esa-sceva/satcom-chunk-collection huggingface dataset:
# Identification of Close Modes on Frequency in Rotating Systems
## 1 Introduction
[14] proposed a new technique for analytically identifying close modes in structures. However, the effect of close modes on the balancing process has been little studied. [PERSON] and [PERSON] mention this issue in rotors in [15], commenting that this presents \"numerous difficulties\" when analyzing the lateral vibration phenomenon in rotors. Although there is currently little information about its effect when using either the influence coefficient or the modal balancing technique due to the modal balancing operating principle, the negative effect would be greater, as it would be necessary to use the modal parameters in resonance from the different modes present in the response diagrams generated (natural frequency, damping ratio, vibration amplitude, and phase angle). During a research stay in the Turbomachinery Management Department at the National Institute for Electricity and Clean Energy (INEEL), one researcher in charge of balancing rotors in the field suggested the presence of close modes as a cause of the high balancing time in very specific equipment. However, the lack of instrumentation to ensure their existence in these problematic rotors, as well as of modal extraction tools that would allow separating the close modes present in the response diagram used for balancing, has prevented quantification during the balancing process. As the number of rotors displaying this behavior was few in comparison to the balancing services they performed (and which were balanced with a considerable increase in runs and time), the economic investment required to solve the problem was not profitable for the company. Therefore, a tool is proposed in this work to identify the presence of close modes in rotating systems. Likewise, the close mode problem in one-degree-of-freedom rotating systems is graphically represented. Finally, a partial result is presented of the study carried out on the vibration signals obtained from two field turbogenerators (units with the same technical characteristics) configured to identify the natural frequencies of the different equipment, with the presence of close modes being found during this process.
# Smart Warehouse Management System: Architecture, Real-Time Implementation and Prototype Design
## 8 Results and Discussion
### Qualitative Metrics
#### 8.2.1 Resilient System  
For any information system architecture to be successful, the reliability and resilience should be its key properties. Our proposed architecture and deployed prototype are reliable and resilient. In the case of a network connectivity problem, the database is maintained at a local server on the systems at each node. When the connectivity is re-established, the stored data are pulled into the main database and respective tables. In the case of a power failure, the power backup is maintained; however, if it is unavailable, the record is maintained manually and then inserted into the database.
## 2 Experimental
### Characterization  
The membrane's hydrophobicity was quantified by measuring the apparent contact angle (CA) value before and after hollow fiber hydrophobization, to determine the hydrophobicity of the fiber surface.  
A Scanning Electron Microscope (SEM) was utilized to observe the morphology and microstructure of the hollow fiber.  
Hollow fibers were characterized by an extrusion method, using a mercury porosimeter to determine the porosity, the average pore size and the distribution of the pore size.  
Pure water permeation was carried out using a home-made device before and after hollow fiber hydrophobization. One end of the hollow fiber was sealed with epoxy resin and then fixed at the outlet of a stainless pipe and covered. Deionized water was fed into the stainless pipe and pressurized by nitrogen at various pressures. The weight of the liquid deionized water penetrating from outside to inside through the hollow fiber was recorded using an electronic balance.  
Liquid entry pressure of water (LEP\({}_{\mathrm{w}}\)), defined as the pressure at which water penetrates through the hydrophobic hollow fiber, used the same device as that used for pure water permeation. The LEP\({}_{\mathrm{w}}\) value is a critical parameter for the hydrophobicity level of the hollow fiber and determines whether it is applicable for the MD process.
## 5 ACTOR Design
### Topology Control  
Providing connectivity for the farther nodes is not a concern when selecting the transmission power in a single-hop setting, but ACTOR is designed for multi-hop networks. A selfish parent could reduce the transmission power and increase its own battery life, leaving its children to find other parents. Another potential problem is that the abrupt changes in the topology may trigger global/local repair mechanisms that incur an overhead of the control packets when the nodes are changing their transmission power.  
