esa-sceva/satcom-chunk-collection huggingface dataset:
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 3 Low Power Wide Area Network
### Brief Origin of LPWAN  
We present a timeline to highlight the progression of LPWAN technologies from earlier versions to the present state-of-the-art LPWAN technologies. Such information should help readers to understand and to appreciate the current growth and future trends in the LPWAN market. The AlarmNet technology suffices as one of the earliest technologies built by alarm device manufacturing company (ADEMCO) in the early 1990s based on a 900 MHz network in order to monitor alarm panels [23]. It sent a small amount of data and transmitted at very low data rates. It served as the bedrock for the migration of remote alarm systems to the 2 nd generation technologies, which provided both data and voice services [23]. A technology similar to modern LPWANs was the advanced radio data information service (ARDIS), which was a wireless wide area network that provided relatively low-speed networksfor sales automation, fleet tracking and other online transaction processes [24]. ARDIS was later acquired by Motorola Incorporation [24].  
Although the above-mentioned technologies lacked some characteristics as compared to modern LPWANs, for example, improved energy efficiency, nevertheless, they offered the foundation for the development of the presently known LPWAN systems. Furthermore, the emergence of the Internet and its present widespread application areas/potentials necessitated the need to improve these technologies as seen today. Much of the resurgence of LPWAN technologies was led by Sigfox, which originated in 2009 [25]. They built the first modern LPWAN in France and their success rate spurred quite a buzz in the industry. Sigfox technologies came at a period when radio devices were declining in cost and their usage was becoming pervasive. They were soon followed by other LPWAN interest groups such as the Ingenu, LoRa Alliance, and even traditional cellular players rolling out NB-IoT devices [4; 9; 26]. Particularly, LoRa technology was initially developed by Cycleo Incorporation in Grenoble, France, but was later acquired by SEMTECH in 2012. An overview of the timeline of the origin of different LPWAN technologies, along with their developers is presented in Table 1. Following this timeline, it is easy to appreciate how the LPWAN market has progressed with potentials for further growth in the nearest future. In this regard, it is our belief that the future of LPWANs will entail the integration and development of CR-LPWAN systems. A number of LPWAN technologies are discussed next, whereas standardization bodies involved in regulating the interoperability of LPWAN technologies will be discussed in subsequent subsections.
## 3 Results  
### Results of Correlation Analysis  
The acquired bands and spectral indices were subjected to a Pearson correlation analysis with measured PC concentrations. A strong correlation between two variables is indicated by a Pearson correlation coefficient closer to 1 in absolute value. The statistical results are presented in Table 4. Among the single bands, B4 had the highest correlation of 0.646. Following closely were B1 and B3 with correlations of 0.633 and 0.640, respectively. This differs from previous researchers [37; 38; 39], who based their monitoring of PC concentration on the prominent absorption valley of PC in the spectral curve at 620 nm. At very low PC concentrations, the spectral characteristics of PC appear at 665 nm. This precisely demonstrates that the spectral feature position of PC changes with variations in the PC concentration [17], rather than consistently residing at 620 nm. Among the spectral indices, the correlation was strongest for (B2 + B8)/B4, measuring \(-\)0.651. Notably, the spectral indices with strong correlations to measured PC concentrations were associated with the B1, B3, and B4 bands. Therefore, the retrieval model was constructed using B1, B3, and B4 bands as input feature vectors.
## 3 Adaptive Data Fusion Method Based on ADCNN for Fault Diagnosis  
In this section, the fault diagnosis and classification method of ADCNN is introduced. The architecture is shown in Figure 1. There are six blocks in total. (1) Multi-sensor information: captures the raw data of mechanical equipment multi-sensors, including vibration X, vibration Y, vibration Z, temperature, sound, and six-dimensional data of electric currents. (2) Data preprocessing: uses a sliding window to divide data. (3) Data fusion: integrates the five-dimensional vibration X, vibration Y, vibration Z, sound, and non-stationary time-varying signals of the electric currents for array data structure fusion. Performs Gaussian filtering and de-noising processing for temperature-stationary signals. (4) Feature extraction: uses CWT to convert the data of vibration X, vibration Y, vibration Z, sound, and electric current channels to adaptive time-frequency map features of different frequency bands. Uses GAF to convert temperature data into GASF two-dimensional image features, and then fuses the six image channels to extract the features as the input of CNN. (5) Feature fusion: the convolutional layer processes the input features into feature maps, and the pooling layer performs adaptive feature fusion for downsampling. (6) Fault classification: uses the Softmax function to output the model prediction results.
