esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Collision Avoidance Decision-Making Method
### Method of Resuming the Original Route  
When the OS is sailing in the general direction of the traffic flow in the traffic lane, the course and position may deviate from the planning route due to anti-collision with obstacles. After passing and then keeping clear of the obstacles, OS will try to return to the planned route and follow the recommended traffic flow direction sailing. This process of sailing is called \"resume sailing\". When resuming sailing, the target point is the intersection of the planned route of the current segment and the line between this segment and the next segment of the traffic lane.  
Figure 10 shows a schematic diagram of the resume sailing method, with \(N_{\rm T}\) pointing to true north, WPT2 representing the target point of the OS's resume sailing, \(C_{w,w+1}\) representing the current segment's planned route direction (the course from waypoint WPT1 to waypoint WPT2), and \(TB\) representing the true bearing of the OS relative to the target point.  
Figure 9: Flowchart of feasible course altering angle obtaining.  
1. Target course of resume sailing To ensure that the OS returns and sails following the planned route, the target course \(C_{T}\) should be determined based on the relative position of OS and the planned route. Equation (19) demonstrates this. \[C_{T}=f(TB-C_{w,w+1})+C_{w,w+1}\] (19) where \(f\) is the approach coefficient, which has a value greater than 1. The approaching speed to the planned route will be too slow if the value of m is too little. If the \(f\) is too great, the difference between the ship's course and the general direction of traffic flow will be too large, and the voyage will be squandered, which will prevent the resume sailing procedure from being completed. This research argues that taking \(f=1.5\) is more acceptable based on several experiments [15].
2. Resume sailing time When the OS alters her course to avoid the TS, whether she can resume sailing will be assessed in the next time step. The discrete method can be used to find the earliest resume time. The diagram of Figure 11 depicts the specific calculating procedure.  
This work develops the maneuvering decision-making method, as shown in Figure 12. The collision avoidance system continually cycles the process at 5 s intervals throughout the operation, based on the above research.  
Figure 10: Schematic diagram of the resume sailing.  
Figure 11: The procedure of resuming sailing.  
Figure 12: Decision-making method.  
In the diagram, \(\Delta\)t represents the calculating time step, which is 1 s in this case. \(K\) represents the coefficient of resume to the planned route time, and \(M\) represents the coefficient of TEBD (\(T_{TS}\)).
## 4 Multilevel Monte Carlo Method  
The multilevel Monte Carlo method [6] (MLMC) is based on the variance reduction principle [6] (p. 3). Many simulation samples are collected at low levels, with less accurate but inexpensive approximations of the model available. While much fewer samples are collected at high levels, there are differences between approximations of the model that are more accurate but computationally expensive. If these differences have a significantly smaller variance, we obtain estimates with the same accuracy but at a fraction of the cost compared to the standard Monte Carlo method.  
Let \(P\) be a random variable and \(P_{1}\),..., \(P_{L}\) be a sequence of its approximations, where \(P_{l-1}\approx P_{l}\). The approximations are becoming more accurate from \(P_{1}\) to \(P_{L}\). Then, the expected value of \(P_{L}\) satisfies the identity:  
\[\mathbb{E}[P_{L}]=\mathbb{E}[P_{1}]+\sum_{l=2}^{L}\mathbb{E}[P_{l}-P_{l-1}]. \tag{6}\]  
The MLMC estimates an individual expectation as follows:  
\[\hat{P}=(P_{1}(\mathbf{x}_{n}^{1}))_{N_{1}}+\sum_{l=2}^{L}(P_{l}(\mathbf{x}_{n}^{l})-P_ {l-1}(\mathbf{x}_{n}^{l}))_{N_{l}}, \tag{7}\]  
where \(L\) is the total number of levels, \(N_{l}\) stands for the number of simulation samples on level \(l\), \(()_{N_{l}}\) denotes the average over \(N_{l}\) samples. Pairs of random inputs \((\mathbf{x}_{i^{\prime}}^{l}\mathbf{x}_{i}^{l-1})\) are both discrete evaluations of a single realization of a random field \(x(\omega_{l,i})\), while the random states \(\omega_{l,i}\) are independent and come from a given probability space \((\Omega,\Sigma,\mathcal{P})\).  
