esa-sceva/satcom-chunk-collection huggingface dataset:
# Flattening the Curve of Flexible Space Robotics [PERSON]
## 3 Results
### Sinusoidal Trajectory Shaping: Gain Stabilization Plus Structural Filters of Only Selected Modes  
Assuming the best performing compensator identified in Section 3.6 (benchmark gain stabilization with classical compensation of the first flexible mode's anti-resonance and resonance), this section addresses shaping the step command to further increase performance, where the results are depicted in Figure 23, comparing the step trajectories versus the single-sinsoidal shaped-trajectories mimicking the step functions. The figure of merits used for comparison include the gain margin and phase margin (listed in Table 5), and a trajectory tracking error in Table 6 with percentage performance improvement.  
Notice sinusoidal trajectory shaping is compared to two other modern benchmarks in Figure 24: shaped-whiplash and time-delay input shaped. The latter two methods do not improve the performance when combined with classical feedback compensation, which seems logical since the methods are derived to act alone. Meanwhile, trajectory shaping with single-frequency sinusoids with simultaneous classical mode compensation improve the performance two orders of magnitude.  
Section 3 includes the simulation results presented in both qualitative fashion vis multi-plots, but also quantitatively using ubiquitous figures of merit: gain margin, phase margin, mean error, and finally the percent performance improvement. Conclusions include:  
1. _Superior performance (based on gain margin, phase margin, and mean tracking error) is attained by only compensating for the first complete flexible mode (resonance and anti-resonance) with notch and bandpass filters, respectively, with step commands shaped by novel single-sinsoidal trajectories._ 1. The next-best performer was the novel shaped-whiplash control based on [23].
2. Specific compensation of higher modes did not substantially improve the performance, with the exception of fully compensating the rigid body, the first three flexible modes  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Shaping Method** & \
\begin{tabular}{c} **Mean Tracking Error** \\ **(Deviation)** \\ \end{tabular} &
\begin{tabular}{c} **Percent Performance** \\ **Improvement** \\ \end{tabular} \\ \hline No trajectory shaping & 0.010682 & - - \\
**Single sine shaping** & **0.00028611** & **97** \\ Shaped whiplash & 0.014454 & 35 \\ Time-delayed shaping & 0.015681 & 47 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Controlling rigid body with four flexible modes (resonance and anti-resonance) with step inputs.  
Figure 23: Utilizing sinusoidal-shaped trajectories to improve the step response: (**a**) responses for step and sinusoidal trajectories; and (**b**) tracking error following sinusoidal and step trajectories.  
and bandpass filter, and fourth the anti-resonance. This option very substantially improved the gain margin, while slightly decreasing the phase margin.  
The results from Section 3 are summarized in general terms in the discussion of Section 4, emphasizing the percent performance improvement over the declared benchmark.
# Secure Image Transmission Using Multilevel Chaotic Encryption and Video Steganography
## 3 Methods and Materials
### Image Encryption Using AES and Chaotic Maps  
Image encryption is critical in securing data during transmission, particularly in multimedia applications. Chaotic systems are widely used in cryptographic systems because of their desirable properties, such as sensitivity to initial conditions, pseudo-randomness, and nonlinearity, which align with cryptography requirements [32]. This approach ensures high key sensitivity and large key spaces, making it resistant to various assaults, such as statistical and differential attacks. In addition, parallel computing significantly speeds up the encryption and decryption processes, further improving performance [33].  
Three factors are considered when developing an algorithm:  
The method should be sufficiently straightforward for easy and thorough evaluation and analysis;  
The security margin offered by an encryptor must be greater than what is needed to fend off known threats;  
The design should use established, thoroughly researched, trustworthy tools and concepts.  
