esa-sceva/satcom-chunk-collection huggingface dataset:
# LEO-Based Satellite Constellation for Moving Target Detection
## 2 Materials and Methods  
Suppose that the observation scene is covered with multiple beams, each generated by a LEO-based satellite illuminator, as shown in Figure 1. Due to the distinct orbit parameters of different satellites, the echoes are concentrated over the dwell time and combined within an identical coordinate [11; 12]. Accordingly, the enhanced multi-source fusion detection can be achieved.  
Figure 1: Multi-source fusion detection diagram.  
For LEO satellites with different orbital velocity, moving direction and incident angle, there exists great differences in the range and Doppler history. This poses a considerable challenge for the robust fusion processing of multi-source echoes. Assume the echo of the \(k\)th satellite can be expressed as \(S_{k}(R)\) in range domain, where \(k=1,2,\cdots,K\), \(R\) is the monostatic range, and \(K\) stands for the platform number. In terms of back projection (BP) operation, the surveillance area in rectangular coordinate is divided into several cells, and the monostatic range of each cell can be calculated by utilizing the observation configurations. Therefore, \(S_{k}(R)\) can be represented as  
\[R_{k}(x,y)=2\big{|}\mathbf{P}_{k}(x,y,z)-\mathbf{P}_{\mathbf{Tg}}(x,y,z_{0}) \big{|} \tag{1}\]  
where \(\mathbf{P}_{k}(x,y,z)\) and \(\mathbf{P}_{\mathbf{Tg}}(x,y,z_{0})\) denote the satellite position vector and the target position vector, respectively, \(x,y,z\) are the corresponding coordinate value. To facilitate the subsequent processing, let the target locate on the earth's surface, and its rectangular coordinate is given by \(\mathbf{P}(x,y,z_{0})=(x,y,0)\).  
After the BP operation, \(S_{k}(R)\) can be renewed as \(S_{k}(x,y)\). According to the above projection relationship, the echoes with multiple satellites can be combined in the Cartesian coordinate \(X-Y\). Here, the target signals corresponding to different satellites are presented as distinct iso-range lines, and the target position is located at the intersection of these iso-range lines. The multi-source fusion process [12] can be summarized as  
\[S(x,y)=\frac{1}{K}{\sum_{k}}|S_{k}(x,y)| \tag{2}\]
# Provably Secure ECC-Based Anonymous Authentication and Key Agreement for IoT
## 1 Introduction
### Motivation and Contributions  
To summarize, previous ECC-based AKA schemes suffer from more or less vulnerabilities, i.e., failure to provide user anonymity ([PERSON] and [PERSON], 2016; [PERSON] and [PERSON], 2016; [PERSON] and [PERSON], 2016; [PERSON] and [PERSON], 2016), and vulnerability to specific attacks ([PERSON] and [PERSON], 2016; [PERSON] and [PERSON], 2016; [PERSON] and [PERSON], 2016; [PERSON] and [PERSON], 2016; [PERSON] and [PERSON], 2016; [PERSON] and [PERSON], 2016). Next, high computational and communication costs eliminate the suitability of some solutions for resource-limited IoT ([PERSON] and [PERSON], 2016; [PERSON] and [PERSON], 2016; [PERSON] and [PERSON], 2016; [PERSON] and [PERSON], 2016). Their security proofs are performed in the Random-Oracle model model ([PERSON] and [PERSON], 2016; [PERSON] and [PERSON], 2016). It is attractive to design an efficient AKA scheme for IoT and provide security proof under the standard model and eCK model.  
We propose an improvement over the scheme of [PERSON] et al. (2018) with the ECC-based message exchange mechanism and the one-way hash function message authentication technique. During registration, the TA only possesses part of the entity's private key, solving the key escrow issues. In addition, the proposals provide PFS and can resist ESL attacks since session keys are generated using both long-term and ephemeral credits. The protocol encrypts entity identities dynamically with random numbers and transmits them anonymously from session to session.  
The paper's contributions can be summarized as follows:  
(1) As an example, the cryptanalysis of the protocol scheme of [PERSON] et al. (2018) for the previous scheme reveals security issues and vulnerabilities.  
(2) A secure-enhanced AKA protocol for IoT is presented. Its security is formally proved under the standard model and the eCK model with the elliptic curve encryption computational assumptions and verified with ProVerif.  
(3) The proposed protocol has better security features with lower communication and computational overheads than existing schemes.
