esa-sceva/satcom-chunk-collection huggingface dataset:
# Error Performance of Amplitude Shift Keying-Type Asymmetric Quantum Communication Systems
## 4 Derivation of Analytical Expression for the Error Probability of the Quasi-Bell State
### Step 1: Representation of the Received Quantum States by an \(8\times 8\) Matrix
In references [29; 30; 31], the order of these coherent states was rearranged to be \(\{|-\sqrt{R_{0}}\alpha\rangle_{\mathrm{S}},|\sqrt{R_{0}}\alpha\rangle_{ \mathrm{S}},|-\sqrt{R_{1}}\alpha\rangle_{\mathrm{S}},\\ |\sqrt{R_{1}}\alpha\rangle_{\mathrm{S}}(=:\{|\alpha_{0}^{\prime}\rangle,|\alpha _{1}^{\prime}\rangle,|\alpha_{2}^{\prime}\rangle,|\alpha_{3}^{\prime}\rangle \rangle\})\) to take advantage of the partial symmetry of the coherent state signal, and the Gram matrix \(\Gamma\) then became as follows:  
\[\Gamma=\left[\begin{array}{ccc}\langle\alpha_{0}^{\prime}|\alpha_{0}^{ \prime}\rangle&\langle\alpha_{0}^{\prime}|\alpha_{1}^{\prime}\rangle&\langle \alpha_{0}^{\prime}|\alpha_{2}^{\prime}\rangle&\langle\alpha_{0}^{\prime}| \alpha_{3}^{\prime}\rangle\\ \langle\alpha_{1}^{\prime}|\alpha_{0}^{\prime}\rangle&\langle\alpha_{1}^{ \prime}|\alpha_{1}^{\prime}\rangle&\langle\alpha_{1}^{\prime}|\alpha_{2}^{ \prime}\rangle&\langle\alpha_{1}^{\prime}|\alpha  
Finally, by substituting Equations (24) and (50) into Equation (18), we obtain  
\[\begin{split}\Psi^{(\text{b})}_{\text{SA}}&=\text{Tr}_{ \text{E}}\Big{\{}\big{(}\mathbb{U}^{(R_{\text{b}})}_{\text{SE}}\otimes\mathbb{I }_{\text{A}}\big{)}\big{(}|\Psi_{2}\rangle_{\text{SA}}\langle\Psi_{2}|\otimes |0\rangle_{\text{E}}\langle 0|\big{)}\big{(}\mathbb{U}^{(R_{\text{b}})}_{\text{SE}} \otimes\mathbb{I}_{\text{A}}\big{)}^{\dagger}\Big{\}}\\ &=h_{2}^{2}\Big{(}|\sqrt{R_{\text{b}}}\alpha\rangle_{\text{S}} \langle\sqrt{R_{\text{b}}}\alpha|\  
\[\mathcal{A} = -U_{11}^{(+)}-U_{22}^{(+)}-U_{33}^{(+)}-U_{44}^{(+)} \tag{61}\] \[= -\frac{(L_{0}+1)(a\pi-1)+(1+L_{1})(1-d\kappa)}{2(\kappa^{2}-1)},\] \[\mathcal{B} = -{U_{12}^{(+)}}^{2}-{U_{13}^{(+)}}^{2}-{U_{14}^{(+)}}^{2}+U_{11}^{ (+)}U_{22}^{(+)}-{U_{23}^{(+)}}^{2}-{U_{24}^{(+)}}^{2}+U_{11}^{(+)}U_{33}^{(+)}\] (62) \[+U_{22}^{(+)}U_{33}^{(+)}-U_{34}^{(+)}{}^{2}+U_{11}^{(+)}U_{44}^{( +)}+U_{22}^{(+)}U_{44}^{(+)}+U_{33}^{(+)}U_{44}^{(+)}\] \[= -\frac{(L_{0}+1)(L_{1}+1)\{(ax-1)(dx-1)-(b-c\kappa)^{2}\}}{4( \kappa^{2}-1)^{2}}.\]  
We confirm directly that two of the eigenvalues \(\{\tau_{i}^{(+)}\}\) are \(0\), and let \(\tau_{3}^{(+)}=\tau_{4}^{(+)}=0\). To determine the signs of the other eigenvalues \(\tau_{1}^{(+)}\) and \(\tau_{2}^{(+)}\) for \({\tau_{i}^{(+)}}^{2}+\mathcal{A}\tau_{i}^{(+)}+\mathcal{B}=0\), we prove that \(\tau_{1}^{(+)}\tau_{2}^{(+)}=\mathcal{B}<0\) (see Appendix C for this proof), and we then know that these two eigenvalues have opposite signs \(\tau_{1}^{(+)}=\frac{-A+\sqrt{A^{2}-4B}}{2}>0\), and \(\tau_{2}^{(+)}=\frac{-A-\sqrt{\mathcal{A}^{2}-4B}}{2}<0\). Note that the corresponding eigenvalues \(\{\tau_{i}^{(-)}\}\) for \(U^{(-)}\) can be obtained in the same manner.
# Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization
###### Abstract  
Indoor localization systems are susceptible to higher errors and do not meet the current standards of indoor localization. Moreover, the performance of such approaches is limited by device dependence. The use of Wi-Fi makes the localization process vulnerable to dynamic factors and energy hungry. A multi-sensor fusion based indoor localization approach is proposed to overcome these issues. The proposed approach predicts pedestrians' current location with smartphone sensors data alone. The proposed approach aims at mitigating the impact of device dependency on the localization accuracy and lowering the localization error in the magnetic field based localization systems. We trained a deep learning based convolutional neural network to recognize the indoor scene which helps to lower the localization error. The recognized scene is used to identify a specific floor and narrow the search space. The database built of magnetic field patterns helps to lower the device dependence. A modified K nearest neighbor (mKNN) is presented to calculate the pedestrian's current location. The data from pedestrian dead reckoning further refines this location and an extended Kalman filter is implemented to this end. The performance of the proposed approach is tested with experiments on Galaxy S8 and LG G6 smartphones. The experimental results demonstrate that the proposed approach can achieve an accuracy of 1.04 m at 50 percent, regardless of the smartphone used for localization. The proposed mKNN outperforms K nearest neighbor approach, and mean, variance, and maximum errors are lower than those of KNN. Moreover, the proposed approach does not use Wi-Fi for localization and is more energy efficient than those of Wi-Fi based approaches. Experiments reveal that localization without scene recognition leads to higher errors.  
Keywords:convolutional neural networks; scene recognition; indoor localization; deep learning; magnetic fingerprinting; pedestrian dead reckoning +
Footnote †: journal: Applied Physics Letters
# Removal of Cesium from Radioactive Waste Liquids Using Geomaterials
## 3 Results and Discussion
### Adsorption Mechanism  
The adsorption of cesium on fly ash-based geomaterials and geomaterials doped with slag was studied. The influence of the pH of the solution, the adsorbent dosage, and the contact time on the adsorption efficiency were discussed. The following matters can be considered:  
1. The adsorption experiment data of Cs on fly ash-based geomaterials and slag-based geomaterials conform to the Langmuir isotherm adsorption equation. The isothermal fitting of the Langmuir model shows that the surface is heterogeneous, which indicates that the adsorption of Cs on the surface of the adsorbent mainly occurred as single-layer adsorption.
2. The best fit was obtained with the pseudo-second-order kinetic model while investigating the adsorption kinetics of Cs adsorption on the fly ash-based geomaterials, indicating that the adsorption process was mainly chemical adsorption. The \(R^{2}\) was greater than 0.996. On the other hand, Cs adsorption on the slag-based geomaterials was suitably described by the pseudo-first-order model as well as by the pseudo  
Figure 8: The Cs adsorption of the Langmuir isotherm on the fly ash-based geomaterials and slag-based geomaterials.  
Figure 9: The Cs adsorption of the Freundlich isotherm on the fly ash-based geomaterials and slag-based geomaterials.  
second-order model (the \(R^{2}\) values were both about 0.999), indicating that physical as well as chemical adsorption were dominant in the adsorption process.  
The results show that the fly ash-based geomaterials can remove Cs\({}^{+}\) more effectively. The adsorption kinetics of the geomaterials with higher adsorption capacities are compatible with the pseudo-two-stage kinetic model, which indicates that it is controlled by complex multi-step adsorption. The negative correlation between cesium and sodium is evidence of a typical cation exchange reaction that occurs in fly ash-based geomaterials, in which sodium zeolite or its precursor exchanges its charge-balancing cations. Specifically, zeolite or its precursor is provided as an adsorption site, where sodium is replaced by cesium [40; 41]. Calcium silicate hydrate geomaterials may interact with the adsorbate by a weak interaction through van der Waals forces (i.e., Physisorption). On the other hand, the (sodium)-aluminosilicate-hydrate geomaterials may share or exchange electrons (cations) between the adsorbent and the adsorbate (i.e., Chemisorption). These results provide new insights, demonstrating that the properties of aluminosilicate geomaterials with a large microporous surface area promote more effective cesium fixation compared to calcium silicate hydrate.
