esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Phase One: Spatial Audio Attention Testing  
A two-way mixed ANOVA was conducted to investigate the impact of 3D audio and auditory stimuli on the accuracy metric during Phase One. A comparison between test groups yielded a significant difference (\(F(1,26)=29.43\), \(p\leq 0.001\), \(\eta_{p}^{2}=1.642\)) across all auditory events, with the 3D audio test group (\(M=0.661\)) scoring higher accuracy scores than the mono audio controls (\(M=0.487\)). Figure 6 shows a comparison of group mean accuracy scores with 95% confidence intervals for each event.  
Figure 6: Mean accuracy (\(\alpha\)) scores for each audio condition across all auditory events in Phase One. The whiskers denote 95% confidence intervals.  
In addition, there is a significant effect of stimulus type on spatial attention of both groups (\(F(7,175)=7.657\), \(p\leq 0.001\), \(\eta_{p}^{2}=0.227\)), with a significant interaction between groups and stimulus type (\(F(7,175)=2.426\), \(p=0.021\), \(\eta_{p}^{2}=0.072\)). Finally, the use of visual accompaniment had no significant effect on the accuracy of spatial attention in the spatial audio group yielded by visual accompaniment (\(M=0.627\)\(SD=0.187\)) and no visual accompaniment (\(M=0.695\)\(SD=0.184\)) paired-samples t-test; \(t(55)=-1.903\), \(p=0.062\). However, there was a significant difference between visuals (\(M=0.543\)\(SD=0.235\)) and no visuals (\(M=0.431\)\(SD=0.176\)) within the mono control group; \(t(51)=-2.827\), \(p=0.007\).  
To better understand behavioral response to spatial audio within the virtual environment, tracked positional data across the two groups was also analyzed. The tracked positional coordinates of all participants on the horizontal axis collected throughout the entirety of Phase One and are visualized as 2-dimensional density plots shown in Figure 7. In addition, the figure also displays the directions and distance of all auditory events inside and outside of the tracked area.  
The figure shows differences in the extent of exploration depending on the auditory condition. Despite both groups showing a condensation of exploration around the center of the tracked space, the 3D audio group show a distribution of exploration in the direction of the majority of auditory targets. Further analysis of the distance between each participant and the auditory target using a two-way mixed ANOVA also yielded a significant difference between the auditory conditions (\(F(7,175)=23.111\), \(p\leq 0.001\), \(\eta_{p}^{2}=0.308\)). With participants within the 3D audio group moving closer towards the virtual sound sources during their respective playback times.
## 2 Data and Methodology of Inversion
### Tomographic Inversion  
The tomographic inversion was performed using the LOTOS code for body wave tomography and passive sources [35]. This code has also been applied in several studies on seismic areas in Romania [27; 36; 37], as well as on regions worldwide where, among seismicity, volcanoes have been studied [38; 39; 40; 41; 42]. The calculation procedure begins with the absolute location of the sources within the reference 1D velocity model, using a grid search method to determine the optimal coordinates of the events. At this stage, travel times are calculated in a simplified manner along straight lines using the 1D velocity distributions. In the next stage, the sources are relocated using a more precise algorithm based on the 3D ray-bending method initially proposed by [43]. The velocity model was parameterized using a set of nodes distributed throughout the study volume, depending on ray coverage. Horizontally, these nodes were distributed regularly with a constant spacing of 5 km only in areas with sufficient ray coverage (see Figure 4), while vertically, the distance between nodes was set to 2 km. To minimize potential artefacts related to the node geometry, the LOTOS code allows for performing the inversion using four grids with different baseline azimuths for node distribution (i.e., 0\({}^{\circ}\), 22\({}^{\circ}\), 45\({}^{\circ}\), and 67\({}^{\circ}\)). After computing the models for all grids, they were averaged and merged into a regular spacing model, which was then  
Figure 3: Map view of ray coverage for P-waves within the 12.5–17.5 km depth range. Black dots are the events, blue triangles are the seismic stations and the gray lines are P-wave ray paths.  
used in the next iteration for source relocation. In total, we stopped at five iterations as a compromise between computational cost and minimizing nonlinear effects.  
