esa-sceva/satcom-chunk-collection huggingface dataset:
# LTM-LoRaWAN: A Multi-Hop Communication System for LoRaWAN
## 5 Conclusions  
In this paper, a multi-hop system for the LoRaWAN network, named Listen-To-Talk-based Multi-hop LoRaWAN (LTM-LoRaWAN), is proposed. It targets extending the coverage of the LoRaWAN network when increasing the number of gateways is infeasible due to gateway deployment constraints. The proposed system is dynamic and flexible enough to be adopted using different LoRaWAN applications that require an extended coverage capability while ensuring delivery reliability. The proposed system in this paper does not require any specific topology. It can be deployed using standard LoRaWAN end devices. The protocol ensures the ability of devices to discover themselves if they are out of the gateway coverage. This would be the trigger of the protocol operation. For out-of-coverage devices, the proposed system utilizes a procedure of transmission using a special type of medium access mechanism. The proposed system also entitles any standard end device that receives traffic from out-of-range devices to forward such traffic to a gateway while operating as a standard LoRaWAN device.  
The performance of the proposed system is comprehensively evaluated using a network simulator. The result indicates the ability of the proposed system to increase network coverage while maintaining traffic reliability. The proposed system outperformed the standard LoRaWAN as it achieved a better delivery rate. The protocol also demonstrated scalability since it yielded a good performance while network sizes were increased. However, as a cost for providing data reliability and coverage extension, the proposed system showed higher energy consumption compared to the baseline system. In future work, the power consumption by the proposed system needs further investigation and analysis to improve the proposal's performance regarding power consumption. This may involve investigating the listening time by out-of-range devices and the forwarding mechanism in order to decrease the consumed power. In addition, the impact of the proposed system on the traffic delay will be further investigated.  
This research received no external funding.
## 3 Model and Methodology
### Path Length Constraint  
When UAVs are flying from a specified starting point \(S\) to a destination point \(D\), as in the scenario described in Figure 1, the distance should be guaranteed to be as short as possible to minimize UAV energy consumption and further prolong the battery life. Thus, the objective becomes how to obtain an optimal path, namely in terms of the shortest one. In order to realize this objective, the path length needs to be one of the constraints in the proposed optimization model.  
Figure 5: Quantization of path turn constraint.  
Considering an actual operation scenario, to simplify the path-planning model, the mileage during the UAVs' turns on a path is ignored, and only the straight-line distances between sequential waypoints are taken into account. Based on this hypothesis, the length of a UAV path can be defined as the sum of all the path segments between any two neighboring waypoints. According to Figure 5, the length of a UAV path is the sum of the straight-line distances between any two waypoints \(WP_{i}\) and \(WP_{i+1}\), which is denoted as \(Len_{i}\). In this way, the value of \(Len_{i}\) can be obtained using Equation (5). In this equation, (\(WP_{i}\) _\(\_\)x, \(WP_{i}\) _\(\_\)y_) and (\(WP_{i+1}\) _\(\_\)x, \(WP_{i+1}\) _\(\_\)y_) are the two-dimensional coordinates of the waypoints \(WP_{i}\) and \(WP_{i+1}\) on a generated path.  
\[Len_{i}=\sqrt{\left(WP_{i+1}\_x-WP_{i}\_x\right)^{2}+\left(WP_{i+1}\_y-WP_{i} \_y\right)^{2}} \tag{5}\]  
The path length constraint can be quantized by the sum of all the path segments, which is given in Equation (6). \(N\) is the number of all the waypoints on a generated UAV path.  
\[J_{L}=\sum_{i=1}^{N}Len_{i} \tag{6}\]
## 2 Proposed Methodology of This Study
### Types of Faults on Signals  
To understand what kind of inputs the FDIR algorithm has to deal with, it is useful to have an overview of the main kind of trends that could be expected on signals coming from faulty sensors. In practice, there is a limited number of typical behaviors that can summarize most possible faults that a sensor or an actuator could encounter [1]. A collection of common fault errors is reported in Figure 6, with a brief description for each of them.  
* Bias: The signal has an abrupt increase or decrease from its normal trend.
* Drifting signal fault: The output shows an unexpected linear trend, drifting away from the nominal state.