In light of the considerations above, we suggest a picture encryption technique that is effective in terms of security and speed by combining the modified AES algorithm with Arnold chaos mapping. This paper uses the general framework of the conventional AES algorithm. The next part provides a detailed explanation of the suggested algorithm [6]. The encryption process uses AES and multilevel chaotic maps to secure the image, guaranteeinghigh unpredictability and defense against cryptographic attacks. The next step incorporates the image between video frames using steganographic techniques after safely encrypting it using the suggested hybrid chaotic-AES scheme. This procedure guarantees extra security and undetectable levels.
# Modeling and Performance of the IEEE 802.11p Broadcasting for Intra-Platoon Communication
## 3 Platoon System
### Communication Model  
Intra-vehicle communication is important for a platoon to maintain stability, which is due to the need for frequent and reliable exchange of information between vehicles in a platoon. The core of platoon communication is V2V communication. In this work, we adopt DSRC to support the V2V communication. The 75 MHz band allocated for DSRC is divided into one CCH and six SCHs. The control channel is responsible for transmitting safety information, while service channels are used for non-safety applications. Broadcast mode based on IEEE 802.11p is applied in platoon communication. IEEE 802.11p is the only standard medium access control protocol for VANETs [34]. There are several differences between broadcast mode and traditional unicast mode. Firstly, to avoid an acknowledgment (ACK) storm, each vehicle in a platoon does not need to transmit an ACK to the origin vehicle when it receives information. Secondly, although there is a failed transmission in broadcast mode, the source vehicle would never retransmit the lost packets. Compared with unicast communications, broadcast communication has a higher transmission failure probability. Lastly, the overhead of RTS/CTS access mode weakens the performance of systems with high mobility, such as platoon. Thus, the RTS/CTS handshake is not applied in broadcast model.  
The platoon communication framework is shown as Figure 2. Since a vehicle's longitudinal motion is noticeably affected by the mobility of the leader vehicle, we must ensure that the information of the leader vehicle can be received by each vehicle in a platoon. To simplify the communication model, we assume the length of platoon does not exceed the communication range \(R\) of a leader vehicle, which indicates the size of platoon is restricted. Vehicles in a platoon are equipped with a transceiver used for sand and receive messages. A leader vehicle can send information to any vehicles within a platoon, while member vehicles only send information to its following vehicle. In terms of contents and functionalities, messages transmitted in platoons are classified into two types: control information and non-control information. The control information includes vehicle driving behavior, traffic conditions, and accident warning; the non-control information includes application information shared among vehicles, such as media, entertainment, and office services. The control information flow among vehicles can significantly affect the stability and security of a platoon. Therefore, we focus on the transmission process of the control information. Note that we establish our analytical model without considering priority since all safety messages have the same priority.  
Figure 2: Platoon communication framework.
# Ten Years of TerraSAR-X Operations
## 9 TerraSAR-X Mission Utilization  
Since the beginning of the operational phase, the number of yearly acquired data takes for basic product generations has been continuously increasing, with only one exception in 2011. When compared to the operations that started in 2008, the acquisitions have almost tripled. Figure 17 visualizes the yearly numbers per imaging mode. Adding the experimental TerraSAR-X-like products we have close to 226,000 acquisitions which are available for user catalog ordering in EOWEB under the headings _TSX-1 Products_, _TSX-1 Experimental Products_, _TanDEM-X Pursuit TSX-1 Like_, and _TanDEM-X Pursuit TSX-1 like Experimental_.  
The TerraSAR-X mission philosophy to let a user decide which acquisitions are performed under what conditions even comprises data takes taken in left-looking mode. Ordering of these acquisitions  
Figure 16: Bias of the In-phase Channel and its readjustment in 2014.  
Figure 17: Total number of basic product acquisitions per year.  
is consistently integrated into the nominal ordering and the production workflow. In total, over 3000 left-looking data takes covering all imaging modes were acquired. Of particular benefit are those that were taken at latitude south of \(-80^{\circ}\) over Antarctica [33]. Figure 18 shows the left-looking Stripmap acquisitions over Antarctica, as executed upon user request.
