esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of DOMs on Static Adsorption and Desorption  
Figure 7 shows the adsorption amounts of NO\({}_{3}\)\({}^{-}\)-N using AEE-3 and A520E in a bi-solute system (HA/NO\({}_{3}\)\({}^{-}\), SDBS/NO\({}_{3}\)\({}^{-}\) or TA/NO\({}_{3}\)\({}^{-}\)) for four adsorption-desorption cycles. It was seen that the adsorption capacities of A520E for NO\({}_{3}\)\({}^{-}\)-N distinctly declined over the four cycles. DOMs can occupy more active sites through hydrophobic and electrostatic interactions on the A520E resin with a polystyrene matrix [12]. By contrast, AEE-3 exhibited greater NO\({}_{3}\)\({}^{-}\)-N adsorption in bi-solute systems, and the reduction of NO\({}_{3}\)\({}^{-}\)-N uptake was slight with increasing the number of adsorption-desorption cycles. This result validated the hypothesis that AEE-3 has a strong resistance to interferential organics due to its hydrophilic matrix. Besides, over 94% of the adsorbed NO\({}_{3}\)\({}^{-}\)-N on AEE-3 was desorbed, while the desorption rates of A520E in the presence of organic matters declined gradually with the number of adsorption-desorption cycle (Figure 8). In addition, Figure 9 presents the desorption rates of DOMs regenerated by a NaCl solution, and the low values for A520E resin can be attributed to the strong affinity between the polystyrene matrix and the hydrophobic organics, causing the DOMs to not be removed fully by regeneration. Hence, these results demonstrate an improved performance on adsorption/regeneration of AEE-3 compared to A520E.  
Figure 7: Effect of different DOMs (HA, TA or SDBS) on NO\({}_{3}\)\({}^{-}\)-N removal by AEE-3 and A520E.
## 2 Materials and Methods
### Measurements  
More than 240 body measurements were collected in each body scan including the torso surface area and torso volume of each garment configuration. To determine the air gap formed between each clothing configuration and the nude body (CO) for the torso (where the SCBA is worn), Equation (1) was used to calculate the air gap distance in millimeters. This equation was previously established by [NO_PRINTED_FORM] [11] to quantify air gaps between clothing layers utilizing 3D body scanning technology and has since been used to assess garment ease based on variations in sizing and fit of firefighting PPE [12].  
\[\textit{Torso }A_{gap}=\textit{Torso }V_{\textit{airgap}}/\textit{Torso }SA_{\textit{co}} \tag{1}\]  
where  
\(\textit{Torso }A_{gap}=\) the average distance between the nude body and the clothing layer worn over the surface area of the torso (mm),  
\(\textit{Torso }V_{\textit{airgap}}=\) the torso volume of the clothing layer minus the torso volume of the compression/nude body (cm\({}^{3}\)),  
\(\textit{Torso }SA_{\textit{co}}=\) the torso surface area of the nude participant in the compression garment (CO) (cm\({}^{2}\)).  
In addition to torso air gap distance, an additional methodological approach was utilized to more specifically investigate the impact of the SCBA on garment ease and the formation of air gaps in specific upper body areas. Of the 240+ body measurements collected in each scan, only the front torso measurements of the upper body were considered to avoid interference from the SCBA tank. This included the following 10 individual upper body measurements: front shoulder width, across chest tape measure, across axilla chest front length, front waist tape measure, left bust prominence, right bust prominence, left stomach circumference tape measure, right stomach circumference tape measure, left waist circumference tape measure front, and right waist circumference tape measure front. From these measurements, garment ease was calculated by subtracting the measurement in the TS from the same measurement in the TS-with SCBA, per previously established methodologies in the literature [4; 12]. All measurements are reported in centimeters.  
Figure 1: A side view gray-scale avatar from the Size Stream SS20 body scanner of a male firefighter participant wearing (**a**) a turnout suit and (**b**) a turnout suit with a provided self-contained breathing apparatus (SCBA).
