esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Water Content  
The findings of the tests (Figure 1) involving samples of rhubarb exposed to ozone and samples not subjected to such treatment and stored in identical conditions (18 \({}^{\circ}\)C) show effects of the concentration and duration of ozonation on the loss of weight. The largest weight loss amounting to 25.50% was observed in the control sample on the 14 th day of storage, while the lowest weight loss of 12.39% was recorded in the sample subjected to ozone treatment at a rate of 100 ppm, for the duration of 30 min.  
A mobile solution has been designed [19] that will allow for the ozone treatment after harvesting. It was proposed to equip the transport devices with appropriate chambers that would allow the ozone treatment to be carried out during the initial transport.  
Transport lasting up to several days usually takes place at ambient temperature, leading to weight loss, mainly due to water loss. The ozonation process carried out under optimal conditions allows keeping 11.5% of the mass, translating into an increase in profit by 64.30 \(\epsilon\) t\({}^{-1}\), assuming the average price of rhubarb at the level of 559.30 \(\epsilon\) t\({}^{-1}\).  
It should be assumed that the cost of ozonation, excluding the cost of equipment, is 0.5 kWh t\({}^{-1}\) of electricity, which is 0.11 \(\epsilon\) t\({}^{-1}\). The cost of energy consumption was estimated on the basis of the power of the ozone generator used, which was used under the conditions of the experiment [19].  
A different response of the raw material to ozone treatment was observed in the case of apples. After ozonation, apples have much worse preservation [20]. However, [PERSON] et al. [20] reported a similar loss of weight in apples subjected to ozonation and in the control sample. Notably, however, the researchers applied periodic ozone treatment using ozone stream at a concentration of 1 ppm. This was due to the sensitivity of the fruit to a higher concentration, and the observed storage-related effect was associated with the impact of ozone on the pesticide residues in the stored fruit.
# Mechanical and Microstructural Studies of High Performance Concrete with Condensed Silica Fume
## 3 Results and Discussion
### Microstructure
[3] found that ultra-high strength concrete with the addition of 15-25% SF was characterized by a higher CSH content, as well as lower porosity and CH content compared to the reference mix. In this study, 10% and 15% CSF substitutions were adequate to improve ITZ along with bond strength to aggregate grains and cement paste. The relationship for HPC between the microcrack width and the fracture energy without and with CSF is demonstrated in Figure 15b. This correlation reveals linearly decreasing microcrack width with increasing fracture energy for HPC specimens with and without CSF. The correlation demonstrates strong R\({}^{2}\) values for the HPC specimens. The predicted equation of the microcrack width for HPC specimens with and without CSF is specified by:  
\[w_{m}=8.21G_{Ic}-52.19 \tag{13}\]  
The above results show that the compressive strength, tensile split strength, elastic modulus, flexural strength, fracture properties, as well as the microstructure of HPC with changes in CSF replacement are closely related. Moreover, the addition of CSF can increase the CSH content and thus reduce microcracks and porosity due to the high SiO\({}_{2}\) content and ultra-fine particle size. The higher packing density of the supplemental cementitious material in the paste matrix reduced microcracks and porosity. For this reason, a higher content of CSH is necessary to obtain high mechanical properties. Figure 14: Differences in microcracks widths of HPC with CSF content of (**a**) 0%; (**b**) 10%; (**c**) 15%; (**d**) 25%.
## 4 Discussion  
The experimental results in Section 3 indicate that the proposed on-line point cloud data compression algorithm for free-form surface scanning measurement has the following features:  
* It can further compress point cloud data and obtain a higher data compression ratio than the existing methods under the same required accuracy. Its compression performance is obviously superior to the bi-Akima and chordal methods;
* It is capable of tightly controlling the deviation within the error tolerance range, and deviations in most measured area are far less than the required accuracy;
* Test A preliminarily verifies the application feasibility of the proposed method in an industrial environment. Test B demonstrates that the method is equally effective for complex surfaces with a large number of details, edges and sharp features, and it has stable performance;
* The proposed method has the potential to be applied to industrial environments to replace traditional on-line point cloud data compression methods (bi-Akima and chordal methods). Its potential applications may be in the real-time measurement processes of scanning devices such as contact scanning probes, laser triangle displacement sensors, mobile laser scanners, linear structured light systems, industrial CT systems, etc. The application feasibility of this method needs to be further confirmed in subsequent case studies.  
