esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods  
### Methodology  
As previously mentioned, the more data related to the TPMs used in the synchronization that the attacker is able to obtain, the higher their chances of success. Due to this, the authors propose a novel approach: instead of relying on a single synchronization rule, the rule is selected quasi-randomly, based on the number of ones present in the input vector. This method introduces additional uncertainty for a potential attacker who is then faced with a choice: either to select a rule at random or to consistently apply a fixed update rule. In scenarios where the communicating parties alternate between different update algorithms, an attacker adhering to a static rule risks desynchronizing their machine, potentially decreasing rather than increasing their machine's synchronization.  
In this paper, the following two new approaches are introduced:  
* dynamic_rows,
* dynamic_matrix.  
Both update algorithms utilize the values generated as the input vector (which consists of \(-1\)'s or \(1\)'s). This strategy leverages data already known to both communicating parties, without requiring the transmission of any additional information over the public channel. Each algorithm selects either the hebbian or random walk rule for a specified vector. Although these algorithms operate in a similar manner, they differ in the resulting weight distribution, which affects the number of updates required for synchronization [39,40].  
The generalized pseudocode for both methods is as follows:  
Initiate function with vector V.  
If number of \(1\)'s in V > floor of (V length/2):  
Update part of the key corresponding to V using hebbian rule.  
Else:  
Update part of the key corresponding to V using random walk rule.  
where: V is the chosen vector (which may be a part or the entirety of the input vector). and floor function rounds the value to the largest integer that is less than or equal to that value.  
The key difference between dynamic_rows and dynamic_matrix algorithms lies in the granularity of their application. In case of the latter algorithm, the entire input vector is used as V, resulting in a single update rule applied per iteration of synchronization. In the first algorithm, each subtree corresponding to a hidden layer neuron is treated as a separate V. Therefore, in that case, the algorithm is chosen K times per iteration of synchronization, each time based on a segment of the input vector of length N.
# Time Series Analysis to Predict End-to-End Quality of Wireless Community Networks +
## 4 Time-Series Analysis to Predict EtEQ
### Experimental Framework
To do so, it removes the temporal ordering of individual inputs, by encoding the time dependency via additional input fields. More details about the tools and algorithms assumed in this study can be found in Table 1.  
Usually, classification studies assess the predictive power of their model by using Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE), both widely used in related work. We considered MAE in our experiments, as it is a common method to evaluate the performance of prediction approaches and gives the same weight to all individual differences. This metric is computed through the following formula: \(MAE=sum(abs(predicted-actual))/N\). Finally, we assumed a lag window size of 12 in all simulations. This value was set based on the analysis of the impact of lag window size performed in [48] that predicts next link quality values in the routing layer of large-scale, distributed and decentralized systems also by means of time series analysis.
## 5 Conclusions  
A modified ellipse method using nonlinear S0 mode Lamb waves with approximate group-velocity matching is proposed for localization and imaging of micro-cracks. Compared with the traditional ellipse method based on the intersections of the elliptical loci, the modified ellipse method utilizes the elliptical rings between two elliptical loci to localize damages accurately.  
In the experimental testing, a rectangle-like in-situ array of PZT piezoelectric transducers was used to excite and receive Lamb wave signals in an aluminum plate. When the length of a small crack (about 1 mm) is smaller than the wavelength of the incident wave (around 20 mm), linear Lamb waves are insensitive to the small crack, while the small crack can be identified and localized more precisely by the proposed method with nonlinear Lamb waves. For the small crack centered in the aluminum plate, the crossover area determined by the proposed method includes, but is not limited to, the intersections based on the ellipse method. Moreover, the dimension of the crossover area can be used to estimate the length of the small crack in the \(y\)-axis direction, while an intersection point is defined by the ellipse method. Extensive FE simulations were conducted to localize a breathing crack in the aluminum plate. The numerical results are in good agreement with the experimental findings. In addition, the breathing crack not situated in any of the propagation paths can also be localized by the proposed method as well as by the PDI.  
In total, the results presented here demonstrate that the modified ellipse method based on nonlinear S0 mode Lamb waves can accurately realize the localization and imaging of  
Figure 13: Localization and imaging of the breathing crack at the coordinates (118.75, 70) with nonlinear Lamb waves using (**a**) modified ellipse method; (**b**) probability-based diagnostic imaging.  
micro-cracks, and it has the potential to evaluate quantitatively the size of micro-cracks and even to monitor crack growth.  
