esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Techniques for Discovering, Using, and Paying for Third-Party IoT Sensors
## 3 Related Work for Sharing IoT Devices
### Related Research for Paying IoT Devices  
IoT device payment techniques involve supporting a pay-as-you-go model for sharing IoT devices in a method similar to cloud computing [4]. Such a model allows IoT applications to pay IoT device providers as long as they use their IoT devices.  
Ether cryptocurrency [54] is proposed by several solutions, such as [32] as a payment technique to pay for using IoT devices. However, based on the performance of the Ethereum blockchain [54], it is not scalable for paying for a large number of IoT devices. Also, it does not support a pay-as-you-go-model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Scalable**} & \multirow{2}{*}{**IoT-Owned**} & \multirow{2}{*}{**Interoperable**} & \multicolumn{2}{c}{**Fully Support Integrating**} \\  & & & **of IoT Devices** \\ \hline Refs. [36; 52] uses up to & & & \\
10,000 IoT devices to & & & \\ evaluate their & & & \\ integration techniques & & & None of the related works fully support integrating \\ Ref. [53] uses up to 50 IoT & & & IoT devices \\ devices to evaluate the & & & \\ integration & & & \\   technique & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: The related research that addresses integrating Internet of Things devices challenges.  
There are other techniques that can be used to provide payment for IoT devices, such as PayPal [55] and cryptocurrencies like Bitcoin [56] and MIOTA [57]. However, there is no paper that proposes using PayPal [55] or Bitcoin [56] to support paying IoT devices. Also, PayPal [55], Bitcoin [56], and MIOTA [57] require development to support a pay-as-you-go model for using IoT devices.  
Existing IoT device sharing solutions/marketplaces do not support a pay-as-you-go model for using IoT devices.  
[PERSON] et al. [58] provide preliminary work on supporting a payment mechanism for IoT device sharing by using the IOTA wallet [57] to enable IoT clients to purchase data packets. However, [PERSON] et al. [58] did not provide any model or mechanisms to process payment, calculate required payment, nor support pay-as-you-go payment for IoT devices. [PERSON] et al. [59] support IoT device sharing via a blockchain-based marketplace. [PERSON] et al. [59] have proposed using the Raiden network [60] off-chain payment channel to support micro-payments for IoT device providers. [PERSON] et al. [61] have proposed a blockchain-based marketplace for sharing IoT devices. [PERSON] et al. [61] use EOS permissioned blockchain [62] to support paying IoT devices for their data. However, [PERSON] et al. [59] and [PERSON] et al. [61] did not provide information for a pay-as-you-go model. Also, they did not provide a mechanism to calculate the required payment.  
All of the discussed solutions, including [54; 56; 57; 58; 59; 61], do not fully support paying IoT devices through a pay-as-you-go model as can be seen in Table 8.
## 2 Experiments
### Data Pre-Processing  
As mentioned above, the measurement data is collected by the Rx part of the channel sounder. It is saved as the format of the time-variant channel transfer functions (CTF) \(H(f,t)\). According to the principle of the channel sounder [21; 22], the format of the measurement data can be expressed as a matrix of \(m\times n\), where \(m=1933\) is the total number of chirp signals per second, denoting the time index and \(n=2560\) is the number of samples in each chirp, representing the delay index. We can then obtain the discretized channel impulse response (CIR) \(h(t,\tau)\) by computing the inverse discrete Fourier transform (IDFT) of CTF with respect to the Doppler frequency.  
Based on the classical assumption of wide-sense stationary uncorrelated scattering (WSSUS) [23], we should ensure that the radio channel satisfies the WSSUS assumption by setting windows along the measurement time \(t\) in the time-variant channel analysis. According to previous related research [24; 25], the channel within a few tens of wavelengths can be regarded as a quasi-stationarity channel. Therefore, we adopt 20\(\lambda\) to be the non-overlap window in order to make the channel analysis.
## 4 Case Study A: LED Array MIMO Multiplexing  
The next phase of this study is to empirically evaluate proof of concept of DNN-based OCC demodulation. In this regard, the first case study involves studying the performance of DNN demodulation of VGG16 for a 2 \(\times\) 2 LED array under multiplexing configuration. We consider packetized communication to conform with traditional communication modalities.  
