esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Statistical Model-Based Ultrasound Envelope Statistics Parametric Imaging Techniques
### Acoustic Structure Quantification Imaging  
ASQ (Aplio XG; Toshiba Medical Systems, Otawara, Japan) is a novel ultrasound imaging modality for liver tissue characterization that measures the difference between the envelope statistics and the Rayleigh distribution. In ASQ, the envelopes are used to compute \(C_{m}^{2}\), a statistical parameter for describing the backscattered statistics [23].  
\[C_{m}^{2}=\frac{\pi}{4-\pi}\frac{\left[E(r-E(r))\right]^{2}}{\left[E(r)\right] ^{2}}, \tag{1}\]  
where \(r\) denotes the envelope amplitude, and \(E(.)\) is the expectation operator. Using the signals with amplitude \(<\mu+4\sigma\), where \(\mu\) and \(\sigma\) denote the mean and standard deviation of the envelopes, respectively, the parameter \(C_{m}^{2}\) is recalculated to be \(rC_{m}^{2}\). Then, \(rC_{m}^{2}\) is compared to the original \(C_{m}^{2}\) to derive the focal disturbance ratio (FD ratio) [24].  
The performance of ASQ imaging in assessing hepatic steatosis was investigated in some animal studies [18; 24; 25]. Using a rat model (\(n=32\)), [PERSON] et al. [18] showed that ASQ had a correlation \(r=-0.90\) (\(p<0.001\)) with MR spectroscopy measurements. Using a mouse model (\(n=9\)), [PERSON] et al. [24] reported that ASQ had a correlation \(r=-0.72\) (\(p=0.0017\)) with histopathology. [PERSON] et al. [25] investigated ASQ and Nakagami imaging for characterizing hepatic steatosis, using a mouse model (\(n=24\)); the reference standard was histopathology. They showed that both ASQ and Nakagami imaging techniques can distinguish normal and fatty livers.  
However, the diagnostic value of using ASQ to quantify the degree of liver fat in the context of human hepatic steatosis remains questionable because of inconsistent findings. For example, [PERSON] et al. [26] demonstrated that the FD ratio had a correlation coefficient of \(-0.87\) (\(p<0.001\)) with MR spectroscopy measurements for 89 patients, whereas [PERSON] et al. [27] obtained a correlation coefficient of \(-0.43\) (\(p=0.004\)) with MR spectroscopy measurements for 70 patients. In addition to its inconsistent findings for fatty liver characterization, ASQ also results in different findings in liver fibrosis assessment [28]. Probably, the criteria used for rejecting signals and comparing \(C_{m}^{2}\) and \(rC_{m}^{2}\) in the ASQ algorithm are empirically determined [25], increasing the uncertainty in ASQ analysis.  
Figure 3: Rayleigh and non-Rayleigh distribution models for ultrasound backscatter envelope statistics.
## 2 Capacity Influence Analysis and Index Systems Construction  
### Capacity Influence Analysis  
Buses travel on the road following the fixed route and arrive at the stop for service, and in the process the waiting area and the lane-changing area shown in Figure 2 form, which constitute traffic bottlenecks, resulting in additional delays and reduced road traffic efficiency. With the development of CAVs technology, there are many changes in road traffic. From a microscopic perspective, the characteristics of vehicles lane-changing, car-following, and the features of bus operation have changed compared with the all HVs environment. At a macro level, the state of traffic flow on the road is also be affected. Affect by such changes, the impact of transits on road capacity in the bottleneck area also becomes a variety. Therefore, it is necessary to study the influence of various factors on the capacity of bus stops under the CAVs environment.  
This capacity of bus stops can be affected by bus arrivals per hour [35], bus dwell time [22], and station types [11], and different factors can cause changes in road capacity, but these influences are mainly characterized in practice by vehicle-following and lane-changing behavior. When buses slow down to change lanes and enter the bus stop, which may cause the surrounding social vehicles to slow down, and when there is no available berth when arriving at bus stops, the bus needs to wait in line for a free berth. A bus stop failure occurs when a bus arrives at the loading areas but with no available berth [36]. The level of road capacity is closely related to the length of failure duration at the stop, measured by the average length of waiting time for buses at a given period. When a bus stop fails, the buses waiting for the free berth form a queue, affecting the lane's upstream traffic flow, which has a negative impact on road traffic. The longer the bus queue, the longer the average failure duration and the lower the capacity level.  
