esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Enhancement of Second Harmonic Generation via Noninvasive Modulation
## 3 Results and Discussion
### Different Scattering Mediums  
As mentioned above, it is possible to improve the light intensity of the SHG by introducing a structured light filed with a specific phase and amplitude. The degree of scattering of the scattering medium can affect the phase gradient, resulting in the change of second harmonic intensity. To explore the relationship between scattering medium and optimized effect, we used ground glass with different degrees of scattering and zinc oxide film (a thickness of 25 \(\upmu\)m) to respectively construct such a structured light field. The different speckle patterns of the same size were generated in the same position after the light passed through the different scattering mediums, as shown in Figure 3. To intuitively distinguish the intensity gradient information of the speckle, the intensity of the same-row pixels (514 th line) of different patterns were mapped. It can be seen from Figure 3 that the larger the grid of the ground glass, the finer the scattering particles of the speckle light field, and the speckle of zinc oxide film is more dense than that of ground glass.  
Figure 2: The enhancement of SHG without scattering medium using wavefront shaping technique. (**a**) Comparison of enhancement effects of different phase mask resolutions (results from the statistical average values of multiple experimental data). (**b**) Comparison of the second harmonic spectrum before and after optimization (detecting the spectrum of the best results). Note: the optimized light field is collected after three times attenuation. Scale bar: 1.468 mm.
## 4 Simulation and Analysis
### Sinusoidal Steering Condition  
In order to further evaluate the effectiveness of the algorithm under a variety of operating conditions, the algorithm is evaluated using a sinusoidal steering condition, where the initial vehicle speed is set to 80 km/h and the adhesion coefficient of the road surface is set to 0.85. The corresponding system inputs to the observer are shown in Figure 6, and the simulation results of the corresponding SCKF algorithm are compared with those of the IMM-SCKF algorithm as shown in Figure 7.  
As shown in Figure 7a, which is a comparison of the longitudinal observation results, both algorithms have high observation accuracy, but the IMM-SCKF algorithm is able to  
Figure 6: Sinusoidal steering condition: (**a**) observer input, including front wheel angle and four-wheel speed; (**b**) vehicle trajectory.  
Figure 7: Simulation results of vehicle state under sinusoidal steering condition: (**a**) longitudinal velocity; (**b**) lateral velocity; (**c**) yaw rate; (**d**) probability of each IMM’s model.  
estimate with smaller following error at higher vehicle speed conditions. In Figure 7b lateral vehicle speed observation, due to the higher vehicle speed, the steering angle is larger at this time, and the tire enters the nonlinear region, and the lateral force calculation result has some error with the real result at this time. Therefore, both observers have certain errors in the steering condition, but the IMM-SCKF algorithm can quickly adjust to a smaller error model output after the steering condition is over, and the error of the SCKF algorithm is about 10% at this time. For yaw rate observation, both IMM-SCKF algorithm and SCKF algorithm have high accuracy, but IMM-SCKF is relatively more resistant to noise, while UKF algorithm has a certain estimation error of about 15% under large steering angle conditions.
## 4 Simulation Analysis  
### Performance Analysis under Different Slip Ratios  
Taking the short-circuit fault between the fourth coil of the first branch of Phase A and the ninth coil of the second branch of Phase A as an example, Equation (18) is used to calculate the protection action value, where \(N=48\). When \(s=0.1\), the calculation result of the protection action value \(S_{op}\) is shown in Figure 6, and the protection proposed in this paper can be operated at 47.5 ms after the fault.  
Figure 5: The action value \(S_{op.normal}\) with different phase voltage drops when the slip ratio \(s\) is 0.1.  
Figure 6: The action of the protection when the slip ratio \(s\) is 0.1.  
When \(s=0.05\) and \(0.01\), the calculation results of the protection action value \(S_{op}\) are shown in Figures 7 and 8. The protection can be operated at 91.7 ms and 365.0 ms after the fault. When the rotor speed is closer to the synchronous speed, the frequency of the rotor current is lower and closer to the DC, so that the action time of the proposed protection is gradually extended, which is inevitable. Ultimately, however, the protection method can operate correctly.
