esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Image Resolution  
The image resolution used for neural network training is an important indicator that affects the performance. The micrographs used were high resolution images, and almost every image had a pixel count of 3000 \(\times\) 2000. If the image is compressed, it is helpful to improve the training speed, but a too low resolution weakens or even destroys some  
\begin{table}
\begin{tabular}{l l c c} \hline
**Amount of Data for Training** & **Backbone** & **Image Resolution** & **mIoU** \\ \hline
14 (5\% of the whole images) & Xception & 2048 \(\times\) 1024 & 37.13\% \\
28 (10\% of the whole images) & Xception & 2048 \(\times\) 1024 & 56.95\% \\
85 (30\% of the whole images) & Xception & 2048 \(\times\) 1024 & 70.12\% \\
169 (60\% of the whole images) & Xception & 2048 \(\times\) 1024 & 76.88\% \\ \hline \end{tabular}
\end{table}
Table 7: Comparison of results with different amounts of training data using the pre-trained network “Xception” with improved hyperparameters.  
Figure 5: Prediction results depending on the amount of training data of 14 to 169 images.  
features; see Figure 6. A crack is a thin and long weld defect. If the resolution of the image is decreased to 512 \(\times\) 256, most of the crack disappears. If the resolution is further changed to 256 \(\times\) 128, almost all of the cracks are not learned. The target is to achieve a good balance of training speed and performance. According to the relationship between the side length of an image and the refinement quality investigated in [19], images with a large resolution are important for the segmentation accuracy. For this reason, the resolution of 2048 \(\times\) 1024 in this case comes along with a sufficient training speed while ensuring that all features are learned as well as possible.
## 2 Automatic Identification of Analysis Methods
### Vmd  
In recent years, wavelet transform and empirical mode decomposition have been the most widely used time-frequency analysis methods [16]. However, wavelet transform has some disadvantages: wavelet transforms require an artificial wavelet basis selection and lack self-adaptability. Meanwhile, [PERSON]'s uncertainty principle limits the wavelet transform, reducing the frequency accuracy while increasing the time accuracy. Empirical mode decomposition (EMD) is commonly utilized in signal decomposition, and mechanical defect identification is used after wavelet transform [17]. The EMD technique lacks a strict mathematical foundation for support and suffers from issues such as a low computational efficiency and modal aliasing [18]. Due to its quick calculation ability and excellent signal-to-noise ratio, VMD is a new signal decomposition approach that can successfully suppress modal aliasing.  
The VMD algorithm was created by [PERSON] et al. [19] in 2014 as a new signal processing method. This is a non-recursive, adaptive signal decomposition method. To  
Figure 3: Identification method flow chart.  
break down each frequency-part of the signal, the optimum solution of the constrained variational problem is obtained during a decomposition by loop iteration to estimate the modal component's center frequency and bandwidth near the center frequency. The VMD decomposition approach is more durable, has a faster convergence time, is less prone to modal aliasing, and has a stronger mathematical theoretical background than the EMD decomposition method. The VMD algorithm decomposes the signal into a set of bandwidth-limited IMFs with an adaptive quasi-orthogonal variation:  
\[u_{k}(t)=A_{k}(t)\cos(\varphi_{k}(t)) \tag{1}\]  
where \(A_{k}(t)\) is the instantaneous amplitude of \(u_{k}(t)\), \(u_{k}(t)\) is a set of discrete values, and the derivative of \(\varphi_{k}(t)\) is the instantaneous frequency of \(u_{k}(t)\).
## 4 Blending Ensemble Learning-Based Landslide Susceptibility Mapping and Risk Assessment
### Optimization Method Based on Blending Ensemble Learning
#### 4.1.2 Random Forest  
Random Forest, as a representative ensemble algorithm, employs a strategy of constructing multiple decision trees in parallel to achieve predictive functionality. It determines the final prediction outcome by using random subsets of training data to build decision trees, conducting majority voting for classification and averaging individual tree predictions for regression problems [32]. Each decision tree is trained based on different sample sets and feature sets, thereby reducing overfitting while enhancing the model's robustness and accuracy. Sample perturbation and feature perturbation introduce diversity among the decision trees. The generalization performance can be improved by increasing the variability among the decision trees [33]. The initial performance of random forests is often relatively poor, which necessitates adding more decision trees to reduce generalization errors and enhance the overall accuracy of the model.  
