esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Proposed CC-MPSO Algorithm
```
0:\(\omega,c_{1},c_{2},r_{1},r_{2}\): PSO operational parameters \(\varepsilon\): swarm convergence parameter \(M\): number of swarms \(N\): number of particles in each swarm \(MaxIt\): The maximum number of iterations per swarm run Output: Optimal search paths of Multi-UAVs
1: Initialize \(M\) motion-encoded swarms randomly
2:For\(k=1:M\)
3:For\(i=1:N\)
4: Compute the fitness of particle \(S_{k}x_{i}\) according to \(F(S_{1}x_{i},\ldots S_{M}x_{i})\)
5: Initialize individual optimal position \(S_{k}y(0)\) accordingly
6:End for
7: Initialize the global optimal position \(S_{k}\tilde{y}(0)\) of each swarm
8:End for
9:While\(t<MaxIt\)
10:For\(k=1:M\)
11: Select the \(swarm_{k}\)
12: Compute the normalized sum of distances \(S_{k}d_{norm}(t)\) for \(swarm_{k}\)
13:If\(t>1\) and \(S_{\{1:M\}-\{k\}}\tilde{y}(t-1)\
eq S_{\{1:M\}-\{k\}}\tilde{y}(t-2)\) and \(d_{k}<\varepsilon\)
14:Then reset particles of \(swarm_{k}\) to the random position
15:For\(i=1:N\)
16: Compute fitness of \(S_{k}x_{i}(t)\)
17: Update personal optimal position \(S_{k}y_{i}(t)\) according to Equation (21)
18: Calculate velocity \(S_{k}v_{i}(t+1)\)
19: Calculate position \(S_{k}x_{i}(t+1)\)
20:End for
21: Update global optimal position \(S_{k}\tilde{y}(t)\) according to Equation (22)
22:End for
23:End while
```  
**Algorithm 1**The pseudo-code of CC-MPSO.
## 2 Historical Data  
There is significant interest and scientific value in reconstructing past landscapes. In some disciplines, that means going to the geological record or to ice cores. In remote sensing, it means looking back to the middle of the 20 th century and rescuing data that have since been relegated to reels and tapes or collected by analog systems. Much early remote sensing data were acquired for regional reconnaissance, mapping and charting, not science. Thus, a large scientific hurdle to overcome is that of sparse coverage. We can only work with what was acquired at the time and devise experimental methods to unlock the available data. To add to this challenge, metadata and image quality problems can provide challenges for quantitative use. Still, with patience and innovation, historical data can be an untapped resource for understanding environmental change over recent decades.
## 3 Tests and Results
### Circuit System Test Results  
#### 3.2.1 System Trajectory and Spectral Model Measurements  
The transfer function is an important basis for analyzing the system's performance and also plays a good auxiliary role in designing the system. The PI (proportional integral) transfer function is usually expressed as in Equation (1)  
\[PI(s)=Kp+Ki/s \tag{1}\]  
where \(Kp\) is the proportional gain, \(Ki\) is the integral gain, and s is the complex variable in Laplace transform. This means that the \(PI\) transfer function can provide proportional control and integral control, so as to achieve accurate control of the system; that is, the smaller the \(Kp\), the faster the system response, the larger the \(Ki\), the more stable the system.  
Figure 9: PV power generation. (**a**) 12 V 80 W (830 mm \(\times\) 550 mm) PV panel daily power generation. (**b**) Days 98–130 total daily power generation (The red dashed line in (**b**) indicates the average daily irradiation).  
The design of the PI controller [40] for the microgrid system is carried out via Matlab's SISOTOOL tool, and the root trajectory and frequency domain characteristics of the designed system are shown in Figure 10.  
