esa-sceva/satcom-chunk-collection huggingface dataset:
# Multilevel Coding for the Full-Duplex Decode-Compress-Forward Relay Channel
## 1 Introduction  
Shortly after the introduction of the three-node relay channel by [PERSON] [1], [PERSON] and [PERSON] [2] proposed and analyzed block-Markov encoded decode-forward (DF) and compress-forward (CF) for the relay channel [3; 4; 5]. Decode-forward is capacity achieving for the degraded relay channel, but, due to the relay decoding constraint, it does not perform well when the source-relay link is weak. Compress-forward can also be optimal under certain conditions [6], but it also falls short under certain other conditions [3; 4]. This motivated a generalization of DF and CF into a hybrid technique by [PERSON] and [PERSON] ([2], Theorem 7), denoted _decode-compress-forward_ (DCF). This technique, its performance and implementation via coded modulation are the subjects of this paper. Other hybrid relaying protocols include hybrid Decode-forward and Amplify-forward [7], and also a variation of DCF has appeared in the context of selective cooperation [8].  
The known achievable rate of DCF in the additive white Gaussian noise (AWGN) full-duplex relay channel reduces to either DF or CF achievable rates. This is a result due to [9] that we re-derive in the following under backward decoding. In the discrete input full-duplex relay channel, we show via examples that the DCF performance can exceed both DF and CF.  
A coding implementation for the DCF in the AWGN channel is then proposed based on multilevel coding (MLC). Multilevel coding was proposed by [PERSON] and [PERSON] in [10]. More details about the performance and the design of MLC can be found in [11; 12; 13]. [PERSON] et al. [14] showed that MLC with linear mapping does not require active shaping to achieve the capacity. MLC was extended to the MIMO (multiple-input multiple-output) transmission [15], and was used for diversity coding [16; 17; 18; 19] and in data storage [20]. Much less is known about MLC in the context of multi-node networks. Exceptions exist, for example, [PERSON] and [PERSON] [21] used MLC in the context of compute and forward and [PERSON] et al. [22] studied multilevel coding in the two-way relay channel.  
At the source and the relay, the proposed DCF multilevel coding decomposes the overall coded modulation into a group of binary codes, each either operating via a DF or a CF protocol. The mappercombines these constituent level-wise codes into a hybrid DF-CF coded modulation. The assignment of each level to either DF or CF and the rate allocation to each level is an optimization problem. We demonstrate the operation of this system by an implementation that employs for the DF components the DVB-S2 LDPC codes [23], and, for the CF components, a group of polar codes that are designed according to Blasco-Serrano [24; 25].
# Energy-Aware System Design for Autonomous Wireless Sensor Nodes: A Comprehensive Review
## 4 Energy Management
### Wake-Up Receiver for Sleeping/Idle Mode
In [106], a TRF duty cycled WuRx is presented with a sensitivity in the range of \(-90\) dBm. The achieved latency of \(32\) ms is used for the detection of a \(64\)-bit wake-up packet with a bit rate of \(128\) kbit/s. The power consumption is in the range of \(3\) uW. Improving the sensitivity can also be realized with architectures allowing a better filtering of signals, e.g., super-heterodyne (SH) or super regenerative (SR) architectures. The authors of [107] introduced a SR-based WuRx with a power consumption of \(40\) uW and a sensitivity of \(-97\) dBm. The decoding mechanism is performed on an off-the-shelf complex programmable logic device (CPLD), requiring much higher power than most recently published WuRx decoders. Figure 9: Block diagram of WuRx.
# Analysis of Multi-Antenna GNSS Receiver Performance under Jamming Attacks
## 2 Methodology
### Measurement Distortions Analysis
#### 2.3.1 Pseudorange Measurements Analysis  
The pseudorange measurement of the \(k\)th satellite after beamforming in the presence of interference can be expressed as  
\[P^{k}_{array}=\rho^{k}+d\rho^{k}+c\left(dt^{k}-dT\right)+d^{k}_{iono}+d^{k}_{ tropo}+\rho^{k}_{bias}+\epsilon^{k}_{\rho} \tag{17}\]  
where \(\rho^{k}\) is the true range, \(d\rho^{k}\) is the orbital error, \(dt^{k}\) and \(dT\) are the satellite and receiver clock errors, \(d^{k}_{iono}\) is the ionosphere error, \(d^{k}_{tropo}\) is the tropospheric delay, \(\epsilon^{k}_{\rho}\) is the receiver code noise, and \(\rho^{k}_{bias}\) is the possible bias introduced by antenna array processing.  
