esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time FPGA-Based Balance Control Method for a Humanoid Robot Pushed by External Forces
## 3 FPGA-Based Balance Control Method
### Trajectory Planning
As shown in Table 2, the oscillator parameters of the stepping gait are taken to execute the forward and backward balance strategies. \(\rho^{x}_{RA}\) (\(\rho^{x}_{LA}\)),\(\rho^{y}_{RA}\)(\(\rho^{y}_{LA}\)), and \(\rho^{z}_{RA}\)(\(\rho^{z}_{LA}\)) are the step length, step width, and lifting height of the right (left) foot, respectively. \(\omega^{x}_{RA}\)(\(\omega^{x}_{LA}\)), \(\omega^{y}_{RA}\)(\(\omega^{y}_{LA}\)), and \(\omega^{z}_{RA}\)(\(\omega^{z}_{LA}\)) are the frequencies of the right (left) foot. \(\varphi^{x}_{RA}\)(\(\rho^{x}_{LA}\)), \(\varphi^{y}_{RA}\)(\(\rho^{y}_{LA}\)), and \(\varphi^{z}_{RA}\)(\(\rho^{z}_{LA}\)) are the phase differences of the right (left) foot. In this paper, the oscillator parameters \(T\) and \(\gamma\) are set as 0.3 s and 0.2, respectively. Moreover, the lifting height \(H_{Sup}\) of the foot is set as 3 cm when the robot takes this stepping gait. For example, in the stepping trajectories of the forward balance strategy for the robot taking a step, two trajectory oscillators on the right and left ankle are shown in Figure 10 based on the results described in Equations (13)-(18). \begin{table}
\begin{tabular}{c c} \hline \hline
**Oscillator Parameters** & **Value** \\ \hline \((\rho^{x}_{RA},\rho^{y}_{RA},\rho^{z}_{RA})\) & \((-\frac{x_{Sup}}{2},0,0)\) \\ \hline \((\omega^{x}_{RA},\omega^{y}_{RA},\omega^{z}_{RA})\) & \((\frac{\pi}{2T(1-\gamma)},\frac{2\pi}{T(1-\gamma)},\frac{\pi}{T(1-\gamma)})\) \\ \hline \((\varphi^{x}_{RA},\varphi^{y}_{RA},\varphi^{z}_{RA})\) & \((0,0,0)\) \\ \hline \((\rho^{x}_{LA},\rho^{y}_{RA},\rho^{z}_{LA})\) & \((\frac{x_{Sup}}{2},0,H_{Sup})\) \\ \hline \((\omega^{x}_{LA},\omega^{y}_{LA},\omega^{z}_{LA})\) & \((\frac{\pi}{2T(1-\gamma)},\frac{2\pi}{T(1-\gamma)},\frac{\pi}{T(1-\gamma)})\) \\ \hline \((\varphi^{x}_{LA},\varphi^{y}_{LA},\varphi^{z}_{LA})\) & \((0,0,0)\) \\ \hline \end{tabular}
\end{table} 
Table 2: The oscillator parameters of the stepping gait in the forward and backward balance strategies.
## 2 Background and Methods  
### Overview of Sewage Treatment Plants in East China  
This study focused on 98 operational municipal WWTPs in the East China region (excluding Taiwan Province and Shanghai Municipality). These facilities are located in cities characterized by subtropical monsoon and temperate monsoon climates, with annual mean temperatures ranging from 15 to 18 \({}^{\circ}\)C. The geographic distribution of the selected WWTPs across this region is visualized in Figure 1, demonstrating their spatial concentration patterns relative to urban agglomerations and hydrological systems.  
According to the 2022 National Statistical Yearbook, the East China region contains 844 operational municipal WWTPs with an aggregate treatment capacity of 7.24 \(\times\) 10\({}^{7}\) m\({}^{3}\)/d. The present study investigated 98 WWTPs featuring a total designed capacity of 4.48 \(\times\) 10\({}^{6}\) m\({}^{3}\)/d and an average daily throughput of 3.55 \(\times\) 10\({}^{6}\) m\({}^{3}\)/d. These facilities exhibited design capacities ranging from 0.2 \(\times\) 10\({}^{4}\) to 20.0 \(\times\) 10\({}^{4}\) m\({}^{3}\)/d, with 73 plants (74.5% of the cohort) having  
Figure 1: Geographical distribution of 98 sewage treatment plants in East China.  
