esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Transmission Time Measurement
#### 4.4.1 Measurement of Times Corresponding to the First Test with the Kruskal Algorithm  
Different times are obtained when measuring each node's transmission times by configuring a default route generated by the Kruskal algorithm, as indicated in Figure 19. The diagram illustrates that frames from nodes 6 and 7 require approximately 30 msec to reach the controller, whereas the frame from node 1 takes 18 msec for the same journey. Nodes 5 and 7 are the fastest to reach the controller by not making additional hops. The frames passing through node 1 describe a similar graph until they reach the controller node.  
Figure 19: Sequence diagram 3: Test 1.1 Default route generated by the Kruskal algorithm with nodes having similar battery levels.  
Figure 18: Test 2.2 Default route generated by the Dijkstra algorithm where nodes three and four have higher battery levels.
# Application of Image Fusion in Diagnosis and Treatment of Liver Cancer
## 3 General Procedure of Medical Image Fusion
Common functions are root mean square (rms), correlation, normalized cross-correlation, gradient cross-correlation, gradient difference, image entropy, mutual information, normalized mutual information, etc. Then, this function is maximized by optimizing the transformation parameters. This converted the problem to multiparametric and multipeak optimization problems. Traditional optimization methods include the gradient descent method, conjugate gradient method and genetic algorithm, etc. Recently, deep learning has become more and more popular for image fusions and registration [10]. One approach of deep learning used for image registration is to drive iterative optimization using deep learning. In such a method, instead of using a traditional feature descriptor, we train deep learning to learn the feature descriptor to guide the fusion of the image. For instance, a deep learning algorithm, the convolutional autoencoder, is often used to extracted features from the images for the registration [11]. Then by optimization methods such as gradient descent, the measurement function is maximized and the image is registered. In the medical field, this kind of method has been used in the registration and fusion of CT/MR [12; 13] and MR/US [14; 15; 16]. However, such iterative method often requires a long time, and has a difficulty to achieve efficient real-time registration. The second approach is to pretrain the deep learning network to directly obtain the transformation parameters for the image registration. Such a deep learning network can be further divided into two categories: supervised learning and unsupervised learning. Supervised learning has been applied to register/fuse CT/US [17] and MR/US [18; 19; 20; 21]. The training of the supervised learning network requires the ground truth. Two kinds of ground truth are generally used: ground truth data from the traditional registration method, as shown in above (i.e., step 1 to step 4) [22; 23] and simulated ground truth data. For example, [PERSON] and [PERSON] generated image pairs using random transformations and obtained ground truth data for the image registration of CT images [24]. Similar ground truth generation methods are used for the fusion of MR images [25]. For unsupervised learning, the deep learning network is trained without the need of ground truth data. The most widely used network is Voxelmorph. This framework, which was proposed by [PERSON] et al. [26], trains the network using a metric that quantifies image which are similar to input images. In the medical field, unsupervised learning has been used for the registration/fusion of CT/MR [27; 28; 29] and US/MRI [30].
## 3 NDN: The Networking Paradigm for Fog Computing  
### Fog Computing: Key Pillars and Issues  
The fog computing paradigm has been initially proposed in the pioneering work of [PERSON] et al. [25], was highly promoted by Cisco [26], and was further refined by the industrial and academic communities [4] to enable computing, storage, and networking close to the end-user.  
End-user devices or near-user edge devices can be leveraged to carry out communication and computation in different IoT domains, such as industrial plants [27] and vehicular environments [28; 29].  
Other similar paradigms to fog computing such as edge computing, mist computing, and cloudlets have been proposed by the research community with similar purposes [30]. Despite the similarities in terms of targeted objectives, the OpenFog consortium [4], bringing together industry companies and academic institutions across the world for the sake of standardization and promotion of fog computing, recognizes a more comprehensive scope and flexibility of the fog in that it seeks to enable computing services anywhere from cloud to things, as data typically moves from the IoT devices to the processing nodes. This is a clear departure from many existing solutions that treat network edges as isolated computing platforms and are not inclusive of the cloud.  
