esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
## 3 OCR Process
### Segmentation
#### 3.2.2 Word segmentation  
After line segmentation, each word from the line is segmented by dividing the line of the text into individual words. Several techniques are used for word segmentation, i.e., the Spacing method involves using the spaces between words to segment the text into words. The dictionary-based method uses a dictionary of words to match against the text and segments the text into words based on the matches. Character-based methods use a combination of known character patterns, such as word breaks and punctuation, to segment the text into words. Deep-learning approaches use supervised learning on annotated datasets to learn the complex relationships between adjacent characters in order to infer word boundaries [73; 74].  
The authors of [75] present word segmentation in Arabic handwritten images using a convolutional recurrent neural network (CRNN) architecture. The authors employ a sliding-window approach for word segmentation, where each window is classified as either a word or non-word using a support vector machine (SVM) classifier. The experimental results show that the proposed CRNN architecture achieves state-of-the-art performance in Arabic handwriting word recognition, with an accuracy of 86.95% on the IFN/ENIT dataset.  
[PERSON] et al. [76] propose a semantic segmentation approach for images containing mixed text to segment the image into different regions based on their content. The segmented regions are then processed using different OCR methods that are specifically tailored to the type of text in each region.
## 4 Experimental Results
### \(F_{1}\) score  
Next, we utilize the \(F_{1}\) score as another metric of detection accuracy. The \(F_{1}\) score is considered a measure of the precise detection or classification of the subject dataset. The \(F_{1}\) score is obtained as follows:  
\[F_{1}=2\left(\frac{P_{r}\times R_{e}}{P_{r}+R}\right), \tag{21}\]  
where \(P_{r}\) is precision and is calculated as follows:  
\[P_{r}=\left(\frac{TruePositive}{PredictedPositive}\right). \tag{22}\]  
True positive corresponds to samples that the proposed algorithm detects as positive samples and that are, in fact, positive. Predicted positives may include both compromised and normal sample points, but the algorithm detects them all as positive. \(R_{e}\) is recall, calculated as follows:  
\[R_{e}=\left(\frac{TruePositive}{ActualPositive}\right). \tag{23}\]  
Figure 11 shows the \(F_{1}\) score of the proposed schemes for different IEEE standard bus systems. The \(F_{1}\) score of the statistical model-based anomaly detection method [7] employing FE for dimensionality reduction is included for comparison. The proposed schemes are also compared with the NCA-based scheme. It is obvious that the proposed FS-based schemes have a higher \(F_{1}\) score for all test cases, whereas the FE-based scheme requires many historical samples from the SE-MF dataset for learning to achieve a higher \(F_{1}\) score. The proposed EDADS-2 has a higher \(F_{1}\) score than EDADS-1 due to the reason that it employs only normal data samples for training. Hence, the average of the distances of the normal training samples from their centroid is close to the value required for accurately separating the normal class from the compromised class. The performance of the NCA-based scheme is lower than that of proposed schemes; however, it performs better compared to the FE-based scheme [7].  
Next, to investigate the impact of several compromised load profiles (compromised samples), we consider different numbers of compromised load profiles, i.e., 24, 30, 36, 40, 45 and 60. The SE-MF dataset load profile is comprised of 360 samples collected through sensors or RTUs at regular intervals of four minutes over 24 h. We use 75% of the data for training and the rest of the samples for testing. Figure 12 shows the \(F_{1}\) score as a measure of the accuracy of the proposed FS-based proposed detection  
Figure 10: The CCD assault detection accuracy of the proposed EDADS-1 and EDADS-2 in standard IEEE 14-, 39-, 57- and 118-bus systems with varying number of training samples. (**a**) Accuracy of EDADS-1; (**b**) accuracy of EDADS-2.  
schemes for standard IEEE 14-, 39-, 57- and 118-bus systems. Figure 12 shows that the FS-based proposed methods have an accuracy of more than 90% for all the employed test systems.
