esa-sceva/satcom-chunk-collection huggingface dataset:
Sensors_**2009**, \(9\), 2944-2950; doi:10.3390/s90402944  
_Sensors_  
ISSN 1424-8220  
www.mdpi.com/journal/sensors  
_Article_  
Compact Integration of a GSM-19 Magnetic Sensor with High-Precision Positioning using VRS GNSS Technology  
**[PERSON] *, [PERSON], [PERSON], [PERSON] and [PERSON]**  
Department of Cartographical Engineering, Geodesy and Photogrammetry. Polytechnic University of Valencia. Camino de Vera s/n, 46022, Valencia, Spain; E-Mails: [EMAIL_ADDRESS];  
[EMAIL_ADDRESS]; [EMAIL_ADDRESS]; [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]  
_Received: 24 March 2009; in revised form: 8 April 2009 / Accepted: 20 April 2009 / Published: 22 April 2009_  
**Abstract:** Magnetic data consists of a sequence of collected points with spatial coordinates and magnetic information. The spatial location of these points needs to be as exact as possible in order to develop a precise interpretation of magnetic anomalies. GPS is a valuable tool for accomplishing this objective, especially if the RTK approach is used. In this paper the VRS (Virtual Reference Station) technique is introduced as a new approach for real-time positioning of magnetic sensors. The main advantages of the VRS approach are, firstly, that only a single GPS receiver is needed (no base station is necessary), reducing field work and equipment costs. Secondly, VRS can operate at distances separated 50-70 km from the reference stations without degrading accuracy. A compact integration of a GSM-19 magnetometer sensor with a geodetic GPS antenna is presented; this integration does not diminish the operational flexibility of the original magnetometer and can work with the VRS approach. The coupled devices were tested in marshlands around Gandia, a city located approximately 100 km South of Valencia (Spain), thought to be the site of a Roman cemetery. The results obtained show adequate geometry and high-precision positioning for the structures to be studied (a comparison with the original low precision GPS of the magnetometer is presented). Finally, the results of the magnetic survey are of great interest for archaeological purposes.  
**Keywords:** Magnetic data, GNSS positioning, VRS approach, sensor integration.
## 5 Discussion  
The presented research work investigated the simulation results and hardware-based results of a flight dynamical system while focusing on PI, PID, and a robust control strategy referred to as ISTSMC. It is evident from the achieved results that a flight dynamical system such as a vertical takeoff and landing system, subjected to harsh weather conditions such as ambient air pressure, leads to variations in various parameters of the model, known as modeled uncertainties. In terms of addressing the parametric variations, ISTSMC outperformed the cascaded control mechanisms such as PI and PID for the flight dynamical system.  
The analytical analysis of any control technique is significant to prove its stability.  
The limitations of other control techniques, such as H Infinity, PID controllers, and fuzzy logic controllers, are evident for flight dynamical systems because we cannot verify the stability analysis after controller design. The significance of ISTSMC is clear due to the possibility for analysis after controller design as compared to other control strategies.  
Furthermore, the presented research validated the stability analysis of the control law using Lyapunov theory and proved that the Lyapunov candidate function was negative definite as \(k_{1},k_{2}>0\). In addition, the various performance parameters in the transient phase, including the peak time, overshoot, and steady-state response, as well as steady-state error, were also compared for both control strategies. In the closed-loop system, the input of the controller is quite significant for the lifespan of the actuator. If the variations in the control input signal are numerous and have high frequency, this leads to extensive mechanical vibrations and faults in the actuators due to fast switching. To minimize this fluctuation in the input signal, ISTSMC provides a uniform and smooth input signal to the actuator as compared to a cascaded control strategy based on PI or PID.
# Experimental Verification of the CFD Model of the Squeeze Film Lifting Effect
## 1 Introduction
### Unresolved Issues  
There is a lack of clear methods to predict the levitation height found in the research. Recalled applications (i.e., bearings,) due to the geometrical limitations, do not deal with the height but rather an acceptable load [7; 9].  
Calculation models utilized in the papers describing the behavior of the gaseous film are commonly based on Reynolds equations. The equations are derived and simplified Navier-Stokes equation of motion of the fluid. The limitation of using Reynolds in comparison to general motion equations was discussed by [PERSON] et al. [19]. Their work showed that aside from Reynolds, Helmholtz's number should also be considered in choosing the proper model for the film.  
