esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Temporal Trend of UBLGUE  
In accordance with Equation (1) and the indicator system discussed in Section 3, the UBLGUE of the CPUA from 2003 to 2020 is calculated via the MATLAB R2016a software (Figure 3). The average UBLGUE values are less than 1.000 in most years and are located in the middle-high-level range, meaning that the UBLGUE of the cities is in an inefficient state. To address this nonoptimal state, it is essential for cities to promote UBLGUE by optimizing the resource allocation, green production, and environmental governance levels for built-up land in future development. Overall, the average UBLGUE values in the CPUA display fluctuating stage characteristics ranging from 0.7802 to 1.1694 in 2003-2020. Specifically, the UBLGUE values increased from 2003 to 2006 in the first phase. This result may be generated via the national rapid urbanization policies that were continuously conducted to expand built-up land and weaken the resource dependence in the urban agglomeration. The economic growth brought via rapid urban expansion exceeds the land economic investment and environmental impacts. With the rapid gathering of urban land use, economic output value, and population size, the CPUA has become a key economic and social hub. This centralized growth model has promoted economies of scale and led to a significant improvement in urban built-up land green use efficiency. From 2006 to 2014, UBLGUE maintained a smooth trend because of imperfect resource-saving mechanisms. And excessive supply of urban built-up land by local governments leads to low utilization efficiency for industrial land. Environmental governance systems prevented coordination between land resource utilization and environmental protection, thereby resulting in low inputs and high output rates. Notably, the UBLGUE values decreased from 2006 to 2009, which was correlated mainly with the financial crisis that triggered an economic downturn and environmental problems, resulting in blind competition for investment, low-level expansion, low resource utilization efficiency, and a low level of UBLGUE. By 2015, the UBLGUE value had reached the peak of the curve. However, from 2015 to 2020, UBLGUE quickly declined again, probably because of the government's strictly controlled construction land scale and restriction of the occupation of ecological space and agricultural space under the tight constraints of resources and environment, especially after the COVID-19 epidemic, which caused the shutdown of economic production and a decrease in fixed investment, thereby leading to a downward trend in UBLGUE.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Time** & **Urban Living Land (km2,\%)** & \multicolumn{2}{c}{**Industrial and Mining Land (km2,\%)**} & \multicolumn{2}{c}{**Urban Built-Up Land (km2,\%)**} \\ \hline
2005 & 4293.10 & 1.44 & 910.24 & 0.32 & 5203.34 & 1.76 \\
2010 & 6488.38 & 2.21 & 1639.86 & 0.54 & 8123.23 & 2.75 \\
2015 & 7401.01 & 2.52 & 2188.77
# Chaotic Optical Communication with Wavelength-Hopping Technology Based on Tunable Lasers
###### Abstract  
With the development of communication technology, there is a more urgent demand for enhancing the security of data transmission. In order to improve the security of the traditional chaotic laser communication system, we propose a new scheme by the introduction of wavelength-hopping technology based on tunable lasers. In our new scheme, due to the hopping of wavelengths and the pseudo-randomness of the wavelength-hopping sequence, it is difficult for the eavesdropper to intercept the information and predict to which wavelength the hopping station will hop. The numerical simulation results show that the average bit error rate of eavesdropping is about five orders of magnitude higher than that of working normally. This indicates that the introduction of wavelength-hopping technology can improve the difficulty of message decoding and improve the confidentiality of the system. This scheme can be used to realize a high level of privacy in the design of chaotic laser communication systems in the future.  
optical communication; chaotic communication; wavelength hopping; tunable laser +
Footnote †: journal: Applied
# A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n
## 3 Cotton Boll Disease Detection Algorithm and Improvement
### Improved Cotton Boll Disease Detection Algorithm Model Framework
#### 3.3.3 Loss Function Optimization  
Loss functions are fundamental components in deep learning that quantify discrepancies between predictions and ground truth. To address limitations in conventional bounding box regression where shape attributes are inadequately modeled, the CloU loss in YOLOv11n is replaced with Shape-IoU [15]. Shape-IoU enhances detection accuracy through three key mechanisms: distance penalty, aspect ratio constraint, and edge alignment. The distance penalty enhances the localization accuracy of small objects, the aspect ratio penalty effectively prevents shape distortion, and the edge alignment term ensures more precise bounding box alignment [16]. This enables Shape-IoU to better handle variations in object shape, size, and position, thereby improving the model's performance in complex scenarios.  
