esa-sceva/satcom-chunk-collection huggingface dataset:
# A Performance Evaluation Approach for Satellite Attitude Control System in Tracking Mode
## 2 Problem Description
### Challenges in System Performance Evaluation under Tracking Mode  
In order to achieve a reasonable performance evaluation of the satellite attitude control system under the influence of multi-source uncertainties, the research faces the following difficulties:  
(1) Dynamic environment influence: The satellite attitude control system typically operates in a dynamic environment under tracking mode, where factors such as environmental noise, atmospheric resistance, magnetic interference, and solar radiation may influence data, leading to fluctuations that cannot accurately reflect the tracking control performance. Addressing the formidable challenge at hand involves devising methodologies to mitigate the impact of uncertainties, extracting operational status information from attitude control systems, and thereby achieving precise assessments of system efficiency.  
(2) Multi-indicator assessment and trade-off: The performance evaluation under the tracking mode of the satellite attitude control system often involves multiple indicators,  
Figure 1: Schematic diagram of satellite operation in orbit in sun-pointing mode.  
such as pointing accuracy, control energy consumption, and control response speed. However, the baselines of these indicators are not the same, and the coupling between the indicators is strong. How to establish a reasonable evaluation model for multiple performance indicators is a very challenging problem.  
To solve the above problems, firstly, this paper measures the uncertainty in the calculated indicators by evaluating the grey number standardization method; secondly, in this paper, the principal component analysis method is improved under the operation rules of grey number, so that each index can be objectively and reasonably weighted according to its own characteristics.
# GNSS-Based Vertically Monitoring of Super-Tall Buildings
## 2 Calculation of the Monitoring Point's Coordinate Time Series  
In order to estimate the motion of a super-tall building, it is necessary to calculate the monitoring points' coordinates time series. For this purpose, the coordinate time series of monitoring points are obtained by adjusting the GNSS monitoring network composed of three reference stations and one monitoring station at each epoch. According to the coordinate time series of the monitoring points, the authors can analyze the horizontal motion of a super-tall building and its effect on vertical reference transmission. The flowchart diagram of the GNSS processing strategy is shown in Figure 2. The GNSS data is post-processed in a kinematic mode to obtain the epoch wise baseline vectors and covariance matrix between the monitoring station and three reference stations. All these baselines form a GNSS monitoring network, including the baseline vectors and covariance matrix between the three reference stations solved in a static mode. Then, eliminating the gross errors of baseline vectors according to the misclosures and fixing the coordinates of the reference points obtained from the construction control network, the coordinate time series of each monitoring point are obtained by a constrained GNSS network adjustment. Finally, the precise coordinates of a monitoring point are obtained by the Daubechies wavelet filter, which allows us to analyse the horizontal motion of a super-tall building.
## 6 Results and Discussion
### MIMO Parameters  
In the MIMO antenna design, the diversity parameters play a vital role in determining the MIMO performance. Among these parameters, the Diversity Gain (DG), the Envelop Correlation coefficient (ECC), and the mean effective gain (MEG) are important. The ECC is used to determine the extent to which the MIMO antenna elements are isolated. Ideally, the value of ECC should be near to zero, but, in practical applications, a value less than 0.05 is considered acceptable. The value of the ECC for the MIMO antenna can be calculated based on the S-parameters using the following formula [37]:  
\[ECC=\frac{|Sj\ast Sji+Sij\ast Sii|^{2}}{\left(1-|Sij|^{2}-|Sji|^{2}\right)\left( 1-|Sij|^{2}-|Sii|^{2}\right)}\]  
where \(S_{ii}\) & \(S_{jj}\) are reflection coefficients and \(S_{ji}\) and \(S_{jj}\) are the transmission coefficients. ECC can also be calculated from 3D radiation patterns, which can be measured in an anechoic chamber using the equation below [38].  
\[|\rho|=\frac{\left|\oint E_{i}\cdot E_{j}^{*}\ d\Omega\right|^{2}}{\left|\oint E _{i}\cdot E_{i}^{*}\ d\Omega\times\oint E_{j}\cdot E_{j}^{*}\ d\Omega\right|^{ \prime}}\]  
where E is the far-field radiation pattern (E field), which is received by the measurement probe, and \(\Omega=\sin\theta\ d\theta d\beta\) is the beam solid angle.  
