esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 3 System Model
### Interference and Noise Considerations  
The interference is simulated as zero-mean additive white Gaussian noise (AWGN) with variance \(\sigma^{2}\). Interference in the mmWave band is low because of very directional beams; yet, inter-user interference (IUI) is taken into account in a multi-user environment. The signal-to-interference-plus-noise Ratio (_SINR_) at user \(k\) is shown as follows:  
\[SINR_{k}=\frac{\left|h_{k}w_{k}\right|^{2}}{\sum_{j\
eq k}\left|h_{k}w_{j} \right|^{2}+\sigma^{2}} \tag{3}\]  
where \(h_{k}\) and \(w_{k}\) are the channel vector and beamforming weight for user k.  
Mobility model: User mobility is simulated through the Gauss-Markov mobility model, updating the direction and velocity of users at every time step to make movement realistic in dynamic environments.  
Mathematical Formulations  
Beamforming optimization model: To maximize the data rate with minimal power consumption, the problem of beamforming optimization can be defined as follows:  
\[\underset{w,\Theta}{\text{max}}\sum_{k-1}^{K}log_{2}(1+SINR_{k}) \tag{4}\]  
Subject to the following:  
\[\left\|W\right\|^{2}\leq P_{max,}\ \Theta=diag\left(e^{i\phi_{1}},\ldots.e^{i\phi_{N}}\right) \tag{5}\]  
where \(P_{max,}\) is the maximum transmit power, and \(\Theta\) consists of IRS phase shift coefficients.  
Congestion Control Mechanism  
The mmS-TCP congestion window (cwnd) adaptation follows the following equation:  
\[cwnd_{t+1}=cwnd_{t}+\alpha\cdot\frac{RTT_{min}}{RTT_{t}} \tag{6}\]  
where \(\alpha\) is the learning rate for congestion control, and \(RTT_{min}\) and \(RTT_{t}\) are the minimum and current round-trip times.
## 3 Methodology
### Satellite Image Preprocessing
#### 3.2.1 Calculate Flight Plan  
We provide the user with two options: they can either upload a previous flight plan (a list of latitude/longitude coordinates), or we can calculate a flight plan based on the elevation map, a starting point, an end point, and a working altitude. Firstly, we load the elevation map as an image and operate this image to calculate the safe zone based on the working altitude of the plane.  
We then employ Rapidly-exploring Random Tree (RRT*, with the ''*' indicating that the algorithm is admissible, ensures that it will eventually find the shortest path as the number of samples increases) [63] to determine coordinates on the map that avoid terrain collisions. An RRT* is a random algorithm that generates random extensions from the start point. It can be applied in non-structured environments if we can calculate intersections between a known point and a newly generated point.  
In Figure 8(a), we show a map of a mountainous area in Hidalgo, Mexico. For better understanding, we include in Figure 8(b) an example of the RRT* drawn in 3D for the same map.  
Figure 8: Preprocessing flowchart (offline preprocessing). This process ensures the best keypoints are maintained to compare the real-time application. We included images of every subprocess for a better understanding, but they are shown better in Sections 3.2.1-3.2.3.  
Figure 9: Example (3D) of RRT* path planning over a section of Hidalgo State in Mexico. We show the final path. Geodetic reference data: the datum corresponds to ITRF92 epoch 1988.0, GRS80 ellipsoid, geographic coordinates. In (**b**), we use a colormap to represent elevation and the \(*\) symbol represent the calculated flight plan.
# Fabric Defect Detection Using L0 Gradient Minimization and Fuzzy C-Means
## 3 Methods
### Texture Removal by the L0 Gradient Minimization (LGM)  
Due to the complexity of background texture information, it often increases the challenge of fabric defect detection. The L0 gradient minimization method [27] is widely used to smooth texture information. It is often adopted for filtering the image while preserving edge feature. The L0 gradient minimization method enhances the significant edge portion of the image by increasing the steepness of the transition portion of the image while removing the low-amplitude detail portions. Inspired by the L0 gradient minimization, we apply it to remove the background texture of fabric.  
As shown in Figure 4a, the fabric defect sample has three different directions of texture, and it is a mesh diagram, as shown in Figure 5a. After smoothing via LGM, the unimportant background texture of the fabric is removed, as shown in Figure 4b. Notice that the high-contrast edges on the defect are preserved, and the defect feature is more prominent, as shown in Figure 5b.  