## 4 Results and Discussion
### Horizontal Openings Effect on Compartment Fire Dynamics
#### 4.2.2 Time to Flahsover  
It is expected that adding and varying the size of horizontal openings will have an effect on the time to flashover, or in well-ventilated cases, the start of the steady-state regime. However, the data obtained from these experiments are perhaps insufficient to draw a reliable correlation (Figure 11). It appears that time to \"flashover\" decreases as ventilation area increases, although the data have a significant scatter (R\({}^{2}\) = 0.1789). This suggests that adding horizontal openings accelerates fire growth, but this is as yet an unreliable correlation and so further work is required on this subject. Indeed, these results do appear to contradict the intuitive theory that ventilation in the roof should reduce the build-up of hot gases and slow fire growth. It is therefore likely that these results are skewed by the looser definition of flashover for the well-ventilated cases.
## 2 Materials and Methods
### Associated Trust and Security Issues  
There are several trust and security issues in CEEC networks as follows:  
Privacy protection issues: When maintaining trust in edge computing, users need to exchange trust data frequently, which may contain their private information. Disclosure of private data will create additional security concerns and most research does not take privacy into account. With the continuous promotion of edge computing and the rapid growth of IoT devices, the problem of data privacy warrants further study. Privacy protection models also need to adapt to the needs of various complex edge computing scenarios.  
Trust weight allocation issues: In the trust evaluation methods based on traditional mathematical statistics, the trust value is usually determined by aggregating trust factors through weighting and other related calculations. However, there is no basis to determine the trust weight. Some studies determine the trust weight based on subjective judgment, while others determine the trust weight based on experience. All of these will lead to inaccurate trust weight allocation, thus affecting the results of trust evaluation. Therefore, it is necessary to further study the allocation of trust weight and develop a new trust aggregation method.  
Edge intelligence trust management problem: In the edge intelligence scenario, there is no trust management mechanism for heterogeneous data owners. In edge intelligence networks, a single data owner may hold multiple training data sets that are heterogeneous in quality and type. The existing trust model is mainly oriented to a single application scenario and cannot realize the compatible trust management of multiple heterogeneous training  
Figure 2: Cloud-Edge-End Collaboration (CEEC) computing power architecture.  
data sets held by a single data owner. There is a lack of unified management mechanism to fully and flexibly manage the trust of the data owners of the edge intelligence networks.
## 4 Trade-Offs among Performance Parameters
### Quality of Experience  
Several studies have been conducted to analyze the quality of experience (QoE) [108], which implies the detailed subjective experience of the effects observed during task completion with the use of a VR simulator. It has been found that the latency has a huge impact on the QoE from the subjective studies performed on a log-loading task [109] of a forestry crane with a VR simulator. These subjective studies were performed on the VR system by adding a controlled delay to the display update and the joystick update. The added delays observed from the task were the controlled delays ranging from 0 to 30 ms for the display update and from 0 to 800 ms for the hand controller when referred to the delays in the crane control interface. The simulator used for performing the task was VR goggles (Oculus Rift) [110], providing stitched stereo camera views, joysticks for controlling the crane, and a simulation environment required for lifting logs onto the truck. The delays obtained by the simulator software developer baseline system for the VR simulator were estimated to be 25 ms in the screen update based on the movement of the head for rendering and about 80 ms for the motion of the joysticks to the visual feedback on the screen. No significant delay effects were observed in the task performance in the joystick delay study within a range of 200 ms. In addition to delays in the task, the delay range was recorded as less than 30-35 ms for the latency of the joysticks or hand controllers. With the evolution of the high speed of the fifth-generation communication technology, Multi-access MEC technology has been proven as a potential tool to satisfy the ultra-low latency requirements of 360\({}^{\circ}\) video delivery to promote users' QoE [111].
## 5 Properties of Hierarchical Entropy Functions  
We conclude the paper with a discussion of some properties of the self-affine hierarchical entropy functions \(h_{2,k}\) and \(h_{3,k}\). For each \(m\in\{2,3\}\) and each \(k\geq 2\), hierarchical entropy function \(h_{m,k}\) obeys the following properties.  
* **P1:**\(h_{m,k}\) is a continuous function on \(\mathbb{P}_{m}\).
* **P2:** If two probability vectors \(p_{1},p_{2}\) in \(\mathbb{P}_{m}\) are permutations of each other, then \[h_{m,k}(p_{1})=h_{m,k}(p_{2})\]
* **P3:** If \(p\in\mathbb{P}_{m}\) is degenerate (meaning that it is a permutation of the vector \((1,0,0,\cdots,0)\)), then \(h_{m,k}(p)=0\).
