esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Convergence Speed of a DCD-Based Algorithm for Sparse Solutions
## 2 Preliminaries
### DCD Algorithms
#### 2.2.4 Numerical Results  
In this section, numerical results show that cyclic and leading DCD algorithms deal with system matrices \(\mathbf{R}\) with different condition numbers. We consider real-valued systems here. The condition number of the matrix \(\mathbf{R}\) is attained by modifying the ratio between \(Q\) and \(U\). Usually when \(U\) is closer to \(Q\), the matrix condition number is higher. For example, performing a highly loaded multi-user detection is equivalent to solving a linear equation \(\boldsymbol{\beta}=\mathbf{Rx}\), where \(\boldsymbol{\beta}\) is the output vector of the matched filter. \(\mathbf{x}\) is a randomly generated \(U\times 1\) transmitted data vector whose elements are uniformly distributed on \([-1,+1]\).  
Using the DCD algorithm to solve (1), an estimated solution \(\boldsymbol{\hat{\kappa}}\) is obtained. We determine the misalignment between \(\boldsymbol{\hat{\kappa}}\) and \(\mathbf{x}\) as  
\[\epsilon=\frac{[\boldsymbol{\hat{\kappa}}-\mathbf{x}]^{T}[\boldsymbol{\hat{ \kappa}}-\mathbf{x}]}{\mathbf{x}^{T}\mathbf{x}}. \tag{4}\]  
The misalignments \(\epsilon\) are averaged by 100 simulation trials and plotted (in decibels) against the number of updates.  
Figure 1 depicts the misalignments of the cyclic and leading DCD algorithms. A system matrix \(\mathbf{R}\) is designed with small condition numbers in the interval \([2,5]\). It is seen that the cyclic DCD algorithm has a slightly slower convergence than the leading DCD algorithm.  
Figure 2 depicts the misalignments of the cyclic and leading DCD algorithms in the system with high condition numbers in the range of \([300,400]\). It shows that the cyclic DCD algorithm has a faster convergence than the leading DCD algorithm. Also, the convergence speed of the two DCD algorithms is slower than that in Figure 1.  
When we consider a scenario with a sparse true solution, the number of updates \(N_{u}\) is expected to be reduced. Figures 3 and 4 display misalignments of the DCD algorithms in the case of a system matrix with high condition numbers. It is seen that the solution is sparser, the convergence of the DCD algorithm is faster.  
algorithm has a lower steady-state misalignment; however, the leading DCD algorithm has a faster convergence speed in the initial iterations. When \(K=64\) (non-sparse system), the cyclic DCD algorithm has faster convergence and lower steady-state misalignment than the leading DCD algorithm.  
Figure 4: Misalignments of the DCD algorithms in the system with the high condition numbers in the range of \([300,400]\) and sparse solutions; \(U=64\), \(M_{b}=15\), \(Q=75\).  
Figure 3: Misalignments of the DCD algorithms in the system with high condition numbers in the range of \([300,400]\) and sparse solutions; \(U=64\), \(M_{b}=15\), \(Q=75\).  
Figure 2: Misalignments of the DCD algorithms in the system with high condition numbers in the range of \([300,400]\); \(U=64\), \(M_{b}=15\), \(Q=75\).
# Visible Spectrum and Infra-Red Image Matching: A New Method
## 3 Proposed Method
### Projection Error  
Projection error (\(\epsilon\)) is defined as the Euclidian distance between the reference image feature points and the projected feature points [18]. The projected feature points are obtained by projecting the feature points of the target image onto reference image with a ground truth homography \(K\). This homography is known in advance between the reference and target images according to Reference [18,33]. We use a threshold of 2 pixels as projection error in this paper to identify corresponding feature points between the reference and target images for the proposed methods.