capacities below 5.0 \(\times\) 10\({}^{4}\) m\({}^{3}\)/d contributing 43.4% of the total treatment volume (Table 1). Process configurations were dominated by anaerobic-anoxic-oxic (AAO), anoxic-oxic (AO) with multi-stage variants, and oxidation ditch systems, which collectively represented 91.9% of the facilities (Table 2) and 92.1% of the total treatment capacity. Influent characteristics during 2024 averaged COD 205.1 mg/L, BOD 76.6 mg/L, TP 3.34 mg/L, TN 32.5 mg/L, and NH\({}_{4+}\)-N 25.3 mg/L. The top five cities with the highest influent COD levels were Qingdao, Linyi, Suzhou, Huzhou, and Taicang, indicating localized industrial or municipal discharge patterns that warrant targeted pretreatment optimization. Effluent quality compliance was evaluated against three tiers of the Discharge Standard for Pollutants from Municipal Wastewater Treatment Plants (GB 18918-2002) [18]: Class 1A, Class 1B, and superior to Class 1A standards.
# UAV Support for Mission Critical Services
## 6 Conclusions  
In this paper, we presented the usage of UAVs in virtualized mission-critical solutions. First, we briefly described the most popular narrowband mission-critical solution, i.e., TETRA, and the ongoing work on the LTE-MC, which is seen as the fundamental solution for mission-critical broadband communications. Later, we provided a short characteristic of UAVs, considering their usage in MCS. We also listed UAV approaches to enhancing connectivity and computing services. To formulate additional requirements, we described two scenarios of exploitation of UAVs in MCS.  
Finally, we proposed and described an innovative mission-critical solution, VMCS, that leverages network virtualization and the orchestration of resources of all system nodes, including end-user terminals and UAVs. TheVMCS solves the communication problem of users with no dedicated MCS terminals. It provides programmable connectivity and a service platform that can support services that suffer from the lack of support by the infrastructure (lack of access to clouds, etc.). The proposed approach has its price, namely complex orchestration and service platforms. Both platforms cope with unreliable infrastructure with constrained resources. The orchestration additionally exploits the position and mobility of the nodes that are part of the infrastructure, particularly concerning UAVs.  
The orchestration of unreliable resources using unreliable orchestration platforms is a new challenge, and thus far, no solution has been proposed. This paper outlines the problem and draws an Edge orchestration architecture able to cope with such a case. The benefits of virtualization combined with UAV usage for MCS justify the complex orchestration of VMCS. The paper outlines the proposed concept; however, much more work is needed on the details of the proposed architecture and intelligent algorithms for resource monitoring, disruptive connectivity handling and computing/storage node placement. These are the goals of our future works.  
Conceptualization, S.K. P.C., and K.S.; methodology, S.K., P.C. and K.S.; writing--original draft preparation, S.K., P.C. and K.S.; writing--review and editing, S.K.; funding acquisition, S.K. All authors have read and agreed to the published version of the manuscript.  
This research was partly funded by Warsaw University of Technology under research grant IDUB/VMC no. 45.010301.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 8: VMCS Fog Cells and Nodes architecture.
## 4 Analysis and Discussion
### Experimental Validation
When \(f\cdot d\) is known, the modal phase velocity and the group velocity basically remain unchanged, whereas there is a one-to-one correspondence between the type of the reflected pulse wave and the location of the flaw. Therefore, in this study, we use a triangular flaw (40 kHz, \(D0\) = 5 mm, \(L\) = 5700 mm) as another example. Based on the data analysis shown in Figure 12, we can obtain a similar conclusion; the flaw area (\(L\) = 5700 mm) has a significant correlation coefficient (\(\rho\)) of 0.3091. Therefore, \(L\) = 5700 mm is then selected as the estimated value of \(L\) (the location of the flaw). The solution remains valid, but the characteristics of the waveform superposition have changed, which can be used to identify the triangle type of the flaw. Figure 12: _Cont._  
The relevant analysis and the solution presented in this paper are still valid for low-frequency ultrasonic guided wave detection in a rod-like structure medium. We can also obtain a similar conclusion for the non-detection zone problem for a rail structure. For example, a rail experiment using 60 kHz as the actual measurement frequency was conducted by [PERSON] (2016). Although the experiment used different excitation pulse waveforms, which were affected by other derived modes wave and pulse trailing, the superimposed reflected pulse waveform obtained in the second monitoring time domain and the peak value was far higher than the peak value of the first reflected pulse; this result is very similar to the results of the characteristic analysis in this study.
