esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of the October 2024 Storm over the Global Ionosphere
## 4 Results
### Storm Effects on Swarm Observation
This timing also coincided with intensified auroral activity, indicating electrodynamic coupling between polar and equatorial ionospheric regions. By 15:00 UT, Swarm A data indicated minimal storm-time expansion, while Swarm B continued to show moderate depletion over the Southern Hemisphere dayside. After 18:00 UT, Ne values from both Swarm A and B returned to near-quiet levels, marking the end of significant ionospheric disturbance. Overall, the analysis underscores the significant role of storm-driven PPEFs in expanding the EIA and driving vertical plasma transport. Swarm A and B data, supported by global TEC maps from Madrigal, confirm that the topside ionosphere experienced substantial restructuring, particularly during the PRE period. The emergence of two distinct EIA crests in Swarm Ne further supports the influence of PPEF-driven vertical drift, consistent with results reported by [PERSON] et al. (2025) [6]. Additionally, the study highlights clear Hemispheric asymmetries in the storm-time ionospheric response, manifested in plasma circulation patterns, thermospheric wind variations, TEC distributions, and magnetic field perturbations. These findings are consistent with earlier research by [PERSON] et al. (2016) [23], who emphasized the fundamental role of Inter-Hemispheric differences in shaping space weather responses during geomagnetic storms.
# High-Level Smart Decision Making of a Robot Based on Ontology in a Search and Rescue Scenario
## 3 Methods
### Reasoning Based on SWRL Rules
#### 3.1.3 Robot Task Reasoning Based on JESS  
In this section, JESS is adopted to map ontology knowledge into the internal format of inference engine and provide the tasks for robots to complete by matching the factual knowledge and rules. JESS is a java-based CLIPS reasoner and its core language is compatible with CLIPS [36]. The structure of JESS is shown in Figure 7. It includes three parts--Ontology, Rules Base, and Inference Engine. In Ontology, the facts library stores the initial fact information hierarchically. In the Rules Base, the rules library stores domain rules related to inference, and JESS internally uses the \"if-else-then\" grammatical structure to describe rules. Ontology provides the factual basis for inference, and the rules base provides inference rules. Since JESS inference engine cannot directly understand the knowledge of OWL and SWRL format, it first needs to analyze ontology and rules and convert them into the input format of the inference engine.  
When the real-time information in SAR is updated to the ontology, the inference engine can automatically complete the matching between ontology and rules. When the scene information meets the precondition of a rule in the rules base, the rule is called and the result of the rule is the task that the robot needs to complete in the current state.
# Design of a Noise Mitigation System Using Lightweight Graded Micro-Porous Material
## 1 Introduction  
Urbanization and the development of transportation systems have led to increased noise pollution and health issues, making noise control essential in living environments [1; 2]. There are three possible means of noise mitigation: (1) a passive system, (2) an active system, and (3) a hybrid (combined passive and active) system. This study specifically investigates passive systems, focusing on porous sound absorption materials. These materials are gaining interest in noise mitigation due to their internal channels, cracks, and cavities, which allow sound waves to penetrate. Sound energy dissipates through thermal loss caused due to heat exchange between compressions and rarefactions in the air caused by the passage of the sound wave through the pores and pore walls. Air flow resistance is a consequence of friction between moving air particles and pore walls and results in the conversion of sound energy to heat. Further, a dissipation in sound energy through viscous loss is due to friction within air molecules as sound waves propagate. These energydissipation mechanisms enable porous materials to absorb sound across a broad frequency range [3; 4]. Further, porous materials also offer a low cost, ease of molding, and weight reduction, making them suitable for noise control in buildings and transportation [5; 6; 7]. The properties of these materials can be tailored through design. They are usually cellular (open-cell foams), fibrous (natural or synthetic), or granular (see Figure 1); this classification is based on the microscopic structure [8; 9].  
Among these types of porous material systems, cellular open-cell foams [10] and fibrous materials [11] are nonstructural and effective sound absorbers but exhibit a low transmission loss [10; 12] depending on the sample thickness. Additionally, they are suited for residential acoustic applications due to their low-temperature operation characteristics. On the other hand, granular materials stand out due to their favorable structural and insulation properties, making them suitable for robust noise control solutions. Hence our primary focus lies on granular materials.
# CBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements
## 4 Discussion
### Effect of Sensing Geometry and Crop Canopy Effect  
The LiDAR sensor needs to detect a dense number of leaves in a vertical plane to estimate the crop height correctly. A smaller number of leaves towards the top of the canopy is unlikely to produce a substantial LiDAR return pulse due to less dimension. Instead, a subsequent return pulse could be produced further down from inside the canopy, where the density of the leaves is higher. In such situations, a shorter height is measured by the LiDAR than the one that is estimated by the regression model, and thus, the measurement falls under the regression line (Figure 9a).  
It is also possible that crop canopies contain gaps in the outer layer of leaves. When the sensor's detector is aligned over these gaps, the returns are likely to be produced by the surrounding canopy leaves. Therefore, the measured distance return remains above the regression line. The scanning geometry of the sensor's individual detector with respect to the target and its FoV determines the nature of the LiDAR return pulse. As the detector's FoV varies with the distance to the object, it is challenging to detect gaps in the canopy at large distances. A smaller FoV for individual detectors could be selected to obtain a better representation of the canopy structure. However, such systems are more expensive, and require more storage and computational power to process the large data, limiting its seamless adoption in phenotyping. Therefore, selection of a suitable LiDAR unit for phenotypic application needs to consider such practical trade-offs. While the CBM sensor produced good returns against a flat surface, the increase of _RMSE_ in wheat crop suggeststhat variability in the response is higher. In general, the height that was estimated from the CBM sensor was slightly lower than with manually measured heights with rulers in the field wheat experiment, although this relative difference was consistent across the genotypes.  
The ground-based laser scanners are prone to variation in FoV coverage at the canopy level due to changes in the crop height. Increasing the mounting height assures the sensor's FoV to cover the entire plot, but it might reduce the scanner's ability to map the subtle variations within the plot due to a lower resolution. The mounting height of the sensor needs to be adjusted with practical settings such as the average crop height and width of the plots. In this study, the CBM sensor was mounted at the height of 1.8 m and used analytics to trim or extrapolate its FoV to match with the plot width at the canopy level.
## 4 Experiments and Results
### Implementations  
#### 4.1.1 Dataset  
We conduct experiments on the MSTAR (The Moving and Stationary Target Acquisition and Recognition) dataset [44]. The MSTAR dataset was established by the Air Force Research Laboratory (AFRL) and Defense Advanced Research Project Agency (DARPA), and it was widely used in the research of SAR target recognition. This dataset contains SAR images under Standard Operation Condition (SOC) [45] and Extended Operation Condition (EOC) [46], and these images are imaged in the X-band and with HH polarization.  
In addition, the range and azimuth resolution of them are both 0.3 m. Here, we utilized the images under SOC to conduct experiments. The SAR images of these targets are shown in Figure 3, and they include 10 classes of military vehicles, including BMP2, BTR70, T72, BTR60, 2S1, BRDM2, D7, T62, ZIL131, and ZSU234. The azimuth angles of these images range from 0 to 360 degrees. In our experiments, images with depression angles of 15 and 17 are used to establish the training set and testing set. The configuration of ten classes of targets is presented in Table 1.  
In our experiments, all images are resized to 64*64 by center cropping. After that, these images are reshaped into vectors to construct a training set and test set.
# Design of a Noise Mitigation System Using Lightweight Graded Micro-Porous Material
## 3 Design, Fabrication, and Validation of Graded Micro-Porous Material System
### Validation by Normal Incidence Impedance Tests
#### 3.4.2 Graded uPHB Composite Material Combination of L37.5, M37.5, S25
This liner was tested on NASA-Glenn Research Center's (GRC) Advanced Noise Control Fan (ANCF) low-speed test bed aircraft engine located at the Turbomachinery Laboratory at the University of Notre Dame [52]. The Technology Readiness Level (TRL) 3-4 test was successful.
