esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Gesture Definition  
#### 4.1.1 Requirements and Considerations  
In order to develop a viable alternative drone control system, it should allow users to perform the same motions as a regular handheld remote: pitch, roll, yaw, throttle, and their combinations, as well as camera motions. Similarly, it should allow users to define the sensitivity of each command, and by extension, the amplitude of the corresponding motion.  
Designing Natural User Interfaces (NUI), such as gestures, some additional requirements must be taken into account to achieve intuitive interaction. [PERSON] et al. [33] explained that the gestures should be related to each other while following a single metaphor. In the same study, the use of multi-modal interaction, such as a combination of gesture and speech commands, is encouraged in order to increase naturalness. [PERSON] et al. [11] additional requirements were highlighted including avoidance of non-ergonomic positions (as described in [34]) and the presence of some form of feedback. In an AR environment, several forms of feedback can be integrated in the real-world view of the user as overlaid virtual objects.  
Hence, UAV gesture control has three primary aims:  
1. To provide an easy-to-learn and intuitive interface;
2. To be physically comfortable and ergonomic;
3. To allow the full extent of drone motions, including combination motions and variable velocities.
# Multi-Target Detection Algorithm for Fusion Images Based on an Attention Mechanism
## 6 Discussion  
This study confirms that integrating a small-target detection layer, Global Attention Mechanism (GAM), and WIoU-based loss into a YOLO-based framework can significantly enhance detection performance on fused infrared-visible imagery. The proposed approach demonstrates strong adaptability across diverse environments, highlighting the advantages of multimodal fusion in improving detection accuracy and robustness. GAM enhances  
Figure 21: Comparison of detection effects between fusion images and single-modality images.  
global feature perception by reinforcing both spatial and channel dependencies, while the small-target layer helps retain fine-grained positional cues critical for detecting small or distant objects. The use of WIoU as a loss function promotes more stable training and better alignment with challenging label distributions. These components work synergistically to address the demands of real-world applications such as intelligent surveillance, autonomous navigation, and public safety monitoring.  
While each enhancement contributes specific benefits, there are also trade-offs to consider. For instance, increased model complexity may introduce higher computational requirements, which should be evaluated based on the deployment scenario. The method is particularly suitable for tasks requiring reliable performance under varying lighting, weather, and terrain conditions. Moreover, although the current framework focuses on static image-based detection, it can be extended to video analysis or real-time processing with appropriate adaptations.  
Another important factor affecting detection performance is the quality of the fused images. Imperfect fusion results--such as spatial misalignment between modalities or low contrast in critical regions--can introduce noise or distortions that impair feature extraction and classification accuracy. In our experiments, we observed that while the model maintains a degree of robustness, degraded fusion quality does impact the confidence and localization precision, especially for small or partially occluded targets. These findings suggest that ensuring high-quality image fusion is crucial for the downstream detection task. Future research could explore adaptive fusion strategies that incorporate alignment correction or contrast enhancement as preprocessing steps.
# Decision Optimization for Power Grid Operating Conditions with High- and Low-Voltage Parallel Loops
###### Abstract  
With the development of higher-voltage power grids, the high- and low-voltage parallel loops are emerging, which lead to energy losses and even threaten the security and stability of power systems. The multi-infeed high-voltage direct current (HVDC) configurations widely appearing in AC/DC interconnected power systems make this situation even worse. Aimed at energy saving and system security, a decision optimization method for power grid operating conditions with high- and low-voltage parallel loops is proposed in this paper. Firstly, considering hub substation distribution and power grid structure, parallel loop opening schemes are generated with GN (Girvan-Newman) algorithms. Then, candidate opening schemes are preliminarily selected from all these generated schemes based on a filtering index. Finally, with the influence on power system security, stability and operation economy in consideration, an evaluation model for candidate opening schemes is founded based on analytic hierarchy process (AHP). And a fuzzy evaluation algorithm is used to find the optimal scheme. Simulation results of a New England 39-bus system and an actual power system validate the effectiveness and superiority of this proposed method.  
