esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Case** & **Clock Instability** & **Mean Offset (m\({}^{2}\)/s\({}^{2}\))** & **Standard Deviation (m\({}^{2}\)/s\({}^{2}\))** \\ \hline
1 & \(10^{-13}\tau^{-1/2}\) & 1.063 & 1320.475 \\
2 & \(10^{-15}\tau^{-1/2}\) & 0.243 & 13.231 \\
3 & \(10^{-17}\tau^{-1/2}\) & \(-1.08\times 10^{-3}\) & 0.143 \\
4 & \(10^{-18}\tau^{-1/2}\) & \(-1.17\times 10^{-4}\) & 0.014 \\
5 & \(10^{-19}\tau^{-1/2}\) & \(-2.94\times 10^{-6}\) & \(1.47\times 10^{-3}\) \\
6 & 0 \({}^{*}\) & \(2.74\times 10^{-8}\) & \(6.96\times 10^{-4}\) \\ \hline \hline \end{tabular} \({}^{*}\) This row demonstrates the REGM derived from true GP values; hence, no clock error exists.  
\end{table} 
Table 4: The statistical analysis of the GP offset between the values calculated by EGM2008 (considered as true values) and those calculated by the recovered Earth Gravitational Model (REGMs). The positions cover the target satellite’s orbit. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Case** & **Clock Instability** & **Mean Offset (m\({}^{2}\)/s\({}^{2}\))** & **Standard Deviation (m\({}^{2}\)/s\({}^{2}\))** \\ \hline
1 & \(10^{-13}\tau^{-1/2}\) & 1.200 & 12,815.256 \\
2 & \(10^{-15}\tau^{-1/2}\) & 0.243 & 128.086 \\
3 & \(10^{-17}\tau^{-1/2}\) & \(-1.86\times 10^{-3}\) & 1.662 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The statistical information of the GP offset at the target satellite’s orbit between true values (calculated by EGM2008) and simulating observations using the SFST method. those recovered from the true values (as indicated in Figure 9f). The REGMs' precision on TS's orbit for cases 4, 5, and 6 are detailed in Table 4.
# SC-FDE Layer for Sensor Networks in Remote Areas Using NVIS Communications
## 6 Results
### CR Sweep  
Taking into account these results and in a bid to raise the average power, a CR sweep was studied, with values starting at 3 dB with 1 dB steps up to 8 dBs. The higher the CR, the higher the Error Vector Magnitude (EVM) due to the saturation produced by the signal limitations. Despite having a higher EVM, the BER could be better due to the average power increase. The trade-off between power consumption and BER results must be considered.  
Figure 6: CDF plot of SC-FDE modulation with 6 W, 12 W, and 25 W of peak power.  
Figure 7: BER vs. EbNo comparison between FSK, QAM, OFDM, and SC-FDE.  
As shown in Figure 8, we analyzed the CR tests with a transmitting power of 6 W. A significant difference can be observed when CRs were applied and when they were omitted. Initially, a probability of about 70% for a BER of \(10^{-3}\) with a CR of 3 dB was obtained. If a CR of 6 dB was applied, a probability of more than 85% for a BER of \(10^{-3}\) could be attained.  
The graph below also shows how the CDF increases when the CR is higher. By dropping the peaks produced by the multicarrier splitting, the SNR and MER improved. However, from 6 dB (which is the best CR value) the CDF started to decrease again. This result was produced by the signal limiting, as the high-power peaks disappear and, therefore, relevant data was lost. This is the point where the EVM was large enough, and the increase of average power did not help due to the large in-band distortion.
## 4 Experimental Methods and Results  
### Experimental Setup  
The performance of two pose estimation approaches using single and multiple graphical markers was compared. The experiments all used wall-mounted ArUco markers and two different camera setups: a fixed-camera setup and a drone-mounted camera setup. When there are insufficient types of ArUco markers available for constructing multiple-marker groups for pose estimation, alternative approaches can be employed, such as combining different types of graphical markers, such as AprilTags with ArUco markers, or treating multiple ArUco marker combinations as a single unified marker.  
In the experiments with the fixed-camera setup, a threshold value of 1 was used for both invalid marker detection and outlier detection. In the experiments using the drone-mounted camera setup, a threshold value of 5 was applied.  
Figure 3 depicts the configuration of a wall-mounted marker. Each marker had a width and height of 100 mm. Figure 3,b shows the configurations of the single marker and the multiple markers. In Figure 3, four markers were installed on the same plane with 125 mm intervals between adjacent markers. One marker was designated as the representative marker, as indicated in the figure.  