## 3 Network Design  
EfficientNet [12], proposed by the Google Brain team in 2019, is a network that has demonstrated appealing performance in image classification projects in recent years. This paper uses the EfficientNetv2-S network and calligraphy dataset for training. Our modifications involve improving the attention mechanism and introducing the CBAM and Self-Attention modules to enhance the model's generalization ability and achieve higher accuracy in calligraphy authentication.  
EfficientNetv2-S Network and Attention Module Improvement  
The characteristics of ResNet inspire EfficientNetv2-S, and thus, it employs MBConv blocks and Fused-MBConv blocks for higher accuracy and faster inference speed. The MBConv block includes dilated, depth-wise separable convolution and the SE attention module. Multiple Fused-MBConv blocks and MBConv blocks are stacked to form the EfficientNetv2-S network.  
[PERSON]'s improved CNN models for calligraphy style classification tasks using the CBAM attention module have achieved better classification results than the SE attention module [13]. Therefore, based on the EfficientNetv2-S network, the CBAM attention module and Self-Attention module are further utilized to improve the SE attention module in the MBConv blocks, resulting in the CBAM-EfficientNet network and SA-EfficientNet network. The CBAM module is a commonly used attention mechanism module in image processing that adaptively weights each channel and spatial position of the image, allowing the model to learn the most important features better and exhibit good image generalization and processing performance [14]. The Self-Attention module can identify the relationship between the features of each position in the image and all other positions. Furthermore, it calculates the correlation scores between each position and other positions in the image and weights the features of other positions. The advantage of the Self-Attention module is that it can establish global dependencies between different positions in the image, which is beneficial for the network to learn the overall features of calligraphy characters [15]. The network structures of both modules are depicted in Figure 7. Note that the attention mechanism of the original EfficientNet_v2-S network is SE attention, while the new network employs Self-Attention and CBAM.  
Figure 6: Data augmentation.  
This paper employs the regular script (Kaishu) and cursive script (Caoshu) calligraphy works of two calligraphers to create a training set. We train the EfficientNetv2-S [16], CBAM-EfficientNet, and SA-EfficientNet networks and horizontally compare Inception-ResNetV2 [17], InceptionV3 [18], MobileNetV3L [19], ResNet50 [20], and MobileNet [21]. The training platform is an i9-13900k CPU, RTX4090 GPU, with 128 GB of memory. The batch size for training is set to 64 and runs for 20 epochs in a loop. The optimizer used is Adam, and the loss function is binary cross-entropy.
## 3 Results and Discussion
### The Crystal Structure of Hexagonal Wurtzite ZnO NPs  
The final Rietveld refinement settings were used to standardize crystal structure and fractional coordinates modeled by the VESTA (visualization for electronics and structural analysis) tool, as illustrated in Figure 5[26] ZnO NPs have a well-known crystal structure of hexagonal wurtzite, which corresponds to the space group P63 mc. The hexagonal unit cell of wurtzite has two lattice parameters, a and c, with a ratio of c/a = 1.6. (in an ideal wurtzite structure). Figure 5 shows a schematic representation of the wurtzitic ZnO structural model with the final Rietveld refinement parameters. The structure is made up of two interpenetrating hexagonal close packed (hcp) sublattices, each of which is made up of one kind of atom displaced by u = 3/8 = 0.375 along the three-fold of the c-axis, as shown in Table 2 (in fractional coordinates in an ideal wurtzite structure). The internal parameter u is the length of the link parallel to the c-axis (anion-cation bond length or nearest-neighbor distance) divided by the c lattice parameter.