Figure 5 illustrates the experiment setup. As can be observed, a 2 \(\times\) 2 LED grid was used with spacing of 3 inches in the vertical and horizontal direction and a Raspberry Pi 4 was used to operate the LEDs. The receiver was a Raspberry Pi Camera V2 which was controlled by the same Raspberry Pi as the LEDs. The top left and bottom right markers are ARUCO markers, which are synthetic square marker similar to QR codes but for object detection and location [33]. They were used to consistently locate the LEDs in each frame captured by the camera. The LEDs operated at 30 Hz and the camera recorded at 60 FPS, following the Nyquist criterion. However, the exact synchronization of the system was inaccurate and caused problems when transmitting a large volume of data in the order of 20 packets or more. This was primarily because the OS running on the Raspberry Pi was not a time-critical (Real-Time) OS, which meant that the accuracy of the operations could  
be variable to a degree and, therefore, causes synchronization issues between when the LED flashed and when the camera captured a frame.  
Figure 6 illustrates what each packet looked like. In addition, the transmitter and receiver software files were both written in python and ran from the Raspberry Pi 4 python IDLE. The transmitter processing and receiver processing for analysis were conducted as follows.
## 3 The Proposed Method
### Modification Strategies
#### 3.3.1 Elastic Modulus of the Cables  
The first modification is made on the elastic modulus of the cables. Among the three typical expressions of geometric nonlinearity, namely, sag effect, \(p\)-\(\Delta\) effect, and large geometric deformation [29], sag effect has the most obvious impact on the optimized cable forces under dead load. Instead of performing the time-consuming nonlinear analysis, the Ernst formula modifying the elastic modulus of cable is adopted.  
\[E_{eq}=\frac{E}{1+\frac{EA}{12T^{3}}(qH)^{2}} \tag{5}\]  
where \(E_{eq}\) is the effective elastic modulus of the cable, \(E\) is the material elastic modulus, \(A\) is the section area, \(T\) is the cable prestressing force, \(q\) is the cable weight per meter, and \(H\) is the horizontal projection length of the cable.
# Explainable Internet Traffic Classification
## 4 Experimental Results
### C4.5 Decision Tree
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **Class** & **TPR** & **FPR** \\ \hline \multirow{4}{*}{Statistical} & Mail & 0.96 & 0.005 \\  & Skype & 0.92 & 0.008 \\  & Firefox & 0.98 & 0.001 \\  & Safari & 0.97 & 0.02 \\  & BitTorrent & 0.97 & 0.001 \\  & Amule & 0.96 & 0.006 \\ \hline \multirow{4}{*}{Composite (\(H=3\))} & Mail & 0.78 & 0.07 \\  & Skype & 0.59 & 0.008 \\ \cline{1-1}  & Firefox & 0.98 & 0.002 \\ \cline{1-1}  & Safari & 0.83 & 0.11 \\ \cline{1-1}  & BitTorrent & 0.96 & 0.001 \\ \cline{1-1}  & Amule & 0.83 & 0.058 \\ \hline \multirow{4}{*}{Composite (\(H=5\))} & Mail & 0.85 & 0.05 \\ \cline{1-1}  & Skype & 0.7 & 0.01 \\ \cline{1-1}  & Firefox & 0.98 & 0.01 \\ \cline{1-1}  & Safari & 0.86 & 0.07 \\ \cline{1-1}  & BitTorrent & 0.97 & 0.001 \\ \cline{1-1}  & Amule & 0.84 & 0.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: C4.5: Performance over UniBS data set.
# Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review
## 5 Discussion
### Current Challenges
#### 5.1.2 Edge Computing Paradigm for UAV Real-Time Object Detection
Another issue is the offloading delay, which was raised in [131] and which has several causes, such as obstacles, low-frequency channels, and task size, as well as the constant innovations in neural networks that cannot be ignored [126]. Additionally, service latency could affect UAV real-time object detection, as mentioned in [131], and a higher latency causes system overhead, which severely deteriorates offloading performance.
