esa-sceva/satcom-chunk-collection huggingface dataset:
# Economic Loss of Pine Wood Nematode Disease in Mainland China from 1998 to 2017
## 1 Introduction  
Pests and diseases are the main cause of forest damage [1], and the economic losses caused mainly include the loss of forest assets [2], the discounted expenditure of the disposal, removal and replacement of damaged trees [3, 4], the loss of forest recreational and landscape value [5] and the loss of carbon sinks [6]. Infected by diseases and insect pests, 85 million hectares of forests worldwide were destroyed between 2003 and 2012, and the most of the disease occurred in temperate North America [7]. China's average annual economic losses caused by forest pests and diseases in 1996-2010 exceeded CNY 100 billion, including direct economic losses and loss of ecological service value [8, 9, 10].  
Pine wood nematode disease is native to North America, and is mainly distributed in Portugal and Spain in North America, East Asia and Europe [11, 12, 13]. In China, Japan, South Korea, Portugal andSpain, pine wood nematode disease has caused massive deaths of pine trees, leading to severe economic losses [14; 15]. China first discovered pine wood nematodes in the Sun Yat-sen Mausoleum in Nanjing in 1982. After nearly 40 years of spreading, the area infected by pine wood nematode disease in 2019 expanded to 588 counties of 18 provinces (autonomous regions, municipalities) in the country [16]. What is more serious is that pine wood nematodes have a tendency to break through the previous suitable geo-environment range and spread northward and toward high altitudes. The middle temperate zone has become the front line for pine wood nematode disease prevention and control [17].  
The rapid spread of pine wood nematode disease has caused huge economic losses to the infected countries. These losses come from direct wood loss on the one hand [18; 19; 20], and expenditure incurred to reduce the spread or impact of pine nematode disease [21; 22], including spraying pesticides, felling, fumigation or burning infected trees [23; 24], as well as economic losses due to the loss of export markets and import restrictions due to pine wood nematodes [25]. According to evaluations, the average annual economic loss of pine wood nematode disease in China is CNY 7.8 billion [26], and the economic loss in Zhejiang province, where the pine wood nematode disease is the most severe, was CNY 621 million in 2004 [27].  
The research on the economic loss evaluation of forestry biological disease is mainly focused on the construction of evaluation theory and index system [28; 29; 30; 31]. There are very limited studies on the economic loss evaluation covering macro-geographic scales and long-term sequences. In view of this, using the provincial-level incidence data of pine wood nematode disease in China from 1998 to 2017, this study constructs a pine wood nematode disease economic loss evaluation index and quantifies the economic loss in China.
# Experimental Study on Mechanical Properties of Mask-Improved Calcareous Sand
## 3 Test Results and Analysis
### Seepage Characteristics  
The permeability coefficient \"\(K\)\" is commonly used to characterize the permeability of granular materials. Its value can be calculated based on the seepage volume, the cross-sectional area of the inner ring, and the infiltration time.  
\[K=(Q\times L)/(A\times t\times h) \tag{7}\]  
where \(Q\) is the quantity of water discharged, \(A\) is the cross-sectional area of specimen, \(h\) is difference in head on manometers, \(L\) is distance between manometers, and \(t\) is total time of discharge. Additionally, during the particle infiltration performance test, fine particles washed by the water flow were collected. Due to variations in the absolute number of particles in the specimens, the particle loss ratio was used to quantify the effectiveness of the masks in resisting erosion by water flow.  
The seepage test data were fitted, yielding the following expression:  
\[L(c)=L_{0}\times e^{-a/p}+L_{res} \tag{8}\]  
\[K(c)=k_{res}\times e^{-a/p}+k_{0} \tag{9}\]  
where \(L_{0}\) is the initial attrition rate (when the mask content is 0), \(L_{res}\) is the residual attrition rate (asymptotic value of the smooth segment), \(k_{0}\) is the initial permeability (when the mask content is 0), \(k_{res}\) is the minimum permeability (predicts permeability at maximum mask content), \(a\) is the decay rate constant (to control the rate of the descending segment), and \(p\) is the non-linear correction factor (to control the inflection point of the curve).  