parallel loop; line switching; complex network theory; GN algorithm; comprehensive evaluation model +
Footnote †: journal: Applied
## 3 Results and Discussion
### The Effect of Diallyl Disulfide on the Cell Membrane Permeability of Phytophthora Nicotianae  
The cell membrane permeability change is shown in Figure 3. The relative electrical conductivity of the _P. nicotiana_ mycelium solution treated with DADS was higher than that of control mycelia, and its conductivity increased over time and with increased DADS concentration. This indicated that DADS treatment destroyed the integrity of the mycelial cell membrane, rupturing the cell membrane and increasing intracellular electrolyte extravasation, thereby causing an increase in cell membrane permeability. This confirms that EOs severely interfere with the lipid layer of cell membranes and cause intracellular leakages, followed by cell lysis [32]. [PERSON] et al. [33] analyzed the protein leakage effect of the ethanol extract of _Mentha arvensis_ on multidrug-resistant _Acinetobacter baumannii_. The intracellular protein released by the application of EO from _A. sativum_ was significantly higher than that of intracellular protein released from _Acinetobacter baumannii_, which induced dose- and time-dependent rupture of the bacterial cell [34].  
Figure 3: Effect of DADS on relative conductivity of _P. nicotianae_ mycelia.  
Figure 2: Fumigation effect of 20 \(\upmu\)L/L DADS on _P. nicotianae_. (**A**) Control; (**B**) mycelia treated with 3 \(\upmu\)L/dish (equivalent to fumigation concentration 20 \(\upmu\)L/L) of DADS.
# Research on Performance Metrics and Environmental Conditions for 5G MIMO OTA
## 3 Experimental Results and Analysis
### Mode Analysis under 3 GPP Environmental Condition  
There are three modes in the 3 GPP TRMS procedure under UE noise-limited environmental condition, namely, FS DMP, FS DML, and FS DMSU. On the contrary, there is only one mode in the CTIA MARSS procedure under interference-limited environmental condition, i.e., Portrait 45, the position of which is the same as FS DMP. In this section, a TRMS comparison between the three modes and the averaged TRMS at all thresholds is performed for comparison. Figures 2 and 3 are for the UMa and UMi channel models, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Experimental Setup** \\ \hline DUTs & 10 Smartphones \\ Test Methodology & MPAC (reference) \\ MIMO & \(2\times 2\) \\ Band & Band 5 (881.5 MHz) \\ Environmental Condition & UE Noise-Limited \& Interference-Limited \\ DUT Placement & Black Box \\ FoMs & TRMS \&MARSS \\ Outage Levels & 95\%/90\%/70\% \\ Channel Models & SCME UMa \&UMi \\ \hline \hline \end{tabular}
\end{table}
Table 2: A summarization of the MIMO OTA testing setup.  
Figure 2: A TRMS comparison between the three modes and the averaged TRMS at all thresholds, under the UMa channel model; (**a**) threshold = 95%, (**b**) threshold = 90%, (**c**) threshold = 70%.  
It can be observed that for both channel models, the averaged TRMS is almost the same as TRMS in FS DML for all DUTs at three thresholds, with a maximum deviation of around 1.6 dB. We can conclude that TRMS performance is mainly determined by FS DML mode under UE noise-limited environmental condition, which can be seen as the dominant  
Figure 3: A TRMS comparison between the three modes and the averaged TRMS at all thresholds under the UMi channel model; (**a**) threshold = 95%, (**b**) threshold = 90%, (**c**) threshold = 70%.  
mode. In later analysis, TRMS in FS DML mode will be selected to be compared to MARSS for variation analysis per 12 azimuth positions.
# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 3 Proposed Method
### General Architecture
#### 3.1.5 Step 5: Redundancy Encoding and Temporal Distribution  
In video watermarking, the channel is not memoryless. Frames may be lost, dropped, or corrupted due to compression, transmission errors, or editing operations. Therefore, to ensure robustness, watermark data must be distributed redundantly across the temporal domain. This step enhances resilience by employing error-correcting strategies and intelligent frame selection to protect the embedded watermark bits over time.  
The watermark bitstream \(\mathrm{w}=\{w_{1},w_{2},\ldots,w_{n}\}\) is encoded using repetition or a low-rate error-correcting code (e.g., BCH, LDPC). For simplicity and high decoding reliability, we implement repetition:  
\[\mathrm{w}_{r}=\underbrace{\{w_{1},\ldots,w_{1}\}}_{r\text{ {times}}},\ldots, \underbrace{\{w_{n},\ldots,w_{n}\}}_{r\text{ {times}}}, \tag{9}\]  
where \(r\) is the redundancy factor (e.g., 3, 5, or 7). Each watermark bit is embedded across multiple independent frames.  