In the random forest classifier, the output results of the decision trees are determined by majority voting. The mean of the decision trees' output is evaluated using the Sigmoid function [34] with a threshold of 0.5.
# Free Vibration Analysis of Triclinic Nanobeams Based on the Differential Quadrature Method
## 5 Conclusions  
In this paper, the free vibration of size-dependent nanobeams made of triclinic material has been investigated. The equations of motion and the associated boundary conditions have been handled by means of the Hamiltonian principle and the Timoshenko beam theory in the context of a nonlocal strain gradient theory. The GDQM has been applied as numerical tool to solve the problem under different boundary conditions assumptions. First, a convergence study verifies successfully the accuracy of the proposed formulation against the available literature. It follows a systematic investigation aimed at checking for the sensitivity of the structural response to small-scale parameters, geometrical dimensions, or possible variations in thickness. According to the parametric results, it is concluded that, the fundamental frequencies increase as the strain gradient parameter increases and nonlocal parameter decreases for all boundary conditions, except for the first mode (in the only case of clamped-free nanobeams). The structural sensitivity to the small-scale parameters becomes much pronounced for higher modes rather than the lower ones. Moreover, the thickness variation impact depends on the vibrational modes and boundary conditions. The highest frequency of the nanobeam is reached always for clamped-clamped boundary conditions, for the same nonlocal parameters and geometrical assumptions. A higher flexibility of the nanostructures is gradually permitted moving from clamped-simply supports, to simply-simply supports, and clamped-free supports. The last combination of boundary conditions yields the lowest values of the vibrational frequency.  
Conceptualization, B.K., R.D. and F.T.; Formal analysis, M.J., R.D. and F.T.; Investigation, B.K. and F.T.; Validation, B.K., M.J., R.D., and F.T.; Writing-original draft, B.K. and M.J.; Writing-review & editing, R.D. and F.T.  
This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results and Discussions
### Experimental Results and Comparison  
#### 4.3.1 Results Comparison on the Single Diode Model  
The experimental results of the first case are tabulated in Table 2. The symbols Min, Max, Mean, and Std. dev. represent the minimum, maximum, mean, and standard deviation values, respectively, over 50 independent runs. The experimental results of some well-designed methods, including SA [18] IADE [23], ABSO [24], GOTLBO [25], IJAYA [28], differential evolution (DE) [33], biogeography-based optimization algorithm with mutation strategies (BBO-M) [34], grouping-based global harmony search (GGHS) [35], chaotic asexual reproduction optimization (CARO) [40], bird mating optimizer (BMO) [44], and pattern search (PS) [45], are also provided in Table 2 for comparison. It can be seen that, compared with ANS, BLPSO, CSO, CTLA, and LWOA, SOS can acquire the lowest RMSE value (9.8609 \(\times\) 10\({}^{-4}\)). Considering the mean, maximum, and standard deviation values, SOS also consistently performs better than them. In addition, SOS is also highly competitive against other recently proposed methods. It is better than IADE, ABSO, BBO-M, GGHS, GOTLBO, CARO, PS, and SA, except not better than DE, IJAYA, and BMO. Although DE, IJAYA, and BMO beat SOS, the disparities are very small. The best extracted values for the five unknown parameters of single diode model are given in Table 3. We observe that these listed methods almost extract close values for the unknown parameters. Utilizing the extracted parameters in Table 3, we reconstruct the characteristic curves as illustrated in Figure 3. We see that both the output current and power calculated by SOS match the measured values well throughout the whole range of voltage. In addition, we also tabulate the output current data calculated by ANS, BLPSO, CSO, CTLA, LWOA, and SOS in Table 4. An error index the sum of individual absolute error (SIAE) given in Equation (15) is used to evaluate the fitting error. It is obvious that the SIAE value of SOS is the smallest, followed by that of ANS, LWOA, BLPSO, CTLA, and CSO, meaning that SOS achieves more accurate values for the relevant parameters of single diode model. \[\text{SIAE}=\sum_{i}^{N}\left|I_{L_{i,measured}}-I_{L_{i,calculated}}\right| \tag{15}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Single/Double Diode Model**} & \multicolumn{2}{c}{**PV Module Model**} \\ \cline{2-5}  & **Lower Bound** & **Upper Bound** & **Lower Bound** & **Upper Bound** \\ \hline \(I_{\text{ph}}\) (A) & 0 & 1 & 0 & 2 \\ \(I_{\text{sd}}\) (uA) & 0 & 1 & 0 & 50 \\ \(R_{\text{g}}\) (\(\Omega\)) & 0 & 0.5 & 0 & 2 \\ \(R_{\text{sh}}\) (\(\Omega\)) & 0 & 100 & 0 & 2000 \\ \(n\), \(n_{1}\), \(n_{2}\) & 1 & 2 & 1 & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter boundaries of solar cell models.
