esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Background: The Internet of Medical Things, often known as IoMT, is a revolutionary method of connecting medical equipment and the software that operates on it to the computer networks that are used in healthcare 5.0. The rapid development of smart medical devices on IoMT platforms has led to the adoption of major technologies in the modernization of healthcare procedures, the administration of diseases, and the improvement in patient treatment standards. The IoMT offers a variety of cloud-based applications, including data exchange, data screening, patient surveillance, information collection and analysis, and hygienic hospital attention. Wireless sensor networks (WSNs) are responsible for both the gathering and delivery of data. Method: The safety of patients and their right to privacy are the top priorities in the healthcare sector. Anyone may see and modify the patient's health information because the data from these smart gadgets are sent wirelessly through the airways. Hence, we developed a unique elliptic curve cryptography-based energy-efficient routing protocol (ECC-EERP) to provide a high level of security and energy efficient system for healthcare 5.0. Data can be encrypted using the key-based method ECC-EERP. It employs pairs of public and private keys to decrypt and encrypts web traffic and reduce the amount of energy needed by a WSN in aggregate. Result and Discussion: The efficiency of the suggested method was evaluated in comparison with that of a variety of existing methods. The suggested method was evaluated with the use of many parameters such as security, encryption throughput, energy efficiency, network lifetime, communication overload, computation time, and implementation cost. The results showed that the proposed technique provides enhanced security and energy efficiency.  
Internet of Medical Things; energy optimization; wireless sensor networks; privacy; security +
Footnote †: journal:  
0000-0001-9991-4600-0000][PERSON]  
1 Information Technology Department, University of Technology and Applied Sciences-Shinas, Al-Aqr, Shinas 324, Oman 1  
Footnote 1: [[https://www.mdpi.com/journal/infrastructures](https://www.mdpi.com/journal/infrastructures)]([https://www.mdpi.com/journal/infrastructures](https://www.mdpi.com/journal/infrastructures))  
2 Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal 576104, India 2
## 3 Experimental Results and Discussion
### Selection of Hyperparameters  
The choice of optimizer and learning rate can improve the training of neural networks, making them faster and better at achieving the desired results. In this research experiment, we combined four different optimizers (SGD, Adam, Adagrad, and AdaDelta) and five different learning rates (lr). We conducted a comparison experiment to find the most suitable optimizer and learning rate for this experiment. We used five learning rates of 0.1, 0.05, 0.01, 0.005, and 0.001, which were combined with the four optimizers. We tested the experiment on the SBG-Dataset test data, setting the epoch of the experiment to 30 rounds. The comparison results of different optimizers and learning rates are shown in Tables 3 and 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Optimization** & **lr = 0.1** & **lr = 0.05** & **lr = 0.01** & **lr = 0.005** & **lr = 0.001** \\ \hline SGD & 98.11 & 98.90 & 99.23 & 98.56 & 98.88 \\ Adam & 97.97 & 98.85 & 99.53 & 99.55 & 99.66 \\ Adagrad & 95.25 & 97.26 & 99.71 & 99.68 & 99.21 \\ AdaDelta & 85.44 & 87.82 & 99.65 & 85.32 & 90.56 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Recognition accuracy (%) of RiceDRA-Net on SBG-Dataset under different optimization algorithms and learning rates.  
According to Tables 3 and 4, we can see that the highest accuracy was obtained by using the Adagrad optimizer with a learning rate of 0.01, which resulted in an accuracy of 99.71% and the lowest total loss value on the test set. Therefore, we can conclude that the optimal combination of optimizer and learning rate for this study's model was Adagrad with a learning rate of 0.01.  
The spatial attention mechanism in the Res-Attention module is impacted by the choice of its convolutional kernel size, and for this experiment, we compared the impact of using convolutional kernels of (3 \(\times\) 3) or (7 \(\times\) 7) on the recognition accuracy of the model. We used the Adagrad optimizer with a learning rate of 0.01, ran the experiment for 30 epochs, and tested the model on the SBG-Dataset.  
Based on Table 5, we observed that modifying the size of the convolutional kernel of the spatial attention mechanism in the Res-Attention module to (3 \(\times\) 3) was more suitable for identifying rice disease infestations in this network.
