esa-sceva/satcom-chunk-collection huggingface dataset:
# LaRaWAPS: A Wide-Area Positioning System Based on LoRa Mesh
## 6 Evaluation
### Ranging Experiment  
We first evaluated the transmission capability of the LoRa signal. We conducted outdoor ranging experiments using standard LoRa hardware, the specifications of which are described in Section 4. The LoRa hardware tried 40 channels per ranging, and not every channel was successful. The quality of the signal and the effect of ranging can be evaluated by recording the number of successful ranging channels at different distances. We also recorded the RSSI in each experiment, and calculated the ranging error from the ranging result and the GPS result.  
In the experiment, the working mode of master-slave TOF ranging was adopted, the spread factor SF = 6 and the transmission power was 20 mW. This experiment was carried out between two nodes on a straight and relatively clear road, ranging was performed approximately every 25 m, the result was then filtered and evaluated as shown in Figure 10.  
In Figure 10, the horizontal axis indicates the distance between two LoRa devices. The green bar chart shows the number of successful ranging channels, which decreases gradually as the distance increases. The left vertical coordinate indicates the ranging error at different distances, corresponding to the yellow curve, while the right vertical coordinate indicates the RSSI at different distances, corresponding to the blue curve. As the distance increases and the signal quality decreases, the RSSI value decreases gradually, and the measurement error increases. RSSI and ranging errors are not only affected by distance, but also by non-Gaussian noise such as multipath, so the curves in the figure are not monotonically changing. All in all, LoRa's ranging ability is strong, the ranging distance reaches up to 1000 m. The ranging error is below 15 m. In particular, LoRa's ranging capability is excellent up to 500 m without shading, which provides a good basis for location services.  
Next, we evaluated the proposed ranging algorithm. Other mainstream LoRa systems were compared in the experiment [35; 49]. In the experiment, the LoRa anchor was placed at a high point in the center of a square area with a side length of 200 m. The target was measured at 30 different locations in the area. We recorded the errors between the ranging results and the actual distance in each experiment. The cumulative distribution function of ranging errors is shown in Figure 11.  
Figure 10: TOF Performance test.  
In the figure, the blue lines represent our proposed ranging method with an average error of 6.46 m, 75% of which are less than 10 m. The other two mainstream LoRa systems have ranging errors of 11.48 and 13.58 m, respectively. They use the mean filter or Kalman filter to reduce the influence of noise on the ranging results, but can not overcome the interference of non-Gaussian noise caused by the multipath effect. The proposed method overcomes the influence of the multipath effect by clustering on the basis of filtering, and is proved to be effective by experiments.
## 3 Materials and Methods
### Anomaly Classification
#### 3.5.2 Temporal Stream  
In order to perform temporal learning without incurring a high computational load, we made use of Stacked Grayscale 3-channel Image (SG3I) (Equation (6)) [59].  
\[SG3I(x,y)=R(x,y),G(x,y),B(x,y) \tag{6}\]  
Here, SG3I(x, y) represents the function notation for the SG3I value at pixel coordinates \((x,y)\), and \(R(x,y),G(x,y),B(x,y)\) represents the intensity value at the same pixel coordinates. SG3I relies on the simple idea of combining multiple frames of video into single frame. The objective is achieved by converting the RGB frames into grayscale images. These grayscale images are combined to form a single 3-channel RGB image, and then, combining the three grayscale images forms the SG3I image, which acts like a single RGB video frame. The frames are selected in sequential order, and each subsequent grayscale frame is fitted to the red, green, and blue channels to yield a single RGB image. This new image is fed to the same pre-trained CNN model as the spatial stream, which serve two purposes: the SG3I image preserves the short temporal characteristics, and the grayscale conversion lets the model focus more on motion-related features.
# Measuring the Lense-Thirring Orbital Precession and the Neutron Star Moment of Inertia with Pulsars
## 4 Measuring the LT Precession via Pulsar Timing  
The first binary pulsar where the presence of the LT precession was shown was PSR J1141 - 6545 [75]. In this case, the precession is not caused by the pulsar, but instead by a fast-spinning, massive WD companion, and is superposed (to an unknown extent) to classical spin-orbit coupling caused by the quadrupolar moment of the WD. This system shows some of the possibilities and limitations of pulsar-timing measurements of LT precession.  