# Fusion of Linear and Mel Frequency Cepstral Coefficients for Automatic Classification of Reptiles
## 2 Proposed Method
### Feature Extraction  
For each syllable, two cepstral features were extracted to characterize the acoustic signal. MFCCs and LFCCs have been broadly used in speech recognition with success [21], and they have also been applied in animal bioacoustic classification [22; 23; 24; 25], due to their easy implementation and high performance. Reptiles mainly produce sounds in low frequencies within the human auditory range. For this reason, MFCCs have been adopted to get high resolution in the low frequency region. However, reptiles are capable of producing sound above 20 kHz, so LFCCs have also been computed to obtain the information in high frequency ranges.  
They are calculated using STFT (a 25 milliseconds Hamming window with an overlap of 50%) and applying Discrete Fourier Transform (DFT) over each frame of the signal. The resultant magnitude spectrum is wrapped by a bank of 40 triangular band pass filters. For MFCCs, the filters are non-uniformly sparse to perform the mel scale transformation. Finally, the coefficients are retrieved taking the lowest Discrete Cosine Transform (DCT) values from the log-magnitude filter outputs, \(\log|\gamma_{i}|\). They are computed following Equation (2):  
\[MFCC_{j}=\sum_{i=1}^{B}(\log|\gamma_{i}|\cos|j(i-0.5)|\pi/B),\ 0\leq j\leq N-1 \tag{2}\]  
where \(j\) denotes the index of the cepstral coefficient, \(B\) is the number of triangular filters, and \(N\) denotes the number of cepstral coefficients to compute.  
LFCCs are calculated similarly, but using a linear sparse triangular filter bank instead of the mel-scale filters. Furthermore, the number of coefficients, \(N\), has been established by experimentation in order to reach the highest classification rate in the last stage. As a result, 18 coefficients have been taken for both features.  
In this work, the feature vectors have been fused, appending the characteristics horizontally as in Label (3), where each row represents a syllable from the segmentation stage. These features have been combined to hold information of higher as well as lower frequency regions, obtaining a broad spectral representation of the reptile calls. Therefore, these rows contain 36 coefficients, which are used as inputs to the classification stage.  
Figure 2: Example of four reptile call spectrograms.  
\[DATASET=\left(\begin{array}{ccc}MFCC_{1}&LFCC_{1}\\ \vdots&\vdots\\ MFCC_{n}&LFCC_{n}\end{array}\right) \tag{3}\]
# Piston Error Measurement for Segmented Telescopes Based on a Hybrid Artificial Neural Network
## 3 Simulation  
Using the hybrid network proposed in this paper to detect the piston errors of sub-mirror consists of three steps. Firstly, establish the data sets for network training. Within the coherent length range of the input broadband light, multiple groups of piston errors are randomly generated and loaded onto the corresponding sub-mirrors. By taking the piston errors to the established simulation segmented optical system, the corresponding focal plane degraded images are generated for training the Resnet network, which is used to detect the signs of piston errors. Then, taking the absolute value of the piston errors and the optical system parameters into Equation (10), the values of the MTF sidelobes can be obtained to train the BP network. Secondly, train the hybrid network. The generated focal plane degraded images and the signs of the corresponding piston errors are used as the input and output of the Resnet network, respectively, and with the cross-entropy loss function used as the index function of the network optimization, the Resnet network can be well trained through a gradient-based stochastic optimization algorithm: Adam. Then, the calculated values of the MTF sidelobes and the absolute values of the corresponding piston  
Figure 8: The structural diagram of BP neural network.  
errors are used as the input and output of the BP network, respectively, and by setting up a specific training algorithm, the BP network can also be well trained. Finally, after the hybrid network training is finished, by taking a degraded focal plane image into the Resnet network, the signs of all sub-mirrors' piston errors can be obtained. Then, performing the Fourier transform on the focal plane image obtains the values of the MTF sidelobes which can be input to the BP network; the absolute values of all sub-mirrors' piston errors can be solved. One thing to note here is that the corresponding relationship between the MTF sidelobe and its related sub-mirror should be established in advance, as the absolute values of all the sub-mirrors' piston errors can be measured simultaneously by one CCD broadband image. Thus, the measurement of the piston error with high precision and a wide detection range can be realized by combining the output of the two networks.  