# Multiclass Segmentation of Concrete Surface Damages Using U-Net and DeepLabV3+
## 3 Results and Discussions
### U-Net and DeepLabV3+ Training Results
#### 3.1.4 Qualitative Comparison  
Figure 14 shows the prediction results of U-Net and DeepLabV3+ alongside the corresponding ground truth. Based on the result, U-Net and DeepLabV3+ successfully ignored the complex background and still had decent performance for damage detection. Based on the prediction results, U-Net can segment smaller objects and has a more accurate detection of boundaries. DeepLabV3+ often ignores small objects and overall has more-smoothened edges than U-Net. This result is also reflected on the quantitative comparison where the values of mean IoU and mean F1 of DeepLabV3+ are lower than those of U-Net. This phenomenon can be seen closely in Figure 15, where in the regions highlighted in yellow, U-Net detects small cracks and voids while DeepLabV3+ does not.  
\begin{table}
\begin{tabular}{c c c} \hline
**Metrics** & **U-Net** & **DeepLabV3+** \\ \hline Accuracy & 0.9800 & 0.9744 \\ Mean IoU & 0.5993 & 0.5174 \\ Mean F1 & 0.7199 & 0.6478 \\ Background IoU & 0.9828 & 0.9711 \\ Crack IoU & 0.4787 & 0.3979 \\ Spalling IoU & 0.6303 & 0.3769 \\ Void IoU & 0.3052 & 0.3237 \\ Background F1 & 0.9913 & 0.9854 \\ Crack F1 & 0.6475 & 0.5693 \\ Spalling F1 & 0.7732 & 0.5474 \\ Void F1 & 0.4677 & 0.4891 \\ \hline \end{tabular}
\end{table}
Table 1: U-Net and DeepLabV3+ training results comparison.  
Figure 14: Models’ prediction on validation data.  
Figure 15: U-Net and DeepLabV3+ comparison on fine-grained object segmentation.
# A New Method for Evaluating the Bearing Capacity of the Bridge Pile Socketed in the Soft Rock
## 5 Conclusions  
In this paper, the analytical expression of the vertical restraint stiffness at the pier bottom was derived based on the Rayleigh energy method and the frequency synthesis method. The functional expression of the vertical restraint stiffness and the fundamental frequency of the pier was obtained. Then, the bearing capacity coefficients of the pile under different impact parameters were calculated and fitted by multiple nonlinear regression through the iterative algorithm. Based on the above results, a method to evaluate the bearing capacity of pile foundations is proposed. The main conclusions are as follows:  
1. For the pier bearing capacity coefficient, the pile length, rock-socketed depth, and pile modulus have a more significant influence. The pile diameter and bearing stratum rock modulus have a smaller influence, whereas the side rock modulus has almost no impact. The bearing capacity coefficient increases with the increase of the pile length, socketed depth, and rock modulus, and it decreases with the increase of the pile diameter and pile modulus.
2. A new method for evaluating the vertical bearing capacity of a single pile is proposed. First, the analytical expression between the vertical fundamental frequency and the constraint stiffness of the pier is derived. Based on dynamic field tests, the pier frequency is measured. Then, through multiple regression, the bearing capacity coefficient model is obtained. Finally, with the design and measured data, the predicted bearing capacity of a single pile can be calculated.
3. According to the engineering example, the accuracy of the evaluation method was assessed through the finite element simulation calculation. The results showed that the error between the constraint stiffness calculated by the code and the constraint stiffness calculated by the frequency synthesis method was about 0.7%. The bearing capacity difference between the analytical solution and the finite element numerical simulation was small, and the method is accurate and effective.  
Conceptualization, Y.L. and D.L.; methodology, Y.L. and S.J.; software, S.J. and K.W.; validation, D.L.; formal analysis, Y.L. and S.J.; investigation, Y.L and D.L.; resources, D.L.; data curation, S.J. and K.W.; writing--original draft preparation, Y.L. and S.J.; writing--review and editing, Y.L.; supervision, D.L.; project administration, D.L.; funding acquisition, D.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 External Perturbations Affecting Geolocation  
In the absence of an atmosphere, tectonic plate movements and tidal effects, position measurements of stable targets on the Earth's surface using SAR would be predictable and virtually unchanging from day to day. In reality, the sub-metric geolocation accuracy required for geometric calibration of today's high-resolution spaceborne SAR sensors can only be achieved if proper account is taken of the most important dynamic effects acting on the radar echoes and the position of reference targets on the ground. These effects are illustrated in Figure 4. They were described in [7]; the height dependent atmospheric PD model was described in detail in [11].
