esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Android smartphones that can record raw GNSS pseudorange (and most recently carrier phase) measurements have been available since late 2016 and initial results have shown promising potential for positioning (e.g., [1, 2]). Since a main purpose of a smartphone is communication, the GNSS capability comes at limited additional expense. The data can be processed in real-time or post-mission. In the latter case, online precise point positioning (PPP) services are available to process data collected in static or kinematic mode. Open source software such as RTKLib (rtklib.org) is also available for single point and differential processing.  
Low cost portable and wearable units with high sensitivity GNSS chips, similar to those available in smartphones, have been available for years for a variety of applications. It was only a matter of time before manufacturers offered raw data availability to potentially enhance performance for applications where needed. These units are robust and all weather. Garmin (Kansas City, MO, USA) has recently announced its GPSMap 66 with the optional recording of observation files in the RINEX format. The unit, shown in Figure 1 (left) is equipped with a quadrifilar helix antenna and a high sensitivity chip capable of tracking and recording either GPS, GPS + Galileo or GPS + GLONASS raw data. The latter consists of raw code, Doppler, carrier phase and carrier-to-noise ratio (C/N0) measurements every second, and associated time tags. The evaluation reported herein focusses on GPS and Galileo. Performance is compared to that of a P10 cellphone (Huawei, Shenzhen, China) running under the Android 8 operating system phone when possible. The P10 is also shown in Figure 1. The unit can track most GNSS signals. The P10 is the first generation of Android phones than can record continuous raw carrier phase data, subject to the limitations of the operating system.
## 3 Results and Discussion
### Prediction of Ultimate Damage Load
#### 3.2.3 Prediction of Artificial Neural Network of Acoustic Emission Multiparameter Data  
In this section, five acoustic emission parameters were used together, including rise time (1), duration (4), amplitude (5), center frequency (9), and peak frequency (10) as the input data for three different rubber contents. The ultimate load of the crumb rubber concrete was predicted by the acoustic wave multiparameter data artificial neural network, and the prediction result was compared with its real value to calculate the prediction error. The training tools and algorithms are the same as the prediction using AE single parameter.  
In the process of reporting the prediction results in Table 15, the input data are the rise time (1), duration (4), amplitude (5), center frequency (9), and peak frequency (10) of the six crumb rubber concrete specimens. Therefore, it is a multiparameter matrix composed of five column vectors. The hidden layer is composed of nine neurons, and the output end is the predicted value of the ultimate load. As for the prediction results of the artificial neural network using AE multiparameters, the optimal number of training iterations is 52 (Figure 18). At the same time, Table 15 shows that the maximum prediction error of the specimens with three different rubber contents is only 0.89%. Therefore, the multiparameter neural network is useful to predict the ultimate load of crumb rubber concrete.  
Ten acoustic emission single parameters, rise-time-amplitude (RA) two-parameter and multiparameter neural network prediction error absolute values are also shown in Figure 19, which can be summarized by comparison: the acoustic emission multiparameter neural network prediction results are the most accurate and the prediction error is no more than 1%. The rise-time-amplitude (RA) two-parameter neural network prediction results are also small.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rubber Content** & **Actual Value (kN)** & **Predictive Value (kN)** & **Prediction Error** \\ \hline
5\% & 16.24 & 16.35 & 0.71\% \\
10\% & 15.27 & 15.20 & \(-\)0.47\% \\
15\% & 11.32 & 11.42 & 0.89\% \\ \hline \hline \end{tabular}
\end{table}
Table 15: Multiparameter neural network prediction results.  
Figure 17: Two-parameter rise-time–amplitude (RA) neural network training performance.
## 3 Results and Discussion
### Pumping Power  
The pumping power which is needed to circulate the coolant in the heatsink versus volumetric flow rate is studied in Figure 14. Pumping power is defined as:  
\[P_{p}=Q\Delta p \tag{6}\]  
Figure 12: Heat transfer coefficient (_h_) in different volumetric flow rates for conventional offset strip fin design and new offset design.  
