esa-sceva/satcom-chunk-collection huggingface dataset:
# IG-Based Method for Voiceprint Universal Adversarial Perturbation Generation
## 5 Generic Adversarial Perturbation Generation Process  
During the iterative optimization process, a subset of audio training samples will be selected for traversal in each round of iteration. Before proceeding, each input sample from the original audio data set will be normalized within the interval [0, 1] to adhere to constraints. Here, 0 indicates the minimum amplitude, and 1 represents the maximum amplitude. Subsequently, the perturbation signal transform of each audio sample will be computed to determine the objective function gradient based on the perturbation signal. After traversing all of the relevant samples, the optimization process will be complete. Combining the gradients obtained from solving the objective function optimally using Adam's rule, we calculate the generic adversarial perturbation. We then iteratively update the optimized perturbation to the original audio samples, ultimately generating the generic adversarial perturbation. We stop iterating and save the adversarial samples once the attack success rate reaches the preset threshold value. Details of the training process can be found in Algorithm 1.  
```
0: Raw audio training Sets \(x_{train}\), Training set labels \(y_{label}\), Expectation of deception rate \(\delta\), Targeted attack category \(t\)
0: Generalized counteracting perturbation \(v^{\prime}\)
1: Initialize the generic adversarial perturbation \(v^{\prime}\) to 0
2: Selecting small samples S in the dataset, \(x_{i}\in(x_{train},y_{label})\)
3: Initialize the update gradient g to 0
4: Normalize the audio samples \(x_{i}\) to the interval range \([0,1]\), \(x^{\prime}_{i}=\tan^{-1}((2x_{i}-1)\times(1-\varepsilon))\)
5: Calculate the acoustic perturbation signal transform for sample \(x_{i}\):\(w_{i}=\frac{1}{2}\big{(}\tan\big{(}x^{\prime}_{i}+v^{\prime}\big{)}+1\big{)}\)
6: Calculate the gradient of the objective function. \(\mathrm{g}\leftarrow\mathrm{g}+\frac{\partial L(\omega_{i},t)}{\partial\omega_ {i}}\) if it is a targeted attack and \(\mathrm{g}\leftarrow\mathrm{g}+\frac{\partial L(\omega_{i},w_{i})}{\partial \omega_{i}}\) if it is an untargeted attack
7: Repeat steps 4 to 6 until each sample \(x_{i}\) in the small batch sample set S has been traversed
8: Based on the obtained update gradient g, which is updated according to Adam's rule, yields \(\Delta v^{\prime}\)
9: Updating generalized counter perturbation \(v^{\prime}\)\(\leftarrow\)\(v^{\prime}+\Delta v^{\prime}\)
10: Repeat steps 2 through 9 until \(\mathrm{ASR}(X,V_{G})>1-\delta\)
11: Output generalized anti-perturbation \(v^{\prime}\)
```  
**Algorithm 1** IG-Based Method for Vioceprint Universal Adversarial Perturbation Generation
## 7 Simulation and Results
### Varying Density of the Network  
This section focuses on the discussion of simulation results with varying density of nodes. We started the simulation with a sparse network and then noticed the difference in reliability parameters with an increasing node density.  
Figure 19: Average end-to-end delay _versus_ number of hops. Hotline assisted routing has fewer hops from source-to-sink due to routing through gateways.
## 3 Results and Discussion
### Comparison with Carnot Cycle COP  
Carnot cycle COP was used to evaluate the model results.  
The chiller allows for running at optimum parameters for cycle time, switching time, heat recovery time, mass flow rates and activated carbon mass and optimum heat transfer coefficients for beds and evaporator heat exchangers. These results are compared with a Carnot cycle COP at different operating conditions, as shown in Table 2. All results are at nominal operating chiller conditions except hot water inlet temperature; the chiller without heat recovery mode operated at a very low COP when compared with the Carnot COP, while the heat recovery mode results are very close to the Carnot COP, as shown in the last column of Table 2.