Figure 4 illustrates the experimental environments for a fixed-camera setup and a drone-mounted camera setup. In the fixed-camera setup shown in Figure 4, a camera (Logitech C920, Switzerland) was positioned perpendicular to the wall where the ArUco markers were mounted. The initial camera position was set so that the representative marker's origin was 100 cm along the positive z-axis of the camera coordinate system, with zero offset in both x and y directions. The representative marker's pose was estimated by rearranging the camera from its initial position. First, the camera was moved 0 cm, 5 cm, and 10 cm away from its initial position along the negative y-axis. The camera was then tilted 30\({}^{\circ}\), 45\({}^{\circ}\), and 60\({}^{\circ}\) about the x-axis from its initial position.  
For the fixed-camera setup, the relative pose estimates between the camera and the representative marker were compared with mechanical measurements as ground truth to calculate 3D position errors as well as roll, pitch, and yaw errors.  
In the drone-mounted camera setup shown in Figure 4, a DJI Tello drone equipped with a camera was used to hover perpendicular to the wall where the ArUco markers were mounted. The drone's target hovering poses matched the six poses used in the fixed-camera setup. However, due to inherent hovering instabilities, the actual drone positions deviated from the target poses. Therefore, instead of absolute errors between estimation and target values, the standard deviation of estimated 3D positions and roll, pitch, and yaw angles over a 60 s hovering period were assessed.  
Figure 3: ArUco marker setup. (**a**) Single marker, (**b**) Multiple markers.
# Cooperative Communication Based Protocols for Underwater Wireless Sensors Networks: A Review
## 7 Conclusions  
The realization of an efficient UWSN requires the challenges rising from the signal propagation to be properly addressed. In this regard, the paradigm of cooperative communication and networking firstly developed for terrestrial RF systems has been recast in the context of UWAC as well. This paper discusses the main cooperative strategiesand protocols adopted in UWSNs to improve the communication reliability, effectively manage channel usage, error control and routing. Hence, our contribution provides a structured perspective that clarifies how cooperation in UWSNs can be fruitfully exploited at different layers of the protocol stack. The literature review, supported by some numerical results, highlighted the broad benefits brought by the use of a cooperative approach, which reflect in significant network performance improvements in terms of reliability, throughput and energy efficiency. The systematic approach here followed to present the state of the art highlights both the results currently achieved by scientific research and those gaps requiring further investigation. In this regard, future works will be focused on machine learning aided UWSNs, representing one of the emerging and attractive topics in the field of UWAC.  
[PERSON], [PERSON] and [PERSON] equally contributed to the paper conceptualization, formal analysis, data curation, writing, and reviewing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This work was partially supported by the European Union under the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, partnership on \"Telecommunications of the Future\" (PE0000001-program \"RESTART\").  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AF & Amplify and Forward \\ AUVs & Autonomous Underwater Vehicle \\ ARQ & Automatic Repeat Request \\ BER & Bit Error Rate \\ BPSK & Binary Phase Shift Keying \\ CDMA & Code Division Multiple Access \\ COPPER & COOperative Protocol for PERvasive \\ CSI & Channel State Information \\ DCC & Dynamic Coding Cooperation \\ DF & Decode and Forward \\ DIF & Decode Interleave Forward \\ FEC & Forward Error Correction \\ GBm-ARQ & Go-Back-m ARQ \\ HARQ & Hybrid Automatic Repeat Request \\ ISI & Intersymbol Interference \\ JSW-ARQ & Juggling Stop and Wait ARQ \\ MAC & Medium Access Control \\ MRC & Maximum Ratio Combining \\ NOMA & Non-Orthogonal Multiple Access \\ OFDM & Orthogonal Frequency Division Multiplexing \\ RF & Radiofrequency \\ ROV & Remotely Operated Vehicle \\ SIC & Successive Interference Cancellation \\ SNR & Signal-to-Noise Ratio \\ SR-ARQ & Selective Repeat ARQ \\ STCBC & Space-Time Cooperative Block Coding \\ SW-ARQ & Stop and Wait ARQ \\ TDMA & Time Division Multiple Access \\ UWAC & Underwater Acoustic Communication \\ UWSN & Underwater Wireless Sensor Network \\ \end{tabular}
## 5 Experiment and Evaluation
### Experimental Setup  
We used the open implementations of five different action recognition methods and applied them to our new dataset benchmark. Among them, Inflated 3D ConvNets (I3D) [39] and Temporal Segment Networks (TSN) [40] use RGB video for action recognition. HON4D [41] extracts features directly from depth maps without using the skeletal information, whereas 2s-AGCN, ST-GCN, and LSTM-DGCN are skeleton-based methods.
# Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN
## 4 Proposed Push-Based Data Forwarding Scheme with Fuzzy Logic
### K-Means Clustering  
The term \"clustering\" can be utilized to manage frequent communication between vehicles. In order to arrange the vehicles into groups, a K-means clustering approach is employed in this paper. K-means is an unsupervised machine learning approach that can be used for clustering. It is a technique that automatically divides vehicles into clusters or groups. Every vehicle is automatically assigned to a cluster upon receiving its Received Signal Strength Indicator (RSSI) value. However, each vehicle can only belong to one cluster. The center point of that cluster is called a centroid [29]. At first, the algorithm takes the number of clusters \"K\". Then, \"K\" points are randomly selected as centroids. To find the optimum number for \"K\", an Elbow algorithm is used.  
Based on the centroids, the vehicles are grouped together. In the next step, central points are chosen to indicate the new centroids. The vehicles then group themselves according to the new centroids, and this process continues until no change in the group occurs. Figure 3 demonstrates the clustering with the use of the K-means clustering algorithm. The K-means algorithm follows the expected maximization to address the problem. After every second, these steps are repeated, allocating the vehicles' locations to the nearest cluster. Each step calculates a new centroid for each cluster. The normalized function of \"_J\"_ is calculated by using Equation (3).  
\[J=\sum_{i=1}^{m}\sum_{k=1}^{K}\omega_{ik}\||x_{i}-\mu_{i}\||^{2} \tag{3}\]where \(\omega_{ik}=1\) for data \(x_{i}\) if it belongs to cluster \(K\); otherwise, \(\omega_{ik}=0\). Furthermore, \(\mu_{k}\) is the centroid of each \(x_{i}\) cluster. On the other hand, the normalized distances from centroids are calculated according to Equation (4).  
\[dist=\sqrt{(x_{1}-y_{1})^{2}+(x_{2}-y_{2})^{2}} \tag{4}\]  
where \(x\) and \(y\) are the coordinates of each vehicle.
# Optimization Design of the Spaceborne Connecting Structure for a Lightweight Space Camera
## 4 Engineering Analysis and Performance Comparison
### Static Analysis  
Static analysis is the performance analysis of the camera under the action of 1 g of gravity and 4 \({}^{\circ}\)C temperature rise loads. The maximum displacement of the secondary mirror relative to the primary mirror has been obtained in Section 3.2, as shown in Table 3. In addition, the displacement of  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Design Variables** & \(\mathbf{L_{1}}\) & \(\mathbf{L_{2}}\) & \(\mathbf{L_{3}}\) & \(\mathbf{L_{7}}\) & \(\mathbf{L_{9}}\) & \(\mathbf{L_{12}}\) & \(\mathbf{L_{14}}\) \\ \hline Initial values/(mm) & 5.0 & 10.0 & 5.0 & 5.0 & 5.0 & 5.0 & 5.0 \\ Optimized values/(mm) & 7.3 & 14.2 & 2.8 & 6.3 & 6.3 & 4.3 & 5.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Size parameters before and after parametric optimization.  
Figure 8: Iteration history of design variables.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Response** & \(\mathbf{R_{1}^{G}}\)/\(\mathbf{\mu m}\) & \(\mathbf{R_{1}^{T}}\)/\(\mathbf{\mu m}\) & \(\mathbf{R_{5}^{G}}\)/\(\mathbf{\
u}\) & \(\mathbf{R_{5}^{T}}\)/\(\mathbf{\
u}\) & \(\mathbf{f_{1}}\)/\(\mathbf{\mu}\)Z & \(\mathbf{M\Delta S/kg}\) \\ \hline Initial values & 9.0 & 7.3 & 6.1 & 6.0 & 130 & 2.4 \\ Optimized values & 9.8 & 9.7 & 4.3 & 6.5 & 112 & 1.1 \\ Requirement & \(\leq\)10.0 & \(\leq\)10.0 & \(\leq\)10.0 & \(\leq\)10.0 & \(\geq\)100 & Min \\ \hline \hline \end{tabular}
\end{table}
Table 3: Camera performance before and after the optimization of a spaceborne connecting structure.  
