esa-sceva/satcom-chunk-collection huggingface dataset:
## 10 Soft Decision Cognitive Radio and Hybrid Overlay/Underlay Cognitive Radio Waveform Design
### Soft Decision Cognitive Radio  
In cognitive radio, the transmitter continuously monitors the radio spectrum and dynamically identify frequency bands into two categories: used bands or unused bands. In other words, the cognitive radio makes the usability of one frequency band by employing a hard decision based on spectrum sensing result. However, the coexistence of underlay CR transmission and primary users indicates that all the primary users' transmissions can tolerate some level of interference. Hence, we can further increase the channel capacity of cognitive radio by making a soft decision on the usability of each and every used band. If we can determine the interference tolerance level of each primary user, the cognitive radio can transmit over both the unused bands and the used bands to optimize the spectrum usage and maximize the channel capacity. We name this system Soft Decision Cognitive Radio. Figure 47 shows such a system.  
Assume the interference tolerance level (the maximum allowed interference power spectrum density) of the \(i^{th}\) used band is \(I_{i}\). Current cognitive radio assumes that \(I_{i}=0\), i.e., no transmission is allowed if the band is being used. Employing knowledge of the interference tolerance level \(I_{i}\), the channel capacity of such a proposed system is  
\[C_{new}=W\log\left(1+\frac{\sum_{k=1}^{N}\Phi_{CR1_{k}}W_{u_{k}}+ \sum_{i=1}^{M}\Phi_{CR2_{i}}W_{p_{i}}}{n_{0}W+\sum_{i=1}^{M}\Phi_{p_{i}}W_{p_{i}}}\right) \tag{84}\]  
where \(\Phi_{CR1_{k}}\) is the cognitive radio transmission power spectrum density on \(k^{th}\) unused band, and \(\Phi_{CR2_{i}}\) is the cognitive radio transmission power spectrum density on \(i^{th}\) used band.  
To maximize the channel capacity of the proposed system, we need to maximize \(C_{new}\) subject to the following constraints:  
\[\Phi_{CR2_{i}} \leq I_{i},\forall i\] \[\Phi_{CR1_{k}} \leq\phi_{k},\forall k \tag{85}\] \[\sum_{k=1}^{N}\Phi_{CR1_{k}} W_{u_{k}}+\sum_{i=1}^{M}\Phi_{CR2_{i}}W_{p_{i}}\leq S \tag{86}\]  
where \(I_{i}\) is the interference tolerance level at \(i^{th}\) used band, \(\phi_{k}\) is the maximum allowed transmission power spectrum density regulated by FCC at \(k^{th}\) unused band, \(S\) is the total transmission power.  
Since the number of unused bands \(N\) and the number of used bands \(M\) are not gigantic numbers, this optimization is relatively small scale and a Lagrange multiplier method with numerical optimization could quickly generate a solution.  
It is evident that current overlay CR transmission and underlay CR transmission are just two special cases of the general soft decision cognitive radio paradigm: if we force \(I_{i}=0\), the system reduces to current cognitive radio; if we force \(I_{i}\) and \(\phi_{k}\) to be FCC UWB spectrum mask, the system reduces to underlay CR transmission.  
Figure 48 illustrates a block diagram of the proposed soft decision cognitive radio system:  
Figure 47: Soft decision cognitive radio.
## 5 Security Analysis
### Requester Privacy  
A keyword token (\(T_{i}=(\{T_{0i},T_{1i}\}_{i\in[1,l]})\)) is created to safeguard the \(dr\)'s keyword privacy. The data generator checks to see if the keyword is \(\hat{k}\in K\) when it receives \(T_{i}\) from the \(dr\). The data generator does not pick up any keyword information from \(T_{i}\) during this operation. Additionally, the aggregated contract creates a search trapdoor (\(\tau_{2}\)) and aggregates several keys in a searchable key \(P_{agg}\). Following receipt of the trapdoor (\(\tau\)), the FS computes \(\alpha\) to confirm its authenticity. If it is found to be legitimate, it aggregates several ciphertexts \((E^{1}_{agg},E^{2}_{agg})\). The original sensing data are then made available to the \(dr\) by sending the result \((E^{1}_{agg},E^{2}_{agg})\). During the \(dr\)'s searching process, data generators and the FS are completely unaware of any information regarding files or keywords. This prevents any private information pertaining to the \(dr\)'s keywords from being accessed by data generators or the FS. Thus, the secrecy of the \(dr\) is successfully protected.
