esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Efficient Constellation for Wireless Connectivity of IoT Devices
## 2 MAP Criterion for Z-BPSK in AWGN Channel  
The recall of the maximum a posteriori criterion (MAP) is reported in Appendix A. Since this paper aims to describe the fundamentals of the new proposed mapping technique, we focus hereafter on a simple binary antipodal constellation (BPSK). Extension to multilevel constellations (QAM) is straightforward and can be easily derived from the following results.  
In the case of a binary signaling plus the zero symbol, the matched filter vector representation is scalar and the signal space is \(s_{m}\in\left\{0,\sqrt{E_{1}},-\sqrt{E_{1}}\right\}\). It should be noted that \(E_{1}\) does not equal the average signal energy \(E_{s}\), which is  
\[E_{s}=E\left[s_{m}^{*}s_{m}\right]=(1-P_{0})E_{1} \tag{1}\]  
where \(E_{1}\) is the energy per non-zero symbol. The received signal in the matched filter scalar representation is  
\[r=s_{m}+z \tag{2}\]  
where \(z\) is Gaussian i.i.d. process with zero mean and variance \(\sigma_{n}^{2}\). Under these conditions we have:  
\[p(r|s_{0})= \frac{1}{\sqrt{2\pi}\sigma_{n}}e^{-\frac{r^{2}}{2\sigma_{n}^{2}}} \tag{3}\] \[p(r|s_{1})= \frac{1}{\sqrt{2\pi}\sigma_{n}}e^{-\frac{(r-\sqrt{E_{1}})^{2}}{2 \sigma_{n}^{2}}}\] (4) \[p(r|s_{2})= \frac{1}{\sqrt{2\pi}\sigma_{n}}e^{-\frac{(r+\sqrt{E_{1}})^{2}}{2 \sigma_{n}^{2}}} \tag{5}\]If we assume the information symbols equally probable, we have from Equation (A5) in Appendix A  
\[\begin{cases}P(s_{0})=&P_{0}\\ P(s_{1})=&P(s_{2})=\frac{1-P_{0}}{2}\end{cases} \tag{6}\]  
The MAP criterion applied to this case yields a decision for the symbols \(s_{0}\) when  
\[p(r|s_{0})P_{0}>p(r|s_{i})\frac{1-P_{0}}{2}\quad\forall i\
eq 0. \tag{7}\]  
By substituting Equation (3) in Equation (7) and taking the base-2 logarithm of both sides, we obtain the MAP decision region of symbol \(s_{0}\) as  
\[\begin{cases}r<&\frac{\sqrt{E_{1}}}{2}+\frac{\sigma_{n}^{2}}{\sqrt{E_{1}}}\ln \left(\frac{2P_{0}}{1-P_{0}}\right)=\theta_{0,1}\\ r>&-\frac{\sqrt{E_{1}}}{2}-\frac{\sigma_{n}^{2}}{\sqrt{E_{1}}}\ln\left(\frac{ 2P_{0}}{1-P_{0}}\right)=\theta_{-1,0}\end{cases} \tag{8}\]  
i.e., \(\theta_{0,1}\) and \(\theta_{-1,0}\) are the boundary points between the decision region of symbol zero and symbol +1 and -1, respectively. In the special case when all symbols, including the zero-energy one, are equally probable, Equation (8) becomes  
\[-\frac{\sqrt{E_{1}}}{2}<r<\frac{\sqrt{E_{1}}}{2} \tag{9}\]  
Analogously by defining the Euclidean distance \(d_{m}\) of received signal from transmitted signal \(\bar{s}_{m}\):  
\[d_{m}=d_{m}(\bar{r})=\sqrt{\bar{r}-\bar{s}_{m}} \tag{10}\]  
substituting into Equation (7) yields the decision region of symbol \(s_{0}\) expressed as:  
\[d_{0}^{2}<d_{i}^{2}+2\sigma_{n}^{2}\ln\left(\frac{P(s_{0})}{P(s_{i})}\right) \quad\forall i\
eq 0 \tag{11}\]
# A Treatise on Reconnoitering the Suitability of Fuzzy MARCOS for Assessment of Conceptual Designs
## 1 Introduction
The MARCOS method was applied in the process for powder-mixed electrical discharge machining of cylindrical-shaped parts using a chromium silicon steel tool, and the result obtained was compared with TOPSIS and MAIRCA (Multi-Attributive Ideal-Real Comparative Analysis). The results obtained showed that the three methods selected the same alternative as the optimal alternative from the eighteen alternatives considered in the decision process [35]. Similarly, the MARCOS method was also compared with MAIRCA, TOPSIS and EAMR (Evaluation by an Area-based Method of Ranking) considering the turning process. The cutting speed, feed and depth of cut were the input parameters in the cutting process in order to determine the material removal rate and surface roughness of the workpiece. The results obtained from the application showed that the four models are in conformity, as they identified the same alternative as the optimal process from the sixteen alternatives considered in the decision process [36]. The result was similar to the application and