esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Fiber-wireless technology has gained significant momentum as the \"last mile connectivity\" in the last two decades in order to address the increasing demand for larger capacity and faster transmission speeds, fuelled by the proliferation of affordable smart phones and tablets packed data-intensive multimedia applications [1; 2; 3]. In addition, this hybrid technology is able to leverage on existing optical fiber infrastructure and, hence, is able to unify the telecommunication backhaul infrastructure by using a common backhaul network to support both wired and wireless services. Figure 1 illustratesa typical infrastructure of a fiber-wireless system with a central office (CO) serving multiple base stations (BSs) interconnected by high speed optical fiber based backhaul while the BSs serve end users wirelessly. It is well-established that centralized control in fiber-wireless system enables simple, low-cost design antenna base stations to be widely deployed where most hardware intelligence are located in a central location.  
One strategy to establish centralized control is to optically distributing the wireless signals at the wireless carrier frequencies in an analog fashion. Despite the elegance of the hybrid fiber-wireless infrastructure, the performance is limited by the inherent properties of analog optical link [3, 4]. Although many techniques have been proposed to improve the performance of analog optical links for the transport of wireless signals including linearization [5, 6, 7, 8, 9, 10], predistortion [11], and modulation depth optimization [12, 13, 14, 15], they demand for complex implementation with precise control which negates the cost effectiveness of fiber-wireless scheme.  
We have previously demonstrated an alternative transport scheme based on digitization of the wireless signals using bandpass sampling technique [16, 17] to improve the fiber-wireless link dynamic range and to overcome the inherent issues of analog optical link. In this paper, we review the work we have carried out in this area in particular focusing on strategies to improve the performance, capacity and energy efficiency of fiber-wireless links with respect to other transport schemes. The paper is organized as follows. Section 2 describes and reviews the different transport schemes with respect to their roles and the base station configurations in fiber-wireless link, and Section 3 presents the experimental investigations comparing analog optical transport _versus_ digitized transport based on bandpass sampling technique for the transmission of broadband wireless OFDM signals. Section 4 governs the study and quantification of energy consumption for different transport schemes with respect to base station site power consumption and wireless cell size.  
Figure 1: Fiber-wireless network.
# Advancing in RGB-D Salient Object Detection: A Survey
## 3 Attention Mechanisms in RGB-D SOD
### Classification of Attention Mechanisms
By incorporating self-attention, it enhances the richness of depth features; through channel attention, it amplifies important features while suppressing noisy channels; and with spatial attention, it focuses on the most relevant regions within the image, thereby improving spatial resolution.
# A Secure and Scalable Authentication and Communication Protocol for Smart Grids
## 4 Experimental Setup
### Evaluation Metrics  
The effectiveness of the protocol was evaluated using the following metrics:  
* **Time analysis:** The time taken for each protocol step was recorded to assess the system's responsiveness, including authentication, key exchange, and message exchange.
* **Throughput:** The server's capability to manage multiple clients was assessed by systematically increasing the number of connected clients to 10, 50, and 100.
* **Power analysis:** The power consumption of the protocol quantification during its execution.
* **Cryptographic Overhead:** The time taken to perform ECDH key exchange and AES encryption/decryption operations was measured.
# YOLO-PWSL-Enhanced Robotic Fish: An Integrated Object Detection System for Underwater Monitoring
## 2 Materials and Methods
### Design of a Bionic Mechanical Fish
#### 2.1.2 Bionic Fish Composition Modules  
The bionic fish modules are shown below in Figure 3. We will explain the functions of each module in detail.  
1. STM32H7B0 VBT6 System Board  
This method uses the STM32H7B0 VBT6 (Geek STM32 Electronic Development Network, Shenzhen, China) chip to achieve data exchange and coordination between modules, driving each module to work closely together. Water pressure sensors and PID algorithms are used to control the reservoir module for buoyancy, and the power module is used for movement. Data is obtained in real time through three sensors and transmitted to the host computer in real time through the transmission module.  