In addition, buses may need to change lanes before entering a stop. The bus needs to find an available gap to perform a lane change. Otherwise, the bus has to decelerate to wait for the available gap or to be forced into the target lane. The forced lane change is defined as changing lanes after the critical position of lane change. When the traffic flow is high, as the study by [PERSON] et al. [37] shows, the headway spacing is relatively short, and the available gap for lane change is small. In this case, the difficulty of bus lane change increases. The bus needs to wait long for an available lane-change gap, or even worse, has to travel to the critical position of lane change to complete the lane change, which further impacts traffic capacity.
# The Status of Space Weather Infrastructure and Research in Africa
## 1 Introduction  
### What Is Space Weather?  
Space weather refers to the highly variable conditions in the geospace environment, including those on the sun, the interplanetary medium, and the magnetosphere-ionosphere-thermosphere system.  
Space weather can have serious adverse effects on the advanced technology that our society depends on, such as satellite communications, Global Navigation Satellite Systems (GNSS) positioning, navigation and timing (PNT) services, and power grids, among others. The primary sources of space weather are solar flares and coronal mass ejections (CMEs), both initiated by the Sun. Solar flares produce sudden bursts of radiation, while CMEs are associated with bursts of plasma, embedded with magnetic field structures, that travel in the solar wind before interacting with the Earth's magnetosphere [1]. Energy and radiation from these events can harm astronauts, damage electronics on spacecraft, and impact GNSS precision, tracking, and acquisition. The geospace response to these changes includes impacts on the radiation belts, multiple large-scale and small-scale changes in the ionosphere, and the production of intense geomagnetically induced currents. To better mitigate space weather impacts on humanity and technological systems, there is a recognized need for improved forecasts, better environmental specifications, and more durable infrastructure. Improved monitoring and modelling of space weather has been identified as critical for the better protection of infrastructure and national economies during periods of large space weather events.  
Understanding space weather is of great importance for awareness and avoidance of the consequences attached to space weather events either by system design or by efficient warning and prediction systems. Providing timely and accurate _space weather_ information, nowcasts and forecasts are possible only if sufficient _observation_ data are continuously available. Based on a thorough analysis of current conditions, comparing these conditions to past situations, and using numerical models similar to terrestrial weather models, forecasters can predict space weather on time scales of hours to weeks.
# Product Demand Prediction with Spatial Graph Neural Networks
## 3 Methodology
### Attributed Graph Prediction Problem Formulation  
We define the product demand prediction problem in the context of SGNN as a regression task on an attributed graph \(\mathcal{G}=(\mathcal{V},\mathcal{E},\mathbf{X})\). Here, \(\mathcal{V}\) represents the set of vertices corresponding to different retail locations, and \(\mathcal{E}\subseteq\mathcal{V}\times\mathcal{V}\) denotes the set of edges reflecting the spatial connectivity between these locations. The vertex feature matrix is denoted by \(\mathbf{X}\in\mathbb{R}^{|\mathcal{V}|\times f}\), encapsulating \(f\)-dimensional features that capture each location's attributes relevant to product demand, such as demographic data, store characteristics, and past demand trends.  
Each node \(v_{i}\in\mathcal{V}\) is associated with a target variable \(y_{i}\) representing the product demand to be predicted. Our goal is to learn a function \(f:\mathcal{G}\rightarrow\mathbb{R}^{|\mathcal{V}|}\) that maps the attributed graph to a vector of predicted product demands. Formally, we define the predicted demand for a node \(v_{i}\) as \(\hat{y}_{i}=f(\mathcal{G},\mathbf{X})_{i}\), where \(f\) is parameterized by the weights of a GNN.
# Structured Urban Airspace Capacity Analysis: Four Drone Delivery Cases
## 3 Flight Planning Algorithm
### Four Metrics for the Capacity Estimation
#### 3.5.4 Normalised Number of Conflicts (\(M_{cnf}\))  
Algorithm 1 generates a flight plan that only fulfils the destination separation requirement within the permissible speed range when the algorithm could not find a flight route that satisfies the separation requirement during the whole flight as well as the destination separation requirement and the permissible speed range. Namely, the sUAS could conflict with other sUASs during the flight based on the obtained flight plans. Although this paper does not provide overtaking manoeuvres, it is assumed that overtaking manoeuvres via vertical manoeuvring is sufficiently possible using current CD and R (conflict detection and resolution) technologies. Then, the number of conflicts is counted whenever a sUAS overtakes a preceding sUAS. Naturally, the number of conflicts increases with the number of sUASs in the urban airspace. We divide the number of conflicts by the total number of possible conflicts to normalise, and use it as a metric:  
\[M_{cnf}=\frac{n_{\text{conf}}}{n_{\text{tot}}} \tag{13}\]  
where \(n_{\text{tot}}\) is the total number of possible conflicts, which is \(\frac{A\times(A+1)}{2}\), and \(n_{\text{conf}}\) is the number of conflicts.