ACTOR's topology control mechanism tries to avert excessive changes in the RPL's DODAG. When children acquire a level of certainty in their desired transmission power, they demand the parent keep their transmission power at least as high. In other words, nodes send a packet to their parent asking for a minimum transmission power. As illustrated in Figure 4, the parent node can choose a low transmission power for its own data packets, but it commits to higher levels to support its children. Topology control not only avoids the RPL's repair mechanism but also keeps the children connected.
# A Transfer Learning Method for Pneumonia Classification and Visualization
## 4 Proposal
### Data Augmentation and Hyperparameter Tuning  
Data augmentation was performed as a regularization mechanism to avoid immediate overfitting. Operations performed at real-time over the training set are as follow:  
* Horizontal flipping
* Zoom range of \(\pm 10\%\)
* Random rotation of \(\pm 0.1\) degrees.  
After several experiments, we found that almost no rotation gave better results. Even though random rotation is almost inexistent, the samples with these operations are not the same as the ones that are not rotated. We tried other data augmentation techniques such as horizontal and vertical shift with \(\pm 10\%\), and a change of brightness of \(\pm 10\%\) without finding any improvement.  
Moreover, apart from preprocessing the images, hyperparameters represented a vital part of the training. The best model for the classification of the Pneumonia dataset was obtained after multiple configurations. The hyperparameters of the final model used for this work are shown in Table 2.  
As indicated in Table 2, the optimization target function also known as cost function or loss, used in this CNN was Binary Cross-Entropy, which is defined as follows:  
\[H(p,q)=-\sum_{i}p_{i}\log\mathrm{q_{i}}=-y\log\hat{y}-(1-y)\log(1-\hat{y}). \tag{7}\]  
If we apply the weights matrix of CSL, we will have:  
\[H(p,q)=-5y\log\hat{y}-0.5(1-y)\log(1-\hat{y}). \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cost Function** & **Learning Rate (Lr)** & **Optimizer** & **No. Epochs** & **Batch Size** & **Lr Decay** \\ \hline \multirow{2}{*}{Binary cross entropy} & \multirow{2}{*}{\(1\times 10^{-3}\)} & Adam & \multirow{2}{*}{100} & \multirow{2}{*}{32} & 10 times after a \\  & & & \(\beta_{1}=0.9\) & & & plateau \\ \cline{3-5}  & & & & & plateau \\ \hline \hline \end{tabular}
\end{table}
Table 2: Network hyperparameters.  
Figure 8: Exit flow blocks modified in the proposed model.  
As shown in Equation (4), we used CSL in order to avoid the bias prediction of the network when training the model. In other words, we optimize the cost function giving preference to the NORMAL class instead of PNEUMONIA because of the nature of the dataset, in which healthy patients are minority.
## 5 Results
### Evaluation of the modeling results
#### 5.3.3 Comparison with MODIS GPP product  
The final evaluation of the modelling results was done using a pixel-by-pixel comparison between the growing season GPP modelled in this study and the MODIS GPP product for the growing season 2004. A scatter plot between these both data sets is shown in Figure 5. The figure proofs strong relationship between the both GPP products, even though individual pixels show relatively high scattering in the plot, particularly at high values of GPP. The reason for the scattering may be a number of distinct artefacts within the MODIS GPP product, which cause a mismatch between the MODIS GPP and the GPP from this study. The global approach used for construction of the MODIS GPP cannot model the conditions of specific regions in details. The MODIS approach uses global  
Figure 4: Relationship between the growing seasons GPP modelled in this study and ground-based GPP. Points represent the observed GPP collected at the 14 test sites across the study area.  
climate data with a spatial resolution of \(1^{\circ}\times 1^{\circ}\) to parameterise the growing conditions. All other input variables are considered as biome-specific constants, which ignore local variations in environmental factors and vegetation conditions [37].  
