esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigating the Reliability and Dynamic Response of Fully 3D-Printed Thermistors
## 4 Conclusions  
This study presents a novel 3D-printing approach for fabricating multi-material thermistors, integrating the substrate, Ag electrodes, and PEDOT:PSS thermosensitive layer using FDM and micro-dispensing technologies. The practical applications of this work include but are not limited to the following: smart textiles, health monitoring, and aerospace engineering, where fully 3D-printed sensors can offer new capabilities, such as integration into complex geometries.  
Key findings from this work include the following:  
* The thermistors were characterized in terms of measurement capability, dynamic performance, and mechanical reliability over the temperature range of 25 to 45 \({}^{\circ}\)C.
* The highest recorded thermal indexes were 905.64 K for D1 and 813.03 K for D2 thermistors, comparable to values reported in the literature.
* Both designs showed similar dynamic performance, with the smaller sensing area of D2 providing quicker response times and faster relaxation.
* Mechanical reliability was material-dependent: strong bonding was observed between PEDOT:PSS and the Ag layer, while weak adhesion with the PC substrate resulted in over 65% material removal.  
Although this study demonstrated measurement capability and dynamic performance comparable to the existing literature, future work should focus on improving the surface properties of FDM-fabricated substrates to improve the adhesion performance towards achieving highly reliable, fully 3D-printed thermistors. The remaining challenges include material compatibility and interface engineering, with a focus on incorporating additional functionalities such as on-board heating and closed-loop thermal regulation. These advancements will help the realization of fully printed smart systems for wearable and biomedical applications.  
Conceptualization, U.C., D.S. and A.J.; methodology, U.C., D.S. and A.J.; validation, U.C.; formal analysis, U.C.; investigation, U.C.; resources, D.S. and A.J.; supervision, D.S. and A.J.; data curation, U.C.; visualization, U.C.; writing--original draft, U.C.; writing--review and editing, U.C., D.S. and A.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the GREAT Scholarships from the British Council in Turkish and Loughborough University [No Grant Number Applicable].  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors declare no conflicts of interest.
## 4 Conclusions  
In this study, a novel finite element model, by taking into account of the penetration of strands in one layer into the previous layer, was developed to represent and predict the mechanical properties of scaffolds fabricated by the 3D bioplotting technique. Our experimental results show the penetration within layers has a significant influence on the mechanical properties of printed scaffolds, along with the number of layers and pore size of scaffolds. To these experimental results, the predictions from our model were compared, showing a strong congruity between them. Based on the simulations from the developed model, a simple regression equation was developed to show the effects of penetration, pore size and number of layers on the elastic modulus of printed scaffolds. The method used to develop both finite element model and regression equation for alginate in the present study can also be implemented for other hydrogels so as to achieve the desired mechanical properties in tissue engineering.  
**Author Contributions:** Formal analysis, S.N. and M.R.K.-R.; Funding acquisition, M.R.K.-R. and X.C.; Investigation, S.N., M.D.S., M.R.K.-R., A.D.M., and X.C.; Methodology, S.N., M.D.S., M.R.K.-R., and X.C.; Project administration, S.N. and X.C.; Resources, M.R.K.-R. and X.C.; Software, M.R.K.-R.; Supervision, M.R.K.-R. and X.C.; Validation, S.N., M.D.S., and A.D.M.; Visualization, S.N.; Writing-original draft, S.N.; Writing-review & editing, M.D.S., M.R.K.-R., A.D.M., and X.C.  
