esa-sceva/satcom-chunk-collection huggingface dataset:
# Multifunction RF Systems for Naval Platforms
## 3 MFRF System Design Considerations
### Practical Limitations and Trade-Offs  
The ideal MFRF system architecture described above is not currently achievable in practice, due to costs or performance limitations in existing hardware technology that force design trade-offs and compromises to be made. The trade-offs broadly fall into three categories: (1) combined vs. separate Tx/Rx arrays [21]; (2) wideband vs multiband operation [22]; and (3) element-level vs. subarray-level digitization/waveform generation. It is expected that all three of these trade-offs would factor into a system design. These trade-offs and their implications, which are further explored in the subsequent sections, generally result in a larger number of AESAs and less flexibility in array utilization than would be the case for the ideal architecture.  
Some comments on costs are also included in the following discussion. However, meaningful estimates and comparisons of system-level costs cannot be provided without detailed system designs, which are beyond the scope of this study.  
There is no discussion in this section of potential constraints in system design and performance that may be imposed by signal/data processing resources. This type of technology continues to advance rapidly, driven by requirements in diverse fields such as artificial intelligence, cloud computing and gaming. For example, Graphics Processing Units (GPUs), which have been developed and used extensively for all three of these applications, are also well-suited for use in massively parallel architectures that exhibit the large data throughput, high computational performance, low latency and easy scalability required by AESA-based radar signal processing algorithms [23]. Given its continued fast pace of development, computing technology is consequently viewed as a much less significant limiting factor for MFRF system performance than the other hardware issues that will be addressed below.
# Q-Learning Approach Applied to Network Security
## 4 Results
### Q-Learning Implementation and Training Strategy  
The decision agent finds itself in a particular state, selects an action, receives feedback from the environment, receives an immediate reward, and transitions to the next state. This interaction with the environment occurs over several episodes. The algorithm maintains a Q-value table to record the discounted reward expectations, and after each immediate reward that the agent receives, it updates the Q-values using the Bellman Equation (3). Firstly, a Q-table, which stores the expected reward for each state-action pair, is initialised with zeros. It is initialised with a 4 \(\times\) 5 matrix due to the number of actions (4) and the number of states (5). The action-selection process is an essential part of the Q-learning algorithm because it involves a constant trade-off between exploration and exploitation. The epsilon-greedy policy provides a balanced approach to this trade-off, which is why we select it as an action selection process. At each time step, a random number is selected and compared against the epsilon \(\epsilon\). The agent chooses a random action if the number is less than \(\epsilon\). Then, he explores the environment and discovers new state-action pairs that could lead to higher rewards. Otherwise, the agent exploits the current knowledge and chooses the action with the highest Q-value for the current state, as determined by \(\arg\max_{\alpha}Q(s,a)\). As a summary, when a random action is selected with a probability \(\epsilon\), then the agent is in the exploration phase, and when it selects the best known action with a probability \((1-\epsilon)\), then it is in the phase of exploitation. In network security, we would like our agent to find a good balance between exploration and exploitation. We would also like to ensure that it explores the environment with a high probability so it can familiarize itself with new and unseen attacks and patterns.  
After taking an action, the agent receives a reward and transitions to a new state. The Q-value for the current state-action pair is then updated using the Bellman equation, which incorporates the observed reward and the maximum expected future reward from the next state.  
This process is repeated across 50 steps over 300 episodes, with an average window size of 50 iterations, allowing the agent to refine its policy. Over time, the agent reduces exploration using the epsilon decay and focuses on exploiting the best policy it has learned. The Q-table stabilizes as the model converges to an optimal strategy.
## 3 Discussion
The cationic nature of the PEI most probably destabilizes the biofilm components such as the eDNA, proteins and extracellular polymeric matrix via electrostatic interaction [52]. Therefore, PEI could help the deeper penetration of the CHL in order to exert its action against the biofilm life forms. It is well known that the persister cells are less susceptible to antimicrobial agents than the growing cells and their predominant role in treatment failure [53; 54]. Interestingly, our study revealed that the persister cells are more vulnerable to CHL and PEI combinations than the growing cells. CHL/PEI 31.25/100 ug mL\({}^{-1}\) eradicated the persister cells by a 1.7 \(\pm\) 0.12 log\({}_{10}\) reduction after 15 min of exposure to red LED, whereas the same condition eradicated the growing cells by a 0.4 \(\pm\) 0.1 log\({}_{10}\) reduction. This could be because of the reduced membrane potential of the persister cells [55] and the presence of the cationic polymer PEI, which facilitates the phototoxicity of CHL besides the ROS that may not require active target sites to exert their effects. In the study by [PERSON] and colleagues [55], minocycline antibiotic was more effective against _E. coli_ persister cells than growing cells. The authors proved that the reduced membrane potential and drug efflux of the persister cells facilitated the penetration and accumulation of the antibiotic inside the cells and killed the cells while reverting to metabolically active cells. Based on the above mentioned, the results emphasized the synergism between CHL and PEI and the possibility of eradicating persister cells via aPDI. However, more research is required to determine the mechanism behind this, which is a part of our ongoing research for better insights into the potential applications. Chlorophyllin could be a promising candidate in various applications, for instance, wound healing, food preservation and water purification. However, to fully understand its benefits, limitations and safety in each specific context, further research and rigorous testing, including the optimization of the light doses, are necessary.
