esa-sceva/satcom-chunk-collection huggingface dataset:
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 3 Signal-Processing Techniques
### Accelerometer Data Preprocessing  
Because of the nature of the raw data provided by accelerometers to obtain quality data, different filtering techniques are employed to remove noise or other interference from the data signals. It is worth mentioning that these disturbances cannot be eliminated 100%, but a proper selection of the filtering method can diminish the noise without significantly affecting the relevant acceleration data.  
In reference [46], a least-squares estimation using an inverse square root filtering procedure is used to obtain data estimation from an accelerometer sensor mounted inside a car. Because of the high level of interference, the data cannot be used efficiently for the onboard-embedded systems. Applying such a filter indicated a very low uncertainty of measurement, low nonlinearity, acceptable sensitivity, and a good signal-to-noise ratio, as the system was able to output at a 45 dB level.  
Another filtering technique is using Kalman filters, such as the one used in the paper [47]. This filter reduced the noise affecting two accelerometers for automotive tests. The authors claim that because the noise is challenging to distinguish, such a filter allowed them to obtain optimal results and a 30 dB signal-to-noise ratio.  
Other scientific research presents various filtering algorithms that can be designed using analogue or digital electronics [48]. Digital filtering is proposed in [49] and completed in a low-cost microcontroller that acquires the data. Paper [50] discusses the integration of signals from position, velocity, and acceleration sensors targeting the same subject, enhancing the observation of the subject's kinematics by aggregating filtered versions of diverse kinematic quantities. The paper [51] introduces a novel complementary filter for manned aerial vehicles that integrates gyroscope data with readings from accelerometers and magnetic fields. The filter corrects data from IMU (Inertial Measurement Unit) and MARG (Magnetic, Angular Rate, and Gravity) sensors. The paper [52] discusses real-time noise filtering in the acquired acceleration data by MEMS devices. These algorithms can be optimal concerning specific performance indices, robust against structured and unstructured uncertainties, or may lack both optimality and robustness.
## 3 Results
### Functional Connectivity Analysis
#### 3.2.2 Homotopic Pair Connectivity  
Figure 4 illustrates the PLV values obtained from the homotopic pair in the centro-parietal area (Pair A) of the AD and HC groups in the five frequency bands. The Mann-Whitney U-test displayed a significantly higher PLV in the theta band for AD participants (\(Mdn\) = 0.64) as compared to HCs (\(Mdn\) = 0.52), \(U\) = 292, \(p\) = 0.01. No significant results were found for the other four frequency bands.  
Figure 5 illustrates the PLV values obtained from the homotopic pair in the fronto-central region (Pair B) of the AD and HC groups in the five frequency bands. The Mann-Whitney U-test displayed a significantly higher PLV for AD participants in both the delta (AD \(Mdn\) = 0.57, HC \(Mdn\) = 0.45, \(U\) = 275, \(p\) = 0.04) and theta bands (AD \(Mdn\) = 0.65, HC \(Mdn\) = 0.50, \(U\) = 282, \(p\) = 0.03). No significant results were found for the other three frequency bands.  
Figure 4: The PLVs obtained from the homotopic pair in the centro-parietal region (Pair A). Plots marked with * indicate a statistically significant difference (\(p\) < 0.05) between AD patients and HCs.  
Figure 5: The PLVs obtained from the homotopic pair in the fronto-central region (Pair B). Plots marked with * indicate a statistically significant difference (\(p\) < 0.05) between AD patients and HCs.  
Figure 6 illustrates the PLV values obtained from homotopic pairs in the parieto-occipital region (Pair C) of the AD and HC groups in the five frequency bands. The Mann-Whitney U-test indicated a significantly higher PLV for the AD group (\(Mdn\) = 0.64) as compared to the HCs (\(Mdn\) = 0.49), solely in the delta band (\(U\) = 293, \(p\) = 0.01). The test resulted in an insignificant outcome for the other four frequency bands.  
Lastly, Figure 7 shows the PLV values obtained from homotopic pairs in the centro-temporal region (Pair D) of the AD and HC groups in the five frequency bands. The Mann-Whitney U-test indicated a significantly higher PLV solely in the theta band of AD participants (\(Mdn\) = 0.48) as compared to the HCs (\(Mdn\) = 0.40, \(U\) = 280, \(p\) = 0.03). The results of group comparisons in the other four frequency bands remained insignificant.  
Summing up, the comparison of PLV in the selected homotopic pairs resulted in observing the main differences in the low frequency bands of delta and theta.  
