esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Strategy for Timed Cooperative Guidance of Multiple Missiles
### Movement Law of the Virtual Leader Missile  
The relative motion relationship of the leader-follower missiles can be seen form Figure 2. Considering that Missiles Leader (ML) is the leader missile, Mi is the ith follower missile, and T is the target, the velocity of the leader missile is \(V_{m0}\), the trajectory inclination angle and the trajectory deflection angle are \(\theta_{0}\) and \(\psi_{0}\), respectively; the vertical and azimuth LOS angle are \(\theta_{0L}\) and \(\psi_{0L}\), respectively; the velocity of the ith follower missile is \(V_{mi}\); the trajectory inclination angle and the trajectory deflection angle are \(\theta_{i}\) and \(\psi_{i}\), respectively; and the vertical and azimuth LOS angles are \(\theta_{0i}\) and \(\psi_{0i}\), respectively.  
In the later stage of the missile movement, the guidance of the missiles makes \(\theta_{0}\) and \(\theta_{i}\) tend to zero. At this time, the curvature of the trajectory curve of the leader missile and the follower missiles tends to be consistent, that is, the relationships \(r_{0}/V_{m0}=r_{i}/V_{mi}\) and \(\psi_{0}=\psi_{i}\) (or \(r_{0}/V_{m0}=r_{i}/V_{mi}\) and \(\psi_{0}=-\psi_{i}\)) are satisfied, and the leader missile and follower missiles reach and hit the target simultaneously. The important aspect is that this strategy can make a same curvature of the leader missile and follower missiles relative to the velocity. Under this condition, the flight of the missile consumes the same time. Hence, the leader-follower strategy can also be suitable for different missile speeds. The leader missile adopts an augmented proportional guidance method as:  
\[A_{z0}=K_{01}|\dot{r}_{0}|\dot{\theta}_{L0}|/\cos\theta_{0} \tag{3}\]  
\[A_{y0}=-\frac{K_{01}|r_{0}|\dot{\theta}_{L0}\sin\theta_{0}\sin\psi_{0}}{\cos \theta_{0}\cos\psi_{0}}-\frac{K_{02}|\dot{r_{0}}|\dot{\psi}_{L0}\cos\theta_{L0}} {\cos\psi_{0}} \tag{4}\]  
In order to quickly make the attitude angle \(\theta_{0}\) of the leader missile tend towards zero, a larger value of \(K_{01}\) needs to be chosen. The follower missiles also adopt an augmented proportional guidance method on the pitch channel:  
\[A_{zi}=K_{i1}|\dot{r}_{i}|\dot{\theta}_{Li}|/\cos\theta_{i} \tag{5}\]  
Figure 2: Relative motion relationship of the leader-follower missiles.  
In order to quickly make the attitude angle \(\theta_{i}\) of the leader missile tend towards zero, a larger value of \(K_{l1}\) needs to be chosen. Next, according to the position relationship between the leader missile and the follower missiles, the lateral acceleration \(A_{yi}\) of the leader missile is designed so that \(r_{i}/V_{mi}\to r_{0}/V_{m0}\) and \(\psi_{i}\rightarrow\pm\psi_{0}\) are satisfied during the guidance process.