* **P4:** For each \(p\in\mathbb{P}_{m}\), \[0\leq h_{m,k}(p)\leq\log_{2}m\]  
Properties P1-P4 are simple consequences of what has gone before. For example, to see why the symmetry property P2 is true, first observe that \(H_{m,k}(\lambda_{1})=H_{m,k}(\lambda_{2})\) if \(\lambda_{1},\lambda_{2}\) are types which are permutations of each other; this symmetry property for entropy on types then extends to \(\mathbb{P}_{m}\) using the fact that the finitary source which induces \(h_{m,k}\) is entropy-stable.  
The well-known Shannon entropy function \(h_{m}\) on \(\mathbb{P}_{m}\) is defined by  
\[h_{m}(p_{1},p_{2},\cdots,p_{m})\triangleq\sum_{i=1}^{m}-p_{i}\log_{2}p_{i}\]  
where \(p_{i}\log_{2}p_{i}\) is taken to be zero if \(p_{i}=0\). We point out that \(h_{m}\) also satisfies properties P1-P4. In addition, \(h_{m}\) satisfies the property that it attains its maximum value at the equiprobable distribution \((1/m,1/m,\cdots,1/m)\). This property fails in general for the \(h_{m,k}\) functions, although it is true for some of them; for example, referring to Figure 2, we see that \(h_{2,2}\) and \(h_{2,4}\) do not reach their maximum at \((1/2,1/2)\), but \(h_{2,3}\) does. It is an open problem to determine the maximum value of each \(h_{2,k}\) and \(h_{3,k}\) and to see where the maximum is attained.  
The inequality  
\[h_{m,k}(p)\leq h_{m}(p),\ \ p\in\mathbb{P}_{m},\ m\in\{2,3\},\ k\geq 2\]  
gives us a relationship between hierarchical entropy and Shannon entropy; it follows from the fact that every string in a hierarchical type class is of the same type. It is an open problem whether this inequality is strict at every non-degenerate \(p\in\mathbb{P}_{m}\); we have proved this strict inequality property in some special cases (for example, \(m=k=2\)).
# Supporting Heterogenous Traffic on Top of Point-to-Multipoint Light-Trees +
## 5 Conclusions  
The optical P2 MP is compared against traditional P2P when dealing with dynamic P2P and P2P + P2 MP traffic. Two optical P2 MP technologies, OCS and DSCM, are studied and compared.  
The simulation findings suggest that DSCM decreases non-linearities and outperforms OCS for long-haul applications. However, in access and metro applications, where non-linearities are assumed to be low, the optical performance of OCS and DSCM is similar.  
For the sake of a quantitative comparison, aggressive and non-aggressive transceiver availabilities are defined. In the case of pure P2P traffic, both OSC and DSCM greatly reduce the number of transceivers in the network by the same amount, between 25% and 38%, while simplifying the network architecture. DSCM is more cost effective than OCS under both transceiver availabilities. However, when considering P2P + P2 MP traffic under the non-aggressive approach, OCS is able to serve them simultaneously without the need for additional transceivers, whereas DSCM requires around 40% more transceivers. In this scenario, the costs savings in OCS are noticeably larger than those in DSCM. Finally, it is worth noting that OCS and DSCM can co-exist and that this combination can be exploited to develop further efficiency improvements.  
Conceptualization, L.V., M.R. and J.C.; methodology, M.I. and L.V.; investigation, M.I.; data curation, M.I.; writing--original draft, M.I. and L.V.; writing--review and editing, M.R., N.C., A.N., J.P. and J.C.; supervision, M.R., N.C., A.N., J.P. and L.V. All authors have read and agreed to the published version of the manuscript.  
This research was funded by European Commission through the H2020 MSCA REAL-NET (G.A. 813144) and the HORIZON ALLEGRO (G.A. 101092766) projects, by the AEI through the IBON project (PID2020-114135 RB-I00), and by the ICREA institution.  
Not applicable.  
The authors declare no conflict of interest.
# An Analysis of Sound Event Detection under Acoustic Degradation Using Multi-Resolution Systems
## 3 Results and Discussion
### Results under Dynamic Range Compression  
The results obtained after applying dynamic range compression to the DESED Public evaluation set are presented in Figure 5. In this scenario, the best overall performance (macro \(F_{1}\)) is obtained by the multiresolution systems, _3 res_ and _5 res_. However, for some particular classes the best performance is obtained with a single-resolution system, as observed in the clean set results. Such is the case of Alarm/bell/ringing (\(F_{++}\)), Cat (\(F_{+}\)), Dishes (\(T_{++}\)), and Running water (\(T_{++}\)).  