1. Power system  
The tail fin section consists of two servos, which are driven to provide power. This module achieves different states by adjusting the duty cycle of the servo motor. Acceleration, deceleration, and swinging are achieved through the coordination of the working states of the two servo motors.  
Figure 3: (**a**) STM32H7B0 VBT6 system board; (**b**) power system; (**c**) reservoir module; (**d**) water pressure sensor; (**e**) turbidity sensor; (**f**) temperature sensor; (**g**) image transmission module; (**h**) communication module; (**i**) robotic arm.  
3. Reservoir module This module controls the proportion of water storage through two needle tubes controlled by DC motors. The system's buoyancy is controlled through feedback from a depth sensor and PID algorithm regulation.
4. Water pressure sensor The model number of this water pressure sensor is XGZP6847A (China Jinhua Tugeli Technology Co., Ltd., Jinhua, China). It is installed inside the machine and connected to the outside of the machine via a hose. It is used to measure the current water pressure in order to determine the depth of the machine underwater. The sensor is connected to the STM32H7B0 VBT6 chip, providing corresponding water pressure information.
5. Turbidity sensor This module is a TDS sensor that collects data synchronously through two channels, offering high real-time performance. The chip is located inside the machine, while the probe is located outside the machine. The sensor is connected to the STM32H7B0 VBT6 chip, providing corresponding water quality information.
6. Temperature sensor This module is a ds18b20 sensor. The sensor is connected to the STM32H7B0 VBT6 chip, providing corresponding water temperature information.
7. Image transmission module The image transmission module is installed on the head of the underwater intelligent robot and connects directly to the host computer via Wi-Fi. The image transmission module can transmit stable underwater images to the host computer.
8. Communication module This module is a Bluetooth module that transmits data collected by sensors to the host computer in real time, while receiving corresponding information from the host computer.
9. Robotic arm The robotic arm consists of four servo motors, which can control its bending, folding, picking, and releasing.
# Towards Digitalization of Water Supply Systems for Sustainable Smart City Development--Water 4.0
## 4 Digital Technologies in the Water System: Key Drivers of Water 4.0
### Big Data Analytics
Several data analytical tools such as Apache Hadoop and Apache Spark may be employed to analyze the data for decision making. In recent times, machine learning, data mining, and computational intelligence algorithms [63; 69] have proven their accuracy and scalability in providing analytical solutions to BD. The South East water utilities is implementing Xylem's Visenti for Software analytics to manage and analyze sensor data installed on water system infrastructure. Smart meter data produce accurate insights on the end user's water consumption pattern, and improve accuracy of demand- and supply-side forecasts. The analysis of data from a smart meter could also provide relevant critical insights into what might happen to the infrastructure in terms of future prediction of pipe failure. The potential of BD analytics in the water sector has been the subject of several research projects. [PERSON] and [PERSON] [70] present a framework for processing and analyzing big data related to water resources for use in real-time applications. The use of big data analytics in the water, sanitation, and hygiene sector was proposed by [71]. Investigation shows that it is possible to effectively monitor system data performance and post implementation for sustainability. [PERSON] et al. [72] present big data open platform architecture which helps to provide an effective tool that permits water utility managers to solve water resource and water modelling challenges. The platform could also be used to aid decision making. With the inclusion of a geographic information system, database management, data analytics and communication, and a knowledge-based expert system, the water utility manager can compare the effect of different current and future management scenarios and make choices to preserve the environment and water resources. In [73], a framework and prototype for big data analytics-based water resource sustainability evaluation was proposed. Results obtained show that the proposed prototype can be used to evaluate regional water resource sustainability and environmental performance in practice and provide scientific basis and guidance to formulate water supply policies. Research studies on leveraging big data analytics for the management of water resources can be found in [74; 75; 76; 77]. Hence, in Water 4.0, smart analytics solutions are required to improve overall system performance. Once the necessary data are obtained from water utility facilities, good analytics and decision frameworks may pilot water utilities to a well-optimized efficiency.