**Funding:** This scientific work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC, grant number RGPIN-2014-05648).  
**Conflicts of Interest:** There is no conflict of interest.
# Data Assimilation of AOD and Estimation of Surface Particulate Matters over the Arctic
## 2 Experiments
### CMAQ-Derived and Assimilated AODs
\[\tau_{m}^{\prime}=\tau_{m}+K(\tau_{0}-H\tau_{m}) \tag{6}\]  
\[K=BH^{T}(HBH^{T}+O)^{-1} \tag{7}\]  
The \(\tau\)', \(\tau_{m}\), and \(\tau_{o}\) represent hourly assimilated, model-calculated, and MODIS-observed AODs, respectively. \(H\) is the linear observation operator allowing the calculation of the model equivalents in observation location. \(K\) denotes the Kalman gain matrix combining the error covariance matrix for the modeled (\(B\)) and observed (\(O\)) fields. \[O=(f_{o}\tau_{o}+\varepsilon_{o})^{2}I \tag{8}\]\[B=\left(f_{m}\tau_{m}+\varepsilon_{m}\right)^{2}\exp\left[-\frac{{d_{x}}^{2}+{d_{y}} ^{2}}{2{l_{xy}}^{2}}\right] \tag{9}\]  
where, \(f_{o}\) and \(f_{m}\) represent fractional error coefficients in the observed and modeled \(\tau\), respectively. \(\varepsilon_{o}\) and \(\varepsilon_{m}\) are minimum error in the observed and modeled \(\tau\), respectively. \(d_{x}\) and \(d_{y}\) are horizontal grid resolution (i.e., \(d_{x}=d_{y}=18\) km in this study). \(l_{xy}\) is the horizontal correlation length for errors in the modeled \(\tau\). In the sensitivity test, the best free parameters of \(f_{o}\), \(f_{m}\), \(\varepsilon_{o}\), and \(\varepsilon_{m}\) on a monthly basis were determined by minimizing a statistical parameter of \(\chi^{2}\) defined in Equation (10). This approach was introduced in the study of [PERSON] et al. [42]. The best values from the sensitivity test were summarized in Table S2. \[\chi^{2}=\frac{1}{N}{\sum\limits_{i}^{N}}\left[\frac{(\tau_{i,AERONET}-\tau_{ i,m}^{\prime})^{2}}{\tau_{i,m}^{\prime}}\right] \tag{10}\]  
\(N\) is the number of available data set from the AERONET stations.
## 6 Case Studies in AI for Communication Networks
### Case Study 2: AI for Managing and Securing IoT and Edge Networks  
The rapid increase of IoT devices and edge computing has introduced both opportunities and challenges for network management and security. AI is being applied to enhance the management of large-scale IoT networks, optimizing device communication, resource allocation, and security in real-time [133].  
In IoT networks, AI-based models analyze data from a vast number of connected devices to detect potential issues such as faulty devices, resource inefficiencies, and security threats. By performing real-time analysis at the edge, AI systems can reduce latency, improve response times, and protect the network from malicious activities like unauthorized access or data breaches. Moreover, AI-based security protocols ensure that devices are continuously monitored for anomalous behavior, minimizing the risk of attacks or compromises [134].  
Figure 11 illustrates an AI-enhanced IoT and edge network, showcasing essential components such as IoT devices, edge computing nodes, and AI-based security systems. In this setup, various IoT devices--such as smart thermostats, wearable devices, and connected sensors--generate data and connect to nearby edge computing nodes for local processing. These edge nodes are positioned closer to the data source to reduce latency and enable real-time analysis. AI-driven security mechanisms are integrated within the network to monitor and detect any unusual device behavior, ensuring that data transfers are secured and threats are identified promptly. The diagram highlights how AI algorithms at the edge can optimize device management by predicting potential failures, adjusting resource allocation as needed, and continuously scanning for cybersecurity threats. This setup demonstrates AI's critical role in maintaining the efficiency and security of IoT and edge  
Figure 10: AI optimization for bandwidth management and congestion prediction in dense urban 5G/6G networks.  
networks, where rapid data processing and real-time security measures are essential for sustaining a large ecosystem of connected devices. The flow between devices, edge nodes, and AI-based security indicates a comprehensive approach to managing and securing IoT networks.
## 3 Results
### Research Contribution
#### 3.3.2 Evaluation Results  
As shown in Figure 12, among the 38 papers that included the evaluation of machine learning/gamification applications, the vast majority (87%) reported positive results. Only 3% reported negative results, with the remainder reporting neutral or mixed results.
