esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Dynamic Cluster-Head Selection and Clustering for Massive IoT Access in 5G Networks
## 4 Numerical Results
### Parameters
In Figure 7, there is no significant reduction in the throughput of the K-means algorithm. With the increase in the number of devices greater than 10000, the superiority of the scheme emerges  
Figure 6: Average transmitted data amount per unit RB. Figure 7: The throughput of the base station. gradually. As shown in Figure 3, under the condition of same IoT device number, with a decrease of the proportion, the less the number of groups, the smaller the collision probability and the lower the average number of using RB in unit time. So, the access success probability and average transmitted data amount per unit RB are optimal at a ratio of 1:30 and the throughput performance is further optimized. The simulation results show that the K-means algorithm can indeed improve the efficiency and throughput performance of the system. Compared with the optimal scale K-means algorithm, in our scheme, when the number of devices is between 10,000 and 22,000, we can observe that the collision probability tends to be stable and the average number of RBs used per unit of time increases gradually with the rise in device number. Also, it indicates that the system access capacity and resource utilization are not saturated. Therefore, the system can keep access success probability close to 100% and the average volume of data transmitted per unit increases steadily. As a result, the throughput increases linearly, which is different from the way that the K-means algorithm uses to alleviate the throughput decline. With the further increase in the number of devices greater than 22,000, the collision probability begins to increase and the average number of using RB per unit time is still growing. So the access success probability begins to decline, and the increase in throughput also tends to stabilize. When the number of devices achieves 30,000, the access success probability is still close to 90%, and the performance of the base station is up to 345 kbits. Besides, the average amount of data carried by unit RB tends to stabilize at 70 bits/RB. The two metrics for the K-means algorithm improved by adaptive clustering is 3.5 times that of the optimal K-means algorithm. Also, the energy efficiency of the system has grown significantly. With the increase in the number of devices, the performance degradation of random access and data transmission leads to deterioration of system performance.
## 3 Proposal
### Realistic Simulations vs. Surrogate Models  
We initially tested our formation algorithm in a 2D environment using E-Puck2 robots [24] and also compared it with other approaches. We then proposed an extension of the algorithm to deal with 3D formations using UAVs [7]. The inherent complexity of this problem required the use of a meta-heuristic, e.g., our HEA, to successfully calculate the optimal parameters of the formation algorithm. As aforementioned, evaluating each configuration required costly simulations using detailed dynamics. Although our HEA successfully optimised the parameters of the DFA3, we observed that the whole process was taking too long for large swarms (720 h for 30 runs optimising a swarm of 10 UAVs), limiting the number of UAVs we were able to use. Therefore, in this article, we study the use of surrogate models [25] to speed up the evaluation of the formation parameters, allowing not only having more robots in the swarm (we plan to reach 30 UAVs), but also allowing more accurate optimisations by increasing the number of evaluations and improving the optimisation algorithm's solutions.
## 4 Conclusions  
Our investigation demonstrated that by replacing the solvents used in SM (the adapted AOAC method developed by [PERSON] et al.), hexane:acetone:ethanol:toluene (10:7:6:7, \(v/v/v\)), with a mixture of methanol:acetone (1:1, \(v/v\)), the use of the toxic solvent n-hexane was reduced and toluene was completely eliminated. This new solvent mixture contributed to a 52% higher extraction of lutein from the hens' feed mixture. It was also found that using the modified standard method (MSM) and the application of direct ultrasound (MSM-UAE) did not lead to a higher concentration of extracted lutein, likely due to the shorter waiting time following ultrasound treatment. The volume of solvents used can be further reduced, but the volumes of each component in the solvent mixture must be reduced by the same percentage, and the ratio of total volume of solvent to sample used must be 40:1. The MSM was successfully used for determination of lutein concentration in hens' feed mixture samples. The concentration of lutein in food is influenced by numerous factors, such as cultivar, soil quality, light exposure, fertilizer use, etc. By obtaining lutein concentrations in food samples that are consistent with the literature, our investigation confirmed that the MSM is suitable for determining lutein concentrations in food samples. Although solvent extraction is somewhat outdated, it is still successfully used for the extraction of lutein from hens' feed mixtures and food samples, especially if the number of samples to be analyzed is relatively small. The simplicity of the method and the reduced amount of toxic solvents make this method readily available. Moreover, the potential for further investigation of the influence of ultrasound and modification of solvent composition on improving lutein extraction from feed and food samples offer opportunities to develop methods that are effective but simple.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15052360/s1](https://www.mdpi.com/article/10.3390/app15052360/s1)]([https://www.mdpi.com/article/10.3390/app15052360/s1](https://www.mdpi.com/article/10.3390/app15052360/s1)), Table S1: Composition of standard hens feed mixture.  