The pseudorange from the \(k\)th satellite for the reference antenna without interference can be expressed as  
\[P^{k}_{ref}=\rho^{k}+d\rho^{k}+c\left(dt^{k}-dT\right)+d^{k}_{iono}+d^{k}_{ tropo}+\epsilon^{k}_{\rho} \tag{18}\]  
The single difference between the pseudorange measurements obtained from the reference antenna and after beamforming is  
\[\Delta P^{k}=P^{k}_{ref}-P^{k}_{array}=\rho^{k}_{bias}+\epsilon^{k}_{\Delta\rho} \tag{19}\]  
where \(\epsilon^{k}_{\Delta\rho}\) is the single differenced receiver code noise.  
Since this single differencing acts as a zero-baseline, the atmospheric error and multipath cancel out. The code noise standard deviation depends on receiver tracking strategies such as chip spacing and the discriminators used and it should ideally be zero mean. Hence, one can estimate the pseudorange bias induced by the antenna array processing in the presence of interference. It is assumed that the residual multipath effect is small compared to the antenna array induced biases. In the simulations, GPS signals were simulated in multipath free conditions. In the case of actual data collection, a low multipath environment was selected.
# Balloon Design for Mars, Venus, and Titan Atmospheres
## 4 Balloon Design for Different Atmospheres
### Balloons on Mars
Further, the OZP balloons of a particular fineness ratio ascend faster than the corresponding SP balloons of the same fineness ratio. Figure 12 presents the mass characteristics for the SP and OZP balloons of different shapes with varying fineness ratios. The OZP balloons are heavier than the SP ones in terms of both gas and envelope mass. For oblate shape SP and OZP balloons, the mass of both envelope and gas increases with increase in the fineness ratio. The envelope mass of the balloon with \(\mathrm{oblateness}=8\) is almost 3 times that of the balloon with \(\mathrm{oblateness}=2\) for both SP and OZP balloons (Figure 12a). In the case of prolate and airship shapes as the prolateness decreases towards sphere, the envelope mass also decreases. The envelope mass of the balloon with a prolateness of 0.1 is twice that of the balloon with prolateness of 0.8 (Figure 12b). The gas mass for oblate shape increases with an increase in oblateness and for prolate shape it decreases with a decrease in prolateness. In order to compare the behaviour of different shapes of the SP and OZP balloons with a sphere shape, a fineness factor of 0.4 for airship and 0.4 for prolate and airship, and 4 for oblate shape were used. Figure 13 presents the ascent speed variation of these shapes. Once deployed, it takes almost one hour for different shapes of the SP and OZP balloons to reach the stable altitude. The prolate shape for both SP and OZP balloons has the highest ascent velocity in comparison to that of the other shapes. The OZP balloon shapes have higher ascent speed in comparison to the corresponding shape of the SP  
Figure 11: Altitude variation of the OZP balloons for different shapes with varying fineness ratios \(f\) in order to find the stable fineness ratio on Mars (**a**) oblate (\(f\): 2-8) (**b**) prolate (0.1–0.8) (**c**) airship (0.1–0.8). Figure 12: Mass variation of different balloon types at varying fineness ratio on Mars (**a**) airship and prolate (**b**) oblate. balloons. For both SP and OZP different shapes, there are almost no oscillations in velocity once the balloon reaches the stable altitude. For the balloon mission on Mars, the OZP balloons are in general more mass efficient. For both SP and OZP balloons it is preferred to use the balloons with low oblateness, a low prolateness or sphere. In terms of flight behaviour, the OZP balloons have a faster ascent speed, in comparison to that of the corresponding SP shapes. In terms of flight characteristics depending upon the requirements, all shapes for both OZP and SP balloons can be used, except a highly prolate shape as it ascends and oscillates very fast; therefore, it might be at risk to loose its heat and eventually burst.