To maximize the effectiveness of temporal redundancy, we distribute the watermark bits only across scene-representative keyframes, not arbitrarily across the timeline. Scene segmentation is based on inter-frame content changes [26]. For each frame \(F_{k}\), compute the DC map \((D_{k})\):  
\[D_{k}(i,j)=\frac{1}{64}\sum_{m=0}^{7}\sum_{n=0}^{7}F_{k}(8i+m,8j+n)\, \tag{10}\]  
Then, the histogram difference between adjacent frames \(d_{k}\) is computed as follows:  
\[d_{k}=\left\|hist(D_{k})-hist(D_{k+1})\right\|_{2}\, \tag{11}\]  
A scene boundary is defined if \(d_{k}>\theta\). Then we select a keyframe \(K_{s}\) for each scene as the temporal midpoint:  
\[K_{s}=F_{\left[(t_{start}+t_{end})/2\right]}\, \tag{12}\]  
where \(t_{start}\) and \(t_{end}\) mark the beginning and end of a shot or scene. If there are \(M\) keyframes in a video, each bit in \(\mathrm{w}\) is assigned to \(r\) of these keyframes, distributed uniformly or pseudo-randomly. This ensures error tolerance (multiple chances for recovery) and resilience to frame drops (if any copy survives, decoding remains possible). Please note that selecting parameter values involves a tradeoff. A higher redundancy factor \(r\) implies increased robustness, although diminished capacity. The scene-change threshold value \(\theta\) will dictate the quantity of video keyframes, while keyframe density signifies a balance between capacity and temporal distribution. This tradeoff is dynamically regulated according to video duration, watermark dimensions, and embedding intensity.
## 3 Quality Assessment of the BIC/B2a Signals from BDS-3 Operational Satellites  
Based on the raw data collected from 3-4 June 2018 with iGMAS and M300 Pro receivers, this section evaluates the quality of the BDS-3 BIC/B2a signals by comparing their signal carrier-to-noise density ratio (C/\(N_{0}\)), pseudorange multipath and noise, as well as the double-differenced carrier phase and code residuals to those of the GPS/Galileo L1-E1/L5-E5a signals. The GPS satellites here only restrict to the Block IIF satellites that can simultaneously transmit L1/L5 signals, while the Galileo signals come from both In-Orbit-Validation (IOV) and Full-Operation-Capability (FOC) satellites, which is the same as our previous research [7]. However, since the signals transmitted by the Galileo IOV satellites show a lower level (approximately 5 dB-HZ) of C/\(N_{0}\) in comparison with their FOC counterparts [31], and the C/\(N_{0}\) of first two FOC satellites (E14 and E18) that were injected into incorrect orbital planes  
Figure 1: Observational environment and GNSS antennas used in the experiments at SGG building of Wuhan University. (**a**) shows the observation site on the roof of SGG building, and (**b**) shows the Trimble Zephyr Geodetic 2 antenna and the short baseline configuration.  
Figure 2: Sky plot of BDS-3 operational (**a**), Galileo (**b**), and GPS (Block IIF) (**c**); GPS (other Blocks) (**d**) satellites and their availability (**e**,**f**) from 3–4 June 2018 with 10\({}^{\circ}\) elevation cut-off angle.  
could be higher than regular Galileo FOC satellites [32], only regular FOC satellites are involved in the computation of average _C/N\({}_{0}\)_ for Galileo.
## 3 Key Internet of Things Scenarios
### Smart Grid  
Energy production and transport will highly benefit from advanced IoT for the efficient exploitation of intermittent renewable energy sources. Since their random nature makes power balance and control difficult, the distribution network needs to leverage active control energy flow management to address emerging customer needs. Furthermore, energy transportation requires highly efficient processes to achieve high equipment utilization and low line losses, through the accurate regulation of the grid load.  
