esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design, Implementation and Test of the DXN Engine
### DXN Tests Scenarios and Experiments
#### 3.4.2 Network Activity-Based Optimisation Algorithm  
As shown in Figure 7, EN was blocked but it is supposed to send a message every 5 min. The only available MN node is scheduled to send a message every 1 h. As it stands, EN will be waiting until MN wakes up every hour to forward its messages. DXN can see this situation from the host server data.  
Therefore, the second part of DXN is performing two tasks. First, it will set a new Tx interval for both EN and MN in this scenario (using the DNN model). This Tx will force both to be active at similar times to reduce the listening time of EN and also the traffic generated from it. Second, it will study the collected data and continuously forecast new Tx intervals to both MN and EN based on the predicted trend of the received data (using the Time Series model). This is useful in seasonal applications where the collected data might matter more during specific periods of time. The new Tx interval of the MN will be reconsidered when load balancing, as well (change of the MN score). This might be applied to several groups of nodes based on their activity.  
To test this part, the arrangement of Figure 5 is used. The transmitting intervals were set differently from SN to another and were set in specific different patterns. In addition, a delay was created manually to simulate the clock time drift at the SNs for rapid results obtaining and short experiment time. As shown in Figure 4, there are two MNs and five ENs with each nodes' Tx intervals. The collected data at the host server that DXN saw for each group and the new adjustments are as shown in Table 3.  
Figure 6: DXN load balance with 5 MNs scenario.  
Figure 7: Different Tx intervals scenario.  
As shown in Table 3, the new Tx interval for group 1 is set to 8 h, meaning each node will transmit its message every 8 h including MN1, and for group 2, the new Tx interval is now 6 h. It is also noticeable that MN1's score was 59, and now that it is sending more often (every 8 h instead of previously every 12 h), that will affect its power consumption and thus its score, which dropped to 55. Meanwhile, the score of MN2 remained the same because the new Tx interval of its group (group 2) was set to 6 h (meaning, nothing is going to change in the MN2 status). Figure 8 shows the results of this normalisation process by DXN.
## 6 Simulation Results and Analysis
### Results  
Based on the simulation results described in Section 6.2, the proposed AIFG algorithm demonstrates significant advantages over traditional framing algorithms across multiple dimensions:  
* Reduction in Queue Delay: As claimed in the abstract, AIFG effectively reduces the average queuing delay for delay-sensitive services. Simulation results show a reduction of at least 8.90%, and up to 43.90%, compared to benchmark methods such as AFG + HEFG, IFG + HEFG, and DQN + HEFG. It demonstrated effective delay control under diverse QoS constraints.
* Improvement in Throughput: The throughput achieved by AIFG increases by at least 6.10%, with improvements up to 78.05% under low delay constraints, confirming its efficiency in data delivery across time-sensitive and non-sensitive flows.
* Enhancement of Multiplexing Efficiency: AIFG demonstrates improved frame utilization, with the multiplexing efficiency increasing by, at most, 92.36% in comparison with traditional methods, especially under stricter time thresholds.  
These quantitative results fully support the core contributions presented in the abstract and validate the effectiveness of AIFG algorithm in adapting to differentiated QoS requirements in Advanced Orbiting Systems.  
It is important to note that in the proposed AIFG algorithm, the framing time threshold is not statically set. Instead, it is dynamically adjusted at runtime by the reinforcement learning agent. The PPO algorithm continuously updates the threshold value based on the observed system state, including packet arrival rate, queue length, and frame generation time. As such, the threshold evolves differently under each delay requirement scenario (e.g., 5 ms, 10 ms, 15 ms, 20 ms), and there is no single fixed optimal value applicable across all conditions. During simulation, different delay requirement environments are used to train the agent, and reward parameters are tuned accordingly.  