The definition of the Shape-IoU loss function is as follows:  
\[IoU=\frac{\left|B\bigcap B^{GT}\right|}{\left|B\bigcup B^{GT}\right|} \tag{1}\]  
\[ww = \frac{2\times\left(w^{gt}\right)^{scale}}{\left(w^{gt}\right)^{scale}+ \left(h^{gt}\right)^{scale}} \tag{2}\] \[hh = \frac{2\times\left(h^{gt}\right)^{scale}}{\left(w^{gt}\right)^{ scale}+\left(h^{gt}\right)^{scale}} \tag{3}\]  
Figure 8: C3k2_PConv module structure diagram.  
\[distance^{shape}=\ hht\times\left(x_{c}-x_{c}^{gt}\right)^{2}/c^{2}+ww\times\left(y_{c }-y_{c}^{gt}\right)^{2}/c^{2} \tag{4}\]  
\[\Omega^{shape}=\sum_{t=w,h}\left(1-e^{-w_{t}}\right)^{\theta},\theta=4 \tag{5}\]  
\[w_{w}=hh\times\frac{\left|w-w^{gt}\right|}{max(w,w^{gt})} \tag{6}\]  
\[w_{h}=ww\times\frac{\left|h-h^{gt}\right|}{max(h,h^{gt})} \tag{7}\]  
\[L_{Shape-10U}=1\ -\ IoU\ +\ distance^{shape}+0.5\times\Omega^{shape} \tag{8}\]  
In the equation, \(scale\) is the scaling factor, which is related to the size of the objects in the dataset. \(ww\) and \(hh\) represent the weight coefficients in the horizontal and vertical directions, respectively, and their values are related to the shape of the GT bounding box. \(w^{gt}\) and \(h^{gt}\) correspond to the length and width of the GT bounding box, while \(x_{c}^{gt}\) and \(y_{c}^{gt}\) represent the center coordinates of the GT box. \(w\) and \(h\) are the length and width of the anchor box, while \(x_{c}\) and \(y_{c}\) are its center coordinates. \(w_{w}\) and \(h_{h}\) are the weighted coefficients in the horizontal and vertical directions, and these coefficients are related to the shape of the GT bounding box. \(distance^{shape}\) and \(\Omega^{shape}\) represent the distance and shape loss components, respectively.
# Design of 1-Bit Digital Reconfigurable Reflective Metasurface for Beam-Scanning
## 2 Materials and Methods  
### Description of the 1-Bit Digital Element and RRM  
In this study, the 1-bit digital reconfigurable reflective metasurface element is designed to obtain the digital phase compensation to replace the accurate phase compensation. As shown in Figure 1a, the 1-bit digital reconfigurable reflective metasurface element adopts a single layer patch structure with a varactor diode through the gap between the two patches. The unit is etched on a dielectric substrate and the bottom is the metal ground. The dielectric constant of the dielectric substrate is 2.65 and the thickness is 3 mm.  
The remaining structure parameters of the 1-bit digital element are provided in Table 1. The proposed element is simulated with the infinite periodic boundary condition in HFSS 15.0 [16]. By controlling the bias voltage across varactor diode, the phase compensation of the element can be adjusted as desired. A 1-bit 20 \(\times\) 20 RRM is present and fabricated at 3.5 GHz fed by a horn antenna using the above element, as shown in Figure 1b,c. The size of the RRM is 500 mm \(\times\) 500 mm. Figure 1d gives the diagram of incident wave, reflected wave, incident angle and reflection angle in the study. The incident wave is in the _xoz_ plane, and the reflection wave is in the _yoz_ plane.  