## 4 Assessment of Performance
### Field Coverage Drop  
The throughput produced by each of the three methods is compared in Figure 4, which shows the comparison in regards to the total number of nodes. The throughput is measured in terms of the number of bits that are transmitted in a certain amount of time (second). If all of the transmitters and receivers follow the same channels and are not required to change their radio between channels, we believe that we have achieved 100% of the theoretical throughput over the whole network. It takes a node two hundred microseconds to change its radio from one channel to another, which reduces the period of time that can be devoted to transmission and, as a result, the amount of data that can be sent in one second. It also  
Figure 3: The average number of nodes moved throughout the recovery process.  
Figure 2: Number of nodes vs. distance travelled.  
indicates that our proposed methodology is upgraded relative to the baseline techniques. The total number of nodes increases, which results in an increase in the throughput overall.  
Figure 5 illustrates the effect that the overhead has on the lifetime average of sensor nodes as a function of the size of the network. We compared the node lifetime when PACR, SNR and proposed techniques were utilised, with particular attention paid to the average lifetime when no recovery mechanism was implemented. It has come to our attention that the lifespan average drops when the number of nodes in the network is increased. For instance, while using 50 nodes, the two strategies provided lifetime averages that were extremely similar to the case when there was no recovery. On the other hand, in sparse networks, PACR performs somewhat better than proposed technique. When applied to dense networks, the proposed technique shows improved performance, with efficiencies of 83.76%, 84.84% and 87.3% for SNR, PACR and proposed technique, respectively, with 250 nodes. When the proposed technique is utilised, there is a significantly lower occurrence of interferences and retransmissions, which is directly responsible for this result. On the other hand, the network lifetime is increased as a result of the failure of an articulation node, which causes WSN to become non-functional. The implementation of a recovery strategy will, thus, extend the lifetime of the network even though it will produce some additional overheads. Regarding the benefits that can be gained from the various recovery strategies, we believe that the reduction in the average node lifetime is still quite negligible. Therefore, compared to the other baseline techniques, the suggested methodology performs better in extending the network's lifespan.  
Figure 4: Number of nodes vs. throughput.
## 1 Introduction
Therefore, in our case, the shaping of the bursts is realized just after the seed laser system at the lowest accessible pulse energy (see Figure 1). The modulated bursts are then amplified by a cryogenically-cooled Yb:CaF\({}_{2}\)-based laser amplifier (see [9,13] for the details of the system) to an average output power of up to 800 W during the burst. The changes of the burst envelope introduced due to the amplification characteristics, especially for bursts with a non-uniform envelope, will be analyzed. Finally, a theoretical approach to estimate the appropriate initial burst envelope for a targeted output envelope is developed and tested with the amplifier system.
## 2 Theory and Models
### Instantentious Power Representation
#### 2.4.2 Average Power Distribution  
Each component, which can be denoted as \(P_{r}^{(kF)}=\Re\epsilon S_{vi}^{(0,k)}\), is treated as a part of the active power associated with frequency \(mF\), as it appears to be contributing to the total average power separately. The consideration lying behind this idea is the following. Suppose one applied a band pass filter tuned so as to let only the \(n\)th harmonic in the Fourier series, representing the voltage (3) or current (4), pass through it. Then, according to (34), there will be only the pair made of \(\mathcal{S}_{vi}^{(0,n)}\) and \(\mathcal{S}_{vi}^{(0,-n)}\) remaining in the sum for the average power (33). These components are firmly associated with frequency \(nF\). Repeating with the family of filters tuned to other frequencies, one may deduce that the contribution brought to the average power by any harmonic does not depend on what other harmonics put in.  