# Forecasting Ionospheric foF2 Based on Deep Learning Method
## 2 Models and Data
### Data and Processing
#### 2.3.1 Data Sets
The data sets are available from different sources listed as below: The geomagnetic indices _ap_, SSN, and F10.7 used in this work are available in convenient ASCII format by the website at [[https://www.gfz-potsdam.de/en/kp-index/](https://www.gfz-potsdam.de/en/kp-index/)]([https://www.gfz-potsdam.de/en/kp-index/](https://www.gfz-potsdam.de/en/kp-index/)) via FTP server [[ftp://ftp.gfz-potsdam.de/pub/home/obs/Kp_ap_Ap_SN_F107/](ftp://ftp.gfz-potsdam.de/pub/home/obs/Kp_ap_Ap_SN_F107/)]([ftp://ftp.gfz-potsdam.de/pub/home/obs/Kp_ap_Ap_SN_F107/](ftp://ftp.gfz-potsdam.de/pub/home/obs/Kp_ap_Ap_SN_F107/)), accessed on 20 April 2021. While the data of magnetic declination \(D\) and magnetic inclination \(I\) are calculated by using World Magnetic Model in MATLAB with relevant factors as LAT, LON, and year.
# Optimizing HAWK Signature Scheme Performance on ARMv8
## 2 Background
### Number Theoretic Transform  
The Number Theoretic Transform (NTT) is a transformation technique based on principles similar to the Fourier Transform, utilizing mathematical properties defined in finite fields and modular arithmetic. It is particularly useful in fields like cryptography and polynomial multiplication, providing efficient computation. NTT operates using modular arithmetic, where integers are reduced modulo a specific prime \(p\). This allows the use of integers instead of complex numbers, which is crucial for performing cryptographically secure operations.  
NTT is based on the primitive root of a specific prime \(p\), and the transformation is computed using discrete roots of unity defined in the finite field. Unlike the Fourier Transform that operates over complex numbers, NTT uses mathematical structures in finite fields. This characteristic makes NTT highly efficient for performing fast polynomial multiplication.  
Polynomial multiplication plays an important role in post-quantum cryptography (PQC) algorithms, and NTT reduces the computational complexity of polynomial multiplication from \(O(n^{2})\) to \(O(n\log n)\). As a result, many PQC algorithms are implemented using NTT, and numerous studies have been conducted to optimize the efficient implementation of NTT for enhanced performance. [15; 16; 17].
# Evaluation Method for Rock Burst Hazards in Strip Filling of Working Faces in Deep Coal Mines
###### Abstract
However, in-depth research on the effects of goat filling on the distribution of the supporting pressure and the identification of the impact risk of the working face is lacking. In terms of rock burst risk assessment, on the basis of analyzing the main factors affecting the occurrence of rock bursts, [PERSON] et al. [20] presented a systematic discussion of several methods, the scope of application, and the evaluation of impact risk indicators for evaluating impact risk. [PERSON] et al. [21] proposed an impact risk evaluation method based on the increment in stress generated by various influencing factors on the basis of self-weight stress. By using an engineering analogy, theoretical research, and actual measurement, the increment in stress generated by each inducement factor was estimated. The ratio of the total stress of the coal body to the uniaxial compressive strength of the coal body obtained by adding increments in stress was used as an equivalent indicator for evaluating the impact risk. It could more quantitatively reflect the impact danger area and degree during face mining. [PERSON] et al. [22] analyzed the factors affecting the rock burst, and a variable-weight identification model for the comprehensive evaluation of the impact risk degree of coal rock was established with impact hazards according to the attribute identification theory. The variable-weight coefficient was determined by using the contribution rate of each evaluation factor in the location to be evaluated. The impact hazard degree was comprehensively evaluated by using the given confidence degree, and the attribute identification and classification were carried out. [PERSON] et al. [23] considered the difference in energy accumulation and release of the coal rock medium and the main control effect of deep high stress on rock bursts. Two indicators were proposed: the combined impact energy velocity index of coal rock and the impact energy velocity index of unloading pressure. In addition, the evaluation index system for deep coal rock's impact tendency was improved. Based on field monitoring and seismology, [PERSON] et al. [24] proposed a criterion for impact hazard assessment and successfully verified it in the field. The existing evaluation methods provide good results for the impact risk assessment of a whole mining area and the traditional collapse of a coal mining face. However, there is still a lack of effective theories and methods for the \"risk area-hazard level\" of rock bursts under strip-filling mining conditions. Based on this, this paper proposes an evaluation method for the effectiveness of filling anti-impact. The method can qualitatively analyze whether filling can effectively prevent a rock burst. A method to identify and evaluate the impact hazard of filling a working face based on fuzzy mathematics is proposed, which provides guidance for the prevention and control of rock bursts in strip-filling mining.
