esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### LSTM Network  
An LSTM [22] network uses cell state, input, output, and forget gates to store long-term dependencies to overcome vanishing gradient problem in typical RNNs and was introduced in 1997 by [PERSON] and [PERSON] The LSTM processes the data sequentially passing the information as it propagates forward. The operations within LSTM allows it to forget or keep the information. The architecture of LSTM model is shown in Figure 4.  
The cell state which is shown as a horizontal line runs through the entire network and has the ability to add or remove the information with the help of gates. The process of the cell state is to carry the information through the sequence processing and theory information from earlier time steps can be carried all the way through the last time step thus reducing the effect of short term memory. As the process goes on, the information is added or removed from the cell states to gate states. Gates decide which information is allowed on the cell state. The first gate that is the forget gate is responsible for learning what information is necessary to keep or forget as they contain sigmoid function. The sigmoid function generates numbers between zero and one, describing how much of each component should be let through. The tanh function generates a new vector, which is added to the state. The cell state is updated based upon the outputs generated from the gates.  
The sigmoid function is given as  
\[sigmoid(x)=\frac{1}{1+e^{-x}} \tag{2}\]  
Equations (3)-(8) represent the flow of information at each gate and cell state of LSTM network:  
\[f_{t}=\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}) \tag{3}\] \[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i})\] (4) \[\tilde{C}_{t}=tanh(W_{C}\cdot[h_{t-1},x_{t}]+b_{C})\] (5) \[C_{t}=f_{t}*C_{t-1}+i_{t}*\tilde{C}_{t}\] (6) \[o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o})\] (7) \[H_{t}=o_{t}*tanh(C_{t}) \tag{8}\]  
\(f_{t}\), \(i_{t}\), and \(o_{t}\) represent the outputs generated by forget gate, input gate, and the output gate, respectively. \(W_{t}\), \(W_{i}\), \(W_{C}\), and \(W_{o}\) are the input weights, respectively. \(b_{t}\), \(b_{i}\), \(b_{C}\), and \(b_{o}\) are bias terms and \(H_{t}\) is the output of LSTM network.  
Figure 4: The architecture of LSTM Network.
# Underwater Target Recognition Based on Multi-Decision
## 5 Numerical Results
### Multi-Step Decision LOFAR Line Spectrum Enhancement Algorithm Validity Test
#### 5.4.2 Identification of Measured Ship Radiated Noise  
The testing data set adopts the same feature preprocessing as the training set and inputs the trained model to complete the test.  
Figure 11 shows the standardized confusion matrix. The recognition accuracy of the radiated noise of the four types of ships is different. Among them, the recognition effect of the Y-signal is the best and the recognition accuracy rate reaches 100.00%. The recognition accuracy rates of the W-type and Z-type are slightly worse, and they are 95.10% and 97.68%. Additionally, the recognition effect of the X-type is the worst, which is only 87.60%. In summary, the total recognition accuracy rate is 95.22%. The recognition accuracy of four kinds of measured ship radiated noise is shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multirow{2}{*}{**ID**} & \multicolumn{2}{c}{**Training Set**} & **Testing Set** \\ \cline{3-4}  & & **Number of Samples** & **Number of Samples** \\ \hline \multirow{3}{*}{W} & 46, 48, 66, & & \\  & 73, 74, 75, & & \\  & 80, 93, 94, & & \\  & 95, 96 & & \\ \hline \multirow{3}{*}{X} & 21, 26, 29, & & \\  & 30, 50, 52, & \\  & 57, 70, 72, & & \\  & 77, 79 & & \\ \hline \multirow{3}{*}{Y} & 6, 7, 8, 10, & \\  & 11, 13, 14 & 1016 & 526 \\ \hline \multirow{2}{*}{Z} & 18, 19, 20 & 1149 & 603 \\ \cline{1-1} \cline{2-4}  & Total & & 3838 & 2176 \\ \hline \hline \end{tabular}
\end{table}
Table 6: various sample training sets and testing sets.  
Figure 12 shows the ROC curve and the corresponding AUC value of the four types of signals. The horizontal axis uses a logarithmic scale to enlarge the ROC curve in the upper left corner. The ROC curves of the signals of W, Y, and Z are relatively close to the \((0,1)\) point, and their classification effects are relatively good. However, the ROC curve of the signals of type X is closest to the 45-degree line, so the classification effect is worst. Judging from the AUC value, the AUC of the Z-type signal is the highest, which reaches 0.9981. The AUC values of the W-type and Y-type signals have respectively reached 0.9952 and 0.9925. The AUC value of the X-type signal is only 0.9702. Therefore, the classification effect of the X-type signal is also inferior to the other three types of signals.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Recognition Rate** & **Class W** & **Class X** & **Class Y** & **Class Z** & **Average** \\ \hline CNN & 95.10\% & 87.60\% & 100.0\% & 97.78\% & 95.22\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: The recognition accuracy rate of the four types of measured ship radiated noise.  