Formally, the imaginary parts of the same components \(\mathcal{S}_{vi}^{(0,k)}\) can be also put under investigation. They may be called imaginary (or reactive) powers \(P_{q}^{(kF)}=\Im\mathcal{S}_{vi}^{(0,k)}\). However, as the voltage and current are real valued and, therefore, symmetry (36) holds, those components will inevitably satisfy the equation:  
\[\sum_{k=1}^{+\infty}P_{q}^{(kF)}=\sum_{k=-\infty}^{+\infty}\Im\mathcal{S}_{vi} ^{(0,k)}=0. \tag{38}\]  
The distribution of the components contributing to the average power, or furthermore, the extraction of their real (active) or imaginary (reactive) parts, among the discrete set of frequencies \(\Lambda_{1}=\{\alpha\,|\,\alpha=kF\}\), appears to be useful in the power analysis of the circuit, since it helps identify the main frequency channels of the energy absorption taking place in the circuit and measure their intensities.
# IoT-Enabled Precision Agriculture: Developing an Ecosystem for Optimized Crop Management
## 4 Experiments and Simulation
### Simulation Software: Contiki and COOJA  
The experimentation utilized wireless sensor nodes that were built on the open-source, highly portable, multi-tasking operating system, Contiki [57]. This operating system is specifically designed for memory-efficient networked embedded systems and wireless sensor networks (WSNs). It is capable of running on microcontrollers with minimal memory resources, with a typical configuration of 2 kB of RAM and 40 kB of ROM. The use of Contiki in our sensor nodes allowed for efficient resource management and ensured compatibility with the low-power requirements of our precision agriculture applications.  
Contiki was selected as the operating system for the sensor nodes and used C as the programming language. Contiki has many advantages over other sensor-network operating systems; for example, it includes an embedded simulation tool, COOJA, which can compile  
Figure 4: IoT System for horse monitoring.  
Figure 3: IoT system for monitoring olive trees in a fixed position.  
and simulate application software conveniently and quickly [41]. For simulation, a single sink node was assumed in each application and 36 normal nodes. The sink node is assumed to be fixed in the olive farm case and mobile in the horse farm case. The static nodes were simulated in COOJA using 36 clients and a single server (Sky Mote IPv6 RPL-UDP). The mobile nodes were modeled using a WSN 6 lowPan in COOJA, where the mobility model was modeled using RWM. The data analysis and processing server were fixed at the simulation area's center. Finally, the simulation duration was set as 4 h. Tables 3 and 4, summarize all used summation parameters.
## 4 Results and Discussion
### Experimental Determination of the Permeability
#### 4.2.2 SLM  
Three samples were tested (90, 130, and 180 \(\upmu\)m pore size). Figure 13b shows the linear effect of meniscus-pore size on its permeability, revealing the same tendency as in the previous tests. Whatever the pore size, scaffold permeability was two orders of magnitude higher than that of the sintering copper. For example, the permeability of the scaffold with 90 \(\upmu\)m pore size was 2276% higher than the permeability of sintering copper 90.  
The scaffold has an outstanding permeability compared with the one specified by the application (\(4\times 10^{-12}\) m\({}^{2}\)), resulting in a lower pressure drop than in the specifications of the application, 100 Pa.
## 3 Results and Discussion
### SG4 Induced Apoptosis of H-29 Cells Via a Mitochondria-Mediated Signaling Pathway  
The functions of mitochondria may be mediated by the mitochondrial ATP-sensitive K channels and the mitochondrial permeability transition pore. The irretrievable opening of the mitochondrial permeability transition pore is an indicator of early apoptosis and is fatal to cells [29]. MMP is an essential component in the production of energy (ATP) and in the maintenance of cellular homeostasis [30]. As a result, the loss of MMP is directly linked to the induction of apoptosis [31]. A potentiometric TMRE dye can be adopted to quantify loss of MMP [8]. TMRE may bind to active mitochondria (possessing negative charge) due to its ability to permeate cells as well as its positively charged properties. Thus, the malfunction of mitochondrial results in a decrease of TMRE accumulation in mitochondria. Figure 3a,b suggests that the percentage of cells with high TMRE intensity in the control was \(88.7\%\pm 0.3\%\). When HT-29 cells were treated with \(200\) ug/mL SG4 for \(48\) h, the percentage of cells with high TMRE intensity was significantly decreased to \(37.4\%\pm 1.3\%\) (\(p<0.001\)), which suggests that SG4-mediated mitochondrial dysfunction occurs. Release of cytochrome \(c\) from mitochondria is  
Figure 2: The cell cycle profile of HT-29 cells treated with \(200\) ug/mL SG4 for \(48\) h: (**a**) In the histograms, \(x\) axis and \(y\) axis denote DNA content and cell counts, respectively. The cell cycle distribution (sub-G\({}_{1}\), \(G_{0}\)/G\({}_{1}\), \(S\), and \(G_{2}\)/\(M\)) is indicated in each histogram; (**b**) the percentage of sub-\(G_{1}\), \(G_{0}\)/\(G_{1}\), \(S\), and \(G_{2}\)/\(M\) cells is presented as determined for (**a**). The experiments were performed in triplicate. \({}^{**}\)\(p<0.01\); \({}^{***}\)\(p<0.001\).  