# A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions
## 7 Contributions to the Use of USVs for Disaster Applications
### Fleet Modifications  
We have proposed a new payload system for the N-Boat II [250], including environmental monitoring sensors and real-time and online communications. The new N-Boat II sailboat architecture is vital for autonomous long-term missions since it can stream available sensor data over the Internet to DM and environmental monitoring agencies. The idea is to prepare the N-Boat II to be used as an early warning system for environmental and natural disasters.  
For the Lutra boats, we integrated an embedded image processing payload, consisting of a sealed acrylic box with a Zed(r) RGBD camera (RGB + depth) ([[http://www.stereolabs.com](http://www.stereolabs.com)]([http://www.stereolabs.com](http://www.stereolabs.com))), capable of outdoor use with a depth range of about 20m. For image processing, we are using NVIDIA(r)'s Jetson TX2 board with Pascal GPU architecture, including 256 CUDA cores. This payload is designed to run Convolutional Neural Networks (CNN), in applications for real-time obstacle detection and other image processing tasks. Finally, the boats can also carry bathymetry sensors and a system to collect water samples remotely. In the future, we intend to include an ADCP sensor to measure water flow--an important parameter for hydrologic studies associated with extreme weather.
## 4 Discussion
Although it is reasonably straightforward to optimize the parameters with each dataset to obtain better retrievals, it would be extremely difficult (if not impossible) to do so when in situ measurements from the target location are hard to obtain. Instead, a set of globally optimized parameters were obtained using a dataset that covers a large dynamic range of the global ocean. Another challenge for \(a_{\text{Gau}}(\lambda)\) retrieval in the open ocean lies in the low contribution of pigment absorption to the total absorption coefficient around 550-650 nm. Thus, it is difficult to directly invert \(R_{\text{rs}}(\lambda)\) to obtain pigment absorption coefficients at the longer wavelengths (>550 nm). This version of the MuPI model should be considered interim because the model could be further updated when more global data become available. In its present form, the model is optimized to work with \(R_{\text{rs}}(\lambda)\) data from the first nine MERIS bands. As a first step, several components of the Gaussian model were deliberately formulated by use of empirical relationships to limit the number of unknowns to be solved via the spectral optimization procedure. This is particularly true for estimation of 13 \(a_{\text{Gau}}(\lambda)\) from two independent Gaussian curves, which significantly reduced the unknowns. Instead of the Gaussian scheme, another potential way to obtain different pigment information from remote sensing data is through the specific absorption coefficients as adopted by many studies [46; 86]. However, the specific absorption coefficients have significant limitations, such as the variation of the coefficients in different waters and the lack of routine measurements of some pigments, such as PE and PC [46; 86; 87]. With the information of accessory pigments obtained from MuPI, different biogeochemical studies could be conducted: 1. Remotely sensed PE and PC concentrations could be validated and applied to the estimation of cyanobacteria on a global scale. 2. The pigment ratios could be used as a direct indicator for estimation of phytoplankton functional types or functional traits, and phytoplankton physiological variation over space and time. 3. The pigment absorption coefficients (photoprotective and photosynthetic) could be estimated from satellite remote sensing data and incorporated into models for more accurate estimation of primary productivity.
# Novel Design of Expandable Spinal Cage for Efficient Lumbar Spine Fusion Operation
## 2 Design of an Expandable Cage
### Final Modeling  
The final prototype consists of an upper plate, a lower plate, and a linkage structure. To prevent displacement within the spine, surfaces of upper and lower plates are designed with  
Figure 2: Link mechanism for angle extension.  
a spiked configuration. These plates are connected via a linkage mechanism that enables simultaneous height and angle adjustments. Joints 1 and 3 of the linkage mechanism are positioned in posterior and anterior sliding blocks, respectively, and screws are used to adjust the positions of these sliding blocks. This screw-based actuation achieves one of the study's primary goals: continuous adjustment of the height and angle.  