Conceptualization, Y.X.; data curation, W.Z.; formal analysis, J.X. and W.Z.; funding acquisition, Y.X.; investigation, J.X.; methodology, J.X.; project administration, Y.G.; resources, Y.X.; software, J.X.; supervision, Y.X.; validation, X.Q.; writing--original draft, J.X.; writing--review and editing, Y.G. and X.Q. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work was supported by the National Natural Science Foundation of China (grant nos. 12025403, U1930202, 11834008 and 51835003); and China Postdoctoral Science Foundation (no. 2020M671015).  
The authors declare no conflict of interest.
# Comparison of Machine Learning Methods to Up-Scale Gross Primary Production
## 3 Results  
### Validation of Up-Scaled GPP with Field Data  
#### 3.1.1 Comparison of Up-Scaled GPP with GPP at Flux Tower Sites  
The up-scaled GPP with a spatial resolution of 1 km in 2014 are shown in Figure 3. We found that the spatial distribution of up-scaled GPP, using five machine learning methods,were generally consistent. However, GPP using SVM were higher than those using other models (Cubist, RF, ANN and DBN) in upstream areas. While the opposite situation occurs in the downstream areas; GPP from SVM were lower than those using other models (Cubist, RF, ANN and DBN). In the middle stream, GPP from RF and DBN were higher than those using other models (Cubist, SVM and ANN).  
Figure 3: Up-scaled GPP using (**a**) Cubist, (**b**) RF, (**c**) ANN, (**d**) SVM, (**e**) DBN from May to September in 2014.  
In general, a good linear relationship exists between up-scaled GPP and GPP at flux tower sites, as shown in Figure 4. R\({}^{2}\) was as high as 0.86 and RMSE was only 0.99 g C m\({}^{-2}\) d\({}^{-1}\) of the up-scaled GPP using RF, which demonstrates that up-scaled GPP could obtain the highest accuracy using RF. Followed by up-scaled GPP using Cubist, R\({}^{2}\) was 0.86 and RMSE was 1.08 g C m\({}^{-2}\) d\({}^{-1}\). Up-scaled GPP using DBN gained the lowest precision, R\({}^{2}\) was 0.79 and RMSE was 1.45 g C m\({}^{-2}\) d\({}^{-1}\). The accuracy of up-scaled GPP using RF were the highest.
###### Abstract  
In the passive bistatic radar (PBR) system,methods exist to address the issue of detecting weak targets without being influenced by non-ideal factors from adjacent strong targets. These methods utilize the sparsity in the delay-Doppler domain of the cross ambiguity function (CAF) to detect weak targets. However, the modeling and solving of this method involve substantial memory consumption and computational complexity. To address these challenges, this paper establishes a target detection model for PBR based on batch processing of sparse representation and recovery. This model partitions the CAF into blocks, identifies blocks requiring processing based on the presence of targets, and improves the construction and utilization of the measurement matrix. This results in a reduction in the computational complexity and memory resource requirements for sparse representation and recovery, and provides favorable conditions for parallel execution of the algorithm. Experimental results indicate that the proposed approach increases the number of blocks by a factor of four, and reduces the number of real multiplications by approximately an order of magnitude. Hence, compared with the traditional approach, the proposed approach enables fast and stable detection of weak targets.  
passive bistatic radar (PBR); cross ambiguity function (CAF); sparse representation; sparse recovery; fast calculation
# Reconfigurable Intelligent Surface Assisted Target Three-Dimensional Localization with 2-D Radar
## 4 Simulation Analysis
### Localization Accuracy Simulation  
A 2-D radar assisted by three RISs carried by UAVs are considered. The 2-D radar is located at [0, 0, 0] km. The location of the target to be located is [24; 48; 12] km. The locations of three RISs are [1.34, 2.68, 0.1] km, [1.34, 2.68, 1.3] km and [1.34, 2.68, 2.5] km. The azimuth angles of the targets and RISs are almost the same to ensure that RISs and targets are always in the beam of 2-D radar. The radar has a bandwidth of 5 MHz and a beam width of \(\theta_{3 dB}=2.7^{\circ}\).  
Since the localization accuracy depends on the radar measurement error, which is determined by the SNR of the received target echoes, the first target is selected as a reference, and it is assumed that its SNR in the direct channel is set to change from 5 to 30 dB. The target SNR in the auxiliary channels can be deduced according to the basic radar equation.  
As shown in Figure 3, the estimated GDOP by averaging 5000 Monto Carlo simulation results is reported, in which the CRLB for different values of the SNR is also calculated and plotted.  
Figure 3: Simulated GDOP and CRLB versus the target SNR.  
