esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Static Form of the Problem
### Scalability and Error Analysis
#### 3.3.2 Error Analysis  
Even though we have derived an upper bound for the approximation error introduced, we are still interested in typical values. In fact, we observe in most cases that the error level is significantly lower than the upper bound. Given a set of parameters, we define the error rate as the probability of getting a scenario that results in an error out of all of the generated scenarios. The average error is defined as the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & \(N\) & \(p\) & **Mean(\(w\))** & SD(\(w\)) & \(Q\) \\ \hline Value & 20 & 0.15 & 100 & 50 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Baseline scenario parameter settings.  
Figure 4: Processing time sensitivity to \(p\).  
Figure 5: Processing time sensitivity to mean(\(w\)).  
Figure 3: Processing time sensitivity to \(N\).  
average amount of error over all of the cases. Since the program stops at the 10 th minute, any scenario with a running time longer than 10 min does not come with a precise result value. Therefore, we only sample the ones that have results worked out within 10 min.  
Surprisingly, the measured error is significantly lower than what we have expected. Within the three sets of experiments where we adjust \(N\), \(p\) and \(\text{mean}(w)\), the error rate are 1.92%, 1.08% and 6.38%, respectively. The average errors are also very small, at 0.17%, 0.09% and 0.15%, respectively. Therefore, the overall error rate is much smaller than the upper bound we set, which is 5%. Note that the overall error rate is given by the error rate multiplied by the average error, as we have defined. Further analysis leads us to conclude that this is because of the limit we put on the processing time. For those test cases where the processing time is longer than 10 min, there is a higher probability of getting an error because of the greater amount of branching. Now that we have excluded those cases, the measured error level is underestimated. Nevertheless, this experiment at least gives us some hints on the level of error introduced. It also demonstrates that in real-life scenarios with real-time processing requirements, the error introduced is negligible.  
We also measure the amount of error introduced by adjusting the parameter \(Q\). A larger \(Q\) means a finer quantization and usually less error, as analyzed in Section 3.2.2. A smaller \(Q\) will lead to higher levels of errors. The extreme case of \(Q=1\) is in essence a naive \"all-or-none\" algorithm. From Figure 6 we observe that: (1) our algorithm performs significantly better than the naive solution; and (2) a \(Q\) value greater than five can give good-enough performance (with empirical errors smaller than 5%). The values in Figure 6 are obtained by running the baseline scenario 50 times. Both the error rate and the average error are presented in the figure.
# Multiple Mobile Sinks for Quality of Service Improvement in Large-Scale Wireless Sensor Networks
###### Abstract  
The involvement of wireless sensor networks in large-scale real-time applications is exponentially growing. These applications can range from hazardous area supervision to military applications. In such critical contexts, the simultaneous improvement of the quality of service and the network lifetime represents a big challenge. To meet these requirements, using multiple mobile sinks can be a key solution to accommodate the variations that may affect the network. Recent studies were based on predefined mobility models for sinks and relied on multi-hop routing techniques. Besides, most of these studies focused only on improving energy consumption without considering QoS metrics. In this paper, multiple mobile sinks with random mobile models are used to establish a tradeoff between power consumption and the quality of service. The simulation results show that using hierarchical data routing with random mobile sinks represents an efficient method to balance the distribution of the energy levels of nodes and to reduce the overall power consumption. Moreover, it is proven that the proposed routing methods allow for minimizing the latency of the transmitted data, increasing the reliability, and improving the throughput of the received data compared to recent works, which are based on predefined trajectories of mobile sinks and multi-hop architectures.  
cluster-based routing protocol; energy consumption; quality of service; multiple mobile sinks; large scale wireless sensor network +
Footnote †: journal: Sensors
# Wideband Printed Wide-Slot Antenna with Fork-Shaped Stub
## 3 Results and Discussion  
### Simulation Results  
The two proposed antennas were designed with the help of simulation software. The final structural parameters were optimized, as shown in Table 1. All these parameters were being optimized by the Ansoft HFSS sweeper and optimizer tool to obtain the best parameter set, except the width of the microstrip feed line, which was kept fixed to satisfy the standard input impedance of 50 \(\Omega\).  