Figure 1: Geometry of (**a**) The 1-bit digital element; (**b**) The fabricated reconfigurable metasurface (RRM); (**c**) The experimental scene; (**d**) The incident wave, reflected wave, incident angle and reflection angle.
### Recent Situation and Trends  
In 2019, 60 GW of wind power capacity was added globally, with the cumulative capacity reaching 650 GW. Costs have fallen rapidly for both onshore and offshore wind power, increasing dramatically the amount of bid prices in auctions around the world, especially in the past year. This development can be explained considering the constant technology innovation, the reduced financing costs and the ever-growing competition in the industry, with electric utilities and large oil and gas companies continuing to invest further in that direction. Additionally, incentives such as robust regulatory reforms, wind energy's cost-competitiveness and its potential environmental benefits show that there is no expected deceleration in the wind power sector progress. Finally, the world's first commercial floating wind park has been commissioned in Scotland, while the sizes of turbines continued to increase, with some manufacturers producing turbines of up to 10 MW.  
Meanwhile, development in solar PV sector is even faster, with almost 100 GW of new capacity added and increased the global capacity to 580 GW at the end of 2019. This growth can be explained mainly due to the raised awareness of the technology's potential to alleviate pollution, reduce carbon dioxide emissions and provide energy access to developing countries. Subsequently, intense competition, increasing efficiencies and reduction in energy costs have led to record-low auction prices.  
Hydro-pumped storage advancements over the past few years continue to upgrade an already proven and reliable technology that represented more than 95% of all energy storage solutions globally, in terms of cumulative capacity. These include improved efficiencies with modern reversible pump-turbines, adjustable-speed pumped turbines, advanced equipment controls such as static frequency converters and generator insulation systems, as well as innovative underground construction methods and design capabilities, leading to faster response times of load follow for intermittent renewables more efficiently and cost effectively. Globally, there are approximately 270 pumped storage plants either operating or under construction, representing a combined generating capacity of over 127,000 MW. Of these total installations, 36 units consist of adjustable speed machines, 17 of which are currently in operation, totaling 3569 MW and 19 of which are under construction, totaling 4558 MW [23].
# Simulation and Design of Three 5G Antennas
## 2 Materials and Methods
### The Unique Performance Parameters of MIMO Terminal Antennas  
In MIMO antenna systems, the number of antenna elements, their spatial arrangement, correlation coefficient (ECC), average effective gain, and overall efficiency are all key parameters for performance evaluation.
## 2 Related Work
### Sensor-Based Cross-Checking  
[PERSON] et al. (2016) used sensor fusion and particle filters to check the plausibility of the position information in the incoming messages and assign a trust level to the message sending vehicle. A separate particle filter was used for each tracked neighbor vehicle. The particle filter combines all available different position information from a variety of input sources such as V2X message, radar, road map, etc., to detect inconsistencies among them, introduced by faulty nodes or malicious attackers. For example, a radar, lidar, or camera could detect no neighbor vehicle at a position claimed through V2X. Not only each V2X message is evaluated in terms of the trustworthiness using the particle filter framework, but also the trust level of the message sending vehicle is computed based on the message trust rating. [PERSON] et al. (2017) presented a similar heuristic framework to combine the plausibility ratings from different sensor modules such as radars, lidars, and ultrasonic to check the message plausibility. The sensor-based check is part of a battery of tests using the movement analysis, sensor-proofed position check, minimum distance moved check, map-proofed position check, etc. [PERSON] et al. (2018) also combined various information sources such as sensors, maps, and input from other vehicles. [PERSON] et al. (2019) checked the position information in the received message through radar sensors. Each vehicle first monitors the neighboring vehicles using the radar in its range. Then it propagates the information to other vehicles to share the information globally. [PERSON] et al. (2019) relies on road-side units (RSUs) to provide GPS reference values to defeat false position attacks.
