esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Design of the Auxiliary Buck DC/DC Converter  
The auxiliary Buck DC/DC converter was designed with a switching frequency, f\({}_{\rm bu}\), of 100 kHz, and a second-order output filter with a cut-off frequency, f\({}_{\rm c-bu}\), of 10 kHz,  
Figure 17: Photograph of the prototype of the proposed TIBuck DC/DC converter working as a VLC driver.  
Figure 18: Schematic and control block diagram of the proposed TIBuck DC/DC converter prototype used as a VLC driver.  
one decade below the switching frequency. As shown in Figure 10, there is a trade-off on the selection of \(\rm f_{bu}\) and \(\rm f_{c-bu}\). The higher the switching frequency, the smaller and lighter the converter, which is an advantage in VLC drivers. On the other hand, the higher the switching frequency, the higher the power losses and the more difficult it is to design the filter. In this design, a conservative approach was chosen, making the output voltage ripple negligible.  
The auxiliary Buck DC/DC converter works in a closed loop, and the control was implemented in the FPGA. The output current \(\rm i_{o}(t)\) of the HB-LED load is measured by a shunt resistance in series. The current \(\rm i_{o}(t)\) has both DC and communication-signal components. Since only the average value, \(\rm I_{o}\), needs to be controlled, there is a low-pass filter, so the only component sampled by the FPGA ADC is Io. The PI compensator was designed with 10 Hz of bandwidth.  
By means of this control, the auxiliary Buck DC/DC converter ensures the HB-LED always works in the middle of its linear region, regardless of temperature. The slow nature of thermal behaviour simplifies the design of the current control, which does not need a fast response or a wide bandwidth. It is worth noting that the dynamics of the output average control throughout \(\rm V_{2}\) in the proposed TIBuck DC/DC converter can be approximated by the dynamic of the auxiliary Buck DC/DC converter if the following conditions are met: \(\rm f_{bu}<<f_{ii}\) and \(\rm f_{c-bu}<<f_{c-ii}\). These conditions are implicit in Figure 10. If they are met, Equation (14) holds true. This means that only \(\rm f_{bu}\) and the output filter of the auxiliary Buck DC/DC converter limit its dynamics.  
The list of components used for the auxiliary Buck DC/DC converter can be found in Table 1. The converter was implemented in a synchronous configuration (to increase the overall efficiency of the proposed TIBuck DC/DC converter, following the conclusion in Section 4) by two MOSFETs integrated in the same chip CSD88539 (switches \(\rm Q_{bu-1}\) and \(\rm Q_{bu-2}\)) and a half-bridge gate driver \(\rm ISL6700\).
# A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks
## 3 Detailed Description of the Proposed Localization Scheme  
The proposed localization scheme consists of four steps: Firstly, the network system architecture and trajectory of the mobile anchor node are designed to obtain a set of measured RSSI values. Secondly, an SVR-based interpolation method is designed to process measured RSSI values to estimate the projection of a sensor node on the trajectory. Thirdly, a curve matching method is developed to process measured RSSI values to obtain the perpendicular distance from a sensor node to the trajectory. Finally, the location of the sensor node is calculated by a geometric method. Table 1 shows the main notations used in this paper.  
Network System Architecture, Mobile Anchor Node Trajectory, and Obtaining a Set of Measured RSSI Values
# A New Compact Octagonal Shape Perfect Metamaterial Absorber for Microwave Applications
## 3 Results and Discussions
### Analysis of Design Structure with Resistive Load  
The effects of the resistive load of the octagonal resonators on the absorption have been analysed. The absorption of design structure with different values of a resistive load are shown in Figure 9. The maximum absorption peaks of the structure are 99.40% at 8.17 GHz and 99.74% at 11.23 GHz, respectively, for 1500 \(\Omega\) resistive load. Similarly, the maximum peak values of the absorption are  
Figure 8: Absorption of the design structure for different thickness of metallic materials.  
Figure 7: Absorption of the design structure for different thickness of substrate materials.  
99.54% at 8.19 GHz, and 99.77% at 11.41 GHz, correspondingly for 3000 \(\Omega\) resistive load. Additionally, absorption peak values are 99.41% at 8.20 GHz and 99.76% at 11.26 GHz, respectively, for 4500 \(\Omega\) resistive load, as well as 99.33% at 8.21 GHz and 99.94% at 11.26 GHz, respectively, for 6000 \(\Omega\) resistive load that placed between two rings. The high absorption peaks are 99.54% at 8.19 GHz, and 99.77% at 11.41 GHz for 3000 \(\Omega\) resistive load.  
