esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Validation Cases
### Surface Roughness Measurements  
The small-scale surface roughness \(\sigma_{\mathrm{S}}\) was estimated using manual in situ measurements. These measurements were performed using a laser range finder mounted on a spirit level supported by two tripods, as shown in Figure 4.  
The measurement was made by first leveling the spirit level about both horizontal axes (pitch and roll). Second, the height to the ground surface was measured every 2 cm. The level is about 120 cm long; therefore, the measurement was repeated three times to obtain a larger baseline. This procedure was performed in both the north-south and the east-west direction. During the measurements, special care was taken to avoid measuring vegetation height instead of ground surface height. The surface roughness was calculated by first calculating the height values relative to the surface slope line. Then, the root mean square (RMS) surface roughness was calculated. The slope line was assumed to be captured by the DEM.  
A roughness measurement was taken at each site using a baseline close to 3 m. Thus, three roughness measurements were taken in the JER area, and two measurements were taken in the WG area. The roughness measurements at JER were taken in May 2022, and the measurements at WG were taken in August 2022.  
The surface height measurements and the surface correlation of the measurement at site JR-1 are shown in Figure 5. The dashed lines in Figure 5a are the surface slope lines. The RMS roughness \(\sigma_{\mathrm{h}}\) and the surface correlation length of the WG and JER sites are given in Table 1. The roughness measurements at the WG sites were taken in the flat area within the sites. Moreover, the location of the measurements was in an area with lower vegetation to facilitate the measurement. Some data points in the measurement at the Kendall site were skipped due to vegetation. Thus, the roughness measurements of WG sites are believed to be lower than the mean RMS surface roughness.  
For the wavelength of the GPS L1 signal, these in situ measurements can be used to estimate the characteristics of the surface height in the spatial domain from a fraction of a wavelength to a few wavelengths. This makes the in situ measurements more suited for estimating the small-scale roughness of the IGOT model. Since the in situ roughness measurements were sparse and did not capture the heterogeneity of the sites, the in situ measurements guided the selection of the small-scale roughness parameter \(\sigma_{\mathrm{S}}\).  
Figure 4: Photo of the surface roughness measurement device.
## 4 Results
### Spatial Correlation Analysis of Human Activities and EQI  
The bivariate global Moran's I of EQI and human activity for 2000, 2005, 2010, 2015, and 2018 are \(-\)0.418, \(-\)0.427, \(-\)0.414, \(-\)0.487, and \(-\)0.477, respectively (\(p<\) 0.01). The results indicate that there is a significant negative spatial correlation between human activities and EQI, and that the increase in human activities leads to a decrease in EQI. The spatial dependence between human activity and EQI is dominated by the \"high-low\", where both high EQI values are surrounded by areas of low human activity (Figure 11). The \"high-low\" is distributed in the Dabie Mountains and Southern Anhui Mountains, of which the Dabie Mountains is a national key ecological function area to lay the foundation for high EQI. The \"low-high\" is concentrated in urban centers, where intense human activity, urban sprawl, and low vegetation cover result in low EQI values. The \"high-high\" is mainly located in the city edge or in the combined suburban areas, where EQI is affected by human transformation and construction, but its vegetation and ecological environment are also strongly protected and maintained under the ecological protection policy.  
Figure 10: Distribution of partial correlation coefficients between EQI and average temperature (**a**) and precipitation (**b**) during the growing season in Anhui Province, 2000–2020.
# Investigation of the Characteristics of NLS-PNA: Influence of NLS Location on Invasion Efficiency
## 3 Results and Discussion
### Insertion of Proline between the PNAs and Valine of the NLS  
Besides the location of the NLS itself, there is a further difference between C-PNAs and N-PNAs. In order to maintain the directionality of the NLS, in C-PNAs, proline (P) of the NLS connects to the PNA, whereas in N-PNAs, the NLS is connected via valine (V). Unlike valine, proline possesses a unique cyclic structure bestowing it with a much higher backbone rigidity than any of the other canonical amino acids. In order to verify its significance, we synthesised N-Pro-PNAs, which are identical to N-PNAs except that a proline residue has been inserted between the PNA and valine. Interestingly, the insertion of proline proved unfavourable for invasion efficiency, and N-Pro-PNAs  
Figure 3: Comparison of (**a**) U-PNAs’, (**b**) C-PNAs’, and (**c**) N-PNAs’ invasion efficiency. Invasion conditions: [DNA] = [each PNA] = 50 nM, [HEPES (pH 7.0)] = 5 mM, and [NaCl] = 0–125 mM at 50 \({}^{\circ}\)C for 1 h.  
displayed invasion properties between those of N-PNAs and C-PNAs (Figure 4). These results strongly indicate that the rigid amino acid connecting the NLS to PNA may unfavourably affect invasion.