###### Abstract  
Stiffness parameters are very important and effective in the constitutive models used in finite element analysis. It is not easy or common to obtain these parameters in the laboratory. However, even if the modulus is determined in the small and medium deformation range, there is a need to make transitions in both static and dynamic parameters. In almost all studies, the Alpan approach is used for the relationship between static and dynamic moduli of elasticity. Therefore, a better understanding of this approach is required. In this study, the relationship between static and dynamic stiffness was determined by monotonic triaxial and resonant column tests on five different sand samples with different relative stiffness and grain distributions, and the results were compared with Alpan's approach. It is not clear which of the initial or maximum modulus of elasticity (E\({}_{0}\)), unloading-reloading modulus (E\({}_{\text{ur}}\)) or secant modulus of elasticity (E\({}_{50}\)) are used by [PERSON] for static modulus of elasticity (E\({}_{\text{stat}}\)). Therefore, the coefficient R\({}_{\text{sec}}\) = E\({}_{\text{stat}}\)/E\({}_{50}\) was introduced and queried to indicate which E\({}_{\text{stat}}\) is a multiple of E\({}_{50}\). In connection with this, the dynamic modulus of elasticity (E\({}_{\text{dyn}}\)) was calculated using the small deformation shear modulus (G\({}_{0}\)) obtained from resonant column experiments and assuming [PERSON]'s ratios (\(\
u\) = 0.2, 0.3, 0.4). It was found that [PERSON]'s empirical approach achieved a significant degree of agreement for the sands in this study and the studies of other researchers. It was observed that the best agreement between dynamic and static stiffness ratio (E\({}_{\text{dyn}}\)/E\({}_{\text{stat}}\)) and static modulus of elasticity (E\({}_{\text{stat}}\)) for sand specimens in this study was obtained with \(\
u\) = 0.2 and R\({}_{\text{sec}}\) = 2. According to the experimental results, it is safe to say that [PERSON]'s empirical approach is still valid when the values of [PERSON]'s ratio and E\({}_{\text{stat}}\) in the very small deformation region are used. Since there are limited studies on E\({}_{\text{dyn}}\)/E\({}_{\text{stat}}\) ratio in the literature, it is thought that the findings in this paper will assist engineers and researchers. However, this work would also assist engineers in selecting appropriate stiffness parameters for calibrating constitutive models.  
Keywords:stiffness; dynamic and static elastic modulus; [PERSON]'s approach; resonant column test; monotonic triaxial test +
Footnote †: journal: Applied Sciences
## 2 Analytical Methods
### Use of [PERSON]'s Theorem  
A remedy to deal with linear partial differential equations (e.g., heat equations) subjected to a non-homogeneous time-dependent boundary and/or heat generation is the use of [PERSON]'s theorem. The main thrust of this theorem is to solve an auxiliary problem with a time-independent condition reduced from the original formulation, and then integrate it over a desired time interval. This integration term is also known as the Duhamel's superposition integral. The exact solution to our problem by using the [PERSON]'s theorem is expressed as:  
\[\theta(\xi,\tau)=\int_{\tilde{\tau}=0}^{\tau}F(\tilde{\tau})\frac{\partial \Phi(\tilde{\xi},\tau-\tilde{\tau})}{\partial\tau}\mathrm{d}\tilde{\tau}, \tag{5}\]  
where \(\Phi(\xi,\tau)\) is the temperature function in the auxiliary problem and \(\tilde{\tau}\) is the time integration variable. First, the corresponding auxiliary problem is given by:  