Due to different resistive load, the absorption values have been different at different frequencies. The resistive load with 3000 \(\Omega\) has shown the maximum absorption value. On the other hand, the minimum value of absorption has been attained from resistive load with 1500 \(\Omega\). The effects of resistive load also showed that the performance of absorption of PMA also depends on lumped elements. Furthermore, the absorbing performance has been optimized by proper choice of the lumped resistor of the structure.
# Satellite Measured Ionospheric Magnetic Field Variations over Natural Hazards Sites
## 5 Results
### Rocket Launches
#### 5.4.1 Falcon 9 Launch  
The statistical evaluation of _Swarm_ A/B/C VFM magnetic field fluctuations for the SpaceX Falcon 9 launch on 2020-11-21T17:17:08 UTC (payload Sentinel-6 Michael Freilich satellite) from Vandenberg Air Force Base, California, USA, is shown in Figure 9. In general the components are similar, but a distinct attenuation of fluctuations over the entire frequency band is observed in the Y-component of _Swarm_ C VFM, the Y- and Z-components of _Swarm_ B VFM are clearly enhanced. In this particular event with the given parameters (box size lat/long \(\pm\) 5.5 deg, time span \(\pm\) 1 day) there are four crossings for each _Swarm_ satellite. On the basis of the orbit constellation _Swarm_ A/C are close in the temporal and spatial domains, which basically enables an intended dual satellite investigation. However, for this event the two post crossings have time gaps of \(\Delta t\) = {+1.15} days, i.e., ionospheric disturbances triggered by the rocket launch are presumably dissipated, magnetic field fluctuations cannot be linked to the launch. _Swarm_ B has one crossing before and three crossings after the SpaceX Falcon 9 lift-off, \(\Delta t\) = {-0.40, +0.11, +0.59, +1.10} days, the measurements show distinct enhancements in the fluctuations for the first crossing after the launch. We contend that this magnetic field variation can be linked to ionospheric structures caused by atmospheric waves generated along the first part of the launch vehicle ascent trajectory.
## 2 Materials and Methods
### Experimental Setup  
We conducted a pot culture with completely randomized factorial design in the greenhouse. Two factors were included, namely, plant species (two levels: _S. guianensis_ and _P. natatum_) and soil treatments (three levels--control with no treatment, liming, and plant residue incorporation), and thus, six treatments were produced with each plant species comprising three soil treatments. For liming treatment (named as CaCO\({}_{3}\)), CaCO\({}_{3}\) of chemical grade was applied to soils at the rate of 2.5 g\(\cdot\)kg\({}^{-1}\), equivalent to 6.5 t\(\cdot\)ha\({}^{-1}\). For plant residue incorporation treatment (named as OM), oven-dried, and homogenized plant shoot biomass was incorporated into the soil at the rate of 30 g\(\cdot\)kg\({}^{-1}\), equivalent to 78 t\(\cdot\)ha\({}^{-1}\). To simulate the status in the field conditions, the soil (grown with each of _S. guianensis_ or _P. natatum_) was incorporated with the plant residues of respective species.  
Each plastic pot (18 cm in diameter and 15 cm in height) was filled with 2.0 kg soil. Five replicates were set up for each treatment, thus, totally producing 30 pots with 15 pots for each plant species. Seeds of _S. guianensis_ and _P. natatum_ were surface sterilized with 10% NaClO\({}_{2}\) for 15 min, rinsed with sterilized water for five times, and then germinated at 28 \({}^{\circ}\)C until the primary roots of about 2 mm appeared. Approximately 50 germinated seeds were sown into each pot, and the seedlings were thinned to 20 plants of similar size per pot after two weeks of growth. Finally, 30 pots were prepared for two plant species and placed in a greenhouse with a temperature range of 23-30 \({}^{\circ}\)C and natural radiation. Pots were irrigated to maintain the stable water content of approximately 18% with the weighing method. To maximize the effects of ameliorating soil acidity, the soil was grown with _S. guianensis_ or _P. natatum_ for two growth cycles, with each lasting for three months. Briefly, after growth of three months at the first growth cycle, plants were carefully removed, and the root fragments in the soil were completely picked out, then, the germinated seeds were sown again for the second growth cycle of three months. Plant residues were incorporated into the soil only once at the beginning of the first growth cycle.  