The performance improvements reported in this section--such as higher throughput, reduced average queue delay, and increased multiplexing efficiency--are the result of adaptive threshold selection throughout the simulation. The reward function parameters used to guide this adaptive process are provided in Tables 3 and 4. These results demonstrate the effectiveness of using policy-based learning to dynamically regulate the frame generation process. AIFG achieves superior performance not through a static configuration but via a policy-driven dynamic adjustment under varying QoS conditions.
## 4 Results
### Warm-Workpiece Measurements  
In the manufacturing line, each workpiece leaves the forming press at nearly 800 \({}^{\circ}\)C. Each part is then left to be cooled naturally by air convection, which takes around 60 min. For this section, two different workpieces are measured continuously during the cool-down. The height of each workpiece is 161.63 mm, and the runout height \(h\) (from the axis point \(a_{0}\) to the runout plane--see Figure 9b) is 64 mm. The objective of this test is to evaluate the accuracy of the system (agreement with the CMM result). Figure 10 plots the runout measurements at different temperatures for the two different workpieces. The workpieces have been measured in the temperature range \(27\ ^{\circ}\)C \(\leq\) T \(\leq\) 560 \({}^{\circ}\)C. Each workpiece runout also has been measured with a CMM after cool-down (27 \({}^{\circ}\)C). The CMM value is used as ground-truth for assessment purposes. The CMM value obtained for the first workpiece is 0.66 mm. Figure 10a shows that our system measurements deviate in less than 0.1 mm from the CMM measurement. The CMM value obtained for the second workpiece is 0.8 mm. Similar to the first workpiece, our system measurements deviate in less than 0.1 mm from the CMM measurement (Figure 10b). It is worth noting that this deviation is dependent on the height \(h\), increasing as longer workpieces are measured (and decreasing for shorter ones). Such a deviation (vs. height) is small enough for the dimensional assessment purposes of the forging process in which the system is deployed.  
Figure 9: Calculation of the workpiece runout \(\Delta\Phi\) regarding the revolution axis \(A\). \(\Delta\Phi=r_{large}-r_{small}\). **(a)** Runout plane \(T_{h}\) perpendicular to the revolution axis \(A\); **(b)** The runout plane \(T_{h}\) is defined at a height \(h\) from the reference point \(a_{0}\); **(c)**\(r_{small}\) and \(r_{large}\) radii computed at the plane \(T_{h}\).  
In Figure 10, there is no apparent correlation between the runout and the temperature of the workpiece at this scale of uncertainty (0.1 mm). Consequently, assessment of the workpiece runout can be performed in our system without the necessity of a correction due to thermal contraction. A more robust study on this matter for this measurement (and other dimensional measurements on the workpiece) is out of the scope of this manuscript, and it is left for future work.
# WIPP: Wi-Fi Compass for Indoor Passive Positioning with Decimeter Accuracy
## 1 Introduction
The main challenge is about the accurate estimation of AOA and TOA measurements under the multi-path fading. To address this problem, some previous work [22; 23] proposed that the number of antennas should be larger than the number of signal paths for each AP, which limits the application. By considering the fact that under the IEEE 802.11n protocol, the CSI contains the information related to the AOA and TOA measurements in each subcarrier, the WIPP conducts the joint estimation of AOA and TOA measurements based on the CSI. The proposed algorithm is able to achieve the accurate estimation of AOA measurement even if the TOA measurement is inaccurate (The TOA measurement cannot be accurately estimated due to the synchronization error between the target and APs [16]). To enhance the accuracy further, we introduce an enhanced two-dimensional (2D) spatial smoothing algorithm to de-correlate the coherent multi-path signal. In our system, we rely on the affinity propagation clustering algorithm to identify the direct signal path from the coherent multi-path signal. Many previous studies [24; 25] have addressed that the indirect signal paths deteriorate the positioning accuracy since they generate the bias of AOA and TOA measurements. Although various approaches have been proposed to overcome the negative impact of indirect signal paths on positioning accuracy [26; 27], the problem of the misidentification of direct signal path still exists. Since the estimation of AOA and TOA measurements with respect to the indirect signal paths has larger variance compared with the one with respect to the direct signal path, we select the affinity propagation clustering algorithm to identify the direct signal path. After the direct signal path is identified, we continue to conduct the positioning by using the Least-Squares Method (LSM). Under the actual deployment, the performance of WIPP is compared with the one achieved by the well-known radio-frequency (RF) based system for locating and tracking users inside buildings (RADAR) and the conventional system using the AOA solely. We find that:  