## 3 Governing Equations of CNTRC Beams
### The Governing Equation for CNTRCs Considering the Thickness Effect
If the inertial axial moments are assumed to be negligible (\(I_{1}=I_{3}\approx 0\)), and since the considered end supports of the composite beam are immovable: \(u(0)=u^{\prime}(0)=u^{\prime\prime}(L)=0\), \(u(L)=-\frac{P_{0}L}{A_{11}}\), from Equations (47) and (51), we can get:  
\[A_{11}\bigg{(}1-l^{2}\frac{\partial^{2}}{\partial x^{2}}\bigg{)}\bigg{(}u^{ \prime}+\frac{1}{2}{u^{\prime}}^{2}-\frac{1}{8}{u^{\prime}}^{4}\bigg{)}=C\ (constant) \tag{52}\]  
or in equivalent form:  
\[u^{\prime}-l^{2}u^{\prime\prime\prime}-\frac{1}{A_{11}}\bigg{(}1-l^{2}\frac{ \partial^{2}}{\partial x^{2}}\bigg{)}\bigg{(}\frac{1}{2}{w^{\prime}}^{2}- \frac{1}{8}{w^{\prime}}^{4}\bigg{)}=\frac{C}{A_{11}} \tag{53}\]  
For \(u(0)=u^{\prime\prime}(0)=u^{\prime}(L)=0\) and \(u(L)=-\frac{P_{0}}{A_{11}}\), from the last equation, it holds that:  
\[N=P_{0}-\frac{A_{11}}{2L}\int_{0}^{L}{w^{\prime}}^{2}(x)dx+\frac{A_{11}l^{2}}{ 8L}\int_{0}^{L}{w^{\prime}}^{4}(x)dx+\frac{A_{11}}{L}\Big{[}w(L)\Big{(}1-{w^{ \prime}}^{2}(L)\Big{)}-w^{\prime\prime}(0)\Big{(}1-{w^{\prime}}^{2}(0)\Big{)} \Big{]} \tag{54}\]  
For the composite beam considered here, the boundary conditions for the simply supported beam are \(w(x)=w^{\prime\prime}(x)=0\) for \(x=0\) and \(x=L\), such that Equation (54) becomes:  
\[\frac{\partial N}{\partial x}=I_{0}\ddot{u}+(I_{3}-I_{1})\ddot{w}^{\prime}-I_{3}\ddot {\theta} \tag{47}\]0  
From Equations (31), (35) and (51), we can obtain:  
\[\frac{\partial N}{\partial x}=I_{0}\ddot{u}+(I_{3}-I_{1})\ddot{w}^{\prime}-I_{3}\ddot {\theta} \tag{47}\]1  
\[\frac{\partial N}{\partial x}=I_{0}\ddot{u}+(I_{3}-I_{1})\ddot{w}^{\prime}-I_{3}\ddot {\theta} \tag{47}\]2  
The initial conditions for the system of (66) and (67) were:  
\[\frac{\partial N}{\partial x}=I_{0}\ddot{u}+(I_{3}-I_{1})\ddot{w}^{\prime}-I_{3}\ddot {\theta} \tag{47}\]3  
For the simply supported beam, the shape functions can be chosen as:  
\[\frac{\partial N}{\partial x}=I_{0}\ddot{u}+(I_{3}-I_{1})\ddot{w}^{\prime}-I_{3}\ddot {\theta} \tag{47}\]4  
The nonlinear differential Equations (66) and (67) were difficult to be analytically solved by exact solution, but a very accurate analytical solution can be found by means of OAFM [31; 32; 33; 34; 35].