According to the root trajectory and frequency domain characteristics of the designed system shown in Figure 10, the designed \(PI\) controller transfer function for the microgrid system is obtained in Equation (2):  
\[PI(s)=\frac{0.0035481\ (s+952.2)}{s} \tag{2}\]  
According to Equations (1) and (2), it is clear that \(Kp\) is about 0.003548, and \(Ki\) is about 3.38. From the \(PI\) simulation of the system, it is clear that the proposed microgrid system is very responsive and more stable compared to other conventional systems.
## 3 Results  
### Comparative Findings: Control Zone vs. Post-fire Zone in Mineral Soil  
Descriptive statistics about bulk density, organic and mineral matter are reported in Table 5. Soil bulk density (BD) increases significantly with depth, both in the control zone (\(U\) (26) = 29, \(p\)\(<\) 0.001) and in the post-fire zone (\(t\) (28) = \(-\) 6.25, \(p\)\(<\) 0.001, r = 0.76). BD presents significantly lower levels in the post-fire zone at depths of both 0-5 cm (\(t\) (28) = 2.92, \(p\)\(<\) 0.01, r = 0.48) and 5-10 cm (\(U\) (27) = 44, \(p\)\(<\) 0.01). Soil organic matter (OM) content falls with depth both in the control zone (\(t\) (27) = 4.27, \(p\)\(<\) 0.001, r = 0.64) and in the post-fire zone (\(U\) (28) = 9, \(p\)\(<\) 0.001). Levels are slightly higher in the burned area, at depths of both 0-5 cm (\(U\) (28) = 56.5, \(p\)\(<\) 0.05) and 5-10 cm (\(t\) (27) = \(-\) 2.62, \(p\)\(<\) 0.05, r = 0.45). Soil inorganic matter (IM) content is inversely proportional to the SOM content.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{A_{0}}\) & \(\mathbf{A_{1}}\) & \(\mathbf{A_{2}}\) & \(\mathbf{A_{3}}\) & \(\varepsilon\) \\ \hline
0.514 & \(-\)0.545 & \(-\)0.325 & 0.151 & 0.173 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Linear regression model coefficients described by [PERSON] et al. [9].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Soil Properties** & **Samples** & **Mean** & **SD** & **Median** & **Variance** & **Min** & **Max** \\ \hline \multirow{4}{*}{BD (kg/m\({}^{3}\))} & C (0–5) & 737.76 & 143.55 & 702.89 & 20.61 & 546.29 & 949.77 \\  & C (5–10) & 1043.30 & 257.33 & 972.70 & 66.22 & 649.28 & 1567.90 \\  & F (0–5) & 593.45 & 138.01 & 597.32 & 19.05 & 295.34 & 797.79 \\  & F (5–10) & 851.19 & 87.66 & 869.18 & 7.68 & 689.50 & 1044.47 \\ \hline \multirow{4}{*}{OM (\%)} & C (0–5) & 15.73 & 3.00 & 16.02 & 9.01 & 11.82 & 20.68 \\  & C (5–10) & 11.16 & 2.66 & 11.55 & 7.08 & 7.14 & 17.36 \\  & F (0–5) & 19.83 & 6.28 & 18.88 & 39.44 & 14.10 & 38.50 \\  & F (5–10) & 13.29 & 1.39 & 12.92 & 1.93 & 10.74 & 16.34 \\ \hline \multirow{4}{*}{IM (\%)} & C (0–5) & 84.28 & 3.00 & 83.98 & 9.01 & 79.32 & 88.18 \\  & C (5–10) & 88.84 & 2.66 & 88.45 & 7.08 & 82.64 & 92.86 \\ \cline{1-1}  & F (0–5) & 80.17 & 6.28 & 81.12 & 39.44 & 61.5 & 85.90 \\ \cline{1-1}  & F (5–10) & 86.72 & 1.39 & 87.08 & 1.93 & 83.66 & 89.26 \\ \hline \end{tabular}
\end{table}
Table 5: Descriptive statistics of the mineral soils analyzed. Bulk density (BD), organic matter content (OM) and inorganic matter content (IM). For C (control zone) and F (post-fire zone) at 0–5 cm or 5–10 cm. Mean at 95%; N = 66.
