esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review
## 1 Introduction
Hence, as the DL techniques significantly rely on the distribution of data in the feature space while ECG signals vary considerably from person to person, the models trained based on particular ECG datasets may not be applied reliably in practice. As many existing reviews [5;6;8] concentrate mainly on DL algorithms, we consider various factors across the whole DL workflow for the ECG arrhythmia classification. Specifically, our major contributions are as follows. * We perform a systematic review for DL-based arrhythmia classification with ECG signals from perspectives of ECG database, preprocessing, DL methodology, evaluation paradigm, and performance metrics in the complete DL workflow as well as the code availability of the reviewed studies;
* The trend of techniques in each perspective in recent years is analyzed to summarize the historical road map and illustrate possible future research directions;
* We present the detailed performance gap between the ECG arrhythmia classification under intra- and inter-patient paradigms. To the best of our knowledge, there is no systematic review on the comparison of DL-based ECG arrhythmia classification under different evaluation paradigms, i.e., intra-patient paradigm vs. inter-patient paradigm. Most existing works consider the intra-patient paradigm; while the investigation in the inter-patient paradigm is limited but more desirable in clinical applications, a thorough comparison between the two paradigms could shed light on future research opportunities.
# A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks
## 3 Detailed Description of the Proposed Localization Scheme
### Calculating the Node Location
#### 3.4.1 Transforming 3D Localization into 2D Localization  
As shown in Figure 9, the to-be-localized node A is at the lower right of the trajectory, B is the projection of node A on the trajectory, and C is the projection of node A on the trajectory plane. The coordinates of point A, B, and C are \(\left(x_{A},y_{A},z_{A}\right)\), \(\left(x_{B},y_{B},0\right)\), and \(\left(x_{C},y_{C},0\right)\), respectively. \(\left(x_{B},y_{B},0\right)\) can be obtained by the method in Section 3.2. \(d_{AB}\) is the perpendicular distance from node A to the trajectory, which can be obtained by the method in Section 3.3. \(d_{AC}\) or \(z_{A}\) is the depth of node A, which can be measured by a pressure sensor. \(d_{BC}\) is the projection of \(d_{AB}\) on the plane, as well as the perpendicular distance from C to the trajectory. \(\Delta ABC\) is a right-angle triangle, and \(d_{BC}\) can be calculated as  
\[d_{BC}=\sqrt{d_{AB}^{2}-d_{AC}^{2}} \tag{43}\]  
Figure 9: Transforming 3D localization into 2D localization.  
After the above operations, the task of localizing node A in a 3D space has been transformed into localizing C in a 2D plane.
# TwSense: Highly Robust Through-the-Wall Human Detection Method Based on COTS Wi-Fi Device
## 5 Results and Evaluation
### Analysis of Experimental Results
#### 5.2.2 Influence on Different Wall Thicknesses  
To verify that different wall thicknesses had a particular impact on recognition accuracy, we first selected three kinds of walls: concrete walls, plaster walls, and glass walls. Since the thickness of the door in daily life was roughly the same, we did not consider the difference or effect of the thickness of the door here in the experiment. Then, under the same material as the wall, three different thicknesses of walls were selected for experiments. The thicknesses of the gypsum wall and the concrete wall were selected as 27 cm, 30 cm, and 37 cm, respectively, and the thickness of the glass was generally higher than that of other walls, and the selected thicknesses were 3 cm, 6 cm, and 12 cm. The final experimental results are shown in Figure 11. It can be seen from Figure 11a that when the wall was made of concrete, the recognition accuracy of the wall thickness of 27 cm was the highest, and the accuracy was slightly lower when the thickness was 30 cm. The worst accuracy was 37 cm. In addition, in Figure 11b, when the wall was made of gypsum material, the  
Figure 10: The influence of different materials of different people: (**a**) Recognition effect of different users; (**b**) Recognition effect of each person under different wall materials.  
recognition accuracy of the wall thickness of 27 cm was the highest, and the recognition accuracy of the thickness of 37 cm was the worst. In Figure 11c, when the wall was made of glass, the recognition accuracy of the wall thickness of 3 cm was the highest, and the recognition accuracy of the thickness of 12 cm was the worst. It can also be seen from the figure that when the wall was made of concrete and gypsum, the difference in the recognition accuracy caused by the change in the wall thickness was more remarkable; when the wall was made of glass. The difference in the recognition accuracy caused by the change in the wall thickness was slight. Therefore, the experimental results showed that the thickness of the wall was related to the experimental accuracy; when the wall thickness was thicker, the wall interference was more excellent and thus the accuracy of recognition was lower.