Figure 13: Local heat transfer coefficient (_l_\({}_{x}\)) enhancement of new offset design as a function of position within a serpentine passage at volumetric flow rate of 3.6 cm\({}^{3}\)s\({}^{-1}\) (start and end of each U-turn is shown by dashed lines).  
where \(Q\) is volumetric flow rate and \(\Delta p\) is pressure drop.  
As it can be seen in Figure 14, a downside of the new design is the large increase in the pumping power required to circulate the water through the microchannels.  
It should be noted that these values of pumping power are just in the order of 0.01 W. Considering Equation (6) and the fact that volumetric flow rate is equal for both configurations, it can be concluded that pressure drop is higher in the new design than the conventional one. Pressure drop in the channel is defined by:  
\[\Delta p\sim\frac{\rho LV^{2}}{2D_{h}} \tag{7}\]  
where \(\rho\) is the fluid density, \(V\) is flow velocity, \(L\) is length of flow passage and \(D_{h}\) is the hydraulic diameter. With respect to Equation (7), a part of this higher pressure drop is caused by the longer path and also higher velocity of flow in the new offset design (as a result of smaller cross-section area in new design compared to conventional one).  
As depicted in Figure 15, the flow velocity in the conventional offset strip fin configuration is mostly in the range of 0.1-0.3 m/s, while it is mostly in the range of 0.4-0.6 m/s for the new offset design.  
Moreover, Figure 15b shows that the velocity distribution in the first and second U-turn is different. This phenomenon is the result of the difference in fin configurations in the first and second U-turns.  
In other words, the number of fins in the first and second U-turns is different.  
Also, it is worthy of mention that there are three fins before and after the first U-turn, while there are two fins before and after the second U-turn. These differences in geometry leads to the difference in the velocity distribution. In addition, in the new design, recirculation areas (explained in Section 3.1 and shown in Figure 7 are causing higher pressure drops, leading to higher required pumping power.  
Figure 14: Pumping Power (\(P_{p}\)) in different volumetric flow rates for conventional offset strip fin design and new offset design.
# Analyzing Six Indices for Online Short-Term Voltage Stability Monitoring in Power Systems
## 5 Conclusions  
In this paper, six indices of voltage stability are analyzed for online SVS monitoring including (i) Lyapunov exponent in the basic form, (ii) contingency severity index, (iii) Potential index, (iv) trajectory violation integral, and (v) voltage severity risk index. Furthermore, using six disturbance scenarios related to different short-term voltage transients in a 39-bus system, their corresponding instructions are proposed. Except Lyapunov exponents which are extensively used in literature, other indices defined in this paper have not been used in SVS monitoring until now, so this paper proposes and modifies them along with appropriate algorithms for the successful use in SVS monitoring in modern power systems. These suggested algorithms are related to a 39-bus test system and could be changed for another one easily. For studying the reliability and speed of these indices with proposed instructions, a clustered database has been generated, and a total error of these methods at instability detection has been calculated. The results show that the simply computed indices that are proposed in this paper are reliable indices for SVS monitoring with detection time near 2 s. The paper proposes a novel index, i.e., VSRI, for online SVS monitoring in modern power systems. For the proposed VSRI, the detection time is less than 1 sec, and its error in detection is less than other indices.  