Figure 13 illustrates the effect of mask content on both the permeability coefficient and particle loss ratio of the samples. As the mask content increased, the permeability coefficients of the samples decreased monotonically, a trend also observed during triaxial specimen preparation, where sample saturation was slower with higher mask content [47]. Similarly, a higher mask content resulted in a lower particle loss ratio. This can be attributed to the sheet-like mask, which tightly adheres to the particles and forms a water-blocking layer within the specimen [48]. This layer disrupts the smoothness of the water flow channel [49], thereby effectively reducing particle loss. Notably, at a mask content of 0.3%, particle loss was significantly inhibited, while the water permeability of the samples was only marginally reduced.
###### Abstract  
5G networks and Internet of Things (IoT) offer a powerful platform for ubiquitous environments with their ubiquitous sensing, high speeds and other benefits. The data, analytics, and other computations need to be optimally moved and placed in these environments, dynamically, such that energy-efficiency and QoS demands are best satisfied. A particular challenge in this context is to preserve privacy and security while delivering quality of service (QoS) and energy-efficiency. Many works have tried to address these challenges but without a focus on optimizing all of them and assuming fixed models of environments and security threats. This paper proposes the UbiPriSEQ framework that uses Deep Reinforcement Learning (DRL) to adaptively, dynamically, and holistically optimize QoS, energy-efficiency, security, and privacy. UbiPriSEQ is built on a three-layered model and comprises two modules. UbiPriSEQ devises policies and makes decisions related to important parameters including local processing and offloading rates for data and computations, radio channel states, transmit power, task priority, and selection of fog nodes for offloading, data migration, and so forth. UbiPriSEQ is implemented in Python over the TensorFlow platform and is evaluated using a real-life application in terms of SINR, privacy metric, latency, and utility function, manifesting great promise.  
5G networks; Internet of Things (IoT); fog computing; edge computing; cloud computing; Deep Reinforcement Learning (DRL); privacy; security; energy; quality of service (QoS) Article
# Deep Visual Waterline Detection for Inland Marine Unmanned Surface Vehicles
## 4 Experiments and Evaluation
### Comparison to Other Alternative Approaches
#### 4.4.1 Visual Comparison  
As very common environmental noises to waterline detection within inland water, four environmental interference factors are paid attention to in our experiments, which are linear objects, water ripples, shadow, and fog. Here, we are primarily concerned about their impacts on the detection results.  
Figure 8 shows the visual results by the Canny edge detector and ours against our concerned environmental noises on waterline detection. In the first row of Figure 8, all elements in black depict the estimated waterlines by the Canny edge detector. In addition, the second row of Figure 8 presents our results, in which the blue line indicates the estimated waterlines by our approach.  
From Figure 8, it is observed that our results are obviously better than the compared approach in terms of handling environmental noises. For example, at the top of Figure 8a-c, rails of our USV, parts of water ripples and shadow are wrongly detected as waterlines, and at the top of Figure 8d, the real waterlines are not completely detected due to low visibility. In contrast, our estimated waterlines at the bottom of Figure 8a-d are basically concentrated in the vicinity of ground truth. Therefore, in terms of resisting environmental disturbances, our approach is intuitively superior to the alternative approach.  
Figure 7: Visual comparisons on detection results by performing Algorithm 1 on a single image with three different strides of WLpeephole (here, blue line shows the estimated waterline in the case of \(h=10\) pixels, green line for the case of \(h=15\) pixels, and red line for the case of \(h=30\) pixels).  
Figure 8: Visual comparison of results between edge detection method (upper) and ours (bottom) with different environmental interference variables: (**a**) linear objects, e.g., rails of the USV, (**b**) water ripples, (**c**) shadow, (**d**) fog. (In our results, blue line shows the estimated waterline, and red line acts as the subline for marking manually the ground truth).