The promise of the first measurement of the MoI of an NS arrived with the discovery of the Double-Pulsar system PSR J0737 - 3039A/B in 2003, because of its extremely compact 2.45 h orbit [76; 77] and the alignment of the spin of pulsar A with the orbit, which simplifies the analysis.  
In the following, we discuss these two systems plus two additional binaries where the measurement of the LT effect appears to be promising at the current time: PSR J1757\(-\)1854 and PSR J1946+2052. A schematic comparison of these compact binary systems with the the Hulse-Taylor pulsar PSR B1913+16 and the Sun is shown in Figure 6.  
Figure 6: Schematic diagram of the compact binary pulsar systems discussed in the text (B1913+16 [78], J0737\(-\)3039 [79], J1757\(-\)1854 [34], J1946+2052 [80], and J1141\(-\)6545 [75]) compared to the size of the Sun. The left orbit of each pair corresponds to the pulsar, whereas the right orbit corresponds to the companion (pulsar A and pulsar B in the case of the Double Pulsar). The sizes of NSs are scaled \(\sim\)10\({}^{3}\) times larger for illustration purpose.
# Cornering Stiffness Prediction Based on Geometric Method
## 4 Discussion  
The method proposed for obtaining cornering stiffness with varying camber angles is time- and resource-saving. It requires tire dimension and only pure side slip characteristics. Compared with the common techniques, i.e., based on combined slip and camber data, the camber angle input is removed. Table 5 shows the number of measurements for the example data above. A 91% saving in measurements could be made using the proposed method.  
This method does have some limitations:  
1. based on the symmetry assumption, this method does not show the effects of ply steer and conicity [33].
2. considering the load range on each half-tire, only cornering stiffness with middle load and small camber angle could be obtained from pure side slip test data with a wide load range when not using a tire model.
3. cornering versus load curves of the half-tire is obtained from the non-cambered tire with nearly uniform load distribution along the width, but it is used in the cambered half-tire with various load distribution along the width, which could cause errors.  
The effects of ply steer and conicity are usually small. Hence, point I does not contribute largely to the error of the method. The application of the tire model makes possible prediction of half-tire results beyond the corresponding complete tire load range, which avoids limitation II. In the proposed method, the tire is divided into two parts. The tire can also be divided into more parts along the tire width. In this way, the error described in III could also be reduced.  
In the proposed method, the influence of camber angle is treated as load distribution. Currently, this is only used in the prediction of cornering stiffness. The approach could be extended to large side slips and lateral force generation, but requires further inquiry.
## 3 Results Analysis
### Experimental Results for Sleep Measurements
#### 3.2.3 IO/Network-Bound Load Test Results  
The results of this experiment show that medians of sleep misses datasets obtained with _HPTSleep_ are significantly lower than using _uSleep()_ function (tens of nanoseconds with _HPTSleep_ versus tens of microseconds with _uSleep()_). All examined platforms exhibit this behavior. VMware ESXi shows a lower spread than other platforms in the case of _HPTSleep_ function (Figure 14e). Xen has the highest spread with _HPTSleep_ function among all the platforms (Figure 14b). Virtual Box has a relatively small spread with _HPTSleep_ function, however max values are near to Xen's maximums (Figure 14c). Xen has the smallest spread for _uSleep()_. Median for _HPTSleep_ values on all platforms stay the same as in previous experiments, including the idle system tests. However, median values for _uSleep()_ have the same order of median values among all platforms: 57.8 \(\upmu\)s for Host OS, 108 \(\upmu\)s for Xen, 131 \(\upmu\)s for Virtual Box, 74.5 \(\upmu\)s for QEMU, and 62.2 \(\upmu\)s for ESXi.  
The CCDF plot in Figure 15 shows that IO/network-bound load has the greatest effect on the tails of distributions, while the shapes of those tails do not have stable patterns as in previous cases. This might mean that different virtualization platforms handle IO interrupts differently.  
