esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Textural Characterization  
The textural properties were reported before and after ammonia treatment for the pristine and platinum-decorated samples [90]. These samples had a complex structure of mixed micropores and mesopores, with an average micropore volume of \(\sim\)0.4 cm\({}^{3}\) g\({}^{-1}\), which was half of the total pore volume of \(\sim\)0.8 cm\({}^{3}\) g\({}^{-1}\). Their nitrogen adsorption isotherms, at 77 K, showed that ammonia treatment improved the BET surface area, while not much alteration was observed for the micropore volume or diameter with an average micropore width of \(\sim\)2 nm, as displayed in Figure 9a-d. The steadiness of the micropore attributes, while improving the BET surface area for the ammonia-treated specimens, supports the assumption of the erosion effect of ammonia acting on mesopores and macropores, while the micropores were not affected due to their inaccessibility by the ammonia gas molecules [90,91]. On the other hand, platinum decoration had an opposite effect, owing to its additional metal mass and nanoparticle metal filling, as reported for deterioration upon metal decorations of activated carbons [28,101]. In addition, the average size of the decorated metal particles were in the range of meso/macropore sizes [102], implying that pore blocking would not affect the micropores, but might block only the mesopores and/or macropores, which would decrease the overall porosity and the surface area, as was reported [90]. The same conclusions can be applied for all of the other decorated metal samples.  
Figure 8: Scanning electron micrograph of Ru-decorated, ammonia-treated AC its corresponding EDX spectrum.
# A Lightweight and High-Accuracy Model for Pavement Crack Segmentation
## 4 Results
### Loss Function Comparison Experiment  
To validate the effectiveness of the proposed dynamic combined loss function, experiments were conducted using a single loss function, a fixed combined loss function, and various dynamic weight schemes. The model employed in these experiments was the one proposed in this study, and the dataset used was Crack500. The experimental results are presented in Table 3.  
From the table, it can be observed that the model trained with the dynamic combined loss function generally outperformed those trained with a single loss function. This indicates that leveraging the advantages of multiple loss functions can lead to better segmentation performance. Moreover, the dynamic weight scheme demonstrated a significant performance improvement over the fixed weight scheme in the final evaluation metrics.  
Figure 7: Segmentation Results of Various Models on Representative Samples From the AsphaltCrack300 Dataset.
## 4 Performance Evaluation  
This section first evaluates the performance of the proposed MTU-Net-based SE method and then compares it with those of several conventional SE methods based on SNMF [3], SEGAN [8], DRNN [5], and U-Net [15]. Here, SNMF, DRNN, SEGAN, and U-Net were trained with hyperparameters according to [3; 5; 8; 15], respectively. In particular, the proposed MTU-Net-based SE method was implemented in three different ways: MTU-Net(spec), MTU-Net(IRM), and MTU-Net(IBM).  
Next, the performance of the proposed mask-based VAD method was also evaluated and compared with several NN-based VAD methods, including DNN [24], bDNN [24], and LSTM [22]. In this study, all the compared methods were implemented with the same hyperparameters used in their reference papers. Here, spectrograms were used as input features for all the implementations, while multi-resolution cochleagrams were used as per their original implementations in [22; 24]. This was done because the proposed VAD method uses mask values from the proposed MTU-Net-based SE method that utilizes spectrograms as input features. All the methods for SE and VAD were implemented in Python 3.5.2 with Tensorflow 1.13.1, and all the experiments were conducted on an Intel Core i7-7700 workstation with an NVidia GTX 1080 ti GPU.  
Figure 4: Comparison of block diagrams concerning the combination of speech enhancement and voice activity detection: (**a**) a conventionally combined approach and (**b**) the proposed approach.
# Combined Improved CEEMDAN and Wavelet Transform Sea Wave Interference Suppression
## 3 Proposed Algorithm  
One of the cores of AEIC is to eliminate the sea wave interference and ensure normal communication. In this section, we propose and elaborate an improved CEEMDAN joint WT sea wave interference suppression method.
## 3 Results and Discussion  
### Aluminum Powders and CNTs' Characterization  
Figure 3 shows the characterization of the aluminum powders. SEM and EBSD were performed to investigate the morphology and microstructure of the as-receive powders. The secondary electron mode in SEM allows the observation of the shape of the powders (Figure 3a). The powders can be classified as close to spherical in shape, with some more elongated. This can be confirmed in the graph in Figure 3e where the length of the particles is close to their width. The EBSD analysis revealed that several grains form each particle (Figure 3b). Due to the production technique, some misorientation can be observed, confirmed by the Kernel average misorientation (KAM) map that can be associated to some plastic deformation. The analysis using DLS revealed that the aluminum powders show a median particle size (D50) of 32.20 \(\upmu\)m; the size distribution can be observed in Figure 3d.  