As shown in Figure 3, the prototype's basic specifications include a length of 26 mm, a width of 11.8 mm, an initial height of 8.2 mm, and an initial lordotic angle of 4\({}^{\circ}\). Height and angle adjustments are performed through sliding blocks that house Joints 1 and 3. As shown in Figure 4, users can adjust the positions of these sliding blocks by rotating two screws using a hex driver. Screw 2 adjusts the posterior sliding block, increasing the cage height up to 11.7 mm. During this process, the linkage mechanism maintains the cage angle unchanged, thereby achieving independent height and angle adjustments. Screw 1 controls the anterior sliding block, increasing the cage height up to a maximum of 16.6 mm and extending the lordotic angle to 16.3\({}^{\circ}\). Thus, the mechanism successfully meets the target specifications of a height of 14.4 mm and an angle of 12.0\({}^{\circ}\). Additionally, this adjustment method is similar to those of existing expandable cages, which utilize rotational mechanisms, and has been carefully designed to minimize disruption to the established clinical workflow.  
To fabricate the prototype, Ti-6 Al-4V was selected as the material due to its excellent biocompatibility, high mechanical strength, and widespread use in clinical spinal implants. This titanium alloy offers a high strength-to-weight ratio and superior corrosion resistance, making it well-suited for long-term implantation in the human body. Its proven track record in orthopedic applications ensures reliability and safety, aligning with the structural requirements of spinal fusion devices [29].  
Figure 3: Modeling of expandable cage. (**a**) Initial state. (**b**) Maximum extended state.
# Enhancing Target Speech Based on Nonlinear Soft Masking Using a Single Acoustic Vector Sensor
## 2 Problem Formulation
### Nonlinear Soft Time-Frequency (TF) Mask Estimation
\begin{array}{l}\frac{1}{1+e^{-\zeta(1-(\Delta \phi(\tau,\omega)/\delta_{1}+\Delta\phi(\tau,\omega)/\delta_{2})/2)}}\ \ \Delta\phi<\delta_{1}\ \&\ \Delta\theta<\delta_{2}\\ \rho\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ else\end{array} \right. \tag{39}\]  
where \(\zeta\) is a positive parameter and \(\rho\) (\(0\leq\rho<1\)) is a small positive parameter tending to be zero, which reflects the noise suppression effect. The parameters \(\Delta_{1}\) and \(\Delta_{2}\) control the degree of the estimation difference (\(\Delta\phi(\tau\),\(\omega\)), \(\Delta\theta(\tau\),\(\omega\)). When parameters \(\Delta_{1}\), \(\Delta_{2}\), and \(\rho\) become larger, the capability of suppressing noise and interferences degrades, and the possibility of the (\(\tau\),\(\omega\)) being dominated by the target speech also degrades. Hence, selecting the values of \(\rho\), \(\Delta_{1}\), and \(\Delta_{2}\) is important. In our study, these parameters are determined through experiments. Future work could focus on selecting these parameters based on models of human auditory perception. In the end, we need to emphasize that the mask designed in Equation (39) has the ability to suppress the adverse effects of the interferences and background noise, and preserve the target speech simultaneously.
## 3 Semi-Automated Image Analysis on ADC Maps
### Tumor mapping from T1 wCE images to ADC maps  
It is difficult for radiologists to directly delineate the tumor contours on ADC maps, and the scanner-provided T1w images and the derived ADC maps are not inherently co-registered, because they have different slice thickness, different field of view (FOV), and different image resolutions. Therefore, a 3D ROI mapping tool was developed to map the tumor ROIs from T1 wCE images onto ADC maps based on the scanner geometry. Compared to the co-registration technique, the mapping tool only transformed voxels within the tumor ROI rather than the whole image volume; thus it was more computationally efficient. However, the mapping tool could not correct for patient motion; thus a board-certified radiologist was required to visually check the mapped results and perform manual corrections when necessary.  