The results presented in the articles usually show a levitation height [20; 21]. Calculation methods often provide an analysis of pressure distribution in the film [19; 22; 23]. A few works show the transient motion of the lifted object [12]. In many cases, it is due to the higher inertia of the objects, where the amplitude of the vibrating motion of the sample is negligible in comparison with the levitation height.  
In calculations of the NFAL phenomena, the lifted object is usually modeled as a rigid body; while bearing in mind possible applications in transporting vulnerable objects, it is important to be able to assess the deflections and stresses of the lifted object. That is why the authors' goal is to build a full FSI (fluid-structure interaction) model of the phenomena, also because it seems extremely difficult to measure the stresses and deflections of a lifted object in an experiment. The model presented here is a step toward building a full FSI model.
## 5 Implementation
### Regression Analysis between Personal Feelings, Traffic Accessibility, and Urbanization  
In the first scenario of the algorithm, we used regression analysis to verify the hypothesis that citizens' personal feelings about the urban environment are correlated with the physical construction, by again comparing the linear relevance of the citizens' feelings indexes--\(F_{0}(c)\), \(F_{T}(c)\), \(T(a)\), and \(U(\mathrm{e})\)--using a pair plot (see Figure 7a). Through the pair plot, we found that \(F_{0}(c)\) and \(F_{T}(c)\) also had a linear relevance to \(T(a)\) and \(U(e)\). The oblique line (fitted by four functions in the pair plot) illustrates the linear relevance between them. The linear relevance in this pair plot can be seen more clearly than that of the questionnaire answers (see Figure 7a). However, the evidence is not sufficient to confirm the functional relationship via a pair plot comparison alone, because four functions--\(F_{0}(c)\), \(F_{T}(c)\), \(T(a)\), and \(U(e)\)--have to be compared. The current linear regression only compares two of them simultaneously. In addition, the values of the dataset were integers in fixed ranges, so regardless of the calculation being compared, the difference will be in integers; therefore, we applied Pearson correlation algorithms to obtain a more specific comparison (Figure 7b). The Pearson correlation is used to compare the alternative relationship between two functions when the difference is between 1 and 0. We took the differences in the Pearson correlation as the percentage of relevance (1 = 100%), where a higher percentage meant a higher relevance between the two functions. In the Pearson correlation, the relational value r is the contrast between two different values with a unified standardization. The reference values of r in the Pearson correlation are \(r<0.3\) for a low correlation, \(0.3\leq r<0.7\) for a medium correlation, and \(r\geq 0.7\) for a high correlation. In Figure 7b, for the function of citizens' personal feelings regarding traffic selection--\(F_{T}(c)\)--the \(r\) to \(T(a)\) was 0.53, which is the highest correlation in this algorithm. The r between the traffic accessibility function \(T(a)\) and urbanization \(U(e)\) was 0.2, which shows that the linear relevance between \(T(a)\) and \(U(e)\) was low and had a negligible relevance. The function \(F_{0}(c)\), which represents the pure personal feelings of citizens about the urban environment, had a correlation of r = 0.28 with the function of traffic accessibility \(T(a)\), which is even higher than the correlation between \(T(a)\) and \(U(e)\). However, the r between \(F_{0}(c)\) and \(U(e)\) was only 0.088, revealing a very low relevance between the two functions. According to the Pearson correlation comparison, users' feelings about the environment (either positive or negative) are not dependent on urbanization and are only slightly related to traffic accessibility.  
To avoid confusion during programming, the labels in the chart are displayed as \(FOC0=F_{0}(c)\); \(FOCT=F_{T}(c)\); \(TOA=T(a)\); and \(UOE=U(\mathrm{e})\).
# ATOSE: Audio Tagging with One-Sided Joint Embedding
## 5 Conclusions  
In this paper, we proposed an audio auto-tagging model with joint embedding that can learn not only acoustic features from the audio domain but also semantic information from the tag domain. Our approach utilizes asymmetric multi-modal embedding to extract features from diverse audio data and project them onto a simplified tag domain. This allows for the continuous maintenance of various audio feature vectors while encapsulating the correlations between tags and audio data within the features.  
Our experimental results demonstrated that our proposed model surpassed previous methods with the MTAT dataset, which has higher tag correlations. Specifically, the HarmonicCNN model with joint embedding achieved the highest performance, with a PR-AUC of 91.27% and an ROC-AUC of 53.78%. Our approach is a general method that can be applied to diverse datasets where there are inter-tag correlations for the ground-truth labels.  