## 4 Discussion  
There are several factors that may influence the accuracy of the experimental results obtained by the proposed method. First, it is observed from Figure 2b that there is an overetch of nitride during the PSG etch process, which causes the measured thicknesses of the oxide layers to be larger than the actual thicknesses. Second, in our extraction model, we have made the assumption that the step coverage is conformal, which is not always the case. If the sidewall deposited polysilicon film is thicker than the horizontally deposited polysilicon film, resistances of continuous step structures (\(R_{1}\), \(R_{2}\), \(R_{3}\)) are smaller than those with uniform thickness. Accordingly, the obtained thickness of the polysilicon layer is smaller than the actual thickness, while the measured thickness and sidewall etch angle of the PSG layer are larger than the real values. On the contrary, if the sidewall polysilicon film is deposited at a smaller thickness than the horizontally deposited polysilicon film, \(R_{1}\), \(R_{2}\), and \(R_{3}\) increases, resulting in a larger obtained \(t_{1}\) and smaller obtained \(t_{2}\) and \(\theta\). In our experiment, the step coverage of Poly 1 is excellent, whereas the sidewall deposited Poly 2 film is thicker than the horizontally deposited Poly 2 film. This explains the experimental results of \(t_{1}\) for Poly 1 agree with optical results better than those of Poly 2. In addition, the extraction model is no longer valid when the thickness of the polysilicon layer is larger than the thickness of the PSG layer because the value of \(R_{sw}\) is calculated to be negative under this circumstance. However, the polysilicon layer used in our experiment is no thicker than the PSG layer. Thus, the experimental results listed in Tables 1 and 2 are valid.  
To overcome the limitations mentioned above, the thickness of sidewall polysilicon film should be induced into the theoretical model when the step coverage of the polysilicon layer is not conformal. The expressions for the resistances of the sidewall (\(R_{sw}\)) and the corner (\(R_{c}\)) are accordingly transformed. As for the situation where the polysilicon layer is thicker than the PSG layer, the areas of sidewalls and the areas of corners need to be redefined. Taking into account the aforementioned factors, a more sophisticated model might be established. This will be our future work.
# Priority-Based Machine-To-Machine Overlay Network over LTE for a Smart City
## 4 Modelling PRACH for H2H and PM2M Traffic
### H2H PRACH Overload/Outage during Preamble Collision  
LTE PRACH may experience the outage condition by having large access traffic, possibly with preamble collisions. To calculate the probability of PRACH outage/overload, we need to know the total Poisson arrival rate of access on a single PRACH resource block including new, returning and handoff calls, \(\lambda_{total}=\lambda+\lambda_{pc1}+\lambda_{pc3}+\lambda_{h}\). The probability of \(n\) arrivals is:  
\[P_{n}=\frac{(\lambda_{total})^{n}}{n!}e^{-\lambda_{total}} \tag{4}\]  
For \(n\) H2H access attempts in PRACH resources, from overload Equation (2), we obtain the overload probability as:  
\[O\_PRACH_{n}=erfc(\frac{\frac{W}{RT_{1}}-\frac{W\eta_{0}}{I_{1}}-n+1-N_{M}-k_{m,1}}{\sqrt{k_{v,1}}}) \tag{5}\]  
where \(erfc\) is a complementary error function,  
\[erfc(x)=\frac{2}{\sqrt{(\pi)}}\int_{x}^{\infty}e^{-\frac{x^{2}}{2}}dt \tag{6}\]  
Thus, the total overload probability for the first handshake step due to collision and interference is obtained by averaging the H2H load as:  
\[\begin{split} P_{O,1}&=\sum_{n=2}^{n_{max}}P_{n}O\_ PRACH_{n}\\ &=\sum_{n=2}^{n_{max}}\frac{(\lambda_{total})^{n}}{n!}e^{-\lambda _{total}}O\_PRACH_{n}\end{split} \tag{7}\]  
where \(n_{max}\) is a sufficiently large number.  
For the third handshake step, the collision probability that H2H terminals collide when \(j>1\) is:  
\[P_{3}(j)=e^{-\lambda_{i},L_{3}}\frac{\lambda_{i,L_{3}}^{j}}{j!} \tag{8}\]  
where \(\lambda_{i,L_{3}}\) denotes the arrival rate of the third step L\({}_{2}\)/L\({}_{3}\) messages.  