When using estimator (7), we are particularly interested in its total computational cost \(C\) and estimated variance \(\hat{V}\):  
\[C=\sum_{l=1}^{L}N_{l}C_{l}, \tag{8}\]  
\[\hat{V}=\sum_{l=1}^{L}\frac{\hat{V}_{l}}{N_{l}}, \tag{9}\]  
where \(C_{1},\hat{V}_{1}\) denote the cost and the estimated variance of \(P_{1}\). Meanwhile, for \(l>1\), \(C_{l},\hat{V}_{l}\) represent the cost and the estimated variance of differences \(P_{l}-P_{l-1}\). For a comprehensive MLMC theory, see [6].  
In the presented way, it is feasible to estimate the expectations of quantities derived from the original QoI. In particular, we utilize the so-called generalized statistical moments of QoI. In our study, the moments' functions \(\phi(x)\) are Legendre polynomials, as they are suitable for the PDF approximation (see [36]).
# Clover Dry Matter Predictor Based on Semantic Segmentation Network and Random Forest
## 3 Experiments
### GrassClover Image Dataset
#### 3.1.2 Dry Matter Prediction Dataset  
In this study, the dataset used for biomass prediction experiments is also derived from GrassClover. This dataset comprises 435 authentic images, each meticulously annotated with biomass information. These genuine images were obtained through on-site biomass sampling in agricultural fields, with precise annotations reflecting the accurate biomass content of the vegetation in each image. This dataset serves as a valuable reference for our biomass prediction experiments, providing real-world context.  
Each biomass sample includes the following components:  
1. A canopy image of a defined 0.5 m by 0.5 m of grass clover preceding the cut.
2. A composition of the harvested biomass with stems located in the square.  
After cutting the plants at a height of 5 cm, all plant samples were separated into ryegrass, clover and weeds. Among them, 272 samples underwent further subclass separation into red clover and white clover. After drying the samples, each fraction was individually weighed to determine the dry matter yield and composition. A total of 435 biomass samples were collected. The sampling spanned the seasons of 2017, with additional samples in 2018.  
By utilizing this dataset of real images annotated with biomass information from GrassClover, we aimed to further validate the robustness and accuracy of our biomass prediction model. These images from real-world scenarios will provide training and evaluation data that closely align with practical applications, thereby enhancing our confidence and understanding of the performance of the biomass prediction model. We used 261 dataset images with biomass labels from this dataset.
## 5 AZTRM-D Simulated Real-World Scenario
### AZTRM-D Walkthrough  
Company A's security architecture is designed to enforce continuous ZT validation [50; 58] and automated risk assessments based on RMF principles [37]. At its core is a risk-based IAM system where every entity undergoes continuous authentication before being granted access [56]. If an AI-driven behavioral analytic engine detects an anomaly, such as an administrative account acting outside its normal patterns, the system automatically enforces adaptive authentication or revokes access until manual verification is performed [40].  
To prevent lateral movement, Company A implements ZT micro-segmentation, isolating development, testing, and production workloads [26]. AI-driven network monitoring continuously analyzes traffic patterns, automatically blocking unauthorized communications and triggering investigations when deviations are detected [43]. Security monitoring is further strengthened by AI-powered Security Information and Event Management (SIEM) and Security Orchestration, Automation, and Response (SOAR) platforms, which initiate automated incident response workflows to contain threats before they escalate [45]. Data security is paramount; all data is encrypted, and access is governed by strict, AI-enforced policies [66].  
The AZTRM-D methodology is applied throughout the entire S-SDLC. During planning, AI-driven risk modeling defines dynamic security policies [71]. In development, developers use risk-based authentication, and all code is scanned for vulnerabilities before being merged [42]. In the build phase, software is compiled in hardened, ephemeral environments, and each artifact is cryptographically signed and its SBOM validated to prevent supply chain attacks [30]. The test phase uses AI-powered penetration testing and DAST to find flaws [29]. Finally, post-deployment, continuous runtime monitoring ensures workloads remain resilient against new and evolving threats [8].