However, the proposed method is not perfect and still has the following limitations. In future work, the following aspects need to be further developed:* This method can only handle 3D point cloud data streams and is not suitable for processing point cloud data containing additional high-dimensional information (e.g., 3D point cloud data with grayscale or color information). We will try to solve the above problem in our future research work;
* This method can only compress the point cloud data stream which is scanned layer by layer. If the 3D point cloud is randomly sampled and there are no regular scan lines (e.g., 3D measurement with speckle-structure light), our method cannot perform effective data compression. It is a huge challenge to solve the above problems.
## 2 Materials and Methods
### Testing Device Software  
The application software for the testing device was written in the C++ programming language in the QtCreator IDE with help of the QtFramework libraries. The main window of the application is shown in Figure 5. The window is divided into three main regions, excluding the region for the control buttons information text region and three graphic regions.  
Figure 3: The testing device in transport conditions.  
The settings dialog window is shown in Figure 6. Parameters that can be set include the working directory, in which the output files are created, and the base name for the created files. Considering the testing process, the number of testing cycles can be set, and since the number of tasks in one cycle is 6, this setting can be adjusted only as multiples of 6. The last adjustable setting is the COM port name to properly choose the connected testing device MCU.  
There are 3 timers/counters used in the application: the first measures the time to complete the task, the second ensures moving to next task after 20 s, and the third is responsible to play a chosen audio sound repetitively, each being 2 s, up to 9 times in one testing cycle.  
Figure 4: The technical solution of the device for measuring the reaction time and the relative error rate in multitasking tasks.  
Figure 5: Main window of the application of the testing device.  
The three graphic regions serve to draw small squares while the red square, if it is drawn in a region, has a special meaning (see the Results section). The tasks generated for testing one subject are created randomly within the sets of 6 into a complete sequence of tasks in an array. Other sub-arrays used in the application include the arrays filled during this process with randomly generated colors (yellow, green, blue) and with identification of one from the three sectors of each graphic region randomly chosen for the approaches if needed. All the randomization processes are seeded with an unsigned int value of the current time and is handled at the start of the testing, so the generation process does not interfere with the testing process.  
The example output file of the testing is shown in Figure 7. As it can be seen, it is a formatted text file that has chosen statistical parameters in the head of the file at lines beginning with the \"#\" character. The statistical parameters include the complete time taken to finish the testing, average time for one task, total number of tasks, total number of errors, and the error ratio parameter. The last six lines indicate the values to create a histogram, with task type number on the \(x\)-axis and the errors corresponding to each task type on the \(y\)-axis.  
Figure 6: Settings window of the application for the testing device.  
Figure 7: Output file example.  
After the head there are four columns, which form from left to right they are the number of the task within the testing, the task type number, time in seconds, and the number of errors for the given task.
# 25 Years of Bluetooth Technology
## 2 Bluetooth Technologies
### Bluetooth Mesh  
The Bluetooth mesh Standard is a publish/subscribe model in which publishers and subscribers can subscribe to any topic. For instance, electric switches can publish and electric lamps can subscribe to a topic. A mesh node can also subscribe to several addresses but publish to one address. Addresses are generally stored in a subscriber list.  
The mesh standard outlines different addresses: unicast and group address. Every node acquires a unique unicast address when it joins the mesh network. A group of nodes is represented by a group address. A new group address needs to be added to the subscriber list before a node can join that group. Once a node acquires a group address, any other node can send a message to it via its unicast address or its group address. A mesh topology is utilized to connect publishers to subscribers.  
The Bluetooth mesh standard enables communication between nodes via scanning and advertising, and flooding mechanisms. Flooding allows nodes in the network to repeatedly relay messages to other nodes until they reach the correct destination. Bluetooth Mesh nodes transmit packets using a random time interval and not a fixed interval as in the case of BLE. Advertisement channels are scanned using a 100% duty cycle for receiving incoming packets. Therefore, mesh nodes continuously scan for incoming packets except in time intervals when transmission is occurring. It is worthwhile noting that the BLE advertisement packet used by mesh standard [11] is supported by both, BLE and Bluetooth mesh devices.  