# Bayesian Optimal Experimental Design for Race Tracking in Resin Transfer Moulding
## 2 Computational Model
### Numerical RTM Model
#### 2.2.5 Interpolation  
For this investigation, it is of interest to be able to output the measurements that pressure sensors at specific locations would give at specified measurement times. The finite element solution defines the pressure at every location in the domain. However, a disadvantage of the algorithm described here is that the timesteps are dependent on the filling times. Therefore, extracting pressure measurements at specific times requires linear interpolation.
# Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System
## 6 Numerical Results  
In this section, we build a simulation of the architecture we proposed in MATLAB (Version R2020a) and Python (Version 3.10) to experimentally simulate the real allocation decision circumstance at the MS side depending on the model deployed by the controller of the core network so as to prove the proposed better performance. The result will be given about the access ratio as well as the cost of the calculation resources of the devices in the form of the CPU usage.
## 4 Empirical Modeling
### Model for 2 nd-Axis Rotation Motion  
When we placed the velocity profile of the pseudo-impulse input on the 2 nd rotational axis, the compressed response showed the variation of the longitudinal thrust force in the \(x\)-direction, the lateral thrust force in the \(z\)-direction, and the reaction moment in the \(y\)-direction. The 2 nd-axis rotational motion did not rotate the gimbal structure, unlike the 1 st-axis rotational motion. When the moment was measured at the end of the cantilever, the moment in the \(y\)-direction was mixed with the force in the \(x\)-direction. It was too small to separate a meaningful value from the measurement. In this study, the reaction moment in the \(y\)-direction was neglected.  
The model parameters for each PWM are shown in Tables 4 and 5. As an example, Figure 13 shows the Bode plot of the experimental values and the proposed model of \(F_{2x}(s)\) and \(F_{2x}(s)\) at 75% PWM. The tendency of the model parameters according to PWM was analyzed, as shown in Figure 14. Like in the 1 st-axis motion, the gain \(K\) increases with increasing thrust. The model for the change in thrust forces differs somewhat from the 1 st-axis motion. It can be seen that the movement of the gimbal structure has a considerable influence on the change in propulsion.  
Figure 14: Model coefficient of thrust force in 2 nd-axis rotation according to PWM: (**a**) \(K\), (**b**) \(a\), (**c**) \(b\), (**d**) \(c\), (**e**) \(d\), (**f**) \(e\), and (**g**) \(g\).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**PWM** & \(K\) & \(a\) & \(b\) & \(c\) & \(d\) & \(e\) & \(g\) &
\begin{tabular}{c} **Correlation** \\ **Number** \\ \end{tabular} \\ \hline
55 & 1289.06 & 51.75 & 120.17 & 6.19 & 11.49 & 8.10 & 2712.26 & 0.66 \\
60 & 1382.06 & 49.87 & 119.74 & 5.31 & 19.14 & 6.83 & 2713.70 & 0.69 \\
65 & 1456.32 & 59.57 & 120.01 & 5.57 & 11.93 & 7.78 & 2712.20 & 0.64 \\
70 & 1483.13 & 60.73 & 119.41 & 5.05 & 20.00 & 6.61 & 2663.02 & 0.69 \\
75 & 1357.27 & 74.79 & 119.92 & 4.59 & 9.89 & 6.85 & 2712.36 & 0.60 \\
80 & 1705.72 & 75.73 & 119.95 & 4.49 & 17.88 & 7.94 & 2815.79 & 0.67 \\
85 & 1653.80 & 70.92 & 119.85 & 4.65 & 13.14 & 5.84 & 2711.62 & 0.68 \\
90 & 1829.85 & 70.42 & 119.87 & 2.68 & 11.27 & 5.32 & 2715.95 & 0.72 \\
95 & 2029.13 & 95.62 & 117.50 & 3.30 & 19.81 & 7.25 & 2820.89 & 0.71 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model coefficient of \(F_{22}\)(s) in 2 nd-axis rotation.  