Figure 4: Smoothed fabric defect image using L0 gradient minimization (LGM). (**a**) Fabric defect image; (**b**) Smoothed fabric defect image.  
Figure 5: Detection effect of LGM. (**a**) Mesh diagram of fabric defect image; (**b**) Mesh diagram of smoothed fabric defect image.  
Figure 3: The framework of the proposed method.  
In order to illustrate the method clearly, we briefly summarize the theory of the L0 gradient minimization model. Let \(l\) be the input fabric image, its smoothed output result is \(S\), \(\partial_{x}S_{p}\) and \(\partial_{y}S_{p}\) are the partial derivative of the processed image in the \(x\) and \(y\) directions at \(p\) respectively, and the gradient of image \(S\) at pixel \(p\) is denoted by \(\
abla S_{p}=\ \left(\partial_{x}S_{p},\ \partial_{y}S_{p}\right)^{T}\). The image L0 gradient specific objective function is defined as:  
\[min\left\{\sum_{p}\left|S_{p}-I_{p}\right|^{2}+\beta|\
abla S_{p}-h|^{2}+ \lambda|H_{0}\right\} \tag{1}\]  
where \(\lambda\) is a non-negative parameter, which directly controls the weight of the smoothing term. \(\beta\) is an automatically adapting parameter. \(h\) is auxiliary variable. By alternatively computing \(h\) and \(S\), it can obtain the output result. Figure 6 shows the result using the LGM method with their corresponding mesh diagrams. It is observed that the output result retains the defect information and removes the background texture information.
# An Assessment of CyGNSS v3.0 Level 1 Observables over the Ocean
## 3 Results
### Analysis by GPS Transmitter  
Residual dependence on geometry is found to play a prominent role in v3.0 CyGNSS data, with the effect observed to be relatively consistent between receivers (Figure 4). To further understand the geometric dependencies, additional investigations segmented by GPS transmitter (SVN) were performed. Figure 6 shows the dependence of NBRCS estimates on the incidence angle for one CyGNSS receiver (FM03), segmented by individual GPS transmitters (and coloured by the GPS block). At a given wind speed (U10 \(\sim\)7 m/s), a substantial range (of up to \(\sim\)3 dB) of NBRCS estimates can be seen between the individual SVNs across 0\({}^{\circ}\)-65\({}^{\circ}\) incidence.  
Figure 2: Temporal evolution of NBRCS estimates at ERA5 U10 \(\sim\)7 m/s for all SVNs, segmented by CyGNSS Flight Model (FM01-FM08).  
Figure 4: Dependence of NBRCS on incidence angle (from nadir 0\({}^{\circ}\) to 65\({}^{\circ}\)) at U10 ~7 m/s, averaged for all GPS transmitters/SVNs; each line (and colour) represents a different CyGNSS Flight Model/receiver (FM01-FM08).  
Figure 3: **(top)** Geographical distribution of NBRCS reported by CyGNSS FM03 for signals incoming from GPS SVN70 (Block IIF) for U10 ~7 m/s. **(bottom)** Corresponding geographical distribution of incidence angle between CyGNSS FM03 and GPS SVN70 (Block IIF).  
Further, the dependency on geometry seems to depend on both the individual GPS transmitter and the GPS blocks. More specifically, all SVNs within a given block show a similar relationship with incidence; however, individual SVNs within each block show slight variations around this relationship, as well as overall differences in average NBRCS. This difference between blocks is most marked in Block IIR(A) at incidence angles <30\({}^{\circ}\), where NBRCS estimates can be -2 dB higher than those found in the other blocks. Additionally, only Block IIF shows approximately constant NBRCS estimates at incidence angles >50\({}^{\circ}\), whereas the other blocks are associated with NBRCS decreases. Significant variability between SVNs is also seen within a given block (up to ~1 dB), which counters the general  
Figure 5: Dependence of NBRCS on azimuth at ERA-5 U10 ~7 m/s, for all GPS transmitters/SVNs, segmented by CyGNSS Flight Models (FM01-FM08).  
Figure 6: Residual dependence of NBRCS reported by CyGNSS FM03 at U10 ~7 m/s on incidence angle; each line represents a different GPS transmitter/SVN and is coloured by the GPS block to which the transmitter belongs.  
expectation that similar hardware designs within blocks should lead to similar transmitted power levels at a given geometry.