## 4 Effect of Design Parameters on Seismic Performance of SCMSW Specimens
### Effect of Axial Load Ratio on Hysteretic Response of SCMSW Specimens  
In this section, the effect of axial load on load-displacement behavior is evaluated based on the results of two SCMSW specimens which had identical aspect ratio and the same amount of flexural reinforcement. The normalized load displacement envelopes for these comparable specimens are given in Figure 15. Results revealed that the maximum observed lateral load increased from 162 kN for SW2 to 213 kN for SW3, resulting in a strength increase of 31% when the axial load ratio increased from  
Figure 14: Load-displacement envelopes for different vertical reinforcement ratios for SCMSW specimens.  
Figure 13: Normalized load-displacement curves for different aspect ratios for SCMSW specimens.  
0.5 to 1.0 N/mm\({}^{2}\). Moreover, the lateral displacement capacity decreased inversely in relation to an increase of the axial load ratio.
# Deep-Reinforcement-Learning-Based Intelligent Routing Strategy for FANETs
###### Abstract  
Flying ad hoc networks (FANETs), which are composed of autonomous flying vehicles, constitute an important supplement to satellite networks and terrestrial networks, and they are indispensable for many scenarios including emergency communication. Unfortunately, the routing therein is largely affected by rapid topology changes, frequent disconnection of links, and a high vehicle mobility. In this paper, an intelligent routing strategy based on deep reinforcement learning (DRI) is proposed, which is decentralized and takes into account the status of symmetrical nodes in two hops. In order to perceive the local dynamics of the network as comprehensively as possible, the location, moving speed, load degree, and link quality of the nodes are considered into the setting process of state elements in the method. The nodes can select the neighboring node adaptively according to the Q values calculated by the model obtained through the training of Deep Q-Networks. The simulation and analysis show that the proposed method possesses good convergence characteristics and has obviously better performance compared with several common methods.  
FANET; UAV; routing; reinforcement learning; decentralized Article  
Deep-Reinforcement-Learning-Based Intelligent Routing Strategy for FANETs. _Symmetry_**2022**, _14_, 1787. [[https://doi.org/10.3390/sym14091787](https://doi.org/10.3390/sym14091787)]([https://doi.org/10.3390/sym14091787](https://doi.org/10.3390/sym14091787))  
Academic Editor: [PERSON]
## 4 Results and Discussion
### Measurement of the RIN  
Limited by the measurement frequency range of commercial instruments, the measurement of the RIN in the millihertz range (1 mHz\(\sim\)1 Hz) is not straightforward. We developed a precision RIN measurement system, which consists of a low-noise photodetector (Thorlabs, DET01 CFC/M, Newton, NJ, USA), a dynamic signal acquisition device (NL, PCI/PXI-4462, Austin, TX, USA) and a signal acquisition and processing program. The frequency resolution of the RIN measurement system is up to 2 \(\upmu\)Hz. We first measured the noise floor of the RIN measurement system in the frequency range from 0.1 mHz to 10 Hz. The result is shown by the black curve in Figure 7. In the frequency range from 1 mHz to 1 Hz, the noise floor is below \(-90\) dBc/Hz. Then, we measured the RIN of the seed laser and the amplified laser operating at free running. Compared with the RIN of the seed laser (the red curve), the RIN of the amplified laser (the blue curve) obviously deteriorates in the frequency range below 0.1 Hz. As shown in Figure 7, the difference between the red curve and the blue curve at approximately 10 mHz exceeds 20 dB. The main reason for noise deterioration is pump noise, which can be reduced by stabilizing the pump current.  
Figure 6: Additional linewidth measurement results of the amplified laser.  
Figure 7: RIN of the amplifier output.  