The inversion was performed using the least squares with QR decomposition (LSQR) [44; 45]. Its stability was controlled by amplitude damping and flattening regularization which was defined from synthetic modeling as giving the best recovery of known structures. To determine the most appropriate regularization coefficients (damping and smoothing), we performed a series of inversions with systematically varied parameters. We evaluated each combination in terms of the residual RMS reduction, the sharpness of the anomalies in the tomographic model, and the ability to recover synthetic anomalies by a checkerboard test. The optimal value was considered the one that provided the best compromise between fidelity to the data and geologic stability of the model. In our case, the regularization coefficients were amplitude damping and flattening of 4 and 7 for P- and S-wave models.
## 2 Related Works
### Skin Lesion Classification with Deep Learning Methods  
Regarding DL approaches, the use of pre-trained networks is going mainstream for skin lesion classification. The authors of [22] use transfer learning on InceptionV3, ResNet50, and Denset201 to perform multiclass classification of skin lesions. They remove the output layer from these architectures and add pooling and fully connected layers. In [23], new adversarial example images are created using the fast gradient sign method (FGSM), and some pre-trained networks (VGG16, VGG19, DenseNet101, and ResNet101) are trained and tested with them. Adversarial training allows maximizing the loss for the input image. In some papers, different architectures are used in several different situations. In [24], three pre-trained networks (EfficientNet, SENet, and ResNet) are evaluated in different training scenarios: for the first, segmented images are used; for the second, pre-trained images are used; and for the third the first two solutions are combined. In [25], a pre-trained ResNet52 network is used for skin lesion classification in different training situations: without data augmentation (DA); with DA only for the positive class; with DA for the positive class and downsampling (DS) of the negative class; and with DA only for the positive class but adding other images taken from different datasets. The best solution is one in which data are augmented only on lesions belonging to the positive class. The use of custom CNNs is minor but has still been taken into account [26; 27; 28].  
There is also a hybrid approach that involves both automatic feature extraction using CNNs and manual feature extraction to obtain vectors for inclusion in the network upstream of its decision-making process [29; 30]. This approach is still little used in general and even more so in the field of dermatology, but it allows for providing additional important information to the network in order to improve its classification accuracy.  
As previously introduced, melanoma detection can be understood as an anomaly detection problem in skin lesions. In anomaly detection, the concept of \"abnormality\" is contrasted with \"normality\", which must be clearly and concretely defined. For the melanoma detection task, normality is represented by the nevi population, while abnormalities are melanomas. This scenario includes several semi-supervised papers in which DL models are trained with only normal data, and anomalies are identified as those data that deviate from the training ones [31; 32; 33]. In the present work, a supervised anomaly detection approach is proposed in which both normal and anomalous data participate in training the neural network. In addition, the proposed work is in the context of hybrid feature extraction approaches; in fact, important texture features are extracted from dermoscopic images and are injected into the CNN upstream of the classification layer. These features are mixed with those previously extracted from the network, bringing additional information to support correct prediction.
## 6 Performance Evaluation
### Power Consumption in the Network
\begin{table}
\begin{tabular}{|c c|c c|} \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Radio chip & CC 2420 & TsRxWait & 2.2 ms \\ Transmit power (0 dBm) & 57.42 mW & TsAck (26 bytes) & 0.832 ms \\ Receive power & 62.04 mW & TsCCA & 0.128 ms \\ Listen power & 62.04 mW & TsRxTx (TxRx turnaround) & 0.192 ms \\ TsMaxPacket (133 bytes) & 4.256 ms & & \\ \hline \end{tabular}
\end{table} 
Table 7: Energy-consumption parameters. Figure 29: The energy consumption distribution in WirelessHART (**a**) and D-MSR (**b**). Depending on the position of sources and destinations in the network, the bottlenecks in D-MSR can be more spread in the network area compared to the concentration of bottleneck nodes in WirelessHART. The distribution pattern in the total energy maps is more affected by the application traffic energy pattern. Figure 29 also shows the energy consumption for idle listening. This energy depends on the efficiency of the scheduling mechanism. The better the scheduling, the less energy is needed for idle listening. As a response we did not include that energy in the total consumed energy in the network. Table 8 also lists the consumed energy for network maintenance messages in case of 3-9 link failures. D-MSR requires less overhead and less maintenance energy for coping with disturbances (e.g., link failures) in the network. In Section 6.7.3, we evaluate the performance of D-MSR in coping with network dynamicity in more detail.