## 3 Experiment: 90 THz CW Modulator
For this measurement, the D\({}_{2}\) pressure is held constant at 0.3 atm. The highest conversion efficiency that we achieve is \(0.5\times 10^{-6}\). Although the conversion efficiency is low, this is the first time CW modulation of an independent laser at such a high rate has been demonstrated. In addition to the measurement of Figure 3, as we vary the incident pump power, we also measure the transmitted pump and Stokes powers through the cavity. Similar to our previous experiments, the transmitted pump power stays constant at about 1 mW, whereas the transmitted Stokes power increases linearly to a maximum of about 7 mW as we increase the input pump power. From the transmitted power measurements, we can calculate the intra-cavity circulating intensities for the pump and the Stokes beams using the mirror transmittance and the known cavity mode size. The maximum calculated intra-cavity intensities for the pump and Stokes lasers are 10 kW/cm\({}^{2}\) and 62 kW/cm\({}^{2}\), respectively. The solid line in Figure 3 shows the calculated conversion efficiency using the estimated intra-cavity pump and Stokes intensities. There are no adjustable parameters in this calculation; _i.e._, all parameters that are used are experimentally measured. We observe reasonable agreement between the experimental data points and the calculations. The disagreement is likely due  
Figure 2: The simplified schematic of our experiment. We start the experiment by locking a high-power pump laser beam to one of the axial modes of the cavity. With the pump laser locked, the Stokes beam is produced through Raman lasing. The pump and the Stokes beams drive the molecular coherence, which can then be used to modulate a separate 785-nm laser beam in a single pass. ECDL: external cavity diode laser, EOM: electro-optic modulator, Yb FA: ytterbium fiber amplifier, PBS: polarizing beam splitter, GS: glass slide, PD: photo-diode, MML: mode-matching lens, HFC: high-finesse cavity, TA: tapered amplifier, DM: dichroic mirror, G: grating, BB: beam block. to the imperfect spatial overlap of the 785-nm beam with the TEM\({}_{00}\) mode of the cavity. This spatial overlap is difficult to measure precisely in our experiment and the calculations do not take this effect into account. The predicted largest value for the molecular coherence that we achieve in this experiment is \(|\rho_{ab}|=2.5\times 10^{-5}\). Currently, the conversion efficiency in this experiment is limited by the intra-cavity pump and Stokes laser intensities. One drawback of the current experiment is that we rely on Raman lasing to generate the Stokes beam. As a result, the pump and the Stokes intra-cavity intensities are limited by the dynamics of Raman lasing. This drawback can be overcome by locking two independent laser beams to the cavity, rather than relying on Raman lasing, an approach we discuss in the next section.
# Hot Topic Community Discovery on Cross Social Networks
## 1 Introduction  
The era of Web 2.0 has witnessed the rapid expansion of online social networks that allow users to obtain information, communicate with each other, and express different opinions. The content published by these social networks usually includes news events, personal life, social topics, etc. The information is not only used to discover hot topics and analyze topic evolution, but also to analyze and supervise public opinion.  
The existing hot topic discovery methods are mainly limited to a single social network, such as Sina Weibo, Twitter, and so on. Generally, in the same social network, users are affected by each other, resulting in a similar point of view. However, under another social network, it is also possible for users to hold the opposite opinion for the same event. Therefore, research undertaken on a single social network is unable to meet the needs of research on hot topic community discovery.  
In this paper, we propose a hot topic discovery method on cross social networks. \"Cross social networks\" refer to multiple social network platforms. The integration of information from multiple social network platforms form a new unified dataset. In the dataset, information from different platforms for the same event may contain similar or unique topics. First, we fuse data from different social networks and build a unified model, which contains a lot of short text. Then, a new topic model called Labeled Biterm Latent Dirichlet Allocation (LB-LDA) is proposed to get latent topic distributions.  
Thirdly, topic cluster operation is processed to get multiple topic communities consisting of several latent topic labels. Finally, scores of different topic communities are calculated and communities with higher score are regarded as hot topic communities. Experiments on data from three different social networks show the hot topic discovery method can find hot topics at that time, and the hot topics are verified to be effective. The main innovations of this paper are as follows:  
* We conduct hot topic discovery on cross social networks instead of a single social network.
* We propose a new topic model called LB-LDA, which can relieve the sparseness of the topic distribution.  
The remainder of the paper is organized as follows. Section 2 presents an overview of related work. In Section 3, we elaborate on our hot topic discovery method on cross social networks. Section 4 describes an experimental result and presents our final hot topic communities. In Section 5, conclusions and suggestions for future research are made.
## 3 Filter-Level Pruning Based on RL
### Filter Selection and Pruning  
In this paper, the Taylor-expansion-based criterion is used for filter importance evaluation [29].  