Now, to find the overload probability of the third handshake step, the overload probability of Equation (3) can be written for different values of bandwidth, spectral efficiency and SINR:  
\[Q\_PRACH_{j}=erfc(\frac{\frac{W_{3}}{R_{3}}\frac{1}{I_{3}}-\frac{W_{3}\eta_{0}}{I _{3}}-j-k_{m,3}}{\sqrt{k_{v,3}}}) \tag{9}\]  
Thus, the overall PRACH overload probability for the third handshake step with preamble collision and inter-cell interference is:  
\[P_{Q}=\frac{\sum_{j=2}^{j}P_{3}(j)Q\_PRACH_{j}}{\sum_{j=2}^{j}P_{3}(j)} \tag{10}\]
## 3 Proposed GIS-Based Approach
### PSs Optimal Timing  
A side result of the PS sitting is information on the best timing for the new PSs settlement. The temporal order of installation is derived by progressively adding the new PSs and checking the fitness function's evolution. Assuming the fitness value before installing new PSs to be 2000, and assuming locating four new PSs, the proposed method installs, one by one, the PSs and evaluates the fitness variation. For instance, as shown in Table 3, PS4 minimizes fitness more. Thus, it should be the first one installed. In the second iteration the methodology considers PS4 in place and, installing one by one the remaining PSs, it identifies the second-best PS, i.e., PS3. The procedure ends when all the PSs have been investigated. Finally, the list of priorities is PS4-PS3-PS1-PS2.
# Software-Defined Networking: Categories, Analysis, and Future Directions
## 10 Future Research Directions
Network policies are formed intelligently using some machine learning algorithms, for example, random forest, support vector machine (SVM) [273], k-nearest neighbors (KNN), and so on. Apart from machine learning, deep learning models such as the artificial neural network (ANN) or convolutional neural network (CNN) may also be adopted. For example, if we can predict the change in network policies before its occurrence and take the needed measures, then much better throughput can be achieved. These approaches will make the controller more intelligent, which results in more efficient handling of network policy change phenomena. Moreover, quality of service (QoS) parameters also need to be considered to manage the network traffic to improve network throughput. However, machine learning algorithms have their own limitations, such as false negatives [274]. For traffic classification, most of the research conducted so far has been on labeled datasets using a supervised learning approach. Few works are done using semi-supervised learning, where some of the data are labeled and some are not given labels. The same applies to the scenario with unsupervised learning. Another important learning approach is reinforcement learning, which is a black box approach when we consider traffic classification. In this regard, algorithms can be designed to classify the data traffic in such a way that the new classified information can help the algorithm learn from the experience.
## 6 Conclusions  
The impact of time delays on a mixed electric grid was investigated in this work, and two approaches for minimizing the bad consequences of latency affecting TSC-TCR electric improvements were proposed. Temporary and permanent faults, both balanced and unbalanced, were considered at various locations throughout the power network. Based on the simulation results, the following conclusions and recommendations can be drawn:  
1. The presented strategies were suitable in minimizing the negative impact of significant delays on the hybrid power system power quality improvement;
2. In most cases, fuzzy-controlled methods represented excellent alternatives to modified predictor methods;
3. It was possible that the burden could be controlled if the response was fast (less than 1 s), which could help in inertia response;
4. The plan to use SVCs and shunt reactors is not enough when using TSC-TCR (in the REG situation of resolving the Shango SS1 and Bugesera SS11 connection problems).  
Other creative techniques, such as genetic algorithms and branch-and-bound methods, should be developed and evaluated in the future, allowing the application of fuzzy MPC methods to reduce the detrimental impact of random delays. In addition, delays caused by cyber-attacks should also be evaluated, as well as relevant mitigation strategies. Moreover, it would be fascinating to demonstrate the efficacy of the suggested approaches in a big power grid, such as the IEEE 118-bus test system and others, where THDu values can exceed 10%.  
Conceptualization, D.M., L.K.L. and B.B.M.; methodology, D.M., L.K.L. and B.B.M.; investigation, D.M., L.K.L. and B.B.M.; writing--original draft preparation, D.M.; writing--review and editing, D.M., L.K.L. and B.B.M.; supervision, L.K.L. and B.B.M.; funding acquisition, L.K.L. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the African Center of Excellence in Energy for Sustainable Development, University of Rwanda (research core funding no. ACE II-017).  
The data given in this study are available on request from the corresponding author.  
The first author would like to thank the African Center of Excellence in Energy for Sustainable Development (ACEESD), University of Rwanda (UR), for their support in this investigation. The second author appreciates the opportunity presented to conduct more research and learn more by the Department of Electrical and Communications Engineering, Moi University. The third author recognizes the Department of Mechanical and Energy Engineering, University of Rwanda (UR), for their support in strengthening the role of successful systems of knowledge and technology in short-term analysis.  
The authors declare no conflict of interest.
