esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Sample Selection and Preparation  
This study was approved by the Ethics committee (protocol no. 602-2020-OSS-AUSLBO-20032) and was conducted in accordance with the Declaration of Helsinki. The manuscript of this laboratory study has been written according to Preferred Reporting Items for Laboratory studies in Endodotology (PRILE) 2021 guidelines [20]. Figure 1 is a visual representation of the study design and its outcomes.  
Oval-shaped single-rooted teeth (N = 64) extracted for periodontal reasons were selected for the study. Teeth with root caries, multiple root canals, immature apex, and/or previous endodontic treatments were previously excluded. A preoperative X-ray (Durr Dental VistaScan Plus, Biettigheim-Bissingen, Germany) was performed to assess the root canal anatomy. Additionally, a preoperative CBCT (NewTom VGi, Verona, Italy) was obtained to acquire information on morphology of root canals. Only oval-shaped configuration was included. Mesiodistal and buccolingual diameters of each root canal at 7 mm from the apex were measured using a dedicated software (Horos Project, Geneva, Switzerland) (Figure 2).  
Teeth were stored at room temperature in sterile water with 1% NaCl(Niclor 5 Ogna, Muggio, Italy) until use. To ensure uniformity, crowns were removed using a high-speed water-cooled turbine and a diamond-coated bur (Intensiv SA, Montagnola, Switzerland) that was 12 \(\pm\) 1 mm from the apex. To evaluate root canal patency and standardize the working length, a 25 mm #10 stainless K-File (Dentsply Maillefer, Ballaigues, Switzerland) was used as a scouting instrument until its tip was visible at the apical foramen (WL).  
Each specimen was then placed and fixed in a customized mold with a polyvinylsiloxane impression material (Optosil, Kulzer GmbH, Munich, Germany) to clinically simulate the alveolar position. All specimens remained in the same position for the entire study to allow the root canal instrumentation, filling procedure, and radiographic examination.  
Figure 1: PRILE 2021 Flowchart.
# Security Risk Analysis of LoRaWAN and Future Directions
## 7 Conclusions and Future Work  
LoRaWAN is one of the most comprehensive and adopted LPWAN technologies, which is rapidly growing in IoT applications, especially in smart meters and oil and gas operations. LoRaWAN v1.0 had security breaches and drawbacks, which were addressed by the release of LoRaWAN v1.1 recently. Although it has improved security properties compared to the previous version, LoRaWAN v1.1 still has some security risks, some introduced by the new security framework, others by not being covered by the specification, which warrants attention from developers. In this article, the authors' aim was to shed additional light to these risks by presenting a comprehensive Security Risk Analysis, along with providing some remedies such as prevention and mitigation strategies.  
In this work, we have analyzed the security risks of LoRaWAN v1.1 by using ETSI guidelines and created a threat catalog for LoRaWAN v1.1 along with discussions and analysis in view of scale, impact and likelihood of each threat. We also discussed several aspects that are more related to practical implementation of the technology and while they are not necessarily part of the spec, they are important to highlight for practitioners. Furthermore, we also provided our insights regarding security improvements of LoRaWAN that might be adopted for up-coming versions.  
According to the result of this security risk analysis, LoRaWAN v1.1 appeared to have a couple of relevant security threats (especially vulnerabilities against end-device physical capture, rogue gateway and replay attacks). Other than the security flaws mentioned in this text, LoRaWAN v1.1 has proven to be more secure and reliable, compared to its earlier version (v1.0). Therefore, with the addition of these new features of roaming and mobility support (the existing ones were: long range, wide availability of low-cost devices, high community support, easily implementable devices), we predict LoRaWAN to be more and more attractive for IoT applications.  
In the future, we are planning to do a comprehensive security analysis of LoRaWAN v1.1 with one of the security proof tools, such as Tamarin [40] or Scyther [41]. We will also propose new techniques and/or methodologies to tackle these newly recognized security problems. Finally, our future work will focus on finding secure key distribution mechanisms that can be used by LoRaWAN v1.1, in the distribution phase of root keys (i.e., the AES-128 bits root keys: _AppKey_ and _NwkKey_).  