# Distance-Based Resource Allocation for Vehicle-to-Pedestrian Safety Communication
###### Abstract  
Cellular Vehicle to Everything (V2X) has redefined the vehicular communication architecture as something that needs an ultra-reliable link, high capacity, and fast message delivery in vehicular networks. The V2X scenarios are broadly categorized as Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), Vehicle to Pedestrians (V2P), and Vehicle to Network (V2N). Vulnerable pedestrians belong to the V2P category and hence require an ultra-reliable link and a fast message delivery in case the moving vehicle is in the close proximity of the pedestrian. However, congestion in the network calls for an optimized resource allocation that would allow a fast and secure connection between a vehicle and the pedestrian. In this paper, we have proposed a distance-based resource allocation that classifies the pedestrians in different categories, performs a one-to-many weighted bipartite matching, and finally a reinforcement learning based power allocation.  
device to device (D2D); long term evolution (LTE); vehicle to everything (V2X); vehicle to vehicle (V2V); vehicle to infrastructure (V2I); vehicle to pedestrians (V2P); cellular user equipment (CUE); vehicular user equipment (VUE); user equipment (UE); base station (BS) +
Footnote †: journal: Electronics
# Processing Fresh-Cut Potatoes Using Non-Thermal Technologies and Edible Coatings
## 5 Emerging Processing Technologies: Operating Principles and Examined Parameters
### Miscellaneous Technologies
#### 5.5.1 Ultrasound
One of the major advantages of ultrasound is its relatively low energy requirement compared to conventional thermal treatments, which makes it a cost-efficient and environmentally friendly process. Additionally, ultrasound reduces the need for chemical preservatives, enhancing food safety and consumer appeal [16]. In an industrial setting, ultrasound can be used to inhibit enzymatic browning and enhance the firmness of fresh-cut potatoes, making it ideal for vacuum-packaged or minimally processed potato products with extended shelf life requirements. However, there are some disadvantages to ultrasound treatment, as prolonged exposure or high intensity can cause cell structure damage, negatively affecting the texture of sensitive produce. This sensitivity requires a careful optimization of treatment parameters to avoid overprocessing and ensure product quality. [PERSON] et al. (2017) showed that the use of ultrasound (40 kHz, 5 min) as an alternative to blanching can improve the quality attributes and shelf life of vacuum-packaged potato strips [98]. In another study, ultrasound treatment revealed high potential as an innovative pretreatment applied before the drying of purple-fleshed potato slices, which exhibited enhanced quality attributes with respect to the control [159]. Furthermore, [PERSON] et al. (2015) investigated the impact of different ultrasound exposure durations on the quality parameters of fresh-cut potatoes. Sonication for 5 min resulted in a 50% inhibition of PPO activity during the initial 4 days of storage. Samples treated with ultrasound for 5 and 10 min exhibited sustained and consistent firmness after frying throughout the storage period, while an extended immersion time of 10 min induced cell damage in the potatoes [116]. [PERSON] et al. (2021) investigated a new way to prevent browning in fresh-cut potatoes by employing a combination of _Sonchus oleraeus_ L. extract and ultrasound. The study demonstrated the effectiveness of this method in preserving the quality of fresh-cut potatoes, reducing browning, and retaining their texture [163]. [PERSON] et al. (2021) explored the application of ultrasonic treatment to lower the minimum effective concentration of purslane extract on fresh-cut potato slices throughout storage. The findings indicated that a more effective anti-browning outcome could be achieved through the combined application with a lower purslane extract concentration (0.02%, \(w/w\)) compared to the optimal concentration of purslane extract alone (0.05%, \(w/w\)) [164].
