  
\[\mathbf{U}=\mathbf{M}\mathbf{D}^{2}+\mathbf{C}\mathbf{D}+\mathbf{K}\mathbf{A}.\]  
Equation (38) can be solved using different methods, namely the direct method (i.e., omitting \(\Phi_{N}(t)\) from both sides of the equation and assuming it to vanish), the collocation method, or the Galerkin method. In the present paper, the Galerkin method is selected to solve the equation. This method tries to minimize the error toward zero with the inner product of the equations in Legendre basis \(L_{m}^{*}(t)\). Consequently:  
\[\langle\mathbf{U}\Phi_{N}(t),L_{m}^{*}(t)\rangle=\langle\mathbf{P}\Phi_{N}(t ),L_{m}^{*}(t)\rangle,\ \ \ \ \ m=0,1,\ldots,N. \tag{40}\]  
The above equation is an algebraic system of linear equations with \(N+1\) equations and \(N+1\) unknowns that can be easily solved. It is needed, however, to apply the boundary conditions to the problem, as also in accordance with Equation (15). Following Equations (25) and (30), it is:  
\[\mathbf{A}\Phi_{N}(0)=\mathbf{\Lambda}, \tag{41}\]  
\[\mathbf{A}\Phi_{N}(0)=\mathbf{\Pi},\]where,  
\[\mathbf{\Lambda}=[\lambda_{1}\ \lambda_{2}\ \ldots\ \lambda_{f}]^{T},\ \ \mathbf{\Pi}=[\gamma_{1}\ \gamma_{2}\ \ldots\ \gamma_{f}]^{T}.\]  
Finally, in order to find the response of the system in Equation (14) under the initial conditions from Equation (15), by replacing \(2f\) of the rows of Equation (40) with Equation (38), a system of algebraic equations with \((N+1-2f)\) equations (Equation (38)) and \(2f\) equations (Equation (40)) is created. Their solution yields the analytic approximation of the original problem. The last rows of Equation (38) are usually replaced with those equations from Equation (40); however, this is not always obligatory and the rows which will cause the system of equations to be singular can be alternatively replaced.
## 3 Results and Discussion
Note that although the GIF and MHD sites exhibit the same seasonality in terms of phase and amplitude (compare the grey dashed lines in Figure 4), the latter was driven within our modeling framework by distinct processes. It is suggested that this discrepancy likely results from errors in the seasonal cycle in the ocean emissions because a better fit between simulations and observations is observed at MHD when an optimized marine source is adopted (compare the grey dashed line and the black line in Figure S1B,D). Other key features of the GIF COS time series are the 2015-2021 declining trend (Figure 2A, orange curve) and that in the spring maximum of COS (Figure 2B, blue line), both of which qualitatively agree with NOAA's observations (Figure 1). In their global atmospheric COS trend analysis from 22 NDACC stations, the authors of [14] reach the conclusion that a slowing down in the rate of anthropogenic emissions is likely the cause of the recent (i.e., from 2016 to 2020) negative trends in free troposphere (FT) COS because, during the building phase of the COS between 1986-2012, the FT COS showed a high correlation with the revised anthropogenic emissions budget described in [31]. Although our multi-year observations are consistent with [PERSON]'s conclusion as to the role played by anthropogenic emissions in the recent negative trends in tropospheric COS, the discrepancies between the observed and simulated seasonal variations at the MHD and GIF sites (Figures 4 and S1) point to a likely misrepresentation of the marine and anthropogenic surface fluxes in the footprint of the latter.
## 6 Theoretical and Security Analysis
### Security Analysis
#### 6.2.2 Collusion Attack  
In the PoAP consensus mechanism, maintainers receive votes from users that contribute to their weight in the consensus process. The more votes they receive, the more likely they are to become committee members or a proposer. Therefore, there is a potential for maintainers to collude with users, bribing them for votes to launch a collusion attack.  
In the proposed PoAP consensus mechanism, after user voting, the weight of committee members is generated randomly through a cryptographic sortition algorithm. Even if committee members receive more votes by collusion with users, this does not guarantee them greater weight or the privilege to propose blocks in the subsequent consensus process. The randomness of this election algorithm significantly reduces the security risks associated with collusion between maintainers and users. Furthermore, within an election period \(T_{election}\), each maintainer has only one opportunity to be elected as a proposer, mitigating the benefits that maintainers can obtain through collusion.  
From the perspective of users, they aim to vote for maintainers with a higher probability of becoming proposers to gain a share of the transaction fee distributed in the block. However, due to the randomness of the election, voting for a specific maintainer only offers a small chance of earning transaction fee from the block, and it comes at the cost of all their \(PV\), thus preventing any potential for additional profit. Moreover, if users collude with malicious maintainer nodes, voting for maintainers attempting to attack the blockchain system, their accumulated \(PV\) will be forfeited as a penalty for supporting malicious maintainers in the system after the malicious behavior of the maintainer is detected.
## 5 Conclusions  
In this study, the ability of three different types of LUE models (MOD17, TL-LUE and TL-LUEn) to simulate GPP at various temporal scales for different vegetation types was assessed using measurements at 58 flux sites in Asia, Europe and North America. The main conclusions that were drawn as follows:  
1. Optimized model parameters vary distinctly not only among different vegetation types, but also among different sites for the same vegetation type, especially for TL-LUEn. The parameters in TL-LUEn change sizably with temporal scales while the parameters in TL-LUE and MOD17 are almost invariant with temporal scales.