## 2 Materials and Methods
### Cardiac Magnetic Resonance Imaging Protocol  
Cardiac imaging examination was performed using 3T MRI scanners (Skyra and Prima, Siemens, Germany). All subjects underwent a standardized clinical imaging protocol inclusive of retrospective electrocardiographic gating, time-resolved balanced steady-state free precession (SSFP) one imaging in four-chamber, three-chamber, two-chamber, and short-axis views of LV at end-expiration. Contrast usage of gadolinium contrast volume of 0.2 mmol/kg (Gadovist(r), Bayer Inc., Mississauga, ON, Canada) was administrated to acquire a contrast-enhanced 3D magnetic resonance angiogram (MRA) of the pulmonary veins in all subjects for assessing LA structure. Time-resolved three-dimensional phase-contrast MRI with three-directional velocity encoding (4D-flow, Siemens WIP 785A) was performed for 5-10 min, following contrast administration to measure in-vivo blood flow velocities within the whole heart. We have previously reported this whole-heart protocol [8; 13]. Briefly, 4D-flow data was acquired during free breathing using navigator gating of diaphragmatic motion; sequence parameters were as follows: flip angle = 15 degrees, spatial resolution = 2.0 - 3.5 \(\times\) 2.0 - 3.5 \(\times\) 2.5 - 3.5 mm; temporal resolution = 39-48 ms; and velocity sensitivity = 150-200 cm/s. Total acquisition time varies between 8-12 min, depending on heart rate and respiratory navigator efficiency. The number of phases was adjusted to 25.
# An Overview of Design Techniques for Two-Dimensional Leaky-Wave Antennas
## 3 Fabry-Perot Cavity Antennas
### Full-Wave Validation of the Theoretical Model
The generation of conical beams, with a scanning pointing angle \(\theta_{0}\) which varies as the frequency increases through (4), is thus demonstrated.
## 2 Navigation and Localization
### Optical Navigation
This trajectory guarantees that images taken by the AUV cameras cover the complete bottom of the  
Figure 11: Optical localization systems based on active landmarks. (**a**) AUV following an array of active markers, (**b**) AUV locating an entrance by an arrangement of active markers. tank. Once the survey is complete, the flight data recorder is identified on the images by applying an HSV histogram and then located; so, the intervention stage can take place. Even though these techniques are quite popular on land and air robots, working this way has several restrictions at underwater. It is required to know before the mission what the robot is looking for; the robot must be pointed to an object of potential interest and HSV boundaries must be manually selected until it is well detected; it also has the inconvenience that colors are not the same underwater as above water, because they are strongly affected by illumination.
# Calculation of Main Circuit Steady-State Parameters for Capacitor Commutated Converter System
## 4 Calculation Procedure of Main Circuit Parameters  
The design of the main circuit parameters aims to calculate all the steady-state characteristics of the rectifier and the inverter considering the control modes of converters and transformers. Usually, the calculation results of the main circuit parameters mainly include the following parameters: the DC power, the DC voltage, the DC current, the firing angle at the rectifier side, the extinction angle at the inverter side, the overlap angle, the reactive power consumed by the converter, the voltage of the valve side and the on-load tap changer of the converter transformer.  
This subsection firstly discusses the basic factors required for the main circuit parameter design of the proposed CCC system, including the system parameters, the control modes, the restriction conditions and the operating conditions. Then, a detailed calculation procedure is proposed considering these basic factors. Finally, a flow chart is drawn to provide an intuitive procedure of the main circuit parameter design.
## 3 Methodology
### Data Acquisition  
To begin, field surveys were conducted in six agriculture fields across Pakistan, including Thatta, Mirpur Khas, Darsano Chano, Gaddap, Quetta, and Rahim Yar Khan. The goal was to connect with farmers, gain an understanding of the surrounding environment, and collect images of weeds in the fields.  