Figure 13: Magnitude Bode plot of experimental results and model in 2 nd-axis rotation with 75% PWM: (**a**) \(F_{22}\)(s); (**b**) \(F_{22}\)(s).
# Whole-Genome Alignment: Methods, Challenges, and Future Directions
## 2 Classification of Whole-Genome Alignment Algorithms
### Anchor-Based Methods
#### 2.2.6 AnchorWave  
AnchorWave stands as a versatile algorithm designed for whole-genome alignment, adept at efficiently aligning expansive genomic sequences while pinpointing areas of resemblance between them. It executes a process of whole-genome duplication-informed collinear anchor identification across genomes and conducts base-pair-resolved global alignment for collinear blocks, employing a two-piece affine gap cost strategy [42]. Compared with the nearest competitive approach, it accurately aligns up to three times more of the genome, encompassing position matches or indels, particularly notable when contrasting diverse genomes. Its ability to handle large datasets and produce high-quality alignments makes it valuable for comparative genomics studies and evolutionary analyses.  
The algorithm starts by aligning the query genome with a reference genome annotation. Then, it uses a dynamic programming algorithm to find the collinear blocks between the two genomes. It may use the longest path to define these similar regions. The user can also decide to include paths containing inversions, rearrangements, and whole-genome duplication. The next step of the algorithm is optional. In order to reduce the size of non-similar blocks (inter-anchor-s blocks), the user can decide to try to identify new additional anchors. Finally, AnchorWave performs base-pair alignment using the two-piece affine gap cost strategy [43]. This pairwise alignment method reduces the error rate and assemble similar genomes up to 30 faster than existing alignment tools [44].
# Compact Aberration-Free Relay-Imaging Multi-Pass Layouts for High-Energy Laser Amplifiers
## 7 Higher Order Aberrations  
For further discussion, we have also analyzed the higher order Zernike terms generated in the two astigmatism compensated systems described in Section 6. The derived terms are shown in Figure 12. It can be seen that the overall compensation of the astigmatism works slightly better in the Type II system as the round-trips were compensated, as well. Furthermore, there is some low amount of de-focus left in the Type I system, which can also be contributed to the round-trips. In the case of the Type II system, a very small amount of astigmatism under \(45^{\circ}\) still remains.  
Figure 11: Compensated Type II system with \(d=200\,\mathrm{mm}\) and \(f=500\,\mathrm{mm}\) for 10 round-trips. (**a**) Astigmatic wavefront deformation as a function of round-trip number as a result of a ray-tracing calculation; (**b**) different views of the compensated Type II system.  
The higher order terms are even smaller than the residual terms for astigmatism in both compensated systems and should be of no practical relevance. The residual distortions are dominated by coma, which has basically the same origin as the astigmatism. Higher order Zernike terms are negligible. The total wavefront error contributed by all terms higher than six is below 10 nm for the Type I system and 2 nm for the Type II system. The corresponding wave forms are shown in the lower part of Figure 12 after subtracting all terms lower than seven. Here, also the coma can be clearly identified.
# Single and Multivariate Statistics of Jet-Induced Pressure Fluctuations over an Infinite Plate
## 2 Experimental Setup  
Experiments were carried out in the laboratory of fluid dynamics \"[PERSON]\" of University Roma Tre. Measurements were performed in an acoustically treated chamber that measured \(\mathrm{2~{}m}\times\mathrm{4~{}m}\times\mathrm{3~{}m}\). The characteristics of the semi-anechoic chamber have been amply described in previous papers (see, among many, [15; 16; 18]). A jet connected to a \(\mathrm{2~{}m^{3}}\) air tank at 8 bar was installed in the semi-anechoic chamber. The connection was provided through a series of valves, and the duct was equipped with mesh screen and a honeycomb. An electronic pressure regulator was used for the remote control of the jet flow and maintained the nozzle pressure ratio to within 1% of the desired set point. Static pressure and temperature were continuously measured at the nozzle inlet. A J-type thermocouple was used to measure the air temperature inside the duct, whereas the static pressure was measured using properly calibrated ICSensor pressure transducers (50 p.s.i full scale). The determination of the pressure and temperature at the inlet section of the nozzle allowed us to determine analytically the jet Mach number \(\mathrm{M_{j}}\) at the nozzle exhaust through isentropic relations.  
