esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Transformer Inrush Current Analysis: Simulation, Measurement and Effects
## 4 Experimental Test Results  
The inrush current measurement circuit includes: transformers for voltage measurement, Rogowski coils (CWT) for current measurement, equipment for recording and acquiring data from the system (Genesis HV 6600 TRAS HBM Genesis digital recorder, produced by HBM SUA, Waalwijk, The Netherlands), F\({}_{\text{o}}\)--optical fiber. Genesis HV 6600 TRAS HBM Genesis digital recorder allows the acquisition of both analog and digital signals delivered from voltage or current measuring transformers. In the case of the experiment described in this work, the signals to be analyzed were analog. The advantage of performing the measurement with numerical systems completed with data transmission facilities through optical fiber, consists in high dynamic precision and the exclusion of the influence of electromagnetic disturbances existing at the place of execution of the measurements. The measurement diagram is shown in Figure 3.  
In laboratory tests, there is a slight delay between the moment of the command to close the contacts and their actual closing. In this case, this delay must be correlated with the value of the phase angle of the supply voltage.  
Figure 1: Power transformer—EMTP wiring diagram.  
Figure 2: The waveforms of the inrush current on the three phases U, V, W obtained by numerical simulation.  
The results of the experimental testing validate the results of the theoretical study and the simulation. According to the laboratory tests, the maximum value was recorded on the U-phase. The connection current on this phase reaches 3652 A. As shown in Figure 4, this value is recorded at the zero crossing of the supply voltage (\(\gamma=0\)).  
Figure 4: Phase voltage (U_UV) and current(I_U) waveforms.  
Figure 3: Power transformer test diagram.  
The results obtained for phases V and W show lower inrush current values than for phase U. The maximum current on phase V is 1.898 kA and on phase W is 1.703 kA (Figure 5).  
The current value obtained by actual measurement of the inrush current is close to the value obtained by modelling/simulation.  
The difference between the modelled and measured value may be due to residual flux, as this isn't the first connection of the transformer to the mains. Figure 6 shows the voltage waveforms on the three phases U, V, W.  
Figure 5: Current waveforms for the three phases.  
Figure 6: Voltage waveforms for the three phases.  
The method presented can be used to determine the optimum angle of the connection voltage so that the value of the intrush current is minimal.  
Both simulation and measurement of the intrush current allow us to gain an overview of this phenomenon. The value of this current can be greatly increased during operation. To illustrate the effects of intrush current, the following is an analysis of a real situation encountered in operation.
## 3 Information and Communication Technology Solutions for Precision Agriculture
### Data Analysis and Artificial Intelligence
#### 3.2.2 Predictive Models and Decision Support
This solution includes disaster forecasting (e.g., fog, haze, and cold temperatures) and utilizes early warning indicators, including low temperature, sparse sunlight, and diseases induced by unfavorable environmental conditions. The specific system aims to provide a framework for analyzing relationships between the vegetable damage dynamics and the meteorological events. Furthermore, it can also support decision-making and encourage farmers to use greenhouse environment control systems, thus improving the quality of their products [39]. [PERSON] et al. analyze how geospatial big data processing can play a key role in the prevention and the mitigation phases of disaster risk reduction in the agricultural sector. In particular, farm machinery telemetry data, agrometeorological observation data from wireless sensor networks, as well spatial variability and crop status data from satellite imagery were found to be particularly useful not only for the main purposes of PA but also for disaster risk reduction [40].
## 3 Results  
All the collected spectra were imported in MATLAB for the successive data processing. Since two spectra were acquired on each sample, at first, these two replicates were averaged, leading to two sets of 100 profiles each, one corresponding to the MIR results and the other to the NIR signals, which are displayed in Figure 1a,b, respectively. In the same Figure 1 (panels c, and d), the mean MIR and NIR spectra of the two investigated categories (pure Senise or adulterated) are compared: by looking at the profiles in Figure 1c,d it is apparent that the differences between pure Senise and adulterated samples are rather subtle and that the identification of adulterated samples cannot rely on visual inspection of the recorded signals only.  