## 3 Results
### Evaluation of Proposed Cardio Data Protection Procedure
The graphs show a significant increase in PRD at compression ratios greater than 5, leading to distortions in the values of the reconstructed cardio data (PPG, ECG, and Holter). In threshold processing using the EPE-based method, the PRD values were significantly lower (Figure 9), which is an indication of a better identity of the reconstructed data compared to the input. This allows for the realization of high degrees of compression in the implementation of the cardio data protection procedure, with the achievement of good identity between the submitted and received cardio data. Figure 5: Graph of received (transformed) ECG signal. Figure 6: MSE in data reconstruction (amplitude compression method, Db4 wavelet basis, 32 bits watermark length). Figure 7: MSE in data reconstruction (EPE-based compression method, Db4 wavelet basis, 32 bits watermark length). Implementations of the cardio data protection procedure with different wavelet bases affect the dependence of PRD on BER (Figure 10). Using Db4 and Db8, more optimal ratios of the studied parameters were obtained. When using the wavelet bases Db12 and Db16 with an increase in BER above 0.03, a significant increase in PRD values was observed. The results were obtained in studies on PPG. Figure 8: Dependence of PRD on the compression ratio for different types of cardio data (amplitude method of threshold processing). Figure 9: Dependence of PRD on the compression ratio for different types of cardio data (EPE-based threshold processing method). The results of the Signal to Noise ratio in the output cardio signal to the input signal are shown in Figure 11. Daubechies wavelet realizations with four and eight coefficients achieved a higher Signal to Noise ratio in the output signal compared to the input signal studied. The results were obtained in studies on PPG. All results were obtained in research conducted through a software program in the MATLAB environment and all showed the effectiveness, security, stability, and potential use of the proposed scheme in telemedicine.
# Anomaly Detection in IoT Communication Network Based on Spectral Analysis and Hurst Exponent
## 5 Results
### Case of Normal Traffic State
There were no significant deviations in the results, which may suggest a similar conclusion to the Legendre spectra generated for this data set. Figure 6 (right graph) shows a set of the Legendre spectra analyzed with a measure of value. The interval of the point dimension, which contained the determined fractal values of the spectra, shifted towards slightly lower values than in the case of all the previously analyzed network traffic samples. The beginnings of all the spectra for the 2018 dataset B had different areas of the fractal dimension value concerning the point dimension. The subsequent phases overlapped or were very close to each other, which revealed a very similar nature of the estimation of the spectra of these samples.
## 5 Outlook and Perspective
### Comparing Obtained Results with Prior Available Data  
Table 1 compares the results obtained in the current research with previously available data to clarify the contributions of the present work and to reveal the impacts of materials and surface morphology on erosion induced by cavitating bubbles. This table indicates that most studies have been conducted on metals and their alloys exhibiting flat surfaces [10; 49; 50; 51; 52]. While these studies may provide benchmarks for comparing the erosion resistance of materials and their alloys, the comparisons are often inaccurate due to differing experimental conditions (e.g., bubble radius and wall distance). Furthermore, since the effects of surface morphology are ignored in these studies, it is difficult to determine the impact of surface morphologies. Conversely, studies focusing on surfaces with riblets reveal the effects of riblets on erosion but do not compare their data with those for erosion depth and radius on flat surfaces [9; 17; 53].
# Connectivity Analysis in VANETs with Dynamic Ranges
## 4 Discussion
### Single-Hop Probability of Connectivity
#### 4.2.3 Connectivity with Path-Loss  
Figure 8 shows the connectivity probability as a function of path-loss. A significant decline in connectivity probability is observed as path-loss increases for both the exponential and synchronized traffic models. Notably, for a constant transmitter power of \(P_{t}=0.5\) W and a vehicle density of \(\rho=0.025\), the inter-vehicle connectivity drops sharply when the path-loss exponent \(\alpha>2\).  