## 2 Copula-Based Bayesian Reliability Models
### Disaggregate and Aggregate Analyses
The resulting joint posterior distribution will then be propagated to get the system posterior distribution (for the system failure rate \(\Lambda\))  
\[g_{\Lambda|k_{1},k_{2}}(\lambda|k_{1},k_{2})=\int_{\lambda}^{\infty}g_{\Lambda _{1},P_{2}|k_{1},k_{2}}\Big{(}\lambda_{1},\frac{\lambda}{\lambda_{1}}\Big{|}k_ {1},k_{2}\Big{)}\frac{1}{\lambda_{1}}d\lambda_{1} \tag{4}\]  
The posterior mean obtained by using DA approach is thus  
\[\mathbb{E}(\lambda|k_{1},k_{2})=\int_{0}^{\infty}\lambda\Big{[}\int_{\lambda }^{\infty}g_{\Lambda_{1},P_{2}|k_{1},k_{2}}\Big{(}\lambda_{1},\frac{\lambda}{ \lambda_{1}}\Big{|}k_{1},k_{2}\Big{)}\frac{1}{\lambda_{1}}d\lambda_{1}\Big{]} d\lambda. \tag{5}\]  
In the AA approach, copula-based joint prior probability function needs to be propagated first to get the system prior distribution \(g_{\Lambda}(\lambda)\), which can be obtained by using  
\[g_{\Lambda}(\lambda)=\int_{\lambda}^{\infty}g_{\Lambda_{1},P_{2}}\Big{(} \lambda_{1},\frac{\lambda}{\lambda_{1}}\Big{)}\frac{1}{\lambda_{1}}d\lambda_ {1} \tag{6}\]  
The system prior distribution then can be updated with aggregate data to get the system posterior distribution using Bayesian rule  
\[g_{\Lambda|k_{2}}(\lambda|k_{2})\propto g_{\Lambda}(\lambda)f(k_{2}|\lambda), \tag{7}\]  
where \(f(k_{2}|\lambda)\) is the likelihood of \(\lambda\) given by observing \(k_{2}\) system failures in time \(t\), which is formulated using a Poisson process. The resulting posterior mean for the AA approach is thus  
\[\mathbb{E}(\lambda|k_{2})=\int_{0}^{\infty}\lambda\cdot g_{\Lambda|k_{2}}( \lambda|k_{2})d\lambda \tag{8}\]  
For the simplified disaggregate analysis (DAI) with independent component assumption, if the prior distribution for the failure rate and failure probability of Components 1 and 2 are gamma and beta distributed with parameters \((a,b)\) and \((c,d)\) (which are natural conjugate distributions for Poisson and binomial sampling processes, respectively), then the posterior distributions are still gamma and beta distributed with parameters \((a+k_{1},b+t)\) and \((c+k_{2},c+d+k_{1})\). The posterior mean for the DAI analysis is thus simply  
\[\mathbb{E}(\lambda|k_{1},k_{2})=\frac{a+k_{1}}{b+t}\times\frac{c+k_{2}}{c+d+k _{1}} \tag{9}\]  
According to [8], the necessary and sufficient conditions to have perfect aggregation for the product \(\Lambda=\Lambda_{1}P_{2}\) is that \(a=c+d\). In that case, it can be interpreted as the pseudo number of trials of Component 2 must be equal to the pseudo number of failures of Component 1.