comparison of MARCOS to EDAS (Evaluation based on Distance from Average Solution), TOPSIS, MOORA (Multi-Objective Optimization on the basis of Ratio Analysis) and PIV (Proximity Indexed Value) in the milling decision-making process [37]. Further, the MARCOS method was applied in the grinding, turning and milling processes in order to determine the optimum material removal rate and effective surface finish considering nine trials with different machining parameters [38]. Considering the applications of the MARCOS model in different areas of application, it can be observed that the model finds more application in infrastructure and technology assessment, it is suitable for handling several numbers of alternatives and it also has a consideration for the ideal and anti-ideal scenarios in the formation of the decision matrix. This makes it possible for the model to capture the variations of the alternatives from the ideal and anti-ideal solutions considering the utility degree and functions of all the alternatives in order to confirm the optimal alternative. Also, considering the application areas of the MARCOS model, it is necessary to investigate its suitability to decision making on the identification of optimal design concept considering several conceptual design alternatives. Hence, this article attempts to extend the application of the fuzzy MARCOS model to the identification of an optimal design concept considering four conceptual designs of a briquette making machine. The decision process considered eight design features, with each of the design features having several sub-features. The importance of considering several design features is to ensure that the decision process is robust and all-encompassing in order to ascertain the computational integrity of the fuzzy MARCOS model.
## 4 A Hybrid-ISL System with Ranging and LoS Support
### PHY Support for Coarse and Fine Ranging  
#### 4.3.1 Coarse Range Measurement  
The coarse range estimation is based on the round-trip time (RTT) measurement [27] implemented in the PHY layer. This measurement provides the initial estimation of the distance between the 2 satellites (i.e., distance between satellite \(C\) and \(T\)). The RTT measurement and the computation of the effective travel time is illustrated in Figure 5.  
The baseband processor is clocked at \(\mathrm{F_{B}}\), which corresponds to a time base resolution of \(\mathrm{t_{b}}\). Noticeable events in the timing diagram are as follows:  
* \(\mathrm{t_{0}}\)--the baseband processor sent the OFDM burst to the analog front-end;
* \(\mathrm{t_{1}}\)--the OFDM burst is aired on the antenna;
* \(\mathrm{t_{2}}\)--the burst is received by the counterpart PHY;
* \(\mathrm{t_{3}}\)--the counterpart PHY processes the OFDM burst and airs a response;
* \(\mathrm{t_{4}}\)--the front-end receives the OFDM burst;
* \(\mathrm{t_{5}}\)--the PHY detects the beginning of the OFDM burst.  
The time required by the analog front-end to send and receive an OFDM burst, noted \(T_{tx}\) and \(T_{rx}\) in the figure, are deterministic. Also, the time required by the counterpart PHY, \(T_{pmc}\), to process and prepare an OFDM burst in response is deterministic. With a previous calibration, these deterministic values can be obtained, \(T_{calib}\)\(\sim\)\(T_{tx}\) + \(T_{proc}\) + \(T_{rx}\). The PHY starts and stops a counter that is clocked at the baseband processors frequency, at t\({}_{0}\) and t\({}_{5}\), respectively, to measure the RTT. The effective distance can be estimated from the travel time, \(T_{trnel}\), that is the difference between the RTT and the previously determined calibration time, knowing the propagation speed of radio waves in vacuum to be c\(\sim\)300,000,000 m/s:  
\[R_{coarse}=T_{trnel}\)_-c_=\frac{(RTT-T_{calib})}{2}\).c_  
The accuracy of the RTT measurement is given by the smallest time interval the baseband can provide. The H-ISL implementation uses a sample frequency of \(F_{s}\) = 61.44 MHz, which is also the operating frequency of the baseband processor. Thus, the achievable resolution one may obtain is approximately 2.44 m.