Figure 11: Confusion matrix of four types of measured ship radiated noise under CNN.  
Figure 12: The ROC curve and AUC value of four types of measured ship radiated noise under CNN.
## 2 Materials and Methods
### EEG Data Collection and Analysis  
EEG activity was recorded using a 128-channel Biosemi ActiveTwo (BioSemi B.V. WG-Plein 129 1054 SC Amsterdam, The Netherlands) measurement device and Ag/AgCl active electrodes arranged in the Biosemi equivalradial 'ABC' electrode layout [37] held in place by an elastic cap. Conductive saline gel (Signa) was applied to ensure good electrical contact between the electrodes and the scalp. DC electrode offset values were kept stable below the recommended 40 \(\mu\)V amplitude level. The analog input EEG signal was low-pass-filtered (fc = 410 Hz, \(-\)3 dB) by the EEG device before being digitized at a sampling rate of 2048 Hz. Data were saved in BDF EEG file format for later offline processing.
## 4 Discussion
also used ASTER stereo image pairs and satellite altimetry data to obtain a similar estimation: \(-\)0.66 \(\pm\) 0.02 m/yr [19]. ASTER optical data are affected by \"light saturation\", which prevents us from obtaining glacier elevation change data within the accumulation area, which could contribute to the higher mass loss estimations of these studies. [PERSON] et al. used TanDEM-X bi-static InSAR and a C-band SRTM DEM to obtain the surface elevation changes from 2000 to 2014 for five glaciers in Unknown I, Zepu, Unknown II, Maguolong, and Guanxing in the SETP, which were \(-\)0.50 \(\pm\) 0.57 m/yr, \(-\)0.58 \(\pm\) 0.57 m/yr, \(-\)0.67 \(\pm\) 0.57 m/yr, \(-\)1.19 \(\pm\) 0.57 m/yr, and \(-\)1.40 \(\pm\) 0.57 m/yr, respectively [44]. The results for the first three glaciers are similar to our regional average mass balance estimations, while the last two results are relatively large. However, [PERSON] et al. provided data for individual glacier which might deviate significantly from the regional average. [PERSON] et al. and [PERSON] et al. also utilized bi-static InSAR data to obtain glacier mass  
Figure 12: Spatial distribution of surface precipitation change rate in the SETP from 2000 to 2014.  
balance results for certain sub-regions within the SETP such as the Kangri Karpo Range and the Nyaingentanglha [27; 35; 36; 45].
## 3 Trajectory Tracking Control of a Nonlinear Reactor: A Case Study
### Second Simulation Analysis: Implementation of the Analytical SMC and I/O FBL Control Design  
The aim is to ensure a perfect tracking between the CSTR output \(y(t)\) and the reference desired input \(y_{d}(t)\) even in the occurrence of the disturbance \(D_{2}\). Next, after the aforementioned SMC method wherein \(q=1\), the designed sliding surface is described by:  
\[S(t)=y_{d}(t)-y(t) \tag{46}\]  
By using the reaching law as provided in Equation (38), the control input gives:  
\[U=\left[\begin{array}{c}X_{2}-T_{r}Da(1-X_{1})\exp(X_{2}/\left(X_{2}/\sigma \right)+1)+\delta X_{2}-y_{d}^{(1)}\\ -\Gamma_{1}(X_{2}-y_{d})-D_{1}+\theta|S|^{\rho}\text{sgn}(X_{2}-y_{d})\end{array} \right]\delta^{-1} \tag{47}\]  
Figure 7 displays the outcomes attained for several values of \(D_{2}\), \(\vartheta=0.5\), and \(\rho=1\). The control input variable is not steady for all disturbance values. For \(\rho=0.52\) and \(\vartheta=0.5\), the chattering is suppressed (Figure 8).  
Figure 6: Control input \(U\): (red: Dynamics obtained with FBL, violet: Dynamics obtained with disturbance injected to the model, blue: Dynamics obtained with disturbance injected to the model and sliding mode control).  
The output was found to be near the desired value, thus preserving stability as well as representing better outcome robustness. Thus, complete elimination of the disturbing parameter \(D_{2}\) did not happen because the model is incorrect. Thus, the computed value pertaining to the concentration, which also influences the SMC through the reaching law, remains incorrect. Consequently, some uncertainty could also be found associated with the derivative pertaining to the switching surface.  
