esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Applications
### Angular Displacement and Velocity Sensors
The substrates are _Rogers RO3010_ with \(\varepsilon_{r}=11.2\), \(h=1.27\) mm (microstrip line) and \(h=0.635\) mm (resonator), and tan\(\delta=0.0023\). The line widths \(W\) are 2.06 mm and 1.04 mm. In reference to Figure 9, ELC mean radius \(r_{0}=8.05\) mm, \(w_{1}=6\) mm, \(w_{2}=l_{1}=s=0.2\) mm, and \(w_{3}=0.5\) mm. Figure 22: **(a)** Experimental set-up, and (**b**) notch magnitude and frequency _versus_ the angular displacement. The frequency shift between measured and simulated data is attributed to substrate- and fabrication-related tolerances (\(f_{0}\) is very sensitive to the resonator parameters). From [47]; reprinted with permission IEEE 2014.  
velocities are \(f_{r}=0.998\) Hz and \(50.251\) Hz, respectively, validating the approach. Rotation speeds as high as required can be measured by means of this approach since the carrier signal frequency (within the microwave region of the electromagnetic spectrum) is much higher than the angular velocity of actual rotating systems. Precision can be improved by merely averaging the time between peaks. Other related sensors for measuring angular spatial variables are reported in [48; 49; 50].
## 4 Conclusions  
This study provides a comprehensive comparison of the combined thermal and electrical stresses behavior of three oil-impregnated paper insulation systems: a conventional mineral oil and two natural esters (NE 1204 and NE 1215). Through a series of dielectric strength tests, FTIR spectroscopy, and frequency-domain dielectric spectroscopy (FDS), this work highlights the complex interdependencies between chemical degradation and dielectric performance.  
The first major contribution of this work is the validation of a multimodal diagnostic approach combining FTIR and FDS to monitor early degradation. Strong correlations were observed between chemical oxidation markers, such as the increase in carbonyl (C=O) bands and decrease in hydroxyl (O-H) groups, and key dielectric indicators, including tan \(\delta\) and permittivity. This finding supports the feasibility of nondestructive, predictive monitoring techniques for assessing insulation health.  
Secondly, the study addresses the phenomenon of electrical failures and examines their consequences--a topic that, although previously explored, warrants renewed attention in the context of alternative insulating materials. The results clearly show that electrical breakdown exacerbates both chemical and dielectric deterioration, especially in mineral oil-based systems. The structural damage and oxidation observed in cellulose paper were far more severe after breakdown in mineral oil than in natural ester systems.  
Thirdly, the comparative analysis between NE 1204 and NE 1215 reveals that not all natural esters offer the same level of protection and performance. NE 1215 exhibited superior resistance to oxidation, better retention of dielectric properties, and more effective preservation of cellulose integrity, both during thermal aging and post-breakdown. This suggests that the specific formulation and chemical composition of the ester significantly affect long-term insulation reliability.  
From an application perspective, the result of this study reveals the advantages of using NE 1215 as a high-performance, biodegradable, and less hazardous insulating fluid. Moreover, the combined use of FTIR and FDS opens new avenues for developing monitoring systems capable of detecting early-stage degradation.  
To reinforce and validate these findings, future studies should include degree of polymerization (DPv) assessments, moisture quantification, and investigations under actual transformer operating conditions. These steps will help translate laboratory insights into robust industrial practices for insulation condition assessment and fluid selection in power transformers.  
Y.B.: conceptualization, methodology, validation, formal analysis, investigation, visualization, and writing--original draft; S.O.O.: conceptualization, methodology, validation, formal analysis, investigation, visualization, and writing--review and editing; I.F.: validation, visualization, writing--review and editing, resources, and supervision; M.F.: validation, writing--review and editing. All authors have read and agreed to the published version of the manuscript.
# Performance Evaluation of a Mesh-Topology LoRa Network
## 9 Conclusions  
The current LoRaWAN standard performs well as an LPWAN standard for IoT applications; however, nodes located far from a GW are prone to increased transmission power and data-rate requirements to provide coverage for these nodes. This negatively affects the power consumption of these nodes.  