We first conducted simulation experiment analysis on the segmented telescope system composed of two hexagonal sub-mirrors, as shown in Figure 2. Then, the simulation experiments were processed to multiple sub-mirrors (\(n>2\)) segmented telescope system which was composed of four hexagonal sub-mirrors. In the end, we perform some comparation work between our method and the work published by [PERSON] et al. in paper [17], since they also used a single wide-band image of a point source to perform piston sensing by a neural network.
# Exploring Hybrid-Multimodal Routing to Improve User Experience in Urban Trips
## 3 Evaluation Framework
### Data Reduction
Thus, to enhance calculating the required derivatives, we now smooth the data, depending on the desired results, before applying the classifier. In this work, we use interpolation with an exponential function to smooth our data. We select the \"knee\" point in the curve, which is used as a threshold to classify the flows in trending or secondary. If we use an exponential function of the form \(y=e^{ax}\) to model the exponential decay, then the solution of Equation (1) leads to:  
\[\kappa=\frac{a^{2}e^{ax}}{(1+a^{2}e^{2 ax})^{3/2}}, \tag{2}\]  
where the maximum is given at:  
\[\kappa^{\prime}=0\ \longrightarrow\ x=-\frac{\log 2a^{2}}{2a}. \tag{3}\]  
In this study, we use HDBSCAN algorithm [20] to identify functional regions. HDBSCAN is a \(O(n^{2})\) time algorithm, with approximate solutions being given in average \(n\log n\) time [22]. The proposed classification approach has to first sort mobility flow popularity in time \(O(m\log m)\), with \(m\leq n\), but, in general, \(m<<n\). After that, the approach needs to solve a minimization problem to identify the best fit exponential equation to fit the curve that formed by the sorted popularities. We use the Levenberg-Marquardt method, which takes \(O(\epsilon^{-2})\) time [23]--\(\epsilon\) is the precision adopted. After identifying the best fit curve, the classification is done in \(O(1)\). Therefore, the approach is executable in \(O(p^{2})\), where \(p=\max\{n,1/\epsilon\}\). The proposed method maintains the same time complexity of HDBSCAN. To assure the quality of the proposed clustering technique, we first verify whether it complies with the requirements of good clustering techniques [24], i.e., the clustering: (i) should not impose a priori bias on the clusters' shape; (ii) should be able to handle varying densities; and, (iii) should be able to handle varying dimensionality. The origin and destination functional regions are obtained using the HDBSCAN technique, which does not impose a bias on the clusters' shape, and it is also known for good performance with noisy datasets. Additionally, the selection of the trend/secondary flow threshold identifies a point with no bias, thus meeting requirements (i) and (ii) (as discussed in Section 5.3). Regarding requirement (iii), despite using a spatial 2D dataset in this study, neither HDBSCAN nor the curvature equation limits the number of dimensions of the dataset. Thus, the proposed technique can handle higher dimensionalities. We conduced a experiment comparing our approach to the SSTFC algorithm [14]. Section 5 shows that both versions of OD-MFC outperform SSTFC when identifying the most relevant flows in noisy datasets.
## 3 Results
### Stripline Incorporating the Proposed Design Methodology
#### 3.2.5 Study on the Applicability of the Methodology  
To validate the effectiveness of the proposed methodology, it was applied to striplines fabricated on a Rogers 5880 (Rogers, Chandler, AZ, USA) substrate with the following parameters: \(b=0.19\) mm, \(b_{\text{max}}=0.192\) mm, \(d_{\text{a}}=0.2\) mm, \(h=0.128\) mm, \(l=8\) mm, \(s_{\text{a}}=0.4\) mm, \(w_{\text{a}}=0.48\) mm. For the simulations, a relative permittivity of \(\varepsilon_{r}=2.2\) and a loss tangent of 0.0009 were assumed. As shown in Figure 12, the attenuation constant of the stripline implemented with the proposed methodology is reduced by 9.89% compared to the traditional design. These results demonstrate the notable adaptability of the proposed methodology.  
Figure 11: Comparison of different distances between the air cavities and the metal line: (**a**) attenuation constant; (**b**) return loss.