A rigid flat plate was placed parallel to the nozzle axis with a traverse system that permitted the correct plate positioning with respect to the jet axis in the z direction. The plate was made of aluminum to obtain a stiffness comparable to the metal alloy of the aircraft external structures. The plate surface was pre-drilled with 200 taps whose spacings in the stream-wise and span-wise directions were equal to the jet diameter D, that is 12 mm. A picture of the experimental setup, previously reported in [16], is shown in Figure 1.  
Wall pressure measurements were performed using two miniaturized pressure transducers (Kulite-Mic190M) whose size fit the pressure taps. These sensors have a sensitivity of 0.128 V/ubar, a dynamic range up to 194 dB and a frequency response up to 125 kHz, which corresponds to the mechanical resonant frequency. Pressure taps not involved in the measurement were covered to avoid spurious effects on the acquired signals. The pressure signals were acquired by a Yokogawa digital scope DL780E at a sampling frequency of 200 kHz with a cut-off filter set at 70 kHz and for a duration of 10 s. A more complete description of the experimental setup and instrumentation adopted can be found in [15; 16].  
Measurements were performed varying the radial position H of the flat plate from H/D = 0.75 to H/D = 2. The jet Mach number was varied from M\({}_{\text{j}}=0.5\) up to M\({}_{\text{j}}=0.9\). The pressure transducers were positioned for \(1\leq\text{x/D}\leq 25\) in the stream-wise direction and for \(0\leq\text{y/D}\leq 3\) in the span-wise direction. The nozzle diameter-based Reynolds numbers of these experiments were of the order of \(10^{5}\).
# Visible Spectrum and Infra-Red Image Matching: A New Method
## 4 Experimental Setup and Results
### Experimental Results
#### 4.5.1 Comparison of Feature Point Detectors  
We use SIFT, SURF, ORB, BRISK, FAST and Harris [32] detectors. Table 1 shows the comparison results. It can be seen that SIFT detector obtains on average the best performance compared to other detectors. It demonstrates the best number of feature point correspondences and repeatability scores, that is, 903% and 62.3%, respectively, on RGB-NIR dataset. Harris detector achieves the second best results. Harris obtains the best results on MSD followed by the SIFT detector.
# Energy-Efficient Connected-Coverage Scheme in Wireless Sensor Networks
## 4 Algorithm for the Set Coverage Problem
### Minimum Coverage Control Algorithm  
Based on the genetic algorithm, we propose a Minimum Coverage Control Algorithm (MCCA) to solve the SCP. The proposed MCCA contains two parts: genetic operation and wake-up scheme. Through the iteration of the algorithm, the minimum coverage set \(\mathbf{s}_{minCover}\) can be found to monitor all MTPs. The set of other redundant sensor nodes is denoted by \(\mathbf{s}_{netCover}\). With the operation of the network, some sensor nodes in set \(\mathbf{s}_{minCover}\) will be out of energy. To keep full MTPs covered, the proposed wake-up scheme will wake up the optimal node in set \(\mathbf{s}_{netCover}\) to replace the dead node. Accordingly, the lifetime of the network is prolonged.  
The length of the chromosome is the same as the number of deployed sensor nodes in the network. The \(k_{th}\) gene value \(g_{i}\) signifies whether the sensor node \(s_{i}\) selected as a active or sleeping node in current round. There are 10 sensor nodes \(\{s_{1},s_{2},...,s_{10}\}\) randomly distributed in the sensing area to monitor four MTPs, as shown in Figure 3. Therefore, the length of the chromosomes for this network is ten and the string of the chromosome 1010010100 is used to represent the state of 10 sensor nodes. It can be observed that the gene value at position 6 is 1 (i.e., \(g_{6}=1\)), which implies that the sensor node \(s_{6}\) is selected as active node in current round. Similarly, the sensor node \(s_{1},s_{3}\) and \(s_{8}\) are also the active node in current round. The gene value of \(g_{2},g_{4},g_{5},g_{7},g_{9}\) and \(g_{10}\) is 0, which implies that the sensor nodes \(s_{2},s_{4},s_{5},s_{7},s_{9}\) and \(s_{10}\) will be in sleep mode.