esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Database and Acquisition Procedure
On average, each cell will be represented in each thermal image with 8 \(\times\) 8 pixels, which meets IEC TS 62446-3 thermography standards and also the best practice recommendations from other authors (e.g., [24]). Moreover, all of the images were acquired following the main recommendations from the aforementioned norm. More specifically, the images were acquired under stable radiation and weather conditions, exclusively with irradiances above 800 W/m\({}^{2}\), average wind speeds of 3 m/s, and with a clear sky. \begin{table}
\begin{tabular}{l l} \hline \hline
**Group of Parameters** & **Value** \\ \hline Camera model & Dji XT2, Resolution: 640 \(\times\) 512 JPEG \\ Camera features & Focal Length: 25.0 mm, Field Of View: 25.0 deg, Exposure: Program AE, 1/100 s, \\  & f/1.4, ISO 128 \\ XMP metadata & Relative Altitude: 25.000000, Flight Yaw Degree \(-91.699997\), Rtk Flag 1 \\ Thermal values & Emissivity: 1.00, Object Distance: 0.00 m, Reflected Apparent Temperature: 20.0 \({}^{\circ}\)C, Atmospheric Temperature: 20.0 \({}^{\circ}\)C, IR Window Temperature: 20.0 \({}^{\circ}\)C, \\  & IR Window Transmission: 1.00, Relative Humidity: 30.0\% \\ Planck values & Planck R1: 457.785, Planck B: 1.428, Planck F: 1, Planck O: 581, Planck R1: 457.785, \\  & Planck R2: 1 \\ Camera range & Range Max: 135.0 \({}^{\circ}\)C, Camera Temperature Range Min: \(-25.0\)\({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main parameters associated with each thermal image stored using EXIF. Figure 8: Acquisition process of the images over the photovoltaic plant. After calculating the GSD, the thermal camera footprint can be calculated according to (6). \[ \begin{split}&\textit{ footprint\_width}=GSD\times\textit{image\_width}\\ &\textit{ footprint\_height}=GSD\times\textit{image\_height}\\ \end{split}  \tag{6}\]  
This way, a footprint of \(10.88\times 8.704\) m is obtained. The overlapping between the images can be calculated based on this footprint and the GPS location of each image. The obtained overlapping aligns with the theoretical overlapping calculated by the DJI mission planner. More specifically, a 10% frontal overlap ratio and also a 10% side overlap ratio is obtained. This overlap is the minimum that is allowed by the DJI mission planer. This configuration maximizes the survey area per inspection. In summary, it can be concluded that all of the images were acquired using the best practices and conditions.
# Analyzing Zone-Based Registration under 2-Step Paging in Mobile Communication Network
## 3 Analysis of ZBR under 2-Step Paging
### Total Signaling Cost  
The total signaling cost on radio channels is composed of the registration cost and the paging cost. In 12R, the location registration cost per unit time can be calculated as follows:  
\[C_{R}^{\rm 12R}=U\times\lambda_{m}\times P_{R}=U\times\lambda_{m}\times\left[ \sum\
olimits_{i=1}^{K}(\pi_{i}\times p_{i0})\right] \tag{12}\]  
\(U\): location registration cost for one registration  
\(P_{R}\): the probability that a UE performs location registration when it leaves the current cell \(K\): total number of states in a zone  
Note that, in 2 ZR, the location registration cost per unit time can be calculated as follows:  
\[C_{R}^{\rm 2 ZR}=U\times(1-\theta)\times\frac{1}{E[T_{Z}]} \tag{13}\]  
\(U\): location registration cost for one registration  
\(E[T_{Z}]\): the expected value of time spent in a zone  
In this study, a 2-step paging method has been used to perform paging by dividing a zone stored in the network to which the UE belongs into two sub-areas. When the incoming call to a UE arrives, the paging cost can be obtained by multiplying the paging cost per cell by the number of cells to be paged.  
The cells located at the boundary at which there is a probability of returning to the zone visited immediately before are called first sub-area A1, and the other cells are called the second sub-area A2.  