Such a kind of deployment particularly challenges service addressing; discovery and provisioning; and management aspects, like orchestration of services and network resources [30; 31].  
Requests for a computing service (i.e., the execution of a given processing function over a given content) are issued regardless of the position (address) of the executor node, of which the identity is not a priori known to the requester. A service may reside on a number of local nodes, or it may partially reside on local nodes and on the cloud. Conventional addressing schemes are not suited to match the service-centric nature of the targeted context.  
Discovery procedures are typically performed with the help of an application-layer broker mechanism [32; 33] that matches the service request with the best available node that is able to execute the service based on specified requirements and discovered capabilities. These broker entities, however, further complicate the reference scenario.
## 3 WDM-PON Network Architectures
### The RING Architecture of the Metro-Access WDM-PON Network  
This RING metro-access architecture is sometimes called the Long-Reach (LR) passive optical network. It is created by the WDM ring with involved passive AWG elements. The specific AWG element is subsequently directed towards the TDM access branch. By using Erbium Doped Fiber Amplifiers (EDFA), two L- and C- bands can be utilized in the optical fiber. At creating this RING metro-access architecture, various splitting ratios can be selected for optical power splitters and a connection of two TDM branches to the one WDM RN with the AWG element is possible.  
The ring metro subnetwork has modular possibilities for connecting a large number of users within limits of acceptable attenuation values. It is reached by erbium doped fibers remotely pumped from the OLT terminal. Possible values of the power budget can be adjusted according to fiber lengths and power characteristics of lasers. The access branch subnetwork is created by the TDM-PON network, where the Distribution Fiber (DF) connected to the AWG element is directed to the TDM RN node with the Optical Switch (OS) element. Thereafter, it is splitting up to ONU units through the terminal Drop Distribution Fiber (DDF) [24].
# A Shearlets-Based Method for Rain Removal from Single Images
## 5 Numerical Experiments  
In this section, some comparison tests are performed to validate the effectiveness of the proposed method. We compare the proposed method with three recent state-of-art rain removal methods, including a dictionary learning-based algorithm in [11] (15 ICCV), a minimization model with the learned rain layer prior method in [13] (16 CVPR), and a directional global sparse model in [21] (18 UTV). All tests are implemented in a laptop, CPU: Inter Core i5, memory: 16 GB, OS: Windows 10, testing software: Matlab R2016a.  
Since humans are more sensitive to changes in luminance, all the RGB testing images are converted to YUV space. The proposed method is utilized to remove rain streaks only in the luminance channel (three compared methods are also implemented on the luminance channel). To be more persuasive, we use two kinds of data for experiments, the synthetic data, and real data. For the synthetic data, the relative error (RelErr), peak signal-noise ration (PSNR), and the structural similarity (SSIM) [33] are used to estimate the performance of different methods.  
* The relative error (RelErr) is defined as \[\text{RelErr}=\frac{\|u-u^{r}\|_{F}^{2}}{\|u\|_{F}^{2}},\] (25) where \(u\) and \(u^{r}\) are the original signal and the recovered signal, and \(\|\cdot\|_{F}\) represents the Frobenius norm.
* The peak signal-noise ration (PSNR) is defined as \[\text{PSNR}=10\log\frac{MNu_{max}^{2}}{\text{MSE}},\] (26) where \(u_{max}\) is the maximum value of \(u\), and \[\text{MSE}=\frac{1}{MN}\sum_{i=1}^{M}\sum_{j=1}^{N}\|u(i,j)-u^{r}(i,j)\|_{F}^{2},\] (27) where \(M\), \(N\) are the size of the signal \(u\).