# Blind Compensation of \(I/q\) Impairments in Wireless Transceivers
## 2 Model Formulation and Mitigation Methodology
### 1/Q Impairment Model Formulation
\right) \tag{8}\]  
Since, the \(I/Q\) imbalance considered in this case is linear with respect to the modeling parameters, the combined baseband model of the \(I\) and \(Q\) components under the effect of both the imbalances can be written as:  
\[\mathbf{y}_{R}=\zeta\mathbf{c}^{\prime}\sqrt{\frac{2}{\left(1+g^{2}\right)}} \sqrt{\frac{2}{\left(1+{g^{\prime}}^{2}\right)}}\left( \begin{array}{cc}g^{ \prime}\cos(\theta^{\prime})&\sin(\theta^{\prime})\\ g^{\prime}\sin(\theta^{\prime})&\cos(\theta^{\prime})\end{array} \right)\! \left( \begin{array}{cc}g\cos(\theta)&\sin(\theta)\\ g\sin(\theta)&\cos(\theta)\end{array} \right)\!\mathbf{x}+\widetilde{\mathbf{n}} \tag{9}\]  
Or simply  
\[\mathbf{y}_{R}=\mathbf{A}\mathbf{x}+\widetilde{\mathbf{n}}=\mathbf{y}+ \widetilde{\mathbf{n}} \tag{10}\]  
where \(\mathbf{y}=\left[I_{y}(t)\ Q_{y}(t)\right]^{\mathrm{T}}\) and \(\mathbf{A}\) is the matrix containing the joint imbalance parameters. Matrix \(\mathbf{A}\) is defined as:  
\[\mathbf{A}=\left( \begin{array}{cc}\alpha_{1}&\beta_{1}\\ \alpha_{2}&\beta_{2}\end{array} \right) \tag{11}\]  
where  
\[\alpha_{1}=\varsigma\mathbf{c}^{\prime}\sqrt{\frac{2}{\left(1+g^{2}\right)}} \sqrt{\frac{2}{\left(1+{g^{\prime}}^{2}\right)}}\left(gg^{\prime}\cos(\theta) \cos(\theta^{\prime})+g\sin(\theta)\sin(\theta^{\prime})\right) \tag{12}\]  
\[\beta_{1}=\varsigma\mathbf{c}^{\prime}\sqrt{\frac{2}{\left(1+g^{2}\right)}} \sqrt{\frac{2}{\left(1+{g^{\prime}}^{2}\right)}}\left(g^{\prime}\cos(\theta^ {\prime})\sin(\theta)+\sin(\theta^{\prime})\cos(\theta)\right) \tag{13}\]  
\[\alpha_{2}=\varsigma\mathbf{c}^{\prime}\sqrt{\frac{2}{\left(1+g^{2}\right)}} \sqrt{\frac{2}{\left(1+{g^{\prime}}^{2}\right)}}\left(gg^{\prime}\cos(\theta) \sin(\theta^{\prime})+g\sin(\theta)\cos(\theta^{\prime})\right) \tag{14}\]  
\[\beta_{2}=\varsigma\mathbf{c}^{\prime}\sqrt{\frac{2}{\left(1+g^{2}\right)}} \sqrt{\frac{2}{\left(1+{g^{\prime}}^{2}\right)}}\left(g^{\prime}\sin(\theta^ {\prime})\sin(\theta)+\cos(\theta^{\prime})\cos(\theta)\right) \tag{15}\]  
Using this imbalance model, the next step is to derive a closed-form probability density function (PDF) expression of the output signals in terms of the imbalanced parameters and find an estimate of matrix \(\mathbf{A}\) using the maximum likelihood (ML) estimation.
# Research on ECG Signal Classification Based on Hybrid Residual Network
## 2 Method
### ECG Signal Classification
The data eventually traverse an additional fully connected layer, where the Softmax activation function is applied to output the classification probabilities for five categories of heartbeats: normal, supraventricular, ventricular, fusion, and unclassified. Overall, the proposed Hybrid ResNet model possesses numerous advantages compared to conventional CNN architectures. This model integrates separable convolution with residual connections to preserve computational efficiency while effectively capturing complex features in ECG signals and mitigating performance loss in deep network training. This enables the model to classify ECG signals with exceptional precision and robustness. Figure 3: Architecture of the Hybrid ResNet model.
# LUMDE: Light-Weight Unsupervised Monocular Depth Estimation via Knowledge Distillation
## 1 Introduction
In order to evaluate our framework, we trained and tested our model on the widely used NYUD-V2 dataset [17], and achieved state-of-the-art (SOTA) results in the unsupervised distillation field. In addition, the inference time of the lightweight monocular depth network was also evaluated, aiming at real-time performance on lower-cost devices. The main contributions of this paper are as follows:  
* Explore knowledge distillation of dense depth estimation networks in the absence of depth ground truth. * Introduce the idea of a pose network into the student network to construct photometric loss, and Transformer as the discriminator of a GAN to capture holistic information. * Our model achieves SOTA performance for lightweight self-supervised depth estimation models on the publicly available NYUD-V2 dataset. * The model size and the computational complexity of the distilled depth module only account for 22.8% and 4.8% of those from the original depth module, respectively. The inference speed of the compact network has increased by 532%.