* The positioning accuracy of the WIPP is higher than the one by the RADAR system and the conventional system using the AOA solely. In our testbed, the median error of the WIPP is only 0.7 m, while the ones of the other two systems are 2.3 and 1.5 m, respectively. * Another significant advantage of the WIPP is about the simple system design and low maintenance cost. There is no requirement of fingerprint database construction or hardware modification. The rest of the paper is organized as follows. Section 2 presents some related work on Wi-Fi indoor passive positioning. Section 3 describes the WIPP in detail, which consists of the main modules of joint AOA and TOA estimation algorithms based on the CSI, 2D spatial smoothing algorithm, and phase correction approach. Section 4 evaluates the performance of the WIPP in an actual Wi-Fi indoor environment. Finally, the conclusion and some future directions are given in Section 5.
# Integration and Field Evaluation of an IoV System for Enhancing Road Safety
## 5 Results
### User Acceptance
#### 5.2.1 Results Derived from the Pre-Test Questionnaires  
The gender representation amounted to 64.71% for men and 35.29% for women, however, the extracted results refer to all users and were not distinguished based on gender. A total of 33.33% of users have driving experience of up to ten (10) years, 40% have from 11-20 years and 26.67% have 21-30 years. Out of these, it is noteworthy that only 18.75% drive a car that is less than five (5) years old in their daily commutes, with the majority driving older generation vehicles. Most of these drivers (88.24%) indicate that they use their vehicles on a daily basis. Exceeding the speed limit and illegal parking are the most common traffic violations among users, followed by passing through a red light (answered by almost 30%), speeding, and driving under the influence of alcohol.  
Users reported they have been involved in an average of 1.41 accidents per user in years of driving. Regarding the drivers' prior experience with the use of advanced driving assistant systems (ADAS) in their vehicles, the first place takes the use of navigation together with the parking assistance system, which seems to have been integrated into the daily life of most drivers. Blind spot detection and forward collision warning systems follow after with quite less frequency of use. It is noteworthy that applications/systems that belong to the category of road safety have a greater frequency of use, while other auxiliary systems, such as cruise control and parking assistance, appear last.  
Similar to the drivers/riders, the road operators completed pre-test questionnaires, specific to their expertise. Of the respondents, a large percentage (66.7%) have no expertise in road condition monitoring and no answer was recorded for the use of software related to road maintenance and inspection. In the question about what kind of system they think would best suit the needs of their position, they stated (a) Inspection Management System and (b) Asset Management System. Half of them (50%) also responded positively to the question of whether their organization uses predictive analysis software or other decision support module for scheduling a maintenance plan. The operators consider the timely renewal of the transmitted information as an advantage of the existing infrastructure of variable message signs (VMS) but recognize the small number and sparse location as a big drawback. A small number also mentioned the inability to display images for quick and easy information as a disadvantage for the existing VMS.
## 5 Experimental Analysis and Verification
### Experimental Verification of Proposed Method  
It is worthwhile to verify the performance of RDCA under the experimental data set, which is closer to a real power grid environment. We designed a hardware device to capture the PQD signals from the signal source, as shown in Figure 11. This platform consists of three parts: signal generating unit, measurement unit, and monitoring unit. A high-precision three-phase power quality source Fluke 6100A is utilized for signal generating, which can synthesize sinusoidal and non-sinusoidal power with harmonics, fluctuating harmonics, flicker, interrupt, sags, and swells. Then the hardware circuit with the 16-bit analog to digital converter (ADC) ADS8556 is used to sample real-time PQD signal and digital signal processor (DSP) TMS320C6748 with a clock frequency of 375 MHz is used to process the sampled signal via the DRST. After that, the features of real-time data can be extracted by the RDCA as soon as the DSP sends the result data of DRST to the host PC through a USB serial interface.  