* Erratic behavior: Quality of the signal worsens significantly due to an increase in variance or noise.
* Spikes: One or more sudden spikes appear in the output of the signal.
* Stuck signal: Signal output gets stuck at a constant value.
* Data loss: The signal presents gaps in which no data is available and the output is null.  
All of the observed elements in this study, namely the three sensors and the actuator output, are assumed to be possibly subject to any of these faults. Systematic fault injection into nominal signals, for simulation purposes, required fine-tuning to adapt to each of the signals' orders of magnitude and typical behaviors. If present, faults can appear anywhere between the first 5% and the last 20% of the simulated time, and signals can return to nominality before the end of the simulation, or proceed in their anomalous behavior until  
Figure 5: Example structure of a simple MLP with only one hidden layer.  
the end of the observation. For thorough testing and result collection, the simulation can also apply multiple faults of different types at once on the same signal.
### Use-Case  
In the healthcare system, various users such as doctors, nurses, research scholars, insurance agents, etc., may need to access the data. In this case, various authorities, e.g., hospitals, clinical research centers, insurance companies, and pharmaceutical companies, may issue various attribute keys to the concerned users (as shown in Figure 1). The shared EHRs of the patients may be protected by instituting access policies, such as any user who possesses the \"Research Assistant\" attribute in the domain of renowned \"Research Organization\" or \"Insurance agent\" attribute in the domain of \"Insurance Company\" from where the patient is receiving services being permitted to access the data. Therefore, in healthcare industries, where the user fraternity is a large group, multi-authority CP-ABE schemes are a suitable option for facilitating efficient and secure EHR sharing. All the above-stated users in the healthcare system come from the open domain, i.e., they are not known to the data owner.  
To control access to shared data, attributes issued by various open-domain attribute authorities are used in access policies. Another important issue that must be considered in EHR data sharing is to give access to closed-domain users, i.e., the users known to the data owner, e.g., family friends, relatives, etc. Flexibility in handling the access requirements of both closed- and open-domain users makes the sharing of health data more practical and effective [10]. In emergencies, family friends or relatives may access the data. The privacy of the patient is also crucial in this kind of data sharing. In the above instance, if a patient outsources their medical data with policy \"[(Profession = \"Doctor\" and Specialization = \"Cardiology\" and Affiliation = \"AIIMS\") or (Profession = \"Doctor\" and Specialization = \"Cardiology\" and Research Associate = \"University Hospital\") or (Relation = \"Friend\")]\", then everyone including adversaries and the cloud service provider (CSP) can look at the policy formulation and figure out that the shared data are of a patient who is suffering from heart disease. This results in privacy leakage even though the ciphertext of EHR data is protected well. Therefore, it is essential to keep the access policy a secret to protect sensitive data.
## 5 Experiments and Results
### Performances of the Multi-Objective Evolution Algorithm in PI Construction  
To evaluate the proposed EKMOABC for solving the multi-objective optimization problem with PIMOC, three classic multi-objective optimization algorithms, including the basic MOABC, NSGAII (non-dominated sorting genetic algorithm II) [23], and MOPSO (multi-objective particle swarm optimization) [24], are used to optimize the parameters of WNN for contrast experiments. The population size, the maximum number of iterations, and the maximum number of solutions in the archive are set to 40, 200, 20, respectively, for all algorithms. The crossover probability and the mutation probability of NSGAII are set to 0.8 and 0.2, respectively. The inertia weight and the learning factor of MOPSO are set to 0.8 and 2, respectively. The probability choices parameter \(\eta_{1}\) and \(\eta_{2}\) of EKMOABC are set to 50 and 10. The maximum obsolete number of scout bees in MOABC and EKMOABC are all set to 50. The comparative results for PI construction with four different multi-objective evolutionary optimization algorithms are shown in Figure 7.  
Figure 6: Prediction intervals with PIMOC based on MOABC. PIMOC: PIs’ multi-objective optimization criteria; MOABC: multi-objective artificial bee colony; PICAW: PIs covered-normalized average width; PICP: PI coverage probability.  
