esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Phase Estimation of Gaussian States in the Presence of Outlier Quantum States
## 5 Phase Estimation of Noisy Coherent State
### Single Outlier Quantum State  
An outlier quantum state is set as \(z_{0}=15+15i,\kappa_{0}=0.1\) in Equation (29). We first change the sample size \(n=1000,2000,\ldots,5000\) to analyze performances of M-estimators for the contamination parameter \(\varepsilon=0.01\). Comparisons of M-estimators \(\hat{\alpha}_{R}\) and \(\hat{\alpha}_{I}\) vs. the sample size \(n\) are plotted in Figure 2a,b, respectively. In Figure 2c,d, we plot the mean square errors (MSEs) of these estimators vs. the sample size \(n\) for the contamination parameter \(\varepsilon=0.01\). In these figures, estimates from the sample mean (yellow-green), the median (purple), bisquare M-estimator (blue), and gamma M-estimator (red) are plotted, averaged over 500 runs of each setting. The true values are plotted by a dashed-dotted line (orange). From Figure 2a,b, we can see that the bisque and gamma M-estimators both performed well when compared to the sample mean and the median. The observed differences between these two figures come from the fact that \(\alpha_{R}\) is harder to estimate than \(\alpha_{I}\). This is because measurement outcomes at \(\phi=0\) are more sensitive to outlier quantum states, as the two gaussian distributions are close to each other. Good performances in the MSEs were also observed for the bisque and gamma M-estimators, as shown in Figure 2c,d. We should stress that the mean and the median are not consistent estimators for our setting.  
Figure 1: A schematic diagram of our model and estimation procedure.  
Next, we plot the result for phase estimation in Figure 3. The same convention as in Figure 2 is used. From Figure 3,b, we draw the same conclusions for the bisquare and gamma M-estimators as in Figure 2. Namely, they are robust and consistent estimators. This is well understood from the fact that it is sufficient to have good estimators for \(\alpha_{R}\) and \(\alpha_{I}\) to estimate phase accurately.
# Comparing a Fuzzy Hybrid Approach with Invariant MGCFA to Study National Identity
## 6 Conclusions  
The national identity measurements at cross-national level suffers from methodological and comparability issues that has caused a big controversy in the past. Issues about non-response bias and measurement errors [25], as well as equivalence invariance [19] suppose an important threat that lead to biased and inconsistent results. We argue here that stratagems, such as discarding items from the scale that obscure the invariance equivalence, other than being helpful in some cases, might also bring some unwanted penalties, for example, some items might only be important for some countries or minorities. We agree with [25] regarding that there is not any miraculous rule, and that each method needs to be investigated by its own benefits and costs. Social scientists working on national identity have argued that it is difficult to take for granted the number of factors and the respective indicators that should be included in each of the factors. To our knowledge, the univariate factor obtained in the current study as a second order latent variable has not been analysed by previous studies. Consequently, social science research on national identity has fallen victim to what can be named as a multi-dimensional methodological trap in which authors have felt comfort presenting the correlation degree between the ethnic, civic and cultural latent variables. Nevertheless, this approach has also created several controversial issues that have obscured potential trends in the field. In this sense, FHA can also be applied to analyse whether the results of factor latent variables with less indicators based on MGCFA are more or less like those obtained by FHA. The additive and difference scores of ethnic and civic national identities proposed by [3] can also be analysed using FHA to see whether the results are also robust. In the past, researchers have focused on the validity and reliability of the scale, so the ideas and rules applied to build the national identity construct have been constrained by these issues, and as previously said, the indicators and dimensions have been subject to controversy. Building on a well-grounded method (FHA) that has been applied successfully in different fields to construct synthetic indicators that are based on multiple items, this study compares the national identity construct (RNISI) with the results obtained by a MGCFA. The results of both methods are compared to see to what extent the indicators are affected by two covariates: country-year and political orientation-year. The ISSP 2003 and 2013 waves were used to collate the information for ten different countries: Denmark, France, Germany, Ireland, Norway, Portugal, Russia, Spain, Great Britain, and the United States.