Establishment of the Real-Time Experimental Setup for Data Acquisition  
The primary data came from the Agro Living Lab (ALL), located in the Gadap region of Karachi. The purpose of establishing ALL was to allow researchers to collect authentic data without encountering obstacles. It can be difficult to obtain permission from farmers and landowners to photograph their fields. The Living Lab also facilitated quantifying and analyzing the types of weeds that are commonly found and the crops that are commonly cultivated in that particular land of Gadap town. Furthermore, the furrow irrigation system was designed to allow weeding robots to move freely without harming crops.  
The dataset was collected in the form of videos, and 12,000 frames were extracted from the videos acquired through a 60 fps camera. The resolution of the camera was 1280 \(\times\) 1024. During data collection, the shooting distance was maintained at approximately 1 m. The images extracted from the video contained a top view of weeds and crops, which is ideal for autonomous weeding robots, as they will pass over the canopy of several crops in a row to detect weeds. Data collected for this study focused on the most commonly cultivated crops, which include okra, sponge gourd, and bitter gourd. After analyzing the data, it was observed that there are four species of weeds commonly found in these crops, including horseweed, Herb Paris, grasses, and small weeds. Figures 2 and 3 depict the visuals of the crops and weed species, respectively. Figure 4 shows a bar graph that illustrates the frequency of crops and weeds present in the dataset.  
Figure 1: Workflow for the development of the weed detection mechanism.  
Figure 3: Visuals of the commonly occurred weeds in the fields. Multiple images of each weed species are arranged in columns.  
Figure 2: Visuals of various crops collected in the dataset. Each column contains different sample images of the crops in the collected data. Column (**a**) represents sponge gourd, (**b**) okra, and (**c**) bitter gourd, respectively.
## 4 LNA Characterization  
The LNA presented in Figure 12 has chip dimensions of (3.0 mm \(\times\) 1.4 mm) including PADs. The transistors of the first and second stages have similar biasing at: \(V_{GS}=0.76\) V and \(V_{DS}=2.3\) V. The third stage transistor is biased using \(V_{GS}=0.56\) V and \(V_{DS}=3.6\) V. Hence, the total DC power dissipated by this device is 362 mW. The bias voltages and currents of each stage were chosen to meet the design requirements of gain, noise figure, and bandwidth. However, this choice led to a higher DC power dissipation; it is less than the maximum DC power consumption allowed of 0.5 W, which was set in the design requirements.  
Figure 11: The complete schematic containing the biasing networks and the 3-stage LNA, which was introduced in Figure 10.  
Figure 12: Realized 3.0 mm \(\times\) 1.4 mm MMIC of the Three-Stage LNA using WIN PIH1-10 GaAs Technology. Reprinted with permission from Ref. [9]. 2025, IEEE.
# Self-Cleaning Solar Mirror Coatings: From the Laboratory Scale to Prototype Field Tests
## 1 Introduction
* Outdoor durability, mainly understood as persistent adhesion to the substrate even in the presence of adverse meteorological events and chemical-physical instability. A trade-off between self-cleaning performance and optical UV-VIS-NIR transmittance can be reached when the difference in reflectance between the solar mirror as it is and the coated mirror does not exceed 10% in the visible region, and for wavelengths between 850 nm and 2500 nm, a coating transmittance of at least 80% is necessary. When a plant component, like a solar mirror, is modified by means of adding another layer (e.g., a self-cleaning layer), the testing of its performance on-field is crucial. Not even a coating material that is stable indoors or during a climatic chamber testing campaign preserves its performance when positioned outdoors and exposed to adverse and, in any case, variable weather conditions for a long time. For this reason, on-field tests of the solar mirror coating prototypes' stability and performance are part of this work.
## 2 Related Work
### Energy Efficient Routing
#### 2.1.1 Data Centric Protocols  
Data-centric routing protocols decrease the amount of transferred data by querying sensor nodes based on their data attributes or interests. This assumes a data query driven model and a routing based on data attributes. For instance, in SPIN [11], sensor nodes inform each other about the data they have and the data they still need to obtain. Thus, interested nodes can query these data. However, this protocol does not ensure the end-to-end delivery of data if intermediate nodes are not interested in that data. In directed diffusion [12], the sink broadcasts an interest message to nodes, interested nodes reply with a gradient message. Both interest and gradient messages allow the sink to establish paths to sensor nodes.
