esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Pruning of Detection Transformer in Remote Sensing Using Ant Colony Evolutionary Pruning
## 2 Related Works
### Neural Network Pruning  
Over the years, neural network pruning has been proven to effectively reduce model size while retaining neural network performance [27; 28; 29]. Neural network pruning is generally categorized into two main types: unstructured pruning and structured pruning. Unstructured pruning [30; 31] significantly reduces model volume, yet such methods introduce irregular operations and necessitate a specialized algorithm library for support, rendering them challenging to implement in practical applications. On the other hand, structured pruning focuses on trimming filters, channels, or even entire neural network layers, enhancing hardware compatibility and scalability.  
Early structured pruning methods were primarily based on parameter norms to determine importance and eliminate channels with smaller norms [32; 33]. However, the assumption that \"small norm = unimportant\" has been challenged, leading to the proposal of more insightful criteria for importance assessment. For instance, FPGM [34] identifies filters near the geometric median as redundant parameters, as the geometric median signifies shared information within that layer. Some studies have leveraged information entropy, inputting a large number of samples into the neural network, to prune filters with a lower average information entropy [35]. Leveraging the prevalence of batch normalization (BN) layers, Network Slimming (NS) [36] discovered that the \(\gamma\) scaling parameters in BN can be utilized to prune output channels. Moreover, [PERSON] and [PERSON] [37] take into account both BN and ReLU operations, categorizing parameters that become 0 post-ReLU operation as having low importance, along with NS considerations.
## 2 Related Work
### Computation Offloading with Incentives  
The authors in [22] proposed to decompose the system profit optimization problem into three sub-problems, task transmission delay optimization, the offloading ratio optimization, and finally, the offloading payment optimization. The system profit is optimizedbased on incentive compatibility and individual rationality of vehicles. The authors in [23] noticed that there are many parked vehicles on the road and their resources are under-utilized. Therefore, the authors introduce a pricing model to offload tasks in a dynamic environment, which helps vehicles to find economical offload strategies and to improve the resource utilization of parked vehicles. In [24], the authors proposed a novel architecture that integrates satellite networks and 5G cloud-supported vehicular networks. Delay and cost are jointly optimized through a simulated annealing algorithm. Similarly, the work of [25] considered the computation offloading problem in the satellite-terrestrial Internet of Vehicles. An auction-based incentive mechanism is brought forward to motivate vehicle owners to join in the offloading process. The authors in [26] proposed a differentiated pricing scheme that sets incentive factors according to the needs of users and the volume of onboard resources. In this way, the incentive for vehicles can be adjusted in real-time. Moreover, a reverse auction-based task scheduling algorithm was proposed to select the edge nodes for task offloading.  
Although the above works provide compensation for SVs, the existing incentive mechanisms still lack specific considerations for vehicle cooperation intentions. Simple economic compensation will lead to fierce competition among vehicles, and in turn, results in uneven distribution of wealth in the system, which will cause a reduction in the pertinence of the incentive mechanisms. In other words, vehicles that have strong intentions to participate in the offloading may be deprived of the opportunities if they have a small volume of computing resources or are in poor financial conditions, with the existing incentive mechanisms. In view of this, this paper firstly proposes a fuzzy logic-based pricing strategy that jointly considers the moving characteristics of vehicles in the system and the service intentions of vehicles. The unit price of a vehicle to help UV with the computation task is calculated and adjusted according to its real-time situation. After this, a Max-Min fairness-oriented offloading algorithm is designed, which works on the basis of the unit price of each vehicle determined by the pricing strategy. With careful consideration, vehicles that have a small volume of computing resources or are in poor financial conditions are favored in the determination of final SVs. In this way, the performance of the computation offloading, including the completion delay, the overall cost, and the resource utilization ratios are improved significantly.