# A TCP Acceleration Algorithm for Aerospace-Ground Service Networks
## 2 Materials and Methods
The congestion window can be used to characterize network capacity, providing a more accurate identification of congestion. Because of the BBR algorithm's unique mechanism for measuring congestion window size, it neither increases the number of congestion windows indefinitely like ordinary congestion control algorithms nor uses the buffer of the switch node, thus avoiding the emergence of buffer bloat (buffer overflow) [13], which shortens the transmission delay significantly. Another advantage of the BBR algorithm is that it measures network capacity actively, adjusting the congestion window. In addition, the autonomous adjustment mechanism enables the BBR algorithm to control the data flow sending rate independently. In contrast, ordinary congestion control algorithms only calculate the congestion window, whereas the TCP protocol completely determines the data flow sending rate. As a result, when the data flow sending rate is close to the link's bottleneck bandwidth, there is data packet queuing or data packet loss due to the rapid increase in the sending rate. Initially, the BBR drew great attention from researchers and was considered a paradigm-shifting achievement in the field of congestion control. However, with research progress, it has been discovered that the BBR protocol has several shortcomings, including a slow convergence speed in the bandwidth detection stage, a low sensitivity, and a lack of consideration for delay and jitter.
# A Long Short-Term Memory Network-Based Radio Resource Management for 5G Network
###### Abstract  
Nowadays, the Long-Term Evolution-Advanced system is widely used to provide 5G communication due to its improved network capacity and less delay during communication. The main issues in the 5G network are insufficient user resources and burst errors, because it creates losses in data transmission. In order to overcome this, an effective Radio Resource Management (RRM) is required to be developed in the 5G network. In this paper, the Long Short-Term Memory (LSTM) network is proposed to develop the radio resource management in the 5G network. The proposed LSTM-RRM is used for assigning an adequate power and bandwidth to the desired user equipment of the network. Moreover, the Grid Search Optimization (GSO) is used for identifying the optimal hyperparameter values for LSTM. In radio resource management, a request queue is used to avoid the unwanted resource allocation in the network. Moreover, the losses during transmission are minimized by using frequency interleaving and guard level insertion. The performance of the LSTM-RRM method has been analyzed in terms of throughput, outage percentage, dual connectivity, User Sum Rate (USR), Threshold Sum Rate (TSR), Outdoor Sum Rate (OSR), threshold guaranteed rate, indoor guaranteed rate, and outdoor guaranteed rate. The indoor guaranteed rate of LSTM-RRM for 1400 m of building distance improved up to 75.38% compared to the existing QOC-RRM.  
5G network; burst error; grid search optimization; frequency interleaving; long short-term memory network; Long-Term Evolution-Advanced system; radio resource management +
Footnote †: journal:  
_Future Internet_**2022**, _14_, 184. [[https://doi.org/10.3390/f14060184](https://doi.org/10.3390/f14060184)]([https://doi.org/10.3390/f14060184](https://doi.org/10.3390/f14060184))
## 2 Related Work
### Sensor-Based HAR  
Due to the recent improvements in IoT devices and sensors, wearable sensors are widely used in most electronic devices, such as smartphones, watches, and bands. These data can be used to analyze and interpret body movements. However, the performance of an activity recognition system depends mainly on the sensor modality. Some of the widely used wearable sensors are accelerometers, inertia measurement unit (IMU) sensors, electromyography (EMG) sensors, and electrocardiography (ECG) sensors. Among these types of sensors, accelerometers and IMU are the most used in HAR.  
An accelerometer can be used to measure the acceleration of the object. In HAR, it is usually mounted on the body parts, such as the waist [15], arm [16], ankle [17], and wrist [18]. In addition to the accelerometer, IMU is one of the choices that have been widely chosen due to the reason that it can measure strength, angular velocity and orientation in the fulcrum part of the body. They are made possible from the incorporation of accelerometers, gyroscopes, and magnetometers inside the IMU sensors. Changes in pitch, roll, and yaw in the IMU sensor can help detect body movement.
## 3 Flight Control System Health Assessment under Uncertainty
### Uncertainty Representation and Interpretation  
At present, the uncertainty representation is usually based on the following theories: probability theory [23], fuzzy set theory [24], evidence theory [25], interval analysis theory [26], etc. Among them, probability theory is widely used in health assessment [27]. In this section, the parameters' uncertainty will be described in the form of probability distributions.