## 3 Time-Domain Analytical Solution of the Received Signal
### Time-Domain Analytical Solution of the Received Voltage Signal  
First, an ideal step current signal is applied to the excitation coil, denoted as:  
\[I(p)=I\frac{1}{p} \tag{24}\]  
The relative magnetic permeability of coil is 1, and its electrical conductivity is close to 0. Substituting the conditions \(\mu_{\tau 1}=\mu_{\tau}\), \(\sigma_{1}=\sigma\), \(\mu_{\tau 2}=1\), and \(\sigma_{2}=0\) into Equation (22), the reflection coefficient is obtained as:  
\[\Gamma_{U}(p)=pI(p)R(p)=\frac{2I-\frac{2\xi I}{\lambda_{i}\mu_{\tau }d}(\tan\xi-\cot\xi)}{\left(\tan\xi+\frac{2\xi}{\lambda_{i}\mu_{\tau}d}\right) \left(\cot\xi-\frac{2\xi}{\lambda_{i}\mu_{\tau}d}\right)} \tag{25}\]  
where \(\xi=-jd\gamma_{k}{}^{\prime}/2\). Thus, we can obtain the relationship between the poles of the equation and the corresponding Laplace variables, yielding:  
\[p_{\text{tc}}=-\frac{\lambda_{i}^{2}+\left(2\xi_{\text{tc}}/d\right)^{2}}{\mu _{0}\mu_{\tau}\sigma} \tag{26}\]  
According to the Cauchy residue theorem [28], and using the residue calculation property of the poles, we can obtain the inverse Laplace transform of the reflection coefficient, denoted as:  
\[f(t)=\frac{1}{2\pi i}\int_{0}^{+\infty}F(p)\text{e}^{p\prime t}\text{d}p=\sum _{k=1}^{n}\text{Res}\big{[}F(p)\text{e}^{p\prime t}\big{]}\Bigg{|}_{p=p_{k,k}} \tag{27}\]  
By applying Equation (27) to Equation (25) and substituting the result into Equation (17), we obtain:  
\[U_{ref}(t)=\frac{8\pi I}{\sigma d}\sum_{i=1}^{\infty}\frac{Y(\lambda_{i})Y_{ ref}(\lambda_{i})}{h^{2}J_{0}^{2}(\lambda_{i}h)}\Bigg{[}\sum_{k=1}^{n}\left(A_{ t\text{k}}\text{e}^{p_{k}t}+A_{ck}\text{e}^{p_{k}t}\right)\Bigg{]} \tag{28}\]  
where \(A_{tk}=-\frac{\xi_{k}\sin 2\xi_{k}}{2\cos^{2}\xi_{\text{tt}}+\lambda_{i}\mu_{\tau}d}\), \(A_{ck}=\frac{\xi_{\text{cd}}\sin 2\xi_{ck}}{2\sin^{2}\xi_{ck}+\lambda_{i}\mu_{\tau}d}\). \(\xi_{tk}\) and \(\xi_{ck}\) are the \(k\)-th positive poles of equations \(\tan\xi=-\frac{2\xi}{\lambda_{i}\mu_{\tau}d}\) and \(\cot\xi=\frac{2\xi}{\lambda_{i}\mu_{\tau}d}\), respectively.  
Finally, we consider the actual rectangular pulse current signal applied to the excitation coil, \(i(t)=I\bigg{[}\bigg{(}1-e^{-\frac{t}{\tau_{0}}}\bigg{)}\varepsilon(t)+ \bigg{(}e^{-\frac{t-\tau}{\tau_{0}}}-1\bigg{)}\varepsilon(t-\tau)\bigg{]}(0 \leq t\leq T)\). Where \(\tau\) is the pulse width, and \(\tau_{0}\) is the actual rise or fall time of the rise or fall edge. According to the convolution theorem, the time-domain expression for the reflected field induced voltage in the receiving coil under actual conditions can be obtained as:  
\[U_{ref}(t)=i(t)*\frac{8\pi I}{\sigma d}\sum\limits_{i=1}^{\infty}\frac{Y(\lambda_ {i})Y_{ref}(\lambda_{i})}{h^{2}I_{0}^{2}(\lambda_{i}h)}\Bigg{[}\sum\limits_{k=1 }^{n}\left(A_{tk}\mathrm{e}^{p_{tk}t}+A_{ck}\mathrm{e}^{p_{ck}t}\right)\Bigg{]} \tag{29}\]
## 3 Results  
### The Results of the Single-Factor Test for Sandstones with Different Pore Structures  
#### 3.1.1 Influence of Confining Pressure on Permeability Evolution  
The variation in the permeability of four subcategories of sandstone with different confining pressures was calculated according to the stable seepage flow measured during the permeability testing of the four subcategories of sandstone under the sole influence of confining pressure. See Figure 9 for the test results.  
The test results revealed that the permeability of the four subcategories of sandstone showed a nonlinear decline with increasing confining pressure. The permeability rapidly decreased in the low confining pressure range of 0-10 MPa; for example, when the confining pressure increased from 1.5 MPa to 10 MPa, the permeability of the four subcategories of sandstone decreased by 39.65%-75.90%. As the confining pressure continued to increase, the permeability changed less and less.  