## 4 Case Studies
### Port of Los Angeles  
The Port of Los Angeles (www.portoflosangeles.org, accessed on 4 February 2024) is the largest container port in the US, located on the west coast. The port handles a variety of cargo including container, automobile, breakbulk, and dry and liquid bulk. The Port of Los Angeles has been focusing on establishing a \"zero emissions pathway\" for cargo movements from ship to terminal and to final destination. It set targets to achieve zero emissions from cargo handling equipment by 2030 and from trucks by 2035. Meanwhile, the port aims to help develop technologies to reduce carbon emissions from ships, harbour craft, and trains to move toward a carbon-free port. The relevant decarbonisation activities include the following:  
* **Electrify cargo handling equipment:** Electric terminal tractor development and demonstration in 2009; capacity plug-in hybrid electric terminal tractor and hybrid yard tractor development and demonstration in 2010; lithium-ion battery demonstration in 2011-2012; in 2020, demonstrated two zero-emission battery electric top handlers and three battery electric yard tractors.
* **Hydrogen vehicles**: In 2021-2022, demonstrated 10 hydrogen fuel cell class 8 trucks.
* **Infrastructure to supply electricity**: The Shore Power Program supported vessels to plug into the electrical grid in the port; in 2020, installed charging infrastructure and smart charging system for charging cargo handling equipment; constructed necessary electrical infrastructure to deliver electricity power to all cargo handling equipment.
* **Infrastructure for hydrogen**: In 2022, built two heavy-duty hydrogen fuelling stations in Ontario and Wilmington.
* **Technology**: The Ports' Technology Advancement Program (TAP) provides continuous support (e.g., funding, guidance, testbed, demonstration) to test clean technologies and associated infrastructure in a real-world environment in and around the port.
* **Policy and incentive**: the OffPeak program was launched by PierPASS in 2005 to incentivise the truckers to use off-peak shifts, which can reduce cargo-related congestion in the surrounding areas of the ports of Los Angeles and Long Beach, and reduce truck waiting time and emissions at ports.
* **Policies and collaboration**: Adopted the Clean Air Action Plan (CAAP) in 2006 and updated it in 2010 and 2017, which set up the \"zero emissions pathway\" for cargo movements throughout the port. The CAAP puts in place a few specific port emission control measures, e.g., the Clean Truck Program introduces progressively strict standards on the external trucks to access the port; the Vessel Speed Reduction Program offers financial incentives to encourage vessels to slow down their sailing speed near the port; the CAAP also initiates the concept of cooperation among port stakeholders, including port authorities, terminal operators, ocean carriers, truckers, shippers, national and regional regulatory bodies, and local communities.
# IoT-Based System for Improving Vehicular Safety by Continuous Traffic Violation Monitoring
## 2 Related Work
### Seatbelt Monitoring Systems  
Different onboard seatbelt monitoring systems have been proposed for improving passenger safety. The first monitoring approach is based on sensors, such as the seatbelt reminder proposed in [43]. To detect the presence of passengers on seats, sensor mats for sensing pressure can be installed on the seat heater or on the seat cushion foam [9]. The authors in [10] used a reed-based sensor to produce a signal regarding the status of the seatbelt, where the signal is generated due to a magnetic component installed on the male buckle. Another method is by installing a sensor inside the female buckle to detect the male buckle plate and send a Bluetooth signal to a smartphone [44; 45].  
The second approach for onboard monitoring of seatbelts is based on image processing, such as [13; 14; 46]. In these systems, different image processing techniques are used, including neural networks and deep learning. These algorithms consume a lot of computational time and may not be compatible with real-time applications, so researchers need to propose faster algorithms. For example, faster R-CNN (proposed in [47]) can be a solution to increase the efficiency of seatbelt detection [48; 49]. Moreover, seatbelts can be detected from the outside of the vehicle using cameras installed on roads [50]. This image processing approach is affected by light, window reflection, rain, and snow, and computation time is still an issue that needs improvement [48].  