Figure 1: (**a**) Image of the fracture tensile specimen and (**b**) schematic illustration of the sections observed using SEM and EBSD techniques.  
Figure 2: Schematic illustration of Vickers microhardness matrix in the top view fractured tensile specimen.  
SEM and HRTEM analysis were used to characterize the as-received CNTs. Figure 4 exhibited the images of the CNTs. The HRTEM image of Figure 4b demonstrates the CNT structure, revealing multi-walled CNTs with a bamboo-like structure. The detail shows the presence of 17 walls.  
During the dispersion process, the CNTs undergo exfoliation, losing some outer layers, as presented in Figure 5a. The CNTs exhibited an average of 17 \(\pm\) 6 layers as received with an outer diameter of 18.7 \(\pm\) 6.5 nm [17]. However, after ultrasonication, the number of walls decreased. Aluminum powders do not suffer significant changes in morphology during the dispersion process, as shown in Figure 5b.
## 3 System Design and Implementation
### ECG Monitoring System Implementation
#### 3.2.1 Data Aggregation Process  
In order to obtain the ECG sensory data accurately from the ECG device, the communication between the sensor nodes and the ECG device is of primary importance. Figure 7 shows the flow chart of the communication between the sensor nodes and the ECG device. To start a communication, a user sends a START mode to the sensor nodes through the query button. The START mode will open the serial port in the ECG device to initialize the data aggregation process. When the serial port is open, it indicates that the ECG device is ready to receive commands from the sensor node and that it is ready for transmission. A sensor node then sends a GET mode to obtain the ECG data from the ECG device. Once the reading process is done, a sensor node will communicate with the base station node in the network and transmit the sensory information back to the base station at the smart phone.
## 5 Simulation Study
### Analysis of Network Life  
The _network lifetime_ is defined as the duration from the very beginning of the network operation until the first sensor node dies. We observe that the DG protocol enjoys a longer network lifetime, since a sensor transmits events only when mobile subscribers come within the communication range of the sensor node, and thus much energy can be saved. However, the CED protocol maintains a shorter network lifetime. This is reasonable because more copies are generated and more events are transmitted in CED, thus producing more overhead. On the other hand, we can see from Table 3 that though the network lifetime of CED is shorter than DG, the data delivery ratio of CED is much higher than that in DG protocol, demonstrating that the proposed CED protocol can better deal with the tradeoff between the data delivery ratio and the delivery overhead than DG.
## 4 Performance Analysis of the Generated Models in Virtual Environments
### Performance Analysis of Models
#### 4.2.6 GPU and CPU Levels  
The levels of the GPU and CPU clocks refer to the frequency in MHz. The clock level can be from 1 to 5 and, although they can be set manually, it is recommended to use dynamic clock throttling by default. Thus, these levels automatically increase if the application does not run at an adequate frame rate at the current levels. Figure 12 shows graphs of the evolution of GPU and CPU levels throughout the application execution in each scene.  
At the device level, we observe how the Quest 3 stands out for reaching level 4 or even 5 more frequently in scenes with a higher graphic workload. These graphs are interesting as they show how, for example, in scenes with HP objects, it is demonstrated that the Quest 3 was limited by the GPU (see Section 4.2.4), as the GPU level was 5 while the CPU level remained at 4. The opposite occurs with the Quest 2, where the levels remained the same. In general, until the scene with 48 LP objects, no device reached level 5, with it being in this scene where the Quest 3 increased the GPU level to this number during specific periods of time. We also highlight in general that the Quest 3 and Pro increase the GPU level more frequently. This indicates that the default dynamic adjustment increases the clock frequency to try to process the rendering more quickly, especially in scenes with higher polygonal load, where we can observe complexity spikes in the graphs. Meanwhile, the Quest 2 has maintained the same level for both CPU and GPU in most application executions.  
Figure 11: CPU utilization % obtained of each scene run on each VR Headset. This metric represents the worst performing core. Therefore, for multi-threaded applications, the main thread of the app may not be represented in this metric.  
In general, we can observe how the Quest 3 and Pro adapted the use of the GPU dynamically better. The reason for this may be due to the difference in hardware power between these headsets and the Quest 2. In any case, it is shown once again that, from a certain number of polygons, as a guide, the different devices may start to experience problems due to the continuous increase in the clock frequency, either of the CPU or the GPU.