To illustrate the idea of the Taylor-expansion-based filter sorting criterion, let \(C(h_{i}=0)\) denote the cost value of the pruned feature map \(h_{i}\), and \(C(h_{i})\) is the cost value without pruning \(h_{i}\). Thereby, the change in the cost value before and after pruning \(h_{i}\) can be expressed as (11):  
\[|\Delta C(h_{i})|=|C(h_{i}=0)-C(h_{i})| \tag{11}\]  
The approximation of \(C(h_{i}=0)\) with first-order Taylor expansion is shown in (12).  
\[C(D,h_{i}=0)=C(D,h_{i})-\frac{\delta C}{\delta h_{i}}h_{i}+R_{1}(h_{i}=0) \tag{12}\]  
where \(R_{1}\) denotes the remainder of Taylor first-order expansion. The Taylor-expansion-based filter sorting criterion in (13) can be obtained by substituting (12) into (11) and ignoring the remainder.  
\[|\Delta C(h_{i})|=|C(D,h_{i})-\frac{\delta C}{\delta h_{i}}h_{i}-C(D,h_{i})|= |\frac{\delta C}{\delta h_{i}}h_{i}| \tag{13}\]  
This criterion tends to prune feature maps that have an almost flat gradient of the cost function.  
Given the filter pruning rate and the Taylor-expansion-based sorting criterion considered, the network pruning process is summarized as follows:  
1. Obtain the number of filters to be pruned in the current layer by multiplying the number of filters in the current layer and the pruning rate.
2. Sort filters according to the Taylor-expansion-based criterion.
3. Prune less important filters as determined by steps 1 and 2.  
The network connectivity should be ensured with network pruning. Examples of filter pruning on VGGNet and ResNet are shown in Figure 2.  
Figure 2: Examples of filter pruning. (**a**) Filter pruning in VGGNet; (**b**) filter pruning in ResNet.
# Dynamic Indoor Localization Using Maximum Likelihood Particle Filtering
## 5 Experimental Validation
Also, although particle filtering should yield the theoretically optimal result if the number of particles is large enough, the very large number of required particles result in that, even  
The second experiment aims to show the performance of our method in living and working environments. To this end we use two adjacent offices of \(7.1\times 11.3\) m\({}^{2}\) connected by a corridor. We build the fingerprint database using \(N=72\) positions, as shown in Figure 6, together with the AP's location. We see from Table 2 that, while in the empty room the proposed method with 10 particles performed similarly to the MLKF method, in this second experiment it largely outperforms the latter. The table also shows the clear advantage of the proposed method over its rivals. The reason for this advantage is as follows. Due to the signal distortion and attenuation caused by multipaths, walls and furnitures, the localization information provided by measurements is not enough to yield accurate estimates at certain locations. This can be seen in the ECD shown in Figure 7. At those locations, dynamic localization methods resort to motion model information to produce an estimate. Figure 7 shows that only the proposed MLPF method can produce accurate estimates at those locations. Figure 4: Positioning error cumulative distribution (ECD) (empty room). Figure 5: Tracking result of maximum likelihood particle filter (MLPF) with 10 and 50 particles in an empty room. This is because it is the only method that is able to make full use of the rich information provided by a non-linear motion model. While methods based on PF potentially have the same possibility, the extremely large number of required particles make this unfeasible in practice. To conclude the experiment, we show in Figure 8 the ground truth and estimated trajectories. Figure 6: Layout and database points for the two office rooms. The AP is placed at the lower-right corner of the right room. Figure 7: Positioning ECD (office rooms).
## 7 Conclusions  
In this paper, some key issues relating to the fault estimation stage of the correlation estimator method were analysed. First of all, the impact of the fault impedance mismatch between the fault occurrence and fault estimation stages was discussed. An error in the fault location was observed for the fault positions far from the observation point. To tackle this problem, a simple approach was proposed in which the fault estimation stage transfer functions are stored separately for low- and high-impedance faults. Secondly, a new pseudo method was developed to correctly identify fault types and pinpoint their precise locations, in which the fault estimation stage transfer functions were stored for all possible fault types.  