Recently, audio auto-tagging research has focused on pretrained models with large-scale datasets to enhance performance, similar to natural language processing research [26; 27; 28]. Our study stands apart by prioritizing performance enhancement without additional large-scale datasets. We tap into the intrinsic semantic information within tags to improve results for a given dataset. In future work, we will explore some applications, like movie genre prediction [29], and we plan to extend our research to various modalities, including images and texts.  
Conceptualization, J.L. and M.C.; methodology, J.L. and M.C.; software, J.L. and M.C.; validation, J.L., D.M., J.-S.K. and M.C.; formal analysis, J.L. and M.C.; investigation, J.L. and M.C.; resources, J.-S.K. and M.C.; data curation, J.-S.K. and M.C.; writing--original draft preparation, J.L. and M.C.; writing--review and editing, D.M., J.-S.K. and M.C.; visualization, J.L.; supervision, J.-S.K. and M.C.; project administration, J.-S.K. and M.C.; funding acquisition, D.M., J.-S.K. and M.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (no. NRF-2019R1A2C1005360) and the 2022 Research Fund of Myongji University.  
Not applicable.  
The data are available at https://github/jaehwlee/jetatag (accessed on 31 July 2023) or upon request to the corresponding author.  
The authors sincerely thank the computer engineering department of Myongji University for generously providing the necessary computing resources for conducting the experiments.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Feature Extractor**} & \multicolumn{2}{c}{**F1-Score**} \\ \cline{2-3}  & **Original** & **With JE** \\ \hline SampleCNN & 0.4966 & 0.4583 \\ SampleCNN (+se) & 0.5134 & 0.5312 \\ SampleCNN (+res) & 0.5087 & 0.4448 \\ SampleCNN (+rese) & 0.5101 & 0.4534 \\ HarmonicCNN & 0.5052 & 0.5341 \\ Tag-SincNet & 0.4324 & 0.3623 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison with DCASE 2017 dataset.
# Optimal Coordination of PV Active Power Curtailment and EVs Charging among Aggregators
## 3 Centralised Optimisation by the DSO for Multiple Aggregators
### Aggregators Constraints
\[\mid\sum_{k\in K}(P_{k,t}^{PV}\cdot\zeta_{k,t}-(P_{k,t}^{EV}+P_{\textit{Line} _{k,t}}^{fc}))\mid\leq S_{\textit{Transj}}^{rated}\cdot\cos(\varphi)+\sum_{k \in K}OL_{k,t},\ \forall t,\forall j\in\textit{Trans} \tag{12}\]  
\[\mid P_{k,t}^{PV}\cdot\zeta_{k,t}-(P_{k,t}^{EV}+P_{\textit{Line}_{k,t}}^{fc}) \mid\leq S_{\textit{Line}_{k}}^{rated}\cdot\cos(\varphi)+OL_{k,t},\ \forall k\in K,\forall t \tag{13}\]  
\[P_{k,t}^{EV}\leq min\{P_{k,t}^{upper},P_{\textit{Line}_{k,t}}^{rated}-P_{ \textit{Line}_{k,t}}^{fc}\}+OL_{k,t},\ \forall k\in K,\forall t \tag{14}\]\[P_{k,t-1}^{EV}-\Delta_{k,t}^{P}\leq P_{k,t}^{EV}\leq P_{k,t-1}^{EV}+\Delta_{k,t}^{ P},\ \forall k\in K,\forall t\quad\text{if}\quad P_{k,t}^{upper}\
eq 0 \tag{15}\] \[E_{(k,t_{min_{k}}^{lower}+t)}^{lower}\leq\sum_{j\in N_{k}^{EV}} \epsilon_{k,j}^{after}+\sum_{\tau\in I}\left(P_{(k,t_{min_{k}}^{\prime}+\tau)}^{ EV}+\cdot\Delta t\right)\leq E_{(k,t_{min_{k}}^{\prime}+t)}^{upper},\ \forall k\in K,t=0,\ldots,N_{k}^{Pl_{int}} \tag{16}\]  
In Equation (16), \(t_{min_{k}}^{\prime\prime\prime\prime}\) is the minimum arrival time of all the arrivals of the EVs managed by the \(k\)th aggregator, i.e., \(min\left\{t_{k,1}^{after},t_{k,2}^{after},\ldots,t_{k,n}^{after}\right\}\), and \(N_{k}^{Pl_{int}}=t_{max_{k}}^{dis}-t_{min_{k}}^{arr}\), the number of parking time slots of the \(k\)th aggregator. Note that all time parameters are discrete, i.e., these are divided by \(\Delta t\). In addition, it is worth mentioning that the formulated convex problem can be extrapolated into a more extensive distribution system at the medium-voltage (MV) level. This means that the aggregators would operate at the MV side instead of the LV side of the distribution transformers. Hence, the nominal capacity and the initial load in constraints (13) and (14) should be considered per distribution transformer and not per feeder. Moreover, constraint (12) should be limited to the rated power of the main distribution transformer located at the high-voltage (HV)/MV substation.