[PERSON] and [PERSON] conducted the research along with analysis and results. The manuscript is also written by the same authors. M.G. has provided the funding and supervision of this research.  
This work (research as well as open access publication) was supported partially by grants: 20201010 (SMART Project) of the European Regional Fund (ERUF), 20150367 (TIMELINESS Project) of the Swedish Knowledge Foundation (KKS), and the Portuguese funding institution FCT--Fundacao para a Ciencia e a Tecnologia, under the sabbatical leave fellowship SFRH/BSAB/128459/2017.  
The authors declare no conflict of interest.
# Defect Tailoring in HfO2/Si Films upon Post-Deposition Annealing and Ultraviolet Irradiation
## 1 Introduction  
The improvement in the electrical performance of thin film transistors (TFTs) has been a subject of interest over the last decade. High-k dielectric materials such as hafnium oxide (HfO\({}_{2}\)) have been widely used as gate oxide for the fabrication of TFTs because of their low intrinsic leakage current [1,2]. However, structural and electronic defects in gate oxide, such as oxygen vacancies, dangling bonds, or ionic impurities, promote the creation of electronic traps with localized energy within its band gap, which could promote an increased leakage gate current through this material, particularly on films prepared with low-temperature processes, which are compatible with flexible substrates, for example [2]. On the other hand, the application of effective processing techniques at low temperatures, such as ultraviolet irradiation, plasma treatment, or conventional thermal treatments, has been widely used for improving the electronic performance of TFTs [3]. For example, the effect of temperature on the physical properties of HfO\({}_{2}\) thin films deposited on a silicon substrate has been analyzed [4,5], obtaining an increase in grain size, a reduction in surface roughness, organic residues, O-H bonds, and defect concentration with an increase in the applied temperature. [PERSON] et al. [6] analyzed the effect of UV irradiation on HfO\({}_{2}\) nanoparticles. According to the photoluminescence analysis, the emission band centered at 2.3 eV decreased with the application of UV light, which implied an increase in oxygen vacancies, which act as charge traps in the nanoparticles. [PERSON] et al. [7] presented an analysis of the physical properties of HfO\({}_{2}\) thin films upon thermal treatment and UV/ozone plasma treatment. The HfO\({}_{2}\) films treated with UV/ozone plasma exhibited a reduction in impurities because a larger portion of Hf atoms is fully oxidized, and the oxygen vacancies decreased. On the other hand, there is an increased interest in HfO\({}_{2}\) in photocatalysis and electrocatalysis applications. For example, Mn-doped HfO\({}_{2}\) nanoparticles [8] and HfO\({}_{2}\)-rGO composites [9,10] have been reported for methylene blue photodegradation and electrocatalytic H\({}_{2}\) evolution, respectively. For this sake, the role of defect engineering has been demonstrated as a promising way to achieve better efficiency.  
In the present work, the effect of UV and thermal post-deposition treatments on defect density in HfO\({}_{2}\) films prepared by dip coating at room temperature was assessed by X-ray diffraction, Raman spectroscopy, atomic force microscopy, UV-visible spectroscopy, and fluorescence spectroscopy. We propose a semiquantitative treatment of the fluorescence data to measure the relative Density of States contributing to the electronic and electrical behavior, which, together with optical characterization, led to quantitative band diagrams of the films.