Another problem is that even when the parameters \(\rho\) and \(\theta\) are adjusted in an accurate manner, chattering could still impact the system. Moreover, the greater the chattering is reduced, the more the tracking error among \(y(t)\) and \(y_{d}(t)\) increases. To find the optimal controller gains pertaining to these above variables, one must reduce the length gap to the sliding surface, which is a delicate control task.  
To conclude the above results, the concentration observer needs to be enhanced to achieve safe operation and good disturbance-rejection.  
The stability and robustness characteristics are gained when the SMC loop is added to the I/O FBL controller, which made us contemplate applying SMC for determining the immeasurable disturbance.  
To enhance the performance of the studied control design, a heuristic optimization technique combined with fuzzy logic will be implemented with the analytical SMC-based I/O FBL. The main objective is to preserve a high-quality steady-state regime while reducing the chattering phenomena under severe output desired trajectory dynamics.
# Detection of Malware by Deep Learning as CNN-LSTM Machine Learning Techniques in Real Time
###### Abstract  
Cyber-attacks on the numerous parts of today's fast developing IoT are only going to increase in frequency and severity. A reliable method for detecting malicious attacks such as botnet in the IoT environment is critical for reducing security risks on IoT devices. Numerous existing methods exist for mining IoT networks for previously discovered patterns that may be exploited to improve security. This study used a hybrid deep learning approach, namely the CNN-LSTM technique, to detect botnet attacks. Any software that infiltrates a computer system or is installed there without the administrators' knowledge or permission is malicious. There is a wide range of viruses that cyber-criminals use to further their nefarious ends. A revolutionary deep learning system has been developed to counteract the increasing quantity of harmful programs. The system takes advantage of NLP methods as a baseline, mixes CNNs and LSTM neurons to capture local spatial correlations, and learns from successive long-term dependencies. Spatial invariance, often known as symmetry, is the property wherein the dataset size remains constant throughout iterations of an algorithm while undergoing various transformations. Therefore, automated extraction of high-level abstractions and representations aids in the malware categorization process. When compared to its predecessor research study, the current level of categorization accuracy is significantly greater than 0.81. The proposed CNN-LSTM method obtained an R2 = 99.19% in the dataset, with a correlation coefficient for the CNN-LSTM technique of R2 = 100% utilizing the provided dataset. The symmetry correlation of the CNN-LSTM, which illustrates that the CNN-LSTM method has the highest detection accuracy, at 99%, among the other malware detection methods such as the SVM and DT. The rest of classifiers had an accuracy of 98% for DT, and 95% for SVM. The accuracy of the LSTM model is 99%, the precision of the CNN-LSTM is 99%, recall is 99% and F1 score is 1.  
CNN-LSTM; cyber-attacks; malware; IoT; malicious threats; machine learning algorithms; cyber security; suspicious activity; cyber threats; malware detection +
Footnote †: journal: Computer Science and Technology  
1
Footnote 1: [[https://www.mdpi.com/journal/symmetry](https://www.mdpi.com/journal/symmetry)]([https://www.mdpi.com/journal/symmetry](https://www.mdpi.com/journal/symmetry))
## Appendix A Proof of Lemmas Used for the Upper Confidence Bound  
**Lemma A1** (Hoeffding's Lemma).: _If \(a\leq X\leq b\) with probability \(1\), then \(X\) is a \(\dfrac{(b-a)^{2}}{4}\)-subgaussian random variable._  
Proof.: In the first step, we prove the following for any random variable, \(X\):  
\[Var(X)=\inf_{t}[(X-t)^{2}]\] \[=Var(X-\dfrac{(b-a)^{2}}{2})\] \[\leq\dfrac{(b-a)^{2}}{4}\]  
Now, let \(P\) denote the distribution of \(X\) and define \(\rho(\lambda)=\log E_{p}[e^{\lambda X}]\). Let \(Q_{\lambda}\) be the distribution of \(X\), defined as follows:  
\[dQ_{\lambda}(x)=\dfrac{e^{\lambda x}}{E_{p}[e^{\lambda X}]}dP_{X}\]  
\[\rho(\lambda)=\dfrac{E_{p}[Xe^{\lambda X}]}{E_{p}[e^{\lambda X}]}=\int x \dfrac{e^{\lambda X}}{E_{p}[e^{\lambda X}]}dP(x)=E_{Q}[X]\]  
\[\rho^{\prime\prime}(\lambda)=\dfrac{E_{p}[X^{2}e^{\lambda X}]}{E_{p}[e^{ \lambda X}]}-\dfrac{E_{p}[Xe^{\lambda X}]^{2}}{E_{p}[e^{\lambda X}]^{2}}=\]  
\[E_{Q}[X^{2}]-E_{Q}[X]^{2}=Var_{Q}(X)\]  
From Step 1, we have \(\rho^{\prime\prime}(\lambda)=Var_{Q}(X)\leq\dfrac{(b-a)^{2}}{4}\). According to [PERSON]'s theorem, there exists some \(\tilde{\lambda}\in[0,\lambda]\) such that  
\[\rho(\lambda)=\rho(0)+\rho^{\prime}(0)\lambda+1/2\rho^{\prime\prime}(0)+\rho ^{\prime\prime}(\tilde{\lambda})\lambda^{2}\]  
Given the convexity of \(e^{X}\), we can exponentiate both sides, and that finalizes the proof.