## 2 Research Data and Research Methods
### Research Methods
#### 2.3.3 Correlation Analysis  
Correlation analysis is frequently employed to assess the degree of association between variables [22]. To elucidate the impact of soil moisture on ecosystem stability, a quantitative analysis of the correlation between Enhanced Vegetation Index (EVI), Gross Primary Productivity (GPP), and soil moisture is conducted. The formula is as follows:  
\[r=\frac{\sum\limits_{i=1}^{n}[(x_{i}-\overline{x})(y_{i}-\overline{y})]}{ \sqrt{\sum\limits_{i=1}^{n}(x_{i}-\overline{x})^{2}}\sqrt{\sum\limits_{i=1}^{ n}(y_{i}-\overline{y})^{2}}} \tag{8}\]In the formula, \(r\) is the linear correlation coefficient of \(x\) and \(y\) variables; \(x_{i}\) and \(y_{i}\) are the values of \(x\) and \(y\) in year \(i\), respectively; \(\overline{x}\) and \(\overline{y}\) represent the average value of the two variables over \(n\) years, which in this study was 20 years. \(r>0\), indicating that the two variables are positively correlated; \(r<0\) indicates that the two variables are negatively correlated. In addition, \(t\) test was used to determine the significance of the correlation, and the results were divided into four grades: extremely significant (\(p\leq 0.01\)), significant (\(0.01<p\leq 0.05\)), weakly significant (\(0.05<p\leq 0.1\)), and not significant (\(p>0.1\)).
## 3 Results and Discussion
### Effect of Ribs  
Often, rock bolts utilize rebar, as the presence of their deformation improves performances via mechanical interlock [61; 62; 63; 64]. To further explore this effect, 5 specimens were prepared and tested. Differences in strength and secant stiffness between the manufactured smooth bar were observed. Improved strength was seen with the presence of roughness and ribs, in line with other research. It was also noted that the rib specimens had lower secant stiffness. Although residual strength per unit length of the sanded smooth bars in the two annuli of B and A were not very large, there was a 72 kN difference in peak strength for grout annulus A observed. It should, however, be noted that these bars were smoothed by hand and could feature a slight difference in roughness, and that there existed a void in the grout for specimen B-6.  
With the application of DOS, the effects of the presence of roughness and ribs can be explored further. Benefits in bond performance were clearly seen when comparing strain attenuation profiles. However, interestingly, this was not the case for loads below 30 kN. This can be seen in Figure 8. Dilation effects were also observed where, at a higher load ribbed bars generated more dilation (at a load of 10 kN, the smooth bar generated more dilation than the rough bar). This high dilation is in line with the expected behaviour of the load transfer achieved through mechanical interlock. Viewing pre- and post-peak results of specimens B-1-I and B-6 again highlights the presence of dilation. Where the pipe is generally being compressed by forces transferred from the bar, however, dilation generates tension which then reduces the overall compression seen in the fiber positioned longitudinally on the outside of the pipe. In line with the expectation that mechanical interlock is responsible for a significant portion of strength, an evident change in response in the pipe pre- and post-peak for the ribbed bar when compared to the hand smoothed bar was observed. This can be seen in Figure 9.  