Figure 7: Comparative results with different multi-objective evolutionary algorithms. EKMOABC: evolutionary knowledge multi-objective artificial bee colony; MOPSO: multi-objective particle swarm optimization; MOABC: multi-objective artificial bee colony; NSGAII: non-dominated sorting genetic algorithm II; PICAW: PIs covered-normalized average width; PICP: PI coverage probability.  
As shown in Figure 7, Compared with the other three evolutionary optimization algorithms (NSGAII, MOPSO, and MOABC) in WNN optimization, the proposed EXMOABC-WNN can ensure a higher confidence level and narrower width of PIs, especially when the PICP is above 97%, where there is no suitable width of PIs that can be chosen by the other three algorithms. The Pareto optimal set with EKMOABC-WNN has not only better convergent performance, but also distributing performance. One of the reasons is that NSGAII, MOPSO, and MOABC may be easily trapped into a local optimum and their searching capacities will become so weak that the better solutions cannot be searched in the constraint range. Another reason is that the distribution of the Pareto set is not considered sufficiently in the process of iteration in NSGAII, MOPSO, and MOABC, which results in the Pareto set non-uniform distribution. In addition, the EKMOABC is based on the relationship between Pareto domination and distribution. The advanced probability choice equation of EKMOABC can avoid the solutions being trapped into local optimum effectively and it is a benefit for the distribution of the Pareto optimal set. The strategy of the guidance with elite population knowledge plays an important role in searching for better solutions and improving the convergent performance of the Pareto optimal set.
## 2 System Layout
### Voltage Regulation  
In order to ensure that the output voltage tracks its voltage reference a similar study is conducted coming from Equation (6) for n = 1 and \(\mathrm{\tilde{x}}=(\mathrm{V_{out}}-\mathrm{V_{out}^{ref}})\),  
\[\mathrm{S_{2}}=(\mathrm{V_{out}}-\mathrm{V_{out}^{ref}}) \tag{28}\]  
\[\mathrm{\dot{S}_{2}}=\frac{1}{\mathrm{C}}(\mathrm{I_{S}}-\mathrm{I_{load}})- \mathrm{\dot{V}_{out}^{ref}}+\mathrm{\Delta f(x)}+\mathrm{\Delta d} \tag{29}\]  
This is a regulation problem where the input reference is a never changing constant value hence it's derivative could be omitted from the surface derivative equation, the terms \((\mathrm{\Delta f(x)}+\mathrm{\Delta d})\) retains to the system uncertainties arising from the non-linearity and losses of the system. Proceeding to conduct Lyapunov stability analysis  
\[\mathrm{B_{2}}=\frac{1}{2}\mathrm{S_{2}^{2}}>0 \tag{30}\]  
\[\mathrm{\dot{B}_{2}}=\mathrm{\dot{S}_{2}}\mathrm{S_{2}}=(\frac{1}{\mathrm{C}} (\mathrm{I_{S}}-\mathrm{I_{load}})-\mathrm{\dot{V}_{out}^{ref}}+\mathrm{\Delta f (x)}+\mathrm{\Delta d})\cdot\mathrm{S_{2}}<0 \tag{31}\]  
In this case the control variable is the source's current as such one may include cancelling terms to simplify the control law. For the load current it's a measurable state hence can be compensated directly as for the terms \(\mathrm{\Delta f(X)}\) and \(\mathrm{\Delta d}\) they are unknown, however, one approach to constructing a meaningful control law it to define boundaries \(\mathrm{\beta}\), \(\mathrm{\alpha}\) such that the aforementioned uncertainties may be contained within.  
\[|(\mathrm{\Delta f(x)}|\leq\mathrm{\alpha(x,t)} \tag{32}\]  
\[|\mathrm{\Delta d}|\leq\mathrm{\beta(x,t)} \tag{33}\]  
The control law is chosen where K is an arbitrary control parameter.  