## 2 Methods
### Optimization Procedure  
The optimization procedure of the white LED spectrum is shown in Figure 1. The optimal white LED SPD for specific luminance level can be obtained by the following steps: first, apply an equal-energy white spectrum and input a specific luminance level to calculate the total radiance of the spectrum in 400-700 nm range. If the luminance level is below 5 cd/m\({}^{2}\), the modified MOVE-model is adopted to calculate the power of the spectrum. Conversely, only the photopic luminosity function is used. Second, input a series of test SPDs with normalization and acquire the absolute radiant power based on the same total radiance with the equal-energy white spectrum to calculate the photopic, scotopic, and mesopic luminances. Third, Judd modified tristimulus values \(X_{\text{[PERSON]}}\), \(Y_{\text{Judd}}\), \(Z_{\text{[PERSON]}}\), and CIE \(V^{\prime}(\lambda)\) are obtained by the integral of test spectra, spectral reflectance, Judd modified color matching functions, and the scotopic luminous function. Then the tristimulus values are converted to the spectral responses of the cones, \(L\), \(M\), and \(S\). The scotopic luminance \(Y^{\prime}\) is also calculated. Next, the \(L\), \(M\), and \(S\) responses and luminance \(Y^{\prime}\) are regarded as the inputs for the [PERSON]'s mesopic color appearance model.  
The mesopic/photopic luminance acquired from test SPDs shall be converted to the illuminance unit first for applying the mesopic color appearance model. After simulating the color appearance, \(A(E)\), \(r/g(E)\), \(y/b(E)\) under a specific luminance level, the simulated tristimulus values \(X^{\prime}\), \(Y^{\prime}\), \(Z^{\prime}\) are obtained by a \(3\times 3\) transformation matrix and converted to CIE LAB color space for its gamut volume calculation. In the theoretical simulation, the spectral reflectances of 1269 matt Munsell color chips are used to construct a gamut boundary. The gamut volume is the summation of all tetrahedron's volume where the tetrahedra are determined by 3D Delaunay triangulation of the color samples, and the unit of gamut volume is LAB cube. Finally, according to the predicted photoic/mesopic luminance (\(L_{mes}\)) and the gamut volume (\(GV\)), the test spectra with relatively higher \(L_{mes}\) and \(GV\) found by achieving the maximum of the objective function as Equation (13), highest \(L_{mes}\) and largest \(GV\) are selected as the optimal white LED spectra under a specific luminance level.  
\[Z=f(L_{mes},GV)=\frac{L_{mes}-\min(L_{mes})}{\max(L_{mes})-\min(L_{mes})}\cdot \frac{GV-\min(GV)}{\max(GV)-\min(GV)} \tag{13}\]
# Study on Mechanical Properties of Multi-Cavity Steel-Concrete Composite Floor
###### Abstract  
This paper proposes a novel multi-cavity steel-concrete composite floor. The mechanical properties of multi-cavity steel-concrete composite floor were studied by static load test. Based on full-scale tests on \(2500\times 1000\times 120\) mm multi-cavity steel-concrete composite floors, the bearing capacity and failure characteristics of the composite floor were analyzed. Compared with the existing prefabricated floor, the reliability of the test was verified by finite element simulation. The influence of steel plate material thickness, floor thickness, cavity size and span on the mechanical properties of composite floor was analyzed. The results showed that the composite floor had stronger bearing capacity and better ductility and integrity than the existing precast floor. The bearing capacity and stiffness of composite floor were positively correlated with the thickness of steel plate and floor, and negatively correlated with the cavity size and span.  