# Perceived Brightness and Resolution of Holographic Augmented Reality Retinal Scan Glasses
## 3 Results
### Retinal Scan Luminance and Ambient Contrast Ratio
Since the ambient light passes through the spectacle lens, its luminance is reduced by the transmittance \(\tau_{\text{C}}\) of the HOE combiner. Optical power measurements of the RS display are taken at the eye position, so \(\tau_{\text{C}}\) does not influence the numerator in Equation (17). The transmittance could however influence the pupil diameter in Equation (15). This will depend on the size of the combiner in comparison to the adaptive field size. Since the HOE region in our glasses prototype was small, and, more importantly, monocular, we neglected this influence. If this does not hold true for other prototypes, \(\tau_{\text{C}}\) can be included as an additional coefficient to \(\text{L}_{\text{A}}\) in Equation (15). Figure 5 shows the RS luminance \(\text{L}_{\text{RS}}\) and the RS ambient contrast ratio \(\text{ACR}_{\text{RS}}\) as a function of the ambient luminance \(\text{L}_{\text{A}}\) for a fixed set of RS glasses parameters. \[\mathrm{\Phi_{V_{Scan}}}=\mathrm{K_{m}}\int\mathrm{V(\lambda)}\ \mathrm{\Phi_{E_{Scan}}( \lambda)}\ \mathrm{d}\lambda\approx\mathrm{K_{m}}\sum_{\lambda}\mathrm{V(\lambda)}\ \mathrm{\Phi_{E_{Scan}}(\lambda)} \tag{4}\]5 RS luminance \(\text{L}_{\text{RS}}\) and RS ambient contrast ratio \(\text{ACR}_{\text{RS}}\) for ambient luminance \(\text{L}_{\text{A}}\) from 1 to 10,000 cd/m\({}^{2}\) and corresponding pupil diameters \(\text{d}_{\text{p}}\). Radiant fluxes \(\Phi_{\text{i}}\), average wavelengths \(\overline{\lambda}_{\text{i}}\), field of view angles \(\alpha\), \(\beta\), and combiner transparency \(\tau_{\text{C}}\) are constant.
## 4 Results and Discussions of Dynamic Characteristics
### Dynamic Characteristics Under Climbing Condition  
For analyzing the dynamic characteristics of the tracked vehicle under climbing conditions, when the tracked vehicle moves on the following five slop ground, \(5^{\circ}\), \(10^{\circ}\), \(15^{\circ}\), \(20^{\circ}\), \(25^{\circ}\), the corresponding vertical accelerations of the vehicle body at the mass center and the symmetrical suspensions at the hinge point were obtained and shown in Figure 14. From there, the climbing process could be divided into three stages: the horizontal movement stage, the initial climbing stage, and the stable climbing stage. At the horizontal movement stage, the vehicle moves on the horizontal ground, presenting the dynamic behaviors discussed in Section 4.1. According to the kinematic analysis of the vehicle, it can be seen that in the initial climbing stage, the tracked vehicle begins to climb slowly, resulting in significant changes in vertical acceleration. Finally, in the steady climb stage, all tracked vehicles show small variations in vertical acceleration. In addition, the vertical acceleration of the vehicle at the mass center is much lower than that of the symmetrical suspensions at the hinge point, indicating that the symmetrical suspensions could decrease the variation in the vertical body and improve the move stability of the vertical body.  
The pitch angle of the tracked vehicle is a vital parameter to evaluate the climbing ability of the tracked vehicle. Thus, the corresponding pitch angles of the tracked vehicle under the climbing conditions were obtained and shown in Figure 15. As determined from it, in the horizontal movement stage, the pitch angle of the tracked vehicle keeps nearly constant as the vehicle moves on the horizontal ground. During the climbing stage, the pitch angle of the tracked vehicle increases rapidly to the first peak point, which is slightly higher than the slop angle, then decreases slightly before increasing to a second, larger peak point. Finally, the pitch angle of the tracked vehicle decreases gradually from the peak pitch angle to the same angle as the shop, because the entire tracked vehicle has entered the steady climbing stage. The value of the second peak point was taken out for analysis, and the curve of maximum pitch angles of the tracked vehicle evolution with the climbing slope was obtained as shown in Figure 16. The maximum pitch angles increase with the increase in the slope angle, and the difference between the maximum pitch angles and the slope angle increases first and then decreases. The results indicate that the proposed tracked vehicle is capable of climbing slopes with angles greater than \(25^{\circ}\), indicating its good adaptability.  
Figure 13: Acceleration amplitude evolution with various velocities.  
Figure 14: The vertical acceleration of the vehicle body at the mass center and the symmetrical suspensions at the hinge point under the climbing conditions.  
Figure 15: The pitch angles of the tracked vehicle under the climbing conditions.