Furthermore, even with synchronized vehicle movement, connectivity is worse compared to the exponential traffic model. The Weibull distribution with \(C=3\) exhibits the poorest connectivity, followed by the Nakagami-\(m\) distribution with \(m=3\), both of which reflect typical traffic conditions in urban and suburban highways. In general, the path-loss has a major impact on connectivity probability.
###### Abstract  
The Routing Protocol for Low-power and Lossy Networks (RPL) is a popular routing layer protocol for multi-hop Wireless Sensor Networks (WSNs). However, typical RPL configurations are based on decade-old assumptions, leading to a mismatch with: (1) advances in wireless hardware; and (2) growing wireless contention. To soften the impact of external stressors (i.e., jamming and interference), we extended RPL to exploit the capabilities of modern multi-interfaced wireless devices. More specifically, our main contribution is the design, development, and evaluation of a novel RPL Objective Function (OF) which, through simulations, is compared to traditional single-interface approaches and a state-of-the-art multi-interface approach. We examine two scenarios, with and without the injection of jamming, respectively. Our proposed OF is shown to outperform, or otherwise perform similar to, all alternatives considered. In normal conditions, it auto-selects the best interface whilst incurring negligible protocol overhead. In our jamming simulations, it provides stable end-to-end delivery ratios exceeding 90%, whereas the closest alternative averages 65% and is considerably less stable. Given we have open-sourced our development codebase, our solution is an ideal candidate for adoption by RPL deployments that expect to suffer interference from competing technologies or are unable to select the best radio technology a priori.  
definition: [PERSON]; [PERSON] [PERSON]; [PERSON] DRiPLOF: An RPL Extension for Multi-Interface Wireless Sensor Networks in Interference-Prone Environments.  
Sensors 20222239063390/s22103906  
6 TISCH; co-existence; end-to-end IP; IEEE 802.15.4; IIoT; multi-hop; multi-interface; RPL; wireless; WSN
# LIF-M: A Manifold-Based Approach for 3D Robot Localization in Unstructured Environments
## 3 System and Sensor Models  
### Definition of System State and Relevant Concepts  
Next, we commence by defining the framework of the localization system and the associated symbols, with the world coordinate system denoted as \(\mathbf{W}\), the robot's body coordinate system as \(\mathbf{B}\), and the map coordinate system as \(\mathbf{M}\). In the context of the localization system, we aim to minimize reliance on wheel odometry and instead lean towards the utilization of laser odometry. By processing sensor data, it is possible to achieve enhanced reusability of the entire system in a way that is applicable across diverse models such as robots, drones, and autonomous vehicles.  
Consequently, in this paper we approximates the IMU coordinate system \(\mathbf{I}\) to align with the robot's body coordinate system \(\mathbf{B}\), thereby addressing coordinate transformation issues during the state estimation process. To accommodate the usage of manifold-based state quantities, the robot's state \(\mathbf{X}\) can be represented in the following form.  
\[X=\left(\begin{array}{cccc}R_{3\times 3}&v_{3\times 1}&p_{3\times 1}&b_{a}&b_{ \omega}\\ 0&I&0&0&0\\ 0&0&I&0&0\\ 0&0&0&I&0\\ 0&0&0&0&I\end{array}\right) \tag{8}\]  
Due to the distinct properties of manifolds in comparison to Euclidean space, when \(b_{a}\) and \(b_{\omega}\) (assumed to be Gaussian white noise) are employed as augmentations to the Lie group matrices, their updating proves beneficial in preventing divergence of the overall covariance (\(\Sigma\)).
# A New Architectural Approach to Monitoring and Controlling AM Processes
## 4 A Closer Look at the Time-Based Functions
### Real-Time Function Loop  
The real-time functions include data acquisition, preprocessing, process-parameter selection, melt-pool feature extraction, and anomaly detection. Data includes melt-pool temperature measurements, melt-pool images, process settings, and actual laser-power measurements. The preprocessing function is necessary to remove noise from data samples, noise that will result in the uncertainty in the final measurements. Since process parameters play an important role in part quality, their selection is critical. Their selection is also not an easy task due to the dynamic and uncertain natures of both the AM materials and the AM process. Those natures mean that parameters controlling AM processes cannot be fixed during the process. If parameters such as laser power levels and scan speed are fixed, overheating and underheating occurs during fabrication, for example, for the nonconventional continuous scan strategy [19]. Therefore, most process parameters must be set initially and then adjusted based on those real-time measurements. Adjustments can be  
Figure 2: Integrated functional models.  
made manually or by using esoteric optimization techniques such genetic algorithms and self-organizing maps [20].  