\[\mathrm{I_{s}}=\mathrm{I_{eq}}+\mathrm{I_{comp}} \tag{34}\]  
\[\mathrm{I_{eq}}=\mathrm{I_{load}} \tag{35}\]  
\[\mathrm{I_{comp}}=(\mathrm{\alpha}+\mathrm{\beta}+\mathrm{K})\cdot\mathrm{ sgn(S_{2})} \tag{36}\]  
Proceeding to the stability analysis  
\[\frac{1}{\mathrm{C}}((\mathrm{\Delta f(x)}+\mathrm{\alpha})+(\mathrm{\Delta d }+\mathrm{\beta})+\mathrm{K})\cdot\mathrm{S_{2}}<0 \tag{37}\]  
\[\frac{1}{\mathrm{C}}((\mathrm{\Delta f(x)}-\mathrm{\alpha})+(\mathrm{\Delta d }-\mathrm{\beta})-\mathrm{K})\cdot\mathrm{S_{2}}<0 \tag{38}\]  
The dual loop control scheme is displayed in Figure 4
# Soliton Content of Fiber-Optic Light Pulses
## 3 Extensions to the Nonlinear Schrodinger Equation
### Cases Involving Several Solitons
#### 3.3.3 Supercontinuum Generation  
For some metrological purposes like coherence tomography, wide bandwidth light with good focusability is required. Thermal light sources cannot deliver adequate power due to both their insufficient spatial coherence and the limitations set by [PERSON]'s radiation law. One can, however, generate broadband light in a fiber by exploiting the nonlinearity to create new frequency components. Experiments involve, in most cases, a powerful mode-locked laser and a piece of highly nonlinear fiber. Either a modulation instability according to an Akhmediev breather scenario evolves, but the structure then breaks apart, or the input pulse represents a high-order (\(N\gg 1\)) soliton, which undergoes fission [56]. In either event, the initial light signal breaks up into an arrangement of several pulses, many of which have soliton characteristics, and radiation. This arrangement has similarities to the soliton gas described above. Through the action of the Raman effect, four-wave mixing and cross-phase modulation combined with complex phase matching properties in the fiber's dispersion curve (see, e.g., [57]), the light quickly develops a spectrum that may easily be one octave wide and often much more. Such a structure is referred to as an optical supercontinuum and has found much research interest; commercial supercontinuum sources are being offered. A good review is [58].  
A corollary to this is the phenomenon of optical rogue waves. Rogue waves of the ocean have been identified as a real phenomenon, not a myth. Unusually, large single waves can occasionally appear without warning, possibly wreaking havoc on ships, and disappear again just as suddenly. It was first discussed in [59] that in the context of optical supercontinuum generation, single spikes of extreme power can occur every once in a while. There is an ongoing discussion about the mechanisms [60].
# Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study
###### Abstract  
The automatic recognition of human activities with sensors available in off-the-shelf mobile devices has been the subject of different research studies in recent years. It may be useful for the monitoring of elderly people to present warning situations, monitoring the activity of sports people, and other possibilities. However, the acquisition of the data from different sensors may fail for different reasons, and the human activities are recognized with better accuracy if the different datasets are fulfilled. This paper focused on two stages of a system for the recognition of human activities: data imputation and data classification. Regarding the data imputation, a methodology for extrapolating the missing samples of a dataset to better recognize the human activities was proposed. The K-Nearest Neighbors (KNN) imputation technique was used to extrapolate the missing samples in dataset captures. Regarding the data classification, the accuracy of the previously implemented method, i.e., Deep Neural Networks (DNN) with normalized and non-normalized data, was improved in relation to the previous results without data imputation.  
h 1 2020; 12, 155; doi:10.3390/f12090155  
url
# Nondestructive Monitoring Techniques for Crack Detection and Localization in RC Elements
## 3 AE Sensors  
Different damage mechanisms and levels can be verified during the four-point bending test of the RC beam, such as tensile cracking followed by shear cracking, yielding of reinforced steel, and crushing. The fracture propagation mainly includes two stages: accumulation of uncorrelated tensile cracks between the central loads, and appearance of diagonal shear cracks, starting on the bottom surface and developing to the top, interconnecting the previously formed tensile cracks. Statistical analysis of the AE signal features can reveal trends that can be ascribed to different damage stages. For example,  
Figure 2: Front (**top**) and back (**bottom**) sides of the test setup showing the servo hydraulic actuator, the linear variable differential transformer (LVDT) sensors, the location of some AE sensors, and the speckle pattern on the central beam surface for digital image correlation (DIC) applications.  
shear events originate signals with longer rise time (the time delay between onset and peak amplitude) and higher amplitude.  