In this environment, it is possible to identify two service types with different performance requirements. The first type consists of online monitoring through sensors. Typically, it requires E2E latency values in the order of seconds and intermittent single-link bandwidth values in the order of tens of kbps. The second type consists of comprehensive video surveillance of transmission lines through fixed cameras and/or unmanned aerial vehicle patrolling. In this case, an uplink bandwidth of up to 10 Mbps and latency values of \(\leq\)200 ms could be necessary. For both service types, availability values should be at least 99.9%, with a deployment density of 10 information sources (sensors and cameras) for each km\({}^{2}\). It is clear that the first type of service is better suited to serverless computing, since it would be able to exploit all its features: promptness, efficiency and scalability. However, video surveillance requirements do not preclude the adoption of serverless capabilities for these services too, especially if performed non-continuously and triggered by specific events, e.g., associated with the output of the sensor monitoring functions.
## 3 Results  
### Data Sets  
To analyze the ISB characteristics, the data sets collected at 120 globally distributed Multi-GNSS Experiment (MGEX) stations spanning a month from day of year (DOY) 41 to 70 in 2020 are employed. The geographical distribution of the 120 selected MGEX stations is shown in Figure 1, and the receiver types of these stations are listed in Table 1. All the stations can support the GPS/GLONASS/Galileo/BDS-2/BDS-3 data. The selected 120 stations cover seven different receiver types from three manufacturers. During the analysis period, a total of 29 stations, which are in parentheses in Table 1, encountered a change of receiver firmware versions. In addition, there was a replacement of receiver types for the station UNSA from JAVAD TRE_G3 TH to SEPT ASTERX4 on DOY 49 of 2020, and for the station BRUX from SEPT POLARX4 TR to SEPT POLARXSTR on DOY 57 of 2020, respectively.
## 3 Results and Discussion
### TCLP vs. Percolation Column Procedure: A Comparison for As Release  
This study compared the leaching of As from contaminated and stabilized soil (mg/kg) based on a worst-case scenario (TCLP), and long-term As leaching from soil in a natural scenario (using a percolation test). The levels of As release in both leaching tests were compared using the same L/S ratio (20 L/S), because the TCLP was conducted with a liquid-to-solid ratio of 20. The order of efficiency at reducing As release by the stabilizers in the TCLP test was in accordance with the percolation tests (Figure 8). The As release curve by the TCLP test and the percolation column test showed a linear relationship with a strong correlation (R\({}^{2}\) = 0.9547). When compared to the percolation column test, the TCLP test resulted in a higher volume of As released from soils. The TCLP results for As release from the various soils were higher by 1.4-3.4 mg/kg. This result is due to the harsh condition (worst-case scenario) applied in the TCLP test, whereas the percolation column test represents natural field conditions. Although the release of As was not completely prevented, the stabilizing agents enhanced the reduction of As from being released from soil in both the percolation and TCLP tests, showing an improvement in preventing groundwater contamination by binding with As to reduce the risks to the environment. Moreover, this study identified CMDS and cement as the most effective stabilizers for arsenic stabilization in soil.  
Figure 7: Leaching of arsenic from contaminated (control) and stabilized soils using the TCLP test, (**A**) leachate concentration (as \(\upmu\)g/L); and (**B**) release of arsenic (as mg/kg) from different stabilized soils.
# Sustainable Planning and Design of Ocean City Spatial Forms Based on Space Syntax
## 4 Case Analysis
### Evaluation and Analysis of Regional Marine Eco-Economic Zoning in Shenzhen
#### 4.1.2 Data Source  
The research data used in this study primarily included Google satellite images, the previous urban master plans for Shenzhen, other specialized plans, and social and economic development plans. Amid studies on the urban form of Shenzhen City, our work mainly referred to the compilation year of the latest version of Shenzhen's urban master plan, and we processed data using three main reference pictures that depended on the data collection time of the Google satellite map; that is, the status quo map of construction land in 1994, the historical satellite image from 2004, and the status quo image from 2014 (Figures 3 and 4). The image from 2004 represented the year before the start of the \"great construction\" period. In 2004, Shenzhen entered a period of \"great construction\" and the changes in urban form were very dramatic. For this reason, in this study, we analyzed and investigated the basic evolution characteristics of Shenzhen's spatial form in the past and present by comparing the urban form structure data of Shenzhen City from three periods over the past 20 years to lay a foundation to determine the sustainability of the urban spatial form of Shenzhen City [35].