## 3 Key Technologies in Trust Management for WSNs
### Trust Evaluation Methods  
In this section, we seek the answer to RQ4: What are the methods for trust evaluation? As stated earlier, trust evaluation includes direct trust evaluations, indirect trust evaluations, and updating the trust value. Based on the characteristics of trust evidence, direct trust evaluation methods can be divided into two categories: threshold-limiting methods and success-failure methods [10]. The threshold-limiting method can be used for evaluating  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Trust Evidence** & **Extraction Methods** & **Comparison** \\ \hline \multirow{4}{*}{PSR} & Direct interaction. & Included in method ‘nodes’ promiscuous receiving mode. \\  & Nodes’ promiscuous receiving mode. & The statistical information in the flow table is derived using the method ‘nodes’ promiscuous receiving mode. \\  & Retrieve from flow table. & Included in method ‘nodes’ promiscuous receiving mode. \\ \hline \multirow{4}{*}{PRR} & Direct interaction. & Included in method ‘nodes’ promiscuous receiving mode. \\  & Nodes’ promiscuous receiving mode. & \\ \hline \multirow{4}{*}{PFR} & Watchdog plus sender overhearing receiver. & Redundant (with method ‘watchdog plus sender overhearing receiver’ selected); increases complexity. \\  & and receiver. & Watchdog plus method ‘watchdog plus sender overhearing receiver’. \\  & Using ACK messages. & Relics on interactions involving request and response messages related to traffic profiles, leading to communication overhead. \\ \hline \multirow{4}{*}{ECR} & Calculate the energy consumption rate based on the beacon message. & \multirow{2}{*}{Based on sensor nodes monitoring their own energy consumption with high precision, not very realistic.} \\  & Convert the power consumption sequence. & \\ \hline \multirow{4}{*}{DA} & Extract differences in data sequences of the evaluating node and the evaluated node. & Less accurate than the method ‘extract the accuracy of the data using an outlier detection algorithm based on all neighborhood data references’. \\  & Extract the accuracy of the data using an outlier detection algorithm based on all neighborhood data references. & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Methods for extracting trust evidence.  
packet sending and receiving rates and the energy consumption rate, while the success-failure method can be used for evaluating the packer forwarding rate and the data accuracy.
# Reducing Forecast Errors of a Regional Climate Model Using Adaptive Filters
## 5 Experiments
### Capturing Spatial and Temporal Variability  
The proposed framework treats the spatial variability through the clustering procedure and the temporal variability through adaptive filtering. Figure 6 illustrates the clustering results of the meridional wind variable at 250 and 500-hPa levels in the forecast domain. The color-bar indicates the centroids of the cluster regions. One can note more homogeneous and smooth clusters at 250 hPa than at 500 hPa. This is expected since the atmosphere at 500 hPa level height is nearer to the earth's surface, which produces a spatial structure due to surface features such as the continents and mountains.  
Figure 7 presents the 250-hPa meridional wind for a time slice of the forecast, which started on 13 December 2001. This shows the temporal variability of the time series. The filtered data approaches the reference NCEP data and shows the reduction of the forecast errors.
## 2 Materials and Methods
### Statistical Analysis  
For participants' characteristics (age, body mass, height, Body Mass Index (BMI), PA, preferred gait speed), a Mann-Whitney test was used due to non-normal data distribution. Spatiotemporal variables of gait exhibited a normal distribution (Shapiro-Wilk test, \(p\) > 0.05). Multivariate analysis of variance (MANOVA) with repeated measures was performed on the parameters of gait pattern. Provided that the MANOVA was significant, a univariate one-way ANOVA was performed on each dependent variable to determine those affected by factors. To assess the effects of the inclination and load positions, a repeated-measures ANOVA considering incline (2 levels, 0\({}^{\circ}\) and +5\({}^{\circ}\) incline), positioning (3 levels, hand, shoulder, no-load) and side (2 levels, unloaded (unpreferred side) and  
Figure 2: Participant while walking on a treadmill: (**a**) carrying a backpack on the shoulder; (**b**) carrying a backpack in the hand.  
loaded (preferred side)) as factors was performed, followed by a post hoc test using a Bonferroni correction. A Mauchly test was used to check the assumption of sphericity, and Greenhouse-Geisser correction was used to adjust for a lack of sphericity where needed. The ANOVA effect size was expressed by its \(\eta_{p}\)2; 0.01 was interpreted as a small effect size, 0.06 was indicative of a medium effect size and 0.14 was indicative of a large effect size [29]. Statistical analysis was performed using STATISTICA computer software (v. 14.0.0.15, TIBCO Software Inc., Palo Alto, CA, USA; 2020), with the significance level set to \(p<0.05\).  
The minimum sample size required to detect a statistically significant effect was determined using G*Power software (v.3.1.9.7, Germany) [18]. This calculation was based on the \(F\)-test, MANOVA, with repeated measures, within the following factors and input parameters: an alpha level of 0.05, a statistical power of 0.80, an \(\eta_{p}\)2 value of 0.06 (medium effect size f = 0.25) and a number of repeated measures equal to 12 (\(2\times 3\times 2\)). The analysis indicated a necessary sample size of 33. With 41 participants, the present study exceeded this minimum requirement.