# Analysis of the Most Relevant Factors for Routing in Internet of Space Things Networks
## 5 Conclusions
* Thus, if the node density in an ad hoc IoT network is expected to be high, then a reactive routing approach might be better suited for the routing task than a reactive routing approach. Note that these guidelines can be used as a baseline for designing specific routing strategies for ad hoc IoT networking. For instance, if an ad hoc IoT deployment has significant node density variability (e.g., from low to high node density), then the analysis and discussion presented in this paper can be used as a basis for the design of hybrid routing strategies addressing this issue. In future research, we will explore the design of such hybrid protocols for ad hoc IoT networking. Based on the presented results and the corresponding discussion, we consider that the evaluation and analysis methodology for routing strategies in ad hoc IoT networking proposed in this work, the comparative analysis between proactive and reactive routing performed, and the identification of the corresponding critical factors for the routing task provide a baseline upon which the research community can explore the design and implementation of novel routing strategies for ad hoc IoT networks. Conceptualization, E.G.-L., A.G.-M. and S.V.-R.; Data curation, R.C.-G.; Formal analysis, R.R.-R.; Funding acquisition, A.G.-M.; Investigation, R.C.-G.; Methodology, A.G.-M.; Project administration, J.E.L.-R.; Resources, J.E.L.-R.; Software, E.G.-L.; Supervision, S.V.-R.; Validation, E.G.-L., A.G.-M. and S.V.-R.; Visualization, R.R.-R.; Writing--original draft, E.G.-L.; Writing--review and editing, E.G.-L., [PERSON] and [PERSON]-R. All authors have read and agreed to the published version of the manuscript. This research was funded by \"The Council for Science and Technology of Mexico\" (CONACYT), through the Basic Scientific Research under grant number 2016-01-285276. Not applicable. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to large size of the data to maintain a public repository. The authors declare no conflict of interest.
# Research into Robust Federated Learning Methods Driven by Heterogeneity Awareness
## 4 Experiment
### Practical Application Expansion and Critical Analysis
#### 4.5.2 Methodological Critical Review  
The core breakthrough of the HAD framework lies in its pioneering fusion of feature statistics and manifold structure analysis to construct a multi-scale heterogeneity perception mechanism. Compared with the local optimization idea of FedProx, which relies on a single regularization constraint, HAD describes the essential differences in client distribution through the collaborative calculation of a variance basis measure and manifold divergence. Compared with FedSAM's strategy of introducing gradient disturbance to improve its robustness, HAD's feature center constraint shows a more stable control force in suppressing training fluctuations. This multi-dimensional modeling paradigm effectively solves the problem of the low efficiency of FedMOON's contrastive learning in complex nonlinear distribution scenarios, and it establishes a new methodological benchmark for cross-domain federated learning.  
However, at the application adaptation level, the framework still faces three key challenges. The extra overhead introduced by its manifold projection calculation makes the computational efficiency significantly weaker than FedAvg's minimalist architecture in edge device scenarios. The regularization mechanism of FedProx shows a better toughness when facing a highly opposite sample distribution (such as the extreme difference between normal tissue and a malignant tumor in medical images). In addition, the need for the fine adjustment of heterogeneity weight parameters increases the deployment threshold, while FedSAM only needs to set the gradient disturbance amplitude, which is easier to engineer. These limitations essentially stem from the theoretical complexity of the statistic-manifold dual model, which forms a new balance proposition between lightweightness and robustness.