Conceptualization, M.A., M.E.H.-G., and H.H.A.; methodology, M.A., M.E.H.-G., and H.H.A.; software, M.A., M.E.H.-G., and H.H.A.; validation, M.A., M.E.H.-G., and H.H.A.; formal analysis, M.A., M.E.H.-G., and H.H.A.; investigation, M.A., M.E.H.-G., and H.H.A.; resources, M.A., M.E.H.-G., and H.H.A.; data curation, M.A., M.E.H.-G., and H.H.A.; writing--original draft preparation, M.A., M.E.H.-G., and H.H.A.; writing--review and editing, M.A., M.E.H.-G., H.H.A., and P.S.; visualization, M.A., M.E.H.-G., H.H.A., and P.S.; supervision, M.A., M.E.H.-G., H.H.A., and P.S.; project administration, M.A., M.E.H.-G., H.H.A., and P.S.; funding acquisition, M.A., M.E.H.-G., and P.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 5 Proposed Protocol to Secure E-Commerce Transactions
### Procedures of Proposed Protocol
#### 5.3.2. Encryption Procedure  
In our protocol, we use \(PW\), \(K_{r}\), \(K_{u}\), \(R_{i}\) (sell or buy orders) and \(t\)-threshold in encrypting \(\mathcal{M}\) transactions, but before performing the encryption process, the stored \(K_{r}\) and \(FK_{i}\) keys should be extracted as in Algorithm 2 from steps 1 and 2. We extract the keys and then we perform the encryption operation using the \(FK_{i}\) key, which is a FER 256 key and a random increase salt (\(salt_{i}\)) in order to provide more information security. Our protocol encrypts through \(ER_{i}=En(FK_{i}||R_{i}||salt_{i}||N_{ID}\oplus U_{ID})\), where \(En\) represents the encryption process, \(N_{ID}\) denotes the network's ID and \(U_{ID}\) signifies the user's ID; then we use DLD for leak detection. This happens by checking the leakage of the ciphertext \(P_{i}=ER_{i}/D_{i}\). Then, we compare the threshold (\(t\)) with the probability of leaking the ciphertext after it is sent over the network connection to the receiving end. We send an encrypted text to the recipient and hide \(FK_{i}\) and \(K_{r}\) with \(FK_{e}\) so that it is protected and hard-to-hack information.  
```
1:Input: \(K_{r}\), \(K_{u}\) keys with 256-bit, \(PW\) and \(R_{i}\), \(t\)-threshold
2:Output: \(ER_{i}\) and \(P_{i}\)
3:Extracting \(K_{r}\longleftarrow PW\oplus sco\)
4:Extracting \(FK_{i}\longleftarrow FK_{e}\oplus sco\oplus PW\)
5:Using \(FK_{i}\) 256-bit with Ferent encryption
6:Encrypting \(ER_{i}\longleftarrow ER(R_{i}||FK_{i}||salt_{i}||N_{ID}\oplus U_{ID})\)
7:\(P_{i}\longleftarrow ER_{i}/D_{i}\)
8:If \(P_{i}>t\) Declare as Info leakage
9:Else repeat step 5
10:Storing \(FK_{e}\longleftarrow FK_{i}\oplus sco\oplus PW\)
11:Storing connection order on the sender side
```  
**Algorithm 2** Encryption procedure.
## 6 On-Site Data Monitoring  
### Measurement Point Arrangement  
The mining of the 109 working face significantly affected the return air roadway of the 111 working face, as indicated by the mine pressure characteristics in previous mining roadways. To effectively assess the support efficacy of the differentiated roadway during the extraction of the 109 working face, the return air roadway of the 111 working face was monitored. The station layout points are illustrated in Figure 15.  
Figure 14: Class B support design in use at the construction site. (**a**) On-site photo of return airway’s surrounding rock; (**b**) On-site photo of the roof of the return airway.  
Figure 15: Monitoring point’s layout diagram.  
Three anchor stress stations were installed in the 111 return air roadway at intervals of 100 m, 200 m, and 300 m from the working face. The load force of the anchors was specifically monitored at a distance of 100 m from the working face. The cross-measuring point method monitored the convergence of the roadway roof and sides within 20 m before and after the working face. A 10 m deep roof borehole was drilled 15 m ahead of the working face between No. 1 and No. 2 stations for inspection purposes and detection of roadway support construction quality.
