esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theory Background and Methods
### LSTM Network  
The LSTM [24] network designs the forget, input, and output gates in the network structure to effectively alleviate gradient vanishing and explosion and has a strong ability to learn the dependency relationship of time-series data. The LSTM network structure is shown in Figure 3. The LSTM network receives the output and cell state from the previous step as the current input. The processing expression is as follows:  
\[f_{t}=\sigma\Big{(}W_{f}\cdot[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{1}\]  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{2}\]  
\[O_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{3}\]  
\[\widetilde{C}_{t}=\tanh(W_{c}\cdot[h_{t-1},x_{t}]+b_{c}) \tag{4}\]  
\[C_{t}=f_{t}\cdot C_{t-1}+i_{t}\cdot\widetilde{C}_{t} \tag{5}\]  
\[h_{t}=O_{t}\cdot\tanh(C_{t}) \tag{6}\]  
where \(\sigma\) represents the activation function, and \(W\) and \(b\), respectively, represent the weights and biases of the corresponding processing operations. In the \(t\)-th update step, input gate, \(i\), forget get \(f\), output gate, \(O\), and cell state, \(C\) are updated by input \(x\), and the hidden state of step \(n-1\), \(h_{t-1}\) is the output of previous layer.  
Figure 2: Structure of the Inception V2 module.
# Coupling Secret Sharing with Decentralized Server-Aided Encryption in Encrypted Deduplication
## 3 Design
### Architecture  
We first introduce the architecture of ECDedup, which aims at building a decentralized secure deduplicated storage system, which is commonly deployed in outsourcedstorage, particularly for backup workloads with high content redundancy. The idea behind ECDedup's design is also applicable for centralized secure deduplication.  
As shown in Figure 1, ECDedup mainly comprises: (i) multiple clients that own data and wish to outsource their data into the cloud storage for backup or file-sharing; (ii) multiple decentralized key servers, providing the interface for the clients to access coding matrix for encrypting clients' data; and (iii) multiple servers that offer cloud storage services, including data deduplication. Each client first divides its data into variable-size data chunks by defining boundaries based on content, then generates the hash value of each chunk and sends it to key servers to generate the corresponding coding matrix. The coding matrix is collaboratively generated by multiple key servers based on the following two designs: (i) the coding-based secret-sharing scheme to ensure the security of the decentralized matrix generation (see Section 3.2), and (ii) the content-based coding matrix, which enables deduplication on the coded chunks for storage efficiency (see Section 3.3). The client then encodes the data chunks into the coded chunks using the coding matrix, and the coded chunks will be distributed to multiple cloud servers for further deduplication.  
ECDedup benefits both performance and security. It avoids the expensive cryptographic primitives (see Section 2.3) of decentralized server-aided encryption and protects the coding matrix via multipartite matrix generation, such that each key server can obtain a vector of the coding matrix to complete part of the computation, without disclosing the entire coding matrix.
# Allocating Small Transporters to Large Jobs
## 6 The General Transporter Allocation Problem
### Computing the Pareto Frontier Exactly
#### 6.1.2 Solving the Mixed Integer Quadratic Program
Theorem 5 implies that in order to find a solution minimizing the makespan \(C^{*}_{\text{min}}\) for \((\text{QTAP})\) and therefore \((\text{TAP})\), we can instead run \((\rho\text{MIP})\) for different values of \(C^{*}\) using a binary search. The following lemma limits the search interval for \(C^{*}_{\text{min}}\). Lemma 3.: _The minimum makespan \(C^{*}_{\text{min}}\) is between \(C^{\text{sat}}_{\text{max}}\) and the minimum makespan of any solution to the homogeneous transporter fleet problem where every transporter has filling time \(\min_{r}\{\kappa(r)\}\)._  
Proof. : By definition, the completion time of a job \(j\in\mathcal{J}\) cannot be smaller than its saturated processing time \(C^{\text{sat}}(j)\). On the other hand, given an assignment for the homogeneous transporter fleet problem, then increasing the values for \(\kappa(j)\) while keeping the assignment the same does not increase the completion time of any job. Hence, we can use a binary search between these values to approximate an optimal solution. The following Lemma is straightforward. Lemma 4.: _Consider \(\varepsilon>0\), \(C_{1}\) and \(C_{2}\in[C_{1},C_{1}+\varepsilon]\) such that \((\rho\text{MIP})\) has a solution for \(C^{*}=C_{2}\) and no solution for \(C^{*}=C_{1}\). Then, the solution for \(C^{*}=C_{2}\) has a maximum job completion time at most \(\varepsilon\) away from the optimal solution of \((\text{TAP})\). Moreover, it minimizes the number of transporters used among the solutions with a makespan of \(C_{2}\)._  
We denote by \((\text{BS})\) the above described binary search algorithm framework, which requires an inner algorithm to solve \((\rho\text{MIP})\). Note that we can aggregate \((\rho\text{MIP})\) by a transporter model similarly to \((\text{SMIP}g)\).