## 2 FSO Communication System
### Receiver Side
#### 2.3.3 Self-Coherent FSO Systems
Additionally, a significant challenge in the KK implementation is the necessity for a mandatory carrier tone at the edge of the information spectrum to mitigate the effects of SSBN [27]. This carrier tone can be introduced in the optical domain, RF domain, or digital domain. Both optical and RF methods, however, increase the hardware requirements and complexity. Thus, inserting the virtual carrier in the digital domain is a simpler alternative that avoids these additional resource demands. However, this approach increases the signal's dynamic range, which leads to a quantization noise penalty owing to the finite resolution of the Digital-to-Analog Converter (DAC) [26; 27; 43]. A digital resolution enhancer (DRE) method has been introduced to mitigate the impacts of quantization noise in low-resolution DACs. Unlike traditional rounding-off quantizers, the DRE employs a dynamic quantization method that efficiently minimizes in-band quantization noise and enhances transmission performance. Building on this DRE technique, a novel self-coherent FSO scheme has been proposed, further simplifying the KK-FSO system. In this new system, the optical carrier is substituted with a virtual carrier, which is directly incorporated into the signal within the digital domain. This approach eliminates the need for additional hardware to generate and synchronize the optical carrier. Additionally, the DRE method allows the use of extremely low-resolution DACs with just three physical numbers of bits, leading to significant reductions in both power consumption and cost. On the Rx side, the KK transform is replaced with the DC-V method, enhancing power efficiency by lowering the requirements for oversampling and CSPR [33]. Keydistinctions among non-coherent (IM/DD), coherent, and self-coherent FSO systems are presented in Table 1. Also, a thorough analysis of optical transceivers is available in [27].
# Real-time Precise Point Positioning with a Xiaomi MI 8 Android Smartphone
## 2 Experimental Setup  
The main device used in this experiment is a Xiaomi MI 8 smartphone. For comparison analysis, a Huawei Honor 9 smartphone, a Huawei P10 smartphone, and two geodetic GNSS receivers (Hi-Target iRTK2) were also used. The Xiaomi 8 mobile phone is a dual-frequency GNSS smartphone that collects the first frequency band (L1/E1) signals of GPS (Global Positioning System), Galileo (Galileo satellite navigation system), BDS (BeiDou Navigation Satellite System), and GLONASS (GLObal NAvigation Satellite System), and the second frequency band (L5/E5a) signals of GPS and Galileo. Huawei P10 is a four-system single-frequency smartphone, and Huawei Honor 9 is a three-system (GPS, GLONASS, and BDS) single-frequency smartphone, and the geodetic receiver is a four-system three-frequency receiver.  
Three datasets were collected in total with a sampling rate of 1 s. The first and third datasets were in the same site: the top of the teaching building No. 5 in Southeast University Jiulonghu Campus, as shown in Figure 1. This site is in a low multipath environment. Since the precise coordinates of the site are unknown, these datasets are mainly used for the raw measurements' quality analysis. The first dataset was collected on July 12, 2018, over a time span of about three hours. The devices used were a Huawei P10 smartphone, a Huawei Honor 9 smartphone, and two geodetic receivers. The third dataset was collected on October 29, 2018, over a time span about one hour, and the devices used are a Xiaomi MI 8 smartphone and a geodetic receiver.  
The second dataset was collected on October 19, 2018, and the site is the GE01 control point in Southeast University Jiulonghu Campus, as shown in Figure 2. The device used is a Xiaomi MI 8 smartphone. Five time periods were observed, about 6 minutes each time. The precise coordinates of this site are known, and the observations are used for positioning tests.  
Based on the Gnsslogger APP [1], we developed the GnssLiuYS APP for data logging, pre-processing and sending. Besides, two Windows desktop programs were developed to achieve converting the original data (which obtained from the GnssLiuYS APP) to the RINEX (Receiver Independent Exchange Format) format and PPP calculation.
## 2 Materials and Methods
### IMU-Based System  
The tested wearable IMU-based system (Baiobit, Rivelo Srl--BTS Bioengineering, Milan, Italy) was a medical device designed for clinical motor assessment. The device (dimensions: 70 \(\times\) 40 \(\times\) 18 mm, weight: 37 g; sample frequency: 100 Hz) integrates multiple sensors, including a tri-axial accelerometer, gyroscope, and magnetometer, enabling a detailed assessment of motion within its 3D local coordinate system. The IMU provides orientation data that allow the algorithm to compute roll (axial rotation), pitch (anterior-posterior inclination), and yaw (lateral inclination) angles. The IMU is able to wirelessly communicate with a personal computer via BLE technology. The IMU can be attached to the user's body via an adjustable elastic band to address different body locations, preventing  
Figure 1: Visual representation of markers and IMU sensor placement on the body used for data collection.  
unwanted sensor displacement throughout the movement; the tightness of the band was manually adjusted for each participant to ensure comfort and stability.  