## 2 Materials and Methods
PL excitation was carried out by using either a UV LED with a wavelength of 365 nm, power 0.5 W, and beam diameter of about 10 mm or a semiconductor laser with a wavelength of 532 nm, maximal power of 0.2 W, and beam diameter of 2 mm. The PL quantum yield (QY) of prepared Cd-CNPs was estimated by using a 50 mm integrating sphere (Thorlabs Inc., Newton, NJ, USA) and a reference aqueous solution of Rhodamine 6G. Measurements of the proton relaxation times in aqueous suspensions of Cd-CNPs were carried out using a 7 T Bruker BioSpec 70/30 USR MRI scanner. The suspensions were diluted in deionized water at various Gd concentrations and placed in 1.5 mL Eppendorf tubes. The sample temperature during the measurements was kept at 19.5 \({}^{\circ}\)C.
## 3 Seismic Input, Load Scenarios and Analyses
### Nonlinear-Dynamic Analysis (NLDA)--Time History Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Signal** & **Station** & **Action** & **Event** & **Mw** & **Epicentral** & **Epicentral** & **PGA** \\
**Source** & **Code** & & & & & **Location** & **Distance [km]** & **[g]** \\ \hline \multirow{2}{*}{ESESEA *} & \multirow{2}{*}{MI05} & \multirow{2}{*}{Italia} & 7 April 2009 & \multirow{2}{*}{5.5} & \multirow{2}{*}{Fossa (AQ)} & \multirow{2}{*}{3.6} & \multirow{2}{*}{0.66} \\  & & & 17:47 & & & & \\ \hline \multirow{2}{*}{DPC **} & \multirow{2}{*}{MMO} & \multirow{2}{*}{Italia} & 30 October 2016 & \multirow{2}{*}{6.5} & \multirow{2}{*}{Norcia (PG)} & \multirow{2}{*}{19.3} & \multirow{2}{*}{0.19} \\  & & & 06:40 & & & & \\ \hline \multirow{2}{*}{DPC **} & \multirow{2}{*}{BSC} & \multirow{2}{*}{Italia} & 23 November & \multirow{2}{*}{6.9} & \multirow{2}{*}{Laviano} & \multirow{2}{*}{28.3} & \multirow{2}{*}{0.09} \\  & & & 1980 18:34 & & & & \\ \hline \multirow{2}{*}{DPC **} & \multirow{2}{*}{CLO} & \multirow{2}{*}{Italia} & 26 October 2016 & \multirow{2}{*}{5.9} & \multirow{2}{*}{Visso (MC)} & \multirow{2}{*}{10.8} & \multirow{2}{*}{0.22} \\  & & & 19:18 & & & & \\ \hline \multirow{2}{*}{DPC **} & \multirow{2}{*}{LSS} & \multirow{2}{*}{Italia} & 24 August 2016 & \multirow{2}{*}{6.0} & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{26.7} & \multirow{2}{*}{0.02} \\  & & & 01:36 & & & \\ \hline \multirow{2}{*}{DPC **} & \multirow{2}{*}{MTR} & \multirow{2}{*}{Italia} & 6 April 2009 & \multirow{2}{*}{6.1} & \multirow{2}{*}{L’aquila} & \multirow{2}{*}{23.1} & \multirow{2}{*}{0.06} \\  & & & 01:32 & & & & \\ \hline \multirow{2}{*}{DPC **} & \multirow{2}{*}{VGG} & \multirow{2}{*}{Italia} & 9 September & \multirow{2}{*}{1998 11:28} & \multirow{2}{*}{5.6} & \multirow{2}{*}{Laino Borgo} & \multirow{2}{*}{2.5} & \multirow{2}{*}{0.08} \\  & & & 1998 11:28 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Selected accelerograms, providing evidence (underlined) of the main and minor events. * Energyito Seismic Network for Site Effect Studies in L’Aquila town, ** Italian Strong Motion Network (RAN). Figure 6: Illustration of the seismic hazard disaggregation, where the percentage contribution to the hazard estimate provided by all possible pairs of magnitude and distance values for a return period of 2475 years is highlighted. Moving to configuration n. 2, since the purpose of this research paper is to conduct dynamic analyses on the motorway viaduct, the derivation of a new set of accelerograms compatible with the site-specific spectrum derived from local seismic response analyses is needed. For this purpose, Seismomatch(r) [51], a well-known software specialized in the selection and treatment of seismic signals preparatory to structural analyses, was used. The basis for the spectrum compatibility for both directions will be the response spectrum in normalized form. Figure 7 reports the matched spectra for the MTR station in the X direction.