# An Improved NSGA-II-Based Method for Cutting Trajectory Planning of Boom-Type Roadheader
## 4 Experimental Validation
### Analysis of Experimental Results  
Given the pose of the roadheader body, the cutting arm is controlled to move according to the planned trajectory. The left image in Figure 22 shows the cutting head moving autonomously to the boundary, while the right image compares the cutting trajectory with the planned trajectory. The results show that the trajectory tracking is consistent with the planning, achieving sectional forming cutting with good boundary control.  
Figure 20: Comparison of planned and virtual simulation trajectories.  
Figure 23 presents a comparative analysis of the cutting trajectory tracking in the x, y, and z directions. (a) shows the trajectory comparison and error in the x-direction, (b) in the y-direction, and (c) in the z-direction.  
The experimental results show that the average tracking error in the X-direction is \(-7.17\) mm, with a maximum error of 9.32 mm. In the Y-direction, the average error is \(-1.75\) mm, with a maximum error of 23.88 mm, which is notably higher than in other directions. This significant deviation suggests that the Y-axis trajectory tracking may be more susceptible to certain challenges such as sensor noise or limitations in the algorithm's handling of vertical movements. In the Z-direction, the average error is \(-8.16\) mm, with a maximum of 15.93 mm. While these deviations are within an acceptable range for most applications, further refinement of the planning algorithm and sensor fusion techniques is recommended to minimize these errors, particularly in constrained or complex environments. Overall, these results validate the cutting trajectory tracking algorithm and provide a strong foundation for further advancements in autonomous cutting technology. However, the higher deviations observed in the Y-direction warrant attention and could be addressed in future work to ensure more consistent performance across all axes.  
Figure 21: Verification platform for sectional shaping and cutting path planning.  
Figure 22: Comparison of cutting paths.
## 3 Results and Discussion
### Antioxidant Capacity of Spice Extracts
Conversely, cardamom extracts presented the lowest AC values determined by DPPH (15.2-51.1 umol TE/g DM), ABTS (79.8-184.8 umol TE/g DM), and FRAP (8.7-28.1 umol TE/g DM) methods in comparison with the rest of the investigated samples. In a different study, extracts from various cardamom cultivars were developed using sonication and 80% ethanol or ethyl acetate as solvents [31]. The AC of these samples was tested using the cupric reducing antioxidant power (CUPRAC) and DPPH assays. Interestingly, the AC measured by the DPPH method ranged from 0.19 to 0.25 umol TE/g and 0.26-0.40 umol TE/g for ethanol and ethyl acetate extracts, respectively. These notable differences in comparison to our results may be attributed to various factors such as extraction time, solvent type, and particularly the use of different spice cultivars. As shown in Figure 2, the AC of the DES-based extracts was significantly lower (\(p<0.05\), Duncan test) than the AC of the pure ethanolic and 70% ethanolic samples. The only exceptions were found for CinDES3, NutDES3, and CorDES3, measured by the ABTS assay. These results indicated that the ABTS of three DES3-based extracts was significantly higher (\(p<0.05\), Duncan test) than those prepared with an ethanol-water mixture (70:30 \(v/v\)). However, the extraction efficiency of antioxidants using DES3 as an extractant from the studied spices did not match that of pure ethanol. Interestingly, similar DESs have been used for the extraction of bioactive compounds from various plant materials. For instance, a DES prepared with ChCl and Gly in different molar ratios and water content has been employed for the UAE of cinnamon and sappan wood [23]. Remarkably, even extracts obtained via response surface methodology, which yielded the lowest amounts of bioactive substances, demonstrated higher concentrations of trans-cinnamaldehyde, coumarin, and brazilin than maceration and reflux extracts prepared with ethanol. An onion peel sample, extracted by a DES based on ChCl, U, and water in a molar ratio of 1:2:4, exhibited 5 times higher FRAP (1457.19 umol ascorbic acid equivalents (AAE)/g) than methanolic (FRAP = 291 umol AAE/g) and water extracts (FRAP = 300 umol AAE/g) [32]. Additionally, a DES based on ChCl and PG in a molar ratio of 1:2, containing 20% water, yielded a comparable amount of curcumin (22.3 mg/g) to extracts obtained using methanol and ethanol (22.7 and 21.6 mg/g, respectively) [33]. The findings reported by other authors suggest that DESs could act as more efficient extraction media than conventional solvents. However, it is essential to note that multiple factors, including extraction time, sonication power, solvent-to-sample ratio, and the polarity of the used solvents, significantly influence the efficient dissolution of desirable antioxidants present in spice powders.
