esa-sceva/satcom-chunk-collection huggingface dataset:
# Power System Reliability Evaluation Based on Chronological Booth-Balieriaux Method
## 2 Literature Review  
### Loss of Load Probability  
The loss of load probability (LOLP), a representative probabilistic reliability index, is defined as the likelihood that the available capacity is insufficient to meet demand, leading to a loss of load. The mathematical expression for LOLP is presented below:  
\[\text{LOLP}=\text{P}(\textit{Available capacity}<\textit{Demand}), \tag{1}\]  
where the \(\text{P}(\cdot)\) operator denotes the probability that \((\cdot)\) is true. The _\"Available capacity\"_ term refers to the system availability contributed by all resources participating in electricity generation, encompassing non-dispatchable resources. _\"Demand\"_ pertains to the electricity requirements of consumers. The _\"Available capacity\"_ exhibits a probability distribution assuming that the failure risk of a generator is a random variable, denoted as FOR.  
Conventionally, estimating the system availability necessitates a consideration of every combination of commitment states of generators. On the other hand, several studies have proposed efficient reliability evaluation methods for calculating the LOLP because of the exponential complexity of this process, circumventing the problem of dimensions.
## 4 Simulation Analysis
### Test Case 2: Multiple Faults  
In Figure 7, there are multiple faults that occur in the distribution network at the same time. Assuming that a fault point is on the nearest downstream section of FIE-9, another fault point is on the nearest downstream section of FIE-16. Then, there will be a fault current flowing from the substation to fault location 1 and another fault current flowing from the substation to fault location 2. Then, the fault-indicating equipment between the substation and fault points will be forward-triggered, including FIE-1, 2, 7, 8, 9, 11, 12, 13, 14, 15, 16. Then, the line list in this case is shown in Table 4--it will be sent to the topology search algorithm to locate the faulted sections.  
Figure 6: Iterative process of test case 1.  
Figure 7: Multiple faults in the IEEE-33 node distribution network.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Step** & \(k\) & \(m\) & \(j\) & \(n\) & \(i\) & \(R[]\) & \(A[]\) & **Step** & \(k\) & \(m\) & \(j\) & \(n\) & \(i\) & \(R[]\) & \(A[]\) \\ \hline (1) & 1 & 1 & 1 & 0 & 0 & & (5) & 22 & 1 & 1 & 0 & 0 & & & \\ (2) & 1 & 1 & 1 & 0 & 0 & & (3) & 22 & 1 & 1 & 0 & 0 & & & \\ (3) & 1 & 1 & 1 & 0 & 0 & & (4) & 23 & 1 & 1 & 0 & 0 & & \\ (4) & 2 & 1 & 1 & 0 & 0 & & (3) & 23 & 1 & 1 & 0 & 0 & & \\ (3) & 2 & 1 & 1 & 0 & 0 & & (4) & 24 & 1 & 1 & 0 & 0 & & \\ (5) & 7 & 1 & 1 & 0 & 0 & & (3) & 24 & 1 & 1 & 0 & 0 & & \\ (3) & 7 & 1 & 1 & 0 & 0 & & (4) & 25 & 1 & 1 & 0 & 0 & & \\ (5) & 11 & 1 & 1 & 0 & 0 & & (3) & 25 & 1 & 1 & 0 & 0 & & \\ (3) & 11 & 1 & 1 & 0 & 0 & & (4) & 26 & 1 & 1 & 0 & 0 & & \\ (4) & 12 & 1 & 1 & 0 & 0 & & (3) & 26 & 1 & 1 & 0 & 0 & & \\ (3) & 12 & 1 & 1 & 0 & 0 & & (4) & 26 & 2 & 1 & 0 & 0 & \(R[1]=26\) & & \\ (4) & 13 & 1 & 1 & 0 & 0 & & (6) & 26 & 2 & 1 & 0 & 0 & \(R[1]=26\) & & \\ (3) & 13 & 1 & 1 & 0 & 0 & & (7) & 26 & 2 & 1 & 0 & 0 & \(R[1]=26\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The operation steps of test case 1.  