To make a comparison with the proposed antennas, the structural parameters of the normal antenna were also optimized. The simulated reflection coefficients are given in Figure 6, which shows the comparison between the proposed antennas and the normal antenna. By smoothing the stub and slot on the ground plane as much as possible, an impedance BW of 130.7% (0.9-4.3 GHz) was obtained, while the impedance BW for the WS antenna with rectangular corners was 100% (0.9-2.7 GHz). Here, the lowest resonant frequency was in agreement with the previous theoretical calculation.  
It can be seen, by introducing an extra stub, the impedance BWs (S\({}_{11}<-10\) dB) for both antenna A and antenna B became wider, reaching 144% (0.9-5.55 GHz) and 147% (0.9-5.90 GHz), respectively. Adding an extra fork-shaped stub was confirmed to be an effective way to improve the BW characteristics.  
To better understand the antennas' behavior, the simulated current patterns of the proposed antennas were compared with the normal antenna, as shown in Figure 7, which depicts the current distributions along the tuning stub at 1 GHz, 4 GHz and 6 GHz, respectively. At the frequency of 1 GHz, there was a strong current on the outer boundary of the stub for all three antennas. Similar current distributions were observed at the frequency of 4 GHz. At the frequency of 6 GHz, the current on the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Description** & **Antenna A (mm)** & **Antenna B (mm)** \\ \hline Length of the horizontal section & \(l_{a}=17.0\) & \(l_{b}=16.48\) \\ Width of the added stub & \(w_{a}=4.0\) & \(w_{b}=4.22\) \\ Inside radius of the bend & \(r_{a}=12.30\) & \(r_{b}=6.08\) \\ Length of the vertical section & \(d_{a}=4.35\) & \(d_{b}=8.82\) \\ Distance between the two stubs & / & \(d_{s}=9.70\) \\ Width of the main stub & \(w_{0}=4.93\) & \(w_{0}=8.33\) \\ Horizontal length of the main stub & \(l_{0}=21.2\) & \(l_{0}=30.52\) \\ Vertical length of the main stub & \(d_{0}=22.08\) & \(d_{0}=14.77\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimal structural parameters of the proposed antennas.  
Figure 6: Simulated reflection coefficients of the proposed antennas, compared with the normal WS antenna.  
stub of the normal antenna became weak, while for the proposed antennas a strong current could still be obtained on the stub. It can be seen that the resonant mode of the current at high frequency changed as a result of introducing the extra stub. By means of a reasonable structural design and the appropriate loading of the extra stub, the broadband matching was able to be realized.
## 4 Discussion
The extract showed moderate antioxidant activity, with up to 72.8% DPPH radical scavenging and increased superoxide and hydroxyl radical scavenging at higher concentrations. It also showed selective anticancer effects, particularly against HepG2 and MCF-7 cell lines, with limitedtoxicity toward normal HEK-293 cells. These results confirm that the CH6 can produce bioactive compounds that reflect its genomic potential. Of particular interest, we identified a biosynthetic gene cluster with high similarity to the guangnanmycin cluster (76%). It features a simpler structure than leinamycin, including a macrolactam ring, a methyldisulfide side chain, and a unique 1-aminocyclopropane-1-carboxylic acid (ACC) unit. ROS, produced by guangnanmycin, can trigger oxidative stress and induce apoptosis in cancer cells. This mechanism is common among quinone-based compounds and contributes to their cytotoxic effects. The biosynthetic pathway responsible for guangnanmycin production is encoded by the \(gnm\) gene cluster, a modular PKS/NRPS hybrid system. It includes several key enzymes, such as acyl-CoA synthetase GnmS for ACC activation, PLP-dependent cysteine lyase GnmY that produces hydropersulfide from L-thioccysteine, and hydropersulfide S-methyltransferaseGnmP that methylates the -SSH group [48]. These enzymes are essential for constructing the compound's sulfur-containing moiety, which contributes to its unique reactivity and cytotoxic potential of guangnanmycin [13]. The amino acid sequences of GnmP and GnmY from the CH6 strain showed 87.5% and 84.9% similarity (Figure S4) to GnmP and GnmY from the _Streptomyces_ sp. CB01883 strain, respectively, suggesting functional conservation of these key biosynthetic enzymes in _Streptomyces_ sp. CH6. Furthermore, inactivation of \(gnmB\) completely abolishes guangnanmycin production, confirming its essential role in the biosynthesis of guangnanmycin [10]. The presence of \(gnmS\), \(gnmY\), \(gnmP\), and \(gnmB\) homologs in the CH6 genome suggests that this strain possesses the genetic capacity to biosynthesize guangnanmycin-like sulfur-containing compounds, which may contribute to the antioxidant and anticancer activities observed in the crude extract. Although genome mining revealed a guangnanmycin-like biosynthetic gene cluster in _Streptomyces_ CH6 with 76% similarity to a known reference cluster, the crude extract showed strong antimicrobial activity against both Gram-positive and Gram-negative bacteria. The actual production of guangnanmycin or a related metabolite has not yet been experimentally confirmed. In future studies, we will purify and identify the active compounds from the culture extract of _Streptomyces_ sp. CH6 using chromatographic techniques (e.g., HPLC, flash chromatography), followed by structural elucidation via NMR and MS spectroscopy. Additionally, linking the compound to the gene cluster via gene knockout or heterologous expression will help establish a direct genotype-phenotype connection.
## 3 System Hardware and Software Structure
### Hardware and Software for In-Vehicle Driver Behavior Recognition  
In order to implement an evaluation methodology based on the driver's perceptual capabilities and a comprehensive assessment method based on confidence levels, we devised a driver behavior recognition system focused on the driver's head posture and driving operations within the vehicle.  
The in-vehicle driver behavior recognition consists of two main components: (1) the driver's head posture recognition and (2) the vehicle operation recognition. The driver's head posture recognition extracts information about the driver's head position and orientation by deploying a camera at specific angles between the main driver and co-driver seats for image capture and analysis. The vehicle operation recognition primarily collects data from the vehicle's internal communication network. It performs tasks such as recognizing the position of the throttle and brake pedals, determining the angle of the steering wheel, and identifying controls for the vehicle's lighting system. The subsequent sections will elaborate on the primary functions and implementation processes of each system.
## 3 Survey of Techniques for Updating
### The Execution Environment
#### 3.3.8. Mantis Operating System (MOS)  
MOS [52] is a lightweight, UNIX-style O/S with support for threads. These threads can be user application threads; the network stack is also implemented as a set of user threads. The MOS operating system library provides support to write a new code image to EEPROM. A commit function may then be called to write a control block for the boot-loader, which then installs the new code on reset. Any part of the code image (except for the boot-loader) may be updated, from a simple patch, to one or more thread, to the entire image. No specific tools are provided to build an update (except for an entire image), and an application-specific protocol would be needed to transfer the update over the network, apply the update, and reboot the system. MOS thus provides a framework to enable remote software updating, rather than the actual implementation.  
MOS contains no specific features support autonomous updates, but the UNIX-style environment might provide for more flexibility than the single-threaded environment provided by many WSN operating systems.
# A Novel Love Wave Mode Sensor Waveguide Layer with Microphononic Crystals
## 3 Experiments and Results
### Liquid Environmental Experiment  
To further explore the actual performance of the new Love wave mode delay line structure with micro piezoelectric PnCs, experiments with liquid environment were conducted. As shown in Figure 12, the micro liquid channel was designed for the device using polydimethylsiloxane (PDMS), which has good biocompatibility. Using the technology of the boning, the micro-PDMS channel was combined with the Love wave mode sensor chip.  
Figure 11: Responses of fabricated Love wave mode delay lines.  
Figure 12: Liquid environmental experiment.  
For the liquid environmental experiment, phosphate buffer saline (PBS) was used as the liquid sample. Devices with the traditional Love wave mode delay line structure and the new Love wave mode delay line structure were added, respectively, with 2 uL, 5 uL and 10 uL PBS solution. The electrical responses were also measured using the vector network analyzer. The treated results of the Love wave mode delay lines with respective original status, adding PDMS channel, and adding PBS solution, are demonstrated in Figure 13.  