However, the four subcategories of sandstone also had some differences in permeability evolution. The permeability ratio (K\({}^{\prime}\)/K) can reflect these differences, so the concept of the permeability ratio was introduced to reveal the difference in the permeability evolution of sandstones with different pore structures under the sole influence of confining pressure in this paper. Because the permeability ratio can reflect the speed of permeability variation and quantitatively reflect a small difference between the same variation trends, many scholars use the permeability ratio to reflect the extent of variation in sandstone permeability [31]. In this paper, the permeability ratio reflected the decreasing difference between sandstones with different pore structures as the confining pressure increased. Here, the permeability value of each sandstone sample obtained under a confining pressure of 1.5 MPa was taken as the reference point, and the ratio of the permeability measured under other confining pressures and the permeability of the reference point was defined as the permeability ratio (K\({}^{\prime}\)/K) of the sandstone sample. Thus, the variation in the permeability ratio (K\({}^{\prime}\)/K) of each of the four subcategories of sandstone with confining pressure was obtained and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Factors**} & **Confining Pressure** & **Water Pressure** & **Temperature** \\  & **(MPa)** & **(MPa)** & **(\({}^{\circ}\)C)** \\ \hline Level 1 & 7 & 2 & 25 \\ Level 2 & 10 & 4 & 35 \\ Level 3 & 20 & 6 & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Three factors and three levels of the orthogonal test.  
is shown in Figure 10. Figure 10 shows that the permeability ratios (K\({}^{\prime}\)/K) of the four subcategories of sandstone decreased at different rates and within different ranges, and the rates of decrease gradually increased from subcategory I\({}_{1}\) to I\({}_{2}\) to II\({}_{1}\) to II\({}_{2}\).
# Computationally Efficient Robust Color Image Watermarking Using Fast Walsh Hadamard Transform
## 4 Performance Evaluation and Simulation Results
### Effect of Number of Spreading Codes on Robustness  
As the number of spreading codes increases, the number of bits of watermark that can be embedded also increases. When we use 4 spreading codes, in each of the selected mid band row 4 bits can be embedded and as per the proposed algorithm total of 1024 bits watermark can be embedded at the maximum in a 512 \(\times\) 512 image. Same way with one spreading code 256 bits and with 16 spreading codes 4096 bits watermark can be embedded. Different watermarks used in this section are shown in Figure 9. So, in effect as the number of spreading codes increases, the number of watermark bits that can be embedded and thus the robustness increases as shown in Figure 10.  
Figure 10: Compression Quality Vs BER in the case of different number of spreading codes.  
Figure 7: Compression Quality Vs BER in the case of different watermark sizes.  
Figure 9: Watermarks used (**a**) 16 \(\times\) 16 bits (**b**) 32 \(\times\) 32 bits (**c**) 64 \(\times\) 64 bits.
## 2 Geographic Ontologies
### Geographic Features, Types and Languages  
However, the specificity of geographic ontology does not lie only in geographic features (as illustrated in Figure 1) [7]. It lies overall in their geometry and in their spatial relationships [8]. Usually, Egenhofer [9,10] or Region Connection Calculus (RCC) [11] relations (Figure 2) are fully integrated into the definitions of geographic features.  
See Figures 3 and 4 for examples of geographic objects linked with topological relations, the first for the whole planet, and the second for administrative subdivisions of a country. Concerning administrative subdivisions [4], here are a set of remarks:  
* Generally, they form an administrative tessellation (as defined by administrative laws of the concerned countries) and often a hierarchical tessellation (in which a zone of the first level can be decomposed into a second level tessellation (example: in the U.S., country, states, counties);
* However, this tessellation is often disrupted, because of the existence of external territories with special statuses; see, for instance, territories, such as Guam, Northern Mariana Islands, Puerto Rico and the Virgin Islands for the United States;  
Figure 1: Example of the beginning of a geographic ontology with only “is-a” relationships.  
Figure 2: Egenhofer topological relations [9].  
* Even if types are the same, they do not have the same meaning; compare, for instance, a Canadian province, such as \"British Columbia\", and an Italian province (_provincia_ in Italian) for which the size, statuses, prerogatives and governance are very different.  