# Impacts of MT-HVDC Systems on Enhancing the Power Transmission Capability
## 5 Simulations and Results  
The proposed research is assessed via interconnected Kundur's two area AC network and CIGRE B4 5-terminal VSC-HVDC systems as shown in Figure 1. The connections are developed through bus 7 and 9 of the two area system. Simulations are developed in Matlab/Simulink to observe the impact of MT-HVDC grids on the transfer capacity of AC transmission networks. Power transfer capability of test system is further illustrated via security regions (nomograms). The generation groups \({G_{p}}^{1}\) and \({G_{p}}^{2}\) are selected from the generation units of area-1 and area-2, respectively, of Figure 1 (with slack bus). Generating capacity of each group is taken 1800 MW. For the power balance, a third slack group (\({G_{p}}^{3}\)), generator 5 (\({G_{5}}\)) connected at bus 8, is introduced within the system as shown in Figure 7. Slack group is mandatory in order to maintain a power balance within all quadrants.  
To compute security regions (standalone AC grid and integrated AC/DC systems, respectively), 16 directions have been assumed as shown in Figure 8. Thus the anticipated angles are \(22.5^{\circ}\), \(45^{\circ}\), \(67.5^{\circ}\), \(90^{\circ}\), \(\ldots\),\(360^{\circ}\), using (3). In each direction, the power to be re-dispatched in \({G_{p}}^{1}\) and \({G_{p}}^{2}\), calculated using (2). Power change steps are shown in Figure 8 by the points a, b, c, d, e, and f in the direction of arrow. The power-sharing by each generator within the group is computed by (4). Power balance must be maintained and achieved through \({G_{p}}^{3}\). Then, three cases are discussed with the help of power transfer capacity evaluation and security regions.  
Figure 5: Droop control using d-q control.  
Figure 6: The DC droop controller.
## 3 Multi-Objective Optimization
### Multi-Objective Hybrid Butterfly Optimization and Particle Swarm Optimization Algorithm
Therefore, each solution is compared with each non-dominated solution in \(\mathbb{P}_{s}\) in order to determine whether the solution should stay in the archive. Next, by looking into the mechanisms for the updating of particle positions of both PSO and BOA algorithms, we observe that both algorithms have their strengths and weaknesses in finding the optimal solution. Therefore, to improve the global exploration ability of the proposed algorithm, we propose the integration of the term \(c_{2}rand_{2}\Big{(}\mathbf{x}_{gbst}^{(\mathrm{G})}-\mathbf{x}_{i}^{( \mathrm{G})}\Big{)}\) from the velocity-updating equation from the PSO algorithm into Equation (34) of the BOA algorithm, and, substituting the term \(\mathbf{g}^{*}\) with \(\mathbf{x}_{gbst}^{(G)}\), we obtain  
\[\mathbf{x}_{i}^{(\mathrm{G}+1)}=\mathbf{x}_{i}^{(\mathrm{G})}+\Big{(}r^{2} \times\mathbf{x}_{gbst}^{(\mathrm{G})}-\mathbf{x}_{i}^{(\mathrm{G})}\Big{)} \times\varphi_{i}+c_{2}rand_{2}\Big{(}\mathbf{x}_{gbst}^{(\mathrm{G})}- \mathbf{x}_{i}^{(\mathrm{G})}\Big{)}. \tag{38}\]  
On the other hand, in order to improve the exploitation ability, the term \(c_{1}rand_{1}\Big{(}\mathbf{x}_{gbst,i}^{(\mathrm{G})}-\mathbf{x}_{i}^{( \mathrm{G})}\Big{)}\) from Equation (29) has been introduced into Equation (35), and the following is obtained  
\[\mathbf{x}_{i}^{(\mathrm{G}+1)}=\mathbf{x}_{i}^{(\mathrm{G})}+\Big{(}r^{2} \times\mathbf{x}_{j}^{(\mathrm{G})}-\mathbf{x}_{k}^{(\mathrm{G})}\Big{)} \times\varphi_{i}+c_{1}rand_{1}\Big{(}\mathbf{x}_{gbst,i}^{(\mathrm{G})}- \mathbf{x}_{i}^{(\mathrm{G})}\Big{)}, \tag{39}\]  
where, due to the multi-objective nature of the problem, the term \(\varphi_{i}\) is calculated according to the following expression  
\[\varphi_{i}=\sum_{i=1}^{M}w_{i}\cdot f_{i}, \tag{40}\]  
where the set of weights \(w_{i}\), \(i=1,...,M\) is chosen randomly to satisfy the condition  
\[\sum_{i=1}^{M}w_{i}=1. \tag{41}\]  