Following the guidelines shown in Section 2, we turned on the feedback loop and tried to minimize the out-of-loop RIN in the millihertz range by stabilizing the amplifier pump LD current. According to Equation (9), the intensity noise of the amplified laser can be reduced by choosing the optimal PID parameters. We tuned the PID parameters while monitoring the error signal with an oscilloscope, until the smallest error signal was observed. Then, we set the TEC temperature of the pump LD at 298 K and stabilize the pump LD temperature at 298 K \(\pm\) 0.01 K. The RINs of the in-loop and out-of-loop are measured and shown by the green curve and purple curve in Figure 7, respectively. It can be seen that the RIN of the out-of-loop is lower than that when free running. In the frequency range from 1 mHz to 1 Hz, the RIN of the out-of-loop can be suppressed by 5-30 dB compared to the free running case. However, it is above \(-\)60 dBc/Hz below 30 mHz, which is slightly higher than the seed laser.
## 4 Simulation and Calibration
### Simulation Scenarios and Data Collection
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Location**} & **HDC** & **GDC** & **Sudoku** & **Compare to HDC** & \multicolumn{2}{c}{**Compare to GDC**} \\  & **Actual Volume** & **Actual Volume** & **Simulated Volume (ve/h)** & **Difference** & **Error (\%)** & **Difference** & **Error (\%)** \\ \hline
1 & 1583 & 1613 & 1674 & 91 & 5.44 & 61 & 3.64 \\
2 & 1784 & 1573 & 1711 & \(-\)73 & 4.27 & 138 & 8.07 \\
3 & 1671 & 1771 & 1732 & 61 & 3.52 & \(-\)39 & 2.25 \\
4 & 1569 & 1505 & 1525 & \(-\)44 & 2.89 & 20 & 1.31 \\
5 & 1407 & 1313 & 1449 & 42 & 2.90 & 136 & 9.39 \\
6 & 1607 & 1689 & 1804 & 197 & 10.92 & 115 & 6.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of actual and simulated traffic volumes. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Link**} & **HDC** & **GDC** & **Sudoku** & **Compare to HDC** & \multicolumn{2}{c}{**Compare to GDC**} \\  & **Actual Travel** & **Actual Travel** & **Simulated** & **Error (\%)** & **Difference** & **Error (\%)** \\ \hline
1 & 665 & 806 & 737 & 71 & 9.70 & \(-\)69 & 9.38 \\
2 & 661 & 711 & 798 & 136 & 17.09 & 87 & 10.93 \\
3 & 742 & 758 & 876 & 134 & 15.33 & 118 & 13.43 \\
4 & 713 & 732 & 816 & 103 & 12.58 & 83 & 10.  
travel time between simulated network of Sudoku and the real network of GDC is less than 16.33%. According to the study of [PERSON], [PERSON] and [PERSON], an error of 15% to 25% cannot be suppressed in microscopic models [43]. Because the differences between model outputs and field data are within the acceptable ranges, the simulation model is considered to adequately represent the traffic congestion in urban high-density areas. To simulate traffic flow under sudden fire disasters, the VISSIM COM and VC++ were programed to restrict the capacity, speed, vehicle routes based on the temporal and spatial distribution influence model under traffic accident in the urban traffic network [44; 45; 46; 47; 48; 49]. Other parameters such as headway and driver behavior were left to the default VISSIM values.
## 7 Performance Evaluation
### Performance Evaluation of the CryptoSuite
It is well-known that underwater acoustic networks are characterized by very high delay variance which is extremely harmful for efficient protocol design, as it prevents from accurately estimating the round trip time (RTT), which is the key parameter for many common communication protocols [1]. Figure 11 shows the average round-trip time we have experienced in our experiments. Of course, the packet size increment due to integrity is not sufficient to explain the extremely large increments in the round-trip time. Actually, at a transmission rate of 500 bit/s, the additional 4 bytes would introduce just a 64 ms delay. As discussed above, the experiments with security disabled and those with security enabled were carried quite a few hours apart. In such a lapse of time, sea conditions have changed a lot so causing largely different delays. \begin{table}
\begin{tabular}{|l|c|c|} \hline
**Node** & **Distance from GW (m)** & **Depth (m)** \\ \hline GW & - & 90.3 \\ FN1 & 163.5 & 96 \\ FN2 & 860 & 39 \\ FLG1 & 677.8 & 20 \\ FLG2 & 687.1 & 15 \\ \hline \end{tabular}
\end{table} 
Table 1: Configuration parameters for Scenario L.
