esa-sceva/satcom-chunk-collection huggingface dataset:
# Can the WiMAX IEEE 802.16 Standard Be Used to Resolve Last-Mile Connectivity Issues in Botswana?
###### Abstract  
Some of the advantages of using Worldwide Interoperability Microwave Access (WiMAX) technology at the last-mile level as an access technology include an extensive range of 50 km Line of Sight (LOS), 5 to 15 km Non-Line of Sight, and fewer infrastructure installations compared to other wireless broadband access technologies. Despite positive investments in ICT fiber infrastructure by developing countries, including Botswana, servicing end-users is subjected to high prices and service disparities. The alternative, the Wi-Fi hotspot initiative by the Botswana government, falls short as a solution for last-mile connectivity and access. This study used OPNET simulation Modeler 14.5 to investigate whether Botswana's national broadband project could adopt WiMAX IEEE 802.16e as an access technology. Therefore, using the experimental method, the simulation evaluated the WiMAX IEEE 802.16e/m over three subscriber locations in Botswana. The results obtained indicate that the deployment of the WiMAX IEEE 802.16e standard can solve most of the deployment issues and access at the last-mile level. Although the findings suggest that WiMAX IEEE 802.16e is more suitable for high-density areas, it could also solve rural areas' infrastructure development challenges and provide the required high-speed connectivity access. However, unlike the Wi-Fi initiative, which requires more infrastructure deployment and relies less on institutional and regulatory frameworks, the deployment of WiMAX IEEE 802.16e necessitates institutional and regulatory standards.  
W 2022 3 150-162. [[https://doi.org/10.3390/telecom3010010](https://doi.org/10.3390/telecom3010010)]([https://doi.org/10.3390/telecom3010010](https://doi.org/10.3390/telecom3010010))  
A 2022 3 150-162. [[https://doi.org/10.3390/telecom3010010](https://doi.org/10.3390/telecom3010010)]([https://doi.org/10.3390/telecom3010010](https://doi.org/10.3390/telecom3010010))
## 3 Method
### Models Used for Generating Simulated HR Signals  
We have used the following two approaches for generating synthetic signals:  
1. Integral pulse frequency modulation model (IPFM).
2. Amplitude modulation frequency modulation model (AM-FM).  
**Integral pulse frequency modulation model:** The ECG-signal is simulated at a sampling frequency of 1000 Hz and the resulting signal is then decimated down to 1.33 Hz sampling frequency. The quadratic increasing frequency is created from  
\[f_{r}(t)=f_{init}+\frac{f_{end}-f_{init}}{(k_{0}+1)N^{k_{0}}}r^{k_{0}},\quad t= 1...N, \tag{29}\]  
using \(k_{0}=2\), \(N=300,000\) and where \(f_{end}\) and \(f_{init}\) are 0.30 and 0.12 Hz, respectively. The heart rate signals are created from an IPFM, where the pulses of the ECG-signal with specified pulse frequency is jittered in time [47]. The jitter-signal is created from a sinusoidal disturbed by a low- frequency noise, where the sinusoidal amplitude as well as the frequency are time-varying, according to  
\[x(t)=100(1-\frac{f_{r}(t)}{f_{r}(N)})\sin(2\pi\frac{f_{r}(t)}{1000}t)+\phi+v(t), \tag{30}\]  
where the random phase \(\phi\) is equally distributed in the interval 0 to \(\pi\) and the low-frequency noise is Gaussian white noise \(v(t)\in N(0,\sigma_{v})\) with \(\sigma_{v}=1\) is filtered with an FIR-filter of order 200 and cut-off frequency 0.12 Hz. The periodic ECG-signal is described by a pulse train with period \(P=1000*60/p_{n}\), where \(p_{n}\) is a normal pulse rate of 60 beats per minute, giving  
\[x_{ecg}(t)=1\quad\text{for}\quad t=0,P,2P,3P... \tag{31}\]  
and zero for all other samples. The jittered ECG-signal is simulated by moving the periodic peaks according to the variation of \(x(t)\), i.e.,  
\[x_{ecg}(RP+x(RP))=1\quad\text{for}\quad k=0,1,2,3... \tag{32}\]  
and all other values zero. The measured value of the distance between two subsequent peaks is depicted as a function of the corresponding time interval in a diagram for all peaks, and the resulting signal is low-pass filtered and decimated to 1.33 Hz giving the final 400 sample HR signal.  