## 2 System Model
### Cognitive Radio Wireless Sensor Network Energy Consumption Model
#### 3.3.1 Event Sensing Unit Energy Consumption  
The event sensing unit interacts with the physical environment to detect an event and then convert the physical signals to digital signals. Signal sampling, physical signal conversion to electrical signals and analogue signal to digital signal conversion are the main sources of energy consumption in the event sensing unit [45]. Let \(T_{ss}\) denotes the event sensing duration, \(P_{ss}\) denotes power required for the event sensing activity which includes event detection and signal conversion and \(B_{ss}\) denotes the bits packet. The energy dissipation for event sensing activity for \(B_{ss}\) bits packet is given as:  
\[E_{ss}(B_{ss})=P_{ss}\,T_{ss}\,B_{ss} \tag{8}\]
## 2 System Model
### Equalization of the Time-Packed Signal  
The three proposed relaying architectures rely on time-packing signalling in the optical feeder link to increase the throughput even further. Unfortunately, the use of time-packing introduces ISI that must be mitigated in reception at the satellite. The optimal strategy for cancelling this unwanted ISI consists in resorting the Maximum-Likelihood Sequence Decoding (MLSD) [34], which can be implemented efficiently with the Viterbi algorithm [35]. However, to increase the throughput of the optical feeder link, it is necessary use a low roll-off factor and large overlapping factor, two things that increase notably thecomplexity of the Viterbi Algorithm to be implemented in the satellite [27]. Due to the impracticability of this solution, alternative strategies should be considered. Towards this regard, in this paper we use a two-side Least Mean Square (LMS) filter to equalize the time-packed channel. Thus, the equalization strategy consists of two steps: (1) Compute the weights of the interference canceller by means of a training sequence and (2) apply the pre-computed weights to the received time-packed signal from the gateway. These weights have to be computed for each overlapping factor that can be used.  
Let us assume that \(\mathbf{r}\) is the vector that stacks the received samples of the time-packed signal, \(\mathbf{w}=\left[w[0]\cdots w[L-1]\right]^{\mathsf{T}}\) is the vector of \(L\) weights in the LMS equalizer, and \(\mathbf{y}[n]\) is the buffer that contains the received samples that participates in the equalization process of the \(n\)-th time-packed received symbol. Then, the equalization of the \(n\)-th transmitted symbol attains the form:  
\[z[n]=\mathbf{w}^{\mathsf{T}}\,\mathbf{y},\qquad\mathbf{y}\ =\ \left[r[n-(L-1)/2]\cdots r [n]\cdots r[n+(L-1)/2]\right]^{\mathsf{T}}. \tag{6}\]  
During the training process, the ideal values of the signal samples at the output of the equalizer \(z[n]\) are known, enabling to determine the most convenient equalization weights. Specifically, the weights \(\mathbf{w}\) have been computed by using the LMS algorithm strategy [26; 36], i.e.,  
\[\mathbf{w}[q]=\mathbf{w}[q-1]+\mu\,e[q]\,\mathbf{y}[q], \tag{7}\]  
where \(\mathbf{w}[p]\) contains the equalizer weights at the \(p\)-th training iteration, \(\mu\) is the forgetting factor, and \(e[p]=z_{t}[p]-z[p]\) is the error between the training symbol \(z_{t}[p]\) and its estimation from (6).
# Gradient-Based Aerodynamic Optimization of an Airfoil with Morphing Leading and Trailing Edges
## 3 Results and Discussion
### Minimization of Drag by Morphing Both Leading and Trailing Edge
#### 3.4.1 Starting from Baseline  
Starting from the baseline airfoil, after seven iterations, an optimized airfoil with mLETE is obtained with a drag coefficient of 116.82 counts, with an around 9.7% drag reduction. The current angle of attack is 1.09 degrees, which is smaller than the initial angle of 2.286 degrees. Pressure coefficient distribution, morphing leading, and trialing shape of the optimized airfoil are compared with the baseline in Figure 11. Significant deformations are achieved; vertical displacement of leading edge \(z_{\text{LE}}\) is \(-0.01146\) m and that of trailing edge \(z_{\text{TE}}\) is \(-0.0098\) m; the corresponding dropping angles are about 6.5 and 1.9 degrees, respectively. The leading edge radii of both upper and lower surfaces decrease to half of those values in baseline airfoil, which are the lower bound of those design variables. As the dropping leading and trailing edges increase the camber, the angle of attack decreases from 2.286 to 1.094 degrees in order to keep a constant lift. This phenomenon is similar to the previous mTE case.  
Figure 10: Comparison of results for the RAE 2822 optimization using a morphing trailing edge. (**a**) \(C_{p}\) distribution. (**b**) Airfoil shape.  