## 4 Results and Discussion
### Major Cracks and Stress Variations
#### 4.2.1 Evolution of Major Cracks with Horizontal and Vertical Stresses
As shown in Figure 5d, the initial horizontal stress values of each measuring point along the slip surface (except measuring points 41 and 51) were basically the same. During the loading process, the closer to the lower part of the slip surface, the greater was the horizontal stress value of the measuring point. This indicates that the horizontal stresses gradually accumulated along the slip surface during the slope failure process. Combined with the abovementioned horizontal stress distribution characteristics, it can be seen that the landslide process was progressive. From the above analysis, we can conclude that the horizontal stress change process was consistent with the slope crack development process. As for the monitoring points near the slope surface (such as points 14, 24, and 34), there was no inflection point A. On the one hand, these points were far from the loading center and were less affected by the loading; on the other hand, they were in a partially unconfined stress state. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Monitoring Point**} & \multirow{2}{*}{**Distance from Loading Center/m**} & \multicolumn{3}{c}{**Slope of Stress Curve (Mean Value)**} & \multirow{2}{*}{**Vertical Stress Slope (0-140 kN)**} \\ \cline{3-4}  & & & **0–100 kN** & **100–140 kN** & **140–150 kN** \\ \hline
11 & 0.5 & 0.033 & 0.190 & \(-\)0.223 & 0.222 \\
12 & 0.1 & 0.147 & 0.198 & \(-\)0.171 & 0.494 \\
13 & 0.5 & 0.099 & 0.113 & \(-\)0.060 & 0.264 \\
14 & 1.1 & 0.011 & 0.007 & \(-\)0.031 & 0.004 \\
21 & 0.2 & 0.07 Compared with the horizontal stresses, the vertical stresses were much larger and basically maintained linear growth as the load increased from 0 to 140 kN, as shown in Table 2. Affected by the vertical loading mode, the stages of the vertical stresses were not obvious.
## 3 Background
### CA Model  
CA is a biologically inspired computational trust model. It is inspired by biology concepts, particularly synaptic plasticity in the human brain and the formation of assemblies of neurons. In our previous work [28], we provided a detailed explanation of synaptic plasticity and how it is applied in our model.  
An MAS is used to represent a social environment where agents communicate and collaborate to execute tasks. In [28], we formally defined the key concepts necessary for describing our model, but here, we summarize the most essential ones. A trustor is an agent that defines a task and broadcasts a request message containing all relevant details, including the following: (i) the task category (type of work to be performed) and (ii) a set of requirements, as specified by the trustor.  
In the CA approach, the trustee, rather than the trustor, decides whether to engage in an interaction and perform a given task. When a trustee receives a request message, it establishes and maintains a connection weight \(w\in[0,1]\), representing the strength of this connection. This weight reflects the probability of successful task completion and is updated by the trustee, based on the performance feedback provided by the trustor. If the trustee successfully completes the task, the weight is increased according to the following:  
\[w=Min(1,w+\alpha(1-w)). \tag{1}\]  
If the trustee fails, the weight decreases as follows:  
\[w=Max(0,w-\beta(1-w)), \tag{2}\]  
where \(\alpha\) and \(\beta\) are positive parameters controlling the rate of increase and decrease, respectively.  
The trustee decides whether to accept a task request by comparing the connection weight with a predefined \(Threshold\in[0,1]\). If the weight meets or exceeds this threshold \((w\geq Threshold)\), the trustee proceeds with task execution.  
Table 1 summarizes the main differences between FIRE and CA across several core dimensions of trust modeling, including trust evaluation methods, trust update mechanisms, and trust storage approaches.
## 5 Stage-Wise Role of SiL and HiL Across Development Phases  
### Applicability of SiL and HiL Testing  
In the development of automotive lighting systems, selecting between SiL and HiL testing is not a matter of preference but rather a strategic alignment with system maturity and the complexity of requirements.  
Software-in-the-Loop testing proves most effective in early development phases, where control logic and software algorithms must be verified quickly and without hardware dependencies. Simulated environments allow iterative testing under various scenarios, contributing to early fault detection and reduced development costs.  
Hardware-in-the-Loop testing, by contrast, becomes essential when validating real-time interactions between software and physical components. For requirements involving precise response timing, safety-critical behavior, or physical signal processing, HiL is the only viable option. It enables the injection of realistic disturbances and ensures compliance with functional safety standards.
