esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive-Size Dictionary Learning Using Information Theoretic Criteria
## 3 Algorithm
In the tests reported below, we took \(K\geq 200\) and \(c=5\). The second extra operation is the computation of ITC, which involves the recomputation of the representations for each dictionary. Since we already have the representations \(\mathbf{X}\) for the full dictionary of size \(n\), we can progressively recompute only the representations that change as the size decreases. For example, the dictionary of size \(n-1\) lacks atom \(\mathbf{d}_{n}\), but is otherwise identical with \(\mathbf{D}\). We need to recompute only the representations that contain \(\mathbf{d}_{n}\). Their number should be less than \((Ns)/n\), since atom \(\mathbf{d}_{n}\), which is the least used, should appear in less representations than the average. Thus, overall, the number of recomputed representations is likely bounded by \((n_{\text{cand}^{S}}/n)N\), which is comparable with \(N\) (the number of signals represented at each iteration); since this happens only every \(c\)th iteration, the extra complexity is relatively small. In the experimental conditions described in the next section, ITC-ADL is about 3-5 times slower than the underlying AK-SVD, which is one of the fastest DL algorithms. This is not an excessive computational burden. If the sparsity level \(s\) is not known, we simply run ITC-ADL for several candidate values; the best ITC value decides the best \((n,s)\) pair. Adapting also \(s\) during the algorithm may be possible, but seems more difficult than adapting only the size and was left for future work.
## 3 Analysis of Lightning Strike Effects on Patch-Repaired Composite
### Optimizing Aluminum Coating Applications for Repaired Laminate  
Although patching techniques can maintain structural integrity, previous simulation studies have indicated that patch repairs are unable to evenly distribute the current and heat generated by lightning strikes as the original structure does. In order to optimize the patch repair strategy, this study employs a method of applying an aluminum coating on the surface of the repaired structure to address the conductivity interruption caused by the patch repair. The aluminum coating serves a dual purpose: restoring the broken conductive pathways at fiber misalignment areas and providing overall lightning protection by dispersing electric current through a Faraday cage-like effect. The coating is applied to the repaired structure's surface, and its position is not significantly influenced by the randomness of the lightning strike location, ensuring a more effective dispersion of the lightning current. It is anticipated that this improvement will significantly reduce the current density passing  
Figure 8: Lightning ablation damage on patch-repaired laminates. (**a**) First layer; (**b**) second layer.  
through the structure, thereby more effectively directing and dispersing lightning currents. Additionally, a study on the transient behavior of aluminum-coated structures under lightning conditions has shown that the effects of thermal expansion coefficients are primarily significant in long-term environments. In contrast, during short-term high-current impacts, the effectiveness of the coating is more closely related to its conductivity and thermal stability. As this study focuses on the effects of electro-thermal coupling, primarily analyzing the temperature-dependent properties of the materials under lightning conditions, the thermal expansion coefficient of the aluminum coating is not considered.
# Indoor PV Modeling Based on the One-Diode Model
###### Abstract  
The use of photovoltaic (PV) panels in interior spaces is expected to increase due to the proliferation of low-power sensor devices in the IoT domain. PV models are critical for estimating the I-V curves that define their performance at various light intensities. These models and the extraction of their parameters have been extensively studied under outdoor conditions, but their indoor illumination performance is less studied. With respect to the latter, several studies have used the parameter-scaling technique. However, the model's accuracy degrades when the light level decreases. In this study, we propose a simple PV modeling technique that can be applied at various illuminance levels by only using characteristic points (short-circuit current, open-circuit voltage, and maximum-power voltage points) at a reference illumination level. The model uses the characteristic point translation technique to translate the reference characteristic points to other operating conditions. Then, parameter extraction technique is used to extract the model's parameters. The proposed model's accuracy is verified using two commercial PV panels and different indoor lighting technologies. The results indicate that the proposed model outperforms the other examined works in terms of accuracy, with an average improvement of 15.75%.  
PV panels; characteristic point translation; five parameters; PV modeling +
Footnote †: journal: Applied
## 3 Dataset and Experimental Setup
### Metrics and Evaluation  
We evaluate the different models using the four evaluation metrics described below:  
* Sensitivity: Sensitivity (or positive recall), is estimated as the proportion of positive samples that are classified correctly. In the context of this paper, sensitivity describes the percentage of drowsy or distracted samples that are being correctly identified. The formula to compute sensitivity in terms of true positives (TP) and false negatives (FN) is: \(sensitivity=\frac{TP}{TP+FN}\).