The proposed LoRaMesh network offers a network topology that can address this shortcoming. The simulation results shown in Section 7 highlight how the proposed network could potentially reduce the power consumption requirements of devices located further from a GW. Furthermore, it highlights the capabilities of such a network to provide adequate network performance in terms of PDR to nodes located further away from a gateway.  
LoRaMesh proved to be a promising alternative to the current industry standard, LoRaWAN, specifically in the smart agriculture and smart city industries.  
The proposed network protocol is not fully optimised and could potentially be improved by implementing a more advanced rule-based approach or reinforcement learning.  
The future challenges that remain are to explore energy-efficient methods to implement routing information dissemination, especially to implement local routing tables to reduce the overhead required by transmitting routing tables to the gateway, and to address dynamic networks, where new nodes can join networks, failed nodes can be detected, and nodes could potentially have a non-static mobility model.  
**Author Contributions:** Methodology, T.G.D.; Investigation, T.G.D.; Writing-- original draft, T.G.D.; Writing--review and editing, M.J.B.; Supervision, M.J.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Performance Analysis
### Transaction Security Analysis
#### 4.2.2 Security of Legitimacy Verification of Notes  
The BTPP scheme has updated the maintenance of the Note Commitments table and the Nullifier table. It utilizes the uniqueness of the stealth address as the invalid identifier for note and binds the stealth address to the corresponding note. When verifying the legitimacy of an input note, it is only necessary to check the correctness of its invalid identifier, which is the stealth address, by verifying whether there is a binding relationship between the invalid identifier and the input note. No one can tamper with the data on the blockchain. Therefore, the BTPP scheme ensures the security of validating the legitimacy of input notes and prevents \"double spend\" issues.
## 4 Coupling Activation of Iron-Based Catalysts under Auxiliary Action
### Piezoelectric Catalytic Activation  
Piezoelectrics are a noncentrosymmetric crystal structure that separates positive and negative charges under the action of external forces, resulting in a corresponding piezopotential [116; 117; 118]. Piezocatalysis refers to the conversion of mechanical energy into chemical energy. When using an iron-based catalyst piezoelectric material or coupled with other photocatalysts, an electric field near the piezoelectric material assists in charge separation [119]. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
Vibration is a very common motion that produces mechanical energy. Compared with commonly used oxidation methods such as Fenton reaction and photoelectric catalysis, piezoelectric catalytic activation is more resource-saving. Even a very small vibration can drive a deformation of nano/micrometer materials to generate a potential [120]. The degradation mechanism of piezoelectric catalysis follows [121]:  
\[\text{(Piezo-materials) + vibration}\rightarrow\text{(Piezo-materials)}\text{(}h^{+}+ \text{e}^{-}\text{)} \tag{15}\]  
\[\text{O}_{2}+\text{e}^{-}\rightarrow\text{-}\text{O}^{2-} \tag{16}\]  
\[h^{+}+\text{OH}^{-}\rightarrow\text{-OH} \tag{17}\]  
\[h^{+}/\text{e}^{-}/\cdot\text{OH}/\cdot\text{O}^{2-}+\text{pollutants} \rightarrow\text{degradation products} \tag{18}\]  
Ultrasound (US) is the most common wave that can generate mechanical energy. PS is converted into \(\cdot\text{SO}_{4}{}^{-}\) under the action of ultrasound; the reaction equation follows [122]:  
\[\text{S}_{2}\text{O}_{8}{}^{2-}+\text{US}\to 2\cdot\text{SO}_{4}{}^{-} \tag{19}\]  
\[\text{H}_{2}\text{O}+\text{US}\rightarrow\text{H}\cdot+\text{-OH} \tag{20}\]  
\[\text{-SO}_{4}{}^{-}+\text{H}_{2}\text{O}+\text{-OH}\rightarrow\text{H}^{+}+ \text{SO}_{4}{}^{2-} \tag{21}\]  
The study of [PERSON] et al. [123] indicated that in the ultrasonic environment, activation persulfate based on foamed zero-valent iron (Fe\({}^{0}\)f) could remove the oxide film on the surface of Fe\({}^{0}\)f in the reaction process. Thus, more Fe\({}^{0}\)f is exposed to the solution to increase the contact area and speeds the reaction. In the persulfate/chlorite Fe\({}^{0}\)f system, a large number of \(\cdot\text{SO}_{4}{}^{-}\), \(\cdot\text{OH}\) and other free radical ions can be generated through ultrasonic action. The possible reaction mechanism is shown in Figure 13 [123]. As an activation method of piezoelectric catalysis, an ultrasonic wave is partially used as an example in the Table 6 to degrade different kinds of wastewater through the activation persulfate mechanism. Figure 13: The possible reaction mechanism of US/Fe\({}^{0}\)f–PS [123].