Due to the functional limitation of Fluke 6100A, we generate eight types of PQDs including L1, L2, L3, L4, L5, L7, L10, and L13. Table 4 presents the performance of experimental data generated by the hardware platform. It is noted that a total of 50 samples are generated per type of disturbance. The test time consists of DRST and RDCA, which the DRST and RDCA are calculated in DSP and the host PC under all the samples, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **Accuracy (\%)** & **Test Time (ms)** & **Class** & **Accuracy (\%)** & **Test Time (ms)** \\ \hline L1 & 100 & 217.13 & L5 & 98 & 232.28 \\ L2 & 94 & 216.09 & L7 & 92 & 224.31 \\ L3 & 96 & 222.83 & L10 & 94 & 238.76 \\ L4 & 96 & 211.14 & L13 & 96 & 234.49 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The performance of the experimental data.  
Figure 11: Hardware experiment platform and structure diagram for real-time PQD signals diagnosis.  
As listed in Table 4, it is observed that the performance is a bit lower than the simulation because the experimental signal is more complicated than the simulation model. However, the average accuracy still reaches 95.75%. On the other hand, the test time shows that the combined disturbances are time-consuming compared to a single disturbance. In general, it is caused by the fact that the combined PQDs have more points of key frequency in the DRST analysis. The highest detection time namely, L10, is 4.76 ms for each sample, once the training of the RDCA model is completed. The results also reveal that real-time power quality diagnosis can be realized effectively in a high-frequency computing unit.
## 3 System Model
: _Accordingly, the effective SINR of the two-hop D2D link from TX \(m_{t}\) to RX \(m_{r}\) through RS \(r\) by reusing RBs of CUE \(c_{f}\) and CUE \(c_{s}\) can be calculated by [23]:_  
\[\gamma_{m_{t},m_{r}}=\frac{\gamma_{r}\gamma_{m_{r}}}{\gamma_{r}+\gamma_{m_{r }}+1}. \tag{3}\]  
The proof of Lemma 1 can be found in Appendix A. For the cellular transmissions, the SINR received by the BS corresponding to the CUE whose RB is reused by the first hop D2D link, i.e., \(c_{f}\), is given by:  
\[\gamma_{c_{f}}=\frac{P_{c_{f}}h_{c_{f}B}^{2}}{P_{m_{t}}h_{m_{t}B}^{2}+N_{0}}= \frac{P_{c_{f}}d_{c_{f}B}^{-\alpha}h_{s,c_{f}B}^{2}}{P_{m_{t}}d_{m_{t}B}^{- \alpha}h_{s,m_{t}B}^{2}+N_{0}}, \tag{4}\]  
and the SINR received by the BS corresponding to the CUE whose RB is reused by the second hop D2D link, i.e., \(c_{s}\), is given by:  
\[\gamma_{c_{s}}=\frac{P_{c_{s}}h_{c_{s}B}^{2}}{P_{r}h_{rB}^{2}+N_{0}}=\frac{P_ {c_{s}}d_{c_{s}B}^{-\alpha}h_{s,c_{s}B}^{2}}{P_{r}d_{rB}^{-\alpha}h_{s,rB}^{2} +N_{0}}. \tag{5}\]  
Here, \(h_{c_{f}B}\), \(h_{m_{t}B}\), \(h_{c_{s}B}\) and \(h_{rB}\) are the channel responses of the cellular link from CUE \(c_{f}\), the interference link from TX \(m_{t}\), the cellular link from CUE \(c_{s}\) and the interference link from RS \(r\), respectively. \(d_{c_{f}B}\), \(d_{m_{t}B}\), \(d_{c_{s}B}\) and \(d_{rB}\) are the corresponding transmission distances. \(h_{s,c_{f}B}\), \(h_{s,m_{t}B}\), \(h_{s,c_{s}B}\) and \(h_{s,rB}\) are the corresponding small-scale fading channel coefficients.