Melt-pool-geometry measurements are used to control the process and to predict the quality of the AM part. The following steps are taken to measure the geometry of melt-pool: (1) remove the noise from images using a either a \(3\times 3\) or \(5\times 5\) Gaussian filter, (2) find the edge of melt-pool using Canny [21] or any other method for edge detection, and finally (3) calculate the area of melt-pool by fitting ellipses or rectangles or otherwise measuring size of the pool. The melt-pool geometry can be used to predict when the process is outside its normal operating range. Based on a defined threshold, the geometry can also detect anomalies during the AM process. Some researchers use other methods, such as bag-of-words, k-means unsupervised clustering, and convolutional neural networks for anomaly detection and classification in LPBF processes [22; 23].  
The real-time control functions continuously reoptimize the process parameters based on the measurement-data analysis. For this type of optimization, different machine learning algorithms, such as genetic algorithms and artificial neural network (ANN), are commonly used [20].
## 1 Introduction  
Malware is evolving from an existing form of attaching an executable file to an email and then spreading malicious code using vulnerabilities of document files. Document-type malware is not an executable file itself, so it is easy to bypass existing security programs and the security programs have a high risk of false positives when detecting document-type malware. The most frequent type of malicious document type is PDF (portable document format) which is the most used document in the world. PDF supports flexibility, so attackers would exploit this flexibility. Although Adobe patches vulnerabilities in PDF, new types of malware are emerging. JavaScript is one of the most convenient flexibilities that PDF supports. JavaScript in PDF is used for changing document contents in response to some events and restricting the actions of the reader. For example, to pre-fill some form fields or to validate entered form field values. However, JavaScript in PDF can be exploited by attackers to inject malware into PDF documents.  
In order to detect malicious codes embedded in document files, the document structure should be understood first. Previous studies have derived structural and metadata, while previous research has focused on extracting features from malicious document files and constructing algorithms based on the malware-centric feature set (excluding benign features). In most cases, malicious PDFs insert malicious code into stream objects because a stream object has no length constraints while other typeshave some. When analysing long bytes, most of the malicious documents contain JavaScript, which is encoded in various forms to reduce the size and the high risk of being detected when the JavaScript is displayed as a bar. Thus, most of the previous studies focus on whether JavaScript is embedded or not. However, we found that not all malware contains JavaScript and a few benign PDFs contain JavaScript. This implies that benign PDFs containing JavaScript can be exploited as an adversarial attack. Furthermore, to improve previous models for higher accuracy, we need to enrich more diverse features than those introduced in previous studies through PDF structure and content analysis.  
We aim to build comprehensive features encompassing structural features and content features and then we plan to test benign PDFs embedding JavaScript to determine whether the detection algorithm might be biased in detecting JavaScript. To distinguish JavaScript in malware and JavaScript in benign PDFs, we propose to use text features that can be derived from JavaScript code.  
In Section 3, we describe the structure of PDF and its features, and design a different method for adversarial attacks. Section 4 shows the statistical difference of features extracted from benign and malicious PDFs and proposes a malicious PDF detection model. Section 5 contains a discussion about the results. We summarize the findings and introduce future work in Section 6.