\[ \begin{split}\frac{\partial^{2}\Phi}{\partial\xi^{2}}+\frac{ \partial\Phi}{\partial\xi}&=\frac{\partial\Phi}{\partial\tau}, \\ \frac{\partial\Phi}{\partial\xi}\Big{|}_{\xi=1}& =1,\\ \Phi(\beta,\tau)&=0,\\ \Phi(\xi,0)&=0.\end{split}  \tag{6}\]  
As can be seen, the time-dependent boundary condition is now a constant flux condition. An exact solution is available by the method of superposition consisting of a transient homogeneous partial differential Equation (PDE) and a steady-state second-order ordinary differential Equation (ODE). In fact, this is the same problem as the one subjected to a constant heat flux studied in [39]. Employing the method of superposition, we let \(\Phi(\xi,\tau)=\Phi_{H}(\xi,\tau)+\Phi_{SS}(\xi)\), where \(\Phi_{H}(\xi,\tau)\) is the transient part and \(\Phi_{SS}(\xi)\) is the steady-state term. Consequently, we can obtain the solution to the auxiliary problem as follows:  
\[\Phi(\xi,\tau)=\sum_{n=1}^{\infty}C_{n}\left[J_{0}(\lambda_{n}\xi)Y_{0}(\lambda _{n}\beta)-J_{0}(\lambda_{n}\beta)Y_{0}(\lambda_{n}\xi)\right]\times\exp(- \lambda_{n}^{2}\tau)+\ln\frac{\xi}{\beta}, \tag{7}\]  
where the eigenvalues \(\lambda_{n}\) and the corresponding eigencoefficients \(C_{n}\) are found as:  
\[ \begin{split}& J_{1}(\lambda_{n})Y_{0}(\lambda_{n}\beta)-J_{0}( \lambda_{n}\beta)Y_{1}(\lambda_{n})=0,\ n=1,2,3,...\\ & C_{n}=\frac{\int_{\xi=1}^{\beta}-\ln(\xi/\beta)\xi[J_{0}( \lambda_{n}\xi)Y_{0}(\lambda_{n}\beta)-J_{0}(\lambda_{n}\beta)Y_{0}(\lambda_ {n}\xi)]\mathrm{d}\xi}{\int_{\xi=1}^{\beta}\xi[J_{0}(\lambda_{n}\xi)Y_{0}( \lambda_{n}\beta)-J_{0}(\lambda_{n}\beta)Y_{0}(\lambda_{n}\xi)]^{2}\mathrm{d} \xi}.\end{split}  \tag{8}\]  
Last, the exact solution of \(\theta\) by the [PERSON]'s theorem is calculated by substituting \(\Phi\) into Equation (5).
## 3 Results and Discussion
### Thermal Conductivity  
Figure 3 presents the average values of the thermal conductivity \(\lambda\) of the tested hemp concrete. Very low thermal conductivity has been observed for HC0 containing the lowest amount of cement binder. The thermal conductivity of HC1, containing only lime binder, was 0.12 W/(m-K), 71% higher than that of HC0. The obtained values for these concretes are comparable to those of previous studies [12; 13] (\(\lambda\) varies from 0.08 to 0.16 W/(m-K) for density HC of 300 \(\div\) 670 kg/m\({}^{3}\)).  
The thermal conductivity of HC2, containing a cement-lime binder, was 18% higher than that of HC3 (with the exact content of the binder) and was 257% higher than that of HC0 with the exact content of the cement binder. A very similar dry density characterises HC2 and HC3 concretes. The difference in thermal conductivity is due to the difference in the porosity distribution of these two binders. A large scatter of the results has also been obtained in the case of volumetric specific heat (Figure 4).  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Specimen**} & \multirow{2}{*}{**pH**} & \multicolumn{2}{c}{**Average**} & \multicolumn{2}{c}{**Residue after Roasting at 650 \({}^{\circ}\)C**} \\ \cline{3-5}  & & **Conductivity** & **Organic Substances** & **Mineral Substances** \\  & & **[mS/cm]** & **[\%]** & **[\%]** \\ \hline HS & 6.32 & 0.48 & 98.53 & 1.47 \\ HC0 & 11.74 & 3.80 & 40.37 & 59.63 \\ HC1 & 12.32 & 8.59 & 24.42 & 75.58 \\ HC2 & 12.29 & 7.42 & 22.71 & 77.29 \\ HC3 & 12.25 & 5.95 & 23.12 & 76.88 \\ \hline \end{tabular}
\end{table}
Table 3: The pH measured in a water solution of HS and HC concrete and the content of organic matter (according to ISO 14688-2 [34]).  
Figure 3: The average values of thermal conductivity of the tested concretes.  