## 4 Numerical Study
### Injection into a Relative Bounded Trajectory  
The guidance strategy proposed in Section 3.4 is implemented here to inject a reconnaissance satellite \(SC_{2}\) into an invariant bounded trajectory about the target object \(SC_{1}\), characterized by the orbit parameters reported in Tables 13 and 14. Data in these tables allow determining the relative position and velocity of \(SC_{2}\) in \(\mathcal{F}_{r}\) at the initial epoch and, consequently, the corresponding state in Hamiltonian variables, collected in Table 15.  
Developing the transformation \(\mathcal{T}_{7}\), it can be verified that the coefficients of matrix \(\mathbf{T}_{N}\) for the case examined here are \(c_{1}=\frac{\sqrt{2}}{2}\), \(c_{2}=-8.2241\cdot 10^{-4}\), and \(c_{3}=607.9516\); then, applying Equation (37) with \(w_{1}\) and \(z_{1}\) in Table 15 provides \(\delta v_{x}=231.442\) m/s and \(\delta v_{y}=-0.542\) m/s. According to Equation (39), such a \(\Delta V\) provided at the initial time is the minimum one; the state resulting from the maneuver is reported in Table 16 and the complete trajectory is shown in Figure 14. It is worth emphasizing here that the transfer of a spacecraft into a bounded relative trajectory only requires an in-plane change in velocity.  
\begin{table}
\begin{tabular}{c c c} \hline semimajor axis & \(\bar{a}\) & 6828 km \\ eccentricity & \(e\) & 0 \\ inclination & \(i\) & 62 deg \\ RAAN & \(\Omega\) & 0 deg \\ true anomaly & \(\theta\) & 0 deg \\ epoch (UTC) & - & 24 January 2023 12:00:00 \\ \hline \end{tabular}
\end{table}
Table 13: Guidance strategy: orbit parameters of target object.  
Figure 12: Lunar formation: trajectory (black) in \(\mathcal{F}_{r}\) resulting from non-invariant initial conditions; the first orbit is plotted in red.  
Figure 13: Lunar formation: time behavior of \(y\) component.
## 4 Results
### Simulated Results
#### 4.2.3 Short-Circuit Proof Algorithm  
Scenario 1--Phase to neutral short-circuit. Figure 12a,b show the behaviour of the voltage and current of phases \(u^{\prime\prime}\) and \(v^{\prime\prime}\) when a unipolar \(u^{\prime\prime}u^{\prime\prime}\) short-circuit is generated and recovered, respectively. It can be seen that the voltage goes to zero when the fault appears maintaining the current limited with a sinusoidal waveform. When the fault is recovered, the voltage increases progressively without producing any problematic over-voltage. In both cases, the time response is less than 60 ms.  
Scenario 2--Phase to phase short-circuit. Figure 13a,b show the behaviour of the voltage and current of phases \(u^{\prime\prime}\) and \(v^{\prime\prime}\) when a bipolar \(u^{\prime\prime}v^{\prime\prime}\) short-circuit is generated and recovered. It can be deduced \(i_{L2u}=-i_{L2v}\) and \(u_{u^{\prime\prime}n^{\prime\prime}}=u_{v^{\prime\prime}n^{\prime\prime}}\), as can be also observed in Figure 13b. This case is particularly interesting because, although the current is properly managed in the steady-state, it can be seen that the voltage does not go to zero after the fault.  
When the fault occurs, it is possible that, in one of the two involved phases, its voltage control action, PRHC\({}_{u}\) output, plus the short-circuit current, \(i_{L2}\), adds up to more than in the other case (see Figure 7b). The phase with more errors rapidly produces a \(k_{1}\) gain that moves from one to zero. As the other phase operates with higher \(k_{1}\) values, it starts to control the current without necessarily a \(k_{1}\) gain equal to zero. This means without the correspondent phase-to-neutral voltage equal to zero.  
Figure 11: Example of the power over-load supervisor algorithm behaviour.  
When the fault is recovered, the voltage increases progressively without producing any problematic over-voltage. As in the unipolar case, the transients are resolved in less than 2-3 grid cycles.  