This investigation also allows an understanding of the limitation of the selected process for creating the rib spacings. The manufactured smooth bar had much lower strength when compared to rebar which was sanded smooth (145 kN vs. 12 kN). This is most likely due to either a change in friction between the two specimens or uneven sanding, leading  
Figure 8: Strain profile of A-1 (ribbed bar), vs. A-6 (sanded smooth) and A-7 (manufactured smooth bar) at various loads; red arrows show theorical strain at zero.  
to mechanical interlock. Due to this, it should be noted that specimens created through traditional manufacturing would likely have reduced bond performance when compared to the presented results, with the amount varying depending on end tolerances and coating. This is similar to the effect of rust seen on the strength capability of bolts [63]. However, once cracking in the grout occurs, this effect would be minimal for mechanical interlock of ribbed bars.
# Task-Driven Real-World Super-Resolution of Document Scans
## 4 Experimental Validation
### Experimental Results
#### 4.3.2 Qualitative Analysis  
Inspecting the qualitative results in Figures 4-8 allows us to confirm that the image similarity metrics (PSNR, SSIM, and LPIPS) do not correlate well with the visual quality of reconstructed text documents. Employing task-driven loss function components allows the models to learn preserving and enhancing text structures. At the same time, they become blurred after applying bicubic interpolation, and the baseline \(\mathcal{M}_{\text{B}}\) model trained from natural images tends to impair the connectivity within the characters, affecting text detection and recognition capabilities. In our opinion, the \(\mathcal{M}_{+++}^{R}\) model leads to the visually best outcome for both real-world and simulated images presenting text documents (Figures 4-7), making the text most legible, which is coherent with the quantitative results. Even though in some cases (like in Figure 7) there are some color artifacts, they are weaker than those caused by other variants trained from the real-world datasets (e.g., \(\mathcal{M}_{++-}^{R}\) or \(\mathcal{M}_{+-}^{R}\)), as well as from the simulated ones (e.g., \(\mathcal{M}_{+++}^{S}\)). Also, the baseline model \(\mathcal{M}_{\text{B}}\) distorts the letters (see the letter 'e' in the zoomed example in Figure 7). We have also verified the behavior of the investigated models for natural images (Figure 8)--the task-driven models tend to render sharper edges, better reconstructing high-contrast details (compare the Big Ben clock for \(\mathcal{M}_{\text{B}}\) and \(\mathcal{M}_{+++}^{R}\)). However, this is achieved at a cost of some artifacts, especially for smooth regions (see the upper left corner in \(\mathcal{M}_{+++}^{R}\)) that substantially deteriorate image similarity scores.
## 4 Case study
### Preliminary Analysis Application  
As it is depicted in Figure 7, the PPC bears three main loads transmitted by the connecting rods. Three screws fix the part. The original material is aluminium alloy. In Table 7, all the features and the design requirements are summarized. \begin{table}
\begin{tabular}{l l} \hline \hline
**Requirement** & **Value** \\ \hline Application & Structural \\ \hline Expected overall size & 130 \(\times\) 130 \(\times\) 20 mm \\ \hline Load & 3 \(\times\) 3000 N \\ \hline Constraints & Rotation blocked by 3 screws M6 \\ \hline  & Current material: Aluminium \\  & Current density: 2700 [Kg/m\({}^{3}\)] \\  & Current Stiffness: 50 [GPa] \\  & Twork: \(-\)30 \({}^{\circ}\)C to 130 \({}^{\circ}\)C \\  & Conductivity E: Not important \\  & Conductivity T: Not important \\  & Required Hardness: 20 [Rock] \\  & Wear resistance: Good resistance \\  & Corrosion resistance: Good resistance \\  & Current tensile strength: 210 [N/mm\({}^{2}\)] \\  & Fatigue strength: Millions of cycles required \\  & Current yield strength: 120 [N/mm\({}^{2}\)] \\  & Maximum expected powder cost: EUR 75/kg \\ \hline Production method & Milling from block \\ \hline Buy-to-fly ratio & Current: 0.347 \\ \hline Batch size & 150 p/years \\ \hline Time-to-Market & Few weeks \\ \hline Production cost & 25 EUR/p \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Design requirements for the analysed product. Figure 7: (**a**) 3D CAD model of the Propeller Pitch Controller (PPC); (**b**) Real part assembled with the blades. The three main initial constraints outlined in Section 3.2 are fulfilled by the specific application. Most of the constraints push the choice towards metals, mainly aluminium and steel, which are widely available as powders to be employed in PBF processes. The size of the part is limited and compatible with most of the printers. Indeed, the size allows multiple parts to be processed simultaneously, thus small batch sizes, as required, are definitely feasible. The approach depicted in Section 3 has been followed for the PPC and the main results are reported here. The process has moved from the computation of the AHP pairwise matrix to assess the relative weights of the material selection indices (Table 8). The table was compiled with the help of the industrial partner and the resulting weights computed according to the AHP method are reported in Table 9.  