It can be seen from Figure 3 that the proposed algorithm can achieve effective target localization in the RIS-assisted 2-D radar, and the GDOP is lower than 800 m when the target SNR is higher than 13 dB. In addition, the localization accuracy becomes higher with the increase of the target SNR, since the larger SNR will improve the target measurement accuracy in direct and auxiliary channels, thus improving the target localization accuracy of the proposed RIS-assisted radar system. Compared with the calculated CRLB, the estimated GDOP is slightly higher and closer to the CRLB, and their difference becomes smaller with the increase of SNR. It is indicated that the accuracy of the radar localization system is closer to the best possible accuracy.
# Real-Time Vision through Haze Based on Polarization Imaging
###### Abstract  
Various gases and aerosols in bad weather conditions can cause severe image degradation, which will seriously affect the detection efficiency of optical monitoring stations for high pollutant discharge systems. Thus, penetrating various gases and aerosols to sense and detect the discharge of pollutants plays an important role in the pollutant emission detection system. Against this backdrop, we recommend a real-time optical monitoring system based on the Stokes vectors through analyzing the scattering characteristics and polarization characteristics of both gases and aerosols in the atmosphere. This system is immune to the effects of various gases and aerosols on the target to be detected and achieves the purpose of real-time sensing and detection of high pollutant discharge systems under bad weather conditions. The imaging system is composed of four polarizers with different polarization directions integrated into independent cameras aligned parallel to the optical axis in order to acquire the Stokes vectors from various polarized azimuth images. Our results show that this approach achieves high-contrast and high-definition images in real time without the loss of spatial resolution in comparison with the performance of conventional imaging techniques.  
Keywords:real-time observation; optical sensing; stokes vectors; information processing technology +
Footnote †: journal: Applied Physics Letters
## 3 Experimental Results and Analysis
### Test in Yale Dataset  
Through the above analysis, the proposed algorithm has good enhancement effects in both natural scenes and coal mines. To test the enhancement effect of the proposed algorithm for unevenly illuminated face images, face images subjected to different illumination conditions are selected from the extended Yale face dataset for testing experiments, and the images enhanced by the competitive algorithm are shown in Figure 14 below.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metrics** & **Original Image** & **MSR** & **CLAHE** & **HF** & **PSO** & **Proposed Algorithm** \\ \hline ALSD & 29.62 & 35.43 & 39.66 & 42.15 & 39.89 & 44.71 \\ AG & 6.34 & 6.94 & 7.65 & 7.79 & 6.82 & 8.36 \\ IE & 6.11 & 7.38 & 7.26 & 7.42 & 7.14 & 7.96 \\ Contrast & 24,770.7 & 26,014.6 & 27,043.7 & 27,335.3 & 26,506.9 & 27,646.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of mine image enhancement algorithms.  
Figure 12: Enhancement histogram of mine image.  
As can be seen from the above image, compared with other images, the face image enhanced by the proposed algorithm has uniform brightness, clear face contour and rich detail features. For the analysis of the Yale face images, the specific evaluation data are shown in Table 3 below.  
Figure 14: Comparison of the Yale face image enhancement effect.  
Figure 13: Percentage enhancement of mine image.  
As shown in Table 3, compared with the original image, ALSD, AG, IE and contrast of the image enhanced by the proposed algorithm improve by 113.1%, 63.8%, 22.8% and 24.1%, respectively.
## 5 Long-Short-Range Communication
### Short-Range Communication
#### 5.2.2 Zigbee  
Zigbee Alliance company introduced Zigbee standards to support low-energy, low-cost mobile-to-mobile communications. It is constructed using IEEE physical standard 802.15.4 [85], which is common to mesh networks but can also be used in a star network consisting of a wireless body network with one master node and various recognizing nodes. Various modules of Zigbee are available with different characteristics concerning its network capability, energy consumption and data rate.  
Standard Zigbee has a range of 30 m in urban environment with 1 mW of energy for transmission [86]. A higher range of 90 m is available in Zigbee Pro in the same scenario with an output power of 63 mW, and Pro-900 can cover up to 610 m in urban environment scenario with a power of 250 mW [87]. Healthcare wireless body networks require 1 mW of power and short range for on-body exchange. The rate of data is 250 kbps for Zigbee Pro and Zigbee, whereas Zigbee Pro 900 has the highest data rate of 10 kbps. Subsequent modules have lower data rates depending upon power efficiency to cover a longer range. In the case of healthcare, it would be better to choose a higher data rate in order to minimize the delay in the system and ensure that critical health data are delivered on time.  