As a consequence, for structuring geographic ontologies, let us notice that:  
* the so-called administrative tessellations are often not strictly mathematical tessellations due to measurement errors and sliver polygons;
* and the spatial relationships could always be generalized or mutated due to scale effects (See [12] for more details); for instance, a road running along the seashore can have a \"disjoint\" relation or a \"touches\" relation according to the scale.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Amino Acids**} & \multicolumn{4}{c}{**Transglutaminase (\%)**} & \multirow{2}{*}{\(P\)} & \multirow{2}{*}{**SEM**} \\ \cline{2-2} \cline{5-6}  & & **Control** & **0.25** & **0.50** & **1.0** & \\ \hline Aspartic Acid & 32.28 & 30.31 & 31.79 & 25.89 & 0.465 & 1.593 \\ Glutamic Acid & 151.95 & 155.82 & 207.49 & 159.08 & 0.072 & 27.559 \\ Serine & 25.75 & 27.39 & 34.88 & 30.99 & 0.874 & 5.430 \\ Histidine & 36.22 & 32.27 & 28.04 & 33.25 & 0.702 & 2.225 \\ Glycine & 52.13 & 36.48 & 68.16 & 66.08 & 0.239 & 9.158 \\ Threonine & 101.28 & 106.05 & 31.76 & 69.41 & 0.214 & 13.637 \\ Arginine & 256.04 & 192.17\({}^{\circ}\) & 255.57\({}^{\circ}\) & 274.64\({}^{\circ}\) & 0.151 & 11.334 \\ Alanine & 125.68 & 119.68 & 124.75 & 125.24 & 0.132 & 1.616 \\ Tyrosine & 131.15 & 110.86 & 153.74 & 143.39 & 0.140 & 7.671 \\ Cystine & 248.10\({}^{\circ}\) & 279.34\({}^{\circ}\) & 331.22\({}^{\circ}\) & 298.20\({}^{\circ}\) & 0.003 & 20.097 \\ Valine & 230.27 & 229.72 & 299.61 & 236.01 & 0.945 & 5.919 \\ Methionine & 121.75 & 93.84 & 108.69 & 112.10 & 0.347 & 8.842 \\ Phenylalanine & 76.85\({}^{\circ}\) & 91.64\({}^{\circ}\) & 48.82\({}^{\circ}\) & 57.48\({}^{\circ}\) & \textless{}0.001 & 5.082 \\ Isoucine & 15.49 & 14.41 & 15.18 & 13.76 & 0.912 & 0.987 \\ Leuine & 50.62 & 44.67 & 44.28 & 37.83 & 0.351 & 3.459 \\ Lysine & 108.67 & 89.30 & 115.97 & 117.59 & 0.254 & 7.811 \\ Proline & 103.84 & 6.41\({}^{\circ}\) & 75.84\({}^{\circ}\) & 104.2\({}^{\circ}\) & 0.020 & 1.008 \\ \(\Sigma\) FAA & 1774.76 & 1660.35 & 1847.51 & 1847.35 & 0.557 & 42.937 \\ \hline \hline \end{tabular}  
\({}^{\text{a}}\)cc: Different letters indicate statistical difference in each row (\(p<0.05\)). SEM: Standard error of mean.  
\end{table} 
Table 5: Effect of transglutaminase application on free amino acid composition (mg/100 g dry matter). Figure 1: Cluster analysis of correlation heat map showing the relationship between MTGase and volatile compounds.
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 3 Methodology
### Field Measurement Techniques  
Field measurements are crucial to validate the results obtained through computational modeling and provide real-world data on grounding system performance.
## 4 Results
### Evaluation of the Individual Retrofit Strategies
#### 4.2.3 Shading Devices Retrofitting Strategies  
The study also explored the impact of installing shading devices as part of the facade retrofit strategy. Three types of shading devices were analyzed: internal smart aluminum blinds, external window louvers covering the entire window at a 15-degree tilt, and 1-m deep overhangs made from either wood or steel. Figure 8 illustrates the percentage of annual heat gain reduction through glazing for each strategy. The results indicate that external shading devices were more effective than internal ones in reducing heat gains. Louvers achieved the greatest reduction, reducing heat gains by 26%, followed by overhangs at 16.6% and smart curtains at 6.1%. The better performance observed in the external shading devices is due to their ability to block direct solar radiation before it reaches the window.  