The mapping tool used an affine transformation with the parameters extracted from the DICOM header based on physical locations. Equation 3 shows the way to calculate the 3D physical location voxelwise. \(\Delta_{i,j,k}\) is the physical voxel size read from the tag \"pixel spacing\" and calculated from \"slice location\"; \(X_{x,y,z}\), \(Y_{x,y,z}\) is image orientation read from the tag \"image orientation\" which specifies the orientation of the image frame rows and columns, \(Z_{x,y,z}\) is the z-direction orientation calculated from \(X_{x,y,z}\), \(Y_{x,y,z}\), \(S_{x,y,z}\) is read from the tag \"patient position\" which specifies the physical location of the patient's anterior-left-upper corner; i, j, k are voxel index; and \(P_{x,y,z}\) are the calculated physical location of the voxel in millimeters. The transformation matrices are calculated for both source and target ROI respectively. For each voxel in the source ROI, the physical location is first calculated, and then the inverse operation is performed to calculate the corresponding voxel coordinates of the target ROI. Finally, radiologists visually check the contours on ADC maps and manually correct the tumor contours on ADC when necessary. Figure 1(B) shows an example of the mapped tumor ROI on the ADC map from the T1 wCE image.  
**Equation 3.** The physical location calculation of a voxel (i,j,k).  
\[\begin{bmatrix}P_{x}\\ P_{y}\\ P_{z}\end{bmatrix}=\begin{bmatrix}X_{x}\Delta_{i}&Y_{x}\Delta_{j}&Z_{x}\Delta_{k }&S_{x}\\ X_{y}\Delta_{i}&Y_{y}\Delta_{j}&Z_{y}\Delta_{k}&S_{y}\\ X_{z}\Delta_{i}&Y_{y}\Delta_{j}&Z_{z}\Delta_{k}&S_{z}\\ 0&0&0&1\end{bmatrix}\begin{bmatrix}i\\ j\\ k\\ 1\end{bmatrix}\]
## 1 Introduction
In [19], the in-line interference between the LEO and GEO system can be mitigated by tilting the normal direction of the phased array antennas of LEO satellites. In [20], the progressive pitch method and the coverage-expanding method are proposed to reduce interference, but the interference can be only partially solved for the high latitudes [21]. Moreover, the adjustment of the satellite antenna beam pointing of the LEO mega-constellation means a huge computational overhead, which is difficult to achieve with the limited computing resources on the satellite. If the calculation of the optimal beam pointing is completed by the ground control centers with stronger computing capabilities, the time-varying antenna adjusting information should be sent back to the satellites frequently, which leads to an additional delay and signaling overhead. However, the co-channel interference for the dense LEO satellite systems will be more complicated due to the overlapped coverage and time-varying interfering links. The above interference mitigation techniques cannot be directly applied to the dense LEO satellite systems. Thus, a distributed beam pointing optimization method based on interference situational awareness is proposed in this paper. Firstly, the interference situational database composed of the angle of departure (AoD) and the angle of arrival (AoA) of the time-varying interfering links can be collected at each UT. Then, the optimal beam pointing at each UT can be obtained by PSO optimization. The main contributions of this paper are summarized as follows:  
* With the time-varying interfering satellite set and minimum elevation constraint, the AoD and AoA of communication and interfering links can be calculated. Then, the AoD and AoA of interfering links are stored to construct the interference situational aware database, which is used for further beam pointing optimization;
* With the interference situational aware database, the optimal beam pointing can be modeled as a non-convex optimization problem, and a distributed method based on particle swarm optimization (PSO) is proposed to maximize the signal-to-interference plus noise ratio (SINR) of each UT;
* The performance of the proposed interference situational aware beam pointing optimization technology is verified by the simulation results. The rest of this paper is organized as follows: in Section 2, the system model and problem formulation are described. In Section 3, the scheme of interference situational aware beam pointing optimization is analyzed. In Section 4, simulation results and complexity analysis are presented. In Section 5, conclusions are drawn. Matrices and vectors are denoted by bold letters. \(\overrightarrow{AB}\) denotes the vector that goes from A to B. \(|a|\) is the magnitude of \(a\). \(\hat{a}\) is the estimation of \(a\). The major variables adopted in the paper are listed in Table 1.