# An Efficient Resource Allocation Algorithm for Device-To-Device Communications
## 4 Simulation Results  
### Simulation Environment Establishment  
In this section, simulation experiments were carried out in a heterogeneous cellular network environment. The macro cell radius is set to 1000 m, and the cellular users and D2D users are distributed in the cell as independent Poisson point processes. The specific system simulation parameter settings are shown in Table 2. The Monte Carlo method is used to randomly generate 2000 distribution scenarios each time. Finally, the system performance results obtained by the algorithm are averaged. It is assumed that each macro cell user occupies a single independent channel, and each microchannel user has a multiplex resource with the macro cell user, and the accessed D2D user needs to select an appropriate channel to communicate according to the current channel state. Since this paper assumes that there can be up to two pairs of D2D user communications on a single channel resource, the number of D2D user pairs accessed on each channel is set to two.
# Within and Across-Language Comparison of Vocal Emotions in Mandarin and English
## 2 Methods
### Listening Tests  
Listening tests were conducted to confirm that the intended emotions were accurately produced using the Qualtrics online survey tool [32] with respective Mandarin and English stimuli. For each language, the stimuli were divided into five sets produced by each of the five speakers. Native Mandarin and English listeners were recruited online and the number of listeners for each set was at least 15. During the listening tests, participants were asked to listen carefully to randomized audio stimuli and select the most appropriate emotion on a five-choice task (i.e., anger, happiness, fear, neutral and sadness) by pressing a computer mouse.  
Recordings whose identification rate was less than 60% were excluded. This is three times the 20% chance level permitted in our study [7]. In order to compare five emotions with parallel texts in each language, we chose 530 perceptually valid recordings (106 sentences \(\times\) 5 emotions) in Mandarin and 300 perceptually valid recordings (60 sentences \(\times\) 5 emotions) in English.
# A Study on Sensitive Bands of EEG Data under Different Mental Workloads
###### Abstract
Commonly used feature extraction algorithms are: Autoregressive model (AR model) which uses the phenomenon of de-synchronization/phase synchronization in EEG signals as feature information [10]; power spectral density estimation [11]; wavelet transform; chaos method; public space models; new descriptors; multidimensional statistical analysis; etc. [12; 13; 14; 15; 16]. Commonly used classification methods are K-means clustering, multilayer perceptron neural network model [17], Fisher linear discriminant, Bayesian method [18], artificial neural network [15; 19], BP neural network [3; 20], SVM [16; 21; 22], and so on. The EEG signal is a non-stationary random signal [23]. The currently recognized frequency range of human brain electrical activity is between 0.5 Hz and 30 Hz [24]. The EEG is generally measured by multiple electrodes to generate multi-lead signals, and the mutual information between the lead signals contains important information. Modern scientific research shows that spontaneous electrophysiological activity occurs when the human brain works. This activity can be expressed in the form of brain waves through a dedicated EEG recorder. In EEG research, there are at least four important bands [25; 26; 27], and each band performs differently under various tasks. It is possible to choose the specific band to use according to the sensitivity of the four bands to various tasks in different situations. This paper aims to study the sensitive bands of EEG data under different mental loads by sorting the importance of different characteristics of EEG data by selecting sensitive bands for different mental loads and by extracting data of one band separately for subsequent data analysis. By comparing the classification accuracy of the four bands, it is concluded that the \(\beta\) band is most sensitive to different mental loads. Therefore only the data of \(\beta\) wave can be used to effectively remove redundancy, additional noise, and improve the classification accuracy of different mental workloads.
## 6 Applications
### 2D Static Water Simulation  
The water in the tank is 0.6 m high. The initial pressure distribution is shown in Figure 15. Pressure distributions obtained with different boundary treatments 1, 2, 3, 4, and 5 at 3 s are shown in Figure 16 to compare with the initial values.  
Figure 16: Pressure distribution of static water with different boundary treatments.  