Conceptualization, funding acquisition, supervision, writing, resource supervision, O.G., Z.K., M.K. and G.K.; writing (review and revision), O.G.; methodology, visualization, O.G.; analysis and data curation, formal analysis, investigation, validation, writing (original draft), O.G. and D.P. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the European Structural and Investment Fund's grant for the Croatian National Scientific Center of Excellence for Personalized Health Care (grant #KK.01.1.1.01.0010) and by the Ministry of Science and Education of the Republic of Croatia.  
Not applicable.  
Not applicable, as there are no humans involved in this study.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 5 Analysis of the Results  
### Three VP Elements Related to Customer Base Growth  
A straightforward interpretation of the VP enhancement framework shown in Figure 4 suggests that there is a close interrelation between four core VP elements (shown in red boxes in Figure 4): _Stakeholder value propositions_, _Foreign market entry_, _Value created_ and _Customer base_. Our claim about a closer interrelationship between these four core elements is based on the fact that each of these elements is related to the other three. _Customer base_ appears as a natural dependent variable associated with the context of early-stage growth-oriented companies. Our framework suggests that a company's customer base is related to the efficiency of the company's value creation processes (i.e., its ability to access, combine and align resources provided by the key actors engaged in their business ecosystem; see assertions associated with _Value created_ [Topic 1] in Appendix A), its foreign market entry strategy (i.e., its strategic plan to penetrate global market locations by transforming its local offers into global ones; see assertions associated with _Foreign market entry_ [Topic 3] in Appendix A); and the attractiveness and alignment of its VPs in relation to key members of their business ecosystem (i.e., its ability to shape a VP portfolio in alignment with its scaling objectives; see assertions associated with _Stakeholder value propositions_ [Topic 2] in Appendix A). From a logical point of view, the link between Topic 1 (_Value created_) and Topic 2 (_Stakeholder value propositions_) could be considered as a core value creation axis that could be further enhanced by _Foreign market entry_ (Topic 3) and _Cross-border business activities_ (Topic 6) to enable _Customer base_ growth (Topic 4).  
These findings are in line with some of the key insights highlighted in our literature review--the multiple-stakeholder perspective on VPs and business growth. The main value of our suggested framework (Figure 4) is in: (i) the specificity of the activities it incorporates; (ii) the way it structures the actionable insights around specific topics (VP elements); and (iii) its explicit identification of the logical relation between the four core VP elements in the context of early-stage growth-oriented companies.
## 5 Practical Implementation and Application  
Our results highlight the importance of a design feature analysis in improving the printability of 3D printed food products. In comparison with previous studies, which primarily focused on material properties and process parameters, our approach extends the application of the Design for Additive Manufacturing (DfAM) principles to optimize geometric features specifically for food printing prior to the manufacturing stage. For instance, as mentioned in the literature review section earlier, many studies have emphasized optimizing extrusion parameters to enhance the print quality, while our work complements these findings by showing that geometric modifications can further improve the structural integrity and print accuracy. This integration of design-centric and process-centric methodologies represents a novel contribution to the field.  