**Amplitude modulation frequency modulation model:** Although the IPFM is a standard approach for simulating HRV signals, we also use the amplitude modulation frequency modulation (AM-FM) model to generate test signals ([48], p. 419):  
\[x(t)=\sum_{k=1}^{N_{c}}x_{k}(t)=\sum_{k=1}^{N_{c}}a_{k}(t)\cos(\phi_{k}(t)), \tag{33}\]  
where \(a_{k}(t)\) is the instantaneous amplitude, \(\phi_{k}(t)\) the instantaneous phase of the signal component \(x_{k}(t)\) and \(N_{c}\) the number of components. This model assumes that the variation in instantaneous amplitude is slow such that the spectra of \(a_{k}(t)\) and \(\cos(\phi_{k}(t))\) do not overlap. From visual inspection of the measured HR signals, the adopted AM-FM model is  
\[x(t)=\cos(0.000003\pi t^{3}+0.25\pi t)+0.3\cos(-0.000008\pi t^{3}+0.004\pi t^{2} +0.4\pi t). \tag{34}\]
# Multi-Objective Parameter Optimization of Flexible Support System of Optical Mirror
## 4 Experimental Analysis
### Measurement Experiment of Internal Resistance of Support Cylinder  
A BBR with a thickness of 0.2 mm, bending position in the middle, bending angle of 30\({}^{\circ}\), and RD with hardness of 45 Shore are applied in the experiment. According to the test requirements of measuring the internal resistance of the support cylinder, the test bench is shown in Figure 16. The support cylinder is placed horizontally on the test-bed, and the radial resistance of BBR is balanced with the gravity of piston parts, so as to eliminate the influence of gravity on resistance measurement. During the experiment, rotating the screw micrometer knob can pull the steel wire rope and tension sensor to move, and finally pull the support shaft of the support cylinder to move. The tension value that was fed back by the tension sensor is collected and set as the internal resistance of the support cylinder under the current displacement. A laser micrometer is used to directly measure the displacement of the piston shaft of the support oil cylinder in a non-contact manner. In order to ensure that the tension value of the tension sensor can truly reflect the internal resistance of the support cylinder, it is necessary to ensure that the tension sensor and the support shaft have good coaxiality as far as possible. The theoretical resistance and experimentally measured resistance of the support cylinder are shown in Figure 17.  
As shown in Figure 17, when the displacement of the support shaft is larger than zero, the deformation resistance generated by the BBR and RD causes the internal resistance of the support cylinder to increase. There is an error of approximately 0.4 N between the actual measured resistance and theoretical resistance. It is within the permissible range of error. The main cause of the error is the variation in the local elastic modulus caused by thermal deformation and thermal deterioration when the BBR is cut online. The experimental results verify the accuracy and effectiveness of the established mathematical models of BBR and RD deformation resistance. These also demonstrate that the optimization of multi-objective parameters can effectively reduce the internal resistance of support cylinders.  
Figure 16: Internal resistance measurement test bench.
## 4 AI-Based Approaches in Smart Buildings
#### 4.1.2 Wavelet Neural Network  
Building energy usage must be forecasted to achieve effective energy management and reduce environmental impacts. In variable air volume (VAV) technologies, a wavelet neural network is a fault detection solution for temperature, flow rate, and pressure sensors. Wavelet transforms and neural networks are combined in this method. It is possible to predict the reliance on renewable microgrid systems in time-series data using this method,  
Figure 4: IoT integration with machine learning algorithms for smart building energy efficiency.  
which helps to reduce system costs while also maximizing battery charging time. The accuracy has been greatly enhanced by combining the two procedures [60].  
A wavelet neural network approach was proposed by [61] for improving PID controller performance. PID stands for (proportional + integral + derivative) controller. [62] implemented neural network PID as the basis for a modern control system that is based on previous investigation. As it provides the closed-loop control system's predetermined value, the central control's brain is its control strategy management section. Therefore, an intelligent strategic control process is critical because it has the power of generating control logic; it is ideal for smart building technologies that are adaptive depending on the latest environmental parameters [63]. On/off, proportional (P), proportional + integral + derivative (PID), proportional/integral (PI), as well as control methods, are the most commonly used control techniques. The on/off control mode is frequently used in building lighting and shading systems. To monitor the set value, controllers using PI/PID models are widely utilized in the development of heating and ventilation systems. The self-learning and dynamic decoupled control characteristics of a neural network PID are excellent [64].