# Hailstorm Detection by Satellite Microwave Radiometers
## 3 The Hail Detection Model
### The MWCC Training Dataset
The TB differences (BTD) between 90, 150, and 190 GHz are displayed in Figure 6. Both combinations including the 90 GHz frequency appear less significant in terms of sensitivity so that they do not recommend themselves for inclusion in the hail detection model. For d \(<\) 10 cm the distribution (BT90-BT150) is flat and takes a quadratic shape when d \(>\) 10 cm. The difference (BT90-BT190) shows a decreasing exponential distribution when d \(<\) 10 cm while the distribution becomes quadratic when d \(>\) 10 cm. High sensitivity is demonstrated by the (BT150-BT190) difference exhibiting the same distribution in both hail diameter classes becoming zero for large hail signature as detected by \(TB_{184}^{var}\)\(>\) 35%. The skill of the (BT150-BT190) difference in evidencing hail scattering fits the conceptual model, which is an exponential distribution as a function of hail diameters. Thus, the hail detection model will be trained by exploiting this BTD. Figure 5: \(TB_{184}^{var}\) sensitivity classes applied to the hailstorms over Colorado (**a–d**) and South Dakota (**e,f**). The sequencing of CO3 complete domain (**a,e**) using the \(TB_{184}^{var}\) thresholds identifies the majority of convective pixels in the class 15%–25% (**b,f**) while classes with \(TB_{184}^{var}\) values ranging in 25–35% (**c,g**) and \(>\) 35% (**d,h**) enclose the main hail cores. Note that no pixels belong to the class \(TB_{184}^{var}\)\(>\) 35% for the Colorado hailstorm where the measured hail diameter at ground was lower than 3.5 cm.
# Early Ransomware Detection with Deep Learning Models
## 6 Conclusions  
In this paper, we have explored the efficacy of deep learning techniques in the early detection of ransomware through the analysis of API call sequences. We designed and created a comprehensive dataset of initial API call sequences of popular benign processes and verified ransomware processes. We also performed a comprehensive analysis of different baseline and neural-network models applied to the task of ransomware detection on this dataset.  
Our investigation has provided substantial evidence that neural network models, especially CNN and LSTM, can be effectively applied to differentiate between benign and malicious system behaviors. We demonstrated that these models outperform traditional ML classifiers (baselines) and a competing method of [29], providing a positive answer to RQ1. Our findings indicate that the inclusion of the result feature for each API call significantly improved the models' performance, providing a positive answer to RQ2. We also found that 1-hot encoding of text features yielded the best results, answering RQ3. We, moreover, learned that increasing the number W of consecutive API calls used in the analysis improved the classification accuracy and F1-measure and that setting W = 7 was sufficient to achieve state-of-the-art results.  
Across various configurations, the combination of operation and result features yielded the best results. Additionally, our analysis showed that a window size of 7 provided optimal performance, and 1-hot encoding (OH) generally outperformed other encoding methods in terms of accuracy, answering RQ4. Finally, we learned that the test times of neural models are suitable for online ransomware detection, which resolves RQ5.  
We hope the PARSEC dataset will become a valuable resource for the cybersecurity community and encourage further research in the area of ransomware detection. Our findings contribute to the development of more robust and efficient ransomware detection systems, advancing the field of cybersecurity.