Due to the highest porosity and lowest density, HC0 demonstrated the lowest specific heat. In the case of HC2, containing a cement-line binder, the specific heat was about 355% higher than for HC0. HC 1 had a specific heat of 0.47 MJ/(m\({}^{3}\)K), which is by 260% higher than for HC0. The obtained values are characterised by a similar volumetric specific heat as the concretes presented in [35; 36; 37; 38; 39]. The differences between the tested materials regarding the thermal diffusivity coefficient were less significant than in the case of the remaining thermal properties (Figure 5). The highest average thermal diffusivity was noted for HC0; it was 0.39 \(\times\) 10\({}^{-6}\) m\({}^{2}\)/s., i.e., almost 17% higher than for HC3 with the highest cement content. The thermal diffusivity of HC1 and concrete with cement-line binder HC2 was lower by 31% and 21% than HC0, respectively.  
Figure 4: The average values of volumetric specific heat of the tested concretes.  
Figure 5: The average values of thermal diffusivity of the tested concretes.
## 3 Receiver Test Methodology
### Time to Recompute Position (TTRP) Solution Test Method  
This test is used to measure the time taken for the RUT to recover immediately after a strong interference event. The interference is switched on 14 min after the simulated scenario starts and it is applied for 90 s. The interference power is fixed to a value such that the receiver loses position solution. In the tests, the interference power corresponds to a Jamming-to-Signal (J/S) ratio of 90 dB. The time taken between switching off the interference source and the first fix is recorded as the TTRP, after interference. The profile of this test methodology, whose total duration is 30 min, is illustrated in Figure 2.
# Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data
## Materials and Methods
### Datasets
#### 3.1.2 HJ Satellite Data
Finally, images of 24 phases were stacked in sequence to produce NDVI time series with a 30 \(\times\) 30 m spatial resolution.
## 4 Result and Discussion
### Analytical Measurement and Visualization after Preprocessing
#### 4.1.2 Random Forest Algorithm  
The Random forests classifier is an ensemble learning method for classification that operates by constructing a multitude of decision trees in which the outcomes are calculated based on the random selection of data. In our experiment, this was done by classifying the reviews and applying different information on different parameters concerning TF and TF/IDF in which we calculated the accuracy of the classification of each application category. In statistical information, we reported precision, recall and F1 scores. Figure 4 illustrates a visualization bar chart for the random forest algorithm in which series1 indicates the accuracy of the random forest algorithm, series2 indicates the precision, series3 indicates the recall, and series4 indicates the F1 score measurement.  
Figure 3: (**a**) Bar-chart visualization of term frequency (TF) naive Bayes multinomial algorithm after preprocessing; (**b**) bar-chart visualization of term frequency/inverse document frequency (TF/IDF) naïve Bayes multinomial algorithm after preprocessing.
# Research on Short-Term Traffic Flow Combination Prediction Based on CEEMDAN and Machine Learning
## 4 Combined Model Construction and Case Analysis Based on CEEMDAN and Machine Learning
### Short-Term Traffic Flow Combination Model Based on CEEMDAN
#### 4.1.4 A Combined Model Based on CEEMDAN and Machine Learning  
Each forecasting model has its own strengths and weaknesses for different kinds of data. In order to maximize the advantages of each component prediction model and enhance the generalization ability of the model, this paper proposes a combined model based on CEEMDAN and machine learning. The prediction steps for this combined forecasting model are as follows:  
Step 1: CEEMDAN can accurately decompose the characteristics of time series, and decompose short-term traffic flow sequences into multiple component sequences with good performance;  
Figure 3: Flowchart of low-frequency component prediction based on KNN.  