## 5 Experimental Results
### Buck-Boost Topology
#### 5.3.2 Step-Up  
Figure 12 shows the power efficiency of the step-up buck-boost topology in the CV and CC modes. In the CV mode, the efficiency improves in general to 10-80 W and \(P_{Diff}\) increases in the high-power region where the output current increases. In the CC mode, \(P_{Diff}\) decreases with an increase in \(V_{var}\) and the input voltage. The power efficiency decreases more than the conventional converter at a power of 50 W or higher. In the step-up section, the output voltage increases with the input voltage, and the voltage stress of diode D\({}_{1}\) also increases. The efficiency decreases more than the conventional converter because the resulting diode loss exceeds the increase in the efficiency obtained by the removal of D\({}_{2}\) in the switch-off section. Therefore, the proposed converter is suitable for applications below 50 W in the step-up section of the buck-boost topology.  
Figure 11: Power efficiency comparison of the buck-boost topology (step-down): (**a**) CV mode; (**b**) CC mode.
# Spoofing Detection Using GNSS/INS/Odometer Coupling for Vehicular Navigation
###### Abstract  
Location information is one of the most vital information required to achieve intelligent and context-aware capability for various applications such as driverless cars. However, related security and privacy threats are a major holdback. With increasing focus on using Global Navigation Satellite Systems (GNSS) for autonomous navigation and related applications, it is important to provide robust navigation solutions, yet signal spoofing for illegal or covert transportation and misleading receiver timing is increasing and now frequent. Hence, detection and mitigation of spoofing attacks has become an important topic. Several contributions on spoofing detection have been made, focusing on different layers of a GNSS receiver. This paper focuses on spoofing detection utilizing self-contained sensors, namely inertial measurement units (IMUs) and vehicle odometer outputs. A spoofing detection approach based on a consistency check between GNSS and IMU/odometer mechanization is proposed. To detect a spoofing attack, the method analyses GNSS and IMU/odometer measurements independently during a pre-selected observation window and cross checks the solutions provided by GNSS and inertial navigation solution (INS)/odometer mechanization. The performance of the proposed method is verified in real vehicular environments. Mean spoofing detection time and detection performance in terms of receiver operation characteristics (ROC) in sub-urban and dense urban environments are evaluated.  
global navigation satellite systems (GNSS); spoofing; detection; IMU; vehicular navigation +
Footnote †: journal: Sensors
# Variable Speed Limit and Ramp Metering for Mixed Traffic Flows: A Review and Open Questions
## 2 Application of VSL and RM Control in HDV Traffic Flows
### RL-Based VSL  
In [45], the Q-Learning (QL) algorithm was applied to VSL to optimize traffic flow on motorways taking traffic predictions into account. The environment was described by a vector of six normalized variables representing the current and previous posted speed limit, speed, and density on the controlled urban motorway. The reward function was modeled as the proportional negative \(TTS\) measured between the two control time steps. Oscillations between consecutive speed limit changes were prevented by restricting them to 20 km/h. A QL-based VSL (QL-VSL) control approach was also proposed in [46] to reduce \(TTT\) at motorway bottlenecks. The QL-VSL approach significantly outperformed the feedback-based VSL strategy based on the obtained results. In [47], the Reinforcement-Markov Average Reward Technique (R-MART) approach was proposed for VSL. The long-term middle rewards are utilized in the R-MART algorithm opposed to QL algorithm deferred rewards. The application of the R-MART VSL controller was able to reduce \(TTS\) by 18% and almost 20% less CO\({}_{2}\) emissions compared to the case without VSL. In our previous study [48], we proposed a deep QL-based VSL algorithm including a customized learning process and a complex reward function consisting of three separate objectives. The proposed algorithm aims to increase the throughput of the motorway by increasing the average mainline speed, increase safety by decreasing the difference between measured speed and posted speed limit, and minimize speed limit fluctuations in successive control step intervals. The proposed algorithm managed to increase the average mainline speed and reduce traffic density. The oscillations between the posted speed limits and the measured speeds were prevented.  
The QL-VSL strategy was found to be most effective in reducing system \(TTT\) in both stable traffic demand scenario and fluctuating traffic demand scenario. Improvements larger than 20% can be expected depending on the respective traffic situation.