## 5 Experiment Validation
### Image Extraction and CNN Design  
The source data were stored and divided for each rotation cycle, with time placed on the horizontal axis, period on the vertical axis, and RGB (red/green/blue) used for frequency intensity through continuous wavelet transformation. Data were converted into expressed images (Figure 5). In other words, instead of going through the feature extraction and selection steps from the equipment data to monitor changes in equipment status, a continuous wavelet transformation step and an input image preparation step were performed to visualize the vibration data. Since a converted image does not have a label indicating the presence or absence of abnormality, a direct classification algorithm cannot be used. Thus, the equipment status change capture method proposed in this paper was used. Through this, pseudo-labels are assigned, and images are classified using a convolutional artificial neural network. The collected vibration signals were divided at regular intervals (w), and the data were used to create a feature image set through CWT. At this time, the feature image set was labeled PN and PA at a 7:3 ratio and stored using the Eulerian perspective method. After dividing those data into training feature image sets, and testing feature image sets with pseudo-labels, they were labeled pseudo-normal and pseudo-abnormal at a 7:3 ratio and stored. A feature image with a pseudo-label was trained with the CNN and used as a classifier. The structure and number of main parameters of the CNN designed [31] in this paper are shown in Figure 6 and Table 1.  
Figure 4: Dataset preparation process for data analysis (Step1–10).  
Figure 5: Feature image example converted using continuous wavelet transform; DS and NDS, Step = 1, 2, 3, 4, 5, 6, 7, 8, 9.
# A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware
## 1 Introduction
### Problem and Motivation  
The escalating threat of ransomware demands new, effective detection strategies to catch attacks early. While current approaches fall into three main categories--process-centric, data-centric, and resource-centric--they all struggle against modern ransomware's sophisticated evasion tactics [8; 9; 12].  
1. Process-centric methods analyze running programs, but they often need complete data to work, which makes early detection difficult.
2. Data-centric approaches monitor user files for unusual changes. However, they frequently produce too many false alarms because they cannot easily tell the difference between normal file changes and malicious ransomware activity. This leads to slow responses.
3. Ransomware's polymorphic nature (meaning it can constantly change its appearance) and its ability to mimic legitimate software further complicate early detection. The critical pre-encryption phase is especially challenging; it is so brief and elusive that traditional analysis struggles to collect enough data for timely and accurate detection [13; 14].  
Some researchers have tried to identify features of \"immuture\" pre-encryption ransomware, but these features quickly become outdated due to ransomware's rapid evolution [13; 15]. This is where advanced techniques come in. Generative Adversarial Networks (GANs) show promise for generating realistic, synthetic attack patterns, which could help overcome the problem of not having enough real-world data. However, standard GANs have their own issues:  
1. Their Minimax loss function struggles to accurately model the complex differences between real and synthetic ransomware data, particularly during that critical pre-encryption phase.
2. GANs are also vulnerable to the \"curse of dimensionality,\" meaning they need effective ways to select important features. Traditional feature selection methods often are not adaptable enough to keep up with evolving ransomware [16].  
Furthermore, building and training robust detection models, like Deep Belief Networks (DBNs), is complicated by the risk of overfitting--where the model performs well on training data but poorly on new, unseen data. Existing methods for deciding when to stop training (epochs) do not fully account for the unpredictable nature of ransomware [17; 18; 19; 20].  
Our research addresses these challenges head-on. We have developed an innovative framework that enhances GAN-based early ransomware detection. This framework incorporates novel mechanisms to adapt to changing feature significance and dynamically adjust training, significantly improving detection accuracy and preventing overfitting through a new three-phase approach [21; 22]. Our goal is to provide a more robust, adaptable, and effective solution to combat the ever-growing threat of ransomware.
# Hourglass 3D CNN for Stereo Disparity Estimation for Mobile Robots
## 4 Experiments  
### Datasets  
Figure 4 shows example scenes of the following datasets, consisting of left image, right image and ground-truth disparity.  
1. Scene Flow [24]: A large dataset with 960 \(\times\) 540 pixel resolution and 35,454 training and 4370 testing stereo images. Disparity maps are presented as the ground truth. If the disparity exceeds the restrictions established in our experiment (the maximum disparity is set at 192), some pixels with large disparities are removed from the loss computation. We use the Cleanpass of the Scene Flow dataset, which contains lighting and shading effects and no additional effects.