## 3 Experiment Results and Discussion
### Implementation Details  
An Adam optimizer [33] with standard momentum was used for optimizing the objective function. The initial learning rate was 0.00001, and if loss was not reduced during 100 epochs for the validation set, the learning rate was reduced by 0.5 times. For the dataset, a total of 7481 pairs of point cloud top view images and answer data were divided at a ratio of 7:2:1 to form the training set (5237), validation set (1496), and test set (748). The distance, reflection intensity, height, and RGB values of the point cloud top view image used as input data were normalized in the range of \([0,1]\). The x and y coordinates of the center point and the width and height of the bounding box in the answer data were also normalized in the range of \([0,1]\). The size of the batch used for training was set to 8 to apply the maximum size allowed by the GPU according to the network structure or input method.  
All codes for the experiment were written using Python 3. We implemented the deep learning model, and training, inference, and evaluation were performed using the Keras API and Tensorflow framework. The proposed network was trained using a NVIDIA GTX 1080 Ti GPU having 11 GB of memory.
## 6 Discussion
### Challenges
#### 6.2.8 Requirement of Extra Resources  
Blockchain-based solutions are well known for their additional resource consumption, specifically computational, storage, and communication resources. An intelligent network like a KDN already spends additional resources on the knowledge plane to implement knowledge generation models, ontology editors, rule generators, knowledge bases, rule engines, etc. Due to the integration of blockchain, the additional resources required are even higher, as blockchains need storage for storing the transactions, where each validated transaction is essentially replicated in each node, and need computational power to compute hashes, sign and verify using digital signatures, achieve distributed consensus, implement smart contracts for automatic access control and contractual function implementation, etc. In terms of communication resources, blockchains consume bandwidth from both end users and network operators for broadcasting transactions and blocks in the P2P network. However, some academicians have attempted to propose resource-efficient blockchain-based solutions for knowledge-producing networks such as KDN by carefully assessing their reliability against security aims [13]. Thus, resource-efficient blockchain systems must be selected to reduce the burden of additional resources that are used in a distributed blockchain.
## 4 Results and Discussion
### Relevant Feature Subset Based on a CFS Algorithm  
In this study, the feature input consisted of 39 items of LiDAR-derived data (i.e., slope, height, and intensity), texture features (i.e., GLCM StdDev and GLCM homogeneity), and visible band. The optimal combination of features was selected via ten experiments using a CFS algorithm. Selection began from (1, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 39) of the features. The most relevant feature subsets were obtained after 100 iterations in every experiment; this result is in line with the procedure proposed by [PERSON] et al. [52]. High classification accuracy was achieved when 10 of the features were applied, indicating that LiDAR-derived data, visible bands, and textural features were more effective in detecting the landslide location. Table 3 shows the most significant results of feature selection based on the CFS algorithm.
# Radiation Qualification by Means of the System-Level Testing: Opportunities and Limitations
## 3 Opportunities Given by System-Level Testing
for mixed-field tests at CHARM: a number of events and information on the degradation due to cumulative effects in the environment defined by the high energy hadrons spectrum. With these data, the user acquires some level of confidence in the system's radiation performance in a cost-efficient way. The amount and quality of data collected during system-level test will highly depend on the observability of system parameters provided by the test setup. Information from the system-level test might be used for design validation, or as an input for design improvements. Results have different specificity for TID and SEE test campaigns, therefore these two types of tests will be discussed separately.
## 2 Materials and Methods
### Data Analysis  
All measured data were synthetized in an Excel table, compared and analyzed using statistical software (SPSS 12.0; SPSS Inc., Chicago, IL, USA). A \(p\)-value of <0.01 was considered statistically significant. Mean value and standard deviation were calculated for each group.  