# The Impact of 6G-IoT Technologies on the Development of Agriculture 5.0: A Review
## 3 6G-IoT: New Technologies and Emerging Services
### Terahertz Communications  
To address the spectrum and capacity shortage below 6 GHz, the adoption of higher frequency bands has already been standardized in 5G networks, primarily for frequencies between 24 and 48 GHz, permitting the development of new very-high-speed services. Future 6G networks are anticipated to employ frequencies over 100 GHz in the THz frequency range (0.1 to 10 THz) [76]. It is anticipated that systems designed to make use of these ultra-high-frequency bands will feature extremely low latency and enormously high data rates. As the number of IoT devices and the volume of generated data continue to increase, the development of high-bandwidth and low-latency communication links will provide a foundation for scalability, accommodating the growing demands of data transmission and processing.  
However, THz communication systems are still in their infancy, and while some of their challenges are similar to those of 5G mm-wave systems, many others are unique and require the development of novel technological solutions [76; 77]. Consequently, innovative transceiver designs, transmission techniques, and communication protocols must be devised to meet the requirements of 6G THz systems. Once these challenges are sufficiently addressed, the THz communication systems will be able to support new applications with higher requirements and, in light of next-generation agriculture, will be able to enable ultra-high-precision agriculture services [78; 79]. These services are expected, for example, to offer plant-level data and therefore unparalleled accuracy in targeting and treating even individual plants, which will massively reduce chemical use. Clearly, such approaches necessitate extremely demanding real-time, ultra-high-resolution field monitoring, which could be efficiently supported by future 6G THz systems [76].
## 2 Determination of Computational Model
### Turbulence Model and Boundary Conditions  
The wind turbine rotates in the air and the flow field is in an unsteady state. The simulation of the flow field is based on the simulation software, and the SIMPLE method is used to solve the incompressible time-averaged N-S equations. The turbulence model adopts the k-\(\epsilon\) model based on the equation of the near-wall region of the SST, which can accurately simulate the flow phenomena in the region close to the blades, as well as the pressure distribution on the surface of the blades.  
We set the blade surface for the fluid-solid coupling calculation surface, the calculation domain inlet using the velocity inlet for uniform wind speed (rated wind speed) 10 m/s, and the outlet using the pressure outlet. The direction vector of the wind turbine rotation axis is set at (1, 0, 0), the inlet turbulence intensity is 5%, and the wall surface is a free-slip wall surface to eliminate the influence of the velocity shear on the flow field of the wind turbine. The wind turbine, nacelle, and tower are all no-slip wall surfaces. The momentum equations are in second-order windward format with a convergence accuracy of \(10^{-3}\).
## 2 Mathematical Modeling and Methods
### Mathematical Model of Clutch System
#### 2.2.1 Electro-Hydraulic Proportional Valve Model  
The leakage between the main spool of the proportional valve and the valve sleeve is too small to be ignored. The hydraulic oil flows into the main valve cavity, which compensates for the amount of oil compression in the main valve cavity, and the rest flows into the clutch cylinder.  
When the solenoid valve is not energized, the main spool is located at the left end of the proportional valve. The oil in the clutch cylinder flows back to the oil tank through the oil discharge port of the proportional valve, and the clutch is in a disengaged state. When the clutch is energized, the electromagnetic force generated by the solenoid valve drives the spool to move to the right. Then the oil discharge port is gradually closed and the oil inlet port is opened. Finally, the oil flows into the wet clutch cylinder through the throttle port and the clutch gradually engages. The electro-hydraulic proportional valve model mainly consists of the spool force balance equation, the pressure-flow balance equation and the flow continuity equation.  