In order to maintain the population diversity and explore the complete Pareto set, in this paper, an adaptive parameter using the normalized population spacing is employed to regulate which developed expression will be applied, which can be calculated as  
\[PS^{(\mathrm{G}+1)}=\sqrt{\frac{1}{N_{p}-1}\sum_{i=1}^{N_{p}}\Big{(}\bar{d}^{ (\mathrm{G}+1)}-d_{i}^{(\mathrm{G}+1)}\Big{)}^{2}}, \tag{42}\]  
where \(d_{i}^{(\mathrm{G}+1)}\) is the distance between the \(i\)th particle and other particles in the population, and \(\bar{d}^{(\mathrm{G}+1)}\) represents the average minimum distance of all particles. In this regard, a large value of this parameter indicates a diverse and spread-out population, which indicates that the global search should be intensified. On the other hand, a smaller value of this parameter indicates that the population is grouped in one region, so the local search is to be intensified. Based on this, the appropriate pseudo-code of the proposed HMOBPSO algorithm, with the mechanism described above for updating the particle position is outlined in Algorithm 1.
## 3 Flexible Anti-Hydrofracturing Solution
### Onsite Scheme Construction
#### 3.2.2 Application  
The anti-hydrofracturing system on the Xiaowan arch dam's upstream surface employs a grid-like protective block configuration (refer to Figure 11a,b). Each block measures 5 m in height and 10 m in length, with a 30-centimeter gap between blocks. Within these blocks, two schemes are employed: Scheme 1 involves applying a 3-millimeter-thick GB plate and a 4-millimeter-thick polyurea spray, while Scheme 3 utilizes direct 7-millimeter polyurea spraying in interval areas to prevent block overlap. SK hand-scraping polyurea, 20 cm in width, is used to edge-seal the sprayed polyurea. At the upstream surface-fillet corner, Scheme 3 combines a 3-millimeter GB plate with a 7-millimeter polyurea spray. Adjacent to the dam heel's induced joint, 2 m of fly ash backfill is used, transitioning to sand, and then covered with backfilled rock slag for protection. Commencing in December 2007, the flexible anti-hydrofracturing system for the Xiaowan arch dam was completed in May 2009, covering a 19,339 m\({}^{2}\) area from dam sections No. 7 to No. 36. Leakage detection began in December 2008, revealing a mere maximum leakage of 2.78 L/s post-implementation of the proposed scheme. This minimal leakage stands as the smallest among comparable global projects, such as Jinping I (H = 305 m) at 64.1 L/s, Xiluodu (H = 285.5 m) at 24.9 L/s, Laxiwa (H = 250 m) at 6.0 L/s, and Udonde (H = 270 m) at 6.3 L/s [31], underscoring  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Test Area**} & **Adhesion Strength** & **Measured** & **Failure Position** \\  & **/MPa** & **Thickness/mm** & **Failure Position** \\ \hline \hline \multirow{3}{*}{A} & \multirow{3}{*}{0.24} & \multirow{3}{*}{5.67} & 80\% disconnected from the GB board \\  & & & 20\% disconnected from standard \\  & & & block and polyurea \\ \cline{1-1}  & & & 80\% disconnected from the GB board \\ \cline{1-1}  & & & 20\% disconnected from standard \\ \cline{1-1}  & & & block and polyurea \\ \cline{1-1}  & & & 100\% disconnected from standard \\ \cline{1-1}  & & & block and polyurea \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of adhesion strength.  
Figure 10: The effect of Spraying polyurea under two different schemes: (**a**) scheme 1; (**b**) scheme 2.  
the efficacy of the authors' system in meeting expansive underwater sealing needs with expedited construction requirements.
## 2 Background: Data and Autoregressive Models
### Time Series Data: Characteristics  
Data derived from time series may have the following characteristics [6]:  
* Trend: This characteristic is associated with the presence of an upward, downward, or stable course of the series, with respect to the time dimension.