## 5 Conclusions  
In this paper, we proposed UWA communication using GSFM waveforms, which offers advantageous modulation in a fluctuating UWA channel, and the proposed method's performance was demonstrated by a comparison with that of the conventional CSS method. According to the AF and ACF, we demonstrated that the GSFM waveform has a low sidelobe level and a distinct mainlobe level simultaneously, which has better performance in signal interference. Regarding the reliability of the proposed method, experiments were conducted using three kinds of methods: a simulation that considered the multipath propagation and AWGN channel, a lake trial and a sea trial. In this paper, multiple GSFM waveforms orthogonal to each other are used to transmit data in UWA communication. This is what sets it apart from past studies and is our original contribution on this topic.  
The performance of UWA communication is not simply proportional to the transmission distance. It is affected by many factors, such as the SSP, the placement of the transceiver, the sea state and the geometry of the channel. In a lake environment, multipath propagation is very severe, affecting the UWA communication performance. This is also shown in the experimental results presented in this paper. The trial environment was a deep sea, and since it is an open space, it was less affected by such multipath propagation. As a result, the proposed method is more robust in UWA communication with multipath propagation and AWGN channels than the conventional CSS method. The Doppler channel was not taken into account in the simulation, but the acquired data from the lake and sea trial contained Doppler shifts of several Hz. If it is to be applied in a severe Doppler channel, a Doppler shift frequency estimation and a modified correlation function will probably be  
Figure 14: The spectrogram of the data: (**a**) the transmitted signal, (**b**) the received signal of the conventional CSS method, (**c**) the transmitted signal and (**d**) the received signal of the proposed GSFM method.  
added. There is a need for research on performance verification and improvement in the Doppler channel in future research.  
Data curation, H.R. and C.Y.; Methodology, K.K.; Software, J.A. and H.R.; Supervision, K.K.; Writing--original draft, J.A.; Writing--review & editing, K.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Agency for Defense Development, South Korea, grant number UD200010 DD.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Internet of Things Environment  
### System Model  
The end devices in an IoT network consist of a number of IoT sensors generating large volumes of data in irregular patterns. Generally, the IoT network consists of one sink node and a set of \(\mathcal{N}\) sensor nodes. The IoT network, created using the RPL protocol, splits the \(\mathcal{N}\) sensor nodes further into a set of child and parent nodes. The network \(\mathcal{N}=\mathcal{P}\mathcal{U}\mathcal{C}\), where \(\mathcal{P}=\{p_{1},\ldots,p_{x}\}\) is the set of parent nodes and \(\mathcal{C}=\{c_{1},\ldots,c_{x}\}\) are the child nodes. Each sensor node participates in generating the data packet at random intervals. All the generated data are directed toward the root node. The parent-child topology creates a network graph, i.e., a DODAG. The node's position in the network graph is defined with the term 'rank'. Rank is used to assign a numerical value to each node in the network. These numerical values represent the distance (or cost) of the node from the root node.  