# Review of Acoustic Sources Alternatives to a Dodecahedron Speaker
## 4 Conclusions  
Relevant studies have been presented in this review concerning acoustic sources alternatives to a dodecahedron speaker. Fifteen sources were identified in the literature. The majority of them are of the impulsive type: balloons, guns, firecrackers, handclaps, wooden clappers, shotshell primers, an explosive mixture of acetylene gas with air, laser-induced air breakdowns and electric spark sources. Four acoustic sources utilize an electrodynamic loudspeaker: inverse horn design, rotation of directional loudspeakers, ring radiators and directional loudspeakers. The two final sources are a compressor nozzle hiss and piezoelectric transducers through spherical distribution. Emphasis was placed on features such as omnidirectionality, repeatability, adequate sound pressure levels, even frequency response, accuracy in measurement of acoustic parameters and fulfillment of ISO 3382-1 sound source requirements. Elements about the generation of sound and the impulse response were also presented where they were available.  
Results from this review have shown that besides dodecahedron speakers, other sound sources that fulfill ISO 3382-1 requirements are inverse horn designs and possibly firecrackers. Sources that can potentially fulfill ISO 3382-1 source requirements are the rotation of a directional speaker, ring radiator and laser-induced breakdown. Reviewing the literature has led us to conclude that there are alternative sound sources that in some occasions can provide usable results concerning the measurements of acoustic parameters. This study has identified research gaps which can be a fruitful area of future work. A possible way for future research could be the combination of different sources for acoustic measurements. Finally, we hope that this review will be useful in relevant areas such as the measurement of HRTFs which are important for auralization purposes.  
[PERSON] analyzed the data and wrote the manuscript; [PERSON] provided suggestions and guidance for the work, reviewed and edited the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
ANSI American National Standards Institute  
C\({}_{80}\) Clarity  
D\({}_{50}\) Definition  
DI Directivity Index  
DIN Deutsches Institut fur Normung  
EDR Effective Decay Range  
EDT Early Decay Time  
ESS Exponential Sine Sweep  
G Source Strength  
HRTF's Head-Related Transfer Functions  
IEC International Electrotechnical Commission  
ISO International Organization for Standardization  
LIB Laser-Induced Breakdown  
MLS Maximum Length Sequence  
NPL National Physics Laboratory  
RT Reverberation Time  
SNR Sound to Noise Ratio  
STD Standard Deviation  
T\({}_{\mathrm{s}}\) Centre Time
# A Cooperative Transmission Scheme in Radio Frequency Energy-Harvesting WBANs
## 5 Simulation Results
Since the network transmission performance of ordinary nodes far away from the coordinator is poor, the network performance is significantly improved with the cooperation of potential relay nodes. The larger the network load, the poorer the transmission performance of ordinary nodes themselves, and the more significant the performance improvement with the assistance of potential relay nodes. As the distance between potential relay nodes and the coordinator increases and the distance to ordinary nodes decreases, the probability of correctly receiving messages uploaded by ordinary nodes increases, and the probability of successfully forwarding these messages to the coordinator through potential relays after direct upload failure also increases. However, the energy collected by potential relay nodes decreases with the distance from the coordinator. When the harvested energy is insufficient to support the energy required for message forwarding, the improved network performance will decline sharply. Finally, we evaluated the performance of the proposed scheme in a randomly deployed sensor scenario; the sensors are randomly deployed in the 60 cm \(\times\) 40 cm plane. The sizes of packets generated by sensors are in the range of 20 bytes to 100 bytes. Figure 5 illustrates the packet reception rates. For the single-hop transmission scheme, the network density is not the main factor affecting the packet reception rate since no cooperation scheme is adopted. We can see that our scheme performs better when more sensors are deployed as the number of potential relay nodes will increase in a dense network. Packets uploaded by sensors far from the coordinator can be forwarded by potential relays under the guidance of the coordinator. The packet reception rates increase 0.9% and 7.9% when two or ten sensors are deployed if \(\delta\) = 0.7. Correspondingly, the packet reception rates increase by 0.7% and 7.4% when \(\delta\) = 0.9. Figure 3: Analysis of packet reception rate in the first scenario.