In summary, vehicular speed monitoring, passenger counting, and seatbelt fastening detection can be estimated using the sensor-based approach or the computer-vision-based approach. The aforementioned studies do not focus on covering the entire road network or connecting with authorities in real time. Moreover, the computer vision approach has limitations, including the high computational demand and the effects of the surrounding environment. Therefore, this paper proposes a novel system that combines the monitoring of over-speeding, overloading of passengers, and seatbelt unfastening through the use of edge, fog, and cloud computing. This system is important to improve safety in vehicles and reduce the harm caused by accidents. Table 1 shows a summary describing the classification of the related studies based on the objectives and the technologies used to achieve the objectives.
## 8 Results and Discussion of Reviewed Papers
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset Name** & **Reference** & **Methods** & \multicolumn{2}{c}{**Performance**} \\ \hline  & & \multirow{4}{*}{DCPT} & Success Rate & Precision & Normalized Precision \\  & & 0.577 & 0.703 & 0.701 \\ \cline{3-5}  & & & Success Rate & Precision & Normalized Precision \\
[88] & & DIMP50-SCT & 0.562 & 0.717 & 0.71 \\ \cline{3-5}  & & DIMP18 [89] & 0.542 & 0.702 & 0.69 \\ \cline{3-5}  & & & Success Rate & Precision \\ UAVDark135 [18] & & DL+SiamAPN & 0.389 & 0.516 \\ \cline{3-5}  & & & Success Rate & Precision \\
[90] & & SiamAPN [91] & 0.3 & 0.424 \\ \cline{3-5}  & & & Success Rate & Precision \\  & & DL+DIMP50 & 0.544 & 0.7 \\ \cline{3-5}  & & & Success Rate & Precision \\  & & DIMP50 [19] & 0.526 & 0.672 \\ \hline  & & \multirow{4}{*}{[92]} & \multirow{4}{*}{Proposed Novel Method} & mAP & R-1 Accuracy \\  & & & 0.828 & 0.844 \\ \cline{3-5}  & & & TransReID [93] & mAP & R-1 Accuracy \\  & & & 0.814 & 0.826 \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & RotTrans & mAP & R-1 Accuracy \\  & & & 0.848 & 0.838 \\ \cline{3-5}  & & & TransReID & mAP & R-1 Accuracy \\  & & & & \\ \hline  & & DP: Dynamic Precision; NDP: Normalized Dynamic Precision; Top1/Top5: The authors of [83] employed the metric of Top1/Top5 for measuring accuracy in single-label classification. & & \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \end{tabular}
\end{table} 
Table 7: _Cont._ \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset Name** & **Reference** & **Methods** & **Performance** \\ \hline  & & Novel Multifeature+CNN method & Accuracy \\  & & 0.95 \\ \cline{2-3}  & [94] & P-CNN [45] & Accuracy \\  & & MLP\_7] [95] & Accuracy \\  & & DD-Net\_7] [96] & Accuracy \\  & & 0.915 \\ \cline{3-5}  & [95] & P-CNN & Accuracy \\  & & MLP\_7] & Accuracy \\  & & 0.948 \\ \hline  & & BANet & mIoU (\%) \\  & [97] & & 64.6 \\ \cline{3-5}  & & MSD benchmark [11] & mIoU (\%) \\ UAVid [11] & & 57.0 \\ \cline{3-5}  & & A\({}^{2}\)-FPN & mIoU (\%) \\  & [98] & MSD benchmark & mIoU (\%) \\  & & 57.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance metrics and results for different datasets and methods.
