esa-sceva/satcom-chunk-collection huggingface dataset:
# Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize
## 2 Materials and Methods
### Field Experiment Description  
A field experiment for testing the proposed UAS data ferrying design was conducted over a nearly 53 ha maize field (typical US mid-west maize field) at the University of Nebraska's Eastern Nebraska Research and Extension Center (ENREC) near Mead, Nebraska during the 2020 and 2021 growing seasons (Figure 1). Maize was planted in one half of the field and soybean was planted in the other half and was irrigated with a center pivot (Lindsay Corporation Zimmatic 8500). In 2020, the field was planted with soybean in the north half of the field and maize in the southern half. The crops were rotated during the 2021 growing season (soybean in the south, and maize in the north). The study was conducted when the crop was 80% or more coverage and at late crop physiological reproductive stages during 2020 and from mid to late growing season during 2021 (Table 2).  
Stationary sensor node stations comprised of soil water content sensors and infrared radiometer sensors were installed in the maize portion of the field. Four sensor node stations were installed at the beginning of the 2020 growing season and five in 2021. The selected UAS data ferrying design (Design B: LoRa wireless protocol, multi-rotor UAS, external power source, memory storage over the UAS) was constructed and used for wireless communication with the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Maze Growing Season** & **Planting Date** & **Vegetative Period** & **Reproductive Period** \\ \hline
2020 & 11 May & 28 May–16 July & 17 July–29 September \\ \hline
2021 & 28 April & 14 May–16 July & 17 July–26 September \\ \hline \hline \end{tabular}
\end{table}
Table 2: The planting date, the observed vegetative and reproductive growth stage dates for maize during the 2020 and 2021 growing seasons.  
stationary sensor node station; soil moisture and canopy temperature data were to be retrieved from each node station during the UAS flight (Figure 2).  
Figure 1: Close-up view of the stationary sensor node station comprising of infrared radiometer and soil water content sensors communicating with an airborne unmanned aerial system-based data ferrying.  
Figure 2: A schematic diagram of airborne based unmanned aerial system data ferry communicating with stationary sensor node stations on the ground installed in maize over a center-pivot irrigated field near Mead, Nebraska.  
During 2020, communication was attempted with the four replicate node stations at UAS hovering heights of 31 m, 61 m, and 122 m above the node stations for three flight days (Figure 3). The UAS hovered at a lateral distance of 0 m from the node station while the communication attempt was conducted.  
During 2021, communication was attempted with the five replicate node stations at a hovering height of 31 m above the stations and at lateral distances between the UAS and station of 0 m, 38 m, and 76 m during nine flight days (Figure 4). Signal interference due to crop canopy cover was expected to be maximum from the mid to late growing season of maize.
# 1D Barcode Detection via Integrated Deep-Learning and Geometric Approach
## 4 Barcode Localization
### Object Detection  
The method we choose to locate the bounding box of the 1D barcode is the YOLO (you only look once) object detection network [7]. It can precisely locate 1D barcodes based on their shape and texture. Since YOLO is used as a tool and can be replaced by other object detection framework, we do not analyze its performance further. After inputting the source image containing 1D barcodes, the object detection network detects the bounding boxes of the 1D barcodes, and then we cut them from the source image into a collection \(I\) consisting of smaller images.  
Even though the positions of 1D barcodes have been detected, the barcode images in \(I\) still cannot be read directly for the random rotation of the 1D barcode and background pixels. Angle prediction, the following procedure, which is performed with another neural network by [PERSON] et al. [6], is performed with a novel method in this stage. The details of the process are described below. However, more than just angle prediction, our technique also extracts the pure 1D barcode region from the bounding box, as is explained in Section 5.