When a call to a UE arrives, the first sub-area A1 adjacent to the zone visited immediately before should be paged, and the next sub-area A2 should be paged if there is no response. Therefore, the paging cost per unit of time can be obtained by multiplying the paging cost per cell by the number of cells to be paged as follows:  
\[C_{P}^{\rm 12R}=V\lambda_{c}\left[n_{1}\sum\
olimits_{i=1}^{k^{*}}\pi_{i}+ \left(\left(n_{1}+n_{2}\right)\sum\
olimits_{i=k^{*}+1}^{K}\pi_{i}\right)\right] \tag{14}\]  
\(V\): paging cost per cell  
\(\lambda_{c}\): number of incoming calls per unit time  
\(n_{i}\): number of cells in i-th sub-area \(A_{i}\)  
\(K\): total number of states in a zone  
\(k^{*}\): total number of states for the cells of the first sub-area to be paged  
\(\pi_{i}\): the probability that a UE is in state i  
In the above, \(k^{*}\), the total number of states for the cells of the first sub-area to be paged, is \(k\) that makes the paging cost, \(V\lambda_{c}\left[n_{1}\sum\
olimits_{i=1}^{k^{*}}\pi_{i}+\left(\left(n_{1}+ n_{2}\right)\sum\
olimits_{i=k^{*}+1}^{K}\pi_{i}\right)\right]\), minimal for every \(k,k=1,2,\ldots,K\).  
Since the total signaling cost on radio channels consists of location registration cost and paging cost, the total signaling cost per unit time of 1 ZR can be calculated as the sum of location registration cost and paging cost, as follows:  
\[C_{T}^{\rm 12R}=C_{R}^{\rm 12R}+C_{P}^{\rm 12R} \tag{15}\]
# IoT's Tiny Steps towards 5G: Telco's Perspective
## 7 Conclusions  
The IoT paradigm has the potential to revolutionize all areas of daily life of individuals, businesses, and society as a whole. Telcos enjoy a central role in the paradigm of IoT because of owing communication infrastructure which is exposed to the numerous technical challenges due to changing connectivity and performance requirements of various IoT applications. These requirements cannot be met with the current cellular networks which create the need to introduce new types of technologies leading toward 5G networks as the main driver for enabling numerous IoT applications. In this context, this paper proposed the activity-based classification of IoT applications and specification of their performance requirements in order to identify 5G radio technologies used to meet them, all from telco's perspective. Activity-based classification of IoT applications indirectly allowed the construction and distribution of performance, as IoT value proposition, while using 5G enabling technologies, as IoT resources in telco's internal value chain. In this context, high performance requirements of each activity-based IoT application class (i.e., connection density, positioning accuracy, coverage) served as a basis to analyze various enabling technologies in radio access part of 5G network in terms of advantages, disadvantages, and research gaps.  
On the basis of the conducted analysis, we concluded that the following technologies can meet the high performance requirements of mMTC activity-based IoT application classes: new waveform technology (in terms of connection density), large scale antennas (in terms of positioning accuracy), wide and flexible bandwidth technologies, in-band full duplexing technologies, and access architecture related radio technologies (in terms of coverage). According to the identified research gaps, one may conclude that the optimal solutions for each technology are still in its infancy. This implies that any 5G enabling technology should be first implemented and tested through prototype construction before the deployment of specific IoT application. Thereafter, the technology combination that meets the performance requirements of each activity-based IoT class should first be found. Finally, the practical implementation of different technological combinations may further lead to a deep and comprehensive analysis of QoS and QoE in the context of IoT applications.  