Table 10 reports a part of the evaluated materials list, including the values of the selected material properties. So, the upper part of the sorted list of materials according to the computed \(CI\) is reported in Table 11.  
Table 11 provides minimum and maximum values of the \(CI\) in consideration of the ranges of physical properties of the materials in the market.
# Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations
## 2 Materials and Methods
### Simulation of Canopy Reflectance Using the PROSAIL-D Model
A dataset with 19,600 simulations was generated by running the PROSAIL-D model in Matlab (The Math Works, Natick, MA, USA). The simulated reflectance derived from the PROSAIL-D model was converted into the corresponding band reflectance using the MERIS spectral response function [59].
# Cost-Aware IoT Extension of DISSECT-CF
## 3 Cost Modeling in DISSECT-CF
### Configurable Cost Models  
Based on these investigations, we created a cost model for IoT systems (represented by the IoT Pricing component in Figure 1), in which the IoT usage prices can be represented in an XML (Extensible Markup Language) format. Figure 2 shows its structure and the concrete values for the applied categories. We use this description as a configuration file for implementing and executing cost-based experiments in the simulator. We manage all prices in Euro. Some providers publish costs in US dollars (like Amazon)--in these cases, we apply conversion; therefore, we also have to specify an exchange rate to Euros. The first line contains the size and target attributes, which are used to define a VM/container specification for the simulation, and name a provider to be used for price calculation, respectively. In the second line, we define the pricing scheme for Bluemix, which is solely based on the data transferred. From the 7 th line, we define the scheme of Amazon, which the provider charges after the number of messages sent. Here, we denoted that one million messages cost 5 dollars, and the exchange rate to Euros, which is 0.95. The maximum size of a message is 512 bytes. From the 9 th line, we define Oracle's scheme. It defines the maximum number of messages to be sent by a device (15,000), and a price for handling a device from this category (0.93). Once additional messages need to be sent, there is an extra charge, 0.02 Euros per 1000 messages. The period attribute defines the time interval for monitoring in days. Finally, from the 15 th line, the Azure scheme is defined, which restricts the number of messages to be sent in a day (6,000,000) and the message size (4 KiB), and defines a monthly price for this service (421.65).  
```
<iotproviders>  <bluemixcalculate=\"false\">  <price-per-MBmbfrom=\"1\"mbto=\"499999\">0.00097</price-per-MB>  <price-per-MBmbfrom=\"450000\"mbto=\"6999999\">0.00068</price-per-MB>  <price-per-MBmbfrom=\"7000000\"mbto=\"-1\">0.00014</price-per-MB>  </bluemix>  <amazonprice=\"5\"messagecount=\"1000000\"exchangerate=\"0.95\"  calculate=\"true\">512</amazon>  <oraclecalculate=\"false\"period=\"31\">  <messages-per-month-per-device>15000</messages-per-month-per-device>  <deviceprice-per-month>0.93</deviceprice-per-month>  <am-messages-per-month-per-device>1000</am-messages-per-month-per-device>  <am-deviceprice-per-month>0.02344</am-deviceprice-per-month>  </oracle>  <azurecalculate=\"false\"period=\"1\">  <price-per-month>421.65</price-per-month>  <messages-per-day>600000</messages-per-day>  <messagesize-per-KB>4</messagesize-per-KB>  </azure>  </iotproviders>
```  
Figure 2: Cost model of IoT providers.