We can see from Figure 13a that the measured ECC value is less than 0.002 in the desired frequency band (24.25-29.5 GHz).  
The Diversity Gain (DG) is calculated using the formula mentioned in [39], which is stated below.  
\[DG=10\sqrt{\left(1-ECC^{2}\right)}\]  
As shown in Figure 13b, the measured value attained for DG is greater than 9.99 dB.  
The total active reflection coefficient (TARC) is another important MIMO parameter used to qualify the extent of mutual coupling between the ports. For the MIMO antenna array, TARC indicates the ratio of the reflected power with the incident power.  
Figure 12: Radiation characteristics. (**a**) Antenna-1. (**b**) Antenna-2. (**c**) Antenna-3. (**d**) Antenna-4.  
What follows is the equation for the calculation of the TARC:  
\[TARC=\frac{\sqrt{\left(\left|Sii+Sij\right|^{2}\right)+\left(\left|Sji+Sjj\right|^ {2}\right)}}{\sqrt{2}}\]  
The Channel Capacity Loss (CCL) is yet another key MIMO performance indicator. Calculation of the CCL is performed by using the equation below.  
\[CCL=-log_{2}(\Psi^{\mathcal{R}})\]  
where \(\Psi^{\mathcal{R}}\) is the receiver antenna's correlation matrix, which is given below.  
\[\Psi^{\mathcal{R}}=\frac{\Psi_{ii}}{\Psi_{ii}}\quad\Psi_{ij}\]  
\[\Psi_{ii}=1-\left(\left|S_{ii}\right|^{2}+\left|S_{ij}\right|^{2}\right),\ \Psi_{ij}=-\left(S_{ii}*S_{ij}+S_{ji}*S_{ij}\right),\]  
\[\Psi_{ji}=-\left(S_{ji}*S_{ji}+S_{ij}*S_{ii}\right),\ \Psi_{jj}=1-\left(\left|S_{ ij}\right|^{2}+\left|S_{ji}\right|^{2}\right)\]  
The value of the CCL should be lower than 0.4 bits/s/Hz.  
Figure 14a,b show the simulated and measured values of the TARC and the CCL, respectively, which are well within the thresholds.
## 2 Earthquake and Study Area  
On 14 April 2016, an earthquake with a magnitude of 6.2 Mw hit the Kumamoto Prefecture, Japan. The epicenter of the foreshock and main shock was located at the Hinagu and Futugawa fault, respectively. The epicenter of foreshock was reported 12.0 km beneath Mount Kinpo that is located in the northwest of Kumamoto city center. While this was thought to be the main shock, on 16 April another earthquake with a magnitude of 7.0 Mw struck the region. The epicenter of this event was located under Higashi Ward of Kumamoto in the Kyushu Region in southwest Japan. The first shock was known as foreshock and the second as the main shock. However, most of the damage was in the eastern suburbs of Kumamoto in Mashiki. Because of this earthquake, 55 people died and more than 3000 people injured and many people became homeless. The collapse of about 8700 structures and severe damage to more than 35,000 buildings and numerous landslides across the mountains of Kyushu were other effect of this earthquake. Numerous studies have been conducted to assess the damage caused by this event, some of which were mentioned above. In this study, the combination of SAR and LIDAR data has been used for this purpose [5; 8; 12; 18]. The location of pre- and post-event LIDAR data and the study area used in this research are illustrated in Figures 1 and 2. In addition, registration date and other details of SAR and LIDAR images are shown in Table 1.  