Calibration of the IMU was performed according to the manufacturer's instructions, with participants standing in a quiet, upright stance while the sensor automatically calibrated itself. This process allowed the device to detect a static, zero-motion condition and establish a baseline orientation, correcting for sensor bias to ensure accurate motion data during subsequent recordings.
## 4 Experiments and Results
### Comparison of the Feature Types of the LSA  
To find the best feature type after the LSA process, this study focused on comparing three types of feature values, namely the Frobenius norm (_f_), Cos \(\alpha\) with slice (_s_), Cos \(\alpha\) with pad (_p_), and a combination of them. The results of this experiment are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature*** & **Precision** & **Recall** & **F-Measure** \\ \hline Frobenius norm (_f_) & 0.381253 & 0.654639 & 0.465479 \\ Cos \(\alpha\) with slice (_s_) & 0.280162 & 0.331615 & 0.123266 \\ Cos \(\alpha\) with pad (_p_) & 0.158213 & 0.32646 & 0.142965 \\ _fsp_ & 0.4068 & 0.828179 & 0.531536 \\ _fs_ & 0.406962 & 0.797251 & 0.526073 \\ _fp_ & 0.408638 & 0.792096 & 0.528116 \\ _sp_ & 0.172015 & 0.333333 & 0.141452 \\ \hline \hline \end{tabular}  
* * \(f\) = Frobenius norm, \(s\) = slice, \(p\) = pad.  
\end{table}
Table 3: Results of various features using the Frobenius norm (_f_), Cos \(\alpha\) with slice (_s_), and Cos \(\alpha\) with pad (_p_).  
Figure 6: Effect of variation in the alpha and \(n\) values at information retrieval measurement values from the evaluation of a plagiarism detection system using the learning vector quantization algorithm.  
Table 3 shows the test results using various combinations of the Frobenius norm (_f_), Cos \(\alpha\) with slice (s), and Cos \(\alpha\) with pad (_p_) as the feature. The combination of the feature value was accomplished using the AND operation. From this experiment, it can be seen that the combination of two or more feature values is better than using a single feature value. Moreover, the combination of all three feature values provides the highest results of precision, recall and F-measure. The combination of the Frobenius norm with Cos \(\alpha\) and with either slice or pad also provides a comparable result. In contrast, using the combination of Cos \(\alpha\) with slice and Cos \(\alpha\) with pad provides a very low accuracy. This result implies that both the Frobenius norm value and Cos \(\alpha\) are important features in the LSA-based, cross-language automated plagiarism detection system; however, using a single feature will significantly reduce the accuracy.
# A Real-Time GNSS/PDR Navigation System for Mobile Devices
## 2 Materials and Methods
### Methods
#### 2.2.1 Sensor Fusion for Device Orientation Estimation
The orientation of the sensor frame, \(\overset{S}{E}\hat{\mathbf{q}}\), is that which aligns a predefined reference direction of the field in the Earth's frame, \(\overset{E}{E}\hat{\mathbf{d}}\), with the measured direction of the field in the sensor frame \({}^{S}\hat{\mathbf{s}}\), using the rotation operation  
\[\overset{S}{E}\mathbf{R}= \begin{bmatrix}2q_{1}^{2}-1+2q_{2}^{2}&2(q_{2}q_{3}+q_ {1}q_{4})&2(q_{2}q_{4}-q_{1}q_{3})\\ 2(q_{2}q_{3}-q_{1}q_{4})&2q_{1}^{2}-1+2q_{3}^{2}&2(q_{3}q_{4}+q_{1}q_{2})\\ 2(q_{2}q_{4}+q_{1}q_{3})&2(q_{3}q_{4}-q_{1}q_{2})&2q_{1}^{2}-1+2q_{4}^{2}\\ \end{bmatrix} ,\]  
therefore, \(\overset{S}{E}\hat{\mathbf{q}}\), can be determined by solving  
\[\underset{\overset{S}{E}\hat{\mathbf{q}}\in\mathbb{R}^{4}}{min}\mathbf{f}( \overset{S}{E}\hat{\mathbf{q}},\overset{E}{E}\hat{\mathbf{d}},\overset{S}{ \mathbf{s}}), \tag{3}\]  
where \(\mathbf{f}\) defines the objective function:  
\[\mathbf{f}(\overset{S}{E}\hat{\mathbf{q}},\overset{E}{E}\hat{\mathbf{d}},\overset{ S}{E})= \overset{S}{E}\hat{\mathbf{q}}^{*}\otimes^{E}\hat{\mathbf{d}}\otimes \overset{S}{E}\hat{\mathbf{q}}-\overset{S}{\mathbf{s}}, \tag{4}\]  
\[\overset{S}{E}\hat{\mathbf{q}}= \begin{bmatrix}q_{1}&q_{2}&q_{3}&q_{4}\\ \end{bmatrix} ;\ \ \overset{E}{E}\hat{\mathbf{d}}= \begin{bmatrix}0&d_{x}&d_{y}&d_{z}\\ \end{bmatrix} ;\ \ \overset{S}{\mathbf{s}}= \begin{bmatrix}0&s_{x}&s_{y}&s_{z}\\ \end{bmatrix} . \tag{5}\]  
Although there are many optimization algorithms in literature, the gradient descent is one of the commonly implemented [12].