Moreover, the managerial implications of our study are significant. Food manufacturing managers can leverage our methodology to enhance the product quality by ensuring that designs are better suited for the 3D printing process. This can reduce trial-and-error in prototyping, minimize material waste, and improve production efficiency. By optimizing the design process, companies will be able to accelerate their production timelines and achieve more consistent results that ultimately lead to operational cost reductions. Moreover, as the 3D food printing industry grows, our methodology offers a scalable solution for companies aiming to enter the market with innovative, customized food products. Practically, our method is implementable with existing 3D food printer systems, especially the extrusion-based techniques, as it usually requires support structures and is also intended mostly for fast consumption. This makes our presented method a cost-effective solution for food manufacturers who are looking to enhance their current 3D printing operations. The value to the business community lies in the ability to streamline production processes, reduce material waste, and enhance product quality, offering food manufacturers a competitive advantage in an emerging market.
# A Novel Spatiotemporal Analysis Framework for Air Pollution Episode Association in Puli, Taiwan
## 3 Proposed Methods  
### Framework Architecture  
Figure 1 shows the framework architecture of the proposed methods. The innovations in our methods rely on the use of video processing techniques that have never been applied to air-pollution analytics. We discover there is a great analogy between the episodes of PM\({}_{2.5}\) activities and the shots of video scenes. The data acquisition and conversion method obtains the long-term air-quality concentration time series, which is then converted to a 10-category air-quality alert time series for episode analysis. The time series is actually a  
video when considering the air-quality alert category as pixel intensity level. The cycle of PM\({}_{2.5}\) episodes--pollution accumulation, transportation, dispersion, and diminishing--can be considered as various shots in the video. We developed four video shot detection algorithms for comparing the quality of the segmented episode shots. In a particular region, some salient meteorological phenomena and anthropogenic activities repeatedly happen. These patterns, if correctly extracted and grouped according to their similarity, would reflect the reasons that cause the major air pollution episodes that frequently appear in the monitored region. We first transform the shots into corresponding GEIs for efficient processing. Three GEI clustering algorithms are proposed to partition the GEIs into episode clusters. These clusters disclose interesting pollution patterns that are implicitly related to local climate and economic activities.
## 4 Experimental Methodology and Measurement Results  
Through extensive measurements, this section demonstrates two main advantages of the proposed architecture: (1) BISDoF links can deliver good quality data, which meets the 3 GPP error vector magnitude (EVM) requirement for 256-QAM (3.5%) [21]; (2) the BISDoF-based network guarantees the frequency synchronism between RRUs without an extra reference clock signal provided.
## 4 Results  
The proposed method is evaluated both qualitatively and quantitatively using computer-simulated and experimentally obtained wrapped-phase images. To assess its effectiveness, we train other deep learning-based networks, including DBDNet [26], DBDNet2 [27], AD-CNN [28], LGCT-Net [29], and LRDUNet [34], using the same training dataset and loss function as our model WPD-Net. The performance of each method is quantitatively measured using the metrics of MSE, PSNR, and SSIM. Additionally, real noisy phase images are analyzed qualitatively to further demonstrate the feasibility and practical applicability of our approach.