2. KITTI 2012 [25]: A real-world dataset containing 194 and 195 moving street view color stereo images (1240 \(\times\) 376 resolution) with and without ground-truth disparities, respectively, taken from an autonomous car. In this experiment, the images with ground-truth disparities were used as training data and were split into a training set (160 images) and a validation set (34 images).
3. KITTI 2015 [26]: A newer version of KITTI 2012, which increases the number of images both with and without ground-truth disparities to 200. Similar to KITTI 2012, the images with ground-truth disparities were used as training data and were split into a training set (160 images) and a validation set (40 images).  
Figure 3: Proposed 3D CNN architecture.
## 4 Research on Platform Performance Testing
### Summary of Platform Performance Indicators  
Based on the test results reported above, important performance indices, such as the platform repeated positioning accuracy and the fundamental frequency, are presented in Table 3.  
The repeated positioning accuracy of the parallel platform studied in this paper is more excellent overall than that of the parallel platform in [23; 24]. The important indices for the platform indicate the rationality and the effectiveness of the platform design. They also show that the configuration of the entire machine, including the layout and the structural forms of the joints and legs, the processing and assembly technology used, and the benchmark system design, play important roles in determining the kinematic accuracy and the stiffness of the entire platform.  
Figure 23: Test results for mobile platform translation along \(Z\)-axis: (**a**) Step size: 1 \(\upmu\)m; (**b**) Step size: 0.5 mm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Project Name**} & \multicolumn{1}{c}{**Performance**} \\ \hline  & pitch motion & 0.440\({}^{\prime\prime}\) (step size of 0.5\({}^{\circ}\)) \\ Repeated positioning accuracy & (Rotating around \(X\)-axis) & 0.181\({}^{\prime\prime}\) (step size of 0.001\({}^{\circ}\)) \\  & yaw motion & 1.734\({}^{\prime\prime}\) (step size of 0.5\({}^{\circ}\)) \\  & (Rotating around \(Y\)-axis) & 0.108\({}^{\prime\prime}\) (step size of 0.001\({}^{\circ}\)) \\  & focusing motion & 0.225 \(\upmu\)m (step size of 0.5 mm) \\  & (Translating around \(Z\)-axis) & 0.168 \(\upmu\)m (step size of 1 \(\upmu\)m) \\ \(X\)-directional static stiffness of the whole platform & 7.84 N/\(\upmu\)m \\ \(Z\)-directional static stiffness of the whole platform & 21.71 N/\(\upmu\)m \\ Natural Frequency (with 30 kg load) & 86.25 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance characteristics of the parallel platform.  
Figure 24: Test results for mobile platform rotation along the \(X\)-axis: (**a**) Step size: 0.001\({}^{\circ}\); (**b**) Step size: 0.5\({}^{\circ}\).
## 5 Application of STPA with POMDP in Collaborative Tasks
### Process Analysis  
We seek to define the possible accidents and system-level hazards in the human-robot collaborative manufacturing task. It is apparent that the potential accidents are related with the injury of the human while working with the robot in the collaboration area. This type of accidents can be occurred on specific phases of the collaboration procedure:  
* **Accident 1**: The robot injures the human while a task is performed in the common shared workspace.
* **Accident 2**: The robot injures the human while they work in separate workspaces.  
Before the beginning of the analysis, the STPA demands the identification of the stakeholders as well as the determination of the losses on which this analysis should be focused. Thus, in a human-robot collaborative task that takes place in an industrial environment, the stakeholders with the associated losses could be:  
* **SK1: Manufacturing Company**: where idle and unusable robots hinder the return on investment and lead to the reduction of productivity. The identified losses could be:  
* L-1: Loss of/or damage to robot,
* L-2: Loss of profit,
* L-3: Loss of/or damage to objects outside/inside the collaborative area (e.g., infrastructure damages).  
Figure 3: A graphical illustration of a fenceless collaborative topology including an operator and a collaborative robot along with the safety mechanisms.  
* **SK2: Human operator**: where the human life can be threatened from injuries, while also the user experience from the involvement in robotic tasks can be reduced. The identified losses for this stakeholder group could be:
* L-4: Loss of human life or injury to people,
* L-5: Loss of trust in automation.