## 4 Observation Simulations of GMR, GeoSTAR, and GIMS  
It has long been recognized that a microwave radiometer onboard a geostationary satellite would be a powerful tool for weather prediction because it could sound the vertical profiles of temperature and humidity inside clouds by frequently monitoring a fixed area and acquiring high-temporal resolution on wide-range Earth observations. The geostationary microwave observations investigated in this study were synthetically generated by the simulated microwave brightness temperature data of GMR, GeoSTAR, and GIMS. In the GEO-MW OSSE, 8 channels in the 50-60 GHz oxygen absorption band were chosen, whose center frequencies are listed in Table 2. The 50-60 GHz band sounds atmospheric temperature by the absorption/emission of microwave radiance. Figure 3 shows the 50.3 GHz simulated brightness temperature map of the full-Earth disk outputted from the observation process model of GMR, GeoSTAR, and GIMS.  
Passive microwave remote sensing uses a microwave radiometer to detect and receive the electromagnetic radiation of the measured object in the microwave band. From a mathematical point of view, the observation process of a microwave atmospheric sounding radiometer can be regarded as a mapping from atmospheric physical states to the observed brightness temperature seen by the sensor. The following three steps are used to establish this mapping. First, the \"true\" atmospheric states are obtained from the Nature Run. Second, the atmospheric state parameters are input into the Community Radiative Transfer  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Channel** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline center frequency (GHz) & 50.3 & 51.76 & 52.8 & 53.596 & 54.4 & 54.94 & 55.5 & 57.29 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Channels for simulated GEO-MW brightness temperature.  
Figure 2: WRFDA-4 DVar system framework.  
Model (CRTM) to calculate the upwelling brightness temperature TB at the top of the atmosphere. Finally, a sensor simulator composed of coordinate transformation, system response, and data processing is used to simulate the observed brightness temperature TA. GMR is a traditional real aperture radiometer, GeoSTAR is a SAIR with a stationary Y-shaped array, and GIMS is a SAIR with a rotating thin circular array. The three GEO-MW sensors have different technical systems and observation data processes; therefore, it is necessary to establish three different simulators to obtain the observed TA by the GMR, GeoSTAR, and GIMS, introduced in detail in Section 4.2.
# Equalization Methods for Out-of-Band Nonlinearity Mitigation in Fiber-Optic Communications
## 2 The Time-Varying ISI Model of NLIN
### Estimating System Performance under the Time-Varying ISI Model  
While the distribution of the NLIN may be fairly close to Gaussian, the various correlations that characterize it make NLIN substantially different from standard additive white Gaussian noise (AWGN), such as ASE. As a result, the system's BER performance cannot be accurately predicted by using the standard noise formulas that were constructed for AWGN [31], particularly when accounting for the presence of equalization mechanisms (designed for NLIN mitigation, or even of the kinds that are standardly included in optical communications receivers). As a result, one is forced to rely on simulations that are based on the split-step Fourier method (SSFM) which are almost unacceptably time consuming, especially when considering large channel-count WDM systems. Moreover, as implied by the results of [32], attempts to avoid this difficulty by simulating only a small fraction of the interfering channels will also suffer from significant inaccuracies.  
In order to overcome this difficulty the Virtual Lab tool was introduced in Ref. [8]. The idea behind the Virtual Lab is that it is sufficient to characterize the properties of the ISI coefficients in order to accurately describe out-of-band NLIN. Therefore, if one artificially generates ISI coefficients with the correct statistical properties, it is possible to reproduce the relevant characteristics of NLIN without using SSFM. The Virtual Lab tool has been shown to provide accurate estimation of system performance (namely, the SNR and BER) in the presence of adaptive equalization [7; 8; 18].  
In following sections we show system performance estimates that are based both on SSFM and on the virtual Lab, allowing the reader to appreciate the accuracy of the tool.
# Traffic Congestion Detection System through Connected Vehicles and Big Data
## 3 Proposal Architecture
### On-Board Unit
#### 3.3.2 Algorithm LORA-CBF  
The LORA-CBF communication algorithm works hierarchically. It is formed by a cluster head, zero or more members in each group and one of more link portals to communicate with the other cluster heads. Each cluster head contains a \"Cluster Table\". A \"Cluster Table\" is defined as a table that contains the address and geographical location of the members and Gateway nodes. The cluster-forming mechanism is the first to be executed and is maintained at all times as shown in Figure 9.  