## 3 Results and Discussion
These results indicate that biosynthesized AgNPs exhibit greater antibacterial activity in comparison to AgNO\({}_{3}\) and chemically synthesized AgNPs. Enhanced antibacterial activity may be attributable to the small size and concentration of the biosynthesized NPs and/or the presence of bioactive compound capping. Small AgNPs may be easily diffused or may penetrate a bacterial cell membrane and inhibit its growth by interfering in the natural metabolism processes. Furthermore, plant-based AgNPs production depends on their metabolism. Metabolites, for instance, phenolics, organic acids, and quinones, as well as ascorbates or catechol (as redox system) play important roles in AgNPs formation [25]. Major chemical constituents of aqueous _D. moldavica_ leaf extracts were identified, and included terpenids, steroids, flavonoids, alkaloids, lignins, phenols, coumarins, and cyanogenic glucosides. These findings indicate that the slightly increased antimicrobial activity of green synthesized AgNPs may be a result of organic content found in _D. moldavica_ seed extracts. A comparison of antimicrobial activity in _D. moldavica_ seed extracts with green synthesized AgNPs (Table 1) confirmed that secondary metabolites play a significant role in capping Ag ions; therefore, despite the low content of synthesized AgNPs as indicated by EDX, secondary metabolites tend to exhibit activity levels that produce effective antibacterial properties at very low concentrations.
###### Abstract  
Addressing the critical technological needs for the real-time monitoring of stress distribution in mining areas, a new method for inverting goal pressure using distributed optical fiber monitoring data is proposed. By coupling the key stratum fracture mechanics model with the subsidence trajectory function model, a theoretical model is established to accurately describe spatial stress evolution during coal mining. The model quantifies the relationship between goal pressure changes and key stratum failures through a two-stage analysis of the subsidence process, based on distinct mechanical properties before and after key stratum fracture. Physical model experiments (3 m \(\times\) 0.2 m \(\times\) 1.1 m) using Brillouin Optical Time Domain Analysis (BOTDA) technology validated the proposed method, with comprehensive monitoring of key stratum deformations. By coupling the fracture mechanics model of the critical layer and the settlement trajectory function model, the dynamic transformation of the pre-fracture and post-fracture stages is realized, and the stress evolution can be monitored and predicted in real time. The results demonstrate spatial consistency between key stratum fracture locations and goal peak stress positions. High-precision optical fiber sensing detected an ultimate strain threshold of 4000 \(\upmu\)c for key stratum failure, with pre-fracture strain measurements consistently below this threshold. The developed stress inversion formula successfully predicted pressure distribution patterns within the goal, achieving real-time monitoring capabilities. Compared with the BPPS measurements, the deviation in the inverted data is less than 8.88%, the root mean square error (RMSE) is 0.98-1.20 in different propulsion stages, and the coefficient of determination (R\({}^{2}\)) is between 0.72 and 0.85. These findings provide a crucial theory for predicting peak stress evolution in mining areas, with implications for improving safety monitoring systems and optimizing mining operations.  
Keywords:similarity model experiment; goal; distributed optical fiber sensing; key stratum breakage +
Footnote †: journal: Applied sciences
# Extending UWOC System Applications through Photon Transmission Dynamics Study in Harbor Waters
## 2 Materials and Methods  
### The Optical Parameters of the Underwater Channel  
The interaction between photons and particles within seawater is both frequent and intense. When a laser beam travels through water, its light is absorbed by various elements such as water molecules, phytoplankton, dissolved organic matter, and organic debris. Concurrently, the light undergoes scattering caused by particles suspended in the seawater [37; 38; 39]. To mathematically describe these effects, two wavelength-dependent optical characteristic parameters are utilized: the absorption coefficient, denoted as \(a(\lambda)\), and the scattering coefficient, denoted as \(b(\lambda)\). Consequently, the attenuation coefficient, \(c(\lambda)\), which represents the cumulative effect of both absorption and scattering, can be expressed as [40]:  
\[c(\lambda) =a(\lambda)+b(\lambda) \tag{1}\] \[\omega_{0} =b(\lambda)/c(\lambda) \tag{2}\]In Equation (1), \(\lambda\) represents the wavelength of light. The ratio of the scattering coefficient \(b(\lambda)\) to the attenuation coefficient \(c(\lambda)\) is known as the albedo, symbolized as \(\omega_{0}\). This ratio, detailed in Equation (2), indicates the proportion of scattering loss relative to the total loss [41; 42; 43]. For the purpose of this study, which aims to analyze the impact of water turbidity on UWOC, harbor water is categorized into four distinct types: Harbor-I, Harbor-II, Harbor-III, and Harbor-IV. A communication wavelength of 532 nm is chosen for analysis.  