Figure 14: Measurements of miss time for the IO/network testbed on: (**a**) Host OS; (**b**) Xen; (**c**) Virtual Box; (**d**) QEMU; and (**e**) VMware ESXi.  
Figure 13: CCDF representation of measurements of miss time for the CPU testbed.
## 2 Preliminary  
An UWA OFDM system with frequency domain equalization is shown in Figure 1. The bit stream \(\mathbf{b}\) to be transmitted is modulated to symbols with digital modulation. After the inverse fast Fourier transform (IFFT) is performed, the signal is up-converted to the carrier frequency. Then it is transmitted through the channel and suffers from noise. In the receiver, the signal is represented as  
\[r(t)=h(t,\tau)\otimes s(t)+z(t), \tag{1}\]  
where \(s(t)\), \(h(t,\tau)\) and \(z(t)\) are the transmitted signal, channel impulsive response and additive noise. \(\tau\) is the channel delay. \(\otimes\) denotes convolution. After being down-convertedto the baseband and performing the fast Fourier transform (FFT), the received symbol in the frequency domain can be written as  
\[\mathbf{R}=\mathbf{H}\mathbf{S}+\mathbf{Z}, \tag{2}\]  
where \(\mathbf{S}\), \(\mathbf{H}\), and \(\mathbf{Z}\) are the transmitted symbol, channel transfer function, and additive noise in frequency domain. Usually, the UWA channels and noise are different from those in terrestrial communications. With more powerful recognizable paths and impulsive noise, the OFDM system does not show good performance in shallow water. To reduce the influence of channels on symbols, before detection, the channel should be estimated and used to equalize the symbol with specific algorithms such as ZF and MMSE.
## 2 The Proposed Fusion Approach
### Flow Diagram of Proposed Approach  
The flow diagram of the complete fusion algorithm is depicted in Figure 1, which comprises two processes: LP-based qshiftN-DTCWT image fusion and MPCA. LP is used for decomposition, DTCWT is used for image fusion, and MPCA is used for feature extraction, as shown in Algorithm 3.  
```
Input: Multi-focus images. Output: All-in-Focus Image Steps: 1. Take the multi-focus images from the source and load them; 2. To use the image fusion technique, two multi-focus images (\(I1\) and \(I2\)) are used as source images. Row (\(I1\) and \(I2\)) and column (\(I1\) and \(I2\)) pixels are used to divide raw images; 3. Multi-focus image row and column pixels are converted from a Two-Dimensional image to a One-Dimensional array of data; 4. Laplacian pyramid is used to divide the resulting 1-D array data (\(I1\)) into minimum (row and column frequencies) and maximum (row and column frequencies) frequency elements. The \(I2\) image is split into low (row and column frequencies) and high (row and column frequencies) frequency components in the same way; 5. To produce low and high-frequency row components, the primary fusion procedure is performed on row elements (both low and high-frequency elements) of \(I1\) and \(I2\). Similarly, to generate minimum and maximum frequency column elements, this fusion technique is used to process the column elements; 6. To produce row and column elements, the fused row, and column frequency components are filtered utilizing the Inverse laplacian pyramid algorithm; 7. The row and column elements of the 1-D array data are converted into a 2-D image; 8. Using qshiftN DTCWT, a final fused image is created from the filtered row and column frequency elements; 9. Apply MPCA on a fused image by qshiftN DTCWT-LP; 10. Featured extracted image i.e., all-in-Focus image.
```  
**Algorithm 3** LP-based qshiftN DTCWT image fusion process and LP
# Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives
## 7 Conclusions  
The study's results demonstrate significant variation in the performance of drift detection algorithms based on the type of drift. The HDDMW algorithm exhibits a superior performance for abrupt and gradual drifts, with HDDMA, DDM, and EDDM following in descending order. Conversely, the EDDM algorithm achieves the best performance for incremental drifts, with HDDMW ranking second, and HDDMA and DDM receiving notably lower scores.  