Finally, the important issue of the spatial step, considered in the fault estimation stage of conventional EMTR and the correlation estimator methods, was studied. Meanwhile, the advantage of the latter method was also highlighted, which was based on the CCv. It was shown that only a smaller spatial step ensures the fault location's accuracy, but at the cost of more memory space required. This problem was solved by proposing a hybrid approach utilising a mixture of the correlation estimator method and regression analysis, in which a few locations along the line length were selected to store their corresponding transfer functions. These transfer functions were correlated with the transfer functions having a smaller spatial step to provide the CCv. The line length and CCv then served as the training data for the regression model's construction, and the Gaussian process regression was the best-fit model to predict the fault location. It was demonstrated that, by employing the existing correlation estimator, the memory space requirement was 75 GB, whereas the same job could be performed with 80 MB by taking advantage of the proposed approach.  
The hybrid approach was also applied in conjunction with the simple and pseudo approaches. It is important to note that the current analyses were based on theoretical and simple cases. Complex networks will be investigated in future studies, and the experimental validation of the method proposed will be presented using field data from real-world systems. Moreover, the study of nonlinear fault impedance scenarios with uncertain parameters is already in progress.  
Conceptualization, A.N.; Methodology, A.N.; Software, A.N. and S.H.; Validation, Y.X. and S.H.; Formal analysis, S.H.; Investigation, Y.X.; Resources, Y.X.; Writing--original draft, A.N.; Writing--review & editing, S.H., N.D., P.C. and M.S.; Supervision, Y.X.; Project administration, Y.X.; Funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant 52007139.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Effect of Operating Conditions on Emissions
#### 3.2.1 Effect of EA on Emissions
A large amount of CO would be splashed and entrained into the freeboard by increasing the fluidized air and react with NO emission (2 NO + 2 CO \(\rightarrow\) 2 CO\({}_{2}\) + N\({}_{2}\)) [23]. SO\({}_{2}\) emissions normally strongly correlate with the sulfur content of the fuel. Previous studies ignored SO\({}_{2}\) emissions due to the low sulfur content in the poultry litter samples. In this study, it was found that SO\({}_{2}\) emission decreased from 49 ppm to 1 ppm when an EA ratio increased from 0.04  
Figure 5: Effect of EA ratio on CO, NO\({}_{\text{x}}\), and SO\({}_{2}\) emissions  
and 1.08. This SO\({}_{2}\) reduction was due to an increase in EA which resulted in three reactions. First, a high O\({}_{2}\) concentration and an optimal reaction temperature (close to 800 \({}^{\circ}\)C) favored CaSO\({}_{4}\) formation. Second, an increase in gas-solid contact benefited SO\({}_{2}\) retention. Third, high amounts of Ca and Mg contents in poultry litter ash can react with SO\({}_{2}\)[6; 9; 21]. Then, SO\({}_{2}\) emission was increased when the EA rose from 1.08 to 1.54. This can be explained by the relatively low temperature (700-750 \({}^{\circ}\)C) within the EA range between 0.79 and 1.54, which does not benefit SO\({}_{2}\) capture and subsequently increase the concentration of SO\({}_{2}\). It was observed that CO and SO\({}_{2}\) emissions were decreased with a minor increment of NO\({}_{x}\) emission within the range of EA between 0.04 and 0.79. On the other hand, the CO and SO\({}_{2}\) emissions were starting to slightly increase when the EA was higher than 1.19. Therefore, the EA ratio is suggested to be in the range from 0.79 to 1.08 for minimizing all three major emissions during the poultry litter and natural gas co-combustion process.
## 3 Results  
### REF Versus IID: First Evidence of Different Damage Progress  
As previously mentioned, a comparative analysis will follow between a REF panel and one that carries unexpected early AE activity hypothesized to be related to skin-core interfacial debonding (IID). It should be noted that in total three panels were tested; in two of them, IID failure was reported.  
In Figure 2, an overview of the loading process for both series is given. An incremental increase in the loading in time is projected together with the scatter plot of the AE hits absolute energy. The AE energy was proven to be a descriptor of fracture energy accumulation [35; 36], but it can also indicate the transition from early micro-cracking to severe crack formation or other internal fracturing [29]. At the early loading stage, sporadic AE activity of low energy is reported for both samples under study. However, a burst in AE energy values was already monitored for the IID series at 0.5 and 1.5 kN/m\({}^{2}\). As the load increased, several additional jumps in the AE energy were captured mainly at the active loading stage (not in the course of the load pause). The same AE feature trends evolved until reaching 5 kN/m\({}^{2}\). In contrast, for the REF panel there was limited AE activity of negligible energy until the load of 3.5 kN/m\({}^{2}\). Beyond that point and likewise in the IID case, the AE hits that were continuously emitted carried many significant bursts of energy.  