When a source tries to send data to a destination, it first checks the routing table to determine if the destination's location is known. If it is, it sends the packet to the neighbor closest to the destination. Otherwise, the sources stores the data in its intermediate memory, starts a timer and Location Request (LREQ) packet transmissions. Only Gateway nodes and cluster heads can transmit a LREQ packet.  
Gateway nodes only transmit gateway packets to one another in order to minimize unnecessary transmissions and only if the gateways belongs to different cluster groups. By receiving a request of location, each cluster confirms that the destination is a member of its cluster. If successful, it generates a Location Reply (LREP) packet that returns to the sender using geographical routing since each node knows the location of the source and the closest neighboring node, based on the information of the received LREQ packet.  
Figure 8: Route Monitoring Algorithm.  
Once the source receives the destination's location, it recovers the data packet from its memory and sends it to the neighbor closest to the destination.  
The location and speed data of each node are inserted in the big data cluster every second as this type of data is the most abundant in the network. For this reason, it is necessary to implement a data aggregation strategy to not affect network performance as the network grows. Data aggregation is performed using a syntactic technique which is applied by the cluster head to all of the data received from its members each second. Figure 10 illustrates the data aggregation process performed by the cluster head.  
If a node functions as the gateway role, it will choose only one cluster head to which to send its data, based on the distance and will notify other cluster heads that it will not send its data to them. In order to archive better network performance with a significantly greater number of vehicle nodes, location and speed data inserts of each node do not have to receive a driver acknowledgement.  
Figure 9: LORA-CBF communication algorithm.
# Stability Analysis of GNSS Stations Affected by Samos Earthquake
## 4 Conclusions
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Station Name** & **Long. E\({}^{\circ}\)** & **Lat. N\({}^{\circ}\)** & \(d_{E}\) (mm) & \(d_{N}\) (mm) & \(\sigma_{AE}\) (mm) & \(\sigma_{dN}\) (mm) & \(d_{U}\) (mm) & \(\sigma_{dU}\) (mm) \\ \hline ASTY & 26.35332 & 36.54513 & \(-5.02\) & \(-4.02\) & 3.37 & 3.87 & \(-9.45\) & 16.98 \\ AYD1 & 27.83788 & 37.84073 & \(-1.79\) & 4.41 & 3.80 & 4.03 & \(-0.53\) & 16.29 \\ AYDN & 27.84614 & 37.84700 & \(-1.97\) & 4.37 & 3.09 & 3.24 & \(-12.42\) & 13.95 \\ AYL & 26.68618 & 39.31144 & \(-1.66\) & 5.96 & 2.40 & 2.63 & 2.30 & 11.63 \\ BOZD & 28.31762 & 37.67294 & \(-2.20\) & 5.33 & 3.21 & 3.33 & \(-7.58\) & 14.37 \\ CESM & 26.37257 & 38.30381 & \(-13.73\) & 52.76 & 3.25 & 3.41 & 1.77 & 14.41 \\ DIDI & 27.26866 & 37.37213 & \(-2.65\) & \(-4.74\) & 3.10 & 3.49 & \(-4.36\) & 14.10 \\ DIDM & 27.27740 & 37.37335 & 0.45 & \(-6.16\) & 3.74 & 3.93 & \(-20.89\) & 16.15 \\ IKAR & 26.22423 & 37.62820 & \(-14.45\) & \(-29.97\) & 3.61 & 3.97 & 1.32 & 16.38 \\ IZMI & 27.08182 & 38.39481 & 12.93 & 35.33 & 2.81 & 3.09 & 1.38 & 13.01 \\ KALY & 26.97615 & 36.95580 & 0.58 & \(-10.34\) & 3.73 & 4.51 & \(-6.81\) & 15.73 \\ KIRA & 27.67220 & 39.10599 & 1.34 & 4.88 & 2.70 & 2.89 & 0.74 & 12.97 \\ LESV & 26.55379 & 39.10008 & \(-0.94\) & 10.39 & 3.64 & 4.02 & \(-2.19\) & 16.42 \\ MNTS & 26.71743 & 38.42658 & 3.75 & 49.06 & 2.60 & 2.87 & \(-4.12\) & 12.33 \\ MYKN & 25.32907 & 37.44164 & \(-1.92\) & \(-1.27\) & 2.79 & 3.11 & \(-1.75\) & 13.41 \\ NAXO & 25.38117 & 37.09819 & \(-4.62\) & \(-0.60\) & 3.03 & 3.42 & \(-6.06\) & 14.15 \\ PAMU & 28.54335 & 37.92378 & \(-1.38\) & 2.76 & 3.70 & 3.90 & \(-20.96\) & 16.33 \\ PRKV & 26.26500 & 39.24570 & \(-1.40\) & 6.16 & 2.46 & 2.68 & \(-2.07\) & 12.11 \\ RODO & 28.16166 & 36.292620 & \(-6.45\) & 1.05 & 3.95 & 4.57 & \(-1.49\) & 17.94 \\ SALH & 28.12354 & 38.48309 & \(-0.60\) & 3.70 & 3.06 & 3.37 & \(-6.19\) & 14.27 \\ SAMO & 26.70533 & 37.79277 & \(-63.13\) & \(-368.04\) & 4.21 & 4.76 & 86.61 & 18.34 \\ \hline \hline \end{tabular}  
* Long: longitude (E\({}^{\circ}\)); Lat: latitude (N\({}^{\circ}\)); \(d_{E}\), \(d_{N}\): east and north displacements; \(\sigma_{AE}\), \(\sigma_{dN}\): standard deviations for east and north displacements; \(d_{U}\): up displacement; \(\sigma_{dU}\): standard deviation for up displacement.  