As the number of drifts increases, the performance of the algorithms deteriorates, albeit with the HDDMW algorithm maintaining the most favorable average F1 score, while the EDDM algorithm exhibits the least favorable average score. In terms of detection time, the DDM algorithm achieves a significantly lower unit of time than the other algorithms, with the drift detection methods based on Hoeffding algorithms ranking next. However, the EDDM algorithm presents significant variability in terms of its detection time.  
Future research may explore the potential of more advanced drift detection algorithms and techniques, such as ensemble methods or deep learning-based approaches, to enhance the accuracy and efficiency of drift detection in synthetic and real-world data streams. Additionally, the use of more diverse and larger datasets would improve the quality of the findings for various applications and industries.  
In conclusion, researchers and practitioners should select the HDDMW algorithm for abrupt and gradual drifts, the EDDM algorithm for incremental drifts, and the DDM algorithm for the best detection time performance. These findings have practical implications for selecting appropriate drift detection algorithms tailored to specific needs.  
Conceptualization, G.Y.S., B.B.Z. and S.B.J.; Methodology, G.Y.S., J.F.L. and S.B.J.; Software, J.F.L.; Validation, S.B.J.; Formal analysis, G.Y.S.; Investigation, J.F.L. and B.B.Z.; Data curation, G.Y.S.; Writing - original draft, G.Y.S. and J.F.L.; Writing--review & editing, B.B.Z. and S.B.J.; Visualization, B.B.Z.; Supervision, S.B.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols
## 4 Performance Comparison
### Data Rate  
Data rate is crucial in IoT applications because it determines how quickly and efficiently devices can communicate, affecting real-time data analysis and system performance. High data rates support the seamless operation of interconnected devices, enabling faster decision making and better user experiences. Among the protocols, RedCap can provide the most significant data rate, up to 150 Mbps in downlink and 50 Mbps in uplink, in some use cases where QoS is essential [22; 34]. LTE-M provides a data rate of up to 1 Mbps, while Sigfox, LoRa, Z-Wave, and NB-IoT have data rates of less than 1 Mbps [60; 61; 62]. Although Sigfox utilizes the UNB very efficiently and provides high receiver sensitivity and low-cost antenna due to very low-level noise, it falls short of the data rate, offering only 0.1 kHz [60]. In the case of NB-IoT, compared to the downlink rate, the uplink data rate is lower and up to 20 kbps [60]. On the other hand, LoRaWAN is perfect for long-distance transmission of small payloads, such as sensor data with small data rates [63].
## 2 Error Magnitude Evaluation in Reed-Solomon Codes
### Forney Method  
The Forney method relies on the Forney equation (2):  
\[\delta_{k}=\frac{Z_{0}\big{(}\alpha^{-l_{k}}\big{)}}{\sigma(\alpha^{-l_{k}})} \,\ \text{for}\ k=1,\ 2,\ \ldots,\ \
u\, \tag{2}\]  
where \(\
u\) is the number of error actually present in the code word (\(\
u\leq t\)), and  
\[\alpha^{-l_{k}}=1/\alpha^{l_{k}}\, \tag{3}\]  
\[\sigma^{\prime}\big{(}\alpha^{-l_{k}}\big{)}=\sigma_{1}+\sigma_{3}\alpha^{-2l_ {k}}+\ \ldots+\sigma_{odd}\alpha^{-(odd-1)l_{k}}\, \tag{4}\]  
\[Z_{0}\big{(}\alpha^{-l_{k}}\big{)}=S_{1}+(S_{2}+\sigma_{1}S_{1})\alpha^{-l_{k} }+\ \ldots\ +(S_{\
u}+\sigma_{1}S_{\
u-1}+\ \ldots+\sigma_{\
u-1}S_{1})\alpha^{(\
u-1)l_{k}}\, \tag{5}\]  
with \(odd\) as the largest odd number less than or equal to \(\
u\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Galois Operation** & **Forney’s Method** & **Lu’s Method** \\ \hline Addition & \(N_{\bigotimes}=\
u\Big{(}\frac{\
u-1}{2}+\frac{3(\
u-1)}{2}\Big{)}\) & \(N_{\bigoplus}=\frac{3\
u(\
u-1)}{2}\) \\ Multiplication & \(N_{\bigotimes}=\
u\Big{(}\frac{\
u+1}{2}+\frac{3\
u-1}{2}\Big{)}\) & \(N_{\bigotimes}=\frac{\
u(3\
u-1)}{2}\) \\ Inversion & \(N^{-1}=\
u\) & \(N^{-1}=\
u-1\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of Galois field operations required by Forney’s and Lu’s methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Errors** & **Forney’s Method** & **[PERSON]’s Method** \\ \hline \multirow{2}{*}{\(\
u=3\)} & \(N_{\bigoplus}=12\) & \(N_{\bigoplus}=9\) \\  & \(N_{\bigotimes}=18\) & \(N_{\bigotimes}=12\) \\  & \(N^{-1}=3\) & \(N^{-1}=2\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Numerical example of equations of Table 1.  