## 4 Discussion
### The Effect of the Earth-Sun Distance to TB Increment  
The orbit of the earth is an ellipse, as can be seen from the distance between the sun and the earth at the perihelion and aphelion. There is periodic variation for Earth-Sun distance, and the angular radius of the sun depends on the distance. According to the Equations (10) and (11), the Earth-Sun distance determines the ratio of solar solid angle of the antenna beam. During the solar observation, the earth is near at perihelion in early January, when the sun is closest to the earth, and the solar solid angle is near the maximum and the average radiation T\({}_{\rm B}\) increment received by the antenna is at its maximum. However, in early July, when the sun is farthest from the earth, the solar solid angle is the smallest in the year, and the average radiation T\({}_{\rm B}\) increment received by the antenna is also at a minimum.  
According to this theory, we analyzed the observed solar radiation T\({}_{\rm B}\) without a solar eclipse and the results showed that the T\({}_{\rm B}\) increment was also variable at three frequencies when there was no eclipse, as shown by Figure 8. The earth was near the perihelion on 27 December 2019 and 1 January 2020, and the average radiation T\({}_{\rm B}\) increment received by the GMR was at a maximum and the earth was farthest from the sun on 30 June and 2 July 2020, where the average radiation T\({}_{\rm B}\) increment received by the GMR was at a minimum. The average percentage of the T\({}_{\rm B}\) variation was 6.6% at three frequencies. These observation data indicate the variation of the Earth-Sun distance. The statistical results are given in Table 5.  
Figure 7: The variation of the T\({}_{\rm B}\) increment (left axis) during the solar eclipse on 21 June 2020 (red solid curve with \(\times\) marks) as compared with that after the solar eclipse on 30 June (black curve with \(\star\) marks). The blue dash line represents the percentage of the unshaded solar disk area (right axis). (a) 22.235 GHz, (b) 25.0 GHz, (c) 30.0 GHz.
# The Legal Framework for Space Debris Remediation as a Tool for Sustainability in Outer Space
## 3 The Definition and Scope of Space Debris Remediation
### Legal Axvenues to Facilitate SDR  
One legal avenue to incorporate SDR mechanisms in the existing legal framework could be through national legislation. The example of space debris mitigation instruments being included in the national authorization requirements for space operators could serve as a model also for SDR. Some states, such as Argentina, Chile, the Netherlands, Poland, Spain and Switzerland have confirmed their adherence to the UNCOPUOS Guidelines. There are also states, such as Australia, Germany and Japan which have not enacted national legislation, but have elaborated state policies or standards for space debris mitigation for their national space agencies [25].  
Furthermore, SDR and OOS measures could be implemented nationally as part of authorization or licensing requirements. This has already been the case with the national adherence to space debris mitigation guidelines. Thus, certain conditions can be prescribed to operators in space legislation: the legal basis for prescribing such conditions is Art VI of the OST which gives a \"mandate\" to states to authorize activities while, according to Art. IX of the OST, taking into account the activities of other states in outer space as per Art. IX.  
Furthermore, the internal regulations of space agencies and intergovernmental organizations (IGOs) such as ESA can contribute to the development and implementation of SDR. Similarly, the 2014 ESA Space Debris Mitigation Policy for Agency Projects [67] is applicable to the procurement of all ESA space systems and all operations under the responsibility of ESA. The 2004 European Code of Conduct for Space Activities [20] is applicable to projects of European space agencies, projects conducted in Europe as well as by European entities outside Europe, and to all space systems and launch vehicles orbiting or intended for orbiting the Earth.  
Thus, this practice of applying mitigation guidelines for ESA projects can serve as an example of how to ensure accordance also to SDR and OOS mechanisms for European projects, even if they have not acquired a binding character.