indicative of early apoptosis and is often an upstream signal of the mitochondria-dependent apoptotic pathway [32,33]. In the present study, we applied an immunodetection method to quantify the release of cytochrome c in cells by flow cytometry. As shown in Figure 3c,d, when HT-29 cells were treated with 200 ug/mL SG4 for 48 h, the high fluorescent cell population significantly decreased from 89.7\(\%\pm\) 0.6\(\%\) (control) to 76.5\(\%\)\(\pm\) 1.0\(\%\) (\(p\) < 0.001), indicating SG4-mediated cytochrome c release from mitochondria occurs. In summary, these results suggest SG4 induced mitochondria-dependent apoptotic effects, as visualized by the loss of MMP and release of cytochrome c.
## 2 Results and Discussion  
### GC-MS Instrumental Conditions  
The development of the experimental procedure started with the improvement of the GC-MS parameters to establish the appropriate instrumental conditions for BVOCs analysis through both analytical methodologies. For such purpose, two standard mixtures of five BVOCs were analyzed by GC-MS using two inlet configurations; a 100 mg L\({}^{-1}\) mix solution was analyzed in the split (S) injection mode (ratio of 1:40) for the HS-SPME/GC-MS methodology, and a 2 mg L\({}^{-1}\) mix solution was analyzed in the splitless (SL) injection mode for the HS-BA\(\mu\)E/GC-MS approach. The total ion chromatograms obtained showed great sensitivity and symmetrical peak shapes for each compound with a suitable resolution under an acceptable analytical time (<10 min). The instrumental sensitivity was evaluated through the limits of detection (LODs) and quantification (LOQs) of each target compound by calculating the signal-to-noise (S/N) ratios of 3:1 and 10:1, respectively. When using the S injection mode, LODs and LOQs of 0.5 and 1.5 mg L\({}^{-1}\) were obtained for the internal standard (IS), \(\alpha\)-pinene and limonene, respectively. Meanwhile, for \(\beta\)-pinene, myrcene and 1,\(\beta\)-cineole, these analytical thresholds corresponded to 1.0 and 3.0 mg L\({}^{-1}\), respectively. As for the SL injection mode injection, the LODs and LOQs achieved for all target compounds were 0.1 and 0.4 mg L\({}^{-1}\), respectively. Afterwards, the instrumental precision was determined through six consecutive injections of the same standard mixtures (under the same injection conditions), obtaining values of the relative standard deviation (RSD) \(\leq\) 10.6% for all analytes. The instrumental calibration was performed through the determination of linear regressions for each analyte, prepared with eleven concentration levels ranging from 20.0 to 700.0 mg L\({}^{-1}\) (S mode, ratio of 1:40) and between 0.4 and 5.0 mg L\({}^{-1}\) (SL mode), where remarkable linearity was attained, as all determination coefficient (\(r^{2}\)) values were above 0.9963.
# Aerial Coverage Analysis of Cellular Systems at LTE and mmWave Frequencies Using 3D City Models
###### Abstract  
Cellular connectivity for UAV systems is interesting because it promises coverage in beyond visual line of sight scenarios. Inter-cell interference has been shown to be the main limiting factor at high altitudes. Using a realistic 3D simulator model, with real base station locations, this study confirms that UAVs at high altitudes suffer from significant interference, resulting in a worse coverage compared to ground users. When replacing the existing base stations by mmWave cells, our results indicate that ground coverage is decreased to only 90%, while UAVs just above rooftop level have a coverage probability of 100%. However, UAVs at higher altitude still suffer from excessive interference. Beamforming has the potential to improve mmWave link budget and to decrease interference and is for this reason a promising technology for ensuring connectivity to aerial users.  
U  
Aerial Coverage Analysis of Cellular Systems at LTE and mmWave Frequencies Using 3D City Models