## Materials and Methods
In Figure 2, with the various dielectric materials listed in Table 1, a dielectric-barrier surface discharge was investigated for the discharge properties, current voltage, and firing voltage. The DBD structure shown in Figure 1c-iii was chosen, which is proper for the bio-medical application of the wearable plasma devices [35; 36]. The plasma erosion of the dielectric surface was also tested. The damage and deformation on the dielectric surface were observed in a prolonged discharge test. The functional groups present in the infrared (IR) spectra of the PP- and PI-surface films in the absence or presence of the plasma were measured using the MB3000 Fourier transform IR spectroscopy device (ABB Bomen, Zurich, Switzerland). Figure 2 shows the schematic diagram of the discharge experiment in the atmospheric environment. An annular-shaped ground electrode was attached to the dielectric upper surface, and a high-voltage circular-shaped electrode was attached to the lower surface. The electrodes were coated with copper (Cu) of the \(T=40\)\(\upmu\)m. The outer and inner diameters of the annular-shaped electrode are 15 mm and 5 mm, respectively. The diameter of the circular-shaped electrode is 5 mm. The high-voltage electrode coincides with the ground-electrode inner circle. The high-voltage electrodes were insulated by the insulation pads attached onto the dielectric lower surface. A long-term discharge experiment was then conducted to assess the durability of the dielectrics. A high voltage from 0.5 to 3.0 kV was applied to the electrodes with the direct current (dc)-alternating current (ac) inverter. The voltage is the sinusoidal signal with the frequency of 40 kHz. The high voltage was measured using the P6015A high-voltage probe (Tektronix, Beaverton, OR, USA), and the current was measured using the Tektronix P6022A ac probe. The voltage and current signals were observed using the Tektronix DPO 4034 digital oscilloscope. All the data of current and voltage are expressed as the root-mean-square (rms) value in all the experiments. Figure 1: Schematic diagrams of the experiments with various dielectric barrier discharge (DBD) types in the atmospheric environment: (**a**) Spatial discharge between two parallel face-to-face electrodes; (**b**) coplanar discharge; and (**c**) planar discharges between both-side electrodes according to various arrangements at (**i**)–(**iv**).
## 8 Discussion and Implications
Our study also indicates that although ICT and internet uses did spread fairly evenly across the nation's provinces, the spread of ICT services and sales/purchases of e-commerce were uneven (Table 2). These purposeful use types are driven by government and businesses and may depend somewhat on broad Chinese economic shifts from export to internal consumption [71] in order to reach evenness throughout the provinces. Interestingly, the need for nationwide improvements in these two areas is addressed in the 14 th 5-year plan for 2021-2025 [67], which emphasizes a productive, flourishing service industry (Article X) and mentions promotion of e-commerce and related smart logistics under the topic of digital industrialization (Article X, Section 2). The theoretical implications of the study are three-fold. First, the SATUM model recognizes the geographical relationships between the dependent variables. For the OLS regression analysis, each dependent variable is examined for its positive or negative spatial agglomeration, i.e., for each dependent variable, are there distinctive spatial partnerships of similarly valued provinces. The OLS regression residuals are examined in the same way [21]. Second, the SATUM model has been expanded in the present research to recognize four distinct groups of dependent ICT variables, rather than only for the full set of variables. Accordingly, in the present study, the focus is on the patterns of spatial agglomeration of each of four groups of dependent variables. The earlier approach of clustering a full set of dependent variables may obscure key underlying geographic patterns, while the present four separate groupings of purposeful uses reveal the distinctive patterns, and, for each group, clusters can be characterized based on the averages of the independent variables. This approach can yield deeper insights into pattern differences of variable groups, including, for example, for e-commerce variables, in a digital divide analysis. As far as we know, this is a novel theoretical approach for ICT research. Third, adding to SATUM a new feature for longitudinal analysis of change provides an understanding of how the relative importance of correlates evolves over a prior time interval. This model's enlargement can be operationalized if there is a comparable set of variables at a prior point, which is increasingly available for nations and for sub-national geographic units, such as states and provinces. Regression change methodologies, such as the technique of regression change scores in dependent variables in this study, can help to explain the dynamics of change in regression correlates over time.