# Complex Battery Storage Fire Propagation Translational Forensic Study Using Cellular Automata
###### Abstract  
The surge in lithium-ion battery (LIB) use, essential for mass-scale renewable energy storage, raises concerns about fire hazards. However, to date, there is a lack of industry-wide understanding of large-scale LIB fire propagation. This paper suggests a translational forensic approach to promote fire safety awareness and introduces the cellular automata (CA) model coupled with the Monte Carlo (MC) approach to address the complex fire propagation simulation within an energy storage system (ESS). The objective is to demonstrate that the CA-MC model can provide a flexible and scalable connection for all levels of battery fire studies. The numerical model is coupled with experimental tests which have been performed to establish the actual timing of fire propagation from a single source. Cellular automata simulation, conducted through hybrid modeling and an applied risk analysis approach to evaluate fire hazards associated with LIBs, offers crucial insights into potential risks. The results demonstrate that, with fire incident initiation at a probability of 0.1 (10%), 33% of batteries will burn, and at a probability of 0.6 (60%) and beyond, the entire battery module will face complete burndown. Achieving full combustion of the entire module will take only approximately 42 timesteps on average, indicating rapid fire propagation. The actual time for a complete fire to occur in the battery module has been estimated to be 304 s per timestep, or 3.5 h total. Using this example, it is shown that the CA-MC approach can be extended to many other aspects of battery fire studies and is ideal as a translational tool, spanning all domains of the LIB industry.  
lithium-ion battery (LIB); cellular automata (CA); Monte Carlo (MC) approach; fire propagation; risk analysis; energy storage system (ESS) +
Footnote †: journal: applied sciences
## 3 Designing Virtualization of Wireless Sensor Network
### Challenges behind Designing VSNs
#### 3.2.9 Resource Scheduling  
When establishing a virtual sensor network, a service provider requires specific guarantees for the virtual nodes' attributes as well as the virtual links' bandwidth allocated to its network. For virtual sensor routers, a service provider might request guarantees for a minimum packet processing rate of the CPU, specific storage requirements, and a lower bound on the size of the memory. On the other hand, virtual sensor link requests may range from best-effort service to fixed loss and delay characteristics found in dedicated physical links. To provide such guarantees and to create an illusion of an isolated and dedicated network to each service provider, infrastructure providers must employ appropriate scheduling algorithms in all of the sensor network elements. Existing system virtualization technologies provide efficient scheduling mechanisms for CPU, memory, storage, and network interface in each of the virtual machines running on the host sensor node. Sensor network virtualization can extend these mechanisms to implement resource scheduling in the physical infrastructure.
# Half-Elliptical Resonator Lowpass Filter with a Wide Stopband for Low Band 5G Communication Systems
## 2 LPF Design Process
### Design of the Proposed Filter  
In the proposed design, to reduce the occupied size, applied lines are bent. The structure of the proposed filter is depicted in Figure 10a, for which all dimensions were previously obtained, and its frequency response is depicted in Figure 10b. The proposed LPF has a cut-off frequency of 1.26 GHz and provides a wide stopband from 1.48 GHz up to 20 GHz with more than 24 dB of attenuation level.  
Figure 8: Two elliptical resonators’ (**a**) structure and (**b**) their frequency response.  
Figure 10: The attenuator resonator (**a**) structure and (**b**) its frequency response.  
Figure 9: The square-shape attenuator resonator (**a**) structure and (**b**) its frequency response.
## 4 Social Media-Driven Traffic Prediction Framework with Illustrative Example
### Data Analysis and Prediction  
#### 4.2.1 Social Network Analysis  
Research conducted on traffic prediction using social media is scarce. It either uses social media and historical traffic data [35; 36; 39] or combines these with the weather [37; 38]. However, data mining methods to identify tweets for traffic information can be extensive and resource demanding, especially if there is a constant inflow of Tweets to be analyzed. A social network analysis with Collaborative Innovation Networks (COINs) is proposed to solve this problem.  
COIN is a term developed by [PERSON], a researcher from MIT Sloan's Center for Collective Intelligence [68; 69], and defines a team of collaborative innovation that uses tools such as email, social networks, chat, blogs, etc., to promote a trend. People collaborating in a COIN are intrinsically motivated, have a shared vision, collaborate through the Web in teams, and work for the same goal. In addition, COINs have a common trendsetter. For example, Figure 6 shows a COIN of technology tech giants (Apple and Samsung) and the respective trendsetters. Trendsetters in a COIN are leaders who have followers and have a big impact on the network.  
For this work, trendsetters will be called \"traffic influencers\". Traffic influencers will have a factor determining the differential change/influence they cause in traffic conditions. Hence, there will be no need to monitor every Tweet (or update) until a relevant one is posted. Instead, traffic influencers will have an intrinsic way of causing variations in road segments and will be less computationally extensive.