Step 2: The sample entropy of each component sequence is calculated, and the random characteristics of each component sequence are measured by the size of the sample entropy. The component sequences are divided into high-frequency components, intermediate-frequency components, and low-frequency components according to the size of the PE value;  
Step 3: Since LSTM has strong nonlinear mapping ability, LSTM is used to predict the high-frequency component sequence of traffic flow with strong randomness, volatility, and nonlinearity. In order to improve the accuracy of the model, on the basis of LSTM, BGWO is introduced to optimize the hyperparameters, and the BGWO-LSTM model is established. Because SVM has a strong learning ability and fast training speed for nonlinear data, SVM is used to predict the intermediate-frequency component sequence of traffic flow. At the same time, in order to further improve the performance of the model, the IGWO is introduced on the basis of SVM to optimize the parameters, and the IGWO-SVM model is established. The KNN model has the advantages of simple structure, mature theory, and short training time. KNN is used to predict the low-frequency component sequence of traffic flow, which is relatively stable and does not have strong variation;  
Step 4: The prediction results of each high-frequency component, intermediate-frequency component, and low-frequency component are linearly superimposed to obtain accurate traffic flow prediction results.  
The flowchart of the short-term traffic flow combination model based on CEEMDAN and machine learning is shown in Figure 4.  
Figure 4: Flowchart of short-term traffic flow prediction model based on CEEMDAN and machine learning.
# Name-Based Security for Information-Centric Networking Architectures
## 2 Background and Related Work
### Related Work  
Name-based security solutions have been considered by many related research efforts. [PERSON] et al. [7] leverage identity-based encryption and identity-based signatures to provide content integrity and confidentiality protection for the CCN architecture. The difference between the content integrity mechanism presented in that paper and our solution is that with our solution only the content owner can generate a digital signature. This has the advantage that delegated storage entities (e.g., CDN nodes) cannot modify a stored item, but it has the disadvantage that it cannot be used for \"live\" content stored in a location outside the administrative realm of the content owner. The content confidentiality solution presented by [PERSON] et al. does not provide forward secrecy. Although we do not propose any particular confidentiality mechanism, our solution can be used with the Transport Layer Security protocol -like handshakes protocols that achieve ephemeral content encryption keys.  
[PERSON] et al. [8] achieve similar goals by using hierarchical identity-based encryption. The advantage of hierarchical identity-based encryption is that once the secret key that corresponds to a content name prefix has been generated, the keys for the names that use this prefix can be generated without the involvement of the \(SKG\). This comes with additional computational and storage overhead. In this paper, we allow each content owner to maintain his own \(SKG\), hence interacting with it does not impose significant network overhead.  
[PERSON] and Uzun [9] use proxy re-encryption to implement an end-to-end content encryption mechanism for CCN that does not prevent content caching. The difference between this approach and our solution is that with our solution a storage endpoint can re-encrypt a content item for a user only if the user is authorized to access it, whereas in the construction presented in [9] a storage endpoint can perform this operation for all items; hence, if access control is required the storage endpoint must be trusted. Similarly, [PERSON] et al. [10] use proxy re-encryption to provide access control by semi-trusted proxies. Their approach has the same limitation as [9].  
[PERSON] et al. [11] use proxy re-encryption to build a privacy enhancing mechanism for the NDN architecture. With their solution, they perform hop-by-hop name and data encryption and re-encryption, in this way hiding user interests. In this paper, we do not provide any privacy-enhancing solution; however, we believe that our constructions are compatible with the solution in [11].  
Attribute-based encryption (ABE) [12] is a type of encryption that is very similar to IBE and for this reason it has been considered by many related ICN-specific efforts (e.g., [13; 14; 15]). However, when it comes to our goals, ABE has bigger computational and storage overhead without offering any additional security advantage. In any case, we believe that the solutions presented in this paper can be easily adapted to use ABE instead of IBE.
# Adaptive Local Context and Syntactic Feature Modeling for Aspect-Based Sentiment Analysis
## 4 Experiments
### Baseline Models
#### 4.2.2 ASC Models  
The group of models compares the performance of aspect sentiment classification models. We choose models based on different word embeddings and models with semantic relative distances or fixed syntactic relative distances, respectively, to verify the importance of the word embedding approach and the effectiveness of adaptive semantic relative distances.  