# Performance Analysis of a New Non-Orthogonal Multiple Access Design for Mitigating Information Loss
## 2 System Model
### OMA System  
In the OMA system, it is assumed that two users receive their data by allocating resources to each user in one resource block. When user \(k\)'s signal is defined as \(s_{k}\), the received signal at user \(i\) is as follows:  
Figure 1: Illustration of downlink non-orthogonal multiple access with two users.  
\[y_{k}^{\text{OMA}}=\sqrt{P_{\text{f}}}h_{k}s_{k}+n_{k},\quad k=1,2 \tag{2}\]  
where \(h_{k}\) and \(n_{k}\) are row vectors of \(\mathbf{H}\) and \(\mathbf{n}\), respectively. \(h_{k}\) represents the channel between the BS and user \(k\), while \(n_{k}\) represents the AWGN noise at user \(k\). Additionally, the data rate for each user can be expressed as follows:  
\[R_{k}^{\text{OMA}}=\lambda_{k}^{\text{OMA}}\log_{2}\Bigl{(}1+\rho|h_{k}|^{2} \Bigr{)},\quad k=1,2 \tag{3}\]  
where \(\lambda_{k}\) denotes the ratio of user \(k\)'s information in one resource block, \(\lambda_{1}^{\text{OMA}}+\lambda_{2}^{\text{OMA}}=1\), and \(\rho\) denotes the transmit signal-to-noise ratio (SNR), \(\rho=P_{\text{f}}/\sigma^{2}\).
# Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation +
## 3 Method
### Output Calculation
#### 3.5.3 Heart Rate Variability Calculation  
Multiple metrics can be computed to express the measure of heart rate variability in different units. In this work, we focus on one of the most commonly used time-domain metric for summarizing HRV called the 'root mean square of successive differences' (RMSSD) [10; 31; 37; 42], expressed in units of time. As the name suggests, this is computed by calculating the root mean square of time difference between adjacent IBIs:  
\[\text{RMSSD}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(\text{IBI}_{i}-\text{IBI}_{i+1})^{ 2}}, \tag{1}\]  
where \(\text{IBI}_{i}\) represents the \(i^{\text{th}}\) inter-beat interval, and \(N\) represents the number of IBIs in the sequence. A graphical example of such HRV calculation is shown in Figure 2. Because RMSSD is more susceptible to noise, only IBIs within the first standard deviation around the mean are considered. Along with RMSSD, we calculate another time-domain HRV metric known as the'standard deviation of NN intervals' (SDNN) [10], which is simply the standard deviation of all filtered IBIs in the sequence.  
In addition, we also compute two frequency-domain metrics of HRV, simply known as 'low-frequency' (LF) and 'high-frequency' (HF) bands [42] (as well as a ratio of them), that are commonly used in the rPPG HRV literature [16; 29; 31]. The LF and HF components are calculated using [PERSON]'s power spectral density estimation [43]. Since [PERSON]'s method expects evenly sampled data, the IBIs are interpolated at a frequency of 4 Hz using spline interpolation and detrended to remove very low frequency components [24]. The power of each band is calculated as total power in a region of the periodogram: the LF band from 0.04 to 0.15 Hz, and the HF band from 0.15 to 0.4 Hz. Both metrics are converted to normalized units by dividing them by the sum of LF and HF. Details about these metrics can be found in [10; 44].  
Figure 2: Example of heart rate variability computation: Even when the heart rate (HR) is almost constant, the underlying inter-beat intervals (IBIs) can have many fluctuations. This is detected by rising squared successive differences (SSD), a measure of heart rate variability.
# Fatigue Crack and Residual Life Prediction Based on an Adaptive Dynamic Bayesian Network
## 3 Fatigue Crack Propagation Experiment Verification
### Single Adaptive Variable DBN Model
#### 3.1.3 Model Results and Data Graphs  
The first 90% of the cycle times are taken as the observation data of this method, and the last 10% are taken as the verification of accuracy. The change in the uncertainty parameters obtained according to DBN reasoning after filtering is shown in Figure 10. The notation \"log(\(\cdot\))\" denotes an operation using the natural logarithm.  
As can be observed from Figure 10, the uncertainty range of parameter \(C\) is quickly narrowed after the first filtering. This demonstrates the efficacy of the DBN in facilitating node adjustment. With the model being updated step by step, the uncertainty range of \(C\) steadily decreased, converging after approximately 4-5 iterations. The updated value was very close to the measured data.  
The final crack propagation prediction of the DBN particle filter is shown in Figure 11. Due to the scarcity of crack observation data, if there is no observation value in the current time step, the prediction value will be used instead.  