## 5 Concluding Discussion
### A Multilevel Learning Process and Implementation
#### 5.2.2 Industry-Level Learning  
There are important barriers when using procurement as a tool for driving sustainability at the industry level. First, a lack of green products and services on the market means that identifying sustainable sources of supply may be challenging and as a consequence fewer suppliers will submit tenders [53]. Lack of information on the actual environmental impact of the products [21] also limits the available choices and creates significant barriers for the procuring organizations. It is, therefore, central to establish inter-organizational relationships at an industry level. Previous research points to different ways for the procuring organization to interact with potential suppliers, to share and receive information [34]. However, in a public procurement setting, this comes with restrictions in relation to the public procurement act in different countries, limiting the collaboration possibilities between procurer and supplier before a contract is signed. This requires supplier engagement [35] and, thus, a translation of policy and compliance activities to achieve this [57]. If this translation works, large projects can facilitate the adoption of new standards for their specific sector and increase performance at a general level over time [58]. They can also promote the development of more sustainable products and services; as their size increases, so does their capacity to develop a green purchasing strategy at an operational level, as they, with their greater scale, can reduce the cost for contractors of engaging in product development [6]. However, our study showed that large projects are not always suitable for developing and testing new products, due to their complexity and long implementation spans, suggesting small-scale pilots, and testbeds as a compliment. Several of the studied large-scale projects engaged in both pilot innovation work and implementation of the status quo to support learning and product development that can reduce carbon emissions and, thereby, increase the capacity of the industry to bid for future projects. The studied cases, especially the frontrunner projects, also worked actively to influence market development, arguing against the deeply rooted idea that green products and services are more expensive than conventional ones.
# Improved Ship Detection Algorithm from Satellite Images Using YOLOv7 and Graph Neural Network
## 4 Results
### Optimization  
To update the network's parameter in order to train it, an optimization algorithm is required. It focuses on the discrepancies between model predictions and the term used to refer to the actual situation. The Adam optimizer is a technique for stochastic objective function optimization using first-order gradients. It is a computationally effective optimizer that can be used with the majority of data types. It also uses a small amount of memory. One of the oldest optimizers, the stochastic gradient descent (SGD) optimizer, does not use momentum while determining the update weights. A performance comparison between the Adam and SGD optimizer for ship detection is shown in Table 3 and Figure 6.  
According to Table 3, Adam optimizer is utilized with 93.4% accuracy to achieve the best classification performance. Compared with a learning rate of 0.01, a learning rate of 0.001 was used to achieve this performance. The batch size for various kinds of optimization is 16.