Figure 4: \(F_{1}\) scores of single-resolution systems (\(F_{++},F_{+},BS,T_{+},T_{++}\)) and multiresolution systems (_3 res_, _5 res_) over DESED Public Evaluation set applying a low-pass filter with cutoff frequencies of 2000 Hz (**top**), 1000 Hz (**center**), and 500 Hz (**bottom**). “Macro F1” shows average score over 10 target categories. Best viewed in color.
## 4 Basic Ideas on Scalar Subdivision Schemes
Let \(\mathbf{U}^{(d)}=[V_{0}^{0},V_{0}^{1},\ldots,V_{0}^{d-1},V_{1}^{0},V_{1}^{1}, \ldots,V_{1}^{d-1},\ldots,V_{n-1}^{0},V_{n-1}^{1},\ldots,V_{n-1}^{d-1}]^{\top}\) be the data that we wish to interpolate and suppose that there exists a sequence \(\left\{t_{j},j=0,\ldots,n-1\right\}\) such that the subdivision curve \(c(t)\) interpolates the data \(\mathbf{U}^{(d)}\), i.e.,  
\[c^{(k)}(t_{j})=V_{j}^{k},\qquad\text{for }k=0,\ldots,d-1,\;j=0,\ldots,n. \tag{13}\]  
If the curve \(c(t)\) is defined by \(n\) control points as in (10), we may get a solution to the point interpolation problem (10) that contradicts the values of higher order derivatives. Thus, in order to interpolate all the information in \(\mathbf{U}^{(d)}\) with an equal amount of variables and equations, we need to use \(nd\) control points \(\mathbf{P}^{0}=\left\{P_{j}^{0}\in\mathbb{R}^{m},j=0,\ldots,nd-1\right\}\) with the periodisation \(P_{j}^{0}=P_{j+nd}^{0},j\in\mathbb{Z}\). A natural choice is to set the parameters in (13) as \(t_{j}=dj,j=0,\ldots,n-1\). Then, from (9) we get the \(nd\) equations  
\[c^{(k)}(dj)=V_{j}^{k}=\sum_{j\in\mathbb{Z}}P_{j}^{0}\beta_{dj-s}^{k},\qquad j= 0,\ldots,n-1,\;k=0,\ldots,d-1,\]  
which can be represented in matrix form as  
\[\mathbf{M}_{n}\mathbf{P}^{0}= \begin{bmatrix}\begin{array}{cccccc}\mathbf{B} _{0}&\mathbf{B}_{-1}&\mathbf{B}_{-2}&\ldots&\mathbf{B}_{2}&\mathbf{B}_{1}\\ \mathbf{B}_{1}&\mathbf{B}_{0}&\mathbf{B}_{-1}&\ldots&\mathbf{B}_{3}&\mathbf{ B}_{2}\\ \ddots&\ddots&\ddots&\ddots&\ddots&\ddots\\ \mathbf{B}_{-1}&\mathbf{B}_{-2}&\mathbf{B}_{-3}&\ldots&\mathbf{B}_{1}&\mathbf{ B}_{0}\end{array}\end{bmatrix}\begin{bmatrix}P_{0}^{0}\\ P_{1}^{0}\\ \vdots\\ P_{md-1}^{0}\end{bmatrix} =\mathbf{U}^{(d)}, \tag{14}\]  
where the \(d\times d\) blocks of the matrix \(\mathbf{M}_{n}\in\mathbb{R}^{nd\times nd}\) satisfy \(\mathbf{B}_{j}=\mathbf{B}_{j-n}\) for \(j=1,\ldots,n\) and  
\[\mathbf{B}_{j}= \begin{bmatrix}\begin{array}{cccccc}\beta_{dj}^{0}&\beta_{dj -1}^{0}&\beta_{dj-2}^{0}&\ldots&\beta_{d(j-1)+1}^{0}\\ \beta_{dj}^{1}&\beta_{dj-1}^{1}&\beta_{dj-2}^{1}&\ldots&\beta_{d(j-1)+1}^{0}\\ \ddots&\ddots&\ddots&\ddots&\ddots\\ \beta_{dj}^{d-1}&\beta_{dj-1}^{d-1}&\beta_{dj-2}^{d-1}&\ldots&\beta_{d(j-1)+1} ^{d-1}\end{array}\end{bmatrix} . \tag{15}\]  
The structure of the matrix in (14) is the block adaptation of the scalar version (10), i.e., it is a block circulant matrix, as described in Definition 3. Indeed, when \(d=1\), \(\mathbf{M}_{n}\equiv M_{n}\) and \(\mathbf{U}^{(1)}\equiv\mathbf{V}^{0}\). Therefore, the Hermite problem represents the block extension of the point interpolation problem in the matrix sense and we can exploit the corresponding linear algebra tools to solve both inverse problems.