Eight 60 kHz resonant AE sensors were attached at one-fourth of the span's length (refer to Figures 1 and 2 respectively for beam design and application). When the sensors are hit by a wave (due to crack growth or another reason), the pressure on their surface is converted to electric voltage, exploiting the piezoelectric effect. If the voltage was beyond a given threshold (60 dB to filter out the signal noise in the specific case), the signal was digitized and stored in an 8 channel Lunitek AEmission system with 5 MHz sampling rate per channel. The software associated to the acquisition board permits automatic extraction and storage of some basic parameters for each individual AE signal waveform (feature-based AE analysis), such as amplitude, duration and ring-down counts (RDC), i.e., the total number of times the signal exceeds the threshold level. RDC divided by duration gives the average signal frequency.
# Compton Scattering Imaging of Liquid Water in Porous Carbon-Based Materials
## 4 Conclusions  
This study applies the high-energy, synchrotron X-ray CSI technique to the GDL material. We have successfully visualized the liquid water penetrating into the GDL material. The depth-resolved intensity analysis shows that the volume fraction of liquid water decreases monotonically as it separates from the surface, and it penetrates at a depth of 50 \(\upmu\)m. These results demonstrate that the imaging water is feasible with the synchrotron-based CSI technique. Finally, since it uses high-energy X-rays with high material penetration and does not require sample rotation, the CSI technique can be applied to visualize the liquid water distribution and its dynamics in a working fuel cell. The challenges are to improve data-accumulation efficiency using a coded aperture system and to improve the spatial resolution using a smaller pinhole aperture.  
Conceptualization, Y.U. and Y.S.; methodology, N.T. and Y.S.; software, N.T.; validation, Y.S., Y.U., H.I. and Y.T.; formal analysis, N.T.; investigation, N.T.; resources, Y.S.; data curation, N.T.; writing--original draft preparation, N.T.; writing--review and editing, Y.S.; visualization, N.T. and Y.S.; supervision, H.I.; project administration, Y.T.; funding acquisition, H.I. All authors have read and agreed to the published version of the manuscript.  
This research was funded by New Energy and Industrial Technology Development Organization (NEDO), grant number 20001309-0.  
Not applicable.  
Figure 8: The depth dependency of the relative volume fraction of liquid water, together with that of the carbon fiber and carbon composite.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.  
Compton scattering imaging was performed with the approval of JASRI (Proposal No. 2020A2148).  
The authors declare no conflict of interest.
To balance BER performance and computational complexity, we note that DNNs have significantly lower complexity than CNNs, RNNs, and LSTM when handling the same tasks [31]. Researchers have shown that a three-layer DNN is sufficient to solve nonlinear problems in RoF systems [32]. Pruning techniques can further reduce BER and computational complexity, making pruned DNNs have lower complexity than Volterra [33]. Therefore, in this paper, we construct a 4.6 km long-distance dual-polarization 2 \(\times\) 2 MIMO D-band photonics-assisted THz communication system. Innovatively applying DNN neural networks combined with iterative pruning techniques to the RoF system, we successfully reduced the BER of 10 Gbaud to 30 Gbaud 16 QAM signals below the HD-FEC threshold of 3.8 \(\times\) 10\({}^{-3}\), increasing the transmission rate from 10 Gbaud to 30 Gbaud compared with the MIMO Volterra nonlinear equalization (MIMO-VNLE) algorithm. Meanwhile, iterative pruning techniques significantly reduced the complexity of the DNN. For 10 Gbaud data, complexity could be reduced by up to 85.9%, and for 30 Gbaud data, by up to 63.0%. The innovative combination of DNN and iterative pruning techniques not only enhances system performance but also provides new ideas and technical paths for the design and optimization of future communication systems. This paper consists of five parts. The next four parts are as follows: Section 2 details the technical principles of the experiment. Section 3 introduces the experimental setup of the dual-polarization photonics-assisted THz wireless transmission link. Section 4 presents the experimental results of neural networks and iterative pruning and conducts in-depth discussions. Section 5 summarizes the main findings and conclusions of this study.