The experiment was finalized after six months of growth. Before the final harvest, the relative chlorophyll content in the leaves was measured using the portable chlorophyll meter SPAD-502 (Minolta Camera Co., Osaka, Japan). Then, plant shoots were separated from roots, and the biomass (fresh weight) of shoots and roots were recorded, respectively. The soil in each pot was homogenized and divided into three aliquots. One aliquot was stored at \(-80\)\({}^{\circ}\)C for DNA extraction within two weeks, the second aliquot was stored at 4 \({}^{\circ}\)C for soil enzyme assay and DOC determination within three days, and the third aliquot was air-dried at room temperature for the soil chemical analysis.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Risk Factors** & **Skier without Falls** & **Skier with Falls** & \(p\) **Value** & **Effect Size** \\ \hline Preferred skiing speed (\%) & & & \textless{}0.001 & Phi: 0.285 \\ Very fast/fast & 54.1 & 38.0 & & \\  & (\(n\) = 550) & (\(n\) = 60) & & \\ Moderate & 41.7 & 36.7 & & \\  & (\(n\) = 424) & (\(n\) = 58) & & \\ Slow /very slow & 4.1 & 25.3 & & \\  & (\(n\) = 42) & (\(n\) = 40) & & \\ \hline \hline \multicolumn{5}{c}{Equipment-related factors} \\ \hline Relativized ski length (\%), mean & 95.4 \(\pm\) 3.6 & 93.1 \(\pm\) 4.1 & \textless{}0.001 & [PERSON]’s d: 0.627 \\ \(\pm\) sd & (\(n\) = 1016) & (\(n\) = 158) & \textless{}0.001 & [PERSON]’s d: 0.276 \\ \hline Sidecut radius (m), mean \(\pm\) sd & 14.6 \(\pm\) 2.9 & 13.8 \(\pm\) 2.9 & \textless{}0.001 & [PERSON]’s d: 0.276 \\  & (\(n\) = 1016) & (\(n\) = 158) & 0.001 & [PERSON]’s d: 0.282 \\ \hline Waist width (mm), mean \(\pm\) sd & 76.1 \(\pm\) 9.5 & 73.5 \(\pm\) 7.3 & \textless{}0.001 & [PERSON]’s d: 0.282 \\  & (\(n\) = 1016) & (\(n\) = 185) & 0.002 & [PERSON]’s d: 0.246 \\ \hline Standing height front & 40.3 \(\pm\) 5.7 & 38.9 \(\pm\) 5.6 & \textless{}0.001 & [PERSON]’s d: 0.282 \\ component (mm) & (\(n\) = 1014) & (\(n\) = 157) & 0.002 & [PERSON]’s d: 0.246 \\ \hline Standing height rear component & 45.5 \(\pm\) 6.0 & 43.8 \(\pm\) 6.5 & \textless{}0.003 & [PERSON]’s d: 0.280 \\ (mm) & (\(n\) = 1016) & (\(n\) = 158) & 0.003 & [PERSON]’s d: 0.280 \\ \hline Standing height ratio (\%), mean & 88.6 \(\pm\) 6.7 & 88.9 \(\pm\) 7.3 & \textless{}0.511 & [PERSON]’s d: \textless{}0.044 \\ \(\pm\) sd & (\(n\) = 1014) & (\(n\) = 157) & 0.511 & [PERSON]’s d: \textless{}0.044 \\ \hline \hline \multicolumn{5}{c}{Environment factors} \\ \hline Snow condition (\%) & & \textless{}0.001 & Phi: 0.155 \\ Fresh snow & 13.8 & 7.0 & & \\  & (\(n\) = 140) & (\(n\) = 11) & & \\  & 57.5 & 43.7 & & \\  & (\(n\) = 584) & (\(n\) = 69) & & \\  & 8.0 & 13.3 & & \\  & (\(n\) = 81) & (\(n\) = 21) & & \\  & 20.8 & 36.1 & & \\  & (\(n\) = 211) & (\(n\) = 57) & & \\ \hline Weather condition (\%) & & & \textless{}0.040 & Phi: 0.074 \\ Sunny & 63.9 & 74.1 & & \\  & (\(n\) = 649) & (\(n\) = 117) & & \\  & 27.8 & 20.9 & & \\  & (\(n\) = 282) & (\(n\) = 33) & & \\  & 8.4 & 5.1 & & \\  & (\(n\) = 85) & (\(n\) = 8) & & \\ \hline Difficulty of preferred slope (\%) & & & \textless{}0.001 & Phi: 0.296 \\ Easy (blue slope) & 6.0 & 31.0 & & \\  & (\(n\) = 61) & (\(n\) = 49) & & \\  & 70.2 & 56.3 & & \\  & (\(n\) = 713) & (\(n\) = 89) & & \\  & 23.8 & 12.7 & & \\  & (\(n\) = 242) & (\(n\) = 20) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)associations with an increase in the risk of falling (OR > 1) were found for a lower skill level (OR 2.785), a larger sidecut radius (1.092), and an easy slope difficulty (OR 2.991).