## 3 Results and Discussion
### Physicochemical Analyses
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multicolumn{4}{c}{**CIELAB Color Space**} \\ \cline{2-5}  & \(L^{\star}\) & \(a^{\star}\) & \(b^{\star}\) & \(\Delta\mathbf{E}\) \\ \hline S\({}_{\text{A}}\) & 35.39 \(\pm\) 0.48a & 11.12 \(\pm\) 0.12c & 43.23 \(\pm\) 0.51b & 10.24 \(\pm\) 0.43b \\ S\({}_{\text{B}}\) & 29.88 \(\pm\) 0.25b & 14.88 \(\pm\) 0.72b & 47.49 \(\pm\) 0.71a & 10.24 \(\pm\) 0.43b \\ X\({}_{\text{A}}\) & 24.38 \(\pm\) 0.52c & 32.91 \(\pm\) 0.25a & 25.32 \(\pm\) 0.48c & 33.06 \(\pm\) 1.76a \\ X\({}_{\text{B}}\) & 34.83 \(\pm\) 0.38a & 11.33 \(\pm\) 0.4c & 49.78 \(\pm\) 0.99a & \\ \hline \hline \end{tabular}  
* Letters in each column indicate statistically significant differences; S\({}_{\text{A}}\): unsterilized dragon’s blood rhizome extract; S\({}_{\text{B}}\): autoclave-sterilized dragon’s blood rhizome extract; X\({}_{\text{A}}\): unsterilized xoconostle fruit extract; X\({}_{\text{B}}\): autoclave-sterilized xoconostle fruit extract; L\({}^{*}\): lightness; \(a^{\star}\): green–red color component: \(b^{*}\): blue–yellow color component; \(\Delta\mathbf{E}\): total color difference between unsterilized and sterilized extracts.  
\end{table} 
Table 2: Changes in the color of non-sterilized or autoclave-sterilized dragon’s blood rhizome and xoconostle fruit extracts.
## 3 Experimental Validation
### Circuit under Test
#### 3.1.1 Sallen-Key Bandpass Filter  
Figure 6 shows the Sallen-Key bandpass filter circuit with a centre frequency of 40 kHz. The square wave signal with a frequency of 10 kHz, duty cycle of 50% and amplitude of 5 V is used as the stimulus of the Sallen-Key bandpass filter circuit. The nominal values of the components in the Sallen-Key bandpass filter circuit are shown in Figure 6. The sensitive components of the Sallen-Key bandpass filter circuit are \(R2\), \(R3\), \(C1\), and C2 [25; 27]. Sensitive components, together with \(R1\) and \(R4\), are used for fault setting and they mainly have 13 fault states, which are represented as \(R1\)\(\downarrow\), \(R1\)\(\uparrow\),\(R2\)\(\downarrow\), \(R2\)\(\uparrow\), \(R3\)\(\downarrow\), \(R3\)\(\uparrow\), \(R4\)\(\downarrow\), \(R4\)\(\uparrow\), \(C1\)\(\downarrow\), \(C1\)\(\uparrow\), \(C2\)\(\downarrow\), and C2\(\uparrow\), and referred to as F0-F12, with F0 indicating that the circuit is not in fault. Table 1 is the specific fault setting in this instance.
###### Abstract  
Solving the vehicle routing problem (VRP) is one of the best-known optimization issues in the TLS (transport, logistic, expedition) branch market. Various variants of the VRP problem have been presented and discussed in the literature for many years. In most cases, batch versions of the problem are considered, wherein the complete data, including customers' geographical distribution, is well known. In real-life situations, the data change dynamically, which influences the decisions made by optimization systems. The article focuses on the aspect of geopositioning updates and their impact on the effectiveness of optimization algorithms. Such updates affect the distance matrix, one of the critical datasets used to optimize the VRP problem. A demonstration version of the optimization system was developed, wherein updates are carried out in integration with both open source routing machine and GPS tracking services. In the case of a dynamically changing list of destinations, continuous and effective updates are required. Firstly, temporary values of the distance matrix based on the correction of the quasi-Euclidean distance were generated. Next, the impact of update progress on the proposed optimization algorithms was investigated. The simulation results were compared with the results obtained \"manually\" by experienced planners. It was found that the upload level of the distance matrix influences the optimization effectiveness in a non-deterministic way. It was concluded that updating data should start from the smallest values in the distance matrix.  