Figure 1: The location of pre- and post-event light detection and ranging (LIDAR) data and the study area [19].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**SAR**} \\ \hline
**Type** & **Date** & **Polarization** &
\begin{tabular}{c} **Spatial** \\ **Resolution** \\ \end{tabular} & **Path Direction** & **Mode** \\ \hline Sentinel-1 & 3 March 2016 & VV & 20 m & D & Interferometric Wide \\ \hline Sentinel-1 & 20 April 2016 & VV & 20 m & D & Interferometric Wide \\ \hline Sentinel-1 & 27 June 2016 & VV & 20 m & D & Interferometric Wide \\ \hline \multicolumn{4}{c}{**LIDAR**} \\ \hline
**Collection** & **Date** & **Data Provider** & **Area** & **Point Density** \\ \hline Airborne LIDAR & 15 April 2016 & Asia Air Survey Co., Ltd. & 151.56 km\({}^{2}\) & 2.94 pts/m\({}^{2}\) \\ \hline Airborne LIDAR & 23 April 2016 & Asia Air Survey Co., Ltd. & 95.85 km\({}^{2}\) & 4.47 pts/m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Registration date and other details of synthetic aperture radar (SAR) and LIDAR data.  
Figure 2: Location of the pre-event (**a**) and post-event (**b**) DSMs and study area. DSMs acquired by Asia Air Survey Co., Ltd. (Tokyo, Japan) [20; 21].
# Flying Real-Time Network to Coordinate Disaster Relief Activities in Urban Areas +
## 6 Discussion  
The network system proposed in this work is flexible and general enough to be applied to support SAR activities in several natural disasters, such as landslides, floods, and earthquakes. The use of UAVs to support opportunistic communication opens the possibility of dynamically deploying a Flying Real-Time Network able to cover large scouting areas. The deployment of these FRTNs will depend on the characteristics of the disaster, the geographic features of the area, and the availability of UAVs to establish the most adequate FWU-based network design. The FWUs are specialized according to the role they play in the network (i.e., TWU/meta-TWU and mule). In order to address this deployment, we consider the following principles for the FWUs' behavior:  
* Each unit is independent and works on its own area.
* Each unit is independent, but it can work on overlapping areas (to decrease transmission time).
* Each unit can work on overlapping areas and it can also interact with an FWU in its neighborhood.
* Each unit works collaboratively and it is part of a large FRTN.  
Several trade-offs should be considered depending on the operational areas of each FWU. The problem of determining the design of the best route to deploy and position the FWUs is an open problem. One critical issue for addressing this problem is considering the variability of the data transmission rate in wireless communication. This rate is much higher when the FWUs are closer to each other (up to 54 Mbit/s in 802.11 g), and much lower when the FWUs are far away (even lower than 1 Mbit/s in 802.11g) [29]. for instance, an FWU with a buffer of tens of megabytes, flying at a speed of few tens of meters per second, could deliver messages to a network bandwidth over 1 Mbps, whereas a direct wireless link within a distance of few kilometers could deliver messages at a much  
Figure 11: Demand bound function (_dbf_, in black) and \(t\) bound (in red) in the TWU and the mules. (**a**) TWU with 10 messages; (**b**) Mule involving 30 messages; (**c**) TWU with 30 messages; (**d**) Mule involving 90 messages.  
slower rate (few tens of Kbps) [30]. However, direct links can deliver a single message with low latency, while an FWU may require a few minutes. The understanding of these trade-offs is key to taking advantage of the FWU network deployment [31].  
We note that the FWUs should be deployed in such a way that the specific areas are covered with a satisfactory periodicity, and thus to effectively transfer the information from the SAR teams toward the IC. Real-time delivery of the messages can be best accomplished in cases where FWUs can work upon controlled mobility and predefined trajectories, or simply using as many meta-TWUs as required. The best alternative will depend on the availability of flying devices and the energy consumption of each solution.
## 2 Two-Stage Time-Domain Equalization Method
### Data Augmentation  
To enhance the generalization and noise robustness of the model, we perform data augmentation by introducing low signal-to-noise ratio (SNR) training samples. Specifically, the received signal is passed through a simulated additive white Gaussian noise (AWGN) channel to generate a noisy version with an SNR between \(-5\) dB and \(0\) dB. These data are then passed through a tanh function to further impose nonlinear effects. The augmented samples are concatenated with the original (high SNR) training data and randomly shuffled to ensure diversity during mini-batch construction. This strategy enables the trained model to better generalize to unknown channel conditions and remain robust under varying degrees of interference.