## Materials and Methods
### Analytical Methods  
COD was measured spectrophotometrically at 620 nm using a BioTek Epoch plate reader (BioTec Inc, Winooksi, VT, USA). Combined with CHEMetrics COD vials (CHEMetrics Inc., Midland, VA, USA) and Bioscience Digital COD reactor (BioScience Inc., Allentown, PA, USA). Potassium dichromate was the oxidizing agent used to oxidize the organic material in the wastewater in the presence of silver catalyst and mercury to withstand chlorine interferences. The vials were heated to 150 \({}^{\circ}\)C for 2 h and the concentration of chronic ions was determined using a spectrophotometer. Fats, oil and grease  
Figure 4: Process flow for post-DAF membrane filtration experiment. a—Baffler; b—Temperature sensor; c—Pressure sensor.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Membrane** & **Retention characteristic (kDa)** & **Operating Pressure (bar)** \\ \hline FP200 & 200 & 6.89 \\ FPA03 & 20 & 6.89 \\ PU608 & 8 & 24.13 \\ ES404 & 4 & 24.13 \\ AFC30 & 0.083 & 58.61 \\ AFC99 & 0.058 & 58.61 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Operation pressures of the various membranes used.  
(FOG) concentration was determined using hexane extraction. Total solids (TS), and total suspended (TSS) and dissolved solids (TDS) were measured by gravimetric analysis, heating to 110 \({}^{\circ}\)C using an oven and a 45 \(\upmu\)m filter. Protein analysis was done by the BCA (bicinchoninic acid assay) method.  
Statistical analyses for comparisons of the different treatment methods and different membrane sizes were done using one-way ANOVA and Tukey test using JMP Pro 12 software. Significance of \(p<0.05\) was used for all comparisons.
## 3 Enhanced SoC-Balancing Strategy with Saturation
### Selection of the Current Limits in CDRs
Meanwhile, each phase current is \(i_{\text{sum}}/i_{\text{sum0}}\) times its original value based on Equation (31). Furthermore, the depth of discharge of each ESU remains unchanged. Thus, Equation (34) is still fulfilled, and a smooth transition of the reference current is guaranteed. When \(i_{\text{sum}}-i_{\text{satt}}-i_{\text{satt}}>i_{\text{satt}}\), the phase currents are \(i_{\text{satt}}\), \(i_{\text{satt}}\), and \(i_{\text{sum}}-2i_{\text{satt}}\), respectively. When \(i_{\text{sum}}-i_{\text{satt}}-i_{\text{satt}}<i_{\text{satt}}\), the phase currents are \(i_{\text{sum}}-2i_{\text{satt}}\), \(i_{\text{satt}}\), and \(i_{\text{satt}}\), respectively. In a similar way, Equations (33) and (34) can be calculated by replacing Equation (31). It can be further verified that the variable current limits formulated as Equation (35) are still effective for preventing step reference currents. In addition, the reachable condition is also analyzed when \(i_{o}\) varies. On the one hand, \(i_{\text{satt}}/i_{\text{sum}}\) is constant with the variable \(i_{\text{satt}}\). On the other hand, the ratio increases with a fixed \(i_{\text{satt}}\) because the accelerating factor and current limits are designed under full load. Thus, \(i_{\text{sum}}\) is lower than \(i_{\text{sum0}}\), and Inequation (16) is always satisfied under load change. To select proper \(i_{\text{satt}}\) for series applications, there are two key points. First, in order to remove the potential step reference currents, \(i_{\text{satt}}\) should not be too high, although a higher \(i_{\text{satt}}\) helps reduce the equalization time. Second, \(i_{\text{sattu}}\) is designed to be proportional to \(i_{\text{sum}}\) in case the step reference currents occur under load change.
# Fog Computing for Smart Cities' Big Data Management and Analytics: A Review
## 8 Challenges and Open Research Issues
### Security and Privacy  
As we mentioned earlier in Section 2.2, edge computing and fog computing dramatically reduce the transfer of data to data centers outside the control of the organization. Analyzing sensitive data on a local gateway or fog node can deeply improve the privacy of the data. However, vulnerabilities come from the limited computing power of IoT devices, as well as hardware design flaws and firmware update constraints. Furthermore, edge and fog systems employ various network components to interconnect IoT devices, storage and computing devices, making them potential targets for various kinds of attacks. Network monitoring solutions can help detect anomalies and security  
Figure 8: Data processing at the fog.  
vulnerabilities [105]. These inconsistencies can provide hackers with an easy entry point into an edge or fog network. Therefore, the security of fog-based solutions should be enhanced at the network level. The network edge might be the place to insert security patches between vulnerable devices and the other components of the network.  