* Specificity: Specificity (or negative recall) is estimated as the proportion of negative samples that are classified correctly. In the context of this paper, specificity describes the percentage of alert or not-distracted samples that are being correctly identified. The formula to compute sensitivity in terms of true negatives (TN) and false positives (FP) is: \(specificity=\frac{TN}{TN+FP}\).
* Average recall: Average recall corresponds to the mean value between specificity and sensitivity. The higher the average recall the less severe the trade-off between sensitivity and specificity.
* Receiver operating characteristic curve (ROC): ROC curve is a graphical way to visualize the classification ability of a binary classifier. ROC curves describe the relation between TP-rate and FP-rate at different thresholds. FP-rate is given as 1-specificity. The area under the ROC curve is equal to the probability that the model will classify a randomly chosen positive instance higher than a randomly chosen negative one. The area under the ROC curve, also known as AUC, is a measure of the general ability of the network to discriminate between the two classes. The higher the AUC, the better the model.
## 3 Methods
### Soil Moisture Retrieval Model  
Studies also showed that backscatter coefficient of bare soil has a good linear correlation with soil moisture [63; 64]. In order to build the linear relationship between backscatter coefficient and soil moisture over vegetated regions using HVBM, the exponential function in Equations (2) and (5) can be expressed as a polynomial following the Taylor approximation. For example, the exponential part of Equations (2) can be expressed as:  
\[\mathrm{e}^{-k_{0}\epsilon_{\mathrm{w}}^{\
u_{0},65}\sec\theta_{\mathrm{Pq} \epsilon}M_{\mathrm{veg}}}\approx 1-k_{0}\epsilon_{\mathrm{w}}^{\
u_{0},65} \sec\theta\beta_{\mathrm{Pq}\epsilon}M_{\mathrm{veg}} \tag{7}\]  
Combining Equations (1), (2), (5) and (7) yields:  
\[\Gamma_{\mathrm{Pq}}\approx(\sigma^{0}-k_{0}\frac{|\epsilon_{\mathrm{w}}|^{1.3 }}{\epsilon_{\mathrm{w}}^{\
u}}\gamma_{\mathrm{Pq}\epsilon}\beta_{\mathrm{Pq} \epsilon}M_{\mathrm{veg}}^{2})/(k_{0}^{1.4}|\epsilon_{\mathrm{w}}|^{1.3}\gamma_ {\mathrm{Pq}\epsilon\epsilon}M_{\mathrm{veg}}\Big{(}1-k_{0}^{2}s^{2}\cos^{2} \theta\Big{)}(1-k_{0}\epsilon_{\mathrm{w}}^{\
u}\sec\theta\beta_{\mathrm{Pq} \epsilon}M_{\mathrm{veg}})), \tag{8}\]  
According to the paper [55], the dielectric constants of water are given from mixing models at various frequencies. Thus, soil moisture content can be written as follows:  
\[SM\approx t_{1}\ln\big{(}(\sigma^{0}-t_{2}M_{\mathrm{veg}}^{2})/(t_{3}M_{ \mathrm{veg}}(1-k_{0}^{2}s^{2}\cos^{2}\theta)(1-t_{4}M_{\mathrm{veg}}))\big{)}+ t_{5}, \tag{9}\]  
where, \(SM\) means soil moisture content. \(M_{\mathrm{veg}}\) has a linear relationship with CVI and related vegetation indices [65]. \(t_{1}\)-\(t_{5}\) are empirical parameters.  
[PERSON] et al. [66] also proposed an empirical model using WCM and NDWI, which is expressed as follows:  
\[SM=k_{1}+k_{2}\sigma^{0}+k_{3}VI+k_{4}VI^{2}+k_{5}VI^{3}+k_{6}VI^{4}+k_{7} \sigma^{0}VI\sec\theta+k_{8}\sigma^{0}VI^{2}\sec\theta, \tag{10}\]  
where, \(VI\) represents NDWI. \(k_{1}\)-\(k_{8}\) are empirical parameters. [PERSON] et al. [67] used [PERSON]'s model to evaluate the applicability of X-band SAR data in soil moisture retrieval over dryland wheat fields. However, the study indicated that the numerical expression between total backscatter coefficient, soil moisture and vegetation parameters in [PERSON]'s model could not be directly derived from the WCM. The presence of NDWI up to the 4 th power seems difficult to understand and suggests that NDWI has more weight than backscattering in computing SM, which is contrasting with the physics.