* Seasonality: This characteristic indicates the existence of periodic patterns in the behavior of the time series, which repeat with a fixed frequency.
* Stationarity: Stationary is a time series whose statistical properties (average, variance, dispersion of values, etc.) are stable over time. A stationary time series with no trend implies that its fluctuations around its mean have a constant width. Furthermore, the autocorrelation of static time series remains constant over time. Based on these assumptions, a time series of this form can be thought of as a combination of signal and noise [11].  
In addition to these basic characteristics, some of the more common patterns seen in time series data are the following [7]:  
-Cyclic behavior: In contrast to seasonal patterns, cyclical patterns appear when the factors influencing the data of each time series are not distinguished by a fixed or known frequency. This particular pattern mainly concerns studies related to financial data, where cyclical behavior is observed according to the cycles of the economy and the business environment. The average duration of these cycles is usually longer than the duration of seasonal patterns, of the order of two years.  
-Diurnality: Refers to the inherent patterns observed in time series originating from a particular application that have a daily or monthly repeat cycle. Data related to solar, or weather observations are some examples indicating this property.  
-Outliers: Regarding the detection of anomalies and outliers in time series data, the analysis focuses on identifying abnormal changes, both desirable and undesirable, in the data set.  
-White noise: This characteristic refers to the cases where the data does not follow a specific pattern.
# A Dependable Localization Algorithm for Survivable Belt-Type Sensor Networks
###### Abstract  
As the key element, sensor networks are widely investigated by the Internet of Things (IoT) community. When massive numbers of devices are well connected, malicious attackers may deliberately propagate fake position information to confuse the ordinary users and lower the network survivability in belt-type situation. However, most existing positioning solutions only focus on the algorithm accuracy and do not consider any security aspects. In this paper, we propose a comprehensive scheme for node localization protection, which aims to improve the energy-efficient, reliability and accuracy. To handle the unbalanced resource consumption, a node deployment mechanism is presented to satisfy the energy balancing strategy in resource-constrained scenarios. According to cooperation localization theory and network connection property, the parameter estimation model is established. To achieve reliable estimations and eliminate large errors, an improved localization algorithm is created based on modified average hop distances. In order to further improve the algorithms, the node positioning accuracy is enhanced by using the steepest descent method. The experimental simulations illustrate the performance of new scheme can meet the previous targets. The results also demonstrate that it improves the belt-type sensor networks' survivability, in terms of anti-interference, network energy saving, etc.  
Keywords:belt-type sensor networks; security; the hop distance; energy-efficient; connectivity +
Footnote †: journal: Sensors
# Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm
## 2 Materials and Methods
### Point Cloud Preprocessing and Segmentation Analysis for Corn Populations  
The point cloud of the maize population was preprocessed and segmented in this investigation using a point cloud library (PCL) configuration [21]. Point cloud collecting, filtering, segmentation, retrieval, feature extraction, and visualization are frequently implemented using PCL, a cross-platform open-source C++ point cloud template library running on the Robot operating system.  
Figure 3: The flowchart of corn single-plant height estimation based on column space segmentation.
## 3 Results
### Results and Analysis  
#### 3.3.1 Overview of the Verification Process  
Firstly, based on the model presented in Figure 10, the base learners corresponding to the three features were individually utilized for meta-training and validation to acquire meta-knowledge. Secondly, leveraging the obtained meta-knowledge and the network architecture depicted in Figure 11, three target task networks were constructed using the three base learners, followed by respective training, validation, and testing processes. After completing the training, three feature extraction networks were derived. Subsequently, based on MFasNetV1 illustrated in Figure 12, the parameters of the feature extraction networks were fixed, and the feature fusion classifier underwent training and testing. Finally, ablation experiments were carried out. A schematic diagram of the verification process is provided in Figure 14.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample Set Type** & \begin{tabular}{c} **Experimental** \\ **Data** \\ \end{tabular} & \begin{tabular}{c} **Simulation Data** \\ **(PC3)** \\ \end{tabular} & \
\begin{tabular}{c} **Simulation Data** \\ **(PC4)** \\ \end{tabular} &
\begin{tabular}{c} **Total** \\ \end{tabular} \\ \hline Training set & 100 & 160 & 0 & 260 \\ Validation set & 65 & 0 & 140 & 205 \\ Test set & 205 & 0 & 0 & 205 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The composition of the target task sample library.