Depending upon the module, Zigbee can operates in frequency ranges of 2.4 GHz, 900 MHz and 868 MHz, each of which are subject to interference issues. Wi-Fi and Bluetooth the 2.4 GHz band, whereas various long-range communication systems operate in unlicensed bands. Collison avoidance using multiple carrier sense approaches is implemented to minimize collisions and retransmit messages [88]. Zigbee technology has been deployed in various health-related applications. An Alzheimer's patient detection application with alerts was developed using a mesh network (Zigbee) [89]. Using Zigbee, a wearable ECG was developed to communicate with master tracking devices [90]. In biomedical applications, improved Zigbee was considered [91] using a low-power transmitter and receiver to increase reliability in terms of interference. Overall, Zigbee performs reasonably and is suitable for healthcare applications owing to its high level of security and robustness to interference.
## 5 Data and Results
### Confidence Threshold Setup
#### 5.4.1 Confidence Thresholds in the Visible-Light Spectrum  
The evaluation measures after segmenting the five video sequences from the visible-light sensor are shown in Table 1. For each sequence, in the first place, the table shows its average grey level value \(\overline{I_{G}}\) and its segmentation statistics (recall, precision and F-score). Based on these input and output data, the last column of the table offers the confidence value that was experimentally assigned.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sequence** & \(\overline{I_{G}}\) & **Recall** & **Precision** & \(F\) & \(C_{V}\) \\ \hline
8\({}^{\circ}\)Night & 49 & 0.08 & 1.00 & 0.15 & \(LOW\) \\
3\({}^{\circ}\)Sunny & 133 & 0.93 & 0.96 & 0.95 & \(HIGH\) \\
15\({}^{\circ}\)Cloudy & 125 & 0.97 & 0.98 & 0.97 & \\
28\({}^{\circ}\)Sunny & 116 & 0.81 & 0.97 & 0.88 & \\ \(-2^{\circ}\)Fog & 147 & 0.52 & 1.00 & 0.68 & \(LOW\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confidence values \(C_{V}\) for each sequence in the visible-light spectrum.  
When examining the results depicted in Table 1, a significant difference is observed between the sequences recorded under adverse lighting conditions (such as fog or darkness) and those recorded with more suitable conditions for the visible-light camera. These results confirm our initial assumption that a low or high average grey level value of the image would produce bad segmentation results, whereas an intermediate mean value would offer good or excellent results.  
Indeed, in the sequences where the average grey level value \(\overline{I_{G}}\) of the frames is between \(95\) and \(135\), the results are usually excellent, with F-score values always close to or greater than \(90\%\). Their sensitivities are also usually very close to \(100\%\) of detected humans in the scene. On the other hand, the evaluation values are always remarkably lower in sequences with frames with an average grey level value outside of the established intermediate range. Thus, confidence threshold \(\zeta_{VL}\) is set to the average grey level value of \(95\), while confidence threshold \(\zeta_{VH}\) is assigned a value of \(135\).
## 1 Introduction
Influential factors that affect dwell time regarding stop location include the relation of a stop to the nearest intersection (far side, near side, or mid-block), the type of intersection (non-signalized and signalized with or without tram priority), and the part of the city the stop is located (close, wide, or outside city centers); with regard to length, it is noted that single stops are generally less susceptible to high values of dwell time than longer double stops [23]. Passenger distribution along the stop platform also has a significant influence on passenger exchange and thus dwell time. Most passengers orient themselves to the future de-boarding situation, and this leads in part to a very pronounced unequal passenger distribution along the platform and overloading of individual tram doors, which significantly extends passenger exchange times [24]. The overview of the current research showed that the methods used to identify influential factors and to select input variables for the calculation of tram stop dwell time, which is required to model tram travel speed, should be researched further. The results of studies previously referred to could be applied but with major constraints. During the development of the analyzed databases and models, many limitations to input data were recognized and acknowledged. Estimated values of influential factors that define stop dwell time were mainly based on experience and experimental investigations. Their values cannot be transferred directly because of the differences in operation and line organization, vehicles, and infrastructure components between different train, metro, and tram systems. The research presented in this paper aimed to reduce the uncertainty related to the tram stop dwell time in tram networks with rolling stock consisting of different tram vehicle types, i.e., vehicles with different numbers of doors, various door widths, door opening/closing mechanisms, door opening/closing times, and floor heights. The stop dwell time at three island stops was determined and analyzed based on field measurements and observations. Island stops selected for this investigation were a part of the City of Zagreb tram network; they were located near the city center and served by three tram vehicle types. As this is the first such investigation performed on the Zagreb tram network, the aims of the research were threefold: (1) to develop and validate a methodology for field data collection and processing, (2) to determine locally representative tram stop dwell time values for three tram vehicle types serving island platforms in Zagreb, and (3) to examine the influence of tram vehicle type on stop dwell time by comparing statistically processed dwell time and passenger data defined for each of the observed vehicle types. The statistical analysis of the obtained empirical results will directly contribute to a reduction in the uncertainties in the process of tram speed and timetable modeling.