* The structural similarity (SSIM) is defined as \[\text{SSIM}=\frac{(2\mu_{u}\mu_{u^{r}}+c_{1})(2\sigma_{uu^{r}}+c_{2})}{(\mu_{u }^{2}+\mu_{u^{r}}^{2}+c_{1})(\sigma_{u}^{2}+\sigma_{u^{r}}^{2}+c_{2})},\] (28) where \(\mu_{u}\) and \(\mu_{u^{r}}\) represent the average of \(u\) and \(u^{r}\), \(\sigma_{u}\), and \(\sigma_{u^{r}}\) represent the standard deviation of \(u\) and \(u^{r}\), \(\sigma_{u^{r}}\) is the covariance of \(u\), and \(u^{r}\), \(c_{1},c_{2}>0\).
# An Automated Behaviour-Based Clustering of IoT Botnets
## 5 Findings and Discussion
### Comparison with AVClass Classification  
Some of the related studies have evaluated the clustering effectiveness using the labels assigned to malware samples by different antivirus engines. A clustering is considered as effective if all samples within a single cluster are assigned the same label. An antivirus label typically indicates a malware class the sample belongs to. However, a malware sample may not be detected by all antivirus engines. Furthermore, a malware sample can be assigned different labels by different antivirus engines. In the case of the latter, it is necessary to identify the most likely malware class the sample belongs to. This can be achieved using AVClass.  
AVClass [39] is a tool which uses the labels assigned from different antivirus engines to identify the most likely malware class the sample belongs to. It takes as an input a JSON report containing the results from the scans performed by the online antivirus scanner VirusTotal. VirusTotal [40] is an online service for scanning files using more than 60 different antiviruses simultaneously. The VirusTotal report contains the labels assigned to the malware sample by the different antivirus vendors. One limitation of AVClass is that it may unable to identify the most likely malware class if there is a lack of decisive antivirus labels.  
We used the API provided by VirusTotal to scan each sample from our dataset and to obtain the scan results. After scanning the samples, we used AVClass to identify the most likely malware class of each sample. We then investigated the malware classes of the samples per cluster and identified two cases: (a) when all samples within the cluster were assigned the same class, we refer to such clusters as pure and (b) when the samples within a cluster were assigned different classes, we refer to such clusters as mixed.  
Sixteen of the nineteen clusters identified by DBSCAN, shown in Table 6, are pure, seven of which were assigned the class 'linux', six the class'mirai', and one cluster was assigned the class 'python'. However, the classes assigned by AVClass may not be helpful for identifying botnet variants. For instance, the distinction between 'linux' and'mirai' classes is unclear since Mirai is a type of Linux malware. We investigated the behaviour profiles of the samples comprising one of the'mixed' clusters and observed samples with identical behaviour profiles which have been assigned different malware classes. This may be a result of malware classification performed by some antivirus vendors using only static features. Therefore, we believe that the clustering evaluation based on labels assigned by antivirus vendors may not be effective in the case of IoT malware.
# An Advanced SAR Image Despeckling Method by Bernoulli-Sampling-Based Self-Supervised Deep Learning
## 2 The Proposed Method
### Multiscale Despecking Network
#### 2.2.2 Dense Residual Block  
To enhance feature extraction, we introduced the DRB in the subblocks of the MSDNet, in other words, the PB, EBs, and DBs. Different from the subblocks of the traditional UNet, the DRB combines the dense connection [42] and the residual skip connection [43], as presented in Figure 5. The DRB consists of three convolutions (i.e., \(\mathbf{W}_{d,1}\), \(\mathbf{W}_{d,2}\), and \(\mathbf{W}_{d,r}\)) along with the parametric rectified linear units (PReLU) [44] (i.e., \(\sigma_{d,1}\), \(\sigma_{d,2}\), and \(\sigma_{d,r}\)). Let the input of DRB be \(\mathbf{F}_{d-1}\). The output of the first convolution along with PReLU is expressed as:  
\[\mathbf{F}_{d,1}=\sigma_{d,1}(\mathbf{W}_{d,1}(\mathbf{F}_{d-1})). \tag{12}\]  
Then, \(\mathbf{F}_{d-1}\) and \(\mathbf{F}_{d,1}\) are concatenated to feed the second convolution by the dense connection, which can be written as:  
\[\mathbf{F}_{d,2}=\sigma_{d,2}(\mathbf{W}_{d,2}([\mathbf{F}_{d-1},\mathbf{F}_{ d,1}])), \tag{13}\]  