\[\left\{\begin{array}{l}m_{p}\ddot{x}_{v}+C_{v}\dot{x}_{v}+k_{p}x_{v}+2C_{d}C_{ v}A_{e}\cos\theta_{s}p_{o}\\ =K_{i}i-K_{b}x_{v}+p_{o}A_{Z}\\ \end{array}\right. \tag{7}\] \[q_{s}=\left\{\begin{array}{l}\operatorname{sign}(P_{s}-P_{c})\ C_{ V}A_{i}\sqrt{\frac{2(P_{s}-P_{c})}{\rho}},x_{v}>S\\ 0,x_{v}=S\\ -C_{V}A_{e}\sqrt{\frac{2(P_{s}-P_{c})}{\rho}},x_{v}<S\\ q_{s}-q_{l}=\frac{V_{0}}{P_{c}}\dot{p}_{c}\end{array}\right.\]  
where \(m_{v}\) is the quality of the spool; \(x_{v}\) is the displacement of the spool; \(C_{v}\) is the viscous damping coefficient of the proportional valve spool; \(k_{p}\) is the return spring stiffness; \(C_{d}\) is the flow coefficient of the orifice; \(C_{v}\) is the flow rate coefficient of the main spool of the proportional valve; \(A_{Z}\) represents the proportional valve spool action area; \(A_{e}\) represents the throttling area of the proportional valve oil discharge port; \(A_{i}\) represents the inlet throttle area; \(\theta_{s}\) represents the jet angle of the main valve throttling port of the proportional valve; \(p_{o}\) represents the proportional valve outlet pressure; \(K_{i}\) represents the driving coefficient of the electromagnet; \(K_{b}\) represents the speed back EMF coefficient; \(P_{s}\) represents the oil supply pressure, \(P_{c}\) represents the outlet pressure of the proportional valve; \(S\) represents displacement of the spool from the initial position to the full closure of the drain hole; \(\rho\) represents the oil fluid density; \(q_{s}\) represents the flow of the proportional valve; \(q_{l}\) represents the flow of the clutch cylinder, \(V_{s0}\) represents the volume of the main valve cavity of the proportional valve, and \(\beta_{e}\) represents the volume elastic modulus of the oil quantity.
## 4 Performance Analysis of QoS Metrics
### Evaluation of PDR for the Transmission Power Level (\(P_{\text{Trans}}\)) and Energy Consumption
#### 4.1.2 Energy Consumption and Network Life at Different P\({}_{\text{Trans}}\) Levels
The node lifetime is calculated for a battery of 5000 mAh capacity with different \(T_{slap}\) intervals for a comparative analysis of different transmission power levels of 1, 3, and 5 dBm. The network lifetime for different sleep intervals, i.e., packet intervals and transmission power levels with a packet size of 30 bytes and battery capacity of 5000 mAh, is presented in Figure 8.  
\[T_{active}=T_{tx1}+T_{tx2}+T_{rx1}+T_{rx2}+T_{idle}+T_{sb} \tag{4}\]  
\[I_{active}=\frac{T_{tx1}T_{tx1}+T_{tx2}T_{tx2}+T_{rx1}T_{rx1}+T_{rx2}T_{rx2}+T_ {idle}T_{idle}+T_{sbT_{sb}}}{T_{active}} \tag{5}\]\[I_{drain}=\left(\frac{T_{active}}{T_{sleep}}\times I_{active}\right)+ \left(1-\frac{T_{active}}{T_{sleep}}\right)\times I_{sleep} \tag{6}\] \[T_{lifetime}=\frac{\frac{B_{capacity}}{T_{drain}}}{365\times 24} \tag{7}\]  
The lifetime of an XBee S2C module can range between 0.93 and 0.94 years with a packet size of 30 bytes, battery size of 5000 mAh, and packet interval of 60 s. Thus, it performs considerably energy efficiently with Zigbee protocol in comparison with the other contemporary technologies, such as Z-wave, Bluetooth low energy (BLE), and LoRa. The node lifetime with unencrypted communication is slightly higher than that of encrypted communication but it is very insignificant considering the security and data privacy AES encrypted communication provides. Focusing on the node lifetime estimation of encrypted communication, the lifetime with transmission levels of 1 and 3 dBm are almost the same whereas the node lifetime decreases with a higher transmission power of 5 dBm. Even though this difference is not very significant, the node lifetime degrades significantly with 5 dBm in comparison with 3 dBm with the decrease of the packet interval and packet size. Thus, for task-intensive applications, a transmission power level of 3 dBm is preferred. Moreover, with 3 dBm, the performance, such as PDR, is quite decent as mentioned earlier, which does not improve drastically with 5 dBm. As it is analyzed, 3 dBm is the most optimized transmission power level considering the trade-off of PDR and network lifetime, the rest of the evaluation with RSSI, latency, and data throughput focuses mainly on 3 dBm, 128-bit AES encrypted communication.