# LSOS: An FG Position Method Based on Group Phase Ranging Ambiguity Estimation of BeiDou Pseudolite
## 3 Algorithmic Principle
### FG Optimization Positioning Method Assisted by SFLA
#### 3.4.3 Optimal Estimation Method of Factor Graph Based on SFLA  
To solve the current nonlinear problems, the least squares method is usually used to make the optimal estimation by Taylor expansion of the covariance function and the derivation of the equation. However, due to the limitation of propagation distance and spatial noise, the conventional first-order Taylor expansion neglects the effect of the expansion on the optimal position estimation. In the early test process, it is difficult to get an effective solution in many cases. As one of the current intelligent group search algorithms, the SFLA algorithm can make up for the shortcomings in the optimal solution estimation of nonlinear functions. The algorithm processing block diagram of this paper is given in Figure 4.
# A Multi-Model Proposal for Classification and Detection of DDoS Attacks on SCADA Systems
## 2 SCADA Systems and Cybersecurity
### Scada System  
SCADA systems perform control and monitoring tasks in critical infrastructure or facilities. Critical infrastructures, such as power generation plants, wind energy turbines, and natural gas distribution facilities are vital structures that produce and (or) transmit natural gas, oil, water, and similar resources to another place. To give more examples, many systems such as municipal water distribution facilities, airlines, and ship systems are also critical infrastructure systems and have a significant place nationally and internationally. SCADA systems are also used in production facilities, factories, or public institutions apart from these infrastructures.  
SCADA systems consist of a master terminal unit (MTU), remote terminal units (RTUs), and a communication network. The MTU controls the processes in the system using a human-machine interface (HMI). There is data exchange and command transmission between RTUs and MTU. RTUs transmit the data collected from the field sensors to the MTU, and RTUs carry out the commands from MTU. Modbus, DNP3, and Profibus communication protocols--specific to SCADA systems--are used for communication between basic units. The sensors and actuators on the RTUs abide by the commands. Elements such as pumps and relays serve as actuators. The HMI also demonstrates the data obtained from the sensors [6; 7].
## 2 Linearization Method  
### Linearization Using Resampling  
Some promising solutions to correct single pulse data are time resampling [15; 16] and phase resampling [17]. In terms of discrete samples, the phase of the baseband signal is given by Equation (4) above. If there were no modulation error, the change in the baseband phase would be only due to round-trip delay of a single point-target and the modulation slope  
\[\Delta\theta=2\pi m_{o}T\tau \tag{6}\]  
When there is modulation error, phase differs from sample-to-sample. The \(n\)th sample is given by:  
\[\Delta\theta+\varphi_{n}=2\pi(m_{o}+\epsilon_{n})T\tau \tag{7}\]  
where \(\epsilon_{n}\) is the error in modulation slope at the time of the \(n\)th sample and \(\varphi_{n}\) is the phase error at the \(n\)th sample. The error term is  
\[\varphi_{n}=2\pi(\epsilon_{n}T)\tau \tag{8}\]  
Figure 1: (**a**) FMCW radar block diagram and (**b**) example waveforms.  
which reduces to one error vector, \((\epsilon_{n}T)\), that can be expressed as a modulation error or a sample time error.  
Time resampling, by interpolation, of each range profile effectively compensates for the phase error for at the cost of computation time [15,16]. Time resampling ratio can be fixed or optimized using phase error and the largest range of interest [17] by the least squares fit at calibration time.  
Autofocus method as described by [PERSON] et al. [18] is accomplished by a measurement of a calibration target at a known range. The baseband signal should be a sinusoid, and the phase error is extracted. Note that the phase error is a function of time delay. Objects will be more out of focus the farther they are from the calibration range. Scatterers at different ranges would need a scaled version of this phase correction. A different matched filter can be applied to each range bin, but is not effective for practical scenes that include clutter and multiple objects.