# Internet of Bikes: A DTN Protocol with Data Aggregation for Urban Data Collection
## 2 Related Work
### Communication Based on Long Range Technologies  
In the last few years, new technologies have been designed to improve communication. Next generation networks (5G) will be launched on the market by 2020. 5G improves the speed as well as it can facilitate the emergence of a huge IoT ecosystem in which networks can communicate billions of connected objects, thanks to a balanced compromise between speed, latency and cost.  
Many low-power wide-area network (LPWAN) technologies have also been deployed. LPWA networks are characterized by having several forms as well as different market approach and technology stack. The most used LPWAN technologies are LoRaWAN, Sigfox and Weightless. LoRa/LoRaWAN technology (LoRa: [[https://www.lora-alliance.org/](https://www.lora-alliance.org/)]([https://www.lora-alliance.org/](https://www.lora-alliance.org/))) was developed by the start-up Cycleo in 2009 in Grenoble city, France and was purchased by Semtech (Newbury Park, CA, USA) in 2012. In 2015, LoRa technology was standardized by LoRa-Alliance and was deployed in 42 countries. Its architecture is based on chirp spread spectrum modulation which uses the same low power characteristics as FSK modulation, whereas immensely raises the communication range. The LoRa physical layer operates on the 868 MHz in Europe, 433 MHz in Asia or 915 MHz in North America ISM bands.  
In 2010, the Sigfox technology (Sigfox: [[https://www.sigfox.com/en](https://www.sigfox.com/en)]([https://www.sigfox.com/en](https://www.sigfox.com/en))) was developed by the start-up Sigfox in Toulouse, France. It is characterized by the application of a technique based on ultra-narrowband IoT communications designed to support IoT deployments over long range communications. It operates in the 869 MHz in Europe and 915 MHz in North America bands. The signal of Sigfox is narrowband using channel bandwidths lower than 1 KHz and forwarding a maximum payload size of 12 Bytes.  
With regard to Weightless technology (Weightless: [[https://www.weightless.org/](https://www.weightless.org/)]([https://www.weightless.org/](https://www.weightless.org/))), the Weightless Special Interest Group has developed three open standards: _Weightless-W, Weightless-N_ and _Weightless-P_. Weightless-W is based on narrow-band FDMA channels with Time Division Duplex between uplink and downlink. It is designed to be performed in TV white-spaces (470-790 MHz). Weightless-N is based on the ultra-narrow-band technology and it gives only uplink communication. Weightless-P offers ultra-high performance LPWAN connectivity.
## 2 Materials and Methods
### Numerical Methods  
Two different approaches were used for modeling landslide propagation in this research: one based on the Finite Volume Method (FVM) and the other based on the meshless Smoothed Particle Hydrodynamics (SPH) method. The first approach was implemented using the RAMMS DBF software, v. 1.5.0 while the second was implemented using code developed by the research group in Madrid [10].  
In propagation modeling, the mathematical formulations used in simulations are often simplified and reduced to two dimensions through depth-integration approximations. Many flow-like landslides are relatively long and wide compared to their depth, allowing the governing equations to be integrated along the vertical axis. This 2D depth-integrated approach strikes a good balance between accuracy and computational efficiency. Models like RAMMS DBF and Geoflow SPH rely on integrated solutions of the mass and momentum balance equations, based on the shallow water flow assumption. While depth-integrated models provide valuable insights into landslide behavior, they have notable limitations due to their simplified representation of 3D dynamics, complex material properties, and terrain interactions. As a result, they are less effective for irregular terrains, complex flow behaviors, or when small-scale processes are important. However, these models are particularly useful for large-scale, shallow landslides and long-runout events, such as the Selanac debris flow. Overall, depth-integrated models offer a practical balance of simplicity, efficiency, and accuracy, making them highly valuable for landslide propagation studies.
## 3 Results and Discussions
### Temperature and Thermodynamic Parameters  
The temperature parameters for Raw-RGL and AC-RGL were evaluated at five temperatures: 25 \({}^{\circ}\)C (298 K), 30 \({}^{\circ}\)C (308 K), 35 \({}^{\circ}\)C (318 K), 40 \({}^{\circ}\)C (328 K), and 45 \({}^{\circ}\)C (338 K); and the experimental data are shown in Figure 8. The adsorption trends for Raw-RGL and AC-RGL are similar, showing a rise in sorption capacity with a temperature increase from 25 to 35 \({}^{\circ}\)C. Later, less adsorption capacity was observed when the temperature increased between 40 and 45\({}^{\circ}\)C. That implies that only a slight energy increase was required for adsorption. The maximum sorption capacities for Raw-RGL and AC-RGL adsorbents are 64.03 and 104.74 mg/g at 35 \({}^{\circ}\)C, respectively.  