The rank serves as an indication of the node's position within the network hierarchy. Rank is measured with a specific objective function (OF). The standardized mechanism uses OF zero (OF0) based on hop counts and minimum rank with hysteresis OF (MRHOF), which is based on the expected number of transmission counts (ETX) metric. DODAGs created using a specific OF are maintained by a control message known as a DODAG information object (DIO). IoT nodes operating in a lossy environment can have fluctuating traffic patterns. In the IEEE 802.15.4-based MAC layer for medium access, channel sensing occurs only once at the end of the BE period, and the BE increases each time the channel is found to be busy.
# An NUTSF at Sub-Region for Suppression of Torque Ripple in Switched Reluctance Motors
## 4 Proposed NUTSF Control Strategy
### Comparison of the Existing and Proposed Schemes  
In recent years researchers have proposed some control strategies to suppress the torque ripple of the SRM. The control strategies for controlling torque ripple are compared in detail in Table 1.  
The APC strategy only controls the turn-on angle of the SRM in [26; 27], and there is no control strategy in the TpE, resulting in a larger current and torque ripple. The current chopping control (CCC) produces a lot of chopping to keep the current constant, leading to a larger torque ripple [28; 29]. The intelligent algorithm is applied to the SRM in [30; 31],  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **APC** & **CCC** & **Intelligent Control** & \multicolumn{2}{c}{**TSF**} & **Proposed NUTSF** \\  & **Strategies** & **Strategies** & \multicolumn{2}{c}{**Strategies**} & **Strategies** & **Strategy** \\ \hline Item & [26; 27] & [28; 29] & [30] & [31] & [24] & [25] & \\ \hline robustness & medium & high & medium & high & medium & medium & high \\ \hline online or offline & offline & offline & offline & offline & offline & online \\ \hline implementation & easy & easy & complicated & medium & medium & complicated & easy \\ \hline torque ripple & high & high & medium & medium & low & medium & low \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of torque ripple reduction methods.  
Figure 10: Block diagram of the proposed NUTSF.  
but it takes a lot of training data to execute. The improved TSF in [24] is built, but it has a lower adaptability in a wide speed range. The parameter selection of the offline TSF and the construction of the GA algorithm are complicated in [25].  
Different from the above-mentioned control strategies. The control strategy proposed in this paper is based on the region division of inductance characteristics, and the non-unit torque sharing function control strategies in different regions are designed according to the motor characteristics. The NUTSF in this control strategy is more in line with the changing trend of the actual torque. The optimization algorithm of the TSF changes the P1 and P2 th-power according to the torque error, so that the proposed NUTSF can be controlled online and effectively reduce the torque ripple in a wide speed range. In addition, the control strategy is simple and easy to implement, and it has good reliability when the speed changes.
## 5 Experiments Results
### Effect of the Principle Component Number  
PCA is applied over the original active HSI data to cut down the spectral redundancy and reduce computational cost accordingly. The degree of the reduction depends on the number of principle components preserved. The fewer principle components are left, the less the redundancy and cost are, but the more information is lost for the data. In this section, we perform the experiment to study the influence of PCA on the performance of the hybrid DenseNet. An overview of percentage contribution explained by different number of principle components is shown in Table 7. As it shows, 40 principle components could explain 97.18% of the total variance. As the number of components decreases, the contribution decreases.  
Figure 13 shows the effect of the number of PCA components to the classification results. For most classes, 10 components are enough to obtain a good accuracy; however, 40 components are need for Class 6 to achieve a satisfactory classification result. The experiment illustrates that there exists giant spectral redundancy in the active HIS dataset, and the spectral bands of the hyperspectral imager could be fewer.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component Number** & **10** & **20** & **30** & **40** \\ \hline
**Contribution (\%)** & 92.24\% & 94.30\% & 95.88\% & 97.18\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: The contribution (%) to variance explained by different number of principle components.  
Figure 12: Classification results with varying proportion of training samples.  
Figure 13: Classification results with different number of Principle Component Analysis method (PCA) components.