## 4 Evaluation Methods
### Nist  
Another metric commonly used in MT evaluation is the National Institute of Standards and Technology or NIST. A variant of BLEU, NIST assigns a higher weight to more informative n-grams and uses the arithmetic mean instead of the geometric mean used by BLEU [30]. The calculation of the BP is also where NIST and BLEU diverge; the variation in length between the translated text and the reference text does not affect NIST as it does with the BLEU score [33]. This is because the precision scores that are calculated in BLEU are replaced with the information gained from each n-gram [34]. This enables the system to obtain more credit if the n-gram match is difficult to obtain or less credit if the match is easier [34].
## 6 Strengths and Limitations  
This research establishes normative performance values based on gender- and maturity-specific benchmarks for Tunisian young basketball players, helping to deepen the understanding of their potential and development. By considering maturity as a factor in performance, we can reduce the risk of disadvantage or misjudging athletes who may be on a slower, but ultimately more successful, developmental trajectory. More precisely, understanding the developmental trajectory of female athletes is vital for creating effective training programs. Moreover, the large sample size enhances the reliability of the reference values, reinforcing their applicability across different maturity statuses and gender groups. One limitation of this study is its focus on a specific population--Tunisian youth basketball players--which may limit the generalizability of the results to other populations with different training practices and social, ethnic, and genetic factors. Moreover, while this study includes basic anthropometric and performance parameters, future research could benefit from incorporating more advanced psychological and physiological parameters to provide a more comprehensive view of athletic development. Lastly, as a cross-sectional study, it does not account for longitudinal changes within individuals, which would be important for understanding the development of physical performance over time.
# A Portable Non-Motorized Smart IoT Weather Station Platform for Urban Thermal Comfort Studies
## 3 Validating the Edge Device  
The weather variable readings provided by the prototyped weather station platform were compared with the Davis Vantage Pro 2 weather station to understand the proposed edge device's accuracy and performance. For the same, the prototype and the Davis weather station were placed adjacent (within 1 m apart) in an outdoor environment at the same height. Six hours of continuous measurements were conducted between 13:00 and 19:00 using both the Davis Weather Station and the prototype. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were calculated from these data sets to understand the prototype's weather measurement accuracy. Table 2 presents the MAE and RMSE values.  
Overall, the edge device performed well, with low MAE and RMSE numbers for all the measured variables. The 3-D printed semi-circular louvre was found to protect the AT sensor from direct solar radiation while providing good ventilation, as the absolute difference between the prototype and the Davis AT was only 0.1 \({}^{\circ}\)C. The MAE and RMSE values of the AT and WS were less than the sensors' actual accuracy (according to the sensors' respective datasheets). The RMSE of the RH sensor was only 0.04% higher than the sensor's actual accuracy of \(\pm\)2%.
## Abstract  
With the continuous development of the networked society, the ability of cyber attackers is becoming increasingly intelligent, posing a huge threat to complex cyber-physical networks (CCPNs). Therefore, how to design a security strategy for CCFNs under attack has become an urgent problem to be solved, which promotes our work. The problem of the distributed event-triggered synchronization of CCFNs in the presence of denial-of-service (DoS) attacks is investigated in this paper. Firstly, a distributed event-triggered controller is designed such that all nodes of networks are synchronized without DoS attacks by relieving the communication occupancy rate of limited bandwidths. Meanwhile, Zeno and singular triggering behaviors are excluded to illustrate the effectiveness of the proposed event-triggered strategy. Secondly, in view of the continuous switching of CCPNs topologies caused by DoS attacks, an event-triggered control (ETC) strategy is proposed to ensure the synchronization of CCFNs under DoS attacks. Meanwhile, the frequency and duration of tolerable DoS attacks that can ensure the stability of the systems are calculated. Finally, two examples are given to illustrate the effectiveness of the proposed method.  
complex cyber-physical networks; distributed controller; ETC strategy; synchronization; DoS attacks
# Design and Numerical Study of Induction-Heating Graphitization Furnace Based on Graphene Coils
## 3 Results and Analysis
### Electromagnetic-Field Distribution  
The magnetic-flux density and current density distributions of the two furnaces were compared under the same alternating current size. Figure 6a shows that the orientation of the magnetic induction starts above the coil and loops back below it. The distribution patterns of the magnetic-flux density in both furnaces are predominantly concentrated in the region between the heater and the coil. Following the universal current law, there is a uniform decrease in the magnetic-field strength within a helical coil that is inversely proportional to the increasing radius of the coil. As highlighted in Figure 6b, the induction coil of the graphene-coil furnace has an enhanced internal magnetic-field strength due to its closer positioning to the heater. This arrangement allows the graphene-coil furnace to achieve superior electrical efficiency for the same amount of current.  