# TopoSinGAN: Learning a Topology-Aware Generative Model from a Single Image
## 2 Materials and Methods
### Experimental Setup
#### 2.3.1 Agricultural Fields  
For this experiment, we used a 175 \(\times\) 240 pixel 3-meter resolution XYZ tile downloaded using QGIS 3.34.0 software from agricultural fields of Tehran province, Iran. This image contains red, green, and blue (RGB) channels. We manually annotated the image delineating agricultural fields in the image and appended it as the fourth channel to this image. Notably, the agricultural plots within this region present considerable variation in shape and size, making them intricate subjects for accurate prediction. This study area is especially challenging given the intrinsic differences in field geometries, topological arrangements, irrigation systems, and the diverse range of crops.
# Flexible Phased Antenna Arrays: A Review
###### Abstract  
Flexibility in phased antenna arrays open the world of new applications. Such arrays can conform to different shapes while ensuring performance in harsh conditions. The purpose of this study is to perform a detailed comparative analysis of numerous studies of flexible phased antenna arrays. This work summarizes the main manufacturing techniques and the commonly used materials with their properties. It also outlines the key challenges and future trends in the development of flexible phased antenna arrays. The paper concludes with research recommendations to address the identified technical issues.  
flexible antennas; phased antenna arrays; additively manufactured antennas 1
## 4 Conjugate Pinion with Ruled Surface Face Gear  
### Generation of Conjugate Pinions  
From the definition of the ruled surface, it can be seen that the tooth surface of the ruled surface is no longer tangent to the tooth surface of the cylindrical gear, except for the contact line, especially at the root and top of the tooth, as shown in Figure 3. If the ruled surface is directly meshed with the cylindrical gear, it will inevitably generate a strong vibration and impact due to not meeting the conjugate relationship, accelerating the wear of the gear. Therefore, in order to avoid the occurrence of the above situations, the ruled surface is regarded as a tool for generating pinions, and is developed into a completely conjugate pinion tooth surface, which can be considered as a modified involute pinion. The coordinate systems of generation for pinions are shown in Figure 9.  
The coordinate systems \(S_{\text{g}}\) and \(S_{2}\) in Figure 9 are the motion coordinate systems and fixed coordinate systems of the ruled surface gear, respectively; the coordinate systems \(S_{\text{n}}\) and \(S_{\text{j}}\) are the motion coordinate system and fixed coordinate system of the pinion, respectively; \(\gamma_{\text{j}}\) is the angle between the shaft of the ruled surface gear and the pinion; \(r_{2}\) and \(r_{\text{j}}\) is the pitch diameter of the ruled surface and the pinion; and \(\varphi_{2}\) and \(\varphi_{\text{j}}\) is the angle of the ruled surface and the pinion, meeting the gear ratio.  
Figure 9: Generation of conjugated pinion.  
By combining the coordinate transformation equation from \(S_{2}\) to \(S_{\rm j}\) and the meshing equation between the two gears, the position vector \(R_{\rm j}\) and normal vector \(n_{\rm j}\) of the conjugate tooth surface of the pinion can be determined:  
\[\left\{\begin{array}{ll}R_{\rm j}(\varphi_{\rm r},\theta,u)=M_{\rm jr}(\varphi _{\rm r})R_{2}(\theta,u)\\ n_{\rm j}(\varphi_{\rm r},\theta,u)=M_{\rm jr}(\varphi_{\rm r})n_{2}(\theta,u) \\ f_{3}(\varphi_{\rm r},\theta,u)=n_{\rm j}(\varphi_{\rm r},\theta,u)\frac{ \partial R_{\rm j}(\varphi_{\rm r},\theta,u)}{\partial\varphi_{\rm r}}\end{array}\right. \tag{28}\]  
where \(M_{\rm jr}\) is the coordinate transformation matrix from the ruled surface to the conjugate pinion, and its specific expression is similar to \(M_{\rm fd}\) and will not be repeated.