A visual analysis of the scatter plot in Figure 5 proved that the relationship between the GPP modelled in this study and the MODIS GPP deviated from the 1:1 line. As a consequence, the model presented in this study underestimates lower values in the MODIS data set and overestimates higher values of MODIS GPP. Our model has a wider value range than the MODIS model. This is also reflected in descriptive statistics calculated for the data sets. The MODIS GPP ranged from 87.07 to 474.3 g C/m\({}^{2}\) with a mean value of 201.4 and a standard deviation of 38.1 g C/m\({}^{2}\), while the GPP from this study varied between 43.86 and 494.1 g C/m\({}^{2}\) with a mean value of 220.4 g C/m\({}^{2}\) and a standard deviation of 55.5 g C/m\({}^{2}\). With regard to the high consistency between the GPP predicted by our model and the ground-based GPP (Section 5.3.2 and Figure 4), the difference between the mean values may suggest that the MODIS algorithm appears to slightly underestimate the growing season GPP in the grassland biome of Central Asia. This suggestion agrees with the results reported by [64] who also found an underestimation of GPP in the grassland biome in the semi-arid Sahel by the MODIS algorithm.
# Improving Energy Efficiency in LoRaWAN Networks with Multiple Gateways
## 6 Performance Evaluation
### Simulation Results
#### 6.2.3 Impact of Idle Current  
With the technological evolution in the transceiver industry, we expect that we can achieve better energy efficiency and a longer lifetime for nodes by decreasing the idle current. We study the impact of reducing the idle current \(I_{id}\) of a LoRa transceiver on the energy efficiency when the number of nodes is 4000 and using \(\mathcal{T}_{1}\) as the TP set for power control.  
Table 13 represents the percentage of energy efficiency improvement when we reduce the idle current. The results show that we achieve a better network efficiency by reducing the idle current. When we reduce the idle current by 16 times, we improve the energy efficiency by 11.6%.
# Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Robot** & **Ground Vehicles** & **Brachiating Robots** & **Unmanned Aerial Vehicles** \\ \hline
**Payload** & Low & Medium & High \\
**Restriction** & Ground & Crossing & Chilean flight \\
**Restriction** & accessibility. & obstacles. & regulation. \\ \hline
**Autonomy** & High & Medium & Low \\ \hline
**Maintenance** & Yes & Temporary & No \\
**work** & & repairs. & \\ \hline
**Development** & Very high & High & Low \\
**cost** & & & \\ \hline
**Industrial** & LineMaster [28] & LineROVer [31] & AIBOT-6 [34] \\
**prototypes** & Elevator IV [29] & Expliner [32] & UAV-Borne [35] \\  & ROBTET [30] & LineScout [33] & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of three main robotic platforms used to perform inspection work in transmission lines. this issue using texturing filtering and perspective correction algorithm. In addition, texturing filtering can simultaneously remove the background noise of power lines as well as generate edge maps. The system applies Hough transform to edge maps to detect straight lines in images that can be related to power-lines. Finally, the system determines power-lines by taking advantage of the regular characteristics of power lines. Additionally, the system uses the prior information of platform position in order to reduce the computational cost and to improve the power line detection. The second stage determines the distance to wires and the robot position using Bayesian methods. The system merges sensor information (IMU and GNSS) with visual information provided by our system using Maximum Likelihood Estimation. This information would allow the equidistant flight over the transmission lines.
# Rockfall Modelling in Forested Areas: The Role of Digital Terrain Model Grid Cell Size
###### Abstract  
This article examines how digital terrain model (DTM) grid cell size influences rockfall modelling using a probabilistic process-based model, Rockyfor3D, while taking into account the effect of forest on rockfall propagation and runout area. Two rockfall sites in the Trenta valley, NW Slovenia, were chosen as a case study. The analysis included DTM square grid cell sizes of 1, 2, 5, and 10 m, which were extracted from LiDAR data. In the paper, we compared results of rockfall propagation and runout areas, maximum kinetic energy, and maximum passing height between different grid cell sizes and forest/no forest scenario, namely by using goodness-of-fit indices (average index, success index, distance to the perfect classification, true skill statistics). The results show that the accuracy of the modelled shape of rockfall propagation and runout area decreases with larger DTM grid cell sizes. The forest has the important effect of reducing the rockfall propagation only at DTM1 and DTM2 and only if the distance between the source area and forest is large enough. Higher deviations of the maximum kinetic energy are present at DTMs with larger grid cell size, while differences are smaller at more DTMs with smaller grid cell sizes. Maximum passing height varies the most at DTM1 in the forest scenario, while at other DTMs, it does not experience larger deviations in the two scenarios.  