## 1 Introduction  
Atrial fibrillation (AF) is associated with an increased risk of stroke, heart failure and cognitive impairment, and it also increases the risk of death in heart failure patients [1; 2; 3; 4]. The distribution of RR interval frequencies in AF patients is mainly unimodal or bimodal [5]. Bimodal RR interval distribution can be a sign of dual atrioventricular nodal pathway physiology [6]. AF and heart failure may coexist and show bidirectional interactions, with heart failure promoting AF and AF with an uncontrolled ventricular rate leading to ventricular tachycardiayopathy [7; 8]. Moreover, preceding and pre-preceding RR interval durations seem to play a role in the left ventricular systolic function in patients with chronic AF and dilated cardiomyopathyopathy [9].  
The prevalence of AF is high in heart failure patients supported by a continuous flow left ventricular assist device (CF-LVAD) and it reaches up to 46% [10]. However, the effects of AF remain unclear in heart failure patients implanted with a CF-LVAD for long-term support, and there is no consensus on the risks associated with AF during heart pump support [11]. There are studies reporting that AF is not associated with risk of death, stroke or thromboembolism during CF-LVAD support [12; 13; 14; 15]. LVADs may even induce favourable atrial structural and electrical remodelling in the patients with AF; however, they may also be associated with increased mortality rates and risk of thromboembolism events after CF-LVAD implantation [10; 16; 17], contradictory to the previous studies.  
Mortality rates in heart failure patients implanted with CF-LVAD may also depend on the type of AF [18].  
Decreased left ventricular ejection fraction under CF-LVAD support during AF is reported in [19], whereas another study reported no significant difference in left ventricular ejection fraction in heart failure patients with LVAD suffering from AF compared to those who did not have AF [20]. As the impact of AF on CF-LVAD therapy remains unclear, more questions such as the effects of concomitant procedures, the implanted device type, the treatment techniques for the arrhythmias and alternative strategies for anticoagulation need to be addressed in heart failure patients with a CF-LVAD [21]. Therefore, a better understanding of the AF in patients with CF-LVADs may help to improve the outcomes related to mechanical circulatory support.  
Computational techniques have already been widely used to simulate different clinical scenarios and healthy and diseased physiology, in addition to CF-LVAD therapies for different conditions, as well as to understand the effects of AF on the ventricular rate and cerebral flow [22; 23; 24]. Computational simulations may also help to understand the effects of AF on the cardiovascular system for heart failure and CF-LVAD support. The aim of the study is developing a numerical model which can simulate atrial fibrillation in heart failure patients and evaluating conditions observed during CF-LVAD support.
# Background and Recent Advances in the Locata Terrestrial Positioning and Timing Technology
## 5 Challenging Issues for Locata  
### RF Interference  
A Locata network faces challenges in the presence of RF interference (RFI), since it operates in the licence-free 2.4 GHz ISM band. One potential interferer is WiFi signals, due to their use of the whole ISM band [63; 64; 65]. It had been confirmed that WiFi signals transmitted at different data rates have different effects on Locata performance. However, a remarkable improvement in RFI rejection was observed in Locata Version 3 devices by using dual-frequency carrier signals, dual-antenna transmissions, Time Division Multiple Access (TDMA) signal coding, and multiple carrier tracking loops [63]. The RFI can also be reduced by restricting the transmission time slots of the LocataLites [64], or by an adaptive inter-loop aiding scheme that helps to maintain lock in weak signal and moderate receiver dynamics environments [66]. However, research has also shown that high levels of narrow-band interference would still compromise the positioning performance [65]. In order to further reduce RFI, WiFi interference detection, as well as adaptively reweighting the Locata measurements, have been investigated, and shown to improve performance [67; 68].
# Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise
## 2 Materials and Methods  
In this section, our model builds upon the strategy from the ByteTrack model that performs two rounds of association for high-score and low-score boxes, and we propose three improvements to enhance the model. First, we introduce a Candidate Boxes Enhanced Filtering (CEF) method, which optimizes the output of the detector to effectively address the sensitivity of tracking results to detection outputs. Second, we propose an adaptive measurement noise (AMN) method, which adaptively adjusts the measurement noise covariance by incorporating prediction information and detection confidence, making the update step of the Kalman filter (KF) more flexible and improving the robustness of the model. Lastly, we refine the calculation of the affinity matrix in the association task, enabling more effective data association for objects undergoing complex motion. Our workflow of the proposed methods is illustrated in Figure 1.