Scenarios 3-4--Three-phase short-circuit and Three-phase to neutral short-circuit. Figures 14 and 15 show the behaviour of the voltage and current of phases \(u^{\prime\prime}\) and \(v^{\prime\prime}\) when a tripolar between phases or a tetrapolar short-circuit is enforced and recovered, respectively. As in the previously studied cases, the behaviour of the current and voltage follows similar time responses.
# Adversarial Sample Generation Method Based on Frequency Domain Transformation and Channel Awareness
## 3 Related Work
### Mathematical Model of OFDM Channel Estimation System
In this paper, it is assumed that the channel impulse response is invariant within an OFDM symbol time, that is, if \(h_{i}(k,l)\) in \(G_{i}\) satisfies that the sampling points are equal at different times, then it can be ensured that:  
Figure 1: OFDM system model. \[h_{i}(l)=h_{i}(0,l)=\cdots=h_{i}(N-1,l)=\frac{1}{N}\sum_{k=0}^{N-1}h_{i}(k,l) \tag{6}\]  
where \(h_{i}(l)\) represents the channel response on the \(l\)th tap within the time of the \(l\)th OFDM symbol. Based on this assumption, the channel impulse response matrix \(G_{i}\) becomes:  
\[G_{i}= \begin{bmatrix}\mathrm{h_{i}}(0)&0&\ldots&\mathrm{h_{i}}(\mathrm{L-1})& \ldots&\mathrm{h_{i}}(1)\\ \mathrm{h_{i}}(1)&\mathrm{h_{i}}(0)&0&\ldots&\ldots&\mathrm{h_{i}}(2)\\ \vdots&\ddots&\ddots&\ddots&\ddots&\vdots\\ 0&\ldots&0&\mathrm{h_{i}}(\mathrm{L-1})&\ldots&\mathrm{h_{i}}(0)\\ \end{bmatrix}  \tag{7}\]  
where \(G_{i}\) is obtained by continuously rotating the channel impulse response \(h_{i}(l)\) on different taps. According to the simulation of the properties of the channel frequency-domain response matrix in the previous section and the properties of the discrete Fourier transform, it can be seen that the channel frequency-domain response matrix will become a diagonal matrix as follows:  
\[H_{i}= \begin{bmatrix}\mathrm{H_{i}}(0)&0&\ldots&0\\ 0&\mathrm{H_{i}}(0)&0&\vdots\\ \vdots&0&\ddots&0\\ 0&\ldots&0&\mathrm{H_{i}}(\mathrm{N-1})\\ \end{bmatrix}  \tag{8}\]  
where \(H_{i}\) represents the channel response of the channel in the \(i\)th OFDM symbol time. Further, we simplify Equation (3) based on this assumption, with:  
\[y_{i}=X_{i}h_{i}+z_{i} \tag{9}\]  
\[X_{i}=diag(s_{i})= \begin{bmatrix}\mathrm{s_{i}}(0)&0&\ldots&0\\ 0&\mathrm{s_{i}}(0)&0&\vdots\\ \vdots&0&\ddots&0\\ 0&\ldots&0&\mathrm{s_{i}}(\mathrm{N-1})\\ \end{bmatrix}  \tag{10}\]  
where \(h_{i}=vec(H_{i})=[H_{i}(0),H_{i}(0),\ldots,H_{i}(N-1)]^{T}\) is the channel impulse response vector; as shown in the formula, \(X_{i}\) represents the channel frequency-domain transmission symbol matrix; function \(vec(\cdot)\) represents the operation of extracting the diagonal elements of a matrix and forming a column vector; function \(diag(\cdot)\) represents the operation of constructing a diagonal matrix with vector E as the main diagonal element.
## 7 Applications in SDN-based VANETs
### Lane Change Assistance  
Without safety applications and programs like path alteration cautioning, changing of the path might bring about dangerous mishaps. A current path changing framework needs to include numerous difficulties such as the bearing and speed of the vehicles, roads situations, and traffic density. This is not possible for multi-hop path alteration tasks. The SDN-based controller could tackle this issue due to the global point of view about the road organization. The SDN-based controller directs the RSUs to assemble data, for example, the speed of vehicles, thickness, guide, and path direction. At the point when path alteration is mentioned by the vehicle, the controller makes a choice by relying upon the data assembled by the RSUs. DSRC is likewise a basic innovation for utilization of path change help [147].