# Research on Fault Diagnosis Method for Rotating Machinery Based on Edge Computing
## 4 Fault Diagnosis Method Based on Edge Computing
### Edge Node Design  
The edge node is designed using a Jetson Orin NX development board and an ADC module with an I2C interface. Each core of the development board operates at a maximum clock frequency of 1.5 GHz. The edge node is equipped with functions for analog-digital conversion (ADC), signal preprocessing, data augmentation, feature extraction, and fault diagnosis classification based on an embedded CNN model. It can also upload the extracted feature values and classification results to the host computer for storage and further analysis. The edge node is configured with two ADC channels to receive analog signals from the  
Figure 7: Vibration data spectrum: (**a**) the original vibration signal without any signal processing; (**b**) the envelope signal obtained through EMD and Hilbert transform.  
accelerometer and temperature sensor of the main motor, respectively. These signals are sampled and stored using direct memory access (DMA) interrupts.
# Experimental Study on Mechanical Properties of Anisotropic Slate under Different Water Contents
###### Abstract
[PERSON] [12] analyzed the acoustic energy, showing that in any bedding direction, the energy release of saturated samples is significantly below that of dried samples. [PERSON] et al. [13], [PERSON] et al. [14], and [PERSON] et al. [15] conducted uniaxial and triaxial tests on shale, highlighting that the strength and Young's modulus of the shale gradually decreased with the increase in water content. [PERSON] et al. [16] noted that the \(m\) and \(K\) values of slate under uniaxial and triaxial compression conditions first decrease and then increase with the rise in bedding angle, and they decrease as the number of freeze-thaw cycles increases. [PERSON] et al. [17], based on uniaxial and triaxial test results of sandstone with prominent bedding characteristics, found that cohesion is directly proportional to water content, while the friction angle is inversely proportional. These studies mainly explore the changes in the macroscopic mechanical properties of shale or sandstone in dry, wet, or freeze-thaw states. However, a few focus on slate and its evolution law of anisotropic properties under different water contents. Until now, scholars have carried out relevant research on the anisotropy properties of different types of foliated rock mass, most of which are shale. Both slate and shale are layered rock masses, but shale is a sedimentary rock with a thin sheet or lamellar joints, mainly formed by clay deposition under pressure and temperature, and easy to break into fragments when hit by hard objects. While slate is an epimetamorphic rock, the possible protolith is mainly shale, mudstone, or tuff. Generally, the slab joints of slate are thicker than the thin foliated joints of shale, thus having denser lithology and higher hardness. In summary, although there are a number of studies on the hydration-mechanical properties and instability mechanism of shale, current research on the failure mode of slate and its changing characteristics of anisotropic mechanical properties under the joint influence of bedding angle, water content, and confining pressure lacks depth. Therefore, it is necessary to conduct in-depth mechanical experimental research to reveal the influence of these three factors on the mechanical behaviors and anisotropic properties of slate, providing references and guidance for the design and construction of underground engineering projects that pass through slate strata.
## 3 Problem Formulation
### Platform Model  
This work targets the hardware platform based on a dynamically partially reconfigurable FPGA, e.g., the Xilinx's Zynq series hardware platform, as shown in Figure 1. The platform consists of a FPGA with two-dimensional DPR capability and a Micro Processor Unit (MPU) that controls the FPGA. The FPGA can be virtualized as a static region and multiple reconfigurable regions [2].  
To reuse the resource of the FPGA efficiently in both time and space, the FPGA with DPR capability needs to be divided into several dynamically partially reconfigurable regions that are denoted as \(\textbf{PR}=\{PR_{0},PR_{1},\)..., \(PR_{i}\}\), each of which comprises multiple types of resources, including CLB, DSP, BRAM and so on. We use \(\textbf{H}=\{h_{1}^{k},h_{2}^{k},\)..., \(h_{|H|}^{k}\}\) to denote the set of resource types contained in the physical space or required by tasks or reconfigured nodes, and \(k\) is used to indicate which \(h_{i}\) belongs to. The volume of each type resource is denoted as \(|h_{i}^{k}|\).  
The data transmission between MPU and FPGA can be completed by reconfiguration port, e.g., Internal Configuration Access Port (ICAP) or Processor Configuration Access Port (PCAP). ICAP can directly write the internal configuration registers of the FPGA with the bandwidth of \(B_{cfg}\) which value is up to 3.2 Gbps for Xilinx FPGA. The MPU configures the reconfigurable regions dynamically by loading the bitstream file through ICAP.  