where \([\mathbf{F}_{d-1},\mathbf{F}_{d,1}]\) refers to the concatenation of \(\mathbf{F}_{d-1}\) and \(\mathbf{F}_{d,1}\). Then, \(\mathbf{F}_{d-1}\), \(\mathbf{F}_{d,1}\), and \(\mathbf{F}_{d,2}\) are concatenated to feed the last convolution, which can be expressed as:  
\[\mathbf{F}_{d,r}=\sigma_{d,r}(\mathbf{W}_{d,r}([\mathbf{F}_{d-1},\mathbf{F}_{ d,1},\mathbf{F}_{d,2}])). \tag{14}\]  
Finally, a residual skip connection is employed to obtain the final output of the DRB. Specifically, \(\mathbf{F}_{d-1}\) is used as the residual to be added with \(\mathbf{F}_{d,r}\), which can be written as:  
\[\mathbf{F}_{d}=\mathbf{F}_{d,r}+\mathbf{F}_{d-1}, \tag{15}\]  
where \(\mathbf{F}_{d}\) is the final output of the DRB.
# Active Noise Control Using Modified FsLMS and Hybrid PSOFF Algorithm
###### Abstract  
Active noise control is an efficient technique for noise cancellation of the system, which has been defined in this paper with the aid of Modified Filtered-s Least Mean Square (MFsLMS) algorithm. The Hybrid Particle Swarm Optimization and Firefly (HPSOFF) algorithm are used to identify the stability factor of the MFsLMS algorithm. The computational difficulty of the modified algorithm is reduced when compared with the original Filtered-s Least Mean Square (FsLMS) algorithm. The noise sources are removed from the signal and it is compared with the existing FsLMS algorithm. The performance of the system is established with the normalized mean square error for two different types of noises. The proposed method has also been compared with the existing algorithms for the same purposes.  
active noise elimination; MFsLMS algorithm; multichannel ANC; HPSOFF +
Footnote †: journal: Applied Physics Letters  
_Article_  
Active Noise Control Using Modified FsLMS and Hybrid PSOFF Algorithm
# Pansharpening by Complementing Compressed Sensing with Spectral Correction
###### Abstract  
Pansharpening (PS) is a process used to generate high-resolution multispectral (MS) images from high-spatial-resolution panchromatic (PAN) and high-spectral-resolution multispectral images. In this paper, we propose a method for pansharpening by focusing on a compressed sensing (CS) technique. The spectral reproducibility of the CS technique is high due to its image reproducibility, but the reproduced image is blurry. Although methods of complementing this incomplete reproduction have been proposed, it is known that the existing method may cause ringing artifacts. On the other hand, component substitution is another technique used for pansharpening. It is expected that the spatial resolution of the images generated by this technique will be as high as that of the high-resolution PAN image, because the technique uses the corrected intensity calculated from the PAN image. Based on these facts, the proposed method fuses the intensity obtained by the component substitution method and the intensity obtained by the CS technique to move the spatial resolution of the reproduced image close to that of the PAN image while reducing the spectral distortion. Experimental results showed that the proposed method can reduce spectral distortion and maintain spatial resolution better than the existing methods.  
pansharpening; spectrum correction; intensity correction; compressed sensing; tradeoff process; IKONOS Article
# Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things
## 3 Performance Evaluation
### The SAC Protocol Performance  
Objective: This experiment was conducted to assess the relevance of using the SAC model in IoT settings by comparing it to a breadth-first search (BFS) and a service-blind clustering (SBC) algorithm. The BFS algorithm is a non-hierarchical routing algorithm that finds routing paths for the sensor readings by considering a flat network topology, while the SBC is a hierarchical routing algorithm that is based on only signal strength for cluster selection and cluster head-member association, discounting energy and service awareness. This experiment was complemented by another experiment aiming at comparing two different versions of the SAC algorithm: (1) the SAC  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Test Attributes** & **Test Value** \\ \hline Beacon Interval & 30 s (LIBP), Adaptive (CTP, RPL) \\ Messaging Interval & 30 s, but following the TDMA schedule computed by SAC \\ Message Contents & “Hello from node” \\ Simulation Runtime & 1000 s, but with 120 s for network self-organization \\ TX/INTRange & 50 m/100 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental setup: Cooja parameters.  