## 8 Evaluation of the MRLG Protocol
### Assessing the Performance in Terms of Node Scalability  
In the tests that follow, we vary the simulation area while maintaining the same number of source nodes, and the same number of packets injected per node. Our purpose is to evaluate the scalability of the routing algorithms when increasing the number of sensors in the WSN. In particular, we seek that the routing overhead increases as linearly as possible to avoid collapsing the WSN, while providing low route convergence times.  
The number of nodes varies from 80 to 400, while the target area varies from \(91\times 91\) to \(210\times 210\) m\({}^{2}\). Notice that we increase the area proportionally to the number of nodes to maintain the same sensor density.  
Figure 9: Varying the packet injection rate on a per-source basis: (**a**) number of routing/injected packets, (**b**) normalized routing load.  
Figure 11(b). Notice that most of the packets traveling along the network are routing related, meaning that minimizing the number of control packets has a clearly positive impact on both packet loss and delay. Once again, it is important to highlight that sink announcement based protocols, such as CTP, may suffer from scalability problems if no actions are taken to minimize the routing overhead introduced, like MRLG does.
## 5 Game-Theoretical Routing Scheme for Video-Streaming in MANETs
### Design of the Utility Function
If there exists a mixed Nash equilibrium, player \(i\) will have a best response. To obtain it, \(U_{i}\) must be maximized:  
\[\frac{\partial U_{i}}{\partial p_{i}}=0 \tag{18}\]  
For the sake of a simpler writing we will omit the \(i\) index to refer the user in the previous variables shown in Table 1. Thus, we will use \(n_{sb}\) instead of \(n_{sb,i}\), and so on. Then, applying Equation (18) in Equation (17) we obtain:  
\[\frac{\partial U_{i}}{\partial p_{i}}=-2\cdot p_{i}\cdot\left(\frac{\phi_{b}} {J_{b}}+\frac{\phi_{m}}{J_{m}}\right)+\frac{\phi_{b}}{J_{b}}\cdot\frac{n_{rb}} {n_{s}}-\frac{\phi_{m}}{J_{m}}\cdot\frac{n_{rm}}{n_{s}}+2\cdot\frac{\phi_{m}} {J_{m}} \tag{19}\]  
To simplify the previous equation, we assume that \(J_{b}\), \(J_{m}\), \(n_{sb}\), \(n_{sm}\) and \(n_{s}\) are greater than zero. This assumption has sense since a jitter equal to zero is very improbable and at least one frame should have been sent as well. We define the following variables:  
\[\hat{\phi}_{b}=\frac{\phi_{b}}{J_{b}},\ \hat{\phi}_{m}=\frac{\phi_{m}}{J_{m}}, \ \hat{n}_{b}=\frac{n_{rb}}{n_{s}},\ \hat{n}_{m}=\frac{n_{rm}}{n_{s}} \tag{20}\]  
Next, we substitute Equation (20) in Equation (19) and we get:  
\[\frac{\partial U_{i}}{\partial p_{i}}=-2\cdot p_{i}\cdot\left(\hat{\phi}_{b} +\hat{\phi}_{m}\right)+\hat{\phi}_{b}\cdot\hat{n}_{b}-\hat{\phi}_{m}\cdot\hat {n}_{m}+2\cdot\hat{\phi}_{m} \tag{21}\]Then, by combining both Equations (18) and (21), we attain the solution for the best probability of sending (I+P) frames through the best path, that gives a NE in the utility function \(U_{i}\). This is called as the best response of the game. Thus, using \(p_{i}^{*}\) to compute the probability of sending I+P frames through the best path, is a strategy which produces the most favorable outcome for player \(i\), taking the other players' strategies as a given. \[p_{i}^{*}=\frac{\hat{\phi}_{b}\cdot\hat{n}_{b}+\hat{\phi}_{m}\cdot(2-\hat{n}_{ m})}{2\left(\hat{\phi}_{b}+\hat{\phi}_{m}\right)} \tag{22}\]  
This way, each player \(i\) will continuously update his/her best response \(p_{i}^{*}\) using Equation (22). To do so, the user easily obtains from the RTCP feedback packets: the number of I+P frames received so far from the best and the medium-quality paths (\(n_{rb}\) and \(n_{rm}\), respectively), the number of I+P frames sent so far (\(n_{s}\)) and the jitter delay through the best and the medium-quality paths (\(J_{b}\) and \(J_{m}\), respectively). In the next section, we explain how the user computes the benefits for the best and the medium-quality paths.
# SMS-Net: Bridging the Gap Between High Accuracy and Low Computational Cost in Pose Estimation
## 4 Experimental Results
### Results  
The model performance was measured using the performance metrics introduced in the previous section on the MPII and COCO validation datasets for detecting and representing pose keypoints in the target images during inference. We compared performances using four models: SMS-Net-Tiny (one stack), SMS-Net-Small (two stacks), SMS-Net-Large (four stacks), and SMS-Net-Huge (eight stacks).