# Assessing Cyber Challenges of Maritime Navigation
## 5 Conclusions  
To summarize, the importance of cybersecurity has been recognized, and there will be a series of new cybersecurity openings in the future through which hackers can attack if systems are not adequately protected. It is expected that cybersecurity challenges will expand as autonomous ships will form a prominent piece of the future maritime landscape, underscoring the growing reliance on interconnected information systems [74]. New, unexpected circumstances may lead to increased cyber risks, as the year 2020 is now seeing with COVID-19, with reports of 400 percent increase in attempted hacks since February 2020 [144]. Moreover, the global security landscape that gives rise to hybrid threats is not expected to improve soon. The maritime industry has shown that it is neither immune to cyberattacks nor completely prepared to combat the risks involved in using some obsolete or modern digital systems. Maintaining seaworthiness given the impact of digital technologies requires robust cybersecurity policy/strategies, cyber-secure maritime technology, a shift in mind-set, and new insurance offers that specifically cover maritime cyber risks. A modest beginning is to have the right people on board and have established trust between maritime stakeholders. A company under attack must have the confidence to share the information with others in the sector, allowing them to bolster their defenses. GNSS signals are indispensable to safe and efficient navigation and are an integral component of maritime operations. To interfere with them jeopardizes maritime safety and security at sea. This paper demonstrates the GNSS vulnerabilities that impact many maritime systems. The use of multiple GNSS constellations (public and private), intelligent processing on the receiver side, and encrypted signals will in the near future provide increased robust defense against jamming and spoofing. The paper also recommends that the maritime community implement an eLoran system as a terrestrial augmentation to space-based GNSS capabilities. Terrestrial signals could be coded and authenticated, further increasing security and, subsequently, safeguarding GNSS satellites by making them less attractive targets. Locally, the positioning systems can be complemented by the harbor's laser-based aid system for berthing and docking, so that the regional shipping industry can be better protected from cyberattacks for the foreseeable future. As presented, building resilience against cybercriminals is a never-ending battle. Conceptualization, A.A. and I.P. ; methodology, A.A. and T.B. ; data collection, A.A., T.B., and I.P. ; EU aspects, H.G. ; validation, A.A., T.B., and I.P. ; formal analysis, A.A. and I.P. ; data curation, A.A., T.B., and I.P. ; writing--original draft preparation, A.A.; internal review, H.G. All authors have read and agreed to the published version of the manuscript. The authors acknowledge the financial support of the Slovenian Research Agency (research core funding No. P2-0394, Modelling and Simulations in Traffic and Maritime Engineering). The authors declare no conflict of interest.
## 4 The SensoTube Architecture's Implementation Reference Model
### Expandability and Inter-Layer Services Mechanisms
#### 4.2.2 Inter-Layer Communication Service Mechanism  
Inter-layer communication includes data and commands transfers among the MCUs of the shields, and among MCUs and various integrated circuits, such as digital sensors, memory chips, analog-to-digital conversion chips, integrated RF modules, etc. In order to support such needs, three types of serial data communication means have been incorporated, namely the I2C-bus, the SPI-bus, and the UART port [143]. The physical access to them can be accomplished through specific connection pins at the S-BECs of the system. From the three, only the I2C-bus can be used for multi-processor communications, because it is a data bus, which can support up to thirty two devices in either a single or multi-master topology [144]. By the use of I2C-bus extenders, the number of supported devices can significantly increased [145]. In addition, the I2C-bus has been proven to be very successful in various applications, as for machine-to-machine interconnection [146]. An additional option for multi-processor communication is the adoption of the CAN-bus, a well-known automotive communication standard [147], which appears to be present in many of the ARM-based MCUs as an integrated peripheral. In this case, some of the general-purpose input/output pins should be reserved for the CAN-bus signals. In the proposed communication signals positions of the S-BEC 1 there are three pairs of UARTs and two chip select signals for SPI in order to avoid resources limitations inherent to the most of the existing expandable platforms. In addition, the SensoTube with the polymorphism in signals connections can ensure unlimited communication resources and, at the same time, it insures the maximum flexibility and openness.