# FPGA-Based Manchester Decoder for IEEE 802.15.7 Visible Light Communications
## 3 Evaluation of the Receiver Performance in Simulations
### Evaluation of the Syncronization on Sequences Affected by Timing Errors  
The timings of the transmitter and the receiver are based on independent local oscillators, which, although they share the same nominal frequency, generate clocks affected by light differences. Typical oscillators have accuracy in the order of some tens of part per million (ppm) that translate in relative frequency differences between transmitter and receiver below 0.1%. Another possible source of frequency shift is the Doppler effect generated when the transmitter and the receiver move one with respect to the other. In addition, the oscillators are affected by jitter noise [38], i.e., rapid variation of the clock phase due to the electrical noise, and frame jitter noise [39], i.e., temporal variation of the start of the bit frames.  
Here, we tested how the proposed algorithm tolerates the frequency shift and jitter noise, starting with the first. We duplicated the experiment proposed in the previous section by changing the bit length \(T_{b}\) by 1%, which, being a tenfold variation with respect to what expected in a real application, represents a quite unfavorable condition.  
The results are presented in Figure 6a. By comparing the curves to those of Figure 5, we note a slight worsening of the performance. In particular, the curve for \(Z_{\%}=2\%\) stands out, indicating that in this condition the reception is never possible regardless of the SNR, while in the previous test \(Z_{\%}=2\%\) granted the best performance (see Figure 5). This result is explained by considering that the limitation of \(Z_{\%}=2\%\) corresponds to a correction of 1 sample per bit with the experimental parameters of Table 5, that is, exactly the out-of-frequency condition we imposed in the transmission sequence. In other words, the  
Figure 5: BER measured when a data packet of 1 Mbit affected by white noise from 0 to \(-25\) dB is received by applying different \(Z_{\%}\).  
maximum correction rate is not enough to catch with the bit temporal variation produced by the simulated frequency error. Apart from \(Z_{\%}\) = 2%, like in the previous test, the performance improves with decreasing \(Z_{\%}\), but there is no significant gain for \(Z_{\%}\) < 10%. No reception is achieved for SNR < \(-\)10 dB.  
The last experiment was designed to simulate the presence of jitter: the bits were generated by varying their duration. If \(T_{b}\) and \(T_{bn}\) are the actual and the nominal bit duration, we varied randomly \(T_{b}\) in the range \(T_{bn}(1\pm 0.01)\), i.e., a 1% variation. Once again, the other parameters were the same as reported in Table 5. The measurements reported in Figure 6b, are pretty similar to those obtained without jitter shown in Figure 5. A light worsening is present for all the saturation levels \(Z_{\%}\) tested, but the receiver easily tolerates this level of jitter.
# Parameter Extraction of Photovoltaic Cells and Panels Using a PID-Based Metaheuristic Algorithm
## 2 Materials and Methods
### The Proposed Algorithm
#### 2.3.2 Calculating System Deviations
With this, the solutions are updated using a combination of output and zero output values, moderated by \(\eta\):  
\[\mathbf{x}(t+1)=\mathbf{x}(t)+\eta\Delta\mathbf{u}(t)+(1-\eta)\mathbf{o}(t) \tag{14}\]where  
\[\eta=\tau_{6}cos(t/T) \tag{15}\]  
\(\mathbf{r_{6}}\) is a vector of random numbers coming from 0 to 1. Finally, the algorithms clip the solutions to ensure they remain within the set bounds. In the extraction of the PV panel parameters, setting the \(K_{p}=1.2\), \(K_{i}=2\), and \(K_{d}=0.75\) helped the algorithm balance between exploration and exploitation for stable and efficient convergence. The selected moderate \(K_{p}\) value ensures responsiveness without oscillations, while the higher \(K_{i}\) value corrects residual errors, and \(K_{d}\) value provides damping to prevent overshooting. Additionally, the Levy flight parameter was set to \(\beta=2\) to allow controlled long-distance jumps, preventing premature convergence while maintaining stability. Empirical tests showed that \(\beta<2\) led to stagnation, while \(\beta>2.5\) caused excessive randomness. This configuration provided optimal accuracy and efficiency, obtaining the best solutions within a few runs. Although a full grid search or sensitivity analysis was not conducted due to computational constraints, the selected parameters were determined through iterative empirical testing and consistently yielded optimal or near-optimal results across multiple test cases. This parameter set achieved low RMSE and MBE values with efficient convergence over multiple runs. After running through all the generations, the code returns the best solution and its fitness value, signifying the optimal parameters and the minimum RMSE achieved by the algorithm. Algorithm 1 and Figure 3 represent the algorithm's pseudo-code and flowchart. Figure 3: PSA flowchart. Due to the PSA's simplicity and ease of implementation, the implementation is built into vs. Code using the Python language version 3.11.9 and runs on a PC with an Intel Core i9 CPU and Windows 11 64-bit operating system. It is straightforward to use and offers stable performance.