The sample size of the study was established based on the fact that a sample size of 150 subjects reaches 89% of power (1-\(\beta\)) to detect an effect size of 0.3 using a chi-square test with two degrees of freedom and a significance level (\(\alpha\)) of 0.05.  
The agreement between each set of anthropometric measurements performed by the same operator at two weeks interval was calculated by using the intraclass correlation coefficient.  
Bland and Altman Plots were drawn to evaluate the correlation between the clinical anthropometric measurements and the same measurements performed on facial scans.  
The Pearson's correlation coefficient was used to assess correlations between OVD (Sn-Gn) and the measured parameters. Simple linear regression analysis with a stepwise forward approach was used to develop a prediction formula for the OVD using other measured parameters as independent variables. The null hypothesis of the present study was that a strong correlation between anthropometric and cephalometric measurements of OVD will be determined.  
Figure 4: Digital anthropometric measurements on facial scans in maximum intercuspation position: (**a**) Subnasion [Sn] to Prementon [PM] (Sn–PM), (**b**) right pupil [Pu] to right buccal commissure (chelion [Ch]) (Pu–Ch_r) and (**c**) left pupil to left buccal commissure (Pu–Ch_l).
## 4 Experiments
### Experiment Settings  
#### 4.1.1 Problem Definition  
We preprocess the check-in sequence (\(CKu\)) of each user (\(u\)) by segmenting it into a set of consecutive trajectories, denoted as \(CK_{u}=S_{u}^{1}\oplus S_{u}^{1}\oplus\ldots\), where \(\oplus\) represents the concatenation. Each trajectory spans 48 h and contains a maximum of 10 and a minimum of 3 unique check-in locations that the user visits. To simplify the temporal analysis, we approximate the check-in times in the trajectories to hourly intervals. Specifically, for each hour within the 48 h, we retain only the earliest check-in location, as multiple check-ins within the same hour are rare in our dataset. This approximation helps maintain data conciseness while capturing the user's overall mobility patterns. If a user visits more than ten unique locations within 48 h, we segment the check-in sequence into multiple trajectories, each containing ten locations. In this way, a user can have multiple trajectories in the dataset, representing different periods.  
In addition to the preprocessing steps mentioned above, we also introduce handling for weekdays and weekends in the trajectory data. When approximating the check-in times in the trajectory as hourly intervals, not only is the check-in time recorded but also whether each check-in point occurs on a weekday or weekend. Moreover, a label is added for each check-in point using binary coding (0 for weekdays and 1 for weekends). Suppose a user visits more than ten unique locations within 48 h, and these locations span both weekdays and weekends. In this case, the trajectories are segmented by weekday/weekend boundaries to ensure the accuracy of the data labels.  
The task of our model is to predict the \(n\)-th check-in location of a trajectory containing n check-ins based solely on the previous \(n\)-1 check-in locations within that trajectory. This prediction capability enables us to capture the sequential nature of the user mobility and infer future check-in locations based on past behavior.
# A Comparative Analysis of Big Data Frameworks:
## 4 Big Data Processing Frameworks
### 4.5. Flink
#### 4.5.4 Fault Tolerance  
Flink provides reliable execution with exactly-once consistency guarantees. It takes regular distributed snapshots of data stream and operator states. These snapshots serve as consistent checkpoints, in case of failure, the system can fall back to these checkpoints. Flink's mechanism for taking these snapshots is introduced in [58] which is inspired by the standard [PERSON]'s asynchronous distributed snapshot algorithm [59]. A general assumption made by the Flink's fault tolerance mechanism is that the data source is replay able. In addition to checkpoints recovery mechanism that is automatically triggered upon failure, Flink also provides SavePoint mechanism which is manually triggered and managed by the user. SavePoints support pause-and-resume jobs and scheduled backup. If a TaskManager fails, JobManager requests ResourceManager for more processing slots, restores the state of failed process using checkpoints, and re-executes it on another processing slot. However, a failure at JobManager is critical, since JobManager monitors the execution of streaming applications and manages related metadata. Flink supports a high-availability mode, which is based on Apache ZooKeeper that stores all necessary metadata on a reliable remote storage system. When the JobManager fails, a new or a standby JobManager takes over the work of the failed JobManager by requesting the Zookeeper to yield JobGraph, metadata and state handles of last successful check point of the application from the remote storage.  
Figure 10: A data flow graph in Flink. Here, edges represent data streams and each vertices represent operators.