rockfall; hazard; modelling; DTM; LiDAR; forest; protective effect; grid cell size +
Footnote †: journal: Applied Sciences
## 1 Introduction
In this paper, we propose a novel incentive mechanism and Proof-of-Active-Participation (PoAP) consensus mechanism tailored for blockchain online social media, named NexoNet, which endeavors to explore the application of blockchain technology in OSM from both technical and economic perspectives. The NexoNet quantitatively assesses and evaluates users' level of participation by assigning a participation value (PV) to users based on their actions, such as content creation, voting, and engagement. This PV is used in the multiple incentive mechanism to distribute the value generated by users. Moreover, it incorporates the PoAP blockchain consensus mechanism to ensure the security and efficiency of the system. By deeply integrating user actions with the underlying mechanisms of the blockchain system, it aims to create a user-centric blockchain social media ecosystem. The distinguishable contributions of this paper are as follows:  
* We propose a user-centric multiple incentive mechanism that integrates rewards for actively engaged users in social media into the blockchain incentive mechanism to enhance user creativity and ensure the quality of content on the social media platform. * We propose a novel consensus mechanism, PoAP, based on the operational characteristics of BOSM and the requirements for efficiency and security. * Based on the multiple incentive mechanism and PoAP, we designed a brand new BOSM system, NexoNet, and conducted a comprehensive analysis on the system model and its operational processes. The rest of the paper is organized as follows. Section 2 introduces related work on blockchain, social media, and blockchain incentive mechanisms. Section 3 outlines the design goals of the NexoNet. In Section 4, we describe the incentive mechanism based on participation assessment algorithm, and Section 5 details the proposed PoAP consensus mechanism. A theoretical and security analysis of the NexoNet is provided in Section 6. Section 7 reports on the performance evaluation results of NexoNet, and Section 8 summarizes and provides an outlook on the research of this paper.
## 3 Results and Discussion
### SCB Test  
#### 3.2.1 Effect of Basalt Fiber on the SCB Test Result  
SCB test results and standard deviation are shown in Figures 15 and 16. It can be concluded from Figure 15 that the \(G_{\text{f}}\) value increased after adding basalt fibers. Therefore, basalt fiber can well enhance the cracking resistance of asphalt mixtures. It can be inferred from Figure 16 that the basalt fiber could help decrease the crack propagation rate of asphalt mixtures because the FI value increase after adding basalt fibers. This test result is in consistence with the IDEAL test results and it help verify the fact that basalt fiber could help enhance the crack resistance of asphalt mixture. Overall, the results show that basalt fiber could well delaying the crack forming and propagation rate of different kinds of asphalt mixtures. The bridge effect formed by the basalt fibers in the micro cracks and the excellent mechanical properties of basalt fiber help to contribute to the enhancement effect on the crack resistance of asphalt mixtures.  
Figure 14: Effect of asphalt type on \(CT_{\text{index}}\).  
Figure 15: Effect of basalt fiber on \(G_{\text{f}}\).