optimization system; vehicle routing problem; distance matrix; geopositioning Article  
[PERSON], [PERSON] Vehicle Routing Optimization System with Smart Geopositioning Updates  
2021  
11, 10933. [[https://doi.org/10.3390/app112210933](https://doi.org/10.3390/app112210933)]([https://doi.org/10.3390/app112210933](https://doi.org/10.3390/app112210933))
# COALA: A Protocol for the Avoidance and Alleviation of Congestion in Wireless Sensor Networks
## 1 Introduction  
The operation of a WSN is interdependently correlated with the existence of data traffic. Network congestion is one of the most serious problems encountered in the management of the data traffic within a WSN. Congestion occurs when current traffic load exceeds available transmission ability at any point in the network. Congestion has an absolutely detrimental impact on WSN performance [1]. More specifically, congestion procures the overflow of node buffers, the degradation of the overall channel quality, and the increase of both loss rates and transmission delays.  
This article proposes a novel protocol that aims, by ruling the routing process, not only to prevent the occurrence of congestion, but also to deter the dispersion of oncoming congestion in WSNs. This protocol performs the discovery of the routing paths that are less likely to be congested, based on the computation of a suitably formulated cost function. This cost function takes into consideration a collection of static and dynamic metrics that are related with congestion.  
The remainder of this article is organized as follows. Section 2 outlines various existing protocols for congestion avoidance and in congestion control WSNs. The description of the proposed congestion protocol takes place in Section 3. In Section 4, the performance evaluation of the proposed protocol is performed through the description and analysis of simulation results. Finally, Section 5 concludes the article.
## 3 Results and Discussion
### Visualization of Lipophilic Compounds via Confocal Imaging  
Microalgal lipophilic compounds were visualized using Nile Red under fluorescence microscopy, as shown in Figure 3. The lipophilic compounds of microalgal cells under BBM control conditions are represented in Figure 3A (1,2,3), while the visualization of cells under abiotic-optimized conditions is shown in Figure 3B (4,5,6). The cells appeared larger, and the accumulation of intracellular carotenoids and other lipids inside the _Monoraphiidium_ sp. cells was found to be higher under optimized conditions (abiotic factors) compared to the control (autotrophic cultivation under BBM medium).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Cultivation Condition** & **Biomass Production (mg\(\cdot\)L\({}^{-1}\))** & **Biomass Production (mg\(\cdot\)L\({}^{-1}\)-d\({}^{-1}\))** & **Doubling** & **Specific Growth rate (mg\(\cdot\)L\({}^{-1}\)-d\({}^{-1}\))** & **Lipid** & **Liloid** & **Chlorophyll** & **Chlorophyll b** & **Categorical** \\
**Direction** & **Coherent** &  
The above findings reinforce our notion that the proportion of light and nutrients has a substantial impact on the accumulation of metabolites in _Monoraphidium_ sp. Generally, carotenoids are classified into two types: primary and secondary carotenoids. Primary carotenoids are photosynthetic carotenoids that are physically and functionally associated with photosynthetic machinery. In contrast, secondary carotenoids have no association with the photosynthetic machinery and are not constrained by stoichiometry with other cellular molecules in terms of content [18]. Nutrient modulation improves carotenoid production for microalgal cells under optimized abiotic conditions, such as light intensity, salinity, and temperature [39]. Hence, the balancing of abiotic factors can lead to the accumulation of substantial quantities of chlorophyll and lipophilic compounds.