multi-cavity steel plate; composite floor; mechanical properties; failure characteristics +
Footnote †: journal: Applied Sciences
# Resonant Power Supply for Plasma Cleaning Based on Fuzzy Logic Power Tracking
## 1 Introduction  
The traditional cleaning methods mainly include mechanical cleaning, chemical cleaning, and ultrasonic cleaning. Mechanical cleaning is used for power tools or related equipment to remove rust, paint, and oxides on the surface of the object. The advantages are a simple operation and low technical requirements but can easily damage the object, for a complex shaped product is helpless, and the process to produce noise pollution, is not conducive to the operator and the health of the residents in the surrounding plants. Chemical cleaning uses chemicals to react with dirt or residue on the surface of the object, so as to achieve the purpose of cleaning. Chemical cleaning efficiency is high, can calmly deal with the appearance of complex products, and there is no noise pollution, but the disadvantages are the consumption of a large number of chemical reagents, it is easy to cause environmental pollution, and also may cause damage to the product itself, for some difficult to use wet cleaning products are powerless. Ultrasonic cleaning is to soak the product in the liquid, and make the liquid produce high frequency oscillation, generate shock waves, and remove the dirt on the surface of the object. This method has high pollution removal efficiency, but the disadvantage is that it also needs dry treatment, and the cleaning time should not be too long, otherwise it is easy to damage the cleaned object [1].  
With the rapid development of industrial products, the continuous improvement of product quality, environmental protection awareness, and green energy saving awareness continues to be popular, and plasma cleaning will be widely used in the manufacturing industry. Plasma can be obtained by arc discharge, glow discharge, flame, laser or shock wave, etc., while the plasma used for cleaning belongs to low temperature plasma and is usually obtained by high-frequency and high-voltage breakdown gas. Depending on the cleaning object, different kinds of gas can be used, or the process can be completed in a vacuum environment. Although plasma cleaning has many of the advantages mentioned above, the current plasma power supply on the market has poor adaptability to generator load and cannot drive a variety of loads. There is a large loss of power from the supply system, a low power factor, the control ability of the plasma current variation is poor, and the system dynamic response is poor. Furthermore, the plasma power supply for an analog chip control power supply, single function, cannot meet the requirements of the application market control. Therefore, the power supply for plasma cleaning and its control strategy are studied in this paper [2; 3; 4].
# Suitability of Graph Database Technology for the Analysis of Spatio-Temporal Data
###### Abstract
The 'Yelp dataset' is a current subset of Yelp's businesses, reviews, and user data comprising spatio-temporal data for 10 metropolitan areas across two countries with about 200,000 businesses and 6.6 million reviews. It is offered for academic use in the yearly Yelp challenge [5], for a variety of data analysis and discovery tasks. In contrast, the ambulance response simulation dataset [6] from Umea University, Sweden, offers more control over the scenario--given a number of hospital resources such as dispatch centres and ambulances, it simulates how emergency calls would be responded to by ambulances. The dataset records spatio-temporal properties such as the time intervals within the response life cycle and the origin and destination of the resource during the response. Other technical properties regarding the emergency itself are also recorded. In summary, we evaluated on two datasets with different representations of spatio-temporal properties, complexities from additional attributes, and total size. Our contributions can be summarized as follows:  
* modelling and importing two characteristic datasets into three state of the art databases,
* rigorously benchmarking them using various analyses that fairly represent the type of querying and data manipulation the dataset would typically undergo, and
* comparing the suitability of JanusGraph vs. TigerGraph by investigating their specific graph query language implementations.
###### Abstract  
The incorporation of digital modulation into radar systems poses various challenges in the field of radar design, but it also offers a potential solution to the shrinking availability of low-noise operating environments as the number of radar applications increases. Additionally, digital systems have reached a point where available components and technology can support higher speeds than ever before. These advancements present new avenues for radar design, in which digitally controlled phase-modulated continuous wave (PMCW) radar systems can look to support multiple collocated radar systems with low radar-radar interference. This paper proposes a reconfigurable PMCW radar for use in vital sign detection and gesture recognition while utilizing digital carrier modulation and compares the radar responses of various modulation schemes. Binary sequences are used to introduce phase modulation to the carrier wave by use of a field programable gate array (FPGA), allowing for flexibility in the modulation speed and binary sequence. Experimental results from the radar demonstrate the differences between CW and PMCW modes when measuring the respiration rate of a human subject and in gesture detection.  