###### Abstract  
Irreversible electroporation (IRE) has gained attention for ablation owing to fewer side effects and fast recovery. However, a high current from the applied high voltage can cause muscle contraction. Adding cationic molecules has been introduced to lower electric field strengths and enhance IRE outcomes by inducing hyperpolarization across the cell plasma membrane. Near-infrared light (NIR) has recently been reported to induce hyperpolarization across membranes in a mode-dependent manner. In this study, we performed IRE in HeLa cells after exposure to 810 nm NIR irradiation. Preconditioning with NIR of 3 J/cm\({}^{2}\) induced changes in membrane potential, resulting in approximately two times enhancement of apoptosis by IRE. The apoptotic signals were governed by the presence of BAX and p53 and were not related to excess oxidative stress. NIR has better spatial and temporal distribution control than chemicals and, therefore, can enhance the spatial selectivity and reduce the side effects of IRE treatment. These results can be used to enhance the clinical outcomes of IRE.  
near infrared; irreversible electroporation; hyperpolarization; apoptosis Article
# Robust IoT Activity Recognition via Stochastic and Deep Learning
## 2 Related Work
### Sensing Approaches  
The use of stochastic processes and statistical signal processing techniques is crucial in the study of human activity recognition [27]. These methods excel at processing and analyzing complex time series data from sensors, enabling the extraction of key features that are critical for identifying various activity patterns [28]. By applying statistical techniques such as autocorrelation functions and power spectral density, researchers can quantify the periodicity and randomness in sensor data, which is vital for understanding the dynamics of complex human movements. In addition, advanced statistical signal processing methods like wavelet transforms have been utilized to analyze non-stationary signals. This approach allows researchers to examine the local characteristics of the signal at different temporal scales, thereby offering new tools for identifying more complex motion patterns [29].  
The integration of machine learning techniques has further propelled the development of human activity recognition technologies, extending their capabilities beyond the recognition of basic motion patterns [30; 31; 32]. By training models such as Support Vector Machines (SVMs), Decision Trees, and Random Forests, researchers can learn complex activity patterns from large-scale sensor data [33]. These models are adept at analyzing hidden patterns and relationships within sensor data, allowing for the detection of subtle variations in human movement, such as fatigue-induced gait changes or falls. Moreover, deep learning technologies, particularly Convolutional Neural Networks (CNNs) [34; 35; 36; 37] and Recurrent Neural Networks (RNNs) [19; 38; 39], have demonstrated robust capabilities in handling time series data by automatically extracting time-dependent features and performing complex pattern recognition. This progress opens up new possibilities for the future development of human activity recognition technologies, as these methods not only enhance recognition accuracy but also provide effective support for real-time health monitoring and preventive healthcare.
# Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement
## 5 Results and Discussion
### Objective Intelligibility Evaluation  
Figure 10 shows the average STOI score (found over all frames for each test condition in Section 4.1). Like the PESQ score comparison (Section 5.1), the KF-oracle method also achieves the highest STOI score for all tested conditions. The proposed method consistently outperforms all competing methods across the tested conditions in terms of STOI score improvements. The STOI score improvement by the proposed method is also very similar to that of the KF-oracle method. Amongst the benchmark methods, MDKF-MMSE [17] is found to be competitive with the proposed method for all tested conditions. Conversely, the noisy one shows the lowest STOI scores for all tested conditions. In light of  
Figure 9: Average SDR (dB) score comparison between the proposed and benchmark SEAs on NOIZEUS corpus corrupted with: (**a**) _babble_, (**b**) _street_, (**c**) _factory2_, and (**d**) _f16_ noises for a wide range of SNR levels (from \(-5\) to \(15\) dB).  
this comparative study, it is evident that the proposed method produces better intelligible enhanced speech than the competing methods for all tested conditions.
## 3 Methodology and Data Description
### Max-Flow Problems for Network Optimization  
Max-flow problems are applied to optimize the movement of goods from Dar es Salaam Port to inland destinations by maximizing the flow through the transportation network, which consists of roads and railways.  
Formulation: The port is defined as the source node, and the hinterland destinations are the sink nodes. The goal is to maximize the flow from the source to the sink, ensuring that no segment of the transport network exceeds its capacity.  
Steps: Data on road and rail capacities are used to define the maximum allowable flow for each segment of the network. The optimization process uses algorithms such as Ford-Fulkerson to calculate the maximum flow, considering capacity constraints and demand at various destinations.
# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## Materials
### Remote Sensing Data
#### 2.3.1 Sentinel-1 SAR Data  
A time series of S1 images collected over our study site was used to generate SAR temporal profiles at both VV and VH polarizations. Eighty-two S1 images obtained from the S1A and S1B satellite constellation operating at the C-band (frequency = 5.406 GHz, wavelength \(\sim\)6 cm) were collected for the period between 28 August 2017 and 27 December 2018. Four S1 (two S1A and two S1B) footprints cover the western part of Catalonia with a six days temporal resolution as shown in Figure 1b. The 82 S1 images were generated from the high-resolution Level-1 ground range detected (GRD) product with 10 m \(\times\) 10 m pixel spacing and acquired in the interferometric wide swath (IW) imaging mode with the VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations. These S1 images are available via the Copernicus website ([[https://sciub.copernicus.eu/dhus/#home](https://sciub.copernicus.eu/dhus/#home)]([https://sciub.copernicus.eu/dhus/#home](https://sciub.copernicus.eu/dhus/#home))). The 82 S1 images were calibrated (radiometric and geometric correction) using the Sentinel-1 Toolbox (S1 TBX) developed by the ESA (European Spatial Agency). The radiometric calibration aims to convert the digital number values of the S1 images into backscattering coefficients (\(\sigma^{\circ}\)) in a linear unit while the geometric correction ortho-rectifies the SAR image using the DEM of the SRTM at 30 m spatial resolution. The radiometric accuracy of the S1 SAR backscattering coefficient is approximately 0.70 dB (3\(\sigma\)) for the VV polarization and 1.0 dB (3\(\sigma\)) for the VH polarization [41; 42]. Since the terrain in the study site is very complex, the radar backscattering coefficients corresponding to areas with high slope values (slope \(>\) 20%) were masked out. The slope mask was generated from the SRTM DEM (Figure 1c) at 30 m spatial resolution available via the earth explorer website ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/))) and applied to the 82 radar image.
## 4 Using the Compact Topological Maps to Localize the Robot
### Resolution of the Localization Problem in a Compact Model  
Image retrieval is an inefficient process due to the fact that the maps are usually composed by a huge number of images and the descriptors have a high dimensionality. Therefore, the computational cost could be a problem. In this case, clustering is used to compact the map. Additionally, indoor environments may present visual aliasing. As explained in Section 3, after clustering, the map \(\mathfrak{M}\) will be formed only by a set of clusters \(C=\{C_{1},...,C_{n_{c}}\}\), where \(n_{c}\) is the number of clusters. For each cluster, a representative descriptor was calculated as the average of the descriptors in it and the coordinates of those representatives as the average coordinates of the descriptors that compose that cluster. Thus, a set of cluster representatives \(\{\vec{r_{1}^{*}},...,\vec{r_{n_{c}}}\}\) and the coordinates of each representative \(\{(x,y)_{r_{1}},...,(x,y)_{r_{n_{c}}}\}\) are known (ground truth).  
The localization in this hierarchical map is carried out as follows. (1) The robot captures a new image \(im_{t}\) from an unknown position \((x_{t},y_{t})\), which must be estimated, and (2) the descriptor corresponding to the new captured image is obtained (\(\vec{d_{t}}\)) by using any of the description algorithms explained in Section 2 (FS, HOG, or \(gist\)). (3) The distance vector is obtained \(\vec{l_{t}}=\{l_{t1},...,l_{m_{c}}\}\) where \(l_{tj}=dist\{\vec{d_{t}},\vec{r_{j}}\}\) is the distance (one of the three types explained in Section 4.1) between the descriptor \(\vec{d_{t}}\) and each representative \(\vec{r_{j}}\). Finally, (4) the estimated position of the robot \((x_{e},y_{e})\) is the position associated with the nearest neighbour \(d_{t}^{mn}|t=arg\min_{j}l_{tj}\).  
The coordinates of the representatives are not used in the localization step. However, to measure the goodness of the estimation, the geometric distance between \((x_{t},y_{t})\) and the centre of the corresponding cluster (obtained as the average position among the positions of the images that belong to that cluster) is calculated: \(error=\sqrt{(x_{e}-x_{t})^{2}+(y_{e}-y_{t})^{2}}\). Furthermore, the required computational cost to estimate the localization is calculated.