## 1 Introduction
[PERSON] et al. [20] studied three algorithms: equal spacing of wavelength range (\"Equal\"), Gram Schmidt orthogonalization in LEDs/light primaries spectral subspace (\"Gram\"), and a generalized version using 200 imaginary light primaries and 40 real LEDs (\"Ortho\"). The performance of the algorithms improved with the higher number of selected LEDs/light primaries, evaluated in terms of RMSE, GFC (goodness-of-fit coefficient--the cosine of the vector angle between the target and the reconstructed vector), \(\Delta\)CCT, and CRI. The result suggested that the \"Ortho\" algorithm might be the most effective. However, it does not include the commercial availability of LEDs. [PERSON] et al. [33] presented a methodology for multi-objective optimization to choose the optimal LED set for simulating daylight illuminants: D50, D65, and D75 with CRI > 90 and an average illuminance of 2000 lx. They used a multi-objective algorithmic approach to select from 5 to 74 LED emitters. Despite the widely understood benefits of LSTLSs and their application potential, a notable gap in research regarding their photometric quality and application in light meter calibration stands has been recognized [10]. Additionally, from the above literature review, it can be inferred that the measures used to evaluate the quality of LSTLS spectral matching are usually based on the criteria that average the differences in the spectra. Additionally, many different solutions are characterized by very high (similar) values of the quality parameters, which could suggest the ineffectiveness of some of these constructions understood as the use of an overestimated number of channels. Our research aims to critically evaluate traditional modeling techniques against measurements to determine their reliability in designing LED replacements for incandescent sources in photometric applications, specifically for photometric calibration procedures. We explored the use of Gaussian models and an exemplary evaluation method (R-squared curve fitting--\(R^{2}\)), widely used to simulate the SPDs of modern LSTLSs based on technical data from LED datasheets, and assessed their fidelity against empirical results. By comparing the effectiveness of these models with exemplary measurements, we aim to identify the areas for improvements in the development of LSTLSs. The main goal is to establish the reliability of these techniques in designing and validating photometric illuminants, providing a systematic approach to retrofitting incandescent sources with advanced LED technology. The findings from this study are intended to refine the understanding and improve the design processes of LSTLSs for photometric applications.
## 3 Numerical Solution  
The governing equations described in Section 2 are spatially discretized by adopting an unstructured colocated FVM. In particular, our solution strategy relies on the well-known OpenFOAM library [22], which is an open-source package for continuum mechanics released under the GNU Public License (GPL).  
It is important to remark that numerical solutions are obtained through simpleFoam, which is the steady solver for incompressible flows available in OpenFOAM official releases. simpleFoam uses the well-established SIMPLE algorithm [23] for pressure-velocity decoupling; the Rhie-Chow correction is adopted to remove oscillations in the solutions [24].  
For all the computations presented in this paper, the diffusive terms and pressure gradients were approximated with second-order accurate central schemes. The convective terms for the momentum, turbulence, and transition equations were handled with a second-order accurate linear-upwind scheme. As regards the linear solvers, a PBiCG with the DILU preconditioner was used to solve the discretized momentum, \(\tilde{v}\), \(\gamma\), and \(\widetilde{\mathrm{Re}}_{\theta,I}\) equations. On the other hand, a PCG with a diagonal incomplete Cholesky preconditioner was adopted for the pressure. Lastly, a local accuracy of \(10^{-7}\) was established for the pressure, whereas other linear systems were considered as converged when the residuals reached the machine precision.