## 2 Materials and Methods
### Global Context Fusion Strategy
state-of-the-art performance in object detection, semantic segmentation, and image super-resolution [31; 32; 33]. These developments support the relevance of Transformer-inspired architectures in high-resolution vision tasks and further validate the design direction of the GCF module. Table 2 summarizes recent Transformer-based advances in remote sensing that inform and support the GCF design. Compared to existing global context modeling techniques, the proposed GCF module introduces several key innovations specifically tailored to real-time fire safety applications. * Flexibility in Context Modeling: Existing methods (e.g., Swin Transformer, Global Context Pooling, Criss-Cross Attention) rely on predefined window structures or axis-aligned static attention mechanisms. In contrast, the GCF module dynamically infers global trends from real-time sensor inputs and responds immediately to local anomalies. This makes it highly effective for modeling nonlinear and uneven heat/smoke diffusion patterns typical in fire events. * Optimization for Embedded Deployment: Prior methods often depend on high-performance GPUs and exhibit high computation and memory demands. GCF leverages a lightweight sensor-driven spatial encoder and localized computation, enabling robust real-time operation on edge platforms. This removes reliance on external servers and avoids communication delays--an essential distinction from existing solutions. * Real-Time Fusion Responsiveness: GCF is designed for high-frequency decision-making, enabling multiple inferences per second. This temporal sensitivity is critical for mitigating fire spread. Pooling-based GCP or fixed-window approaches operate on static intervals and may fail to respond promptly to fast-evolving hazards. * Application Context Suitability: Unlike prior techniques focused on general image understanding (e.g., semantic segmentation, salient object detection), the GCF module is structurally aligned with intelligent firefighting systems. It explicitly integrates environmental architecture (e.g., ventilation, nozzle directionality) into its spatial modeling, supporting actionable decisions in fire scenarios. Through these contributions, the GCF module outperforms traditional global fusion mechanisms in responsiveness, efficiency, and sensor coordination, making it highly suitable for next-generation embedded fire response platforms.
# Emulating Software-Defined Disaggregated Optical Networks in a Containerized Framework
## 1 Introduction
### Motivation and Previous Work  
The state of the art in SDN DON and optical devices' emulation reveals that the community is pushing considerably in this direction. YANG-based models in accordance with the NETCONF protocol are efficient approaches towards minimizing or even removing the drawbacks of the so-called vendor islands. Thus, there are ongoing projects to provide unified models for optical devices. However, to the best of our knowledge, optical device emulation is in its early stages. Only some of the analysed works assume a scenario where SDN DON emulation tackles from the highest application layer down to the data plane. Handling over control and management of emulation to the application layer will permit a more precise optical network design and an automatic deployment of software agents related from device catalogue at the request of the user. Moreover, as pointed out in the data-plane initiatives in the previous subsection, interactions between realistic agent-based SDN implementations and data-plane models are scarce. In fact, the challenge of these initiatives relies on the combination of physical-layer modelling commonly devoted to off-line planning tools with operation-based systems such as SDN controllers. The complementary nature of those systems makes the development of a DON emulator a difficult and challenging task. Such context is the starting point that motivates this paper with the aim at contributing to achieve a such challenging goal.  
This work is a significant follow-up of an open line started in [36], in which we reported our preliminary progress towards an optical emulation framework built on automatic agent creation. In particular, ref. [36] focused on a specific use case of an 18-node European topology with nodes based on open packet transponder (Cassini [37]) in which we were able to modify parameters such as optical launch power, wavelength, and modulation format. Here, we extend that work and present a substantial evolution of our Containerized Framework for emulating SDN-DONs with special attention to the coexistence of the Kubernetes-based [38] master nodes server-client architecture and a new automatic agent creation framework using NETCONF in the SDN controller-optical agent interaction irrespective of the YANG model used. Additionally, to test the practical feasibility of our proposal, a proof-of-concept is also presented.
# Generation of Beam Tilt through Three-Dimensional Printed Surface
## 4 Results and Discussion  
This section details the fabrication of the proposed BDS prototype and the obtained radiation beam patterns along with the VSWR results. The requirement of a full 360\({}^{\circ}\) phase is addressed through the required four-layer cube arrangement. It is noted that the smaller cube sizes generate higher transmission magnitude values, which are close to 0.99, whereas the larger cubes sizes show greater phase values. Interestingly, the side lobe levels are less than \(-5\) dB and the VSWR is less than 2 over the frequency range of interest.