algorithm where over the experimental time, energy awareness and service awareness are combined to find efficient routing paths; and (2) a dual-mode SAC algorithm called SAC-MIX, where energy awareness is implemented during the first half of the experiment when all nodes are assumed to have enough residual energy. Service awareness is implemented during the second half of the experiment when the nodes are assumed to have depleted enough energy.  
Network: Two networks were considered: (1) a hybrid network consisting of 100 nodes randomly distributed within the sensing area; and (2) a conventional sensor network consisting of 100 CSN nodes. The hybrid sensor network consisted of 80 CSN nodes and 20 HSN nodes, where each HSN node consumes 0.5 J more than the energy of a CSN node. The hybrid sensor network running SAC had a total initial energy of 110 joules, while the normal sensor network running the BFS and SBC had a total initial energy of 100 joules. This is because, in practice, the HSN nodes are initially more energized than the CSN nodes.  
Results: The results presented by Figure 9a revealed the relative efficiency of the SAC algorithm compared to both BFS and SBC in terms of residual energy. The results also reveal that through implementing hierarchical routing, a service-blind algorithm can be outperformed by a non-hierarchical algorithm. Figure 9b reveals that SAC-MIX outperforms SAC. Therefore, SAC-MIX will be considered in the rest of our experiments and referred to as SAC.
## 4 Conclusions  
We have numerically studied the two-photon ionization of a hydrogen and helium atom by an ultrashort EUV pulse. Due to the competition between the resonant and nonresonant ionization paths, the relative phase \(\delta\) between the \(S\) and \(D\) photoelectron wave packets from the resonance-enhanced two-photon ionization by fs EUV pulses is different from the scattering phase shift difference, \(|\delta_{sc}|\) or \(\pi-|\delta_{sc}|\) more precisely, which would be expected for single-photon ionization from an excited level [29] and nonresonant two-photon ionization, and rapidly changes with the pulse width when the pulse is resonant with an intermediate excited state and \(2\,\mathrm{fs}\lesssim T_{1/2}\lesssim 10\,\mathrm{fs}\). Accordingly, the photoelectron angular distribution varies with \(T_{1/2}\) as well. Hence, the control of the competition between the resonant and nonresonant paths in H and He by pulse width is a unique feature of a-few-fs EUV pulses.  
Also, \(\delta\) is different from \(|\delta_{sc}|\) and \(\pi-|\delta_{sc}|\) but nearly constant independent of \(T_{1/2}\) when the Rydberg manifold is excited and in the case of above-threshold two-photon ionization. The transition across the ionization threshold is smooth.  
The PAD as well as \(W\) and \(\delta\) is affected by the chaotic nature of SASE FEL pulses and by neighboring harmonic components typical of HHG. For the former case, the relative phase \(\delta\) takes a value between those corresponding to the coherence time and the mean pulse width of the pulses. For the latter case, _i.e._, for the case of 2 PI by high-harmonic pulses with multiple harmonic orders, \(\delta\) does not depend much on the pulse width, similarly to the case of the Rydberg manifold excitation. On the other hand, by selecting an appropriate photoelectron energy range, one can recover the pulse-width dependence of \(\delta\) originating from the competition of the resonant and nonresonant ionization paths.  
The results of the present study stress the importance of the account of the nonresonant paths in the interpretation of resonant two-photon, single- or two-color, ionization experiments, including coherent control, by state-of-the-art ultrashort EUV sources.