Figure 6: The PLVs obtained from the homotopic pair in the parieto-occipital region (Pair C). Plots marked with * indicate a statistically significant difference (\(p\) < 0.05) between AD patients and HCs.  
Figure 7: The PLVs obtained from the homotopic pair in the centro-temporal region (Pair D). Plots marked with * indicate a statistically significant difference (\(p\) < 0.05) between AD patients and HCs.
## 4 Experimental Validation  
The experimental validation of the XAITO-DRGC model is tested using two datasets. Table 1 depicts the detailed description of two datasets. The DDR dataset [23] comprises 13,673 fundus images obtained at a 45\({}^{\circ}\) field of view (FOV). Among these, there were 1151 ungradable images, 6266 normal images, and 6256 DR images. The APTOS 2019 Kaggle dataset [24] has 3662 retina images with various image sizes. The dataset can be classified into five DR stages. Moreover, 1805 of the images are normal and 1857 are DR images. Figure 3 illustrates the confusion matrices given by the XAITO-DRGC model on the APTOS 2019 dataset. On the entire dataset, the XAITO-DRGC model has identified 344 samples as normal, 59 samples as mild, 188 samples as moderate, 21 samples as severe, and 42 samples as proliferative. In addition, on 70% of training (TR) data, the XAITO-DRGC method has identified 243 samples as normal, 43 samples as mild, 132 samples as moderate, 12 samples as severe, and 26 samples as proliferative. Also, on 30% of testing (TS) data, the XAITO-DRGC technique has identified 101 samples as normal, 16 samples as mild, 56 samples as moderate, 9 samples as severe, and 16 samples as proliferative. \begin{table}
\begin{tabular}{c c c} \hline
**Class** & **APTOS 2019** & **DDR Dataset** \\ \hline Normal & 361 & 1253 \\ \hline Mild & 74 & 126 \\ \hline Moderate & 200 & 895 \\ \hline Severe & 39 & 47 \\ \hline Proliferative & 59 & 182 \\ \hline Total No. of Images & 733 & 2503 \\ \hline \end{tabular}
\end{table} 
Table 1: Dataset details. Table 2 and Figure 4 provide an overall classification output of the XAITO-DRGC model on the APTOS 2019 dataset. The experimental values notified that the XAITO-DRGC model has shown enhanced results under distinct aspects. For instance, with the entire dataset, the XAITO-DRGC model has offered an average \(accu_{y}\) of 95.69%, \(prec_{n}\) of 86.04%, \(rec_{q}\) of 78.81%, \(spec_{y}\) of 96.59%, and \(F_{score}\) of 81.69%. Meanwhile, with 70% of TR data, the XAITO-DRGC approach has provided an average \(accu_{y}\) of 95.56%, \(prec_{n}\) of 86.59%, \(rec_{q}\) of 77.01%, \(spec_{y}\) of 96.42%, and \(F_{score}\) of 80.13%. Eventually, with 30% of TS data, the XAITO-DRGC algorithm has rendered an average \(accu_{y}\) of 96%, \(prec_{n}\) of 86.48%, \(rec_{q}\) of 83.14%, \(spec_{y}\) of 96.97%, and \(F_{score}\) of 84.52%. Figure 3: Confusion matrices of the XAITO-DRGC approach under the APTOS 2019 dataset (**a**) entire dataset, (**b**) 70% of TR data, and (**c**) 30% of TS data. Figure 4: Result analysis of the XAITO-DRGC approach under the APTOS 2019 dataset.
###### Abstract  
This study provides an operational solution to directly connect drones to internet by means of 4G telecommunications and exploit drone acquired data, including telemetry and imagery but focusing on video transmission. The novelty of this work is the application of 4G connection to link the drone directly to a data server where video (in this case to monitor road traffic) and imagery (in the case of linear infrastructures) are processed. However, this framework is applicable to any other monitoring purpose where the goal is to send real-time video or imagery to the headquarters where the drone data is processed, analyzed, and exploited. We describe a general framework and analyze some key points, such as the hardware to use, the data stream, and the network coverage, but also the complete resulting implementation of the applied unmanned aerial system (UAS) communication system through a Virtual Private Network (VPN) featuring a long-range telemetry high-capacity video link (up to 15 Mbps, 720 p video at 30 fps with 250 ms of latency). The application results in the real-time exploitation of the video, obtaining key information for traffic managers such as vehicle tracking, vehicle classification, speed estimation, and roundabout in-out matrices. The imagery downloads and storage is also performed through internet, although the Structure from Motion postprocessing is not real-time due to photogrammetric workflows. In conclusion, we describe a real-case application of drone connection to internet thorough 4G network, but it can be adapted to other applications. Although 5G will -in time- surpass 4G capacities, the described framework can enhance drone performance and facilitate paths for upgrading the connection of on-board devices to the 5G network.  