# Characterisation of Dust Particles Deposited on Photovoltaic Panels in the United Arab Emirates
## 3 Results and Discussion  
### Optical Microscopy  
The optical images obtained from the dust samples present several intriguing findings.  
The dust collected from the surfaces of PV panels exhibits a distinct colour that differs from the colour of the ground sand at the same site. This visual dissimilarity suggests that the dust on the PV panels might have unique origins or compositions compared to the surrounding ground dust.  
Despite variations in the locations from which the PV surface dust samples were collected, there is a remarkable uniformity in their colour. This consistency in colour across different sites indicates that the dust on the PV panels possesses a consistent composition, regardless of the specific site of collection.  
Conversely, the ground dust samples gathered from each site display a diverse range of colours. The varying colours of the ground dust grains suggest the presence of different compounds across these samples, hinting at possible variations in their sources and compositions.  
The existence of multiple colours in the ground dust samples, as shown in Figure 1, sparks interest and highlights the need for further investigations. This diversity in colours could be indicative of a wide range of compounds in the dust, making it an intriguing subject for deeper analysis. Some compositions might interact with the polymer top cover on the PV panels, leading to potential effects on their performance and functionality. Moreover, tainted compositions may not respond effectively to conventional cleaning agents, necessitating tailored cleaning approaches based on the specific dust characteristics [19].  
To gain a comprehensive understanding of the dust samples, XRD analysis was conducted to discern the phases present in the dust. The XRD study allowed us to identify the crystalline structure and phase composition of the dust materials, providing valuable insights into their properties.  
Remarkably, despite the variations in the collection sites, the dust samples collected from the PV panels exhibited a consistent composition. This consistent indication of the same composition across different locations within the PV facility offers valuable information. It suggests the possibility of devising a unified approach for PV cleaning that would be effective across various sites. To enhance our knowledge and precisely quantify the degree of homogeneity or deviation across the dust samples, it would be highly desirable to analyse the samples throughout the entire year. Such longitudinal studies would enable us to observe potential seasonal variations and gain a comprehensive understanding of the dynamics of dust accumulation on PV panels.  
Finally, the optical images have revealed fascinating insights into the colour and composition of the dust samples, both on PV panels and the surrounding ground. These findings present exciting opportunities for further research to optimise PV cleaning methods and enhance the overall efficiency and performance of PV systems across different locations.
## 5 Discussion
Importantly, the small difference in canopy gap fraction in dense forests can lead to significant differences in LAI estimates due to the logarithmic expression in the gap probability model [5]. In this study, we assumed a spherical leaf angle distribution due to the lack of field measurements of leaf angles. This assumption might be inadequate for tropical forests because of the diversity of forest canopies. We also assumed the random spatial distribution of leaves, i.e., ignoring the clumping effect, which may cause LAI underestimation [42]. Despite the large uncertainty of ALS-based LAI estimation, it theoretically yielded more accurate estimates than the DAP-based method. We investigated the relationship between LAI and AGB over all forest plots and found relatively high correlations, especially for the mixed coniferous and broad-leaved forest. The high correlation may be partly explained by the competitive growth. The comparison between airborne and satellite LAI estimates basically verified that the ALS-based LAI might be more reliable since they showed high consistency with the widely used Sentinel-2 retrievals using the PROSAIL model and ANN machine learning [43]. As the clumping effect was ignored in both airborne and satellite estimation, there were no apparent systematic differences in LAI between them. Some significant differences in LAI between ALS and Sentinel-2 retrievals may be explained by the topography effect because rugged terrain may cause variation in spectral reflectance. Complex forest background is another uncertainty source that contributes heterogeneous background spectral reflectance, resulting in limited representativeness when using a soil library [44]. Although DAP has proven its merit in forest inventory applications, our study showed it has relatively low capacity in canopy biophysical variable estimation, especially in LAI estimation.