###### Abstract  
Void defects, manifested as distributed porosity, are common in metal additive manufacturing (AM) and can significantly degrade the mechanical performance and reliability of fabricated components. To enable real-time quality control during fabrication, this study proposes a grating laser ultrasonic method for the online evaluation of porosity in AM parts. Based on the theoretical relationship between surface acoustic wave (SAW) velocity and material porosity, a non-contact detection approach is developed, allowing the direct inference of porosity from the measured SAW velocities without requiring knowledge of the exact source-detector distance. Numerical simulations are conducted to analyze SAW propagation under varying porosity conditions and to validate the inversion model. Experimental measurements on aluminum alloy specimens with different porosity levels further confirm the sensitivity of SAW signals to internal voids. The results show consistent waveform and spectral trends between the simulation and experiment, supporting the feasibility of the proposed method for practical applications. Overall, the findings demonstrate the potential of this approach for the accurate online monitoring of void defects in metal AM components.  
additive manufacturing; grating laser ultrasonics; online monitoring; porosity evaluation; surface acoustic wave Article
# Anti-Cancer Drug Screening with Microfluidic Technology
## 1 Introduction
Since different modes of drug testing (mono or combinatorial) could be desired, this study classifies the presented microfluidics into three categories based on sample size and combinatorial or monodrug for drug screening applications. These categories include monodrug screening on a microscale sample, monodrug screening on a macroscale sample, and combinatorial drug screening on a microscale sample. The main goal of the first class (mono drug screening on a microscale sample) is to examine the effect of a single drug on a microscale sample. For instance, in biological applications, micro samples (i.e., single cells or multicellular aggregates) are exposed to specific drug doses. As such, this class is not suitable to evaluate cancer tissues for ex vivo applications. The second class (mono drug screening on a macroscale sample) is designed to eliminate the limitation of sample size associated with the first class. So, the drug gradient is generated in a macroscale sample (e.g., microdissected tissues). However, these two classes of microfluidics cannot evaluate the combination therapy. Accordingly, the third type of microfluidic platform has been designed to test combinations of anti-cancer drugs on microscale samples. However, there is a lack of combinatorial drug screening on macroscale samples. Furthermore, all mentioned classes can only generate and test countable concentrations due to the discrete concentration gradient generator. Therefore, a novel class of microfluidic systems to simultaneously deliver continuous gradients of two drugs on a macroscale sample to address the aforementioned limitations is of high importance. In the following sections, the pros and cons of each of these classes will be thoroughly discussed in more detail.
# A Survey on Moving Target Defense: Intelligently Affordable, Optimized and Self-Adaptive
## 4 Conclusions
### Empirical Insights
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Towards Affordable** & **Towards Optimized** & **Towards Self-Adaptive** \\ \hline \multirow{4}{*}{Main ideas} & \(\bullet\) & To reduce the high overhead & \(\bullet\) & Solutions considering specific & \multirow{2}{*}{\(\bullet\)} & ML \\  & \(\bullet\) & of legacy methods & & types of attacks & \(\bullet\) & ML + defense mechanisms \\  & \(\bullet\) & To minimize the additional & \(\bullet\) & Solutions considering & \(\bullet\) & ML + GT \\  & \(\bullet\) & triggering of overhead & & generalized types of attacks & \(\bullet\) & ML + GT \\ \hline \multirow{4}{*}{Merits and demerits} & \(\bullet\) & Efficaciously processing large & & & \multirow{2}{*}{\(\bullet\)} & Balancing security and resource costs to respond to attacks with optimized strategies & \\  & & data to improve defense efficiency & & & \\  & \(\bullet\) & Rendering additional & & & \\  & \(\bullet\) & overhead and inefficient & & & \\  & & algorithms can also cause & \(\bullet\) & Easy-to-generate sub-optimal & \(\bullet\) & Sophisticated sesing and analysis of attack behavior \\  & & problems in processing & & & & and traits \\  & & efficiency & & & \\ \hline \multirow{4}{*}{Challenges} & \(\bullet\) & To minimize the additional & \(\bullet\) & Scalability problems & \(\bullet\) & Necessary to ensure the high-level capability of sensing attacks \\  & \(\bullet\) & overhead from ML & & & \\  & \(\bullet\) & To increase the computational & & & \\  & power of ML algorithms & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of intelligent MTD techniques. aspects of cyber-attacks and defense to facilitate the convergence of MTD techniques as much as possible.