## 1 Introduction
The multi-constellation kinematic PPP model, Kalman filter and stochastic models applied here are introduced in the second section. Section three describes the multi-constellation kinematic PPP data-processing strategy. The performance of multi-constellation kinematic PPP is elucidated by applying it to real data in the fourth section, which is followed by the conclusions. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Common Name** & **Int. Satellite ID** & **Pseudo Random Noise (PRN)** & **Notes** \\ \hline BEIDOU M1 & 2007-011A & C30 & Not in operation \\ BEIDOU G2 & 2009-018A & C02 & Not in operation \\ BEIDOU G1 & 2010-001A & C01 & GEO 140.0\({}^{\circ}\)E \\ BEIDOU G3 & 2010-024A & C03 & GEO 110.5\({}^{\circ}\)E \\ BEIDOU G4 & 2010-057A & C04 & GEO 160.0\({}^{\circ}\)E \\ BEIDOU IGSO 1 & 2010-036A & C06 & IGSO 120\({}^{\circ}\)E \\ BEIDOU IGSO 2 & 2010-068A & C07 & IGSO 120\({}^{\circ}\)E \\ BEIDOU IGSO 3 & 2011-013A & C08 & IGSO 120\({}^{\circ}\)E \\ BEIDOU IGSO 4 & 2011-038A & C09 & IGSO 95\({}^{\circ}\)E \\ BEIDOU IGSO 5 & 2011-073A & C10 & IGSO 95\({}^{\circ}\)E \\ BEIDOU G5 & 2012-008A & C05 & GEO 58.75\({}^{\circ}\)E \\ BEIDOU M3 & 2012-018A & C11 & MEO \\ BEIDOU M4 & 2012-018B & C12 & MEO \\ BEIDOU M5 & 2012-050A & C13 & Not in operation \\ BEIDOU M6 & 2012-050B & C14 & MEO \\ BEIDOU G6 & 2012-059A & C02 & GEO 80.3\({}^{\circ}\)E \\ BEIDOU I1-S & 2015 & C31 & IGSO \\ BDS M1-S & 2015 & C34 & MEO \\ BDS M2-S & 2015 & C33 & MEO \\ BDS I2-S & 2015 & C32 & IGSO \\ BDS M3-S & 2016 & C35 & MEO \\ BEIDOU IGSO 6 & 2016 & C15 & IGSO \\ BEIDOU G7 & 2016 & C17 & GEO \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Constellation of the BeiDou Navigation Satellite (BDS) regional system. GEO: geostationary orbits; IGSO: inclined geosynchronous orbits; MEO: medium Earth orbits.
# Romanian Fake News Detection Using Machine Learning and Transformer-Based Approaches
## 2 State of the Art
The study in [21] demonstrates that back translation (BT) using Transformer-based models, like mBART, improves fake news detection in Romanian. Models trained with BT-augmented data outperformed those using original data, with Extra Trees and Random Forest classifiers yielding the best results. The research highlights BT's potential to enhance detection accuracy, especially when using mBART over Google Translate. This research aims to implement and train multiple AI models to evaluate their effectiveness for detecting Romanian fake news, with a focus on creating a new dataset of fake and real news in the Romanian language and performing the necessary preprocessing steps to prepare the data for model training.
# Feature Selection Algorithms as One of the Python Data Analytical Tools +
## 2 Background
### Feature Selection Algorithms Categorization by Input Data  
Some researchers categorize feature selection algorithms depending on the input data types. Basically, all data can be divided into streaming and static data. Streaming data can be divided into two big groups: data stream, when new objects are added consecutively, and feature stream, when new features are added to the dataset.  
For static data, which are more conventional for traditional feature selection algorithms, some researchers [18] categorize them into: similarity based, information theory based, sparse learning based, statistical based methods, and others. Similarity based methods build an affinity matrix to get feature scores. As they are kind of univariate filters in traditional classification, they do not take into consideration any model and do not handle feature redundancy. Information theoretical algorithms work the same way as similarity based ones but also utilize the concept of \"feature redundancy\". Sparse learning based feature selection algorithms embed feature selection into a machine learning algorithms, working with weights of features. Statistical based feature selection models use statistical measures for features filtering, thus working exactly as most filtering methods in traditional interpretation. This categorization does not include wrappers, thus they are usually categorized as \"other\".
# Fuzzy Logic-Based Geographic Routing Protocol for Dynamic Wireless Sensor Networks
## 3 FLGR Protocol
### Routing Void Avoidance Scheme and Routing Process  
Based on the fuzzy logic system, the next forwarding node can be selected and the route to the destination node can be established.