The reconfiguration time \(RT\) of a region \(\textbf{PR_{k}}\) is proportional to the amount of resources \(|h_{i}^{\textbf{PR_{k}}}|\), which can be expressed as a linear combination of the reconfiguration time of various types of resources:  
\[RT_{k}=\frac{\sum\limits_{i=0}^{|H|}|h_{i}^{\textbf{PR_{k}}}|}{B_{cfg}},\forall \textbf{PR_{k}}\in\textbf{PR} \tag{1}\]
# Robust Estimation of Vehicle Motion States Utilizing an Extended Set-Membership Filter
## 5 Verification for Robust Estimation Strategy
### Experimental Results  
#### 5.1.1 DLC Maneuver  
The selected vehicle performs the DLC maneuver on flat road at an initial speed of 120 km/h. For the ESMF-based strategy, the bounds of the process noise and measurement noise are set as: \(|w_{i}|\leq 10^{-3}\), \(|v_{1}|\leq 3\times 10^{-3}\), \(|v_{2}|\leq 3\times 10^{-3}\), \(|v_{3}|\leq 10^{-4}\). The corresponding ellipsoid shape matrices which define the process and measurement noises are set as:  
Figure 8: Verification structure for robust estimation strategy.  
\[\mathbf{Q}=\mathbf{I}_{6\times 6}\times 10^{-5},\quad\mathbf{R}=\begin{bmatrix}10^{-4}&0&0 \\ 0&10^{-4}&0\\ 0&0&10^{-7}\end{bmatrix}\]  
The center and matrix that define the shape of the initial state ellipsoid are initialized as follow:  
\[\mathbf{x}_{0}=\begin{bmatrix}\dfrac{120}{3.6}&0&0&0&0&0\end{bmatrix}^{\text{T}}, \quad\mathbf{P}_{0}=\mathbf{I}_{6\times 6}\times 10^{-2}\]  
The ellipsoidal projection \(\mathbf{\hat{x}}\pm\mathbf{Ez}\)[29] can be taken as the upper and lower bounds (see the dot lines in Figure 9) of the estimated results, where \(\mathbf{P}=\mathbf{E}\mathbf{E}^{\text{T}}\), and \(\|\mathbf{z}\|\leq 1\).  
For the UKF-based strategy, the initial value, process noise's covariance matrix \(\mathbf{Q}\), measurement noise's covariance matrix \(\mathbf{R}\), and initial error covariance matrix \(\mathbf{P}_{0}\) are set as follows, respectively:  
\[\mathbf{x}_{0}=\begin{bmatrix}\dfrac{120}{3.6}&0&0&0&0&0\end{bmatrix}^{\text{T}}, \mathbf{Q}=\mathbf{I}_{6\times 6}\times 10^{-6},\mathbf{R}=\begin{bmatrix}10^{-6}&0&0 \\ 0&10^{-6}&0\\ 0&0&10^{-9}\end{bmatrix},\mathbf{P}_{0}=\mathbf{I}_{6\times 6}\times 10^{-3}\]  
Moreover, as the consideration of the mathematical principle for a stochastic distribution, the expression of \(\mathbf{\hat{x}}\pm 3\sigma\) is chosen as the upper and lower bounds (see the dot lines in Figure 10) of the estimated results of the UKF-based strategy.  
Figure 9: Results of the ESMF-based strategy under the DLC maneuver. (**a**) Longitudinal velocity; (**b**) Lateral velocity; (**c**) Vehicle sideslip angle.
# Towards a Near-Real-Time Protocol Tunneling Detector Based on Machine Learning Techniques +
## 4 Protocol Tunneling Detector  
The proposed architecture splits the burden of processing the traffic of a monitored network into two different sub-modules: the first mainly deals with secure connections,  
Figure 1: A DNS tunneling example.  
while the second inspects unencrypted traffic. As previously discussed, the former analytics has been detailed in [11]. At a glance, it detects possible anomalies occurring during an SSL/TLS handshake between a client, located inside the network monitored by the software platform outlined in Section 1, and an external server. The SSL/TLS detection analytics examines information contained in X.509, SSL, and TLS exchanged protocol messages. Instead, the second module looks for anomalies in unencrypted traffic regarding the abuse of specific protocols (i.e., tunneling attack techniques). To provide these detection capabilities, this prototype collects a sequence of bytes from each network packet and inspects its content. The content, along with its features, is fed to a testing module, which detects possible anomalies that are signaled to security analysts.