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## 4 Experiments and Results
### Experimental Setup
#### 4.1.3 Parameter Settings  
The DNN-MHAT model was applied using the Tensorflow1.13.1 with Keras2.24 libraries written in Python 3.7.1 Language and an Ubuntu16.04 system with a CPU of Core Tetranuclear i7-7700k and a GPU of GTX1080 Ti GAMING X 11 GB. To construct the input comment matrix \(C\), the Tokenizer method uses 100,000 words. We assumed the 45 and 100 first words of comments in the tweet and review datasets by setting the padding sizes to 45 and 100, respectively. In the current study, the pre-trained and publicly available GloVe was utilized as the weights in the embedding layer. The \"Gigaword 5 + Wikipedia 2014\" version of GloVe was utilized, comprising six billion tokens and a vocabulary size of 400,000. For the embedding layer, the embedding size of 300 was used. Other parameter settings used in the proposed model are shown in Table 2.
###### Abstract  
In this study, the role of the signalling molecule nitric oxide (NO) in magnetopriming-mediated induction of salinity tolerance in soybean seeds is established. The cross-talk of NO with germination-related hormones gibberellic acid (GA), abscisic acid (ABA) and auxin (IAA) for their ability to reduce the Na\({}^{+}\)/K\({}^{+}\) ratio in the seeds germinating under salinity is highlighted. Salt tolerance index was significantly high for seedlings emerging from magnetoprimed seeds and sodium nitroprusside (SNP, NO-donor) treatment. The NO and superoxide (O\({}_{2}\)\({}^{\bullet-}\)) levels were also increased in both of these treatments under non-saline and saline conditions. NO generation through nitrate reductase (NR) and nitric oxide synthase-like (NOS-like) pathways indicated the major contribution of NO from the NR-catalysed reaction. The relative expression of genes involved in the NO biosynthetic pathways reiterated the indulgence of NR in NO in magnetoprimed seeds, as a 3.86-fold increase in expression was observed over unprimed seeds under salinity. A 23.26-fold increase in relative expression of NR genes by the NO donor (SNP) was observed under salinity, while the NR inhibitor (sodium tungstate, ST) caused maximum reduction in expression of NR genes as compared to other inhibitors [L-NAME (N(G)-nitro-L-arginine methyl ester; inhibitor of nitric oxide synthase-like enzyme) and DPI (diphenylce iodonium; NADPH oxidase inhibitor)]. The ratio of ABA/GA and IAA/GA decreased in magnetoprimed and NO donor-treated seeds, suggesting homeostasis amongst hormones during germination under salinity. The magnetoprimed seeds showed low Na\({}^{+}\)/K\({}^{+}\) ratio in all treatments irrespective of NO inhibitors. Altogether, our results indicate that a balance of ABA, GA and IAA is maintained by the signalling molecule NO in magnetoprimed seeds which lowers the Na\({}^{+}\)/K\({}^{+}\) ratio to offset the adverse effects of salinity in soybean seeds.  
g 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# A Sigfox Energy Consumption Model
## 3 Sigfox Overview
### Sigfox Radio Interface
Note that, in contrast with other technologies, retransmissions due to absence of feedback from the other endpoint of a link do not exist in Sigfox. Figure 2: Sigfox frame formats: (**a**) uplink and (**b**) downlink. All frame field sizes are expressed in bits. The third frame header field starting from the left is the Device Identifier (Device ID) or the Error Correcting Code (ECC), for uplink and downlink frame formats, respectively. The two rightmost frame fields are a Message Authentication Code (Msg Auth Code) and a Frame Check Sequence (FCS).
# Ultra-Wideband Antenna Design for 5G NR Using the Bezier Search Differential Evolution Algorithm
## Appendix A Benchmark Functions  
1. Rotated Hyper-ellipsoid function:where \(\mathbf{x}\) is an \(n\)-dimensional vector. Global solution: \(f(\mathbf{x})=0\) at \(\mathbf{x}=[0,\ldots,0]\).