## 3 Results
### Common Themes for RQ2
Older adults may forget or resist wearing or carrying wearable sensors or other devices, or their batteries may run out of charge. Wi-Fi and radar-based systems were expensive upfront and accompanied by high development overhead costs. Vision-based systems had issues detecting in low-light environments, restricted fields of view, and privacy invasions. There are advantages to remote sensing of vibrations and sounds. Even though the user might forget to engage with the device, continuity in operation was an advantage compared with wearables. Moreover, vibration and acoustic technologies were relatively cheap. By identifying patterns of vibrations or sounds rather than raw signals, the system was minimally intrusive or invasive of privacy compared with vision-based monitoring systems. Nevertheless, remote sensing technologies have disadvantages. One study argued that remote sensing could still be expensive because the system would typically be installed in a confined area where multiple sensors are needed to deliver the intended outcomes and there could be blind spots [48]. The overall cost would then be elevated over the cost of radar-based systems. The accuracy of the acoustic technologies depended on the choice of classifier and signal processing techniques. Differentiating some sounds in the physical indoor environment was challenging; sounds from door knocking and door closing were difficult to distinguish. Environmental noise remained an issue affecting the accuracy of detection for acoustic technologies [51; 53]. According to the two articles [52; 53], vibration technologies were easier to install than acoustic technologies with high accuracy. However, there was not enough evidence to show the extent to which accuracy would change if subjects fell onto carpeted surfaces. Also, there is little detail on the range to which a mobile phone accelerometer would be able to measure the vibration pattern of a fall adequately. For the combined approach, recalibration was necessary for different floor materials, and the system fell short of differentiating between'soft' and'slow' falls. The system also did not consider the correlation between vibration and acoustic signals, thereby not taking advantage of its ability to distinguish airborne sources from those resulting only from ground contact. 3.3 Theme 3: Paucity of Research around Acceptability and Simultaneous Use of Sounds and Vibrations in Remote Monitoring of Assisted Living or Smart Homes in the United Kingdom  
There is limited evidence in the United Kingdom concerning the simultaneous application of acoustic and vibration technologies. Only one study identified was based in the United Kingdom, and it examined acoustic technologies only [49]. The lack of information in other forms of application, the absence of simultaneous consideration of acoustic and vibration data, and the limited number of studies identified in the United Kingdom all suggest a paucity of research in the area. These Themes are summarised in Figure 4.
# A Distributed Algorithm for UAV Cluster Task Assignment Based on Sensor Network and Mobile
## 3 Proposed Rational Clustering Method (RCM) Using Information Exchange
### Performance Analysis  
The performance analysis for the proposed RCM-IE is given here based on the OPNET modeler's simulations. The simulation was performed using a network space of 1000 m \(\times\) 500 m and 80 UAV nodes. Both distributed and clusters were present in the network; the maximum cluster size was 5-6 UAV nodes under 12 clusters. A cluster provided 5-60 task slots for accepting and delivering completed ones. The maximum acceptance time for the slot was 240 ms, and the communication range was 250 m. The UAVs communicate/are interconnected using nine access points across the network space. The metrics task processing rate, completion ratio, reassignment, failure, and delay were considered for the comparative analysis. The methods FDSA [17], CLA-TO [25], and ETCTMA [22] were considered alongside the proposed RCM-IE.
## 4 Experiments
### Ablation Study  
The ablation experiment results are shown in Table 1. Among them, LSKNet is the baseline model, LSK-CLS represents the model trained after the classifier learning stage, LSK-ARC represents the result obtained by replacing the convolution of RPN layers with rotation convolution, and DOUNet represents the model trained with the classifier learning stage added to the previous model. From the detection results, the model with the addition of the classifier learning stage has significantly improved accuracy in tail categories, which also verifies the superiority of our method in dealing with long-tail data. In addition, the overall performance of the network has also been improved after adding rotation convolution, which also verifies the advantages of rotation convolution in extracting accurate features and improving the accuracy compared to 2D convolution.  