Regarding cooling energy reduction, the three shading strategies showed comparable results that are statistically significant, as detailed in Table 9. The smart blinds system achieved a 4.9% reduction in annual cooling energy, followed by louvers at 4.2% and overhangs at 3.5%. Despite the similar energy savings, louvers proved to be the most cost-efficient option with the shortest payback period of 4.6 years. In contrast, the smart blinds and steel overhangs had higher initial costs relative to the cooling energy savings they offered, resulting in longer payback periods of 10.6 and 14 years, respectively, as shown in Table 10.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Strategy** & **Strategy Description** & **Unit Cost** & **Total Retrofit Cost** & **Annual Cooling** & **Payback Period** \\
**Code** & & **[SAR/m2]** & **[SAR]** & **[SAR]** & **[SAR]** & **[Year]** \\ \hline S-W-1 & Smart glass (electrochromic) & 1200 & 118,800 & 10,800 & 11.0 \\ \hline S-W-2 & Low-E Coating & 250 & 24,750 & 2489 & 9.9 \\ \hline S-W-3 & Triple-glazed & 320 & 31,680 & 6522 & 4.9 \\ \hline S-W-4 & Double-glazed with Argon & 20 & 1980 & 193 & 10.3 \\ \hline S-W-5 & Smart glass with Argon & 1220 & 120,780 & 12,067 & 10.0 \\ \hline S-W-6 & Low-E Coating with Argon & 270 & 26,730 & 3503 & 7.6 \\ \hline S-W-7 & Triple-glazed with Argon & 340 & 33,660 & 6963 & 4.8 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Initial cost and payback period for the different windows retrofit strategies.  
Figure 7: Percentage of the annual reduction in glazing heat gains in the different window retrofit strategies (glazing heat gains in the base case were 30,118 kWh).
## 2 Modeling and Degradation Simulation
### Degradation Models  
Five degradation phenomena--ageing, EVA discoloration, LID, cracking, and corrosion--are integrated into the simulator by adjusting \(R_{p}\), \(R_{s}\), and \(I_{cc}\) based on empirical models from [6; 9; 10; 11; 21; 22]. Potential Induced Degradation (PID) was also considered, as it represents a major field-observed degradation mechanism. While full PID simulation is not implemented in the current version due to limited access to robust empirical models, its effects--typically manifested as a rapid drop in shunt resistance and/or increased leakage current across module insulation--are well documented [7]. To support future integration, a theoretical formulation has been included in B. Table 1 summarizes the associated degradation effects.  
Figure 4: Relationship between irradiance levels and PV power output, confirming the expected linear increase in generated power with irradiance at constant temperature.  
Figure 3: Twenty-four-hour environmental data (irradiance and ambient temperature) recorded on 15 July 2011, used to validate model behavior under realistic solar exposure conditions.  
Recent reviews [23] have comprehensively summarized the root causes and detection challenges associated with PV degradation, highlighting the need for integrated diagnostic frameworks similar to the one proposed in this work.
# A Simplistic Downlink Channel Estimation Method for NB-IoT
###### Abstract  
This paper presents a downlink channel estimation method intended for a Narrowband Internet of Things (NB-IoT) access link. Due to its low computational complexity, this method is well suited for energy-efficient IoT devices, still providing acceptable reception quality in terms of signal-to-noise (SNR) performance. This paper describes the physical layer of NB-IoT within the scope of channel estimation, and also reviews existing channel estimation methods for OFDM signals. The proposed method, based on linear interpolation of channel coefficients, is described as a three-step procedure. Next, indicators of channel quality assessment, which may be determined without prior knowledge about the transmitted signal, are defined. Two variants of channel estimation, differing in the frequency domain processing, are evaluated to assess the significance of frequency selectivity in an NB-IoT downlink. The chosen method is compared with another method implemented in MATLAB LTE Toolbox(tm). An analysis of the computation time is conducted, subsequently demonstrating the definite advantage of the proposed method.  
d owlink channel estimation; NB-IoT; Internet of Things; LPWAN; M2M communications; physical layer +
Footnote †: journal: Applied Sciences  
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