## 3 Proposed Solution to Predict the AoA
### Curvature-Adaptive Kernel for Non-Linear Mobility
We define the confidence of each model \(c_{i}\) as the inverse of its prediction error:  
\[c_{i}=\frac{1}{MSE_{i}+\epsilon},\qquad i\,\epsilon\,\{LR,SVR\} \tag{21}\]  
A weighted average gives the fused AoA prediction:  
\[\hat{\theta}_{fused}(t+\Delta)=\frac{w_{LR}\ \cdot\ c_{LR}\ \cdot\ \hat{\theta}_{LR}(t+\Delta)+w_{SVR}\ \cdot\ c_{SVR}\ \cdot\ \hat{\theta}_{SVR}(t+\Delta)}{w_{LR}\ \cdot\ c_{LR}+w_{SVR}\ \cdot\ c_{SVR}} \tag{22}\]  
where \(w_{LR}\) and \(w_{SVR}\) implement minimum dominance logic:  
* If LR is more accurate (\(MSE_{LR}<MSE_{SVR}\)): \(\odot\qquad w_{LR}=\max(0.65,1-w_{SVR})\) \(\odot\qquad w_{SVR}=1-w_{LR}\)
* If LR is more accurate (\(MSE_{SVR}<MSE_{LR}\)): \(\odot\qquad w_{SVR}=\max(0.65,1-w_{LR})\) \(\odot\qquad w_{LR}=1-w_{SVR}\)  
The main features of the models are as follows: (1) the better model receives \(\geq\) 65% weight, (2) both models contribute in a similar way when their performance is comparable, and (3) smooth transitions between dominance regimes are guaranteed. To further refine confidence scoring, we extend the confidence metric to include SNR:  
\[c_{i}^{\prime}=\frac{\gamma\cdot SNR+(1-\gamma)\cdot\frac{1}{MSE_{i}+\epsilon}} {max\ (confidence)} \tag{23}\]  
The parameter \(\gamma\) balances statistical and physical-layer confidence, with the result normalised to maintain scale consistency, a critical consideration in real-world O-RAN settings, where high SNR may coexist with unstable trajectories (risking false confidence) or noisy AoA histories, despite steady user movement. At each time step, the framework computes both the LR prediction \(\hat{\theta}_{LR}\) and the SVR prediction \(\hat{\theta}_{SVR}\), evaluates their recent prediction mean squared error (MSE) over a sliding validation window, and retrieves local SNRs from PHY measurements or RU power levels if available. The fused estimate \(\hat{\theta}_{fused}\), generated from the dynamically adapted weights (\(c_{LR}\), \(c_{SVR}\)\(w_{LR}\), \(w_{SVR}\)) is then used for steering vector computation, beamforming weight calculation, and constraining the MUSIC angular search region if fallback is needed. This adaptive fusion approach ensures stable and high-quality AoA prediction across all mobility regimes. It avoids abrupt switching between models, provides smooth transitions near decision boundaries, and is fully compatible with the distributed O-RAN architecture: LR and clustering at the O-RU for immediate local prediction and SVR plus fusion at the O-DU or near-real-time RIC. As a result, the DE-ACSW-R system delivers robust, low-latency, and data-driven beamforming even under fast mobility and dense multipath conditions, meeting the requirements for 5G and 6G RAN.
## 3 Characterization Results
### Overall Efficiency  
The efficiencies of the optical elements of the configuration (i.e., focusing mirror, plane grating, and plane mirror) were measured in the 12-62 eV region at the Circular Polarization (CiPo) beamline at ELETTRA Synchrotron [24]. The total efficiency of the instrument, defined as the product of the grating diffraction efficiency and the mirrors reflectivity was measured. As shown in Figure 9, the efficiency of the monochromator for s-polarized light is in the 8-15% (in percentage) range. The efficiency of gratings is between 13-27%. The theoretical efficiency for p-polarized light was obtained by combining simulations with s-polarization experimental results. During the measurements, for each photon energy, the two gratings performed the spectral selection at the subtended angle \(k\) for which the tangential defocusing is corrected. In order to retrieve the efficiency, the optics were positioned on an X-ray reflectometer [25] which was installed at the output of the beamline.  
Given that the grating was used at a variable subtended angle, the loss of efficiency due to the introduction of the additional plane mirror was almost recovered because it was maintained close to the blaze condition of maximum efficiency during the full wavelength scan (as represented in Figure 3).
# Design and Control of an Ultra-Low-Cost Logistic Delivery Fixed-Wing UAV
## 2 Overall UAV Design
### Optional Configuration of UAV Bomb-Dropping Mechanism and Power Unit  
#### 2.8.1 Power Device Selection  
Because of the high efficiency and long life of the brushless motor [54], we choose the brushless motor as the power unit in this study.  