From Figure 9, the surface accuracy of the mirrors is good. After removing the rigid body displacements and defocus, the RMS values of the mirror surface error are about 1 nm. From Figure 10, the wavefront error of the system is worst under 1 g of X-direction gravity, but it still meets the space application requirements, and the mirror performance and system performance are good under the temperature rise loads. The results showed that after installing the optimized connecting structure, the static performances of the system meets the design requirements.
# Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review
## 3 Results
### Data Transmission Networks  
Instrumental networks for seismic and volcanic monitoring have different sensor types (e.g., seismic sensors, accelerometers, inclinometers, high-precision GPS, monitoring cameras, pressure sensors, etc.) and components (e.g., data acquisition systems, data loggers, transmission media, energy sources, physical infrastructure, etc.). They bring seismic information to seismological data centers to monitor seismic and volcanic events in real time. The following articles are related to the research questions proposed in this study and deal with seismic data transmission and seismic networks. The information is compiled in Table 4 and presents a summary of the most important findings. Regarding latency reduction in communication networks for earthquake early warning (EEW), the authors [23; 24] identified that some data could be improved by deploying software upgrades and setting the data-logger parameters. In addition, they succeeded in recording latency data per packet in a MSEED (SEED, Standard for the Exchange of Earthquake Data) (MSEED contains raw waveform data, but does not include metadata) format, as traditionally performed with seismic waveform data. This has allowed them to identify the performance of telemetry links in real time. In future work, they suggest developing data-logger algorithms, which reduce data latency by transmitting final solutions or light seismic parameters instead of continuous data that respond to data transmission limitations
# Performance Analysis for COVID-19 Diagnosis Using Custom and State-of-the-Art Deep Learning Models
###### Abstract  
The modern scientific world continuously endeavors to battle and devise solutions for newly arising pandemics. One such pandemic which has turned the world's accustomed routine upside down is COVID-19: it has devastated the world economy and destroyed around 45 million lives, globally. Governments and scientists have been on the front line, striving towards the diagnosis and engineering of a vaccination for the said virus. COVID-19 can be diagnosed using artificial intelligence more accurately than traditional methods using chest X-rays. This research involves an evaluation of the performance of deep learning models for COVID-19 diagnosis using chest X-ray images from a dataset containing the largest number of COVID-19 images ever used in the literature, according to the best of the authors' knowledge. The size of the utilized dataset is about 4.25 times the maximum COVID-19 chest X-ray image dataset used in the explored literature. Further, a CNN model was developed, named the Custom-Model in this study, for evaluation against, and comparison to, the state-of-the-art deep learning models. The intention was not to develop a new high-performing deep learning model, but rather to evaluate the performance of deep learning models on a larger COVID-19 chest X-ray image dataset. Moreover, Xception- and MobilNetV2- based models were also used for evaluation purposes. The criteria for evaluation were based on accuracy, precision, recall, F1 score, ROC curves, AUC, confusion matrix, and macro and weighted averages. Among the deployed models, Xception was the top performer in terms of precision and accuracy, while the MobileNetV2-based model could detect slightly more COVID-19 cases than Xception, and showed slightly fewer false negatives, while giving far more false positives than the other models. Also, the custom CNN model exceeds the MobileNetV2 model in terms of precision. The best accuracy, precision, recall, and F1 score out of these three models were 94.2%, 99%, 95%, and 97%, respectively, as shown by the Xception model. Finally, it was found that the overall accuracy in the current evaluation was curtailed by approximately 2% compared with the average accuracy of previous work on multi-class classification, while a very high precision value was observed, which is of high scientific value.  
artificial Intelligence; chest X-ray; COVID-19; deep learning convolutional neural networks; lung opacity; mobilenetv2; transfer learning; Xception; pneumonia +
Footnote †: journal: Medical Imaging  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
## 5 Results and Discussions
### Temperature Susceptibility of THFS Modified Asphalt Binder
#### 5.5.4 Frequency Susceptibility of the THFS Modified Asphalt Binder  
On the basis of time-temperature equivalence principle and through curve fitting using Sigmoidal function, the \(G^{*}\) and \(\delta\) master curves of asphalt binders with respect to frequencies ranging from 0.01 Hz to 50 Hz were regressed. The \(G^{*}\) and \(\delta\) master curves are plotted in Figure 8.  