By applying the algorithm proposed in Section 3.1, the iterative process in MATLAB is shown in Figure 8, and the operation steps of the topology search algorithm and the fault location result are shown in Table 5. The final result array \(R[1]=9\) correctly indicates that fault location 1 is on the nearest downstream section of FIE-9, and another final result array \(R[2]=16\) correctly indicates that fault location 2 is on the nearest downstream section of FIE-16, which is consistent with the fault points we set in Figure 7. This proves that the fault location method can effectively locate multiple faults occurring at the same time.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Equipment** & **Status** & **Upstream** & **Downstream** & **Equipment** & **Status** & **Upstream** & **Downstream** \\
**Number** & **Equipment** & **Equipment** & **Number** & **Square** & **Equipment** & **Equipment** \\ \hline
1 & 1 & — & 2 & 18 & 0 & 17 & 19 \\
2 & 1 & 1 & 3 and 7 & 19 & 0 & 18 & 20 \\
3 & 0 & 2 & 4 & 20 &
# Angle-of-Arrival Estimation Using Difference Beams in Localized Hybrid Arrays
## 3 Proposed AOA Estimation Approach
### Discussion on Extension of the Proposed Approach  
The proposed approach can be potentially extended to wideband mmWave systems, where each subcarrier or a cluster of subcarriers are assumed to be narrowband, and the proposed approach is performed separately at different subcarriers or clusters. The cross-correlations between subcarriers or clusters can also be exploited to improve the estimation accuracy [6].  
The proposed approach can be extended from a linear array to a planar array, where the proposed phase shift design and cross-correlation operation can be applied similarly along the orthogonal dimension. Since the radiation pattern of a planar array can be representedby the product of independent radiation patterns along two orthogonal dimensions, the AoA estimation between them can be decoupled from each other.  
The proposed approach can potentially be extended to the case in the presence of nonlinear-of-sight (NLOS) or interferences from other transmitters. Since the NLOSs are typically much weaker than the LOS, serial interference cancellation could be performed for sequential AoA estimation with the proposed approach. When the AoA of the LOS is estimated, we can steer all beams of subarrays towards this direction, and then evaluate its channel amplitude and phase. By regenerating the LOS signal component and removing it from the received signals in all subarrays, the second strongest path can be estimated. In the same way, the remaining paths can be estimated and subtracted one by one. When there exist multiple interferences with similar power from different directions, the proposed approach could be conducted in terms of parallel interference cancellation, where multiple AoAs are simultaneously estimated and cancelled.
# Contrast-Enhanced Ultrasound Imaging Based on Bubble Region Detection
## 2 Materials and Methods
### Frequency Domain
#### 2.1.2 Construction of Bubble Wavelet  
Among the existing models, the Doinikov model [20] focuses on microbubbles with phospholipid shells. The Doinikov model has been proven to predict the 'compression-only' behavior [21] of Sonovue very well. It is commonly accepted that its spectrum is closer to that of the Sonovue microbubble and is the optimal choice for the bubble wavelet [17; 22].  
The Doinikov model can be described as:  
\[\rho_{l}(RR^{\prime\prime}+\frac{3}{2}R^{\prime 2})=(p_{0}+\frac{2x(R_{0})}{R_{0}}) (\frac{R_{0}}{R})^{3\gamma}-\frac{2x(R_{0})}{R}-4\chi(\frac{1}{R_{0}}-\frac{1} {R})-P_{0}-P_{drive}(t)-4\eta_{l}\frac{R^{\prime}}{R}-4(\frac{k_{0}}{1+a|\frac {R^{\prime}}{R}|}+k_{1}\frac{R^{\prime}}{R})\frac{R^{\prime}}{R^{\prime 2}} \tag{2}\]  
where \(\rho_{l}\) = 1000 kg/m\({}^{3}\) denotes the density of the surrounding liquid; \(P_{0}\) = 101,000 Pa as the atmospheric pressure; \(\gamma\) = 1.07 as the gas thermal insulation coefficient; \(R_{0}\) = 1.7 \(\upmu\)m as the initial radius of microbubble; \(R\) is the instantaneous radius of microbubble; \(R^{\prime}\) is the first-order time derivative of \(R\), with essentially \(R^{\prime}=dR/dt\) and \(R^{\prime\prime}=d^{2}R/dt^{2}\); \(\sigma(R_{0})\) = 0.072 N/m as the initial surface tension; \(\chi\) = 0.25 N/m as the shell elasticity modulus; \(\eta_{l}\) = 0.002 PaS as the liquid viscosity coefficient; \(k_{0}\) = 4 \(\times\) 10\({}^{-8}\) kg and \(k_{1}\) = 7 \(\times\) 10\({}^{-15}\) kg/s as the shell viscosity components; \(\alpha\) = 4 \(\upmu\)s as a characteristic time constant; and \(P_{drive}(t)\) is the driving ultrasound.  