2. Rastrigin function: \[f(\mathbf{x})=An+\sum_{i=1}^{n}\Bigl{(}x_{i}^{2}-A\cos(2\pi x_{i})\Bigr{)}\] where \(\mathbf{x}\) is an \(n\)-dimensional vector and \(A=10\). Global solution: \(f(\mathbf{x})=0\) at \(\mathbf{x}=[0,\ldots,0]\).
3. Ackley function: \[f(\mathbf{x})=-20\exp\left(-0.2\sqrt{\frac{1}{n}\sum_{i=1}^{n}x_{i}^{2}} \right)-\exp\left(\frac{1}{n}\sum_{i=1}^{n}\cos(2\pi x_{i})\right)+20+\exp(1)\] where \(\mathbf{x}\) is an \(n\)-dimensional vector, and \(n\geq 1\). Global minimum: \(f(\mathbf{x})=0\) at \(\mathbf{x}=[0,\ldots,0]\).
4. Sphere function: \[f(\mathbf{x})=\sum_{i=1}^{n}x_{i}^{2}\] where \(\mathbf{x}\) is an \(n\)-dimensional vector. Global solution: \(f(\mathbf{x})=0\) at \(\mathbf{x}=[0,\ldots,0]\).
5. Powell function: \[f(\mathbf{x})=\sum_{i=1}^{n/4}\Bigl{(}(x_{4i-3}+10x_{4i-2})^{2}+5(x_{4i-1}-x_ {4i})^{2}+(x_{4i-2}-2x_{4i-1})^{4}+10(x_{4i-3}-x_{4i})^{4}\Bigr{)}\] where \(\mathbf{x}\) is an \(n\)-dimensional vector, and \(n\) is divisible by 4. Global solution: \(f(\mathbf{x})=0\) at \(\mathbf{x}=[0,\ldots,0]\).
6. Three-Hump Camel function: \[f(\mathbf{x})=2x_{1}^{2}-1.05x_{1}^{4}+\frac{x_{1}^{6}}{6}+x_{1}x_{2}+x_{2}^{2}\] where \(\mathbf{x}\) is a 2-dimensional vector \((x_{1},x_{2})\). Global solution: \(f(\mathbf{x})=0\) at \(\mathbf{x}=[0,0]\).
7. Beale function: \[f(\mathbf{x})=(1.5-x_{1}+x_{1}x_{2})^{2}+\Bigl{(}2.25-x_{1}+x_{1}x_{2}^{2} \Bigr{)}^{2}+\Bigl{(}2.625-x_{1}+x_{1}x_{2}^{3}\Bigr{)}^{2}\] where \(\mathbf{x}\) is a 2-dimensional vector \((x_{1},x_{2})\). Global solution: \(f(\mathbf{x})=0\) at \(\mathbf{x}=[3,0.5]\).
8. Sum of Squares function: \[f(\mathbf{x})=\sum_{i=1}^{n}ix_{i}^{2}\] where \(\mathbf{x}\) is an \(n\)-dimensional vector. Global solution: \(f(\mathbf{x})=0\) at \(\mathbf{x}=[0,\ldots,0]\).
9. Bohachevsky No. 3 function: \[f(\mathbf{x})=x_{1}^{2}+2x_{2}^{2}-0.3\cos(3\pi x_{1})\cos(3\pi x_{2})+0.3\] where \(\mathbf{x}\) is a 2-dimensional vector \((x_{1},x_{2})\). Global solution: \(f(\mathbf{x})=0\) at \(\mathbf{x}=[0,0]\).  
* (10) Sum of Different Powers function: \[f(\mathbf{x})=\sum_{i=1}^{n}|x_{i}|^{i+1}\] where \(\mathbf{x}\) is an \(n\)-dimensional vector and \(p\) is a positive integer. Global solution: \(f(\mathbf{x})=0\) at \(\mathbf{x}=[0,\dots,0]\).
## 2 The Biofeedback Device and Application
The biofeedback application has a graphical interface (Figure 3) that either appears on screen or embedded in a web environment. The graphical part of the application displays the result of the regression procedure as a visualized response on the computer screen, where users can see their personal code (Figure 3a) and recognize their anxiety states through a chromatic code (red for anxiety, green for relaxation, orange for a little anxiety) along with their anxiety level percentage (Figure 3b). During the real-time measurement, when high anxiety levels are reached, the application encourages users to apply diaphragmatic breathing (Figure 3c) and shows them pictures so as to get them to relax (Figure 3e). Furthermore, the application informs the users about whether the sensors are working properly (green color next to the sensor's name) or not (red color next to the sensor's name) (Figure 3d). Figure 2: The biofeedback system. The tutor can monitor the students' stress level in real-time with the monitor user interface depicted in Figure 4. The user interface of this monitoring application shows the names Figure 4a and user codes (Figure 4b). Moreover, their anxiety level is depicted in color code (Figure 4c) followed by the relevant percentage (Figure 4d). The meaning of the colors is displayed at the bottom of the screen (Figure 4e). Figure 4: Instructor monitoring application. Figure 3: Biofeedback application user interface. (**a**) Biofeedback application interface, (**b**) Anxiety level percentage, (**c**) Encouragement for diaphragmatic breath, (**d**) Indication of well-functioning sensors, (**e**) Display pictures for relaxation.