To fairly compare the convergence efficiency of rotation convolution and 2D convolution, we chose the baseline model LSKNet-T without adding pre-training weights and replaced the large kernel convolution of the backbone network in the baseline model with rotation convolution, marked as LSKNet-ARC. From the experimental results shown in Table 2, adding rotation convolution can indeed improve the overall accuracy and convergence efficiency of the network, Frame Per Second (FPS), parameters, and Giga Floating-point Operations Per Second (GFLOPs) of the model shown in the table; it can be seen that after adding the rotation convolution, the computational load has significantly increased and the inference speed of the network has significantly decreased, but this has also resulted in higher detection accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **AP50** & **AP75** & **mAP.5:95** & **AP\({}_{\mathbf{T}}\)** & **AP\({}_{\mathbf{TN}}\)** & **AP\({}_{\mathbf{H}}\)** \\ \hline LSKNet & 75.86 & 47.65 & 45.46 & 72.33 & 74.25 & 82.32 \\ LSK-CLS & 76.43 & 48.77 & 46.18 & 73.59 & 75.04 & 82.02 \\ LSK-ARC & 75.91 & 47.41 & 45.53 & 72.20 & 74.28 & 82.44 \\ \hline DOUNet(ours) & 76.54 & 49.01 & 46.22 & 73.48 & 75.18 & 81.98 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the ablation study on the DOTA-v1.0 dataset.
# Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients
## 3 Proposed Method
### Datasets  
In this paper, we utilized a dataset comprising 5644 COVID-19 patient records. This dataset included 5628 patient records collected by the KCDC from more than 100 hospitals nationwide from 20 January to 30 April 2020. Additionally, we incorporated 50 patient records obtained from Korea University Ansan Hospital until 30 August 2020. This time period is critically important as it covers the initial outbreak, acute phase, and peak of COVID-19 cases in South Korea, providing a comprehensive overview of the virus's impact. Specifically, this period highlights the urgency of medical responses focused on efficiently managing medical processes with limited resources and rapidly determining treatment priorities.  
The dataset contains 42 clinical variables for patients confirmed positive through real-time PCR testing, categorized into eight target classes that reflect the severity of each patient's condition. The clinical variables are defined as easily measurable features, such as body temperature, blood pressure, symptoms, and past medical history, which can be promptly gathered directly from patients without significant delay.  
Despite its nationwide scope, the dataset does not specify the locations of diagnoses in the Republic of Korea. It includes the records of patients who were treated and subsequently discharged from quarantine or hospitalization, as well as those who died from COVID-19 complications. The discharge criteria for patients included receiving two consecutive negative test results at least 24 h apart and the absence of symptoms. To analyze different combinations of variable groups, we classified the clinical variables into five categories: patient demographics, vital signs, symptoms, underlying diseases, and blood test results.
## 2 Results and Discussion  
### Polyphenolic Compounds Analysis by HPLC Supported by FTIR and Confocal Microscopy  
#### 2.1.1 HPLC Analysis  
In all evaluated fruit wines, four phenolic acids were detected (chlorogenic, gallic, caffeic, and protocatechuic acid). Additionally, \(p\)-coumaric acid was found only in pomergnate wine. The concentrations of phenolic acids identified in wines are provided in Table 1.  
Regarding pomergnate wine, gallic acid was the dominant phenolic compound, followed by chlorogenic acid. [PERSON] et al. (2018) also found gallic acid to be the most abundant compound, followed by hydroxycinnamic acid. Additionally, caffeic, \(p\)-coumaric, and vanillic acid were noted in this wine. [PERSON] et al. (2018) determined the quantities of different phenolic compounds in juices and identified gallic acid as the greatest phenolic compound. In pomergnate wine, punicalagin, ellagic acid, coumaric acid, and gallic acid were noted as the most abundant phenolic compounds (2018). Generally, persimmon wine contained the lowest sum of phenolic acids (0.65 mg/g), with gallic acid as the most dominant, followed in descending order by chlorogenic acid, caffeic, and finally, protocatechuic acids. [PERSON] et al. (2018) obtained comparable results from a qualitative and quantitative profile of phenolic acids in persimmon wine. In kiwi wine, the amount of phenolic acids was four times lower as compared to pomergnate. The most abundant compound was gallic acid followed by protocatechuic and chlorogenic acids. [PERSON] et al. (2018) determined phenolic compounds in kiwifruit wines and noted gallic acid, protocatechuic acid, catechins, chlorogenic acid, epicatechin, coumaric acid, and ferulic acid. For Hongyang kiwi wine, the most abundant phenolic compounds were catechins and chlorogenic acid.