multimode radar; digital radar; PMCW radar; vital-Doppler; gesture recognition Article
## 2 The Efficiency and Errors of Practical Apparatus  
In practical protocols, all apparatuses should be realizable and convenient. All the apparatuses in the protocols are divided into three types: emission apparatuses, channel and detection apparatuses. In a practical protocol, the following situations should be considered.  
* Emission apparatuses. The practical and efficient single-photon sources have not yet been realized, while some researchers have been studying the spectra [49] and efficiency [50] of the single-photonsources. In this paper, the single-photon sources are not adopted. Instead, we use weak coherent pulses with typical average photon number of \(\mu_{S}\) in the following protocols, which can be easily prepared by standard semiconductor lasers and calibrated attenuators [51]. The error rate caused by the emission apparatuses is denoted as \(\varepsilon_{S}\). A pulse is requested to contain only one kind of polarization, but more than one photon in a pulse are allowed.
* Channel loss and error. The existence of the channel loss leads to an imperfect transfer efficiency, and the noise in the channel leads to some channel error. Suppose the transfer efficiency of the channel is \(\eta_{C}\), the error rate caused by the channel is \(\varepsilon_{C}\). Refs. [52; 53] provided the physical setups and detailed properties of some kinds of quantum channels.
* Detection apparatuses. In practice there is no detector with perfect detection efficiency. The quantum efficiency \(\eta_{D}\) is the probability that the detector registers a count when one photon comes in, and the error rate caused by the detection apparatuses is \(\varepsilon_{D}\), where the main error source is the dark count \(d\). The single-photon detectors with high efficiency, like 80-93% have been realized in the laboratory [54; 55].  
Assume all the parameters described above are all known by both parities of the protocol, and the typical average photon number of the whole system is \(\mu\equiv\mu_{S}\eta_{C}\eta_{D}\). Then, the overall error rate is \(\epsilon\equiv 1-(1-\varepsilon_{S})(1-\varepsilon_{C})(1-\varepsilon_{D})\).
## 3 Simulation, Results, and Discussion
### Bended Structure
#### 3.2.2 Effect of the Test Tube Radius
For instance, tubes with r = 0.75 and 1.5 mm had higher Q factors, and were mainly used to extract the imaginary part (\(\varepsilon^{\prime\prime}\)). Tubes with larger radii (r = 3.75 and 5 mm) had a sensitivity range frequency of 400 MHz; hence, they were advantageously used for permittivity and the real part (\(\varepsilon^{\prime}\)) extraction, since \(\varepsilon^{\prime}\) and \(\varepsilon^{\prime\prime}\) are directly related to \(\Delta\)fr and to Q factor, respectively [45].
# A Comparative Study of SOx, NOx, PM2.5 and PM10 in the UK and Poland from 1970 to 2020
## 2 Characteristics and Impacts of Air Pollution
### The Main Sources of Sulphur Oxides, Nitrogen Oxides, and Particulate Matter
#### 2.2.2 Nitrogen Oxides  
Nitrogen oxides are mainly formed due to combustion processes, partly from nitrogen compounds in the fuel, but more significantly by the direct combination of atmospheric oxygen and nitrogen at flame temperatures. For everyday purposes, most of the nitrogen oxides we are exposed to are from road transport [7]. However, in addition to road transport, air transport, water transport, and other heavy machinery are also responsible for emissions of these compounds. After transport, the main cause of high concentrations of nitrogen oxides is the energy and manufacturing industries, and construction (Figure 2). NO\({}_{2}\) accounts for only 5-10% of directly emitted nitrogen oxides, the remainder being NO. However, the cooling of blue gases and the supply of oxygen promotes the oxidation of NO to NO\({}_{2}\). Nitrogen oxides, together with other compounds in the atmosphere, are involved in chemical transformations leading to particulate matter and ozone depletion.