The pressure scattered by the microbubble can be expressed as:  
\[P(d)=\rho_{L}\frac{R}{d}(2R^{\prime 2}+RR^{\prime\prime}) \tag{3}\]  
where \(d\) denotes the distance from the center of the microbubble to the transducer.  
Following this, the bubble wavelet can be obtained by solving Equations (2) and (3) with the initial condition of \(R(t=0)=R_{0}\), \(R^{\prime}(t=0)\) = 0.
# Self-Adaptive Priority Correction for Prioritized Experience Replay
## 1 Introduction
Unlike previous methods, this work solves this problem from another perspective. The proposed method only corrects the replayed samples to fix the update direction of DQL or DDPG. As the learning rate of DQL is usually low, Q value will not change much in the short term. Although there are differences between the real TD-error and stored TD-error, the overall distributions of both are similar (Figure 1). This motivated us to find a solution to correct the priority deviations according to the similarity. Specifically, We use the importance of the replayed samples to achieve a correct estimate of the gradient. To achieve this goal, there are two main challenges must be overcome: One is how to accurately calculate the real priorities with a low computation cost, and the other is how to reduce the time overhead of the correct gradient's estimation and improve data utilization. In this work, we propose a self-adaptive priority correction algorithm Importance-PER (Imp-PER) based on Importance Sampling (IS) [19; 20]. First, Imp-PER uses a regression model to predict the sum of TD-error of all data in EM according to the stored priorities. Then, a mini-batch of data is sampled from PER according to the stored priorities. After that, the real sampling probabilities of data are estimated by the predicted sum and the real TD-error values calculated by the latest Deep Q-Network (DQN) or DDPG. Finally, in the DQN or DDPG loss function, data is multiplied by importance weight, which is estimated by the real sampling probabilities and the stored sampling probabilities. The main contributions of this work are summarized as follows. * We design a linear model to predict the sum of real TD-error and estimate the real probability of the current batch of data. Compared to predicting the real probability of each sample in EM, the cost is much less. * By using IS technology to estimate the DQN or DDPG loss function for the real priority distribution, we improve the data utilization and eliminate the cost of updating the entire EM priority. * We conduct experiments on various video games of Atari 2600 with DQN and MuJoCo with DDPG from OpenAI Gym [21] to evaluate our algorithm. Empirical results show that Imp-PER can significantly improve data utilization on discrete state and continuous state tasks without introducing additional computational costs. The experiments also illustrate that Imp-PER is compatible with value-based and policy-based DRL.
# Bluetooth Communication Leveraging Ultra-Low Power Radio Design
## 1 Introduction
A good example application is a tracking device that establishes a Bluetooth connection to a smartphone. Using a common CR2031 coin battery with an energy density of 653 mWh, the required average power consumption for a 20-year lifetime is 3.7 uW and the TI can be calculated as shown in Figure 2. For a more accurate estimation, several additional factors need to be considered including: (1) the battery self-discharge rate (usually <20 years), (2) the battery capacity degradation by temperature variations and (3) the startup/shutdown overhead energy which might add extra ~50% to the power budget. All the aforementioned factors will limit the device from achieving the desired lifetime values. Figure 1: (**a**) Wide range of consumer IoT applications, (**b**) self-powered wireless sensor systems used in health monitoring purposes. This paper presents a review of recent efforts devoted to realizing low power connectivity solutions for wireless sensor nodes in BLE networks. The proposed solutions can be classified into two main categories: low-power radio design (physical layer) and energy-efficient wireless access technologies (network layer). Ultimately, combining the efforts in both areas will result in a new generation of IoT devices that are mobile and self-powered to help propel IoT applications to new paradigms. The rest of the paper is organized as follows: in Section 2, we discuss different synchronization schemes adopted in IoT applications with the main focus on BLE networks. Then, we present the concept of asymmetric BLE communication, referred to as back-channel communication. A design example of a BC receiver is also described, demonstrating one possible implementation of the BC communication in BLE-based networks. Section 3 reviews the ULP radio design challenges and trade-offs in both receivers and transmitters. A summary of the state-of-the-art fully compliant and back-channel BLE radios is reported, emphasizing the trade-off between the power consumption and other performance metrics. Finally, conclusions and future directions are presented in Section 4.