# Augmenting Environmental Interaction in Audio Feedback Systems+
## 3 System Design and Validation
### Distribution of Room Modes with Timbre  
In Section 3.1, we approximated a measure of the amount of reverberation according to reverberation time. In this section, another approximation regarding reverberation is investigated, emphasizing related to spectral rather than of temporal characteristics. High reverberation diffusion means that reflections are scattered along various paths, producing a smooth magnitude response; on the other hand, strong reflections exist at specific frequencies in low reverberation diffusion, producing an uneven magnitude response. While in the previous Section 3.3 we measure the strongest path of audio feedback, this section is concerned with diffusion of the reflection paths.  
In order to discern such distribution of room modes, we used the variance of magnitudes from the transfer function, derived from the frequency responses of the input and output signals. Table 3 compares actual measures of the distribution through spectral flatness [21] and variances from the impulse response data in several locations, and approximate measures by the variances from the derived transfer functions. Figure 11 also details these approximate measures.  
The resulting approximation of the distribution is mapped to the gains of the unmodified output signal and a one-pole high-pass filter having a cutoff frequency of 4000 Hz, which are mixed thereafter: high variance increases the gain of the high-pass filter (decreases the gain of the unmodified signal) and the sum of the gains is always unity in linear scale. The last column in Table 3 shows the average gain values mapped by the variance measurements. This gain control changes spectral centroid as well as distribution width of spectral energy, which is associated with softness of a sound [22]. Figure 12 evaluates the mappings by comparing the changes in spectral centroids.  
Figure 11: Variances of magnitudes of transfer functions simulated using impulse response data of several locations with different distributions of room modes (measured in Table 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Room Types** & **Spectral Flatness** & **Var\((1\,H(f)\,1)\)** & **Var\((1\,H^{\prime}(f)\,1)\)** & \(g\)** \\ \hline Long Echo Hall & 0.85 & 29.71 & 52.90 & 0.56 \\ Livingroom & 0.83 & 31.52 & 61.08 & 0.74 \\ Church & 0.73 & 52.68 & 70.37 & 0.95 \\ Bathroom & 0.64 & 79.15 & 90.00 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the distributions of room modes measured by spectral flatness and variance of the magnitudes of the impulse response data (\(H(f)\)), average variance of the frequency magnitudes of the estimated transfer function (\(H^{\prime}(f)\)), and average gain values mapped by \(H^{\prime}(f)\) (\(g\)).
## 2 Frequencies and Mode Shapes of the Double-Beam System
### Parametric Studies
#### 2.3.1 The Order of Basic Mode  
Figure 3 shows the effect of the order of the basic mode (\(n\)) on the frequency ratios in two sub-modes (\(\omega_{0,i},\ i=1,\ 2\)) for different boundary conditions when \(s_{\rm e}=100\), \(s_{\rm m}=1\) and \(s_{\rm k}=2\). It can be seen from the figure that \(\omega_{0,1}\) and \(\omega_{0,2}\) vary obviously with boundary conditions in the 1 st basic mode. The effect of \(n\) on the frequency ratios is small when \(n\) is large. Figure 4 shows the effect of \(n\) on the mode amplitude ratios in two sub-modes (\(\phi_{0,i},\ i=1,\ 2\)). \(\phi_{0,1}\) is always positive and \(\phi_{0,2}\) is always negative, which indicates that the modes of two beams are always in-plane with each other in the 1 st sub-mode, whereas the modes of the primary beam are always anti-plane with the modes of the secondary beam in the 2 nd sub-mode. \(\phi_{0,1}\) tends to be very small with the increase of \(n\) and \(\phi_{0,2}\) decreases dramatically after the 4 th basic mode, which means that the double-beam system tends to  
Figure 2: Mode shapes of the first 8 modes for the fixed-fixed double beam system.  
behavior like two individual beams. While \(\phi_{0,1}\) varies a lot with boundary conditions in the 1 st basic mode, \(\phi_{0,2}\) differs obviously with boundary conditions when \(n\) is larger than 5.