# A Survey on Swarm Robotics for Area Coverage Problem
## 3 Algorithms and Methods for Swarm Robotics
### Classical Algorithms
#### 3.2.4 Operative Distributed Asynchronous (CORDA) Model  
Asynchronous CORDA is the primary computational model in the domain of swarm robots. It has a closer approximation to real-life situations when compared to other algorithms. The algorithms developed using the CORDA model view robots as consisting of four sequential cycles: wait, observe, compute, and move. All the cycles do not overlap. The primary objective of the research is to prove the CORDA model is popular and suitable compared to other available computational models for area coverage problems. Many solutions for area coverage have been discussed comprehensively by using this model. Two situations are applied. The first one is when covering the area without an obstacle; in this case, the robot starts to compute the boundaries of the strip that will be covered by itself depending on its closest horizontal neighbours. In the second case, when covering the area with obstacles, the robots start by being deployed randomly in the area and then collect on the left boundary. The robots divide the space into several blocks and draw the size of the blocks. In the beginning, the robot verifies if the region contains an obstacle (another robot or any object); if it has not found an obstacle, it travels directly to the entry point of the next block for exploration. On the contrary, it begins to paint the present block, a region above the horizontal line passing through its current position [63].
# Factors Influencing the Mental Health Consequences of Climate Change in Canada
## 4 Discussion
### Communication and Outreach  
The American Psychological Association (APA) _Mental Health and Our Changing Climate_ guidance document highlights the imperative role for frequent and clear communication to support psychosocial wellbeing in a changing climate [8]. The United Nations Office for Disaster Risk Reduction (UNISDR) suggests that clear, early-warning communication efforts can save lives during extreme weather events [61]. Furthermore, communication can increase community resilience by supporting people to assess risks and respond before an extreme weather event occurs [46].  
Importantly, mass communication often relies upon technology so there is a need to build redundancy into technical systems (e.g., satellite communications, back-up generators, and door-to-door emergency service response communications) before, during, and after extreme events [46]. Other features of communication systems that are required to support and protect the psychosocial consequences of climate change-related extreme weather are communication systems from local and trusted sources, such as meteorologists [62].
# Performance Analysis of Internet of Vehicles Mesh Networks Based on Actual Switch Models
## 6 Conclusions  
In this paper, we proposed a network performance analysis model based on actual switches for IoV mesh networks. Initially, a mathematical model was formulated based on a typical IoV mesh network architecture to obtain the evolving network topology in real-time. Subsequently, by delving into the ingress and egress traffic forwarding mechanisms of individual nodes and expanding them to a multi-node multi-task environment, we established the task generation and the traffic forwarding model using actual switches. By integrating the dynamic network topology, we derived the current traffic distribution within the network. This information was then utilized to extract pertinent parameters for constructing the IoV mesh network performance evaluation indicator system, offering a comprehensive and scientific reflection of the network's condition from both the task and network perspective. Simulation results demonstrated that network performance undergoes different but regular changes under diverse ingress QoS levels, caching capacities, and vehicle densities. The proposed model provides a rational and effective means of assessing network performance under different traffic conditions.  
In future research, we can study more complex dynamic network scenarios, including networks with diverse node types and multiple channels, based on this network performance analysis model. Additionally, we can also explore problems such as task offloading, resource allocation, and load-balanced routing design of the network. The effectiveness and reasonableness of the formulated strategies are assessed by single or multiple metrics.  
Figure 11: Network performance for different vehicle densities.  
**Author Contributions:** Conceptualization, J.H., Z.R. and W.C.; methodology, J.H., Z.R. and W.C.; software, J.H. and Z.S.; validation, J.H. and Z.S.; formal analysis, J.H.; investigation, J.H.; resources, Z.R.; data curation, J.H. and Z.S.; writing--original draft, J.H.; writing--review and editing, J.H., Z.R., W.C., Z.S. and Z.L.; supervision, Z.R.; project administration, Z.R. and Z.L.; funding acquisition, Z.R. and W.C.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Key Research and Development Program of Shaanxi Province of China: 2022 ZDLGY05-09.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Shaanxi Academy of Aerospace Technology Application Company Limited. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.