# A Review of the Challenges in Deep Learning for Skeletal and Smooth Muscle Ultrasound Images
## 3 Results and Discussion
### Classification Architecture in Skeletal Muscle  
CNNs and deep CNNs have been commonly used as base architectures for the classification of muscle ultrasound images [20]. Specifically, VGG-16 and AlexNet are variations of a CNN which are used to classify muscle gender and bicep vibration, while VGG-F is a variation of a deep CNN which is used to classify calf muscle types. The difference between the two lies in the number of layers. The deep CNN uses five convolutional layers followed by two fully connected layers and is useful for classifying many labels. For example, [PERSON] et al. (2018) classified eight classes. Meanwhile, CNN-based AlexNet and VGG-16 were used to classify two and three classes, respectively.  
VGG-16 was used for gender classification in the anterior tibialis area. The VGG-16 accuracy was 95.2% compared to 83.30% for AlexNet and 84.40% for GoogleNet in previous trial studies [31]; however, the training data weakness arises from the fact that the full-frame ultrasound images caused inaccuracy, namely 100% for males and 49.30% for females, thus requiring cropping strategies to perform more detailed classification and eliminate the lower part and right side. The same problem occurred in classification with VGG-F when classifying muscle types in the transverse and longitudinal planes in the musculoskeletal area [30]. A cropping strategy was implemented by excluding the fasciae and muscle tendons, where only the muscle mass in the fiber area was classified. VGG-F outperformed VGG-16 and VGG-19 for muscle type classification with an accuracy of 89.44%. A cropping strategy can help a classification process to gain more leverage for specific area identification [62; 63].  
In contrast, with deep learning classification of bicep vibration, the cropping strategy was not used because it requires an entire image to identify the difference in muscle thickness on the bicep vibration. AlexNet provided 82.50% of the results and is comparable with VGG-16 and VGG-19 for the test results with the same number of images [32].
# Study on the Mechanical Criterion of Ice Lens Formation Based on Pore Size Distribution
## 5 Conclusions  
Based on the microscopic crystallization theory, a theoretical model is developed for mechanical criterion of formation of ice lens considering the initial water content, temperature, pore radius and pore size distribution. A new mechanical criterion of formation of ice lens was proposed based on the existing criterion. Through analysis, the following conclusions can be drawn:  
(1) The mean stress on the pore matrix transformed by crystallization pressure on the pore wall is the driving force for the formation of ice lens. In addition, in the absence of external load, the tensile stress is the full internal constraining force that exerts significant control over the formation of ice lens.  
(2) Within the temperature range of ice lens formation (0 to \(-2\)\({}^{\circ}\)C), the calculated mean stress is greater than the tensile stress, which indicates that the ice lens will be formed under this condition. In this way, the microscopic mechanism of formation of ice lens is connected with the macroscopic mechanics. This illustrates that the microscopic crystallization theory can be used to explain the mechanical mechanism of the ice lens formation.  
(3) Based on the experimental data from the test of unfrozen water content and microscopic crystallization theory, the pore size distribution curves of soil are obtained. Furthermore, a new method for calculating pore size distribution based on the relationship between the ice crystals and temperature is obtained. Through controlling of temperature and initial water content, a larger and wider pore size distribution can be obtained.  
Furthermore, the mechanism of crystallization in porous materials (such as rock, cement, geopolymer, etc.) has received attention in cold region engineering. Due to the intrinsic heterogeneity and complexity of the material in terms of water content, pore geometries, connective channels between  
Figure 15: Variation of tensile stress with temperature under different initial water content.  
pores, solid grain properties, et cetera., the extensive applicability of the crystallization theory still needs to be further extended and studied.  
Conceptualization, Y.L. and F.M.; methodology, Y.L. and D.L.; validation, Y.L., L.C. and F.M.; formal analysis, Y.L. and F.M.; investigation, Y.L. and F.M.; resources, Y.L. and L.C.; data curation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L., D.L., L.C. and F.M.; supervision, D.L. and F.M.; funding acquisition, F.M. and D.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This research was supported by the National Natural Science Foundation of China (No. 41701060 and No. 41271080) and the program of the State Key Laboratory of Frozen Soil Engineering (Grant No. SKLFSE-ZT-17).  
The authors declare no conflict of interest.