ASC models are divided into three main categories, which are LSTM, GCN, and BERT-based models for aspect sentiment classification. The LSTM-based models include TD-LSTM, IAN, RAM, AOA, and MGAN. The GCN-based model is ASGCN. The BERT-based models include BERT-SPC, AEN-BERT, BERT-PT, LCF-BERT, and LCFS-ASC. The following is an introduction to each ASC model.  
TD-LSTM [13] is a model based on two LSTMs capturing contextual information related to the target.  
IAN [15] is an interactive attentional network model based on LSTM and attentional mechanisms that consider the interactive learning of target words and contexts.  
RAM [16] is a model based on BiLSTM and multiple attention mechanisms that focus on important features in complex sentences.  
AOA [17] models aspects and sentences in a federated manner, focusing on the important parts of the sentence.  
MGAN [18] is a new multi-granularity attention network that captures word-level interactions between aspects and contexts.  
ASGCN [19] builds a graph convolutional network on a sentence dependency tree, exploiting syntactic information and word dependencies.  
BERT-SPC [20] is a pre-trained BERT model designed for sentence pair classification tasks.  
AEN-BERT [20] is an attentional encoder network that models between context and target based on attentional encoders.  
BERT-PT [26] is inspired by the reading comprehension task and is suitable for the aspect sentiment classification task.  
LCF-BERT [4] uses a semantic relative distance local focus mechanism to determine the local context to eliminate the influence of irrelevant words.  
LCFS-ASC [5] is a sentiment classification model that uses dependent syntactic trees to determine local context.
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 2 Structure of Filter Bank Multi-Carrier Systems
### Design of Multirate Filters: Polyphase Filters  
In designing an FBMC system, the first step is to design a low-pass prototype filter that meets the requirements of NPR or PR. In [31], three kinds of prototype filters are analysed and compared for the design of FBMC based systems. The authors proposed a prototype filter model called Farhang which offers the best performance in terms of sidelobe suppression in comparison to the widely used square-root raised-cosine (SRRC) filter, as well as the discrete-time square root Nyquist filter model designed in [32]. The prototype filters are adopted for designing interpolation and decimation filters. Above, we show in Figure 1 the basic structure of implementing interpolation in FBMC systems. This figure can be extended to form the basic structure of interpolation in polyphase filter banks over \(M\) sub-carriers, as depicted in Figure 4a [33]. The discrete, real input signal \(x[n,k]\) of Figure 1a is split to yield \(M\) sub-band signals by the use of synthesis band-pass filters whose impulse response is given as \(g[n,k]\) in order to realise the polyphase representation shown in Figure 4a. Similarly, Figure 1b shows the process of decimation in multirate systems that are usually adopted for combating the effects of aliasing. This figure can be used as a foundation to develop an efficient implementation structure for polyphase decimation as shown in Figure 4b.
# Future Directions for Electric Propulsion Research
## 4 Challenges for Electric Propulsion Development  
The future of electric propulsion is mainly pushing in two directions: increasing the specific impulse and longevity of high-power technologies and improving the efficiency and reliability of low-power technologies. In the former, thrusters with longer lifespans and greater \"fuel economy\" will enable new deep space science missions, and thus are of primary interest to civilian institutions. In the latter, dropping launch vehicle costs has ignited interest in small-scale satellites and constellations for both commercial and scientific near-Earth applications. Naturally, a low-power electric propulsion solution is sought for this new wave of satellites. We discuss in the following the existing technologies that meet these developing needs at different power levels, focusing on the challenges they face and the solutions currently being developed.
## 4 Risk Analysis for Current Protocols
### NMEA 2000
#### 4.2.1 Denial of Service  
We posit that NMEA 2000 is highly vulnerable to Denial of Service attacks due to its low-bandwidth design. A malicious device (or one that was simply misconfigured) could produce a large amount of traffic on the message channel, preventing other devices from communicating. It has been highlighted as a dated protocol that is still in use by other studies [30].