# Effect of the Partitioning of Diffuse and Direct APAR on GPP Estimation
## 2 Materials and Methods
### Clumping Index Product  
Clumping index (CI) is a critical canopy structural parameter characterizing the degree of the leaf grouping within a canopy. The CI value of vegetation canopy can be retrieved utilizing a linear relationship between the CI and the normalized difference between hotspot and dark spot (NDHD) angular index [43]. MODIS BRDF products were obtained using a hotspot-adjusted RossThick-LiSparse Reciprocal (RTLSR) model [44]. CI retrievals from the MODIS bidirectional reflectance distribution function (BRDF) products (MCD43A1 and MCD43A2) based on linear CI-NDHD equations proposed by [PERSON] et al. [43] were proposed by [PERSON] et al. [44]. The main CI algorithm was designed to retrieve CIs in a closed interval (0.33, 1.00), and a back-up algorithm was designed to reprocess these outlier CI retrievals. Finally, a data set with a temporal resolution of eight days and spatial resolution of 500 m was obtained. The validated data sets indicated higher accuracy of R\({}^{2}\) values of 0.80 and 0.72, RMSE values of 0.07 and 0.12, respectively, for the main algorithm and back-up algorithm [44].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site ID** & **Latitude (\({}^{\circ}\))** & **Longitude (\({}^{\circ}\))** &
\begin{tabular}{c} **IGBP Land Cover** \\ **Classification 1** \\ \end{tabular} & **Study Period** \\ \hline FR-Pue & 43.7413 & 3.5957 & EBF & 2003–2014 \\ DE-Tha & 50.9626 & 13.5651 & ENF & 2003–2014 \\ DK-Sor & 55.4859 & 11.6446 & DBF & 2003–2014 \\ CG-Tch & \(-\)4.2892 & 11.6564 & SAV & 2006–2009 \\ AT-Neu & 47.1167 & 11.3175 & GRA & 2003–2012 \\ FR-Gri & 48.844 & 1.952 & CRO & 2004–2014 \\ \hline \hline \end{tabular} \({}^{1}\) DBF: deciduous broadleaf forest; ENF: evergreen needleleaf forest; EBF: evergreen broadleaf forest; GRA: grass; CRO: crop; WET: wetland; SAV: savannas; SHR: shrubland.  
\end{table}
Table 1: FLUXNET site information used in this article.
# On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview
### Detection and Locking  
When the switch rails complete their movement, the rails are locked by the locking mechanism and then detected by the arrangements. The switching operation is considered to be completed when the switch rails are detected in their place. Thus, these two systems are of high importance in a full railway S&C system. But, because of the high number of parts involved in locking and detection systems, these systems reduce system reliability and increase the maintenance cost. The different lock and detection systems used in various systems include mechanical linkages, sensors, electrical clutch, etc.  
The rail clamp point lock (RCPL), which is widely used in the UK, has two plunger-type limit switches and a mechanical locking arm. At the end of switch travel by the hydraulic actuator (for clamplock points), the mechanical lock engages with the associated locking piece and a positive lock is achieved as shown in Figure 5b [38]. The commonly used conventional mechanical lock is shown in Figure 8, where two metallic rods are connected to two switch rails [45, 46]. When these two rods are locked in their position after the switch movement, the lock blade engages with a lock dog through an interlocking notch. The stretcher bar connected to the lock blade is then locked in its position, which prevents any movement of the switch rails when the power is off.  
A different approach for detection and locking is used in HPSS, where the switch rail position is measured using a linear variable differential transformer (LVDT) [47]. The LVDT monitors the movement of the switch rails, and when the switch rails are within the allowable range, the clutch in the electrical motor is engaged and the power to the motor is removed. Locking in an HPSS is achieved in two ways: through the electrical brake and by using a non-backdrivable lead screw (as shown in Figure 7.  
The different detection and locking mechanisms explained so far are for linear actuators. Lift-hop-drop actuation mechanisms use passive locking to allow vertical movement of the switch rails. The hopper, as shown in Figure 6, engages with the locking block that prevents any horizontal movement of the switch rails in that position. The locking in the vertical direction is achieved by the weight of the switch rails and the hoppers [36].