## 4 Case Study
### Comparison with the Bayesian Network Method
However, fuzzy estimations often yield invalid results that can be used for reasoning when the individual state probabilities of the variables are similar. Even if the results are valid, some uncertain information will be lost. The reasoning method based on evidential networks does not need a fuzzy estimation of the evidence of uncertainty, thus avoiding the loss of uncertain information to an optimal degree. It is able to obtain more accurate results while avoiding misjudgment. In addition, evidential network-based reasoning methods can combine multiple uncertain evidences that are passed to a node directly through Dempster's combination rules. A combined evidence of \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Capability** & **P (c=H)** & **P (c=M)** & **P (c=L)** \\ \hline Anti-jamming capability & 0.6875 & - & 0.3125 \\ System survivability & 0.7485 & - & 0.2515 \\ Security and confidentiality & 0.5942 & - & 0.4053 \\ Navigation and positioning capability & 0.6404 & - & 0.3596 \\ Space-navigation-and-positioning system capability & 0.4482 & 0.2745 & 0.2773 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The probability distribution of the requirement-satisfaction degree for each upper-layer capability. Figure 5: A comparison of the results obtained through the two methods (Bayesian and evidential). information is then formed and transferred to the adjacent node. This avoids double-counting multiple pieces of evidence in the same path; it can be said that this model is an efficient parallel-computing model. In the Bayesian network, the reasoning method's mechanism-of-combination rule is unable to fuse uncertain evidences due to the fact that the use of probability is the basis of the uncertainty reasoning: every piece of evidence information is required to travel through the entire network. After updating all of the nodes' probability values as one new prior probability, the same operations can be performed on other nodes' evidences in order to update the multi-evidence information. For the complex network, the computational complexity of the Bayesian network makes it difficult to complete the reasoning task. Conversely, the evidential network-based reasoning method greatly improves and accelerates the efficiency of the reasoning.
# Advancing in RGB-D Salient Object Detection: A Survey
## 4 Performance Evaluation and Comparison
### Model Comparison
Attention mechanisms significantly enhance feature selection and model interpretability by focusing on the most relevant aspects of the data, leading to more accurate detection. However, these mechanisms may also introduce computational overhead and the risk of overfitting, particularly in resource-intensive models. Additionally, the effectiveness of cross-modal fusion techniques varies depending on the quality of the depth data and the model's ability to balance RGB and depth information. While some models exhibit exceptional robustness, this often comes at the cost of increased complexity. The future of SOD in RGB-D may see the integration of more advanced technologies, such as self-supervised learning, quantum computing for complex data processing, and further enhancements in multimodal fusion techniques. These innovations will aim to address current limitations, such as the reliance on large annotated datasets and the high computational costs associated with advanced models. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline \multirow{2}{*}{**Time**} & \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Backbone**} & \multicolumn{2}{l}{**Attention**} & \multicolumn{2}{l}{**Multi-Model**} & \multirow{2}{*}{\(\mathbf{F}_{\beta}\)} & \multirow{2}{*}{\(\mathbf{S}_{\alpha}\)} & \multirow{2}{*}{\(\mathbf{E}_{\phi}\)} & \multirow{2}{*}{**MAE**} \\  & & & & **Mechanism** & & **Fusion** & & & \\ \hline
2018 & PCF [57] & VGG-16 & ✗ & ✗ & 0.872 & 0.877 & 0.924 & 0.059 \\ \hline