### Applications of Chaos for Modulation Efficiency  
When chaos is used to modulate a waveform in communication systems, the bit error ratio can be improved with respect to the classical half-sine pulse frequency-modulated waveform. To this purpose, some promising results have been recently published in reference [2]. In particular, in [2] the issue related to the search for efficient solutions for the coexistence of radar and communication systems has been discussed. Specifically, the authors of reference [2] have proposed a new approach to control chaotic trajectories, which assures the coexistence of both radar and communication systems. By selecting a state variable for controlling the trajectory, it is possible to produce a controlled chaos-based frequency modulated (CCBFM) waveform for transmitting a signal in joint radar-communication systems [2]. Moreover, a communication receiver and a radar receiver have been designed in order to decode the information and extract the signature of the target, respectively. Analyses of the conceived chaos-based approach have been conducted [2]. In particular, the bit error ratio highlights that the CCBFM waveform has better performances than the classical half-sine pulse frequency-modulated waveform. Moreover, when the signal-to-noise ratio is about \(-30\) dB, it can be shown that the adoption of CCBFM waveforms assures good target detection and classification [2]. The conducted analysis has clearly demonstrated that chaos-based frequency-modulated waveforms can be effectively exploited for joint radar-communication systems in a shared spectrum [2].
## 3 Relative Importance Measure Based on Distance Distribution and Multi-index Fusion
### Problem Definition  
Under normal conditions, a complex network can be represented by \(G(V,E)\). Here, \(V\) refers to the node in the network \(G\) and \(E\) refers to the edge of the network \(G\). The network \(G\) comprises \(n\) nodes. Among them, \(n\) nodes can be divided into important node set \(V_{1}\) and unimportant node set \(V_{2}\). The important node set \(V_{1}\) has \(n_{1}\) nodes, while the unimportant node set \(V_{2}\) has \(n_{2}\) nodes. The important node set \(V_{1}\) includes known important node set \(R\) and unknown important node set \(U\). The unimportant node set \(V_{2}\) and unknown important node set \(U\) constitute target node set \(T\), i.e., \(T=V_{2}\cup U\).  
The key to finding the relatively important nodes in the target node set \(T\) is to first calculate the importance of a node in the target node set \(T\) relative to a known important node, and then calculate the importance of a node relative to all nodes in the known important node set \(R\).  
The main contents of this work include the following: For the information of known important node set \(R\), the importance of any node in the target node set \(T\) relative to the node in the known important node set \(R\) is analyzed and calculated. The expectation is to find \(top-k\) relatively important nodes in the target node set \(T\). The final results are analyzed and evaluated on the basis of three evaluation indicators, namely, precision, recall, and area under the curve (AUC).
## 2 Materials and Methods  
### Datasets  
#### 2.1.1 Dataset I  
The dataset used in this study is a public dataset, that can be found on bci-horizon-2020: P300 speller with amyotrophic lateral sclerosis (ALS) patients. The experiment paradigm was proposedby [PERSON] and [PERSON] [8]. The interface is shown in Figure 1 which is a 6 \(\times\) 6 character matrix. They used BCI2000 [24] to collect EEG signals from eight ALS subjects. The collection process is as follows. The subject needs to type 35 characters in total. For each character, the subject looks at the character, and then each row and column flash randomly one at a time as a round (a total of 12 flashes, and 2 flashes include the target stimulus), and there are 10 rounds (in order to average the signal to reduce noise). The signal acquisition frequency is 256 Hz, bandpass filtering is from 0.1 to 30 Hz, and eight channels are used for acquisition (Fz, Cz, Pz, Oz, P3, P4, PO7, and PO8). Therefore, for each subject, we can get 420 samples (70 P300 samples and 350 non-P300 samples).