**Acknowledgments:** The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments (research study); [PERSON] performed the experiments (research study); [PERSON] analyzed the data and contributed materials; [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A 1 \(\times\) 2 Two-Dimensional Slanted Grating Based on Double-Layer Cylindrical Structure
## 2 Design of the 2D Slanted Grating
We use RCWA and SA algorithms for grating design and optimization of the parameters {d, f, \(\alpha\), h\({}_{1}\), h\({}_{2}\)}. The rigorous coupled-wave analysis (RCWA) is a purely numerical method. The rigorous coupled-wave approach analyzes the diffraction of an electromagnetic plane wave incident obliquely at a planar grating bounded by two different media. RCWA expands the electromagnetic field and the dielectric constant of the material by a Fourier series, and solves [PERSON]'s equations by solving the eigen-values and eigenvectors of the matrix. Therefore, RCWA is an effective tool for dealing with periodic structures, especially diffraction gratings. SA is a probabilistic technique that can approximate the global optimum of a given function. The purpose is to satisfy the requirements of higher efficiencies in the (0, \(-\)1) order and the (\(-\)1, 0) order, and lower efficiency in the (0, 0) order; thus, the cost function \(\Phi\) was set up as follows:  
\[\Phi=\frac{\left[\mathrm{DE}_{(0,-1)}-\mathrm{DE}_{\mathrm{av}}\right]^{2}+ \left[\mathrm{DE}_{(-1.0)}-\mathrm{DE}_{\mathrm{av}}\right]^{2}}{\sum_{i=-m,j=-n}^{i=m,j=n}\mathrm{DE}_{(i,j)}} \tag{5}\]  
\[\mathrm{I}_{\mathrm{av}}=\frac{1}{2}\left[\mathrm{DE}_{(0,-1)}+\mathrm{DE}_{( -1,0)}\right]\]  
where \(\mathrm{DE}_{(0,-1)}\) and \(\mathrm{DE}_{(-1.0)}\) are the diffraction efficiencies of (0, \(-\)1) and (\(-\)1, 0) orders, and \(\mathrm{DE}_{(i,j)}\) is the diffraction efficiency of the (i,j)th order, respectively. DE is calculated by rigorous coupled-wave analysis. When \(\Phi\) reaches the minimum value, the SA algorithm was cut off, and the optimal parameters solution were obtained. The 2D grating structure parameters were processed with a global optimization, and the optimized parameters are d = 603 nm, h\({}_{1}\) = 330 nm, h\({}_{2}\) = 528 nm, f = 0.47, and \(\alpha\) = 20.6\({}^{\circ}\) when the incident light at 450 nm is normally incident on the grating surface. With this structural parameter, the diffraction efficiencies of the (\(-\)1, 0) and (0, \(-\)1) orders are 39.10% and 39.12%, respectively, under the TE polarization incidence. When the incident light is TM polarized, the diffraction efficiencies of (\(-\)1, 0) and (0, \(-\)1) orders are 39.13% and 39.19%, respectively, at a wavelength of 450 nm. According to the parameters, we can deduce that the polarization-dependent loss (PDL) of this grating is 0.01 db, which means a good polarization-independent characterization was achieved.
## 3 The Proposed Approach
### Test Data Generator Module
It should be noted that the above method can be applied for representing values of integer input variables by setting \(d_{i}\) to 0, and using the following formula instead of the formula in Equation (3):  
\[\mathrm{x_{i}}=\mathrm{a_{i}}+\mathrm{int}(\mathrm{x_{i}}^{\prime}\frac{ \mathrm{b_{i}}-\mathrm{a_{i}}}{2^{\mathrm{m_{i}}}-1}), \tag{4}\]  
Now, each chromosome (as a test case) is represented by a binary string of length m = \(\sum_{i=1}^{k}m_{i}\); the first m1 bits map into a value from the range [a\({}_{1}\), b\({}_{1}\)] of variable x\({}_{1}\), the next group of m\({}_{2}\) bits map into a value from the range [a\({}_{2}\), b\({}_{2}\)] of variable x\({}_{2}\), and so on; the last group of m\({}_{k}\) bits map into a value from the range [a\({}_{k}\), b\({}_{k}\)] of variable x\({}_{k}\). For example, let a program have two input variables, x and y, where -3.0 \(\leq\) x \(\leq\) 12.1 and 4.1 \(\leq\) y \(\leq\) 5.8, and the required precision is four decimal places for each variable. The domain of variable x has a length of 15.1; the precision requirement implies that the range [-3.0, 12.1] should be divided into at least 15.1 \(\cdot\) 10,000 equal size ranges. This means that 18 bits are required as the first part of the chromosome: 2\({}^{17}\) \(<\) 151,000 \(\leq\) 2\({}^{18}\). The domain of variable y has a length of 1.7; the precision requirement implies that the range [4.1, 5.8] should be divided into at least 1.7 \(\cdot\) 10,000 equal size ranges. This means that 15 bits are required as the second part of the chromosome: 2\({}^{14}\) \(<\) 17,000 \(\leq\) 2\({}^{15}\). The total length of a chromosome (test case) is then m = 18 + 15 = 33 bits; the first 18 bits code x and remaining 15 bits code y. Let us consider an example chromosome: 010001001011010000111110010010010. By using the formula in Equation (3), the first 18 bits, 01000010010110000, represents x = 1.0524, and the next 15 bits, 111110010100010, represents y = 5.7553. So, the given chromosome corresponds to the data values 1.0524 and 5.7553 for the variables x and y, respectively.