## 1 Introduction
The equations of flocking correlate with CPSO [38; 39], or more recently with the Extended PSO [28], but no mathematical analysis has yet linked up those two fields. In [40], for our MOSL problem, we proposed an algorithm called LCPSO, inspired by CPSO, which takes into account communication constraints in distance between agents that are representative of underwater systems [41]. Based on simulations, we showed that the limited number of attracting agents allows a reactive tracking of one or multiple _moving_ targets avoiding collisions between agents, with better performances than the CPSO algorithm. In this paper, we intend to demonstrate new results characterizing LCPSO. First, we demonstrate that the positions of the center of gravity and attractor(s) will guide the swarm, and this mechanism is the key to head for the source. Second, we show that a distance parameter, \(r_{eq}\), which is an input of our algorithm, can control the surface of the swarm. Finally, we show that, whatever the initial conditions, the swarm will attain a stable formation, invariant to time and the place of the attractor. We prove these results theoretically under some simplifying hypotheses, whatever the dimension \(D\). We then successively remove the simplifying hypotheses and show that the proposed theoretical results are confirmed by simulations. These flocking behaviors have already been shown in [12; 39]. However, to the best of our knowledge, controlling the surface of the swarm with an optimization algorithm such as PSO and its derivatives is new. Also, the use of specific attractors to head for a goal indirectly and in the same time keeping a strong formation, has never been investigated before, to the best of our knowledge. We organize our article as follows. Section 2 formalizes the MOSL problem while Section 3 describes our models, particularly the PSO algorithm and the LCPSO algorithm to track a mobile target. New contributions begin at Section 4, which provides a mathematical analysis of the behavior of the LCPSO algorithm under some restricting hypotheses. Those hypotheses are removed one by one to show that our algorithm is resilient to a limited communication range. Section 5 provides simulations results of our algorithm in mobile target tracking scenario. Finally, Section 6 concludes this paper and gives some perspectives for future works.
# LoRaWAN Communication Protocols: A Comprehensive Survey under an Energy Efficiency Perspective
## 2 Background Knowledge
### LoRa PHY Layer  
LoRaWAN uses the PHY layer called LoRa, which is based on chirp spreading modulation called chirp spread spectrum (CSS) and is used to enable IoT devices to exchange messages with low energy consumption. In particular, the spreading of the spectrum is achieved by generating a chirp signal that continuously varies in frequency. When frequency increases over time, the signal is called upchirp; otherwise, it is called downchirp. A benefit of LoRa is that timing and frequency offsets between transmitter and receiver are equivalent, which greatly simplifies receiver design. A higher data rate is used to chip the desired data signal and modulate it onto the chirp signal that represents the transmission symbol [25].  
In addition, LoRa PHY layer provides the following transmission parameters [25]:  
Bandwidth (BW): Expresses the difference between the upper and lower frequencies in a frequency band. Typical bandwidth values are 125, 250, and 500 kHz, with 125 kHz being the most commonly used value. It is also identified with the number of chips sent per second (\(R_{c}\)) as  
\[BW=R_{c}(\text{chips}/\sec) \tag{1}\]  
Spreading Factor (_SF_): defines the number of chips contained in each symbol (\(2^{SF}\)).  
Thus, the duration of a symbol can be calculated as:  
\[T_{s}=\frac{2^{SF}}{BW}(\sec) \tag{2}\]  
Figure 2: Star-of-stars LoRaWAN topology.  
