esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Validation and Case Studies  
The validation of the optimized LSP parameters and the predicted extended RUL is conducted using Finite Element Analysis (FEA). The primary objective is to compare the extended RUL obtained from the DNN model with the RUL calculated from FEA simulations after LSP remanufacturing. The comparison validates the effectiveness of the optimization process and the accuracy of the predictive models.  
The FEA model simulates the bearing's response under cyclic loading conditions with the effects of the optimized LSP parameters on the residual stress distribution and fatigue life. The methodology for calculating the extended remaining life using FEA involves the following steps:  
The residual stress profile \(\sigma_{\text{residual}}(d)\) as a function of depth \(d\) is obtained from the simulation. The dynamic response of the material is governed by the following wave equation:  
\[\rho\frac{\partial^{2}\text{u}}{\partial t^{2}}=\
abla\cdot\sigma+\text{F}_{ \text{LSP}}. \tag{15}\]  
where \(\rho\) is the material density, \(u\) is the displacement vector, \(\sigma\) is the stress tensor, and \(\text{F}_{\text{LSP}}\) represents the LSP-induced force. The effective stress amplitude \(\sigma_{\text{eff}}\) experienced by the material under cyclic loading is modified due to the presence of residual stresses:  
\[\sigma_{\text{eff}}=\sigma_{\text{applied}}-\sigma_{\text{residual}}. \tag{16}\]  
where \(\sigma_{\text{applied}}\) is the applied stress amplitude from service loading; \(\sigma_{\text{residual}}\) is the compressive residual stress at the critical location obtained from FEA. The extended remaining life \(N_{\text{FEA}}\) is calculated using the Basquin equation, which relates the effective stress amplitude to the fatigue life:  
\[N_{\text{FEA}}=\left(\frac{\sigma_{f}^{\prime}}{\sigma_{\text{eff}}}\right)^{ 1/b} \tag{17}\]  
where \(N_{\text{FEA}}\) is the number of cycles to failure after LSP, \(\sigma_{f}^{\prime}\) is the fatigue strength coefficient, and \(b\) is the fatigue strength exponent. The DNN model predicts the extended remaining life \(N_{\text{DNN}}\) of the bearing after LSP remanufacturing based on the optimized LSP parameters and the current remaining life \(N_{\text{current}}\):  
\[N_{\text{DNN}}=f_{\text{DNN}}(P,O,D,N_{\text{current}}) \tag{18}\]  
where \(f_{\text{DNN}}\) represents the trained DNN function mapping inputs to the extended remaining life. The accuracy of the DNN model is validated by comparing \(N_{\text{DNN}}\) with the FEA-calculated extended life \(N_{\text{FEA}}\). A low relative error indicates strong agreement between the DNN predictions and the FEA results.  
The relative error is computed as follows:  
\[\text{Relative Error}\left(\%\right)=\left(\frac{\left|N_{\text{DNN}}-N_{ \text{FEA}}\right|}{N_{\text{FEA}}}\right)\times 100\%. \tag{19}\]
## 4 Experimental Results
### Experimental Heat Transfer Coefficients and Frictional Pressure Drops
Figure 5c reports the heat transfer coefficients at different saturation temperatures for a mass velocity of 600 kg m\({}^{-2}\) s\({}^{-1}\) with an imposed heat flux of 60 kW m\({}^{-2}\). The strong contribution of nucleate boiling is highlighted considering the fact that the heat transfer coefficient always increases as saturation temperature increases, even at this high mass velocity. Figure 4: Heat transfer coefficient versus mean vapor quality at \(t_{sat}\) = 30 \({}^{\circ}\)C and G = 400 kg m\({}^{-2}\) s\({}^{-1}\) at different heat fluxes. _HF_ expressed in (kW m\({}^{-2}\)). Besides heat transfer coefficients, pressure drops are another important parameter to be considered to develop heat exchangers. Frictional pressure gradients are plotted against the mean vapor quality in Figure 6 at different working conditions. Figure 6a shows the effect of both vapor quality and mass velocity for a constant saturation temperature at the inlet of the test section of 30 \({}^{\circ}\)C with an imposed heat flux of 30 kW m\({}^{-2}\). Keeping constant the mean vapor quality, frictional pressure gradient increases as mass velocity increases. Keeping constant the mass velocity, the frictional pressure gradient shows the classical two-phase flow behavior: it increases as vapor quality increases up to \(x_{mean}\) = 0.8-0.9, reaching a peak, and then it slightly decreases as vapor quality further increases. Similar trends can be observed for the other heat fluxes. The effect of saturation temperature on the hydraulic performance is shown in Figure 6b, which reports data for a mass velocity of 600 kg m\({}^{-2}\) s\({}^{-1}\) with an imposed heat flux of 12 kW m\({}^{-2}\) for three different saturation temperatures. As previously explained, a higher saturation temperature implies a higher vapor density, with a consequent lower vapor velocity at constant mass velocity. This consideration can be observed in Figure 6b: for mean vapor qualities higher than 0.4, the frictional pressure gradient decreases as saturation temperature increases, due to the lower vapor velocity at constant mass velocity.