Figure 14: Time histories of the pressure at the test point (3.22 m, 0.16 m) obtained from different boundary treatments methods and experiments.  
Figure 15: Initial pressure distribution of static water in a tank.  
The pressure distribution at 3 s with boundary treatment 1, 2, 3, 4, and 5 are shown in Figure 16.  
From Figure 16, it is clear that boundary treatments 2 and 4 fail to prevent inner particles from penetration. The color layer is continuous at the same level, which indicates that the pressure is the same at the same water height. For other boundary treatments, the pressure values obtained in the corner of the tank are not accurate.  
The pressure values of the left corner and the middle point of the tank bottom are traced during the whole simulation and the relative error is exhibited in Figure 17.  
From Figure 17, we can see that additive boundary particles with dynamic dummy particles and ghost particles plus a repulsive force produce similar pressure evaluations for the middle tank point. However, for the corner point, only the additive boundary particle treatment provides correct pressure approximation. So far, it is obvious that additive boundary particles with dynamic dummy particles provide the best simulation of the static water tank.
# Wave Propagation in Laminated Cylinders with Internal Fluid and Residual Stress
## 3 Numerical Examples
### Air-Filled Sandwich Cylinder  
In this example, a sandwich cylinder similar to that analysed in [25] is considered but filled with air at 20C. The ratio between the thickness and the mean radius is \(h/R=0.02\). The structure is made of laminated graphite-epoxy skins, each comprising 4 orthotropic sheets with symmetric lay-up of \([+45/-45]_{S}\), and a ROHACELL foam core. The total thickness of the skins is 2 mm; the core thickness is 10 mm. For this sandwich cylinder, the ring frequency is considered as the first cut-off frequency for \(n=0\), which has been found to be 857 Hz. The arbitrary unit cell analysed has characteristics dimensions \(L_{y}=5\) mm and \(\alpha=0.4^{\circ}\) and it has been meshed using 12 solid elements for the skins and the foam, and 30 acoustic elements for the fluid.  
The complex dispersion curves for the _in vacuo_ and fluid-filled cylinder are shown in Figure 5. It can be seen that the dispersive behaviour is complicated by the FSI and that structural wavemodes couple with the fluid wavemodes over the whole frequency range. For the breathing mode, \(n=0\), this coupling clearly occurs for the flexural mode at low frequency and for the longitudinal mode at higher frequency (when the effect of Poisson contraction becomes non-negligible). For the bending mode, \(n=1\), acoustic curves become similar to those in a hard-walled duct, and similar to the previous case, most coupling occurs between fluid waves and structural waves that involve significant radial motion.
# Xanthone Derivatives and Their Potential Usage in the Treatment of Telangiectasia and Rosacea
## 4 Epidemiology  
Vascular lesions, or more precisely skin manifestations of circulatory disorders, are still one of the main therapeutic but also aesthetic problems.  
The most common vascular lesions in the population include telangiectasias, varicose veins, rosacea, hemangiomas, and vascular malformations. Hemangiomas occur in approximately 4-5% of newborns, especially Caucasians, and in up to 30% of children born prematurely. Additionally, they occur 3-9 times more often in girls than in boys. Vascular malformations occur much less frequently but with the same gender frequency [59; 60].  
The prevalence of venous problems of the lower extremities varies considerably with the latitude, with the highest rates reported in Western countries. Varicose veins, estimated to be more common than chronic venous insufficiency, range from \(<\)1% to 73% in women and from 2% to 56% in men. The main risk factors are age, gender, standing work, pregnancy, and a genetic family history of varicose veins [61; 62].  
The epidemiology of telangiectasia was investigated by conducting a large cohort study, determining the prevalence from facial photographs of more than 2000 participants from Northern Europe. This group was predominantly female (58.8%) with a median age of 66.9. The study showed that the risk factors for telangiectasia are older age, being female, smoking, particularly active and prolonged smoking, a fair complexion, and high susceptibility to sunburn (phototypes I and II on the Fitzpatrick scale). The effect causing the greatest increase in the occurrence of telangiectasias was long-term smoking, increasing the risk by 38.4%. The authors emphasized that facial erythema and telangiectasias are often associated with the erythemat steleangiectatic subtype of rosacea [63].  