# An Efficient Multistage Approach for Blind Source Separation of Noisy Convolutive Speech Mixture
## 4 Results and Discussion
### Experimental Setup  
We considered 10 source speech signals comprised of five female and five male speakers from the TIMIT database [41]. All the source speech signals have the same loudness and a sampling rate of 8 kHz. The Hamming window with a 75% overlapping factor was used. A noisy reverberant environment was used to evaluate the separation  
Figure 2: Second model.  
performance of the proposed multistage approach. For a fair comparison, the methods used in [31; 32; 42] were extended, composed of the respective BSS technique and MMSE filtering mechanism for de-noising, as presented in Figures 1 and 2, respectively. The proposed models were investigated for different parameters such as signal-to-distortion ratio (SDR) and RT. \(\Delta\)SDR is defined as the difference between the desired SDR of the estimated speech signal and SDR of the speech mixtures, i.e., \(\Delta\)SDR = SDR\({}_{\text{desired}}\) - SDR\({}_{\text{mixture}}\).
## 1 Introduction
The point-surface fusion model was then established by using the indices and NMRI at the station, to realize the goal to produce a spatially continuous NMRI map. Due to the spatial variation and complex nonlinear relationship between the above indices and NMRI, it is difficult to map NMRI from satellite-based vegetation index datasets using traditional linear statistical regression algorithms, especially over regions with heterogeneous environments. Compared with the traditional algorithms, machine learning techniques have been reported to be excellent in dealing with complex nonlinear problems, and have advantages in exploring the hidden features and relationships within datasets. Therefore, we analyzed the performance of three machine learning regression algorithms, i.e., the back-propagation neural network (BPNN) [34], the general regression neural network (GRNN) [35], and random forest (RF) [36], to construct nonlinear models. The rest of this paper is organized as follows. Section 2 presents the study area, the data used in this study, and the correlation analysis. Section 3 introduces the fusion models and the statistical methods used for evaluation. Section 4 evaluates the experimental results and analysis. Finally, the conclusions and future research are summarized in Section 5.
# Advanced SDR-Based Custom OFDM Protocol for Improved Data Rates in HF-NVIS Links
## 3 Results and Discussion
### Real Field Conditions
#### Mobile Receiver System (Figure 19b)
mobile receiver's whip antenna, a simple modification was implemented: the antenna tip was tied down using a rope, effectively bending the antenna into a more horizontal orientation (Figure 25). This modification altered the radiation pattern of the antenna, focusing more of the energy upwards rather than towards the horizon, which is necessary for NVIS communication. The results of the NVIS propagation tests demonstrated a notable increase in signal variability when compared to the findings of the ground wave propagation tests. The observed variations in signal strength were attributed to the inherent dynamic nature of ionospheric conditions, which can give rise to sudden changes in both reflection angles and signal path lengths. The signal power received in NVIS mode was, on average, lower than that received in ground wave mode. This is to be expected, given that the signal propagates over a more extensive path, undergoing reflection off the ionosphere prior to reaching the receiver, which inevitably results in a degree of signal loss. Despite the enhanced signal variability and diminished signal power, the communication system remained operational in the majority of instances. The OFDM modulation demonstrated efficacy in maintaining the integrity of the transmitted data, even in the context of fluctuating NVIS conditions. Despite the noted degradation in quality, messages and files were successfully received in most tests, although this was more challenging in certain conditions because of the high variation of SNR level during transmission. The evaluation indicated that the system consistently maintained optimal functionality and data integrity across diverse modulation formats, provided the specified SNR thresholds are satisfied for each digital modulation employed.