# Human-Related Hazardous Events Assessment for
### Innovative Contribution  
The purpose of this study is to develop a comprehensive, human-related hazardous events assessment methodology with full consideration of the influences from social and technical aspects. Using this methodology, the risks stemming from on-site operations and management level can be quantitatively analyzed for accidents which occur during docking repair. For this purpose, a suffocation accident which occurred in a Chinese shipyard is exampled as a case study to illustrate the application of the proposed methodology. By the application of the methodology, the accident report is firstly qualitatively interpreted according to the basic principle of ETA to identify the human-related hazardous events involved in the suffocation accident. Additionally, the logic relationships among the identified hazardous events are analyzed to establish the CN, which is then evaluated by the improved K-shell decomposition algorithm to obtain the node criticality. Meanwhile, the identified hazardous events are reorganized within the framework of the HFACS with reference to the node criticality to develop the framework of the BN. Subsequently, the above-obtained node criticality is utilized to calculate the probability distribution of the nodes involved in the BN, which is essential for Bayesian inference. The innovative contribution of the proposed methodology is summarized as follows.  
1. Theoretical exploration of quantitatively describing the human-related hazardous events involved in the maritime accidents which have occurred during docking repair with a full consideration of factors from social-technological aspects.
2. The application of the CN in this study is able to conquer the shortage of the ETA, in which it fails to consider the logic relationships among hazardous events allocated at different levels.
3. The determination of prior probability and conditional probability of the Bayesian inference is implemented by the improved K-shell decomposition algorithm.
4. An entire solution to assess human-related hazardous events in shipyard accidents is established, and the consistent precision of this is validated with a case study of a suffocation accident.
# Motion-Induced Phase Error Compensation Using Three-Stream Neural Networks
## 3 Experiment
### Network Training  
The network was implemented by Pytorch [37] 1.8.2 and the training process was completed in the hardware environment with Intel Gold 5120 CPU and the 16 GB NVIDIA Tesla P100 GPU. We used an Adam [38] optimizer with initial learning rate of 10\({}^{-4}\) to minimize the loss function in Equation (10). The batch size is set to 4. A scheduler was applied to reduce the learning rate with a factor of 0.1 when there is no improvement on validation set after 10 epochs. The temporal three-streams neural networks are trained separately, and their loss curves are shown in Figure 4. Both curves show the convergence occur after around 30 epochs, then the losses steadily reduce to about 5 in the training set and 8 in validation set, respectively, which indicates the validity of the model.  
Figure 3: Collection of the training data and validation data. The first row shows the different 3D models used in the virtual FPP system. Each 3D model with different motion type was measured by 24 frames fringe patterns of the three-step PSP, in which the 1 st, 10 th, and 20 th images are shown in the second to fourth row. The ground truth numerator and denominator of the first frame are shown in the fifth and sixth row.
# Absolute Capacitance Measurement by Direct Digital Fitting of Proportional Coefficient
## 2 Measurement Principle and Simulation
### Principles of Conventional Charge-Discharge Technology and Noise Effects
Therefore, the traditional time constant method is susceptible to noise interference and the problem of affecting the accuracy of capacitance measurement due to the high requirement of the rising or falling half position. Meanwhile, the conventional time constant method has a long response time when measuring large capacitance, due to the long charging and discharging time, and requires a voltage of 0 value or full voltage for the initial state. We propose a capacitance measurement method of directly fitting the proportional coefficient to try to solve some of the shortcomings of the traditional time constant method.
## 3 Results
### Evoked Activity
#### 3.2.1 Local Node Silencing and PCI
(**b**) Spatiotemporal distribution of the areas with significant changes of their firing rate with respect to baseline activity following stimulation. Representative snapshots are reported at time t = 5 ms, t = 145 ms, t = 245 ms, t = 285 ms, t = 375 ms, and t = 485 ms in the control condition (top row), for the local manipulations L4 (middle row), and L17 (bottom row). (**c**) Top: the temporal courses of the instantaneous firing rate (<\(\Delta\)IFR>) are reported for all nodes and separately for the representative conditions: ctrl (grey), L4 (dark blue), and L17 (light blue). For each condition,  
the highest five <AlIFR> (quantified as the total activity in the interval [0, 500] ms) of the non-stimulated hemisphere (orange traces) are also reported. The horizontal brown line indicates the mean baseline (defined as <AlIFR(t)> averaged over the interval [\(-\)1000, 0] ms and over the latter highest five <AlIFR(t)>). The vertical dotted black line marks the time of the stimulus, and the vertical continuous black lines are relative to the snapshots reported in panel b. Bottom: the corresponding sorted binary spatiotemporal matrices of significant activities highlight the impact of the local manipulations with respect to the control condition (the red line is the sum of significant activity over time). The Perturbational Complexity Index (PCI) decreases significantly (\(p\) < 0.00001, ANOVA and post hoc pairwise T test with Benjamini-Hochberg correction) in L4 and L17 compared with control (see Figure S2).