However, the drag reduction achieved is lower than that achieved by using only the mLE. A possible interpretation could be that the optimization finds a local minimal starting from the baseline airfoil due to the pitfall of the gradient-based optimizer.
# Vision, Enabling Technologies, and Scenarios for a 6G-Enabled Internet of Verticals (6G-IoV)
## 2 Technology Enablers for the Internet of Verticals
### Edge Computing/Edge AI  
ETSI is standardizing Multi-access Edge Computing (MEC) [13], previously known as Mobile-Edge Computing, a technology that empowers a programmable application environment at the edge of the network, within the RAN. The benefits are reduced latency, more efficient network operation for certain applications, and an improved user experience.  
For many vertical applications, latency will determine how close the edge servers need to be to user devices. The 5G networks are striving for 1 msec latency (round-trip time) within the network. Light travels 300 km per millisecond, so designers will need to plan their applications accordingly. Latency is critical in vehicular edge applications, specifically [14]. Moreover, a robotic controller can tolerate around four milliseconds latency (round trip) and therefore, allowing for fluctuations and processing time, MEC need to be placed as close as possible to the controller. The other consideration is the amount of data that needs transportation to MEC. For example, a factory performing AI-based video analytics of its assembly line operation may wish to conduct such calculations on a MEC at the factories' location rather than backhauling a huge amount of data to a more central operator location. The 5G standalone network architecture provides access to user data in a local environment via a distributed User Plane Function (UPF), thus enabling local data breakout and facilitating edge computing applications.  
Variations of distributed AI, such as federated learning, are expected to be evolved in the future [15; 16]. In the 6G era, we will see MEC integrating increasingly more secure, low-latency AI and data analytics services at the edge of the network to a host of verticals.
## 2 Materials and Methods
### Result Analysis  
In general, correlation analysis was the first step for ET\({}_{0}\) estimation, because it determined which variables were qualified as an input factor. Our results showed that air temperature and solar radiation were the dominant factors contributing to ET\({}_{0}\). Other factors such as \(\Delta\)T, RH and S were only correlated with ET\({}_{0}\) in specific climates or cities. The second step was to calculate threshold values of r\({}^{2}\) for the selection of input variables.  
Figure 5: Spatial distribution of seasonal ET\({}_{0}\) in testing phase predicted by (**A**) FAO PM method, (**B**) BF\({}_{0.5}\), (**C**) BF\({}_{0.7}\), (**D**) GABP\({}_{0.5}\) and (**E**) GABP\({}_{0.7}\) models for the 14 national meteorological stations in China.  
With no methodology to refer to, we subjected all \(\tau^{2}\) of meteorological factors to statistical distribution using a box chart (Figure 6) and then used median (\(\tau^{2}\) = 0.50) and third quartile values (\(\tau^{2}\) = 0.70) as thresholds. Correlated factors satisfying the thresholds were applied to BP and GABP models. Our study may provide a reference for the selection of input variables. Significant climatic and seasonal effects was observed on ET\({}_{0}\) estimation. BP models performed worse in cold seasons and in arid to semi-arid climates. However, GABP models significantly improved model performance even with fewer input parameters. In general, GABP\({}_{0.7}\) model was acceptable in annual ET\({}_{0}\) estimation, while GABP\({}_{0.5}\) performed best in seasonal ET\({}_{0}\) estimation.
## Nomenclature  
\begin{tabular}{l l} PT & pulse train \\ WPT & wireless power transfer \\ CC\&OV-PT & capacitor current and output voltage feedback pulse train \\ PWM & pulse width modulation \\ DC & direct current \\ AC & alternating current \\ \(V_{\rm i}\) & input voltage \\ \(V_{\rm ref}\) & reference voltage \\ \(f_{1}\) & switching frequency of Buck converter \\ \(f_{2}\) & switching frequency of Inverter \\ \(P_{\rm H}\) & high duty cycle of pulses \\ \(P_{\rm L}\) & low duty cycle of pulses \\ \(i_{\rm C}\) & the output capacitor current \\ \(i_{\rm L}\) & inductor current of the Buck converter \\ \(i_{\rm 1}\) & the input current of the full-bridge Inverter \\ \end{tabular}