# Portable Prototype of Hydrogen Fuel Cells for Educational Training
## 2 Theoretical Background
### Cell and the Stack  
#### 2.3.1 Operating Principles  
Hydrogen is introduced as fuel, while oxygen acts as an oxidant. The reaction at the anode happens, and hydrogen undergoes oxidation and e- leaves the cell. Another reaction occurs at the cathode. Electrons e- enter the cell at the cathode side O\({}_{2}\) undergo reduction, react with the hydrogen, and form the water. A catalyst helps to speed up the hydrogen splitting into electrons and hydrogen ions at the anode. These hydrogen ions are transferred to the cathode through the electrolyte membrane while electrons pass through an external circuit resulting in an electric current. Therefore the electronic conductivity of the catalyst layer is of crucial importance. The area where electrolytes, electrodes, and reactants come into contact is called the reaction area. The higher the reaction area, the higher the amount of current produced. Reaction kinetics is another crucial parameter. A fast reaction rate has higher output, while sluggish kinetics result in lower output, which is why a catalyst is used to enhance FC's production and overall efficiency. Electrons and ions produced at either electrode must be consumed at the other electrode to keep charge balance as electrons are small in size than ions. Therefore, their movement is effortless. At the same time, ions have large sizes and must pass through electrolytes, making their movement difficult. Ion's movement takes place through a hopping mechanism. For efficient ion movement, the electrolyte must be very thin. Water is produced as a byproduct and must be removed continuously. Otherwise, it may accumulate in the fuel cell and strangle it. Thus the transport of fuel and oxygen will be disturbed [38].
## 3 Experimental Results and Discussion
In addition, they provide ongoing diagnostic data on process variability and make it possible to obtain information on monitored processes' capability. In industrial practice, control charts contribute to optimizing economic aspects (i.e., to the overall reduction of control costs). Production quality control using control charts is used especially in repeated processes when under relatively stable production conditions (i.e., technology, material, technical parameters of machines and equipment) there are other influences (i.e., small deviations from prescribed technology, small deviations in material quality, small inaccuracy of setting parameters of machines and equipment). Significant non-exceedance of effects will manifest itself within certain \"reasonable\" limits as a random effect. [PERSON] emphasized the empirical usefulness of the control chart for recognizing deviations from the state in which the technological process is statistically mastered. Measurement control charts are suitable for several reasons. Most technological processes and the above-mentioned drilling process, with their inputs and outputs, provide measurable features so that their wide use is possible. In the practical application, it was considered that the behavior of the technological process of drilling could be analyzed regardless of the technical specification. The charts reflect the technological process of drilling and give an image of what the process itself is capable of. In industrial practice, the stabilization of the process also contributes to shortening the time difference between an unstable technological process and corrective intervention. The behavior of the measured values and the limits determined on the basis of the control characteristics give a sufficiently reliable visual image of the course of the measured values themselves. Thus, it is possible to evaluate the measured data in real time and provide the measured variables' current state. By their action, the sought systematic causes cause a fundamental change in the variability or position of the process, whereby the process ceases to be in a stable state.
# A Procedure to Prepare Sand-Clay Mixture Samples for Soil-Structure Interface Direct Shear Tests
## 3 Reconstitution Procedure  
### Mixing Protocol of Sand and Clay  
A sand clay mixing protocol named S1 was utilized to prepare the sand-clay mixture slurry. As illustrated in Figure 2, the S1 protocol consists of mixing dry Fontainebleau sand, dry clay, and distilled water in the order of sand-water-clay. Sand-clay mixture samples were prepared with different clay content by weight. The initial water content (\(w_{0}\)) for mixing was 1.5 times of the liquid limit (\(w_{\text{L}}\)) of the sand-clay mixture at each clay content. The liquid limit values as a function of clay content are detailed in Table 2. These were obtained using the Casagrande apparatus in the laboratory. During mixing, the dry clay powder was added progressively into the sand-water mixture, by 5 to 10 g per step and then mixing for 1 min. After adding all clay, the slurry samples were mixed by rotating a stirring glass stick in a container for 10 min until visual homogenization. After mixing, the sand-clay mixture slurry specimens were placed in a vacuum device for 1 h to remove the potential air bubbles inside. This S1 mixing order can provide homogeneous samples from macro-to microstructure, which has been verified before [57; 61].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Composition** & **CaO** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **Na\({}_{2}\)O** & **K\({}_{2}\)O** & **MgO** & **LOI 1** \\ \hline Mass\% & 1.13 & 68.44 & 26.93 & 2.57 & 0.01 & 0.12 & 0.11 & 0.68 \\ \hline \hline \end{tabular}  
* LOI: loss on ignition.  
\end{table}
Table 1: Chemical composition of K Clay.  
Figure 1: Grain size distribution of Fontainebleau sand NE34 and kaolinite clay.