## 5 Simulation
### Experimental Results and Analysis
#### 5.2.6 IoT Devices Overhead  
Table 3 summarizes the breakdown of communication time, training time, round time, and the additional overhead brought by encryption and decryption in our framework. A key distinction of our approach compared to HierFAVG lies in the lightweight  
encryption and decryption operations integrated at the IoT device level, ensuring secure communication without significantly increasing device workloads.  
The results demonstrate that the additional overhead introduced by our framework is minimal. Specifically, the average encryption and decryption time per round is approximately 0.066 s, accounting for less than 1% of the total round time (ranging from 8.48 to 10.28 s). This minimal computational cost underscores the practicality of the framework in resource-limited IoT environments. Additionally, since federated learning involves transmitting fixed-size model parameters, the encryption process does not increase the size of the transmitted data. As a result, the communication time remains unchanged, ranging from 3.94 to 4.35 s, further confirming the framework's scalability.  
Our framework introduces encryption and decryption at the IoT device level as the sole additional operation. This contrasts with the centralized approach of HierFAVG, where no encryption is applied to the device side. However, despite the inclusion of security mechanisms, the overall round time of our framework remains competitive due to the lightweight nature of the encryption operations and the optimized hierarchical aggregation strategy employed at the edge and cloud levels.  
The practical implications of this additional overhead are further validated by the consistent round times observed across devices. Although training time varies slightly (ranging from 4.13 to 5.95 s), the overall round time almost remains consistent, demonstrating the framework's effective balancing of device-level tasks. The negligible impact on device resources ensures that IoT devices can maintain their primary focus on training and communication without being constrained by security mechanisms.  
The results show that our framework balances security and efficiency, introducing minimal additional computational and communication overhead at the IoT device level. By ensuring secure data exchange without increasing the size of transmitted data or imposing significant delays, our framework aligns with the demands of scalable and resource-efficient federated learning in IoT networks.
## 7 Results and Discussion
### Network Delay  
Without authentication, EIGRP exhibited delays ranging from 30.21 to 30.39 s, while OSPF delays ranged from 30.24 to 30.35 s. With authentication, EIGRP delays varied between 30.40 and 30.48 s, and OSPF delays ranged from 30.31 to 30.46 s. These results indicate that authentication introduces some variability in the delay levels of both EIGRP and OSPF. Figure 17 provides a detailed graph illustrating the network delay differences with and without authentication.
## 2 Modeling and Formulation
### Field Partitioning and Sector Area Calculation
This means the communication between the normal node and the \(CH\) is a single-hop communication. Therefore, all normal nodes do not have high-energy consumption rates because there is no load on them. When the \(BS\) receives high recharging requests from these nodes, it knows whether it is an extraordinary or normal node which is supposed to have failed, therefore it knows that this node must be replaced; but if the requests comes from nodes which selected as \(CH\), then this high request is because of the working load on this \(CH\). The \(BS\) automatically informs all nodes in the network about the \(CH\)s for sectors. This technique greatly reduces the energy required for internal communication among nodes to select the \(CH\) (as in Algorithm 1). This study proposes that all nodes send a periodical message to inform the \(BS\) about the node situation (such as in Equation (6)). The \(BS\) analyzes these report messages to study the node status. \[periodical\ message=\left(n_{k},RE_{k},SN_{i}^{j},CH,rt_{k}\right) \tag{6}\]  
```
Input:\(N\) set of nodes. Output:select\(CH\)s for each sector. 1 Let \(N=(n_{1},n_{2},\ldots,n_{k})\) denote the set of n nodes
2\(RE_{k}\) denote the residual energy of nodes
3\(SN_{i}^{j}\) denote the node sector location
4\(CH\) denote the cluster head node
5\(DE_{k}\) denote the threshold value of the node
6 Create node \(n_{k}\)
7 Divide the monitoring field into sectors \(SN_{i}^{j}\)
8 Set node position \(SN_{i}^{j}\) clusters formation
9 Select\(CH\) from \(SN_{i}^{j}\) based on RE\({}_{k}\) & middle location
10 if\(n_{k}\in SN_{i}^{j}\)\(\in\)\(RE_{k}\)\(>\)\(DE_{k}\) & has not been \(CH\) yet then
11\(n_{k}=CH\) for \(SN_{i}^{j}\)
12 else
13\(n_{k}\) normal node
14 endif
15 inform nodes in \(SN_{i}^{j}\) about their \(CH\)
16\(CH\) sends data to \(BS\)
17 Repeat the steps 12 to 16 for different rounds
```  
**Algorithm 1**Find and select Cluster Head