In addition, \(SF\) is inextricably related to energy consumption of the LoRa devices. LoRa employs 6 orthogonal \(SF\) from 7 to 12, with \(SF\) 7 being associated with lower sensitivity; thus, higher SNR and shortest transmission range, higher data rate, and vice versa. Note that as the value of \(SF\) increases, the time-on-air (ToA) also increases and, in turn, the energy consumption per transmission increases. \(SF\) can be evaluated as:  
\[SF=log_{2}\frac{R_{c}}{R_{s}} \tag{3}\]  
where, \(R_{s}\) is the rate of the symbol  
\[R_{s}=\frac{BW}{2^{SF}} \tag{4}\]  
Coding Rate (\(CR\)): is defined as the ratio of useful data to all data during transmission and reception and refers to the number of bits used for error detection and control, while taking values of 4/5, 4/6, 4/7, and 4/8. Hence, the data rate (DR) can be obtained as  
\[\text{DR}=\text{SFR}_{s}\;\frac{4}{(4+CR)}\;1000\;\left(\frac{\text{bits}}{ \text{sec}}\right) \tag{5}\]  
Table 2 presents SF values against LoRa parameters at 125 kHz.
However, there are views explaining impacts of altitude on species richness, diversity, and biomass [4]. The effect of altitude on soil organic carbon (SOC) is also an important concern for climatologists and environmentalists, as it has the potential to help mitigate atmospheric carbon emissions. Furthermore, increase in species may have a positive impact on SOC and varies significantly with altitude and disturbance regime [31]. Different natural and anthropogenic activities in the forest floor promote SOC accumulation by exposing it to higher levels of microbial activity [32]. Counting all these points, elevational gradients could become the most powerful \"natural triggers\" for monitoring the ecological and evolutionary responses of biota to environmental changes [33; 34; 35]. The forest cover in Pakistan is less than 6%; however, due to rough terrain, diverse climatic condition, and edaphic variables, it is a hub having about 6000 known vascular plant species [36]. Both conifers and broadleaved trees are the major components of these forests distributed throughout the country [37]. _Monotheca buxifolia_, a broadleaved evergreen tree species, is one of the highly exploited tree species in the studied area, due to its valuable services to the local community and climate change as well [38; 39]. These forests can store a significant amount of carbon and can play a visible role to moderate the warming temperature by altering the carbon cycling of the ecosystem [40]. In Pakistan, few studies have been carried out on the carbon sequestration capacity of broadleaved tree species, including _Olea ferruginea_ by [PERSON] et al. [37], [PERSON] et al. [41], and Oak by [PERSON] et al. [42]. _M. buxifolia_ forests are under a huge pressure of climate change and human interference [37; 40; 41]. Still, the effect of altitude on species diversity, richness, and carbon sequestration potential remain unexplored. To bridge the gap, the current study was designed (1) to estimate the species richness and diversity in these forests along the altitudinal gradient, (2) to know how much carbon is stored in _Monotheca_ phytocoenosis and in its soil, and (3) to expose the effect of altitudinal gradients on biomass carbon allocation-based stand indices. This first attempt will provide a baseline for forest management in the region, including forest resource utilization and carbon management.
# Affordable Broadband with Software Defined IPv6 Network for Developing Rural Communities
## 4 Affordable Broadband Services with SoDIP6 Network
For node in V: /*record power of active and sleep node on every 2-3 min*/
10. if (node is active):
11. record_active_power(node)*record active power of each switch with average traffic volume passing through it. */
12. else:
13. record idle/sleep mode power of node
14. if no_traffic(node): /* if node is idle then the node is signaled to enter into sleep mode */
15. sleep_node(node)
16. /*(every 2 to 3 min, /*record power of links*/
17. for link in Ed: /*record power of active and sleep switch on every 2-3 min*/
18. if (link is active):
19. record_link_power(link) /*record power of each link*/
20. @10 PM to 6 AM, sleep_all_node() /* sleep the node during the night from 10 PM to 6 AM */
21. @6 AM, wakeup_all_node() /* wakeup nodes if in sleep mode */
```  
**Algorithm 1** Pattern of Energy Consumption Recording of the SoDIP6 Network  
From the different literature [20; 24; 25; 26; 27] and our empirical analysis, it can be seen that SoDIP6 network is more energy efficient, leading to a reduction in organizational OpEX and hence contributes to community and service provider sustainability. Figure 5: Pattern of total energy consumption by links in the legacy IPv4 and the SoDIP6 network. Figure 6: Comparative charts showing the estimated annual energy bill amount by both networks and operational expenditure (OpEX) savings in SoDIP6 network by (**a**) switches, and (**b**) links.