Prior to the creation of the classification models, samples were divided into a training and a test set by the Duplex algorithm [27], and the splitting was carried out on each class separately. In order to create two representative sets of samples taking into account the variability present in both data blocks, the same procedure as reported in [18] was followed. Briefly, for each category, the MIR and NIR spectra were concatenated row-wise, leading to the two augmented matrices: \(\mathbf{X}_{\textit{Senise}}^{\textit{Aug.}}\) (\(=[\mathbf{X}_{\textit{Senise}}^{\textit{MIR}}\mathbf{X}_{\textit{Senise}}^{\textit{NIR}}]\)) and \(\mathbf{X}_{\textit{Adult.}}^{\textit{Aug.}}\) (\(=[\mathbf{X}_{\textit{Adult}}^{\textit{MIR}}\mathbf{X}_{\textit{Adult}}^{\textit{NIR}}]\)). Afterwards, a Principal component analysis (PCA) model was separately calculated on each of the augmented matrices (after block-scaling) and, in both cases, the first five principal components (PCs) were extracted. Eventually, the Duplex algorithm was run individually on each of the two score matrices (i.e., category-wise) and signals were divided accordingly. Of the 100 investigated samples, 65 (40 pure Senise and 25 adulterated) were selected as the training set, while the remaining 35 (20 pure Senise and 15 adulterated) were left out for the validation of the models (test set).  
At first, classification models were built on each spectroscopic block individually, both to investigate how efficient any of the two infrared regions could be in allowing the identification of the adulteration of the product and to compare the resulting outcomes with those obtained by the multi-block strategies. For the analysis of the individual data blocks, two different classifiers were used: partial least squares discriminant analysis (PLS-DA) [28] and a classification strategy based on the fusion of different pre-treatments through SO-PLS-LDA known as sequential preprocessing through orthogonalization (SPORT); [29].  
Briefly, in the context of classification, SPORT is based on applying different pre-treatments to the spectroscopic matrix of interest, so as to create a multi-block data set, which is then processed by SO-PLS-LDA.
## Materials and Methods
### Oxygen-Scavenging Activity  
In order to evaluate oxygen scavenging activity of GR-SO\({}_{4}\) and GR-PEDOT powder, both powders (0.6 g) were put in 60 mL volume airtight container which was previously filled with ambient air (~20 v% of O\({}_{2}\)). Then, the oxygen content of the internal container was measured using an O\({}_{2}\) Headspace gas analyzer (CheckMate 3, MOCON Inc., Minneapolis, MN, USA) in a time-dependent manner (10, 20, 30, 40, 50 and 60 min).
# 2D Phase-Based RFID Localization for On-Site Landslide Monitoring
## 4 Hamaliere Landslide Monitoring
### Application of the Model to a Real Geometry
#### 4.2.1 Random Localization Error of the Experimental Field  
The previous developments (Equations (14) and (15)) have been applied to the geometry installed in the Harmaliere landslide, as shown in Figure 5. A mapping of the random localization error (related to \(\sigma_{rdm}\), Equation (12)) is shown in Figure 5a. We see that the lowest error is obtained when facing the antennas, which are oriented eastward. The plot is separated in two main areas, discriminated by the 2 cm random localization error value. This value was chosen because it reflects the target precision in our application.
# A Location and Velocity Prediction-Assisted FANET Networking Scheme for Highly Mobile Scenarios
## 4 Network Clustering
### Dynamic Clustering Evaluation
#### 4.3.2 Cluster Maintenance Metric  
To characterize the cluster maintenance, [PERSON] et al. in [25] took both the relative mean speed \(S_{k}\) and distance \(D_{k}\) into account and proposed a novel CH selection index to evaluate the cluster maintenance in three-dimensional (2-D) Euclidean space. In this paper, we extend it to the 3-D Euclidean space. For a node \(n_{k,i}\) belong to the \(k\)-th cluster \(\Phi_{k}\), its cluster maintenance metric \(\xi_{k,i}\) can be expressed as  
\[\xi_{k,i}=S_{k,i}+D_{k,i}, \tag{24}\]  
\[S_{k,i}=\frac{\sum_{j=1}^{N_{k}}\|\mathbf{v}_{k,i}-\mathbf{v}_{k,j}\|}{N_{k}\max\{ \Omega_{k,i}\}} \tag{25}\]  
\[D_{k,i}=\frac{\sum_{j=1}^{N_{k}}\|\mathbf{p}_{k,i}-\mathbf{p}_{k,j}\|}{N_{k}\max\{Z_{k}\}} \tag{26}\]  
Figure 4: The diagram of the time-varying topology, (**a**) variation of a CM, (**b**) variation of a CH.  