# Software for Monitoring the In-Service Efficiency of Hydraulic Pumps
## 5 Assessment of a Pump's In Situ Operating Conditions Using the Software Module
The input data associated with pump performance over time have to be updated by the user. Certainly, the input data associated with pump performance have to be updated if repair work is applied to the geometry of the impeller blades. Assessing degraded pump performance in-service should be taken into account. Degraded pump performance methodology based on in-service test data is introduced by [PERSON] [38]. The methodology is based on the prediction of a small difference in the head at near pump shut-off head when compared to a new pump. An extensive degraded pump performance study on 150 pumps ranged from 22 kW to 3 MW installed in the municipal water supply and distribution system was conducted by [PERSON] et al. [39]. The results obtained in this study revealed that the pump efficiency degraded by 9.3% on average when the pump operated at the BEP. Moreover, the efficiency degraded on average by 12.7% when the actual operating point and the original best efficiency point were compared. [PERSON] et al. [39] noticed that while the ages of the pumps ranged from 1 year to 61 years with an average of 25 years, there was no discernible correlation between pump age and efficiency degradation. The above statement was not entirely surprising given that the pumps in service undergo various forms of routine maintenance, repair, refurbishment and modification throughout their working life [39]. The hydraulic performance along with the vibration response of an industrial scale centrifugal pump of 7.5 kW was experimentally investigated by [40; 41] to assess the degradation in pump performance. The results delivered by all these investigations can be used to identify the degradation level in pump performance. As a result, the time when input data associated with pump performance has to be updated in the algorithm due to degradation is determined based on these investigations. The marking of the \(\eta_{P}(Q)\) curve for this pump allows the marking of the maximum efficiency value of \(\eta_{P_{BEP}}=93.6\,\%\) (marked with \(\blacksquare\) in Figure 18) with a \(+2.4\,\%\) deviation compared to the value indicated by the manufacturer, that of 91.4 %. The pump's operating regime (normal operation--NO, operation at limit--LO and abnormal operation--AO) have been determined according to the criteria defined in Section 2 and are marked by green, yellow and red. The pump's duty point at a speed of 993 rpm can be found at the boundary between the green and the yellow areas (at a greater flow than that corresponding to the BEP flow rate). The selection of the pump's duty point for the maximum speed of 993 rpm is justified by the maximum flow rate of drinkable water supplied by this water treatment plant for Timisoara city. The operation of the pump in situ with VSD is considered to provide the variable flow rate needed by the consumers in Timisoara city.
# Hybrid Encoding Scheme for AMBTC Compressed Images Using Ternary Representation Technique
## 4 Experimental Results
### The Performance of the Proposed Method
#### 4.1.1 Coding Efficiency Comparisons
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{\(k=128\)} & \multicolumn{2}{c}{\(k=256\)} & \multicolumn{2}{c}{\(k=512\)} \\ \cline{2-10}
**Images** & **Bitrate** & \begin{tabular}{c} **w/o** \\ **QA** \\ \end{tabular} & **w/QA** & **Bitrate** & \begin{tabular}{c} **w/o** \\ **QA** \\ \end{tabular} & **w/QA** & **Bitrate** & \
\begin{tabular}{c} **w/o** \\ **QA** \\ \end{tabular} & **w/QA** & **Bitrate** &
\begin{tabular}{c} **w/o** \\ **QA** \\ \end{tabular} & **w/QA** \\ \hline Lena & 0.97 & 32.96 & 33.07 & 1.01 & 33.03 & 33.12 & 1.08 & 33.13 & 33.21 \\ Jet & 1.00 & 32.77 & 32.82 & 1.04 & 32.83 & 32.86 & 1.11 & 32.90 & 32.92 \\ Baboon & 1.79 & 28.67 & 28.72 & 1.83 & 28.72 & 28.75 & 1.89 & 28.80 & 28.82 \\ Tiffany & 0.89 & 34.63 & 34.81 & 0.93 & 34.79 & 34.93 & 1.00 & 34.99 & 35.09 \\ Boat & 1.30 & 31.17 & 31.32 & 1.34 & 31.24 & 31.37 & 1.41 & 31.36 & 31.46 \\ Stream & 1.89 & 29.79 & 29.82 & 1.92 & 29.84 & 29.86 & 1.99 & 29.93 & 29.93 \\ Peppers & 1.01 & 32.59 & 32.74 & 1.05 & 32.66 & 32.80 & 1.12 & 32.79 & 32.90 \\ House & 1.36 & 31.52 & 31.57 & 1.39 & 31.57 & 31.61 & 1.46 & 31.65 & 31.67 \\ Average & 1.28 & 31.76 & 31.86 & 1.31 & 31.83 & 31.91 & 1.38 & 31.94 & 32.