# Inversion Analysis Method for Tunnel and Underground Space Engineering: A Short Review
## 3 Inversion Analysis of Initial Ground Stress  
Geo-stress is a type of natural stress that varies spatially and temporally, while initial stress is secondary stress formed relative to the stress redistribution phenomenon during tunnel excavation. In the early 20 th century, people began exploring initial ground stress during tunnel and underground space engineering construction. [PERSON], a Swiss geologist, was the first to propose the existence of initial ground stress within rock strata. He observed and analyzed the construction process of a large section mountain tunnel and concluded that the distribution of initial ground stress is in a state of hydrostatic stress.  
In 1915, the Swedish scholar [PERSON] first measured the initial geo-stress in the Navia Peninsula. He found that the maximum principal stress in the rock stratum is almost horizontal stress, fundamentally shaking the view that the initial geo-stress is dominated by vertical stress. In the mid-20 th century, domestic and foreign scholars used the strain generated in the mining process and geological exploration data in major projects to develop the calculation method of inversion analysis. In China, the founders, mainly [PERSON] and [PERSON], began actual measurements of ground stress in the 1950s.  
Figure 3: The research on inversion analysis method in tunnel and underground engineering in recent 20 years: (**a**) Research compositions, (**b**) Distribution of publications.
## 5 Results and Discussion
Finally, the efficiency of the three-phase VSC is measured utilizing the discussed control strategies in this paper, and the obtained results are plotted in Figures 9 and 10, in which different load points have been considered. Note that these measurements have been performed using the KinetiQ PPA5530 power analyzer. Figure 8: Obtained spectrum using different MPC schemes, where the average switching frequency of each MPC scheme is 5 kHz. (**a**) Conventional predictive scheme; (**b**) improved scheme; (**c**) periodic MPC scheme; and (**d**) MPC scheme with modulator. Figure 10: Measured efficiency of the three-phase VSC using the fixed switching frequency schemes, feeding a linear load. Figure 9: Measured efficiency of the three-phase voltage source converter (VSC) using the variable switching frequency schemes, feeding a linear load. In order to further investigate the effect of different predictive control schemes on the converter efficiency, simulation analysis is conducted. Figure 11a-d shows the distribution of the simulated switching and conduction losses at 4 kW, where the PMPC control strategy introduces much lower switching losses than the M\({}^{2}\)PC. It is worth mentioning that the Infineon-FF50R12 RT4 module was modeled with an ambient temperature of 40\({}^{\circ}\)C in order to obtain the conduction and switching loss using PLECS at 8 kW rated power. Figure 12 shows the simulated efficiency for the MPC algorithms. As can be seen, the simulation results give slightly higher efficacy rather than the actual measured results in Figures 9 and 10. Part of the power is dissipated due to the cable resistivity, which is not included in the simulation analysis. This is another reason that the ambient temperature is difficult to precisely estimate. Finally, for the CPMC, IMPC, and M\({}^{2}\)PC, the conduction loss is approximately equal to 30 W and that due to the similar average time of duty cycles for the mentioned methods. Figure 11: Different configuration MPC schemes for efficiency assessment at 4 kW rated power. (**a**) Distribution Please change labels k–n to a–d for conduction and switching losses of 2L-VSC using the conventional (CMPC) scheme, (**b**) distribution for conduction and switching losses of 2L-VSC using the improved (IMPC) scheme, (**c**) distribution for conduction and switching losses of 2L-VSC using the M\({}^{2}\)PC scheme, and (**d**) distribution for conduction and switching losses of 2L-VSC using the periodic (PMPC) scheme. While for the PMPC the average time of duty cycles is less and therefore the conduction loss is approximately equal to 24 W. On the other hand, the switching loss distribution of the four MPC algorithms show how much transitions occur to deliver the same power at approximately the same switching frequency.