## 3 Results  
Figure 1 illustrates a sample of a CLSM image of the canal sealer penetration from each group at the root canal's middle and apical third. Figure 1: A sample of CLSM image of root canal sealer penetration from each group at the middle and apical third of the root canal. The three-way ANOVA revealed that the tested variables (irrigation activation protocol, sealer type, and root third) had significant effects on the MPD variables and their interactions terms (\(p<0.05\)) except for the sealer type * root third and irrigation activation protocol * sealer type * root third interactions (\(p>0.05\)). Table 1 presents a summary of the values for the mean and standard deviation of the MPD of the sealers for all groups. However, regardless of the irrigation activation protocol, the AHP and AHPB sealers statistically presented similar penetration properties. Regarding the effect of the irrigation activation protocol, the highest MPD values were recorded in the SWEEPS groups (697.90 \(\pm\) 279.30), while the lowest value was observed in the CSI groups (259.68 \(\pm\) 83.74). Multiple comparisons indicated significant differences between all irrigation activation protocols (\(p<0.05\)). Considering the sealer type and irrigation protocol interaction effect, a statistically significant difference was detected between the Er,Cr:YSGG laser and EA groups for the AHPB sealer (\(p<0.05\)), while it was insignificant for the AHP sealer (\(p>0.05\)). Considering the irrigation protocol and root third interaction, a significant difference was detected between the maximum depth to which the sealer penetrated between all irrigation protocols groups at the middle third, while the SWEEPS, Er,Cr:YSGG, and EA values were significantly higher compared to the PUI and CSI protocols at the apical third (\(p<0.05\)). Figure 2A shows the mean penetration percentages with the associated standard deviations, highlighting the intragroup and intergroup efficacy across the various sealers and irrigation techniques. Figure 2B illustrates the penetration effectiveness of the different irrigation methods at two root levels regardless of the sealer type. Figure 2C represents the overall penetration performance of the canal sealers independent of the irrigation method and root level. Figure 2D illustrates the sealer penetration at the different root levels irrespective of the sealer type and irrigation method used. The three-way ANOVA test revealed that the variables tested affected the penetration percentage values (\(p<0.05\)), but the interactions between the variables were insignificant (\(p>0.05\)) except for irrigation technique * root third interaction (\(p<0.05\)). Regarding the effect of the irrigation activation protocol, the SWEEPS and Er,Cr:YSGG laser groups exhibited the highest percentage values, while the CSI group had the lowest value. Significant differences were presented between all the irrigation activation protocol groups (\(p<0.05\)) apart from the difference between the
# Examining Attention Mechanisms in Deep Learning Models for Sentiment Analysis
## 5 Experimental Study
### Datasets  
Reviews constitute the basis for almost all benchmark sentiment analysis datasets in the literature used to evaluate a model's performance [47]. The proposed attention-based methods are evaluated on three sentiment analysis datasets, the movie review polarity (MR) [48], the movie review subjectivity (SUBJ) [49] and the IMDb large movie review dataset [50].  