There was a similar increase in \(G^{*}\) value and decrease in \(\delta\) value for asphalt binders across frequencies ranging from 0.01 Hz to 50 Hz. This could be because the pavement response and deformation were instantaneous and small caused by a higher frequency, which indicates a shorter loading time. The increase in \(G^{*}\) values and decrease in \(\delta\) values resulted in an improved frequency and exhibited elastic behavior during shorter loading times because asphalt binder would recover from some deformations due to elastic stored energy. Under identical frequencies, the \(G^{*}\) values of THFS modified asphalt binders were higher than that of SK-90, but the \(\delta\) values were lower than that of SK-90, which indicated that at a constant frequency, the addition of THFS could improve the stiffness and elastic characteristics as well as the potential resistance to permanent deformation.  
Moreover, for THFS modified asphalt binder, there was a slight increment in \(G^{*}\) values and a minor reduction in \(\delta\) values as THFS content increased at the same frequency. This can be attributed to the interaction of THFS and asphalt binder that form more and more large molecules and limit the movement of molecular chains of asphalt binder. The increase in \(G^{*}\) values and decrease in \(\delta\) values were statistically significant at the 5% significance level, which indicated that THFS content had a significant effect on the frequency susceptibility of THFS modified asphalt binder.
# Evaluation of Ride Comfort under Vortex-Induced Vibration of Long-Span Bridge
## 4 Result and Discussion
### Drivers' Ride Comfort Evaluation
#### 4.2.2 MSI  
Also taking a sedan car as an example, assuming that the vortex vibration frequency is 0.328 Hz and the amplitude is 0.3 m, the MSI values under different road grades and vehicle speeds are calculated, as shown in Figure 10.  
The Motion Sickness Index (MSI), expressed as a percentage, quantifies the morbidity of motion sickness in a given population. As depicted in the figure, the MSI decreases with higher vehicle speeds. This correlation arises from the inclusion of vibration duration in the MSI calculation, specifically, in relation to vehicle speed. Slower vehicle speeds result in an extended vibration duration, leading to higher MSI values and increased motion sickness incidence. Additionally, the MSI rises with heightened road roughness. In contrast to vortex vibration excitation, the MSI value under road excitation is negligible. Furthermore, when assessing MSI values under identical road surface conditions, with and without considering vortex-induced vibration (VIV), a significant disparity between these two values becomes apparent. This highlights that, in contrast to the OVTV, the MSI value is a more suitable metric for evaluating driving comfort in the presence of vortex-induced vibration.  
As previously mentioned, the conditions in Figure 11 were configured with a vehicle speed of 40 km/h on a Class A road surface to investigate the MSI for four vehicle types. In the context of vortex vibration, there exists a relationship between the MSI value and vibration frequency and amplitude. Notably, the MSI value rises with increasing amplitude due to the amplified acceleration response associated with greater amplitudes. Furthermore, the MSI value exhibits an initial increase followed by a subsequent decrease as the frequency increases. This behavior can be attributed to the initial increase and subsequent decrease in the frequency modulation coefficient corresponding to the frequency weighting function \(W_{f}\), occurring within the range of 0-0.5 Hz. Specifically concerning the MSI value, it reaches its peak when the bridge vortex vibration frequency falls within the range of 0.3-0.5 Hz. This indicates a relatively higher incidence of motion sickness among individuals exposed to high-order vortex vibration.  
Figure 10: Relationship between MSI and road grade and vehicle speed. (**a**) The value of MSI calculated without VIV (**b**) The value of MSI calculated with VIV.
# Strategies for Deploying a Sensor Network to Explore Planetary Lava Tubes
## 2 Mission Architecture
### Preliminaries  
To understand the feasibility of our proposed concept of using multiple SphereX robots assisted by a rover/lander, we first developed a 3D model of a prospective cave/lava tube with a vertical entrance using Blender software, as shown in Figure 5a. These models have been developed utilizing terrestrial lava tubes and have been scaled up in size based on pit photos to the expected size on the Moon. Although lava tubes differ in size and cross-section, the low gravity of the Moon and Mars is hypothesized to have created larger lava tube cross-sections. These scaled-up lava tube models will then be used for testing our mapping, navigation, and sensor placement algorithms. For sensor placement, multiple sensors (SphereX robots) will be placed along the cave such that they can maintain a direct-line-of-sight connection link, as shown in Figure 5b. This link will then be used for communication and power transfer within the robots.  
Figure 4: An example of a Lunar lander carrying multiple SphereX robots for pit exploration.