# An Image Processing Method for Extraction of the Stress Wave Reflection Period
## 2 Algorithm for the Automatic Extraction of the Reflection Period Based on Image Processing
### Step 8: Calculation of the Reflection Period  
The primary frequency is the dominant frequency of the pixel's statistical parameters in the time-frequency spectrogram, not the frequency of the reflected wave signal. The reflection period \(T\) of the reflected wave can be calculated according to the time-pixel conversion relationship and the frequency characteristic of the statistical parameters, as shown in Equation (6).  
\[T=2\times f\div F\times\Delta t, \tag{6}\]  
where \(F\) is the main frequency of signal, \(f\) is the maximum frequency of signal, and \(\Delta t\) is the time interval represented by each pixel.
## 4 Experiments without Frame Aggregations  
In this section, we show the experimental results of the proposed MAB algorithms when used in IEEE 802.11g wireless networks. We used the IEEE 802.11g standard to evaluate the proposed MAB algorithms when packets were transmitted one at a time because the standard does not support the frame aggregation. In addition, IEEE 802.11g supports only a single wireless link, i.e., the single-input single-output (SISO) mode, between a sender and receiver. According the standard, there are eight possible MCS levels, i.e., \(K\) = 8. The data transmission rate can be one of {6, 9, 12, 18, 24, 36, 48, 54} Mbps. Here, we use only the ERP-OFDM scheme in the 802.11g standard for simplicity.  
The carrier frequency is set to 2.412 GHz and the channel bandwidth is set to 20 MHz. For the path loss of the radio signal, we used the urban microcell non-line-of-sight model in [38]. Thus, with the carrier frequency and a given distance \(d\) (in m) between a sender and receiver, the path loss (in dB) of the signal was computed as follows:\[40.198+38\log_{10}(d). \tag{6}\]  
Here, we assumed that \(d>1\).  
In all the experiments in this study, all wireless nodes operated in the DCF mode. The request-to-send/clear-to-send (RTS/CTS) scheme was not used for simplicity. As an application in all the experiments, we used a fixed constant bit-rate (CBR) traffic from a sender to its receiver during entire simulation time. The data packet size was set to 1500 bytes including the Internet protocol (IP) and user diagram protocol (UDP) header. All wireless nodes used 100 mW (i.e., 20 dBm) to transmit packets. The noise figure was set to 7 dB. Table 1 provides more details about the simulation parameters for the experiments in this study. All other parameters for simulations remained at their default values in the ns-3 version unless stated otherwise.
# RSS-Based Localization and Mobility Evaluation Using a Single NB-IoT Cell+
## 4 RSS-Based Localization Algorithms  
In this section, three RSS-based methodologies to locate a mobile UE are presented. First, a simple proximity algorithm is presented. Second, we modify a range-based algorithm, which uses a path loss model in combination with the sector information of the received eNB. Finally, we create a fingerprinting database and optimize the hyperparameters of a \(k\)NN algorithm.
# A Novel Non-Coherent \(Q\)-Ary TH-PPM Transceiver
## 3 Performance Analysis
### BER Performance Analysis under Ideal Synchronization Conditions
Also, when the sampling value of the matched filter, which is not correlated with the transmitted data symbol, does not exceed the sampling value of the matched filter correlated with the data symbol, we have (6). If all the sampling values of the \(Q-1\) matched filters, which are not correlated with the data symbol, do not exceed the sampling value of the matched filter correlated with the data symbol, we can obtain a correct symbol decision. Finally, we can have BER of the new TH-PPM receiver as  
\[P_{eb}=\frac{Q/2}{Q-1}\cdot\sum\limits_{k=1}^{Q-1}\binom{Q-1}{k}\frac{(-1)^{k +1}}{k+1}\exp\biggl{(}-\frac{k}{k+1}\cdot\log_{2}^{Q}\cdot\frac{E_{b}}{N_{0}} \biggr{)} \tag{15}\]  
It can be seen that although the demodulation method and the derivation of the BER are different between the conventional TH-PPM receiver and the new TH-PPM receiver, the theoretical formulas of BER are the same under ideal synchronization conditions.