\end{table} 
Table 3: Coseismic NEU displacements and st. deviations derived from GNSS measurements.
# A New One-Dimensional Compound Chaotic System and Its Application in High-Speed Image Encryption
## 4 Experimental Test and Security Analysis
### Information Entropy Analysis  
Shannon information entropy is widely used to measure the randomness of distribution of a given information \(\mathsf{m}\). In image encryption, researchers use information entropy to quantify the information uncertainty in encrypted images. Shannon information entropy is calculated as follows:  
\[H(m)=-\sum_{i=1}^{L}P(m_{i})\log_{2}[P(m_{i})] \tag{34}\]  
When Equation (34) is used to calculate the image information entropy, \(m_{i}\) represents the \(i\)-th gray value of \(L\)-level gray image (the gray level number of a common 8-bit gray image is \(L=256\)), and \(P(m_{i})\) represents the probability of occurrence of the \(i\)-th gray value. For ideal noise images with completely equal probability and random distribution of pixel values, the probability of occurrence of each gray level should be equal, that is, the \(P(m_{i})\) are all constant \(1/L=1/256\). By substituting this result into Equation (34), the ideal information entropy of 256 gray level images can be calculated as eight. If the information entropy of the encrypted image is closer to eight, it means that the more uniform its gray distribution is, the closer it is to the randomly distributed noise image and the more difficult it is to crack it. The information entropy values of several images encrypted by this algorithm are shown in Table 4. It can be seen from Table 4 that the information entropy of the encrypted image obtained by this algorithm is much larger than that of the original plaintext image. Moreover, in most cases, the information entropy of the ciphertext image obtained by this algorithm is higher than that of the algorithm in references, which shows that this algorithm has certain advantages.
## 5 Validation II: Simulation of Open-Hole Tension Tests with GA-Based Uncertainty
### Statistical Comparison of OHT Tests and FE Simulations  
To validate the ranges of FE input parameters listed in Table 7, the OHT8 and OHT12 tests are considered, as 20 test results are available, which ensures statistically meaningful experimental data. The 24 sets of FE input parameters obtained from the last GA generation are applied to the OHT8 and OHT12 models using element sizes of 1 mm \(\times\) 1 mm.  
Figure 12a,b show the distribution of the resulting open-hole strength from the 20 experiments and 24 FE simulations for the OHT8 and OHT12 tests, respectively. The dashed lines represent a best fit normal distribution from each test case. The statistical simulation results agree well with the experimental counterparts for both open-hole tension tests. Table 8 compares the mean values and the coefficients of variation between experiments and FE simulation. It can be seen that the mean values are accurately predicted with errors of less than 2%. However, the coefficients of variation from simulations are nearly double those of their experimental counterparts. Overall, the results demonstrate that the GA-based calibration of input parameters cannot only accurately determine the optimal FE parameters, but it can also elegantly account for uncertainty without the need for an excessive number of FE model evaluations.  
Figure 11: Force vs. displacement results from the last generation within the GA-based optimisation of damage input parameters compared to target curves obtained from experiments [6] on quasi-isotropic \([90/45/0/-45]_{s}\) been veneer laminates subjected to compact tension tests.  
Figure 12: Comparison of open-hole strength in OHT8 and OHT12 between the 24 FE simulations within the last GA generation and 20 experiments.