The current is mainly distributed in the charge, graphite heater, and coil. To facilitate the observation, these three parts of the cloud diagram were selected, and the results are shown in Figure 7. Due to the influence of the skin effect, the current densities of the two types of furnaces are the maximum in the inner wall of the coil, followed by the outer wall of the heater, and there is almost no current distribution in the charge. The skin depth is estimated as \(\delta=\surd(1/(\pi\sigma\mu_{0}))\). For this calculation, the current frequency is f = 1500 Hz, the conductivity of the Cu is \(\sigma\_\)Cu = 6.0 \(\times\) 10\({}^{7}\) S/m, which gives the skin depth \(\delta\_\)Cu = 1.68 mm for the copper coil, and the conductivity for the graphene is \(\sigma\_\)G = 1.1 \(\times\) 10\({}^{6}\) S/m, which gives the skin depth \(\delta\_\)G = 12.40 mm for the graphene coil. The figure also shows that the skin effect of the copper coil is more obvious.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Copper-Coil Furnace** & **Graphene-Coil Furnace** \\ \hline Total energy consumption, kWh & 3940.22 & 2696.83 \\ Energy for the heater, kWh & 3179.19 & 2288.77 \\ Electromagnetic efficiency, \% & 80.69 & 84.87 \\ Thermal efficiency, \% & 14.90 & 20.82 \\ Energy efficiency, \% & 12.02 & 17.67 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of energy efficiencies between different furnaces.  
Figure 5: (**a,b**) Temperature rise curve of each component; (**c**) energy balance; (**d**) structural schematic of insulation layers 1, 2, and 3.  
Figure 6: (**a,b**) Magnetic-flux density distribution; (**c,d**) flux density distribution along x-direction for different Hs.  
Figure 7: (**a**) Current density distribution of original furnace. (**b**) Current density distribution of graphene-coil furnace.
## 5 Engineering Case Analysis
### Feature Extraction  
The typical mine coal-and-rock-mass-fracture signal and blasting-vibration signal are decomposed by LMD. The frequency-spectrum results of the microseismic waveform obtained after LMD decomposition are shown in Figure 7. In order to improve the recognition accuracy in the later stage, the decomposed PF components need to be screened to obtain more effective signal features. A group of coal-and-rock-mass-rupture and blasting-vibration signals are randomly selected and calculated using the LMD algorithm, and the  
Figure 6: Typical microseismic signals and their frequency spectra in mines. (**a**) Coal-and-rock-mass-rupture signal; (**b**) Coal-and-rock-mass-rupture-signal frequency; (**c**) blasting-vibration signal; (**d**) blasting-vibration-signal frequency.  
correlation coefficient of each PF component is obtained by Equation (7). The results are shown in Table 1. It is clear that the correlation coefficients of the coal-and-rock-mass-fracture signal and the blasting-vibration signal have a relatively consistent change trend, and there is an obvious turning point in PF4. Therefore, we take the correlation coefficient threshold as 0.3, and select PF1-PF4 as the optimal component, and use the LMD energy entropy of PF1-PF4 as the feature vector. As can be seen from Figure 7, the frequency spectrum of PF5 and PF6 components show obvious differences compared with the original signal.  
Figure 7: LMD decomposition and frequency spectrum of typical microseismic signals in mines. (**a**) PF component of coal-and-rock-mass-fracture signal; (**b**) PF component frequency spectrum of coal-and-rock-mass-fracture signal; (**c**) PF component of blasting-vibration signal; (**d**) PF component frequency spectrum of blasting-vibration signal.  