Figure 7: Effect of time on ibuprofen uptake by Raw-RGL and AC-RGL adsorbents.  
Thermodynamic parameters are vital for investigating heat variation and the spontaneity of the sorption process [57]. \(\Delta\)G\({}^{\circ}\), \(\Delta\)H\({}^{\circ}\), and \(\Delta\)S\({}^{\circ}\) are calculated at different temperatures (298; 308; 318; 328 and 338 K) of ibuprofen on Raw RGL and AC-RGL solvents and indicated in Table 6. The \(\Delta\)H\({}^{\circ}\) results for both adsorbents indicate that the sorption processes are exothermic. The size of \(\Delta\)H\({}^{\circ}\) of ibuprofen adsorption suggests a weak interactive strength, which also suggests electrostatic attraction [55]. \(\Delta\)S\({}^{\circ}\) data for ibuprofen adsorption by Raw-RGL and AC-RGL indicate an increase in the randomness of ibuprofen in solution as the adsorption reaches equilibrium. The \(\Delta\)G\({}^{\circ}\) figures for both adsorbents indicate that the uptake process is spontaneous and feasible.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Models** & **Raw-RGL** & **AC-RGL** \\ \hline \multirow{2}{*}{PFO} & K\({}_{1}\) & 0.469 & 0.979 \\ \hline \multirow{2}{*}{PSO} & r\({}^{2}\) & 0.995 & 0.993 \\ \hline \multirow{2}{*}{PSO} & q\({}_{\rm e}\) & 34.56 & 56.65 \\  & K\({}_{2}\) & 0.101 & 1.002 \\  & r\({}^{2}\) & 0.861 & 0.757 \\ \hline \multirow{2}{*}{IPD} & C & 33.23 & 47.23 \\  & K\({}_{\rm i}\) & 7.715 & 13.06 \\  & r\({}^{2}\) & 0.867 & 0.823 \\ EPA & \%\({}_{\circ}\) & 12.88 & 16.88 \\ ESA & \%\({}_{\circ}\) & 87.12 & 83.12 \\ \hline \multicolumn{3}{c}{Experimental (q\({}_{\rm e}\))} & 68.97 & 101.96 \\ \hline \hline \end{tabular} EPA—estimated pore adsorption of IPD; ESA—estimated surface adsorption of IPD.  
\end{table}
Table 5: Kinetic studies of Raw-RGL and AC-RGL and their parameters.  
Figure 8: Effect of temperature on ibuprofen uptake by Raw-RGL and AC-RGL adsorbents.
## 3 Results
### Statistical Tests
#### 3.2.1 Wilcoxon Test  
The Wilcoxon signed-rank test is a non-parametric statistical method used in binary classification to assess if two models differ significantly in their predictive accuracy. The formula is given in Equation (7):  
\[W=\min\Bigl{(}\sum_{i:d_{i}>0}R_{i},\ \sum_{i:d_{i}<0}R_{i}\ \Bigr{)} \tag{7}\]  
In this context, \(d_{i}\) represents the discrepancy between paired predictions, while \(R_{i}\) denotes the rank of the absolute discrepancies \(|d_{i}|\). The test statistic W is subsequently evaluated against the critical value from the Wilcoxon signed-rank distribution table, or alternatively, the \(p\)-value is calculated to ascertain statistical significance.
## 2 Islanding Detection Methods
### Computational Intelligent Based Methods
#### 2.6.8 Deep Learning Method (DL)  
This approach can assist in shortening the computation time required to extract the features based on the feature extraction method since the best features are automatically learned from the original input datasets without the use of a separate feature extraction method. A convolution neural network (CNN) with an image dataset as input is used in a deep learning method for islanding detection. The time series is transformed into scalogram picture data using a continuous wavelet transform [51]. It was suggested to detect islanding using a stacked auto-encoder and a deep neural network (DNN). The characteristics were then retrieved using wavelet multi-resolution spectral analysis and fed into the DNN [52].