## 3 Results and Discussion
### In-Raw Seed Distribution Uniformity Evaluation  
The sticky belt experiments were conducted using the same design outlined in Table 5, which was also used in the flow uniformity experiments. The results of the sticky belt experiments can be found in Table 7, where we evaluated the variation factor (Vf) and goodness criteria (\(\lambda\)) discussed in previous sections. Based on the experimental results, values of S\({}^{2}\), Vf, \(\mu\), and \(\lambda\) were calculated.  
While the highest Vf value obtained from the sticky belt experiments with sage seeds was 3.31 in experiment 11 (at a forward speed of 2 ms\({}^{-1}\), seed rate of 6.6 kg ha\({}^{-1}\), and seed falling angle of 35\({}^{\circ}\)), the lowest Vf value was 1.53 in experiment 13 (at a forward speed of 2 ms\({}^{-1}\), seed rate of 15 kg ha\({}^{-1}\), and seed falling angle of 1.36\({}^{\circ}\)).  
In all experiments, including those conducted under conditions numbered 11 and 13, the in-row seed distribution uniformity in sage planting exhibited characteristics of a negative binomial distribution based on the Vf values. The obtained \(\lambda\) values also supported this finding indicating that the seeding quality, in terms of in-row seed distribution uniformity, was largely classified as \"insufficient\".  
The highest \(\lambda\) values recorded were 58.00% (medium) from experiment 12 (forward speed of 2 ms\({}^{-1}\), seed rate of 23.4 kg ha\({}^{-1}\) and seed falling angle of 35\({}^{\circ}\)), and experiment 13 (forward speed of 2.0 ms\({}^{-1}\), seed rate of 15 kg ha\({}^{-1}\) and seed falling angle of 1.36\({}^{\circ}\)), the lowest \(\lambda\) value was obtained as 30.33% (insufficient) from experiment 11 (forward speed of 2 ms\({}^{-1}\), seed rate of 6.6 kg ha\({}^{-1}\) and seed falling angle of 35\({}^{\circ}\)).
# Multi-Dimensional Fusion Attention Mechanism with Vim-like Structure for Mobile Network Design
## 2 Background and Related Work
### Attention Mechanisms  
Attention mechanisms have become a cornerstone in modern deep learning, significantly enhancing the performance of neural networks across various tasks by enabling models to focus on relevant parts of the input data. A prominent example is SENet [6], introduced by [PERSON] et al., which recalibrates channel-wise feature responses by explicitly modeling interdependencies between channels. CBAM (convolutional block attention module) [8] combines both channel and spatial attention, sequentially applying channel attention to capture the importance of different feature channels and spatial attention to focus on critical regions in the spatial dimensions of the feature maps. Subsequent works, such as non-local neural networks [39], GAT (graph attention network) [40], and GALA (global attention local aggregation) [41], EMAttention [42], expand upon this concept by incorporating various spatial attention mechanisms or developing sophisticated attention blocks. EAPT [43] introduced deformable attention, which learns the offset of each position within a patch, enabling the acquisition of non-fixed attention information despite segmenting a fixed-sized patch. [PERSON] [44] uses two self-attention mechanisms, \"spatial token\" and \"channel token\", to solve the problem of computational complexity as channel dimensions increase.  
In contrast to methods that rely on computationally expensive and heavy non-local or self-attention blocks, we have designed a more efficient approach to capturing location information and channel interdependencies, thereby enhancing the feature representation of mobile networks. The method proposed in this research employs a \"\(1+3\)\" structure, which consists of a main body and three one-dimensional encoding processes. This approach not only captures detailed spatial and channel-wise dependencies but also maintains computational efficiency. Consequently, it outperforms other lightweight attention mechanisms, such as CBAM [8] and TA (Transform attention) [1].