## 7 Open Challenges and Future Directions
* **Resource utilization assessment**: considering EFS as a recent development in feature selection techniques, new and sophisticated EFS approaches are speculated to emerge in the coming few years. However, classification models built using such new approaches are often evaluated for their performance in-terms of accuracy whileneglecting their behavior against resource consumption. Such assessments need to be taken into consideration by the research community as the new wave of technological advancements points towards a large-scale deployment of resource constrained IoT devices. Moreover, intelligent cyber-security based solutions are generally known to be resource intensive solutions while incorporating machine learning or deep learning algorithms. Hence, resource efficiency analysis of EFS approaches can add great value towards achieving green intelligent solutions, playing a positive role in saving energy even when deployed over resource adequate devices. * **Application of XAI:** application of XAI techniques, such as LIME, Grad-CAM, and occlusion sensitivity, is suggested for understanding the predictions made by FS/EFS-based classification models. Such analysis may help researchers in further tuning the selection process of suitable features to build lightweight classification models with better predictive power. This additional step may prove vital before large-scale deployments of such models within an IoT environment. In-fact, the application of XAI techniques along with some automation mechanisms can be utilized to re-train existing classification models as an adaptive mechanism to the changing behavior of cyber-attacks, and by that, producing a robust solution for stopping sophisticated cyber-attacks such as zero day attacks.
# Extending the Quality of Secure Service Model to Multi-Hop Networks
## 3 QoSS Applied to a Multi-Hop Network
### Reliability Applied to QoSS  
Applying the definitions from Section 3.1 to the QoS model, a failure is any event where data fail to be transmitted across some portion of a network, securely or otherwise. Any failure will affect the confidentiality, integrity, or availability of the network, both individually and collectively. In terms of QoS, confidentiality and integrity are affected by adversarial actions, whereas availability is affected by natural or non-adversarial events. The QoS model presents a method to quantify security based measures of system reliability, specifically confidentiality, integrity, and availability. As the probability of failure is complimentary to the reliability, then, by extrapolation, the probability of confidentiality and the probability of integrity can both be viewed as measures of reliability in addition to the typical availability metrics.  
Iteratively, each hop in a series may be calculated and then, using typical reliability calculations for a system of components may be multiplied to yield the reliability of the system. To obtain the reliability of the larger system, the probability of interception, decryption, decoding, noise, suppression, and injection may all be multiplied by each other and the full QoS may be calculated in the same manner. Each hop has the similar cumulative effect of reducing the reliability of the network in terms of security because an adversarial listener or disruptor has additional opportunities to infiltrate. A naive approach would simply average the values together, but that overlooks the cumulative effect of multiple hops. The architecture presented here makes the assumption that message packets are reformed and retransmitted at each hop. As such, signal degradation is only a concern on a per-hop basis. Admittedly, this view is overly conservative in that the possibility of a listener obtaining a message at one or more hops may be counted multiple times.
## 4 Simulation Results
### Hidden Node Problem in IEEE 802.15.4
The total transmission success probability is defined as  
\[P\{\text{tx succeeds}\}=\frac{1}{N}\cdot\sum_{i=0}^{N-1}p_{i}. \tag{3}\]  
Few papers have proposed methods to eliminate the hidden node problem [31, 32]. The problem with these proposals is that they require modifications to the standard. We suggest two simple but efficient methods that minimize the effect of the hidden node problem. While these methods do not completely prevent collisions, the collision probability decreases which allows recovery via retransmission procedure. The first method is to decrease the beacon interval. This causes a trade-off between network lifetime and reliability, because energy usage increases with shorter interval as beacons must be received more often [33]. Figure 11: Goodput with different access cycle lengths in ZigBee. Duty cycle and throughput are the same on each case. The second method is to increase the value of the minimum backoff exponent. A large exponent decreases throughput but increases energy-efficiency as collisions are reduced. The trade-offs with the backoff exponent are studied [34], although the hidden node problem is not considered. In the remaining simulations, we set the minimum backoff exponent to 5 which decreases the collision probability between two hidden nodes from 75% to 32%, thus increasing the success probability of a frame transmission with retries from 68% to 99%.