Security challenges in fog computing can manifest themselves at three levels: edge and fog network infrastructures, fog nodes, and device level. An increasing number of efforts are aimed at solving security and privacy concerns in edge- and fog-based systems. For example, [PERSON] et al. [106] provided a detailed analysis of security threats that can hamper the network infrastructure, user devices, edge data center, and virtualization infrastructure. They listed eight potential security challenges and described how existing solutions could address those challenges. [PERSON] et al. [107] analyzed the security and privacy challenges in fog computing. They reviewed existing solutions and approaches and described general perspectives on security and privacy issues in fog-assisted systems. [PERSON] et al. [108] discussed the security requirements of fog-assisted systems in smart healthcare and smart grids. They described fog computing's security challenges and the trust and privacy issues surrounding big data in these fog-assisted systems. Furthermore, they discussed the potential of blockchain technology to address many of these security concerns. In [109], the authors proposed a solution for reliable smart city service delivery at the edge, which utilizes collaboration between edge servers and privacy mediation nodes and an intrusion detection system to improve the security, reliability, and availability of smart city applications.
## 6 Conclusions and Perspectives
We can attempt to combine PRI corrected or calibrated by the most influential factors (e.g., pigment pool sizes and structural changes) with SIF or other greenness indices to establish a generalized global model of carbon uptake. The satellite data from MODIS and GOME (Global Ozone Monitoring Experiment) provide potential regional and global calculations of PRI. The Sentinel program [161] of the ESA (European Space Agency), the new hyperspectral instruments built by the DLR (German Aerospace Center), such as EnMAP (Environmental Mapping and Analysis Program [162]) or DESIS (Earth Sensing Imaging Spectrometer [163]) on board the ISS (International Space Station), as well as the forthcoming missions built by NASA (National Aeronautics and Space Administration), such as the HyspIRI (Hyperspectral Infrared Imager [164]) or the EIS (Europa Imaging System [165]) allow space-based calculation of thePRI and estimation of the RUE at a higher spatial resolution. Incorporating satellite data with over 685 eddy covariance flux sites (FLUXNET [166]) even further enhances the global continuous and accurate monitoring of carbon uptake by terrestrial ecosystems. This research was supported by the European Research Council Synergy grant SyG-2013-610028 IMBALANCE-P, the Spanish Government project CGL2013-48074-P and the Catalan Government project SGR 2014-274. [PERSON] gratefully acknowledges the support from the China Scholarship Council. [PERSON], [PERSON] and [PERSON] planned and designed the research. [PERSON] provided data from 1992 to 2009. [PERSON] wrote the main manuscript text and prepared the figures and tables. All authors contributed significantly to the final revisions of the manuscript. The authors declare no conflict of interest.
## 3 Results and Discussions
### Aerodynamic Performance and Stall Margin
\(PR_{stall}\) is the pressure ratio at the stall, and the mass flow rate at the stall is denoted by \(\dot{m}_{stall}\). Figure 11 shows the stall margin improvement for the different inclination angles from hub-to-shroud. The inclination angle in the range of \(15<\alpha<45\) can significantly increase the stall margin. However, further increase in the hub-to-shroud inclination angle dramatically reduces the stall margin improvement, due to increase in the length of the impeller-diffuser gap. The maximum value in stall margin improvement is at \(30^{\circ}\), and reaches about 3.88%. Figure 12 compares the Mach number and static pressure distributions on the 50% span of the impeller-diffuser interface near stall between the original and \(30^{\circ}\) diffuser vanes. In the case of the original diffuser, a high Mach number region indicated by black circle in Figure 12a is observed in the diffuser throat, which causes a shock, and a large loss. Therefore, as shown in the pressure contour (Figure 12c), the magnitude of the pressure is decreased in the front part of the diffuser, and slowly recovers thereafter. However, in  
Figure 11: Stall margin improvement by inclined leading edge diffuser vane with different inclination angles. Figure 10: Aerodynamic performance variation for three different inclination angles; (**a**) total pressure ratio, (**b**) total-total efficiency. the case of 30\({}^{\circ}\) as shown in Figure 12b, this kind of shock wave is not observed within the passages, and the static pressure increases steadily and continuously. Figures 10-12 confirm that the 30\({}^{\circ}\) inclination from hub-to-shroud not only can improve the pressure ratio and compressor performance at the design point condition, but also can significantly increase the compressor operating range and stall margin.