where \(\Omega_{k,i}\) denotes the speed difference set among different nodes within the cluster set \(\Phi_{k}\) and it is calculated by  
\[\Omega_{k,i}=\{\|\mathbf{v}_{k,i}-\mathbf{v}_{k,j}\|\|\forall n_{k,j}\in\Phi_{k}. \tag{27}\]  
\(N_{k}\) is the element number of \(\Phi_{k}\). Set \(Z_{k,i}\) consists of all Euclidean distances between CMs of \(\Phi_{k}\) and node \(n_{k,i}\) and it can be calculated as  
\[Z_{k,i}=\{\|\mathbf{p}_{k,i}-\mathbf{p}_{k,j}\|\|\forall n_{k,j}\in\Phi_{k}\}. \tag{28}\]  
Based on Equation (24), we can learn that the value of \(\zeta_{k}\) varies within the range of \([0,2]\). Calculate the stability metric values for those nodes belonging to \(\Phi_{k}\), and take the average value as the cluster maintenance metric, which is expressed as  
\[\zeta_{k}=\sum_{i}\zeta_{k,i}/N_{k}. \tag{29}\]
# Analysis of Motion Characteristics of Bionic Morphing Wing Based on Sarrus Linkages
## 4 Conclusions  
The underwater-aerial transmedia aircraft morphing wing designed in this paper could precisely complete the folding action and compress the space of the overall structure as much as possible. The Sarrus linkage acts as a spatial parallel linkage mechanism; its high strength ensured the stability of the morphing wing during the process of movement. Through the two groups of experiments, it was found that the folding time of the wings is about 0.434 s, the unfolding time is about 0.3445 s, and the time of the design requirements is 0.5 s. The folding and unfolding actions in the experiments have effectively taken less time than the design requirements to complete them. In summary, the morphing wing designed by selecting the kingfisher as the bionic object can be better applied to a specific type of underwater-aerial transmedia aircraft.  
When the Sarrus mechanism has dual stability, it has two equilibrium positions, which are precisely adapted to the two states of the underwater-aerial transmedia aircraft morphing wing. Although a rigid hinge was used in this paper, the steady-state characteristics of the flexible Sarrus mechanism can provide ideas for the design of the morphing wing later. Additionally, the operating range of the underwater-aerial transmedia aircraft designed in this paper was limited to low-altitude subsonic speed. We have not analyzed the wing motion characteristics under other operating conditions in depth, but this is the direction of our subsequent work. For the question about the heavy crosswind load, we consider the underwater-aerial integrated propulsion system to control the tilt of the fuselage to adapt to the wind direction to solve this problem. As for the pitch, roll, and yaw of the aircraft, there is no relevant study in this paper, but it is an important part of our future research.  
Conceptualization, Z.Y. and Y.F.; methodology, X.T. and L.C.; software, Z.Y. and Y.F.; validation, Z.Y., X.T., Y.F. and L.C.; formal analysis, Z.Y. and Y.F.; investigation, Z.Y. and Y.F.; resources, Z.Y.; data curation, Y.F. and L.C.; writing--original draft preparation, Y.F. and L.C.; writing--review and editing, Z.Y. and X.T.; visualization, Y.F.; project administration, Z.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Natural Science Foundation of Changsha City, China (Grant No. 2022 kq2202074) and the Natural Science Foundation of Hunan Province, China (Grant No. 2022 JJ30696).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Not applicable.  
The authors declare no conflict of interest.
## 4 Conclusions  
This study investigated the feasibility of using photonic crystal-based mechanochromic sensors for SHM. To this end, discolorations due to direct tensile deformations were analyzed in the RGB and HSV color spaces. The following conclusions were drawn from this study:  
1. The RGB color of the mechanochromic sensor showed different discolorations depending on the extent of displacement. B showed significant discoloration at \(<\)1 mm displacement, but the discoloration reduced for displacements \(>\) 1 mm. At 1.5-2 mm displacement, the discolorations of R and G were dominant, with R showing the largest discoloration behavior at \(>\)4 mm displacement.