# Arabic Cursive Text Recognition from Natural Scene Images
## 3 Advances of Deep Learning Network in Text Analysis
### Recurrent Neural Networks  
Arabic script is represented in a contextual manner. The contextual learning is possible through RNN networks as each character in Arabic script depends on its previous character and predicts the next character at the current point in time. If context is eliminated from Arabic script, then recognition is not possible. Therefore, for cursive scene text analysis, the focus shifts from traditional backpropagation to context learning classifiers. Recent years' work as explained in [4; 5; 6; 84] on Arabic-like script used the Recurrent Neural Network (RNN) approach for text classification. The RNN is suitable for problems where context is important to learn. The MDLSTM is considered as a connectionist approach, which mainly relies on the Multidimensional Recurrent Neural Network (MDRNNN) and Long Short-Term Memory (LSTM) networks. The multidimensional LSTM follows the RNN approach to learn the sequences. All past sequences with respect to the current point in time are accumulated to predict the output character. The RNN provides an appropriate architecture for Arabic scene text recognition. The Arabic scene text image requires extensive pre-processing before applying the learning classifier. The pre-processing includes skew correction, removal of non-text elements from an image, conversion of the text image into standard format and feature extraction, which is a very crucial part machine learning tasks.  
The deep learning architecture is implemented at the hidden layer of the LSTM network. As shown in Figure 13, the dotted lines marked at the hidden layer represent interconnection among memory blocks. The hidden layer can be adapted by inclusion of more than one layer, and the calculation at the hidden layer could be optimized by the interconnection of hidden layers at different levels, as adopted in the proposed thesis. The level of hidden layers and their interconnections among each other present the idea of the deeply interconnected network, which takes more time during gradient computation than usual, but exhibits good results in the learning of complex patterns, as experimented by [5; 85]. Both works presented by [PERSON] et al. rely on the deep MDLSTM architecture for learning the complex pattern of printed Urdu data. The deep MDLSTM network with its adapted architecture is followed in this proposed thesis, as it has not been extensively experimented on Arabic scene text recognition.  
Figure 13: LSTM deep learning view.
# Global GNSS-RO Electron Density in the Lower Ionosphere
## 2 Data and Methods
### \(N_{e}\) Retrievals
#### 2.3.1 Inversion from \(\Delta\)hTEC
Note that \(\mathbf{S}_{\mathbf{a}}\) is a height-dependent parameter, which is empirically determined for stable \(N_{e}\) inversion over a large dynamic range and variability in the \(D\)/\(E\)-region altitudes. The detailed data flow diagram of the new algorithm and changes can be found in Appendix A. For computation efficiency of the inversion from \(\Delta hTEC\) to \(N_{e}\), we employ a variable height range for the state vector such that \(z_{max}\leq h_{max}+10\) km. Because \(\Delta hTEC(h_{t})\) does not have much information on \(N_{e}\) at altitudes above \(h_{max}\), it would waste resources to carry out the \(N_{e}\) retrieval at extra levels above \(h_{max}\). This adaptive height range in the retrieval helps to reduce the number of \(N_{e}\) levels in the state vector, and therefore save CPU time for the inversion calculation in Equation (11).