# Gait Recognition Algorithm of Coal Mine Personnel Based on LoRa
## 3 Results
### Coal Mine Underground Environment
#### 3.3.2 Underground Long-Distance Sensing  
We also verify the gait recognition performance under long-distance conditions in the coal mine underground environment. The experiment schematic diagram is shown in Figure 15. The target was perpendicular to LOS when it was 20 m away from the transceiver. Figure 18 shows the confusion matrix of experiment results. It can be seen from the figure that even if the target is 20 m away from the transceiver, the recognition accuracy only decreases by 0.5% compared with that of 5 m. The target identity can still be recognized effectively, which lays the foundation for long-distance gait recognition application. It is proved that the gait recognition system based on LoRa is feasible for long-distance sensing in the underground coal mine roadway.
###### Abstract  
This study compares a standard monofocal intraocular lens (IOL) with two rotationally asymmetric refractive multifocal IOLs, analyzing power profiles and wavefront data across three nominal powers. Tested IOLs included monofocal Acumex AN6 and multifocal AN6V (addition +1.50 D) and AN6 VM (addition +3.00 D) by Teleon Surgical, at powers +10.00 D, +20.00 D, and +30.00 D. Using NIMO TR1504, power profiles, spherical aberration, and coma were analyzed. Multifocal lenses displayed an asymmetrical design, with zones for far and near vision and a central notch for far power. The multifocal AN6V and AN6 VM showed neutral spherical aberration in the center, increasing to positive values, and finally achieving negative values towards the periphery. The vertical coma coefficient [Z(3:\(-\)1)] remains almost stable for the monofocal IOLs. The lower-addition multifocal IOLs induce primary vertical coma of +0.11 microns at 3 mm away from the lens center compared to +0.21 microns obtained with the high-addition lens. We can conclude that the multifocal AN6V and AN6 VM lenses display an asymmetrical design. The final addition provided by these lenses depends on its nominal power. The variation of SA with optic size is more pronounced for the monofocal model, and it is dependent on the nominal power.  
multifocal intraocular lens; rotationally asymmetric refractive lens; presbyopia; spherical aberration 2020
# Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction
## 2 Materials and Methods  
### Study Area  
In this study multiple almond orchards (AO) are included, from four distinct almond growers (AGs) within the TM region of northern Portugal.  
Regarding AO1, AO2, and AO3, these are located in the Torre de Moncorvo municipality. Regarding AO4, this is located between the Vila Flor and Alfandega da Fe municipalities (Figure 1a). These orchards present different characteristics, namely AO1 has 5.7 hectares with 1387 almond trees; AO2 has 2.9 hectares with 765 almond trees; AO3 has 3.0 hectares with 756 almond trees; and AO4, the largest area, has 12.3 hectares with 3198 almond trees.  
This region of TM is mountainous and presents warm and dry summers and moderately cold and wet winters [22]. These characteristics are typical of the Mediterranean climate, which makes the region suitable for almond cultivation.  
Considering the yield levels recorded from 2017 to 2021, AO4 and AO3 had the highest productivity, averaging 1041 kg/ha and 785 kg/ha, respectively, while AO1 and AO2 had lower productivity with an average of 462 kg/ha and 372 kg/ha, respectively, in the same period (Figure 1b).
## Abbreviations  
The following abbreviations are used in this manuscript:  
STBC space-time block coding CAZAC Constant Amplitude Zero Autocorrelation MIMO-OFDM multiple-input multiple-output orthogonal frequency division multiplexing SISO-OFDM single-input single-output orthogonal frequency division multiplexing CAZAC-TD CAZAC time domain CAZAC-FD CAZAC frequency domain AWGN Additive White Gaussian Noise Milan-FD Milan Frequency Domain CRB Cramer-Rao bound NCRB Normalized Cramer-Rao bound
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 5 Simulation and Results
### Results and Discussion
#### 5.2.4 Throughput  
Throughput is defined as successful packets delivery at the sink node. It only considers the packets receiving without knowing the total number of packets sent. In TBRL, different paths have different throughput values. Path 2 and 4 have higher throughput as compared to paths 1 and 2, as shown in Figure 10. TBRL is sensitive against delay and packet delivery ratio; therefore, it chooses the throughput value of path 1 because of its less delay and highest packet delivery ratio explained in previous sections.