## 4 Numerical Results
### Bi-Objective Antenna Optimization
The optimized corner designs were \(\mathbf{x}_{1:-4}=\mathbf{x}_{1:12^{*}}=[26.07\ 10.56\ 28.56\ 0.06\ 0.5\ 0.51\ 0.38\ 0.44\ 0.46\ 0.41\ 0.47\) \(0.39\ 0.36\ 0.44\ 0.43\ 0.42\ 0.43\ 0.59\ 0.78\ 0.77\ 0.71\ 0.73\ 0.81\ 0.75\ 0.65\ 0.72\ 0.9 0.88\ 0.78\ 0.75]^{T}\) and \(\mathbf{x}_{2:d_{6}}=\mathbf{x}_{2:20^{*}}=[12.54\ 16\ 16.22\ 0.66\ 2.14\ -0.19\ 0.35\ 0.37\ 0.33\ 0.27\ 0.22\ 0.2\ 0.2\ 0.23\ 0.4\) \(0.37\ 0.34\ 0.35\ 0.34\ 0.32\ 0.32\ 0.31\ 0.23\ 0.46\ 0.5\ 0.53\ 0.52\ 0.48\ 0.44\ 0.45\ 0.5\ 0.55\ 0.61\ 0.7\ 0.73\) \(0.66\ 0.62\ 0.65\ 0.64\ 0.69\ 0.74\ 0.73\ 0.64]^{T}\), respectively. Note that the dimensionality of the extreme points was different (12 relative variables per feature--30 total--for \(\mathbf{x}_{1}^{*}\) vs. 20--46 total for \(\mathbf{x}_{2}^{*}\)), which was due to the lack of performance improvement from increasing the number of \(\mathbf{x}_{1}\) variables above 12. The obtained solutions were used as starting points for generation of the Pareto set. In the second design stage, the Pareto designs were obtained using an objective of the following form:  
\[F_{1:c}(\mathbf{x})=F_{1}(\mathbf{x})+(\max\{0,A(\mathbf{x})-A_{c}\})^{2} \tag{15}\]  
The additive component of Equation (15) is a penalty coefficient activated when the threshold \(A_{c}=0.5(A(\mathbf{x}_{m:d})+A(\mathbf{x}_{m+1:d}))\) is violated. In \(F_{1}(\mathbf{x})\), the parameter \(S_{\max}=F_{1:c,\max}\) (see Section 3.3). The number of algorithm iterations was set to \(K=3\). The candidate  
Figure 7: Reflection of the designs optimized in a nested loop with respect to (**a**) objective \(F_{1}\) and (**b**) objective \(F_{2}\). The dimensionalities of the spline-parameterized antenna features (see Section 2) are \(d_{1}=1\) (gray), \(d_{2}=4\ (\circ\circ\circ\)), \(d_{3}=8\ (\) -), \(d_{4}=12\ (\cdots)\), \(d_{5}=16\ (\cdots)\), and \(d_{6}=20\ (\bullet)\), respectively. The gray and red lines represent the characteristics of the initial and final designs obtained in the course of nested optimization. The blue lines denote the threshold values for the design objectives. solutions for optimization were generated using Equation (6). The Pareto set, composed of two corner and seven intermediate designs \([\boldsymbol{x}_{1.20}\boldsymbol{x}_{2.20}\boldsymbol{x}_{3.20}\boldsymbol{x}_{4.20 }\boldsymbol{x}_{5.20}\boldsymbol{x}_{6.20}\boldsymbol{x}_{7.20}\boldsymbol{x}_{8.12}\boldsymbol{x}_{9.12}\boldsymbol{x}_{9.12}^{*}]\), and the frequency responses of the selected optimal solutions are shown in Figure 8. The obtained results indicate that the footprints of the optimized antennas ranged from 230 mm\({}^{2}\) (with an in-band reflection of \(-10.3\) dB) to 757 mm\({}^{2}\) (reflection of \(-20.5\) dB). Hence, the change in antenna size and frequency responses along the Pareto set were 527 mm\({}^{2}\) and 10.2 dB, respectively. It is worth noting that the miniaturization rate of the smallest antenna with reference to the largest one was almost 70%. The gain pattern responses were obtained for the selected antenna designs (i.e., \(\boldsymbol{x}_{1.20}\boldsymbol{,x}_{9.12}\) (corner designs), and