The LMD energy entropy of the PF1\(-\)PF4 components of a typical mine microseismic signal is shown in Figure 8. It can be seen from the figure that there is a certain regularity in the energy entropy of PF component of microseismic signal at the site. Since the coal-and-rock-mass-fracture signal is mainly concentrated in the low-frequency band, most of the energy entropy of PF3\(-\)PF4 is slightly greater than that of PF1\(-\)PF2. On the contrary, because the blasting-vibration signal is mainly distributed in the high-frequency band, with high concentration and rapid attenuation, the energy entropy of PF3\(-\)PF4 is mostly slightly less than that of PF1\(-\)PF2. Therefore, the LMD energy entropy of the PF component can effectively identify mine coal-and-rock-mass-fracture signals and blasting-vibration signals.
## 6 Numerical Simulation Results and Discussions  
In this section, we demonstrate the effectiveness of the proposed new robust saturated tracking controller (RAS-BSC) under the combined influence of parameterized uncertainties, external disturbances, and control input saturation. The quadrotor dynamics from (2) are used to evaluate the stability of the closed-loop system and verify the superior performance of our controller. The advantages specific to our method are discussed and highlighted by comparing the proposed controller with some previous approaches, including the nonrobust adaptive backstepping controller (BSC) [8] and robust backstepping controller (RA-BSC) [34].
## 2 Acquisition of Tunnel Face Images
### Stitching and Fusion of Partitioned Photography Images
#### 2.2.2 Image Fusion of Tunnel Work Face Partitions  
After stitching, unnatural color transitions (shown in Figure 7) are corrected using the fade-in-fade-out weighted fusion algorithm shown in Equation (4).  
\[X(a,b)=\begin{cases}x_{1}(a,b)&(a,b)\in x_{1}\\ (1-\gamma)x_{1}(a,b)+\gamma x_{2}(a,b)&(a,b)\in x_{1}\cap x_{2}\\ x_{1}(a,b)&(a,b)\in x_{2}\end{cases} \tag{4}\]  
where \(x_{1},x_{2}\) are the images to be stitched, \(X\) is the stitched image, \(\gamma\) is the weighting factor, \(\gamma=\frac{w_{d}}{w}\in(0,1),w\) is the horizontal coordinate distance of the overlapping part of the stitched images, and \(w_{d}\) is the horizontal coordinate distance of the pixel points in the overlapping part of the stitched images from the start of the overlapping section.  
The final complete tunnel work face fusion image compared with the full tunnel work face captured image is shown in Figure 8.  
Figure 7: Unnatural edges in image stitching. (As the yellow box marks).  
Stitched and fused images significantly improve quality and restore geological structure information, laying a solid foundation for subsequent joint extraction (see Figure 8).
## 6 Analysis of the eLPRT Protocol
### Contention-Free Retransmissions
Although the average delay with this strategy is higher, for real-time applications, the provision of bounded delay is more important than the reduction of the average delay. As Figure 9 shows, for the \"RP after CAP\" strategy, the maximum delay suffered by the data packets is bounded by the superframe period (100 ms). Figure 10: Current consumption of eLPRT with \(BER_{bad}=10^{-2}\) (data) and \(10^{-4}\) (beacon). Figure 9: Maximum delay of eLPRT with \(BER_{bad}=10^{-2}\) (data) and \(10^{-4}\) (beacon). Figure 10 shows the average current consumption (per sensor node) of the eLPRT protocol using the parameters of the CC2430 included in Section 4. The consumption for both retransmission strategies is equal, and increases slightly with the number of nodes, since the size of the beacon increases due to the ACK bitmap field and the allocations for retransmissions. Neverthelesss, the increase in the current consumption due to retransmissions is small, being largely compensated by the benefit of increased delivery ratio. As an example, the current consumption with 25 nodes is 0.77 mA without retransmissions and 0.84 mA with retransmissions due to channel errors (9% more). Experimental results concerning the use of the frequency hopping mechanism in combination with the contention-free retransmission mechanism are provided in the next section.