Rosacea is a condition that affects as much as 2-22% of the Caucasian population. It appears that the highest risk of rosacea is found in Caucasians with fair, sun-sensitive skin, classified as skin phototypes I and II according to the Fitzpatrick scale. However, a recent study reported that rosacea also occurs in phototypes III and VI, although papulopustular lesions are more frequently observed. The reason for this is the masking of erythema by the pigment of a darker phototype of skin. A prospective study conducted in Germany in 2016 found an overall 12% prevalence of rosacea, with the erythemata-vascular-pustular and papulopustular subtypes accounting for 9% and 3%, respectively. Rosacea occurs with a strong predominance in women and is usually diagnosed after the age of 30 [63; 64; 65; 66].  
The activities mentioned above for the xanthone derivatives are connected with the pathogenesis and formation of teleangiectasia and the early stages of rosacea, which reveals a need for further research about topical formulations containing synthetic xanthone derivatives.
# HPS-Net: Multi-Task Network for Medical Image Segmentation with Predictable Performance
## 2 Related Work
### U-Net  
Due to the outstanding performance of U-Net, many excellent networks [13; 14; 15] are based on or sourced from U-Net. U-Net [8] is derived from the fully convolutional network (FCN) [16] and is a convolutional neural network developed for biomedical image segmentation task. Compared with FCN, the structure of U-Net was modified to adapt to fewer training images and to achieve higher segmenting accuracy. The main idea of U-Net is to supplement a usual contracting network by successive layers, where pooling operations are replaced by upsampling operators. Hence, these layers increase the resolution of the output. Moreover, successive convolutional layers can then learn to assemble a precise output based on the information.  
Figure 2 illustrates the structure of U-Net. From Figure 2, we can see that U-Net has two symmetric paths, namely, the left contracting path and the right expanding path. The contracting path is made up of repeated convolutional layers, ReLU layers, and Max-pooling layers, which focuses on capturing context information. The right expanding path contains repeated convolutional layers, ReLU layers, and upsampling layers, which enables precise localization. There are some skip connections between these two paths, which can compensate for the loss of boundary pixels caused by the convolution operations in the contracting path.  
Figure 1: An illustration of the application case of the proposed HPS-Net. HPS-Net can generate an unlimited number of segmentation hypotheses along with their measure predictions and can provide the segmentation hypothesis based on a specified measurement value.  
Since the input and output are images of the same size, why construct a symmetric structure that includes downsampling and upsampling? The answer can be explained from the theoretical meaning of symmetry principles. The symmetric structure can be divided into two paths--the downsampling path and the upsampling path. The downsampling path can increase the robustness of some small disturbances of the input image, such as image translation and rotation, thereby reducing the risk of overfitting, reducing the amount of calculation, and increasing the size of the receptive field. The main purpose of the upsampling path is actually to transform the features back to the size of the original image. In addition, since a large amount of information is lost in downsampling, some connections are applied between the layers at the same resolution level to supplement the missing information. The above reasons led to the final adoption of a symmetric structure.
## 3 Controller Design
### Cascade ADRC Convergence Analysis
According to the transmission characteristics of the cascade controller, the inner loop system is generally required to be stable before the outer loop system, that is, the movement of the inner loop changes faster than that of the outer loop, that is, the time scale of the inner loop system is smaller than that of the outer loop system. Therefore, in the actual numerical simulation, the sampling step of the inner loop should be smaller than that of the outer loop. According to experience, the sampling step size of the outer loop is generally selected as an integral multiple of the sampling step size of the inner loop.
## 2 Internet of Things (IoT): Specifications & Security Challenges
### Architecture of IoT
#### 2.1.1 Perception Layer  
It represents the first layer of IoT architecture that is used for indicating the objects at the physical level. The objects work within the adjacent environment by sensing, collecting, and processing information. It is defined as smart objects with the following common characteristics:  
* Communication: objects have connectivity to the internet and to other objects to collect data, update status and collaboratively provide proper services.
* Identification: objects have unique identification and can be located based on their physical location. Further characteristics that can be also added to IoT-application are outlined below:
* Addressability: objects can be configured either directly or remotely.
* Processing capabilities: objects have embedded processing capabilities to handle shared information by the sensors and direct the actuators.
* User interface: objects have an appropriate interface for easing the user's experience. One should note that multiple protocols are being currently adopted. Namely, RFID, Bluetooth, IEEE 802.11 ah, IEEE 802.15.4e, and Z-Wave are widely used [17; 18; 19].