## 3 Research Methodology
### Steps in Data Preprocessing and Standardization  
This section includes checking the presence of input features that are redundant in the datasets, translating the available nominal input features into the numerical form, mapping them to the same scale, extracting the most useful input features to prevent the features from biasing the classification results, and reducing the computational burden during the visualization phase [20]. The mapping of input features to the same scale was performed using six algorithmic data transformations, namely normalizers, scalers (Min-Max, Robust, and Standard), and transformers (Quantile and Power), along with their suitability for numerical inputs. In the earlier section, the nominal security features whose encoding was to be performed were recognized, including Proto, Service, and State. Two techniques (encodings) can be used in this context. The use of label encoding was chosen instead of One Hot Encoding because the number of nominal security features is increased when One Hot Encoding is used. All feature values are converted to numeric values using the Label Encoding method [32; 33]. The following step refers to features with different value distributions or scales that are not similar. This technique is considered important for data preprocessing, and should be completed before tree-based IDSs process the data. All features in the dataset that have a significant variation in the scale of the data are rescaled, so that the values for each feature F represent a mean of zero with a variance of one.  
\[F_{newscale}=\frac{F_{original}-\overline{F}}{\sigma} \tag{1}\]  
where \(F_{newscale}\) denotes the feature's new-scaled value, while \(F_{original}\) denotes the feature's original values, \(F\) denotes the feature's mean, and \(\sigma\) denotes the standard deviation. All features are scaled, encoded, and ranked for the selection processes.
## 2 Materials and Methods
### Data Processing and Inversion  
The data processing and inversion relating to both the simulations on the synthetic models and to the landfill survey were performed using the RES3 DINVx64 v3.13 of Geotomo Software [46].  
The discretization of the inverse models was carried out considering the horizontal dimensions of the cells equal to half of the minimum electrode spacing to obtain the adequate resolution allowed by the high amount of data.  
Unlike the apparent resistivity data simulated starting from the block model, the dataset acquired at the landfill included values with a standard deviation greater than 20%, which were discarded. Furthermore, some outliers caused by high contact resistance or by insufficient current intensity were eliminated. As a result, the dataset was heavily reduced to 6093 processable measures, which is, however, an amount sufficient to obtain a high-resolution model.  
Figure 5: Electrode array arrangement on the upper surface of the Bellolampo landfill (Palermo, Italy). The electrode locations are indicated by the blue circles. The DEM has a resolution of 0.5 m and was provided by the managers of the wassteland.  
Figure 6: Pseudospatial distribution \((x,y,z)\) of the logarithm of apparent resistivity data, where \(x\) and \(y\) are the coordinates of the surface center of each electrode array and \(z\) is the pseudo-depth calculated according to [PERSON] [29]. The black dots show the electrode positions.  
For the discretization of the landfill inverse model, a grid of 32 \(\times\) 28 nodes, equidistant 10 m, was chosen to cover a rectangular area of 310 m by 270 m, which included the positions of all the electrodes used. The inverse model is thus composed of 31 \(\times\) 27 \(\times\) 13 = 10,881 cells.  
The topographic correction was made using a Digital Elevation Model provided by the landfill managing body. Considering the resolution of the inverse model, the resolution of the DEM was lowered to 5 m. Furthermore, the inversion was constrained by using the position of the waterproofing membrane placed at the base of the landfill which, due to its insulating characteristics, should not allow the crossing of the current flow, forcing the current to flow almost entirely inside the basin. In this way, the transition from conductive materials (waste and leachate) to the much more resistive limestone rock is forced to take place in correspondence with the bottom membrane [45].  
The inversion process was stopped when the misfit changes fell below 2%, obtaining an RMS misfit between observed and predicted data equal to 7.7% on the apparent resistivity and 23.9% on the apparent chargeability. The dimensions of the cells of the rendered volume of the Voxler 3D models are 5 m \(\times\) 5 m \(\times\) 2 m.