The mesh standard [11; 12] allows backward compatibility feature to allow BLE devices to join the mesh network. Therefore, native BLE devices can also join a Bluetooth Mesh network. This is accomplished via the proxy feature implemented in a \"proxy node\". The \"proxy node\" can communicate either by using the default BLE advertising capability or by utilizing backward compatibility that allows BLE support. In order to prevent the flooding mechanism to cause any scalability issues, Bluetooth mesh utilizes a \"relay\" feature. Only nodes with the \"relay feature\" enabled can forward packets in to the mesh network. A Time-to-Live (TTL) field is associated with each message. A message is only relayed if its TTL field is greater than 1.  
The mesh standard also defines a Friendship feature which helps implement power conservation. The feature allows connected to the main power supply to assist low-power nodes to scan and join the mesh network. The low-power device is the low-power node and the assisting device is the friend node. A friend node stores messages that arrive for its low-power node and relays messages that it gets from its low-power node into the mesh network. Using the friendship feature, low-power nodes need not implement a 100% duty cycle thereby, reducing energy consumption.
## 3 Results
### Audio Analysis  
To characterize the acoustic stimulus, the spectrograms, spectra, and MSC were computed. The spectrograms of (ii) the emotional voice and (iv) the synthetized emotional voice were calculated to analyze the similarity of temporal components (Figure 4a). The spectra of these signals were then calculated (Figure 4b). In the context of the MSC, a value of 1 represents the maximum degree of similarity, whereas a value of 0 represents the minimum degree of similarity. Following a visual inspection of the spectra, we calculated the MSC for all the frequencies between 0 and 5 kHz (Figure 4c).  
Figure 2: Averaged log-transformed power spectrum [in \(10^{4}\)log\({}_{10}\)(\(\mathrm{\SIUnitSymbolMicro V}^{2}\))/Hz] of the Fz electrode across the frequency range of 0.5–45 Hz. Upper: Log-transformed power spectrum in the resting-state (gray) and gratitude (mustard) conditions. Shaded areas denote, for each condition, the standard error of the mean (SEM). Lower: Difference in power spectrum between conditions. The dotted line denotes the zero-power difference. The solid dark line at the bottom of the x-axis denotes the significant cluster of \(p\)-values from a paired \(t\)-test (\(\alpha\)-level = 0.05) for the resting-state and gratitude power spectrum (\(p\)-values were corrected for multiple comparisons via cluster-based permutation test; N = 100,000 randomizations).  
Figure 4: Emotional (ii) and synthesized emotional (iv) voice signal analysis. (**a**) Upper: Spectrogram of the emotional stimulus recorded by the participant after reading aloud the gratitude letter. Lower: Spectrogram of the synthesized emotional stimulus obtained from the text of the gratitude letter written by the participant using VoiceOver. (**b**) Upper: Spectrum of the emotional voice stimulus. Lower: Spectrum of the synthesized emotional voice stimulus. (**c**) Analysis of the magnitude-squared coherence (MSC) value [in AU] shows the similarities between the spectral content of (ii) the emotional signal and (iv) the synthesized emotional signal. The dashed red line denotes the 0.7 threshold for MSC values. The mean MSC value was 0.21 for a frequency bandwidth of 0–500 Hz. AU, arbitrary units.  
Figure 3: Averaged log-transformed power [in 10\({}^{4}\)log\({}_{10}\)(\(\upmu\)V\({}^{2}\))/Hz] across the Fz electrode for each frequency band for the resting-state (gray) and gratitude (mustard) conditions. Frequency bands are considered as theta (4–7.9 Hz), alpha (8–11.9 Hz), beta (12–29.9 Hz), and gamma (30–45 Hz). Solid lines within the violins indicate the mean value. Dashed lines denote the power at 0. Vertical lines denote the limit between frequency bands. Asterisks denote the significance of the difference between conditions established using paired \(t\)-tests: (\(p<0.01\)), and \({}^{*}\) (\(p\leq 0.05\)).
# Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks
## 3 System Model
### Energy Model  
Different from the wired network routing algorithms which focus on transmission bandwidth, WSN is an energy heterogeneous network and the routing algorithm pays more attention to the energy consumption of the whole system [25]. Therefore, the actual communication channel loss model plays an important role in the design of routing algorithm. The wireless transmission energy model used in this paper is referred to [26]. When Nodes transmit \(k\) bit data to a location to where the distance is \(d\), the energy consumption composed of the transmission circuit loss and power amplification is as shown in Equation (2).  
\[E_{TX}(k,d)=\left\{\begin{array}{ll}k\cdot E_{elec}+k\cdot\xi_{fs}\cdot d^{2 },&d<d_{0}\\ k\cdot E_{elec}+k\cdot\xi_{mp}\cdot d^{4},&d\geq d_{0}\end{array}\right. \tag{2}\]  
where \(E_{elec}\) represents the energy dissipation to run the radio electronics, \(\xi_{fs}\) and \(\xi_{mp}\) are the energy coefficients needed to run the amplifier for two kinds of distances (close and far).  