The movie review polarity dataset (MR) is a collection of movie reviews extracted from the website Rotten Tomatoes in 2004. This dataset comes in two different versions, polarity v2.0, which consists of 1000 positive and 1000 negative processed reviews and sentence polarity v1.0, which consists of 5331 positive and 5331 negative processed sentences/snippets. All the reviews are written before 2002, with a cap of 20 reviews per author (312 authors in total) per category. What makes the dataset particularly interesting is the informal language used since each instance is very short and the presence of noisy polarity labeling.  
The movie review subjectivity dataset (SUBJ) was constructed in 2004 by crawling two popular movie review websites to tackle the problem of building a subjectivity corpus. To gather subjective sentences (or phrases), the authors collected 5000 movie review snippets (short text) from the website Rotten Tomatoes. To obtain (mostly) objective data, the authors took 5000 sentences from plot summaries available from IMDb. It worth noting that only snippets of at least ten words and only reviews/summaries of movies released post-2001 were selected, which prevents overlap with the movie review polarity dataset.  
The IMDb large movie review dataset (IMDB) is a collection of 50,000 labeled and 50,000 unlabeled reviews from the website IMDb and was constructed in 2011. It is much bigger than the previous datasets and does not have many correlations between the training the testing set. It was constructed without allowing more than 30 reviews per movie and contains an even number of positive and negative reviews. The authors decided to consider only highly polarized reviews, so reviews with a score equal or greater than 7 are labeled as positive, while reviews with a score equal or less than 4 are labeled as negative and neutral reviews are excluded. In this work, we used 25,000 documents for training and 25,000 for testing.  
The purpose of the text classification task is to assign each document to a sentiment label, e.g., positive/negative/neutral. In the following section, the experimental setup, the parameters and the collected results are thoroughly presented.
## 3 Methodologies
### Model Description
#### 3.2.4 Gradient Boosting Decision Tree (GBDT)  
A GBDT is an advanced ensemble learning technique that operates by incrementally building an ensemble of weak decision tree models to form a strong predictive model. The core idea is to compute the residual errors between predicted and actual values, and to use these residuals as targets to train the subsequent tree. As the number of trees increases, the model can be improved by focusing on examples that are hard to predict. This boosting approach leverages the strength of multiple learners to reduce both bias and variance, leading to improved model accuracy and robustness against overfitting. Through iterative minimization of a differentiable loss function, GBDT effectively adapts to various types of predictive modeling tasks, including both regression and classification. A classical GBDT model is shown in Figure 6. The key steps involved when using a GBDT for regression tasks are as follows: (a) initializing with a constant value that minimizes the loss function, iteratively training decision trees on the residual errors of the current model; (b) updating the model by adding the new tree to the existing ensemble, and (c) optimizing the model using gradient descent to minimize the loss function. The advantages of using a GBDT for regression tasks include its accuracy in capturing complex patterns and feature interactions, flexibility in supporting various loss functions for different types of regression problems, and robustness through subsampling and regularization techniques. The objective function for a GBDT in regression tasks can be formulated as:  
\[Obj(j,n)=\arg\min_{\gamma}\sum_{x_{i}\in\bar{K}_{j_{n}}}L(y_{true},F_{n-1}(x_{ i})+\gamma) \tag{4}\]  
where \(F_{n-1}(x_{i})\) is the prediction value at the \(m-1\)-th iterations, \(\gamma\) is the optimal adjustment value, and \(R_{jn}\) is the region corresponding to the \(j\)-th leaf node in the \(n\)-th tree. In this study, the GBDT model's performance is controlled by seven hyperparameters, including the loss function, the number of estimators (n_estimators), the max_depth, the min_sample_split, the min_sample_leaf, the learning rate, and the subsample ratio. Note that four approaches are usually used to generate loss functions of GBDTs, including square error, absolute, Huber, and quantile.  
Figure 6: Illustration of GBDT building process by adding weak learners.