2. The overall performance of TL-LUEn was slightly but not significantly better than TL-LUE at half-hourly and daily scale, while the overall performance of both TL-LUEn and TL-LUE were significantly better (\(p<0.0001\)) than MOD17 at the two temporal scales. The improvement of TL-LUEn over TL-LUE was relatively small in comparison with the improvement of TL-LUE over MOD17. However, the differences between TL-LUEn and MOD17, and TL-LUE and MOD17 became less distinct at 8-day scale.
3. At the half-hourly temporal scale, TL-LUEn and TL-LUE outperformed MOD17 for all vegetation types but CROP. The outperformance of TL-LUEn and TL-LUE over MOD17 was more distinct for forests than for GRASS and SHRUB vegetation types. With the increase of temporal scales, the improvement of both TL-LUEn and TL-LUE over MOD17 decreased. At the daily temporal scale, both TL-LUEn and TL-LUE performed better than MOD17 for forests and SHRUB. TL-LUE also outperformed MOD17 slightly for other non-forest types (CROP and GRASS). TL-LUEn only performed better than TL-LUE for BF. At the 8-day temporal scale, TL-LUEn only outperformed MOD17 for forests while TL-LUE performed better than MOD17 for all vegetation types. TL-LUEn only slightly outperformed TL-LUE for BF.
4. The improvement of TL-LUEn and TL-LUE over the MOD17 for forests was mainly achieved by the correction of the underestimation of GPP under low incident PAR and the overestimation of GPP under high incident PAR occurring in the MOD17.
5. TL-LUEn is more applicable at individual sites at the half-hourly scale. TL-LUE could be regionally used at half-hourly, daily and 8-day scales, owing to its excellent performance and small parameter variations at different temporal scales and for most vegetation types. MOD17 is also an applicable option at 8-day scale.
# Research on Improving Satellite Positioning Precision Based on Multi-Frequency Navigation Signals
## 2 Methodology and Equations  
Under the assumption that the measurement errors are independent and have equal variance, [PERSON] et al. proposed that the optimal layout of six satellites is that four satellites are equally spaced on the horizontal plane, and the other two are on the zenith; at this time, the minimum value of the geometric dilution of precision (GDOP) is \(\sqrt{2}\), as shown in Figure 2[23]. It is speculated from this that when the optimal layout of N satellites is known, and two satellites are placed in the same position where there was one satellite before, the optimal layout of 2N satellites can be obtained and, at this time, the GDOP is \(1/\sqrt{2}\) times the previous.  
The CAPS system adopts the virtual clock technology with the pseudo-range differential effect proposed by [PERSON] [21], which can eliminate the ephemeris error, clock error, and radial error caused by ionospheric delay and tropospheric delay in measurement errors. Only random errors are left, caused by multi-path effects or receiver thermal noise, etc., which makes the measurement errors of multi-frequency signals independent. It is technically difficult to arrange two satellites at the same position, but it is easy to achieve multi-frequency navigation signals from the same satellite, as shown in Figure 3. The physical augmentation factor of precision (PAFP) proposed by [PERSON] et al. is to improve the spatial layout equivalently through multi-frequency navigation signals [22]. At this time, the PDOP value of the constellation composed of satellites transmitting multi-frequency navigation signals is significantly lower than that of the original system.
# Short Text Clustering Algorithms, Application and Challenges: A Survey
## 3 Components of Short Text Clustering
### Similarity and Distance Measure
#### 3.4.3 Euclidean Distance  
Euclidean distance, also known as the Euclidean metric, is a frequently used distance measure in clustering algorithms, including clustering text and is the default measure of distance in the K-means algorithm [144]. For instance, to calculate the distance between two documents, \(d_{a}\) and \(d_{b}\) are represented by their term vectors \(\overrightarrow{t_{a}}\) and \(\overrightarrow{t_{b}}\) successively; the term set is \(T=\{t_{1}\dots t_{m}\}\). Equation (28) calculates the Euclidean distance between two documents:  
\[\textit{Euclidean}\left(\overrightarrow{t_{a}},\overrightarrow{t_{b}}\right) =\sqrt{\sum_{t=1}^{m}\left|w_{t,a}-w_{t,b}\right|^{2}} \tag{28}\]
# Integration and Flight Test of a 7 kW Turboelectric Vertical Take-Off and Landing Unmanned Aircraft
## 6 Conclusions and Recommendations
### Additional Considerations  
Engine vibration is often an issue with small UAS engines. Accelerometer data confirmed that turbine engine vibrations were so small as to present no issue. The data show that similar turboelectric systems could be utilized in aircraft requiring low engine vibration, such as those carrying sensitive loads. The highest aircraft acceleration was experienced on landing and not during the dive pull-out. Thus, for VTOL aircraft, future designers should focus on airframe landing loads.  
Far-field acoustic data were also recorded to evaluate the noise generated by the turboelectric aircraft in flight. The acoustic data confirmed expectations that the lifting rotors were a major noise contribution, and that the enclosed turbine engine did not significantly contribute to acoustic emissions. Peak auditory noise was around 1000 Hz with a 46 dB SPL at distance of around 210 ft.
## 1 Introduction
To shorten the latency of edge-distributed inference, in this article, we propose EDIJP, which is an edge-distributed inference framework based on joint partitioning. Regarding our framework, joint partitioning integrates model partitioning and data partitioning. We used an iterative algorithm to trade off communication latency and computing latency. The main contributions of this article are summarized as follows:  
* We propose a joint partitioning scheme that effectively reduces the latency of edge inference. * We shorten the inference latency depending on the trade-off between communication and computing in an edge-distributed inference scene. * To trade off the communication latency and the computing latency, we design an iterative algorithm to gradually obtain distributed partitioning and deployment results. * We validated the method's effectiveness using the most general CNN models, VGG16 [45] and Alexnet [46], and the CloudSim [47] simulation platform.