Figure 3: Error magnitude evaluation block.  
In Figure 4, an example of an implementation of Equation (3) is presented. For simplicity, the \(Z_{0}\big{(}\alpha^{-l_{k}}\big{)}\) and \(\sigma\big{(}\alpha^{-l_{k}}\big{)}\) notations are replaced by \(Z_{0k}\) and \(\sigma^{\prime}_{k^{\prime}}\), respectively.  
In this case, the system is configured to perform the error magnitude evaluation of up to three errors (\(\
u=3\)). Inside the system, the two operands of Equation (3) (\(Z_{0k}\) and \(\sigma^{\prime}_{k}\)) can be identified and the number of required operations to perform the result is found to follow the equation of Table 1: 3 inversions (plus 3 required to generate the inverses of \(\alpha\) coefficients), 12 additions, and 18 multiplications.
## 3 Results
### Privacy Management
Visible: SNs structure requires from users to provide several personal information regarding their identity, which are visible to a large number of audiences. * Communication varied: SNs structure enables users to communicate by using several means of communication, as well as using many different devices. * Interactive: SNs structure presupposes that users interact with other users, in order to enhance their experiences within these environments and to adapt to the environment context. * Privacy Risky: SNs structure raises several privacy risks, deriving from unauthorized access to users' personal information by unwanted audience or other third parties. Self-adaptive privacy protection schemes: 1. Context aware: Schemes should be able to identify users' individual and collective values, so as to provide them with the proper privacy choices. * Selective disclosed information: Users should be provided with the opportunity to disclose information in a selective way, depending on the context that they operate, so as to adapt their behaviors accordingly. * Privacy aware enhanced: Users should be informed for the diagnosed privacy risks and be provided with the proper privacy justification for each context in which they operate, in order for their decision-making procedure to be enabled. * Privacy control enhanced: Users should be offered with the opportunity to have control over their information and to decide which of them are willing to disclose accordingly to their individual and social characteristics.
# Dispersive FSO Performance Estimation with Gaussian Pulses and Laplace Modeled Time Jitter
###### Abstract
The third effect studied in this work is related to time fluctuations of the incoming pulse's center around the detection point, which is known as the time jitter effect (TJ) [20; 21; 22; 23; 24]. Thus, the detector's output signal is always fluctuating as the incoming pulse is randomly time jittered (due to many factors), which depends on the propagation path, the electronic accuracy of the transmitter or the receiver, etc. This is especially problematic in very-high-data-rate FSO links. In this study, the TJ is modeled by the Laplace distribution [25; 26; 27], which describes early or delayed pulse arrivals indiscriminately and symmetrically. An atmospheric effect which significantly impacts the performance of FSO links is atmospheric turbulence [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. However, its influence has been studied broadly in the scientific literature and thus, it is out of the scope of the present work since we concentrate here only on the abovementioned effects which are dominant in very-high-data-rate communication systems with dispersive pulse propagation. However, the joint effect of all these factors could be studied further. The remainder of this work is structured as follows: Section 2 contains the mathematical analysis of the physical effects, Section 3 refers to the availability and reliability of the link by means of the outage probability (OP) and average bit error rate (ABER), respectively. The corresponding numerical results are illustrated in Section 4, and the final conclusions are provided in Section 5.