# A Flexible FPGA-Based Stochastic Decoder for 5G LDPC Codes
## 6 Implementation Results and Discussion
### Results
#### 6.2.1 BER Performance  
Figure 8 shows BER performance of the proposed decoder, which delivers BER = \(10^{-6}\) at 2.65 dB of \(E_{b}/N_{o}\) for a block length of 3808 with a base code rate of 1/3. It has been observed that SD provides better error correction performance compared with the conventional sum-product (SPA) and min-sum (MS) algorithms. Notably, decoding iterations of SD require more clock cycles than conventional designs. Additionally, it has been observed that applying noise-dependent scaling of 0.86 [29] to the received channel probabilities improves performance for lower code rates such as 1/3 and 2/5. Conversely, this performance declines for higher code rates such as 5/6 and 8/9.
# Optimization of the Drying Temperature for High Quality Dried Melissa officinalis
## 3 Results
### Energy Consumption, CO\({}_{2}\) Production, and Time Duration  
Table 1 and Figure 2 present the energy consumption, CO\({}_{2}\) production, and time duration of the drying procedure of _M. officialis_ under different drying temperatures. Drying of _M. officialis_ at 42 \({}^{\circ}\)C (followed by 49 \({}^{\circ}\)C) resulted in higher energy consumption (52.36 and 49.18 kWh/kg, respectively) and CO\({}_{2}\) production (49.18 and 45.69 kg CO\({}_{2}\)/kg, respectively) compared to lower drying temperatures (Table 1, Figure 2). Plants dried at 20 \({}^{\circ}\)C, without the use of an oven, were considered as the treatment with no energy consumption or carbon footprint related to the examined parameters.  
Comparing the different examined temperatures, drying at 42 \({}^{\circ}\)C versus 35 \({}^{\circ}\)C, consumed 21.7% more energy and produced up to 67.3% more CO\({}_{2}\), but reduced drying time by up to 3.6 h. Drying at 49 \({}^{\circ}\)C versus 35 \({}^{\circ}\)C, consumed more energy (by up to 5.8%) and produced more CO\({}_{2}\) (by up to 48.3%) but decreased drying time by up to 13.5 h. Interestingly, drying at 49 \({}^{\circ}\)C versus 42 \({}^{\circ}\)C consumed less energy (by up to \(-\)84.1%) and produced less CO\({}_{2}\) (by up to \(-\)81.0%), while also reducing drying time by up to 9.9 h (Figure 2).  
Figure 1: Effects of drying temperature on _M. officialis_ moisture content.
# An Accuracy-Efficiency-Power Consumption Hybrid Optimization Method for CNC Milling Process
###### Abstract  
This study proposes a hybrid optimization method which can help users to find optimal cutting parameters which will provide better efficiency and lower power consumption for a milling process. Empirical models including performance-power consumption characteristic curves of servo motors were built, and an optimization algorithm adopting the empirical models with procedure guiding function was developed. The empirical models were built based on the measurements from planned machining experiments with different combination of machining parameters including spindle speed, feedrate, and chip load, etc. After integrating the models and algorithm, an optimization system with human machine interface, which has procedure guiding function, was developed. The system can recommend optimal machining parameters for a milling process for shorter machining time and lower electricity costs based on the original machining parameters. Finally, cutting experiments were conducted to verify the proposed system, and the results showed that the proposed method can effectively enhance efficiency by 42.06% and save 34.74% in machining costs through reducing machining time and electrical power consumption.  