# Reinforcement Learning Based Query Routing Approach for P2P Systems
## 3 RLQR Approach
### 1-Neighbor-Selection Algorithms
#### 3.2.3 UCBNS: UBC-Based 1-NS Algorithm  
Upper confidence bounds (UCB) is a family of MAB algorithms. Unlike the epsilon greedy algorithm, which pays attention only to the average reward of the actions, UCB algorithms rely on the average reward and on the confidence in the estimated reward's values of the selected actions [26]. It is worth mentioning that the UCB family of algorithms always chooses the best available actions and they do not randomly explore unlike epsilon greedy does. In this paper, we focus on the UCB1 algorithm and adapt it to define our 1-NS algorithm UCBNS. As illustrated in Algorithm 2, UCBNS starts by exploring all the \(m\) neighbors for the first \(m\) queries (see lines 1-9 Algorithm 2). Thereafter, it selects, for each query \(q_{
# Video Captioning Based on Channel Soft Attention and Semantic Reconstruct
## 4 Experimental Evaluation
### Results and Analysis
#### 4.4.1 Study on Modules  
The channel soft attention could have a different structure by using different channel attention module. We use the channel attention part of SEnet [28] and CBAM [7] combined with soft attention, respectively. These two kinds of channel attention are quite similar and both with 200 k parameters. We evaluated these two original attentions and the attention replaced the activate function _sigmoid_ with \(tanh\). The results are shown in Table 1. We use SeqVLAD as a baseline which denotes by \"SVLAD\", \"\(SE\)\" and \"\(CA\)\" denote the original SEnet and CBAM (channel attention part). \"\(SE-tanh\)\" and \"\(CA-tanh\)\" denote the attention module with \(tanh\) activate function. As Table 1 shows, the results of SeqVLAD optimized with different channel soft attention are very close. This is because the difference between these two channel attentions is negligible, CBAM has one more maxpool operation than SEnet, and the performance of CBAM is not much better than SEnet in the classification task. The activate function \(tanh\) may not have much improvement, but it could work well in this model. All these models with attention have made a significant improvement compared to the baseline, which shows the effectiveness of channel soft attention.  
As for semantic reconstructor, the parameter \(\lambda\) in Equation (26) indicates adding how much reconstruction loss in training, too large \(\lambda\) may ruin the caption performance. To select the best \(\lambda\), we did some comparative experiments with different \(\lambda\) on two structures. As shown in Figure 4, The comparison on metric BLEU-4 shows that larger is not better. We empirically set \(\lambda\) to 0.2 in the following experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **METEOR** & **BLEU-4** & **CIDEr** & **ROUGE-L** \\ \hline SVLAD & 33.8 & 45.9 & 77.5 & 68.9 \\ SVLAD\({}_{SE}\) & 34.9 & 48.8 & 80.6 & 70.2 \\ SVLAD\({}_{SE-tanh}\) & 34.8 & 49.8 & 81.9 & 71.1 \\ SVLAD\({}_{CA}\) & 34.9 & **50.2** & 81.5 & 71.4 \\ SVLAD\({}_{CA-tanh}\) & **35.1** & 49.9 & **83.7** & **71.5** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different channel soft attention variation on MSVD dataset.  
The parameters and multiply-accumulate operations (MACs) of each module are shown in Table 2. The MACs of each module denotes the multiply-accumulate operations of one video. \"CA\" denotes the channel attention module, \"SDN\" denotes the semantic detection module and \"CSA-SR\" denotes the final module in our paper. As Table 2 shows, the Decoder module occupies more than half the parameters of \"CSA-SR\" and MACs. The total parameters and MACs are not equaled to the final model \"CSA-SR\" because there are some extra operations to connect these modules.
## 3 Network Datasets for NIDSs Evaluation
### UGR'16 Dataset  
The UGR'16 [14] consists of 4 months of anonymized network traffic flows (_netflow_) gathered from a Spanish Internet Service Provider (ISP) facilities. The authors of UGR'16 split it into two different parts: CAL and TEST. The first one, obtained from March to June of 2016, contains incoming and outgoing ISP network traffic. The second one, obtained from July to August of 2016, contains mostly synthetically generated attacks flows which are added to the normal ISP use network traffic. These attacks were launched in a controlled environment, within the ISP facilities, in order to guarantee the correct ISP operation. Most of them were synthetically generated from up-to-date tools covering harmful and difficult to detect attacks such as _low- and high-rate DoS, Port scanning_ or _Botnet_. Apart from the previous ones, the authors labelled real attacks after an in-depth inspection of traffic flows where several state-of-the-art anomaly detectors suggested anomalous behaviours. They identify _UDP port scanning_, _SSH scanning_ and _Spam campaigns_. Last but not least, some flows were labelled as _Blacklist_ if their IP was found in well-known blacklist open sources.  