The UAV has a wingspan of 1.5 m and is designed with a maximum takeoff weight of 2 kg and an endurance of 0.2 h. To ensure safe flights and sufficient maneuverability, the power-to-weight ratio of the propulsion system is 1.5. Based on the aircraft's takeoff weight and the power-to-weight ratio, the minimum required thrust is calculated to be 2.4 kg, meaning that each motor needs to provide 1.2 kg of thrust. After comparing the power parameters of various motors and considering their sizes and weights, T-MOTOR AS2317 was chosen. Subsequently, the propeller selection was guided by the motor efficiency table (Table 7). Using a 3 s battery and an APC96 propeller, each motor's maximum thrust is 1416 g, resulting in a maximum power-to-weight ratio of approximately 1.57. At a power-to-weight ratio of 0.8, the throttle setting for cruising is approximately 52%, adequately meeting the cruising requirements. Calculating with a 70% throttle setting at a current of 20 A, the consumed electricity (C) = TI = 20/60 \(\times\) 19.87 = 6.62 Ah is deduced. A 4 s 30 C 5300 mAh battery is used, and considering the motor's peak current of 37 A, a Gens ace 40 A brushless ESC is selected, which converts the DC electricity from the battery into the three-phase AC required by the brushless motor [55]; it alters the motor's power or torque based on pulse signals from the flight controller and powers devices like servos, flight controllers, and remote-control receivers.
# Fault Diagnosis Algorithm of Beam Pumping Unit Based on Transfer Learning and DenseNet Model
## 3 Dynamometer Classification Model Based on the Transfer Learning Method
### Training Results and Discussion
#### 3.3.1 Training Results Based on Transfer Learning
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Fault Type**} & \multicolumn{2}{c}{**Recognition Rate**} \\ \cline{2-3}  & **Fully Connected Layer** & **Global Pooling Layer** \\ \hline plunger up bump & 66.0\% & 91.3\% \\ plunger down bump & 59.1\% & 90.9\% \\ insufficient fluid supply & 94.9\% & 97.5\% \\ double valve loss & 62.9\% & 100.0\% \\ fixed valve loss & 81.8\% & 90.9\% \\ broken rod & 95.5\% & 97.7\% \\ normal operating conditions & 94.9\% & 100.0\% \\ gas effect & 93.5\% & 97.8\% \\ pump leakage & 100.0\% & 99.1\% \\ plunger come out & 90.5\% & 100.0\% \\ swimming valve loss & 76.7\% & 98.6\% \\ sand effect & 71.4\% & 90.5\% \\ heavy oil effect & 87.5\% & 87.5\% \\ wax effect & 80.9\% & 95.6\% \\ pump and eject & 50.0\% & 100.0\% \\ training set accuracy & 99.6\% & 100.0\% \\ test set accuracy & 96.9\% & 96.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Single fault type and overall recognition rate. Figure 8: Test results of working conditions affected by wax. Figure 9: Sand affects the test results of working conditions. By comparing the classification results of Figures 8 and 9, the dense connection model of the full-connection classifier has low classification deviation and low data loss in the  
Figure 11: Test results under normal conditions. Figure 10: Gas affects the test results of working conditions. case of correct classification. However, it fails to accurately extract graphic features in some working conditions, and the classification accuracy needs to be improved. The dense connection model of the global pooling layer classifier did not show classification errors in the classification tests of the four test samples in Figures 8c, 9c, 10c and 11c. However, compared with the dense join model of full join classifier, there is some data deviation. In some working conditions, there is an obvious data loss of 13.6%, which is consistent with the fluctuation of the loss curve in Figure 6b of the training data. It can be seen that the recognition models constructed by the two classifiers have good performance, but under the condition of insufficient samples, the dense connection model of the fully connected classifier has errors, indicating that the degree of feature recognition in the image is still insufficient.
## 3 Routing in Satellite Networks
### Routing Protocols for Satellite Networks
#### 3.2.2 Access Routing  
Access routing is accountable for ensuring communication between the terrestrial gateway and LEO, MEO, and GEO satellites according to various parameters. The parameters that access routing, also known as User Data Link (UDL), consider time in providing communication: the lifetime, delay, and signal robustness of UDL [97; 102; 103].