\(d_{0}\) is the distance threshold value shown in Equation (3).  
\[d_{0}=\sqrt{\xi_{f/s}/\xi_{mp}} \tag{3}\]  
The energy consumption required by the node to receive \(k\) bits data is shown in Equation (4).  
\[E_{RX}(k)=k\cdot E_{elec} \tag{4}\]  
After receiving information from member nodes, the energy consumption that the CH has to spend to data fusion is \(E_{DA}\) when handling with 1 bit data.
## 3 Results and Validation
### Validation
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Type** & **CR \({}^{1}\)** & **FR \({}^{2}\)** & **GR \({}^{3}\)** & **SR \({}^{4}\)** & **WE \({}^{5}\)** & **WB \({}^{6}\)** & **UB \({}^{7}\)** & **BL \({}^{8}\)** & **SI \({}^{9}\)** & **Total** & **PA (\%)**[10] \\ \hline Copland & 114 & 1 & 1 & 3 & 0 & 0 & 0 & 3 & 0 & 122 & 93.44 \\ Forest & 0 & 95 & 11 & 0 & 0 & 0 & 0 & 0 & 0 & 106 & 89.62 \\ Grassland & 0 & 0 & 132 & 0 & 1 & 0 & 0 & 4 & 0 & 137 & 96.35 \\ Shrub land & 0 & 0 & 1 & 63 & 0 & 0 & 0 & 17 & 0 & 81 & 77.78 \\ Wetland & 0 & 0 & 0 & 0 & 55 & 2 & 0 & 0 & 0 & 57 & 96.50 \\ Water & 1 & 0 & 0 & 0 & 2 & 75 & 0 & 0 & 0 & 78 & 96.16 \\ UB \({}^{7}\) & 5 & 0 & 0 & 0 & 0 & 0 & 95 & 2 & 0 & 102 & 93.14 \\ Barren & 0 & 0 & 3 & 0 & 0 & 0 & 5 & 231 & 3 & 242 & 95.45 \\ Snow/ice & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 6 & 74 & 80 & 92.50 \\ Total & 120 & 96 & 148 & 66 & 58 & 77 & 100 & 263 & 77 & 1005 & \\ UA (\%) \({}^{11}\) & 95.00 & 98.96 & 89.19 & 95.45 & 94.83 & 97.40 & 95.00 & 87.83 & 96.10 & \\ \hline Overall accuracy = 93.68\%, Kappa = 0.92 \({}^{1}\) CR = cropland, \({}^{2}\) FR = forest, \({}^{3}\) GR = grassland, \({}^{4}\) SR = shrubland, \({}^{5}\) WE = wetland, \({}^{6}\) WB = water & & & & & & \\ body, \({}^{7}\) UB = Urban and build-up, \({}^{8}\) BL = bare land, \({}^{7}\) SI = snow/ice, \({}^{10}\) UA = user’s accuracy and \({}^{11}\) PA = producer’s accuracy. & & & & & \\ \end{tabular}
\end{table} 
Table 4: Confusion matrix for the 2014 HRB land cover map. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Years** & **2015** & **2014** & **2013** & **2011** & **2010** & **2005** & **2000** & **1995** & **1990** & **1986** \\ \hline OA (\%) & 93.5 & 93.7 & 91.3 & 89.6 & 89.8 & 89.9 & 91.1 & 90.3 & 88.8 & 85.2 \\ Kappa & 0.924 & 0.927 & 0.891 & 0.879 & 0.881 & 0.881 & 0.896 & 0.887 & 0.868 & 0.827 \\ \hline \end{tabular}
\end{table} 
Table 5: Accuracy of each year. OA (%) = Overall accuracy (%). Figure 7: Comparison of land cover maps at HRB between the GlobeLand30 and the proposed method in 2000 and 2010.
## 3 Self-intersection Penalty Term
### Definition and Description  
Our method generally applies to meshes satisfying the conditions described below:  
* The mesh is a two-dimensional manifold.
* The mesh describes an orientable surface.
* The mesh is a closed surface.  