In Figure 2c, the cumulative graph of AE hits population at incremental loading illustrates that not only the was AE energy high for the IID series very early, but also that the number of hits was much greater for the IID series at this early loading stage. Indicatively, at 3.5 kN/m\({}^{2}\) the hits emitted at the REF panel were limited to 2263 and the respective hits number in the IID case was up to 14010.  
At the next step, the AE sources related to specific damage modes were assessed considering the wave rise time (RT) and the frequency distributions, since the established literature demonstrates that RT rises and frequency drops as the shear becomes dominant in the presence of debonding/detachment phenomena [29]. The time periods of the burst AE energy activity are isolated, and a representative sampling of each series is demonstrated in Figure 3. As expected, the RT reaches a maximum and the initiation frequency (IF) a minimum at the moment where numerous hits of high energy were emitted, a result that validates the presence of shear-dominated damage, either due to internal TRC skin failure or skin-core interfacial detachment.
# Prediction Interval Adjustment for Load-Forecasting using Machine Learning
## 4 Experiments and Results
### Prediction Intervals Evaluation Metrics
#### 4.1.2 Probabilistic and Point-Forecast RMSE (Root Mean Squared Error) and MAE (Mean Absolute error)  
Probabilistic and point-forecast error is measured as indicated in Figure 11. Point forecast corresponds to the predicted load value, _Outside Prediction Interval_ stands for those load measures that fall above or below the prediction interval range, and _Inside Prediction Interval_ corresponds to the actual load measure that falls inside of the prediction interval range.  
Using all the errors, we estimate probabilistic and point-forecast MAE and RMSE using the respective set of errors. Point MAE and RMSE estimation help us to estimate the precision of the forecast method, also helps us to understand the prediction intervals in general. Probabilistic MAE and RMSE help us to understand better the PICP metric result.  
Figure 11: Probabilistic and point-forecast errors.
# Ground Based Hyperspectral Imaging to Characterize Canopy-Level Photosynthetic Activities
## 2 Materials and Methods  
### Plant Materials and Experimental Design  
To evaluate the utility of diurnal SIF measurements for estimating whole canopy photosynthetic efficiency, a field experiment was established at the University of Georgia Lang-Rigdon research farm near Tifton, GA. Two cotton cultivars (PHY 841 RF and PHY 340 W3 FE) were planted on 19 June, 2018 at a 2.5 cm depth, with a seeding rate of 11 seeds per m\({}^{2}\). Individual plots were 1 row with a length of 9 m and an inter-row spacing of 0.91 m. Plots were arranged along the east-west direction. The two cultivars planted represent two different species of cotton adapted to different cotton production regions of the southern United States (US) and were previously shown to differ in leaf anatomical characteristics and photosynthetic response to light intensity [46]. PHY 841 RF is a Pima cotton (_Gossypium barbadense_) cultivar widely grown in the arid southwestern US, whereas PHY 340 W3 FE is an upland cotton (_Gossypium hirsutum_) cultivar primarily grown in the humid southeastern US. To generate large differences in photosynthetic efficiency of the canopy, once the crop had reached the initial stages of floral bud development (referred to as \"squaring\"), plots received one of two possible herbicide treatments. Untreated control plots received only water applied as a foliar spray at a rate of 130 L/ha. Diuron (3-(3,4-dichlorophenyl)-1,1-dimethylurea) treated plots had a 41% solution of DCMU (\(w/w\); Diuron 4L) applied at a rate of 2.35 L/ha and a total application volume (water plus Diuron solution) of 130 L/ha. Diuron is a highly selective herbicide that specifically blocks the transfer of electrons from PSII to plastoquinone during the thylakoid reactions of photosynthesis. There were two rows of buffer plants between adjacent herbicide treatments to prevent drift onto non-target plants. Fertility, pest control, and irrigation were managed according to University of Georgia's Cooperative Extension recommendations [47]. The experiment was arranged as a split plot,randomized complete block design, wherein DCMU treatment represented the whole plot factor and cultivar represented the sub-plot factor. There were eight replicate plots for each cultivar within a particular herbicide treatment.