As shown in Figures 11 and 12, due to the lack of observational data, the fatigue crack growth and remaining life were significantly underestimated in the first 2-3 filtering processes, with prediction errors reaching as high as 20% to 30%. With the continuous collection of observational data, the uncertainty in the DBN was updated, thereby gradually improving the prediction accuracy. Especially when the filtering process exceeded six iterations, the prediction error for the residual life of specimens could be maintained below 5%, which has practical implications for the safety assessment and maintenance of structures.  
Figure 11: Prediction results of fatigue crack propagation for the specimens with dimensions of (**a**) \(120\times 30\times 15\) mm\({}^{3}\), (**b**) \(160\times 40\times 15\) mm\({}^{3}\) and (**c**) \(240\times 60\times 15\) mm\({}^{3}\).  
Figure 10: Prediction results of parameter \(m\) for the specimens with dimensions of (**a**) \(120\times 30\times 15\) mm\({}^{3}\), (**b**) \(160\times 40\times 15\) mm\({}^{3}\) and (**c**) \(240\times 60\times 15\) mm\({}^{3}\).
# Along-Track Multistatic Synthetic Aperture Radar Formations of Miniatellites
## 4 Control of Sensor Position
### Mitigation of System Performance Degradation  
PRF tuning to mitigate the effect of system performance degradation due to an unequal sensor position is not a new idea since already investigated in [11] or in [18]. Here, we implement such method in a formation, together with a novel way to further displace the phase centers: the twin antenna concept.  
Figure 3: Focused IRF of a point target for \(N=R=5\) and uniform spacing of sensors. **Left**: 2D IRF power; **center**: azimuth cut at maximum range sample; **right**: interpolated main lobe.  
In the next subsections, it shall be clear, however, that _mitigation_ implicitly includes a different definition of quality for random formation of small satellites. In practice, with a small probability, the results may be insufficient for any application. With loose formations, less cost means passing from a deterministic system to a probabilistic one in which performance can be assured within a given degree of reliability. The probability with which any mitigation method may fail is analyzed in Appendix A.  
In the following, just two exemplary cases are presented in which the improvement of the performance are shown after the application of the proposed mitigations. In the shown cases, the sensors are in a total random positions. Moreover, the maximum formation extent is fixed, to have a common imaged area large enough to assure the output azimuth resolution. The minimum sensor distance is fixed too, to keep the satellites safe from collisions, let us say three times the standard deviation of the control position plus three times the standard deviation of the knowledge position. According to the current technology, the minimum distance has been set to 30 m.
## 5 Experimental Results
We have chosen a linear equalizer for which its structure consists of 33-FF and 15-FB taps. The number of taps is chosen in a manner that maximally improves performance by tackling the bandwidth limitations of electrical components and chromatic dispersion. The results show that such post-equalization significantly improves BER performance compared to the previous case without the post-equalization. As one can see in Figure 8e,f, post-equalization can significantly improve the signal quality for Kerr-OFC generated carriers (0) and (+1), or even enable new modulation format alternatives. However, the feed-forward equalizer, due to the intensity fluctuations of the (\(-1\)) carrier, cannot show visible performance improvement (see Figure 8a,d). With the linear post-equalization (33-FF and 15-FB taps), BER performance is significantly improved for carrier (+1), which allows us to achieve the BER floor below the 7% HD-FEC limit for NRZ-COK signals at 60 Gbaud. Figure 8g-i show the corresponding eye diagrams captured for modulated carriers (\(-1\)), (0), and (+1) at \(-10\) dBm received optical power (ROP) that allows detecting signals with BER below \(5\times 10^{-3}\) at 40 Gbaud. Figure 8: BER versus ROP for the IM/DD communication system where the Kerr-OFC light source carriers “\(-1\)”, “0”, and “+1” can be used for NZR-COK signals transmission with baud rates up to (**a**) 50 Gbaud, (**b**) 60 Gbaud, and (**c**) 50 Gbaud without any post-equalization. If the post-equalizer with 33-FF and 15-FB taps is used, the BER is significantly improved for carriers “\(-1\)”, “0”, and “+1” up to (**d**) 50 Gbaud, (**e**) 60 Gbaud, and (**f**) 60 Gbaud. Received signal eye diagrams for carriers: (**g**) “\(-1\)”, (**h**) “0”, and (**i**) “+1” captured at ROP of \(-10\) dBm in the 40 Gbaud case.