2019 & SSRCNN [58] & VGG-16 & ✗ & ✓ & 0.876 & 0.890 & 0.912 & 0.047 \\  & PDNet [59] & VGG-16, VGG-19 & ✗ & ✗ & 0.823 & 0.877 & 0.899 & 0.071 \\  & MMCI [60] & - & ✗ & ✓ & 0.868 & 0.859 & 0.882 & 0.079 \\  & CPFP [61] & VGG-16 & ✗ & ✓ & 0.890 & 0.878 & 0.900 & 0.053 \\  & TANet [62] & VGG-16 & ✓ & ✓ & 0.888 & 0.878 & 0.909 & 0.061 \\  & DMRA [50] & VGG-19 & ✓ & ✓ & 0.896 & 0.885 & 0.920 & 0.051 \\ \hline
2020 & DANet [63] & VGG-16, VGG-19 & ✓ & ✓ & 0.905 & 0.897 & 0.926 & 0.046 \\  & DASNet [64] & ResNet-50 & ✗ & ✓ & 0.911 & 0.902 & 0.927 & 0.042 \\  & CoNet [65] & ResNet & ✗ & ✗ & 0.872 & 0.894 & 0.912 & 0.047 \\  & CMWNet [66] & VGG-16 & ✗ & ✓ & 0.902 & 0.903 & 0.936 & 0.046 \\  & ICNet [67] & VGG-16 & ✗ & ✓ & 0.891 & 0.894 & 0.926 & 0.052 \\  & S\({}^{2}\)MA [16] & VGG-16 & ✓ & ✓ & 0.889 & 0.894 & 0.930 & 0.053 \\  & A2 dele [68] & VGG-16 & ✓ & ✗ & 0.873 & 0.869 & 0.916 & 0.051 \\  & PGAR [69] & VGG-16 & ✗ & ✓ & 0.893 & 0.909 & 0.916 & 0.042 \\  & HDFNet [70] & VGG-16, VGG-19 & ✗ & ✓ & 0.922 & 0.908 & 0.932 & 0.038 \\  & JL-DCF [71] & VGG-16, ResNet-101 & ✗ & ✓ & 0.903 & 0.903 & 0.944 & 0.043 \\  & FRDT [72] & VGG-19 & ✗ & ✓ & 0.879 & 0.898 & 0.917 & 0.048 \\  & CmSalGAN [39] & VGG-16 & ✓ & ✓ & 0.897 & 0.903 & 0.94 & 0.046 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of evaluation metrics based on the NJUD dataset. ✓ indicates the presence of the module in the model’s structure, while ✗ indicates the absence of the module, and - Indicates that the model has not measured this indicator.
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 6 Performances Analysis
### The Reference Point Group Mobility (RPGM)
#### 6.2.3 Throughput  
Figure 9 depicts a line chart illustrating the throughput performance under various conditions, where throughput, measured in megabits per second (Mbps), is plotted against the number of nodes (ranging from 0 to 60) on the \(x\)-axis and Mbps on the \(y\)-axis (ranging from 0.00 to 0.07). Eight lines represent different scenarios involving combinations of mobile and attacker nodes. The first set of scenarios portrays the mobile environment utilizing the  
Figure 8: E2 ED without and with attack using RPGM mobility model.  
RPGM mobility model, ranging from 10 to 60 nodes, without an attack, with mobile node percentages varying from 10% to 40%. Conversely, the second set of scenarios illustrates the mobile environment with the same mobility model, node numbers, and percentages, but with the Hello Flood attack. Analysis reveals that the mobile environment without the attack has higher throughput values than those with the attack. Additionally, throughput tends to decrease as network size increases. Moreover, networks with a higher proportion of attacker nodes experience a more significant reduction in throughput, indicating the adverse impact of malicious activity on network performance. Furthermore, node mobility, which is represented by varying percentages of mobile nodes, influences throughput, implying that it plays a role in shaping network performance metrics.  
The interaction between the RPGM model and the Hello Flood attack severely diminishes throughput in network environments due to a variety of interconnected factors. Firstly, the organized movements of nodes within the RPGM can trigger congestion in specific network zones, heightening packet collisions and retransmissions, which directly harm throughput. The Hello Flood attack's flooding of excessive traffic, saturating channels, and worsening interference, thereby obstructing packet transmission and reducing throughput, compounds this congestion. Secondly, the inherent movement patterns in the RPGM introduce routing instability, leading to suboptimal routing decisions and increased packet loss, adversely impacting throughput. The Hello Flood attack exacerbates this instability by overwhelming routing protocols and disrupting packet delivery, resulting in a further decline in throughput. Additionally, the heightened resource consumption within RPGM groups, along with the influx of malicious traffic from the attack, strains network resources, causing bottlenecks and queuing delays that degrade performance and decrease throughput. Moreover, the RPGM's management of node movement at boundaries introduces extra delays in packet transmission, exploited by the Hello Flood attack to further hinder throughput.