## 3 Marine Environment Monitoring Network Model
### Node Flow Analysis  
Suppose the traffic of node i is x, and its traffic weight coefficient is \(\theta\) (0,1). Then, the weight generated via the traffic of node i can be expressed as follows:  
\[y_{i}=\theta_{i}X(i) \tag{6}\]  
In addition, according to the location information of nodes, assuming the total number of nodes in the cluster is n \(\in\) [1,N], the distance d between node j and node i can be expressed as follows:  
\[d_{i,j}=\sum_{n=1}^{N}\left\|n_{i}-n_{j}\right\| \tag{7}\]  
Then, the traffic weight generated via node j to i can be expressed as follows:  
\[y_{i,j}=\frac{\theta_{i}\theta_{i}X(i)}{d_{i,j}} \tag{8}\]  
If node i is a common member node in the cluster, only the cluster head node communicates with it and generates the single node traffic of the above formula. However, if node i is a CH node, the member node j in the whole cluster will generate traffic to it. However, since some nodes may be in a dormant state at some time, it is assumed that the number of nodes communicating with CH is m. At this time, the sum, W, of the traffic weights generated via node i can be calculated as follows:  
\[\text{W}=\theta_{i}X(i)+\sum_{j=1}^{M}\frac{\theta_{j}\theta_{i}\text{x}( \text{i})}{md_{i,j}} \tag{9}\]  
It can be seen that, when the residual energy of node i is equal to or slightly less than j, if the sum of traffic weights, W, at node i is much larger than node j, it indicates that the location and communication state of node i are more suitable to be selected as a CH node, and the election of a CH node in the cluster cannot be determined solely according to the residual energy.
# Asymptotic Performance of GNSS Positioning Approaches under Cross-Correlation Effects
## 5 Simulation Results
Concerning the CRBs, \(\text{CRB}_{w}^{b}\) and \(\text{CRB}_{1}^{b}\) did not account for the deterioration caused by the cross-correlation errors, thus failing to accurately represent the performance bounds. These bounds were lower than \(\text{CRB}_{w}^{a}\) and \(\text{CRB}_{1}^{a}\), even though the WLS and ILS were not achievable. In contrast, \(\text{CRB}_{w}^{a}\) and \(\text{CRB}_{1}^{a}\) more accurately represented the performance boundaries of the WLS and ILS, especially in the high \(C/N_{0}\) region. Figure 1: RMSE and CRB with respect to the \(C/N_{0}\) values (dB-Hz) of all satellites.
## 3 Results
### Classification Tree  
#### 3.2.1 Classification Tree Development and Evaluation  
In this part of the analysis, a classification tree is being developed. Classification trees are a machine learning technique and they form one of the two types of decision tree family algorithms. This type of algorithm is being used for classification purposes when the outcome variable gets discrete values. The second type of algorithms owning to the decision tree family is known as \"regressiontrees\" and it is being applied in the case that the outcome variable gets continuous values [25]. The graphical display of the classification tree outcomes consists an important advantage since they become easily explained and interpreted even by people with limited knowledge in statistics [26]. Moreover, it is very important the fact that classification trees have the ability to handle qualitative variables without needing to set up dummy variables and that they mirror human decision-making more closely, compared to other statistical approaches [26]. Due to these advantages, but also due to the fact that classification trees are appropriate for customer segmentation, this method was chosen.  
The usage of the classification tree method in this paper mainly targets in assisting the process of determining a group of people (including existing and potential users), that is more likely to shift towards the ThessBike system owing to a state-of-the-art mobile app. It is also sought to examine the current mobility characteristics of those who are more likely to be affected by the app. Based on the above, variables coming from the first and the second section of the questionnaire were used as possible predictor variables.  
It is very important to highlight that a joint analysis of the two groups of users was carried out. This option was considered preferred in order to conduct the analysis with a sufficient sample. It was also considered acceptable since only variables that were in common between the two questionnaires were used. The classification tree was developed, evaluated and optimized through the tree package [27], in the R programming language [28].  
The first step for the classification tree development was to separate the whole dataset randomly into two different sets. The training set includes 166 (332/2) rows and the test set which includes the rest 166 observations. Utilizing only the data from the training set, an initial tree was developed with the tree () function in order to predict the outcome variable (shift towards the ThessBike system owing the sophisticated mobile app). This initial tree consists of seventeen terminal nodes. The next step of the classification tree development process was to prune it, which is to optimize it by reducing the number of terminal nodes, with the aim to avoid overfitting. For the determination of the optimal number of terminal nodes, the cross-validation method was applied and the minimization of the classification error was used as a criterion. The final tree, which was created after the application of the abovementioned process, has eight terminal nodes and 79.88% accuracy on the test set.