The dataset is publicly available with a total of 23 files, one per week: 17 for CAL and 6 for TEST. Each file size is 14 GB (compressed) being all of them available in _nfcapd_ and CSV format.  
A preliminary descriptive analysis of classes distribution of the dataset in the TEST part is numerically shown in Table 2. As expected, in the context of the problem addressed here, the number of traffic flows corresponding to attack and regular background traffic (_Background_) substantially differs. The attack with the highest number of flows (_Spam_) supposes only 1.96% of total flows in TEST. Besides, the TEST part contains all of the mentioned attacks, i.e., those synthetically generated and the ones discovered from anomalous behaviours. Because of that, the authors decided to use this part of the whole UGR'16 dataset to perform supervised classification through several ML-based techniques in this work.
## 3 Method  
### Method of Calculating Shale Gas Diffusion  
#### 3.1.1 [PERSON]'s First Law  
The classic [PERSON]'s first law states that the rate of material flow due to diffusion across unit area perpendicular to the direction of diffusion is proportional to the concentration gradient, the diffusion coefficient, and the diffusion time, given by Formula (1):  
\[Q=D\cdot\frac{dC}{dL}\cdot t \tag{1}\]  
In the formula: \(Q\)--diffused gas per unit area, m\({}^{3}\)/m\({}^{2}\); \(C\)--free gas content, m\({}^{3}\)/t; \(D\)--cap diffusivity, m\({}^{2}\)/Ma; \(L\)--cap thickness, m; \(t\)--diffusion time, and Ma--million years.  
After the initiation of hydrocarbon production from source rocks, light hydrocarbons can diffuse through molecular diffusion from source rocks into permeable strata nearby [36; 37]. As the gaseous hydrocarbons continue to diffuse through the cap for a certain period, a relatively constant concentration gradient will occur in the longitudinal direction. As shown in Figure 2a, when the \"cap\" is securely sealed, the gas that is diffusing from the shale will be fully contained within the \"cap\" and will not diffuse through the permeable layer above it. This implies that \(C_{t}^{0}>C_{t}^{x}>C_{t}^{L}=0\). If the sealing capacity of the cap layer is weak, only a fraction of the gas that diffused from the shale will be confined within the shale, while the remaining fraction will diffuse through the cap layer into the underlying permeable layer; therefore, it is expressed as \(C_{t}^{0}>C_{t}^{x}>C_{t}^{L}>0\).  
Figure 1: Buried depth contour map and well crossing profile of Longmaxi Formation in Weiyuan area. (**a**) Buried depth contour map of Longmaxi Formation in Weiyuan area; (**b**) comprehensive stratigraphic bar chart in Weiyuan area; (**c**) passing through the stratigraphic profile of wells Wei201 and Wei204.  
In Formula (1), the unit of the gas concentration, \(C\), is expressed in mol/m\({}^{3}\). To determine the actual rate of gas diffusion under strata circumstances, the gas concentration should be converted into free gas content (m\({}^{3}\)/t) by incorporating the rock's density. The difference in concentration between the roof and the floor helps calculate the vertical gas content gradient. Thus, during the time interval \(t_{1}\) to \(t_{2}\), the cumulative amount of shale gas reservoir that diffuses into the cap layer per unit area via the lower cap interface (\(x=0\)) can be determined as:  
\[Q=\int_{t_{1}}^{t_{2}}\rho_{\mathrm{s}}\frac{D}{L}\left(C_{t}^{0}-C_{t}^{L} \right)\mathrm{d}t \tag{2}\]  
The calculation points of Equation (2) are the values of \(\left(C_{t}^{0}-C_{t}^{L}\right)\) and \(D\).  
In the formula: Q--diffused gas per unit area, m\({}^{3}\)/m\({}^{2}\); \(\rho_{\mathrm{S}}\)--shale density, kg/m\({}^{3}\); \(D\)--cap diffusivity, m\({}^{2}\)/Ma; \(L\)--cap thickness, m; \(C_{t}^{0}\)--free gas content at cap bottom boundary at any time, m\({}^{3}\)/t; \(C_{t}^{L}\)--free gas content at cap top boundary at any time, m\({}^{3}\)/t; \(t_{1}\)--initiation time of diffusion, Ma; \(t_{2}\)--diffusion termination time, Ma; and \(\rho_{\mathrm{S}}\)--rock density, kg/m\({}^{3}\).