The experimental results show that, for the Love wave mode delay line structures, when PDMS was based on the device, about 1 dB of attenuation was introduced for both the traditional structure and micro PnCs. From Table 1, it can be seen when 2 uL PBS was added, the frequency shift of Love wave mode delay line was about 32 KHz and 39 KHz, respectively, with the traditional structure and micro PnCs. When 5 uL PBS was added, the frequency shift of Love wave mode delay line was about 76 KHz and 87 KHz, respectively, with the traditional structure and micro PnCs. Thus, it has been proven that the new novel structure with micro PnCs can be applied in liquid environments as well as the traditional  
Figure 13: Liquid environmental experimental results of the Love wave mode delay line.  
structure, and even obtains better results. Furthermore, this micro PnCs structure can be extended to microreaction tanks for microcontrol. Therefore, this new device structure has a promising application for combining acoustic sensors and microfluidics.
# Analyzing and Forecasting Vessel Traffic Through the Panama Canal: A Comparative Study
## 2 Materials and Methods
### Methodology
#### 2.2.1 Autoregressive Integrated Moving Average (ARIMA)  
In univariate ARIMA models, the behavior of a time series is explained based on its past or lagged observations [32] and stochastic error terms. This model integrates three components, Autoregressive (AR), Integration (I), and Moving Average (MA), each characterized by specific parameters: \(p\) denotes the number of autoregressive parameters (AR), \(d\) is the number of differencing steps needed to make the series stationary (I), and \(q\) is the number of moving average parameters (MA).  
Thus, the model is denoted as ARIMA (\(p\), \(d\), \(q\)). Mathematically, the model can be expressed by the following equation:  
Figure 1: Vessel Transit Routes through the Panama Canal.  
\[\mathrm{X_{t}}=\mu+X_{t-1}+X_{t-d}+\cdots+\phi_{1}X_{t-1}+\phi_{2}X_{t-2}+\cdots+ \phi_{p}X_{t-p}+e_{t}-\phi_{1}e_{t-1}-\phi_{2}e_{t-2}-\cdots-\phi_{q}e_{t-q}\]  
If the ARIMA model incorporates a seasonal component, it can be represented as \(SARIMA\ (p,\ d,\ q)(P,\ D,\ Q)\), where the first parentheses (\(p,\ d,\ q\)) correspond to the non-seasonal autoregressive process, and the second parentheses (\(P,\ D,Q\)) denote the seasonal variation in the time series. Thus, SARIMA is essentially an extension of ARIMA used when a seasonal pattern is evident in the time series [33].  
Mathematically, the model is expressed by the following equation [34]:  
\[(1-B)\Big{(}1-B^{12}\Big{)}\Big{(}1-\phi_{12}B^{12}\Big{)}y_{t}=\mu+\Big{(}1+ \theta_{1}B+\theta_{2}B^{2}+\theta_{3}B^{3}+\pi_{1}B^{12}+\pi_{2}B^{24}\Big{)} e_{t}\]  
Box and Jenkins [35; 36] is the most widely used statistical procedure for analyzing and interpreting time series data in order to develop forecasting models such as ARIMA and SARIMA. Essentially, it involves identifying a mathematical model that represents the behavior of a time series and enables forecasting for a given time period. This methodology is divided into four stages [37]. The first stage, identification, involves recognizing patterns of trend, seasonality, and periodicity, and transforming the series into a stationary one. The next stage, estimation, consists of plotting the simple and partial correlograms to identify parameters based on the simple and partial autocorrelations. The third phase, validation, entails assessing the model fit through residual analysis and the Ljung-Box test. Finally, the forecasting stage involves generating predictions based on the selected and validated models. The entire process is summarized in the following flowchart (Figure 2).