## 2 Communication Framework Description  
Although different standardization approaches have been established since 2012, recent advancements in AI tools, technological advancements, and the usage or integration into the healthcare sector have increasingly highlighted the significance of on-body sensors. These devices are becoming progressively pivotal for health monitoring and overall well-being, having a growing relevance in contemporary medical practices. It is therefore essential to develop new communication structures that utilize the technologies explored over the last decade while anticipating future advances. Future research therefore should focus on identifying technologies that can be synergistically combined or the ones still needed to fill the existing gaps. In contrast to other standardization approaches like SmartBAN [8], IEEE 802.15.6 [6], mainly focusing on star topologies, the focus in this work for evaluating digital health technologies is based on a multi-hop or token-based communication model, which works comparable to a 'Data train' by concatenating different messages of sensor nodes within the WBAN. To provide a comprehensive understanding of this model, a basic overview is provided. This introduction serves to explain the critical principles and functionalities of this communication protocol and highlight its implications and potential applications in healthcare technology.
# Simulation-Based Design for Inlet Nozzle of Vortex Tube to Enhance Energy Separation Effect
###### Abstract  
The vortex tube, also known as the Ranque-Hilsch vortex tube, is a mechanical device that separates compressed gas into hot and cold streams. It offers a reliable and cost-effective solution to a wide range of cooling applications, as it operates without moving parts, electricity, or refrigerants. Research on vortex tubes has primarily focused on understanding the mechanisms of energy separation and optimizing cooling performance by altering geometric operational parameters. In this study, a Computation Fluid Dynamics (CFD) analysis was conducted to enhance the prediction of energy separation performance and improve the overall energy efficiency of the vortex tube. First, the geometry of the experimental device was modeled to closely match its actual shape, unlike the simplified geometries commonly used in previous CFD studies. Simulations were then carried out with variation in grid systems and turbulence models, and the results demonstrated improved agreement with experimental data compared to those reported in previous studies. Finally, simulations with a modified shape of the inlet nozzle shape were performed, revealing that the energy separation effect of the vortex tube could be enhanced by approximately 15% with an increased inlet expansion ratio (\(e\)) while maintaining a constant nozzle length.  
computational fluid dynamics; vortex tube; energy separation effect; inlet nozzle design; turbulent thermal flow +
Footnote †: journal: applied sciences
# Markov-Modulated Poisson Process Modeling for Machine-to-Machine Heterogeneous Traffic
## 6 Simulations and Result Discussions
### Regular eNodeB Scenarios, Results, and Discussions
#### 6.2.2 Simulating a Disaster Scenario  
In the event of a catastrophe, we assume twice as much H2H traffic is received at a constant average arrival rate of \(\lambda_{(h)}=50\). The five groups are progressively synchronized in the meantime:  
* First emergency storm, when Group(\({}_{(1)}\)) submits its data as a result of a sudden event: \(\lambda_{(E1)}=\lambda_{(1)}=5\);
* Second emergency storm, when Group(\({}_{(1)}\)) and Group(\({}_{(2)}\)) dispatch their payloads simultaneously: \(\lambda_{(E2)}=\lambda_{(1)}+\lambda_{(2)}=15\);
* Third emergency storm, when Group(\({}_{(1)}\), Group(\({}_{(2)}\) and Group(\({}_{(3)}\) send their data at the same time: \(\lambda_{(E3)}=\lambda_{(1)}+\lambda_{(2)}+\lambda_{(3)}=30\);
* Fourth emergency storm when Group(\({}_{(1)}\), Group(\({}_{(2)}\), Group(\({}_{(3)}\), and Group(\({}_{(4)}\) send their payloads all together: \(\lambda_{(E4)}=\lambda_{(1)}+\lambda_{(2)}+\lambda_{(3)}+\lambda_{(4)}=50\);
* Worst-case storm, which occurs when the five storms dispatch their data simultaneously: \(\lambda_{(W)}=\lambda_{(1)}+\lambda_{(2)}+\lambda_{(3)}+\lambda_{(4)}+\lambda_ {(5)}=75\).  
Figure 9's data reveal the following:  
* Receiving the five synchronized groups causes a significant decline in the service completion rate while moving from an Emergency(\({}_{(1)}\)) storm (\(\lambda_{(E1)}=5\)) with a 100% completion rate to an Emergency(\({}_{(5)}\) storm (\(\lambda_{(W)}=75\)) with only an 8% completion rate.  