## 3 Resonant Converter Families  
Studies have been devoted to resonant converters since the 1980s to meet most of the industrial requirements, such as efficient energy conversion, higher power density, smooth waveforms, etc. Initially, the idea was to integrate resonant tanks into converters to generate oscillatory voltage and/or current waveforms that will ensure _ZVS_ or _ZCS_ conditions for power switches [23]. To reduce switching costs and current and voltage pressures as well as _EMI_, soft-switching strategies are introduced. The switching frequency can be increased by soft-switching conditions, and thus the size and volume of the converter could be decreased. Soft-switching converter topologies are considered an improvement or enhanced generation of hard-switching types [74; 75; 76; 77]. Throughout the 1990s, new generations of soft-switching converters had been created, which integrate the advantages of traditional _PWM_ converters with resonant converters. Compared to traditional _PWM_ converters, soft-switching converters have moving waveforms except for the smooth nature of the rising and falling waveform edges, which are devoid of transient spikes. New soft-switching converters usually use resonance in a controlled style, unlike resonant converters. To create _ZVS_ and _ZCS_ conditions, resonance will occur just before and during turn-on and turn-off operations. They also act as conventional _PWM_ converters [23].
## 3 Methods
### Sample Size and Survey Locations  
This research study was conducted in Lahore, Pakistan. It is the second-largest city of Pakistan with a population of about 11.13 million people and an area of approximately 1772 sq. km. It is a business and education hub besides its unique cultural and socio-economic importance in the region. The growing demand for public transport was an important consideration for conducting this research study in Lahore. Both SWVL and Airlift decide the launch of their new routes after proper planning and analysis upon receiving route suggestions from their prospective users. The travelers book their ride by providing origin-destination information using the respective mobile apps and access the buses at the closest bus stop locations designated by both services. The fare structure of choice B is based on distance travelled and routes, and ranges from Rs. 20 to 200, whereas, choice B operates on flat fare basis. Both services use air-conditioned vehicles with choice B offering a live tracking feature as well. Figure 2 shows some routes of both choice A and B.  
The respondents of this research study are the residents of Lahore who have used these app-based DRT services. The survey locations were chosen based on the locations of bus stops for choice A and choice B. The main routes along whi  
Figure 2: Survey locations and route network of app-based demand-responsive transit services in Lahore.  
were effective locations where appropriate responses could be measured. The real-time positions of the respondents were recorded using GPS devices. A total of 440 samples were collected from respondents, out of which 392 (89.09%) were collected at the bus stop locations and 48 (10.9%) using the web-based questionnaire. The number of people using a bus stop was a factor in determining the frequency of the surveys to be collected at that location. The routes and stop locations are shown in Figure 2. As can be seen in Figure 2, the routes of these services overlapped at some bus stop locations. Therefore, the responses were collected at the overlapping locations depending on the demand for each service at that location. Students from University of Engineering and Technology, Lahore, conducted the survey. They were properly briefed about the study before the commencement of the survey. Two types of respondents were targeted in this study: people who were traveling in these services and people who were waiting at the bus stops for these services. The data were collected between November and December of 2019. The responses were totally anonymous in order to maintain the confidentiality of any personal details.
## 3 Information-Centric Networking  
Unlike traditional host-centric protocols, i.e., TCP/IP, ICN focuses on what data or service is being requested rather than on the node providing it [10; 48]. This change reflectsthe growing demand for efficient content retrieval and service provisioning in modern networks, where users are increasingly more interested in accessing information and services than communicating with specific hosts.  
The initial design of ICN was presented in 2009 by [PERSON] et al. in their seminal work in [49], which laid the foundation for a paradigm shift from host-based communication to content-centric communication. The objective was to enhance data retrieval processes by addressing several limitations of traditional networking, such as inefficient content distribution, scalability challenges, and security issues tied to host identities. However, ICN's flexibility and innovative design have made it an ideal candidate for more advanced applications. Today, ICN is being increasingly explored for service retrieval and in-network processing, where computation and service execution can take place within the network itself, leveraging ICN's intrinsic features like data naming, caching, and security.  
In the following, we review the main ICN principles and explain how they become enabling factors for INC. Then, we focus on the most prominent ICN architecture, namely NDN.