## 6 Results and Discussion
### Analysis of Throughput, Trust, and Security  
The analysis of trust, security, and throughput using the BEA-SSA scheme compared with GWO [26], MOFPL [22], PRO, SSA [36], BES [37], ROA, HGS, SSO, RCSO [21], and FCR [20] is shown in Figures 11-13. Here, evaluation is performed for 100 and 200 nodes. Throughput is a measure of how many units of information a system can process in a given amount of time. Throughput is usually measured in bits per second (bit/s or bps), and sometimes in data packets per second (p/s or pps) or data packets per time slot. The throughput has to be high as it plays a chief role in transferring data. In Figure 11a, the throughput is high at 750 th rounds for 100 nodes, while for 200 nodes the throughput is high at 1000-1250 th rounds. In Figure 12, the trust factor is high at the initial rounds, but as the rounds increase, trust values lessen. However, when distinguished over GWO [26], MOFPL [22], PRO, SSA [36], BES [37], ROA, HGS, SSO, RCSO [21], and FCR [20], the BEA-SSA model poses high trust. Likewise, the security in terms of risk is lesser for the BEA-SSA method, while, GWO [26], MOFPL [22], PRO, SSA [36], BES [37], ROA, HGS, SSO, RCSO [21] and FCR [20] shows high-security risk. Chooses the best CH in WSN by predicting energy level using an improved Deep Convolutional Neural Network (DCNN). The BEA-SSA is used to choose the WSN's top CH. The Bald Eagle Assisted SSA (BEA-SSA) model has high RSSI, PDR, and residual energy. These improvements are attributable to better energy evaluation and prediction using DCNN and the installed BEA-SSA-based CHS. Using an improved Deep Convolutional Neural Network (DCNN), which is used to choose the best CH in WSN, predicts energy level. In the WSN, the best CH is chosen using the BEA-SSA. The Bald Eagle Assisted SSA (BEA-SSA) model has high RSSI, PDR, and remaining energy. These improvements are the result of the BEA-SSA-based CHS being implemented, as well as the improved energy evaluation and prediction using DCNN. This promises the superior performance of the BEA-SSA approach.  
Figure 10: Convergence of BEA-SSA approach compared with the other models [20; 21; 22; 26] for (**a**) 100 and (**b**) 200 nodes.
## 5 ADR Results and Discussion  
The DLIT images introduced in Chapter 3--DLIT Trials, were used for validating the proposed two-stage method. In Figures 18 and 19, the DLIT images of thin film Test Sample 1 and Test Sample 2 with and without wire breaks, which are shown previously in Figures 7  
firmation checks. After processing, weak wires such as the bottom two wires shown in Figure 19a, which can barely be seen with naked eyes, are detected and highlighted to avoid being mistakenly classified as breaks. Additionally, the detection results are automatically labelled as image titles. The results can also be summarized in batches to assist operators' post inspection.  
In Figure 20, the DLIT images of thin film Test Sample 3 and Test Sample 4 with partial wire breaks, which were previously shown in Figure 10, were processed. It is clear that the ROI contains hot spots which are correctly highlighted in red color, whereas normal wires are labelled in green color.  
The ADR results presented in figures from Figure 19 to Figure 20 show that the developed ADR algorithm is able to successfully detect the ROI of intact wires, detect wires with hot spots, and detect complete wire breaks. Detected intact wires' ROIs are highlighted with green frames, whereas wires with hot spots are highlighted with red frames. Number of complete wire breaks can be calculated by subtracting number of wires detected from total number of wires. The entire process is automatically completed by the processing computer with a pre-defined coarse ROI arrangement of wires, and the detection results can be incorporated into a separate reporting system to generate batch processing results for human operators, or with an alarm system which notifies operators when wire breaks or hot spots are detected.  
The ADR algorithm, written in Python, runs 0.7 s processing time per image, on a laptop with 16 GB RAM and an Intel i7 processor. The ADR can be further optimized using compiled programming languages such as C/C++ to reduce processing time. The short processing time required means that the ADR is suitable to operate either as an in-line inspection tool to be incorporated into the DLIT system, or as a post-analysis tool to assist post-inspection of the fabricated films.  
The current version of ADR is semi-automatic as it still requires the operators to input a coarse ROI arrangement of wires. To be able to achieve full-automatic ADR, additional experiment data need to be collected to enable edge detection and image object segmentation using Deep Learning methods to detect the coarse ROI as input to the proposed ADR. Simulation work has been undertaken to generate bespoke images to be used as training datasets.