Since the two-dimensional manifolds do not have to be connected, we can say a mesh with several disconnected parts also satisfies the conditions above. We demonstrated that our proposed method also works with a disconnected mesh in Section 4.  
To remove the self-intersection, the triangle mesh should be iteratively deformed by moving each vertex in a specific direction. The moving directions of vertices are obtained by computing the partial derivatives of the self-intersection penalty term which is defined as the volume of the self-intersection region in this paper. This penalty term is denoted as \(E_{SPT}(V)\), where \(V\) is the coordinates of all the vertices. An ideal self-intersection penalty term should satisfy the following conditions:  
* When there is no self-intersection, both the penalty term and the gradient of the penalty term should be zero.
* When there is self-intersection, the value of penalty term indicates the degree of intersection.
* When there is self-intersection, the gradient of penalty term offers meaningful direction for optimization.  
Leaving the strategy of computing the value of penalty term aside, the method of computing gradient is discussed first.  
The first step of computing the partial derivatives is separating the vertices into two sets: (1) vertices in the self-intersection region and (2) vertices not in the self-intersection region. We implemented this separation by emitting a beam of detection rays, the density of rays is manually set according to the number of triangles in the mesh. An appropriate setting of density of rays guarantees the accuracy of classification and low memory consumption. There are two typical ways of intersection shown in Figure 2. According to the type of self-intersection, the set of vertices in self-intersection could be further separated into two sets. Overall, the vertices are divided into three sets: (a) vertices in the self-intersection region due to interpenetration of the outer surface, denoted as \(V_{out}\); (b) vertices in the self-intersection region due to interpenetration of the inner surface, denoted as \(V_{in}\) and (c) vertices not in the self-intersection region, denoted as \(V_{0}\); Based on the classification result, the partial derivative of the penalty term with respect to coordinate of a vertex can be obtained according to the normal vector and which set the vertex is belonging to.  
Figure 2: Two typical way of self-intersection: (**a**) Self-intersection due to interpenetration of the outer surface. (**b**) Self-intersection due to interpenetration of the inner surface.  
It is worth pointing out that self-intersection described in Figure 2b rarely occurs for statistical body shape model. To maintain the generality of our method, both of these two type of self-intersection are considered in the following discussion.
# Lift-Thrust Integrated Ducted-Grid Fusion Configuration Design for a Ducted Fan Tail-Sitter UAV
## 4 Analysis of the Influence of Fusion Configuration Design Parameters
### The Influence of the Number of Circumferential Grids  
Due to the axisymmetric layout of the duct, the internal airflow also exhibits a uniform circumferential distribution. When embedding grid channels on the wall, the number of circumferential grid cells affects the size of the air flow stagnation zone cells within the same circumferential distribution range. Figure 16 shows the effect of different numbers of circumferential grids on the aerodynamic characteristics of ducted propellers.  
It can be seen that as the circumferential distribution of the grid cells becomes denser and denser, the thrust slightly increases and then remains basically unchanged, while the torque changes remain basically stable. The increase in the number of grids actually exerts a certain constraint on the leakage effect of the blade tip, thus benefiting the torque and power characteristics. For generating additional lift, the number of grid circumferential cells is not necessarily the more the better, and there exists a relatively optimal value for the number of cells.  
Figure 17 shows the cross-sectional streamlines and pressure distribution cloud images when the circumferential \(\times\) flow direction values are \(2\times 2\) and \(6\times 2\), respectively. As the number of circumferential units increases, both the low-pressure zone on the inner wall of the duct induced by the grid and the separation phenomenon at the rear of the duct are basically similar. This indicates that the number of circumferential grids has a relatively small impact on the aerodynamic forces of this composite configuration.  
Figure 16: The influence of different numbers of circumferential grids on the aerodynamic characteristics of ducted propellers: (**a**) thrust; (**b**) torque; (**c**) lift.  
Figure 17: Pressure distribution cloud images of the fusion configuration with different numbers of circumferential grids (\(2\times 2\), \(6\times 2\)).
# Residual Echo Suppression Considering Harmonic Distortion and Temporal Correlation
## 5 Experiments
### Computational Complexity of the Proposed RES Algorithm

Table 4: Average PESQ scores in various SNR and SER conditions for the AEC without RES, with the PFRES [27], with the HDRES [28], and with the proposed RES during the double-talk periods for the real-recorded data. The numbers in the bold face indicate the best performances.