Table 1 presents the absorption coefficient (_a_), scattering coefficient (_b_), attenuation coefficient (_c_), and albedo (\(\omega_{0}\)) for these four types of harbor water. The optical parameters for Harbor II water are sourced from [PERSON]'s report [38]. For the remaining three Harbor water types, their optical parameters are extrapolated using the same albedo, \(\omega_{0}\), while varying the attenuation coefficients, \(c(\lambda)\).
# Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems
###### Abstract  
Although Orthogonal Frequency Division Multiplexing (OFDM) technology is still the key transmission waveform technology in 5G, traditional channel estimation algorithms are no longer sufficient for the high-speed multipath time-varying channels faced by both existing 5G and future 6G. In addition, the existing Deep Learning (DL) based OFDM channel estimators are only applicable to Signal-to-Noise Ratios (SNRs) in a small range, and the estimation performance of the existing algorithms is greatly limited when the channel model or the mobile speed at the receiver does not match. To solve this problem, this paper proposes a novel network model NDR-Net that can be used for channel estimation under unknown noise levels. NDR-Net consists of a Noise Level Estimate subnet (NLE), a Denoising Convolutional Neural Network subnet (DnCNN), and a Residual Learning cascade. Firstly, a rough channel estimation matrix value is obtained using the conventional channel estimation algorithm. Then it is modeled as an image and input to the NLE subnet for noise level estimation to obtain the noise interval. Then it is input to the DnCNN subnet together with the initial noisy channel image for noise reduction to obtain the pure noisy image. Finally, the residual learning is added to obtain the noiseless channel image. The simulation results show that NDR-Net can obtain better estimation results than traditional channel estimation, and it can be well adapted when the SNR, channel model, and movement speed do not match, which indicates its superior engineering practicability.  
6G; OFDM; multipath time-varying channel; deep learning; channel estimation; NDR-Net 2023
## 3 Results and Discussion
### Oxygen and Deuterium Isotopes of Groundwater EDB
The results of the stable isotope values of \(\delta\)D, \(\delta^{18}\)O, and D-excess in precipitation from the three selected GNIP stations compared with those of the groundwater from the shallow coastal aquifers of the EDB is presented in Table 2. The results of the wet season groundwater show a range of values \(\delta^{18}\)O (\(-\)5 to 0.3%), \(\delta\)D (\(-\)32 to 2.3%), and D-excess (\(-\)0.3 to 13.8%), which are very similar to those of Douala \(\delta^{18}\)O (\(-\)6.1 to 1.8%), \(\delta\)D (\(-\)38 to 17.6%), and D-excess (2.6 to 18.9%), and Cotonou \(\delta^{18}\)O (\(-\)5.5 to 1.8%), \(\delta\)D (\(-\)36 to 15.8%), and D-Excess (0.3 to 17.4%), which could be a result of a similar climatic condition, especially temperature, humidity, and evaporation. However, the values of stable isotope of \(\delta\)D, \(\delta^{18}\)O, and D-excess values for the groundwater samples in the dry season show range values \(\delta^{18}\)O (\(-\)4 to 0.8%), \(\delta\)D (\(-\)19.7 to 7.5%), and D-excess (\(-\)0.9 to 15%) with slight deviations from the wet season data. The observed slight variation is an indication of the evaporation effect, which is also responsible for the relatively enhanced values of TDS in a dry season's groundwater samples. The precipitation isotopes data from the Kano GNIP station in the northern part of Nigeria (Table 2) with range values of \(\delta^{18}\)O (\(-\)7 to 2.4%), \(\delta\)D (\(-\)58.3 to 22.3%), and D-excess (\(-\)13.4 to 21%) showed a wide variation from those of Douala and Cotonou (Figure 13). This variation is probably due to the elevation effect in addition to the relatively higher elevation and evaporation rate in the northern part of Nigeria, which is characterised by low annual precipitation, as shown in Figure 1. The clustering of the groundwater samples around the Douala and Cotonou Regional Meteoric Water Line (RMWL) and Global Meteoric Water Line (GMWL) (Figures 13 and 14) affirm meteoric water that infiltrates the aquifer within a short time  
Figure 14: \(\delta^{2}\)H vs. \(\delta^{18}\)O diagram for selected precipitation data and the groundwater samples: (**a**) wet season; (**b**) dry season.