# Helping CNAs Generate CVSS Scores Faster and More Confidently Using XAI
## Appendix A Common Vulnerability Scoring System
* T--Exploit Code Maturity (ECM): This measures the likelihood of the vulnerability being attacked and is typically based on the current state of exploit techniques, exploit code availability, or active \"in-the-wild\" exploitation. * T--Remediation Level (RL): This reflects the level of effort required to remediate a vulnerability. It measures the extent to which a vulnerability can be mitigated or repaired through software updates, configuration changes, or other actions. * T--Report Confidence (RC): This measures the degree of confidence in the existence of the vulnerability and the credibility of the known technical details. * E--Modified Base Scores (MBS): These metrics enable the analyst to override individual Base metrics based on specific characteristics of a user's environment. Characteristics that affect Exploitability, Scope, or Impact can be reflected via an appropriately modified Environmental Score. * E--Confidentiality Requirement (CR): This measures the impact on the confidentiality of information that is affected by a vulnerability. It reflects the potential damage that could be caused if a vulnerability is exploited to disclose sensitive information to unauthorized parties. * E--Integrity Requirement (IR): This measures the impact on the integrity of the information that is affected by a vulnerability. It reflects the potential damage that could be caused if a vulnerability is exploited to modify or destroy information in an unauthorized manner. * E--Availability Requirement (AR): This measures the impact on the availability of resources that are affected by a vulnerability. It reflects the potential damage that could be caused if a vulnerability is exploited to disrupt access to or use of critical resources, such as systems, networks, or applications. Each of these metrics has multiple possible values. Tables A1-A4 provide a summary of the potential values for the metrics mentioned above, along with an explanation for each value.
## 4 Experimental Methods and Results
### Experimental Methods and Results of the Decomposition Component by EMD  
For construction of the prediction model, the closing price of stock data as historical data was transformed into the new data using EMD. As shown in Figure 7, this experiment demonstrates decomposing results to create IMFs using EMD. The seven IMFs were decomposed from the original closing price sequence and the results of the IMFs' scale from high to low frequency. However, the number of IMFs are different depending on the raw data. The processes of EMD are repeated until there are only one global maxima and minima value showing on IMF 7 in Figure 7. The number of IMFs will be changed if the raw data is changed. On the other hand, the number of IMFs will still be of the same value when applying EMD to the same data.  
The result shows that it can be divided into three groups. The first group are high-frequency components in the original data. This group was represented by the first few IMFs with a lot of noise. The second group are middle-frequency components. represented by the center IMFs with a medium noise. The last group are low-frequency components. This group was represented by the last few IMFs with little noise. Moreover, the last IMF is comparable to the trend of a stock. It is common to hypothesize that the LSTM can accurately predict low-frequency IMFs, but it will struggle with high-frequency IMFs. To maximize the prediction efficiency, the LSTM is trained individually by each IMF. Thus, the hyper-parameter, the number of hidden layers and weights are different for each IMF. This is the significant difference making a hybrid EMD-LSTM model perform better than a  
Figure 6: The process of the prediction model.  
single LSTM model, which is applied directly to the original closing price time series, with characteristics of noise and volatility.  
IMFs were obtained by subtracting from the original closing price, so the summation of all IMFs is totally identical to the original. For this reason, the summation of the prediction results of all IMFs can be considered as the prediction result for the original closing price.  
Figure 7: The original closing price and decomposed IMFs.
## 2 State of the Art
### Routing Protocols  
Routing protocols are responsible for guaranteeing the delivery of a message from a source node to a destination node. Routing protocols must adapt to the essential characteristics of FANETs: high mobility, energy efficiency, constant changes in the topology, etc. In addition, due to the wide variety of application fields, the requirements of routing protocols in FANETs can be very diverse. For example, a constant jitter would be necessary for real-time video applications, whereas a high level of reliability would be necessary in applications that extend telecommunication coverage in case of disasters (regardless of delay and jitter). Most FANETs' routing protocols are extensions of well-known MANET protocols, such as AODV, OLSR, or DSR [10]. However, there is still a lack of routing protocols that fit all FANETs' needs.  
In this subsection, we will examine the different routing protocols proposed for FANETs and group them into five main categories: (i) topology-based, (ii) position-based, (iii) clustering/hierarchical, (iv) swarm-based, and (v) delay-tolerant network (DTN).