2. HSV also exhibited different behaviors depending on the degree of displacement. In the HSV color space, S exhibited major discoloration at an initial displacement \(<\) 1 mm, which was effective up to 3 mm. At displacements between 1-4 mm, the discoloration of H was major.
3. For displacements \(<\) 1 mm, the discolorations of B and S were the most sensitive and showed the highest fitness for linear relationships, suggesting their suitability for predicting small displacements in concrete. However, owing to the influence of other colors on the small displacement as well as on large displacements, an in-depth study is required to predict the overall displacement.
4. The ViT model, built with six RGB and HSV channels, exhibited a high prediction accuracy of R\({}^{2}\) = 0.997 for the measured displacement. This presents a novel methodology for predicting displacement through the discoloration of the mechanochromic sensor. However, the different discoloration characteristics depending on the angle of incidence of the mechanochromic sensor should be considered in future studies.  
**Author Contributions:** Conceptualization, W.-J.C. and I.Y.; methodology, M.Y.; software, W.-J.C.; validation, Y.-S.Y. and M.Y.; formal analysis, G.-S.R.; investigation, G.-H.A.; resources, Y.-S.Y., G.-S.R., G.-H.A. and J.S.Y.; data curation, I.Y.; writing--original draft preparation, W.-J.C.; writing--review and editing, Y.-S.Y.; visualization, I.Y.; supervision, Y.-S.Y.; project administration, Y.-S.Y.; funding acquisition, Y.-S.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2023-00254093).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions in this study are included in the article, and further inquiries can be directed to the corresponding authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Dual-Stack Single-Radio Communication Architecture (DSSRCA)
### Experimental Evaluation  
This section aims to present practical experiments that make it possible to validate the proposed architecture, including its main ideas and the developed hardware prototype. Different experimental setups were created, in order to separately evaluate the two partitions that compose DSSRCA: Best-effort and reliable communication protocols.  
The experiments were performed using two different types of mobile data collectors: (i) a remotely piloted UAV and (ii) a bicycle. The main reason of using a bicycle equipped with our communication prototype is safety, as long-range experiments-related to the reliable protocol-were performed on a long and busy seaside walkway. On the other hand, the tests of the best-effort communication were performed in an open field using both the UAV and the bicycle.  
The physical layer settings and other hardware characteristics of the experimental setups are presented in Table 1.
## 3 Privacy Preservation of Supply Chain Management Data: New Meta-Heuristic with Ethereum Blockchain
### SCM Privacy Preservation Framework  
SCM is one of the best-known commercial organizations due to its capacity for improving the efficiency of the firm. Supply chains and Ethereum blockchains are coupled to improve the security of supply chain networks. Many methods for ensuring the data privacy preservation and security of the Ethereum network with blockchains were introduced, including a VMI mode system, homomorphic encryption, fully observable supply chain management, PBFT algorithm, and consensus-based collaborative management mechanism. These methods help with cost-effective reconciliation, minimizing dispute settlement, increasing security, reducing dwell time, enhancing transparency, decentralizing data distribution, lowering complexity, increasing throughput, and addressing issues with information sharing and data tracking. The architectural representation of the Ethereum blockchain technology developed for privacy preservation is given in Figure 1.  
Figure 1: Architectural representation of the privacy preservation platform developed in the Ethereum blockchain.  
Owing to these issues, higher credibility and dependability of data sharing and keeping security in the Ethereum blockchain technology, a data privacy preservation model using blockchain has been established. Initially, the desired data are gathered from publicly available online databases. Data restoration and data sanitization are the two stages of secured data transfer in the developed model. The initial manufacturer data are cleaned during the sanitization procedure to guarantee the security of the data during transmission. Here, the originally collected data undergo sanitization. Then, the optimal key is generated with the ABC-ROA algorithm, which follows multi objective functions, such as HF, IP, FR, and DM. Then, the sanitized data progress to the restoration process, where they are stored. The restored data are transferred to the supply chain framework, where the blockchain is adapted to preserve the data during transmission. These overall processes are done in an Ethereum environment using blockchain technology. This technique sends the sanitized data over a single blockchain to prevent unauthorized access and delay in data transmission. The performance of this blockchain-based privacy preservation model is verified through various heuristic algorithms regarding key sensitivity, cost function, Euclidean distance, and harmonic mean.