# The Debris Flow Risk Prediction Model Based on PCA-Elman
###### Abstract  
Accurate prediction of the risk levels of debris flows is crucial for devising effective disaster prevention and mitigation strategies. This study, based on debris flow sample data from Yunnan Province, initially employs Principal Component Analysis to reduce the dimensionality of the raw data, extracting key features and minimizing data dimensions. Subsequently, a 5-fold cross-validation method is utilized to segment the dataset into training and testing sets, and a predictive model integrating Principal Component Analysis with an Elman Neural Network (PCA-Elman) is constructed. The study investigates the impact of data imbalance and spatial variability on the model's predictive accuracy and attempts to enhance the model's generalization capabilities by employing the Adaptive Synthetic Sampling algorithm and incorporating samples from unknown regions. The results indicate that the model demonstrates high accuracy and generalization in predicting debris flow risks, with its Area Under Curve value, Average Precision value, and average precision scores surpassing those of traditional models, achieving an accuracy rate of 88.57%. After processing the data with the Adaptive Synthetic Sampling algorithm, the model's accuracy rate increases to 98.33%. Furthermore, incorporating samples from unknown regions into the trained model significantly improves the prediction accuracy for debris flow risks in those areas. This research offers new insights into the assessment of debris flow hazards and disaster prevention and mitigation efforts, and provides a reference for the construction of predictive models for similar natural disasters.  
debris flow; principal component analysis; Elman neural network; risk prediction; imbalanced dataset; generalization ability +
Footnote †: journal: applied sciences
## 4 Formulation of the Advanced Global Efficiency Index, AGEI  
The aim of the Advanced Global Efficiency Index (AGEI) is to provide an evaluation tool suitable for assessing complex plant configurations, encompassing both hybrid and homogenous solutions while maintaining the straightforward structure of GEI. In this case, the fundamental concept underlying its formulation is that the efficiency of a system composed of \(m\) machines, each consuming \(en\) and collectively producing \(n\) useful effects, is \(n/m\) times the efficiency of a system consisting of \(n\) machines, each producing a single useful effect and consuming \(en\).  
Accordingly, the index expression comes as follows:  
\[\text{AGEI}=\frac{n}{m}\cdot\sum_{j=1}^{m}\sum_{i=1}^{u_{j}}E_{ij} \tag{16}\]  
where  
\[n=\sum_{j=1}^{m}u_{j} \tag{17}\]  
and the term \(E_{ij}\) represents the \(i\)-th efficiency or performance coefficient of the \(j\)-th machine, which provides \(u_{j}\) useful effects simultaneously.  
The above formulation can be considered a generalisation of the GEI equation. The two formulations, AGEI and GEI, are identical in the case of homogenous configurations, i.e., when \(m\) is equal to 1, corresponding to an integrated system covering all the useful effects, or when \(m\) is equal to \(n\), indicating a system composed entirely by non-integrated machines.  
The following section reports two simple examples showing how AGEI supersedes both \(\text{GEI}_{\text{eq}}\) expressions, providing results that align more closely with analytic efficiency calculations. The energies considered in the examples are deliberately maintained in the order of a few kilojoules, to facilitate the comprehension of the steps; however, the same considerations can be applied to cases involving larger amounts of energy.
# NaviSoC: High-Accuracy Low-Power GNSS SoC with an Integrated Application Processor
## 2 SoC Overview and RF Architecture
### Frequency Planning for Dual-Frequency Receiver  
The continuous expansion of the GNSS space segment leads to an increase in the number of navigational systems and signals available [22]. Because of that, choosing the appropriate frequency bands for dual-frequency receivers has been an important task. Bands centered around 1575.42 MHz (E1/L1 band of Galileo (European Global Satellite Navigation System) and GPS (US Global Positioning System)) and 1191.795 MHz (Galileo and GPS E5/L5 band) frequencies were chosen after a detailed analysis of the GNSS signals. The GNSS frequency spectrum depicted in Figure 3 clearly suggests that these bands allow all global and regional GNSS constellations to be received. In this case, setting the receiver bandwidth to 52 MHz allows for the reception of signals from all global systems--Galileo, GPS, BeiDou (Chinese Navigation Satellite System) and GLONASS (GLObal NAvigation Satellite System of Russian government), as well as local systems such as QZSS (Quasi-Zenith Satellite System developed by the Japanese government) and IRNSS (Indian Regional Navigation Satellite System). To reduce power consumption, the wideband RF processing is performed using a direct conversion receiver with complex signal processing. Consequently, the IF bandwidth and sampling frequency may be limited to 26 MHz and 64 MHz, respectively. As a result, a high positioning accuracy is achieved while the power is kept at a reasonable level.  
Both channels' bandwidth and sampling frequency are highly configurable. Therefore, the best accuracy/power ratio may be selected for any given application. This is a unique feature allowing the use of CCNV1-A1 in a wide range of applications. The sampling frequency can be changed from 8 MHz to 64 MHz. The channel bandwidth is automatically set to a frequency close to the Nyquist frequency.