# Channel Estimation for UAV Communication Systems Using Deep Neural Networks
## 2 System Model and Problem Formulation
### Problem Formulation
#### 2.1.1 Adaptive Learning Rate  
Based on the maximization of the local estimate of the likelihood, the learning rate can automatically be adapted while the RBM is trained using the stochastic gradient. Because \(\theta=(W,b,c)\) is the parameter of GBRM modeling, and it represents the adapted parameter of learning rate \(\eta\), \(P_{\theta}(V)=P_{\theta}^{*}/Z_{\theta}\) represents the probability density function (pdf), and \(Z_{\theta}\) is the normalization parameter for the parameter \(\theta\). If the difference between the learning rate becomes small and the models become close to each other, then the likelihood of \(\theta^{\prime}\) is computed as:  
\[P_{\theta^{\prime}}(v_{d}) = \frac{P_{\theta^{\prime}}^{*}(v_{d})}{Z_{\theta}}\frac{Z_{\theta} }{Z_{\theta^{\prime}}} \tag{9}\] \[= \frac{P_{\theta^{\prime}}^{*}(v_{d})}{Z_{\theta}}\left\langle \frac{P_{\theta^{\prime}}^{*}(v)}{P_{\theta^{\prime}}^{*}(v)}\right\rangle_{P_ {\theta}}^{-1}\]  
Now, the learning rate parameters are chosen to maximize the likelihood of the GBRPM parameters \(\theta^{\prime}.P_{\theta^{\prime}}\) represents the unnormalized computed PDF of the training data, which is used to estimate the expectation of the data. The optimal learning rate can be found that can maximize the likelihood of each iteration. Nevertheless, this can lead to a big fluctuation due to the small size of the minibatch. Ref. [20] proposed that the new learning rate is chosen from the set \(\{(1-\mathcal{E})^{2}\eta_{o},(1-\mathcal{E})\eta_{o},\eta_{o},(1-\mathcal{E })\eta_{o},(1-\mathcal{E})\eta_{o},\}\) where \(\eta_{o}\) is the prior learning rate and \(\mathcal{E}\) is a small constant, which was chosen randomly.
## 4 Results and Discussion
### Atomization Experiment Results
#### 4.1.1 Effect of Air Pressure on Atomization  
The experiment measuring the effect of air pressure on atomization was carried out under the conditions of water volume 20 L/h and water pressure 0.1-0.15 MPa. The results are shown in Figure 9.  
Figure 8: Experimental device for dust collection by swirling curtain.  
As shown in Figure 9, there is no linear relationship between the number of droplets and the proportion of droplets below 50 \(\upmu\)m and the air pressure. When the number of droplets is the largest, the atomization effect of ultrasonic atomizer is the best.  
1. When the water supply of the atomizer is constant, the number of droplets increases first and then decreases with the increase of air pressure. When the air pressure is 0.35 MPa, the number of droplets is the largest. The result is similar to a previous study performed by [PERSON] et al. [24].
2. With the change of air pressure, the proportion of fog droplets below 50 \(\upmu\)m has the same trend as the number of fog droplets. When the air pressure is 0.35 MPa, the proportion of fog droplets is the largest.
3. When the water volume is constant, the droplet size is smaller--more droplets. Additionally, the droplets are well-distributed. Consequently, the corresponding atomization effect is quite good.
## 2 Routing Algorithms for Wireless Sensor Networks
### Hierarchical Protocols  
Hierarchical protocols are based on clusters because clusters contribute to more scalable behavior as the number of nodes increases. Furthermore, clusters provide improved robustness and facilitate more efficient resource utilization for many distributed sensor coordination tasks.  
Low-Energy Adaptive Clustering Hierarchy (LEACH) is a cluster-based protocol that minimizes energy dissipation in sensor networks by randomly selecting sensor nodes as cluster-heads [16].  
Power-Efficient Gathering in Sensor Information System (PEGASIS) [17] is a near optimal chain-based protocol. The basic idea of the protocol is to extend network lifetime by allowing nodes to communicate exclusively with their closest neighbors, employing a turn-taking strategy to communicate with the Base Station (BS). Threshold-sensitive Energy Efficient protocol (TEEN) [18] and Adaptive Periodic TEEN (APTEEN) [19] have also been proposed for time-critical applications. In TEEN, sensor nodes continuously sense the medium, but data transmission is done less frequently. APTEEN, on the other hand, is a hybrid protocol that changes the periodicity or threshold values used in the TEEN protocol, according to user needs and the application type.