## 5 Experimental Results
### Interpretation of Results  
Experimental results indicate that the proposed method has higher accuracy when compared with other recent methods [24; 31]. The superior performance is due to the computationally efficient and robust IF estimation algorithm that results in accurate separation of sources thus accurate source localization. Commonly used IF estimation methods compute TFDs as a first step so that the signal energy is concentrated along IF curves in the TF plane and noise is spread out.  
Figure 7: MSE curves of DOA estimates for under-determined scenario.  
However, these TFDs fail to achieve good performance for signals with intersecting components thus resulting in the poor performance of the IF estimation algorithms [32]. This problem is partially mitigated by employing the adaptive directional kernel-based TFDs [28] or Viterbi Algorithm based methods [26; 32], but these algorithms are computationally expensive. The computational cost of ridge detection and tracking method is \(\mathcal{O}(VMN^{2}\text{log}N+VQ^{2}N^{2})\), where \(Q\) is the size of the smoothing mask. The computational complexity of Viterbi based algorithms is \(\mathcal{O}(VM^{2}N^{2}logN+VMN^{3})\)[33]. The proposed multi-sensor IF estimation algorithm accurately estimates the IF by adapting the direction of the analysis window with the signal characteristics thus resulting in accurate source separation and DOA estimates. The computational cost of the proposed method is \(\mathcal{O}(3 VMPWN+VMLW\text{log}W+NWMV)\), which is much less than the ridge tacking and viterbrae-based algorithms.
## 6 Simulation Results and Analysis
### Comparison of Dual-Motor Speed Synchronization Performance with Disturbance Observer
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicator** & **Proposed Method** & \
\begin{tabular}{c} **Method A** \\ **(Reference [31])** \\ \end{tabular} &
\begin{tabular}{c} **Method B** \\ **(Reference [32])** \\ \end{tabular} \\ \hline \multirow{3}{*}{Dynamic Response} & Fast, capable of quick & Moderate, the optimization & The response speed is \\  & recovery and responding to & process may result in a \\  & high, dynamic load variations & slightly slower response & \\ \hline \multirow{3}{*}{Robustness} & Moderate, capable of & High, capable of effectively & Moderate, primarily \\  & effectively responding to & responding to & \\  & friction and load variations & variations, and external & \\  & disturbances & disturbances & \\ \hline \multirow{3}{*}{Computational Complexity} & Moderate, the design of & High, as the particle swarm & High, fractional-order control \\  & sliding mode control and & optimization (IPM) requires & requires more complex \\  & disturbance observer is & more computational resources & \\  & relatively simple & & resources \\ \hline \multirow{3}{*}{Synchronization Error} & High, able to maintain a small & Moderate, the optimization & Moderate, but may be less \\  & synchronization error during & can reduce synchronization & flexible than other methods \\  & rapid load variations & errors to some extent & under dynamic conditions \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative evaluation of the performance of synchronization accuracy of dual motors under external disturbances across different methods. enhances the dynamic response of motor speed error by optimizing the sliding surface design, ensuring strict synchronization of both motors during complex machining tasks. Second, a cross-coupling controller coordinates the operation of both motors, enforcing strict speed synchronization and ensuring smooth beam motion. Finally, as friction occurs at the contact surfaces of the guide rails and transmission components, a DOB is introduced to estimate and compensate for friction in real time, further reducing synchronization errors. To further explore the engineering application value of this control strategy, it is necessary to analyze its applicability in systems of different power levels. Although the simulations in this study were conducted on low-power motor systems, the proposed control method can be extended to high-power applications. In high-power applications, due to the limitations of power devices, the switching frequency is relatively low, and the larger moment of inertia of the system leads to slower dynamic responses. However, through reasonable parameter optimization design, not only can control accuracy be maintained, but the anti-interference characteristics of this method will play an even more important role in high-power systems, as external disturbances have a more significant impact on these systems.