## 4 Conclusions  
Applying jig fixtures to prevent welding deformation is widely used in industry, but there are currently no standards for how to use jigs, and it is common simply to decide based on personal experience. This study applied the finite element analysis software MSC.MARC to simulate residual stress and deformation in butt welding workpieces on 300 \(\times\) 250 \(\times\) 6 mm SUS304 steel with jig fixtures using gas metal arc welding, so as to determine the best location for applying jig fixtures to prevent deformation. Our findings suggest that applying jigs to these steel plates with a depressed displacement of 0 mm produces the best deformation inhibition results for steel plates after welding. They also show that four jigs spaced 200 mm apart inhibit deformation better than six jigs spaced 100 mm apart. The best deformation inhibition results are achieved not when the jigs are attached closer to the welding bead, but when they are attached at a suitable distance from the welding bead. This research suggests that the optimal location for applied jigs is 100 mm from the welding bead, which is similar to actual welding practice. The total difference between the maximum and minimum displacement after welding in the case of 300 \(\times\) 250 \(\times\) 6 mm SUS304 steel plates is about 1.1 mm. Regardless of whether or not jigs are applied, the equivalent von Mises stress or residual stress is highest at the welding bead and reaches the yield stress of the welding plates. Adding jigs to inhibit deformation significantly increases the equivalent von Mises stress/residual stress. Therefore, we suggest that to lessen the residual stress, heat treatment should be applied to the area around the welding bead or to the whole workpiece.  
This article provides a novel method for analyzing residual stress and deformation in butt welding using the finite element method with a customized subroutine program designed to exactly simulate the actual nonlinear physical phenomenon of this welding process. In the future, we will use this approach to conduct simulation analysis of T-shaped steel plates, a very common form of structural steel used in industry. With the help of custom-designed subroutine programs, the proposed approach can be also extended to analyze multi-layer welding as well as welding with different metals.  
Figure 22: Equivalent von Mises stress 3D image of CASE 7 with and without jigs.  
**Acknowledgments:** The authors acknowledge partial financial support under grant no. MOST 106-2221-E-151-017.  
**Author Contributions:** [PERSON] conceived and designed the simulations and experiments; [PERSON] and [PERSON] performed the simulations and experiments; [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# L-Band Erbium Fiber Laser with Tunable and Narrow Sub-kHz Linewidth Output
## 5 Conclusions  
In this work, an EDF ring laser was proposed and experimentally tested. To accomplish the stable SLM and narrow linewidth characteristics, the cascaded RB feedback injection and unpumped EDF-based SA were added to the ring cavity to produce two EDF laser structures. According to the measured results, the second EDF laser was better than the first. In the second EDF laser configuration, the lasing wavelength passed through the cascaded 2 m EDF-based SA and 100 m SMF-induced RB and then entered the FRM for round-trip propagation. Here, the output power and OSNR of the second EDF laser were between \(-14.17\) and \(1.74\) dBm and \(49.09\) and \(68.71\) dB, respectively, in the wavelength scope from 1558.0 to 1618.0 nm (60 nm wavelength tunable bandwidth). Moreover, all the measured 3 dB Lorentzian linewidths for each output wavelength were 313 Hz. The second fiber laser also achieved a flat power output range from 1568.0 to 1598.0 nm with \(\Delta\)P of 1.1 dB (output power was from 0.64 to 1.74 dBm). In addition, the output instabilities of power and center wavelength over the achievable tuning bandwidth were accomplished within 0.22 dB and 0.024 nm, respectively, through a short-term observation of 40 min. As a result, the designed L-band EDF ring laser achieved the output qualities of narrow sub-kHz linewidth, flat power output range, wide tunability bandwidth, high OSNR, and output stability.  
Conceptualization, C.-H.Y. and C.-W.C.; Investigation, L.-Y.C., Y.-T.L., C.-YL., K.-M.C. and J.-H.C.; Data curation, L.-Y.C., Y.-T.L., C.-YL. and K.-M.C.; Writing--original draft preparation, C.-H.Y. and C.-W.C.; Writing--review and editing, C.-H.Y.; Supervision, C.-W.C. and J.-H.C.; Funding acquisition, C.-H.Y. All authors have read and agreed to the published version of the manuscript. This paper was supported by the National Science and Technology Council, Taiwan, NSTC-112-2221-E-035-059-MY3.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.