## 2 Materials and Methods
### Satellite Image Preprocessing
#### 2.3.2 RADARSAT-2
The 30 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) ([[http://www.gdem.aster.ersdac.or.jp](http://www.gdem.aster.ersdac.or.jp)]([http://www.gdem.aster.ersdac.or.jp](http://www.gdem.aster.ersdac.or.jp))) was used to simulate the SAR image based on its imaging geometry and the available orbit image was matched to the simulated image by warping it to the DEM coordinate system [42,43]. After the terrain correction, the dataset was geocoded into a Universal Transverse Mercator (UTM) map projection. Finally the image was rectified using a preprocessed Quickbird image with a root mean square error of one pixel in geographic position. In order to match the HJ-1A/B image data, the pixel number of the RADARSAT-2 image data was \(\sim\)144, as each sample plot was corrected by nine pixels to match the ground observations with the HJ-1A/B image data.
# A Review of Cutting-Edge Sensor Technologies for Improved Flood Monitoring and Damage Assessment
## 3 Overview of Flood Sensor Development
### Flood Satellite Remote Sensing Sensors
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Satellite/Mission** & **Sensor** & **Principle** & **Resolution (m)** & **Launching Country** \\ \hline GCOM-W1 & AMS(R)2 & Microwave & Thousands–Tens of thousands & Japan 2010s \\ \hline GPM & DPR, GMI & Microwave & 250–500 & America-Japan 2010s \\ \hline NOAA-20 & VIIRS, OMPS CrIS, ATMS & Optical Microwave & 375–750 & America 2010s \\ \hline SSC & SIR-A & & & \\ SIR-B & Microwave & 15–45 & America 1980s \\ \hline RADARSAT & SAR & Microwave & 1–5 3–30 & Canada \\  & & & 30–100 & The 1990s (debut) \\ \hline ERS & SAR, WS & Microwave & 30 & ESA 1990s (debut) \\ \hline Envisat-ASAR & ASAR & Microwave & 30–1000 & ESA 2000s \\ \hline TerraSAR-X & X-SAR & Microwave & 1–2 2–40 & DLR and ESA 2000s \\ \hline COSMO-SkyMed & SAR & Microwave & 1–100 & The early 2000s (debut) \\ \hline CALYPSO & IIR & Laser & 30–60 & America-France 2000s \\ \hline SMAP & L-SAR & Microwave & Meters–Tens of meters & America 2010s \\ \hline Gaofen & WFV, P/MS & & & \\ C-SAR & & & & \\ HRI, MSI & Optical & 0.8–8 & China 2010s (debut) \\  & AEMS, PSC & & & \\ LiDAR & & & & \\ \hline TanDEM-X & X-SAR & Microwave & 1 & Germany 2010s \\  & L-BR & & & \\ S-BR & & & & \\ \hline NovaSAR-1 & X-SAR & Microwave & 6–30 & British 2010s \\ \hline FSSCAT & GNSS-R/RO/ & Optical & Meters–Tens of thousands & ESA 2010s \\  & SCAT & HTI & Microwave & thousands & The late 2010s \\ \hline SWOT & RA & & & \\ Ka-RI & Airborne & 0.01–0.02 & NASA and CNES 2010s \\  & AMR & PODS & & \\ \hline NISAR & L-SAR & & & \\ S-SAR & Microwave & 6 & America-India 2020s \\ \hline Qilu 1 & Ku-SAR & Microwave & 0.5 & China 2020s \\ \hline Haisi 1 & C-SAR & Microwave & 1 & China 2020s \\ \hline Chaohu 1 & SAR, WTS & & & \\ PHS, TS & Microwave & 1 & The early 2020s \\  & DO Sensor & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._Some of the satellite data in the table above are completely free of charge: Landsat, TRMM, GCM-W1, ERS, CloudSat, METOP, CALLIPSO, Envisat-ASAR, GPM, SMAP, SWOT, NISAR, as well as MODIS data on the Terra and Aqua satellites, and the data of ESA Sentinel-1, Sentinel-2 are also publicly available through the Copernicus program. Ikonos, World View, Quick Bird, Geo Eye, Qilu 1, Haisi 1, Chaohu 1, NovaSAR-1, TerraSAR-X, Jilin-1, Zhuhai-1, SPOT-6, SPOT-7, RADARSAT-2 are commercial satellites that require payment, but other satellites are partially free of charge (Figure 5). Some of the data sources from early-launched military satellites are old and may not be available for future use; however, once declassified, these data are valuable for generating and reconstructing historical records of disasters and disaster events.