optimization; power consumption; machining efficiency; machining cost +
Footnote †: journal: Applied
# Numerical Investigation of a Novel Grinding Device
## 3 Results
### General Characterization of Collisions
Comparing the data in Figure 3 with the corresponding one for the IWM [23], it emerges that the OWM configuration with the same number of particles presents an higher but constant impact angle, an increase in impact kinetic energy of an order of magnitude, and a similar reduction in the impact frequency. Similarly to the total kinetic energy, this difference is probably related to the higher tip speed and reduced confinement. The collisions between the beads and the container wall are also taken into account, and the corresponding data are reported in Figure 4. The last panel shows the ratio of impact kinetic energy \(k_{\mathrm{e,w}}/k_{\mathrm{e,pp}}\) and impact frequency \(v_{\mathrm{c,w}}/v_{\mathrm{c,pp}}\) between the collision with the wall and those among the beads. The general trends are similar to those of Figure 3a,b for the collisions among particles; however, here, there is a non-monotonous behavior around 200 rpm. This is likely due to a regime change in the particle motion. Indeed, at low \(\Omega\) (i.e., 100 rpm), all the beads remain at the bottom of the vessel and are displaced by the stirring arm in a quasi-rigid collective motion. The friction with the container floor induces rolling and increases the tangential component of collisions, thus determining a low median of \(\theta_{\mathrm{impact}}\). At the same time, the interquartile range is wide, revealing the presence of almost purely normal collisions, likely those with the vessel lateral wall. At 200 rpm, the increased centrifugal force pushes the beads from the center to the periphery, where the tangential velocity of rotation around the axis is greater, and some beads start to jump over the impeller. This results in fewer low-energy, high-angle collisions, as evidenced by the shift in the distributions. As \(\Omega\) continues to grow (300 rpm and above), the beads are more frequently lifted off the ground by the stirring arm and repopulate the center, the collisions become less tangential, and the median of \(\theta_{\mathrm{impact}}\) first increases then stabilizes slightly above 30\({}^{\circ}\). Figure 4: (**a**) Impact kinetic energy, (**b**) impact angle, and (**c**) ratios of kinetic energy and frequency as a function of the stirring velocity for the collisions between the beads and the container wall. In the first and second panel, the median, first, and third quartiles of the distribution are shown. Figure 4c evidences that the collisions with the container wall are more frequent but significantly less energetic than those among the beads. This difference decreases with \(\Omega\) as the impact velocity with the stationary wall grows more than the one among the beads.
# Model Reference Adaptive Control of Vehicle Slip Ratio Based on Speed Tracking
###### Abstract  
This paper investigates a slip ratio control problem of a vehicle braking system in which real-time road conditions are unavailable. To overcome the nonlinearity and uncertainties, the slip ratio control problem is converted to a speed tracking control problem. By introducing a road-condition observer, a model reference adaptive control (MRAC) method is proposed to overcome modeling errors and system uncertainties. The road condition observer based on the Lyapunov theory is designed and a sliding mode observer is also constructed to estimate the unknown vehicle velocity. The asymptotic and robustness property of the adaptive controller is derived by the Popov hyperstability theory. Simulations under different conditions including different road adhesion coefficient, system uncertainties and modeling errors are provided to illustrate the effectiveness and robustness of the proposed method.  
model reference adaptive control; braking control; speed tracking; wheel slip ratio Article
# Analytic Form Fitting in Poor Triangular Meshes
## 1 Introduction  
### Research Target  
This manuscript reports an algorithm for analytic form (cone, cylinder, sphere) submesh extraction and fitting, using as input low statistical quality triangular meshes. Our algorithm has been successfully applied in actual industrial low-quality datasets, when good quality ones are impractical due to their large size and processing times. Our method favors sequential small submesh probing and vector closure formulation for the identification of analytic form parameters. Our method avoids, as much as possible, multi-variable regression, which requires a statistically rich input.
# BL0K: A New Stage of Privacy-Preserving Scope for Location-Based Services
## 1 Introduction
We propose a framework called BL0K that creates an entirely new encryption communication procedure in the problem domains (malicious server) and a secure application for use in resource-constrained schemes such as mobile devices. 2. BL0K is capable of reducing the amount of exposed data during an attack, hence BL0K can be used to lessen data leakage. 3. BL0K can be used to make the outputs of the algorithm appear completely randomized. Thus, increasing the general performance level that would also increase the level of privacy. 4. We evaluated the performance of BL0K framework: using a dataset close to the real-world queries by comparing its performance with the BLOT in terms of privacy (entropy), and the reported performance improvements increased to 93% (averaging 25% to 30%). The rest of the current paper provides the scheme to overcoming the issues described earlier using ZKP and BF. Related work is reviewed in Section 2, Section 3 provides the preliminaries with all its subsets. The BL0K technique is outlined in Section 4. BL0K performance measurements, calculation of the performance entropy for BL0K, the BL0K performance results, and conclusion are provided in Section 5, Section 6, Section 7, and Section 8, respectively.
# Sensing in Inland Waters to Promote Safe Navigation: A Case Study in the Aveiro's Lagoon
## 3 Low-Cost Maritime Data-Sharing System Design and Evaluation
### System's User Interface (UI)
The UI design presented in this section was based on the features of existing technological solutions in academia and industry (as described in the Benchmarking subsection), IMO guidelines [82], and the Nielsen and Norman UI design heuristics [83; 84]. In addition, situational visual limitations [77; 85] have been considered, in particular, the impact of direct sunlight on navigational challenges.