These findings are clear because, despite increasing demand for M2M services (scr(\({}_{(m)}=\) 100%, 40%, 20%, 12%, 8%)), the network only has a fixed number of resources (rb(\({}_{(m)}=6\)) set aside for M2M traffic. Additionally, system utilization peaks to its cut-off point with a ru(\({}_{(m)}=100\%\) due to the high load of arrivals compared to the available resources in the system, except for Group(\({}_{(1)}\)), as mentioned previously.  
* There are no restrictions on H2H traffic because the network assigns the majority of its resources to it (rb(\({}_{(h)}=94\)), while only processing an average of 50 requests every time interval (\(\lambda_{(h)}=50\)), with scr(\({}_{(h)}=100\%\) and ru(\({}_{(h)}=53\%\).  
Figure 9: Regular eNodeB in an emergency situation, where scr is the service completion rate and \(\lambda_{(m)}\) is the average arrival rate for M2M traffic.
# How Should the Transition from Underwater to Surface Swimming Be Performed by Competitive Swimmers?
## 4 Discussion
### Transition Phase
According to regression analysis of transition-phase, the body position and inter-limb coordinative parameters of competitive swimmers statistically predicted an important amount (between 15 and 29% depending on the stroke, Table 2) of the variance in the forward velocity. Statistical models included the body depth in all four strokes and the body or trunk inclination in the alternate strokes (front crawl and backstroke) for predicting transition performance, highlighting the importance of an appropriate body positioning when approaching the water surface. This should be performed while minimizing body depth and maintaining trunk inclination in backstroke but minimizing inclination in front crawl and maintaining depth with water surface. Obviously, on the simultaneous techniques (butterfly and breaststroke) where body undulations occur [47, 48], the effect of trunk/body position for velocity was probably hindered. For the inter-limb propulsion coordination, in butterfly the time gaps related to the beginning of arm propulsion (DRP1 and 2) were included in the regression model. This data probably indicates that swimmers had to primarily adapt their arm to leg movements at the beginning of the arms propulsion (not in DRP3 and 4) when the body position was still underwater. For the alternate strokes, shorter times gaps at the end of the first arm pull in backstroke and longer time gaps at the end of the second arm pull in front crawl were included in the regression model. These results highlight the technical demands of swimmers who successfully adapted to the changing constraints of the transition phase, i.e., increased drag forces (underwater to surface swimming), modified body position (while ascending to water surface) and arm-to-leg propulsion (compared to primarily leg propulsion in underwater).
# Transmitter and Receiver Circuits for a High-Speed Polymer Fiber-Based PAM-4 Communication Link
## 5 Discussion and Conclusions  
The communication link presented in this paper uses a cost-efficient plastic fiber to transfer ultra-high data rates. A demonstration was performed over a one-meter fiber. In the measurements, the input power was close to the detection limit for the PD, which is why the system at the time was limited by power. Therefore, for longer fibers or a higher performance, a low noise amplifier (LNA) can be placed before the power detector. Adding an LNA will contribute with more noise, decreasing the signal-to-noise ratio (SNR), which eventually becomes a limitation. Since the signal has a wide bandwidth, due to low modulation complexity, dispersion of the fiber cannot be neglected for longer fibers. Comparing the difference in group delay over the signal's bandwidth with the baud rate will give an indication when symbol interference becomes problematic.  
In Table 2, the proposed PMF link is compared to previously published PMF links. It can be seen that a highly energy-efficient transmission (4.0 pJ/bit) over PMF was achieved in [3], as well as a high data rate (17.7 Gbps). In this work, 30 Gbps (BER \(<10^{-12}\)) was demonstrated, in real-time over a one-meter long PMF, which is the highest data rate. Moreover, the transmitter and receiver have the smallest combined chip area of 0.83 mm\({}^{2}\), and an energy efficiency of 4.1 pJ/bit, which is competitive considering that it does not include an LO. The proposed link can be used for ultra-high data rate, short-range communication (device-to-device, chip-to-chip etc.), for example within a car.  
**Author Contributions:** Resources, M.B.; Supervision, Z.S.H. and H.Z.; Writing--original draft, F.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.