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: PSNR and bitrate of compressed images with \(8\times 8\) block size. Figure 5a,b shows that the gain in PSNR increases as \(\tau_{1}\) increases, and this is mainly because the number of smooth blocks also increases as \(\tau_{1}\) increases. Because more blocks are classified as smooth for larger \(\tau_{1}\), more blocks will be processed using the QA technique. As a result, the gain in PSNR is higher when \(\tau_{1}\) is larger. It also can be observed that for each test image, the gain in PSNR is larger when \(k=128\) than that when \(k=512\). The reason is that a smaller \(k\) implies larger differences between the original bitmaps and cluster centers. Because the QA technique is capable of reducing the distortion caused by the differences, a larger PSNR improvement can be achieved for smaller \(k\). It is interesting to note that the gain in PSNR of the Stream and Baboon images increases more than that of other test images when varying \(\tau_{1}=10\) to \(\tau_{1}=50\) for both \(k=128\) and \(k=512\). Because these two images are more complex than the others, their bitmaps of smooth blocks are expected to be more different from the selected cluster centers used to replace the bitmaps. As previously mentioned, the QA technique is effective in reducing the distortion caused by the differences, and the bitmaps of Stream and Baboon images are more different from the cluster centers than the other images. Therefore, the improvement in PSNR after applying the QA technique is more significant than for the others.
# Secure LoRa Firmware Update with Adaptive Data Rate Techniques +
## 4 Discussion
### Conclusions and Future Work  
A secure and reliable firmware update methodology is proposed for mobile, energy-constrained LoRa devices. The secure code transfer and execution steps were simulated and then implemented on a battery-powered LoRa device. The most critical aspect of ensuring energy efficient updates is using ADR to find the optimal RF setting with the highest available data rate. Energy consumption scales non-linearly with the settings, making it worthwhile to invest time in converging to the optimal RF setting. The proposed secure authentication and encryption features add only an incremental burden on total energy consumption. The sensor AES computations also consume a negligible amount of time and energy. The most significant security burden is associated with the increased reception time of the MAC address. The proposed methodology could be improved by reducing the number of acknowledgements sent by the LoRa device since transmitting these impose the greatest energy burden. Fewer acknowledgements improves performance in the nominal scenario but introduces risk and redundancy if packets are frequently dropped or have transmission errors. Future work will integrate ADR during the data exchange as a means of maintaining the optimal RF settings when the LoRa device is mobile.  
D.H.--Conceptualization, modeling, software development, and system implementation. Wrote original draft and generated the figures in the manuscript. Inventor of Roper device. M.G.--Investigation, data collection and curation, funding acquisition, writing review, and editing. Inventor of Roper device. E.E.T.--Supervision, writing editing, and formal analysis. J.P.--Supervision, writing editing, and formal analysis. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by a New Mexico Small Business Assistance grant.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Objective** & **Method** & **Mitigation** \\ \hline \multirow{2}{*}{Get Firmware} & Observe firmware update by capturing it over RF. & The update is encrypted so it is meaningless to the observer after it is collected. \\ \hline \multirow{2}{*}{Get Firmware} & Falsely claim to be a cattle that needs a firmware update. & The attacker would not have so it would fail the initial authentication. \\ \hline \multirow{2}{*}{Get Firmware} & Read the firmware from the external memory chip. & The image is still encrypted external to the chip so it would not reveal any useful information. \\ \hline \multirow{2}{*}{Change Firmware} & Falsely claim to be the base station and update the firmware on a sensor. & The malicious actor could not compose a valid MAC to pass the initial exchange. \\ \hline \multirow{2}{*}{Change Firmware} & Wait until after the initial exchange and begin adding new firmware to the device. & Each data packet is signed using \(K_{c}\) so no un-authenticated packets would ever be accepted. \\ \hline \hline \end{tabular}
\end{table}
Table 5: The summary of the security risks and mitigation.
## 7 Conclusions  
The FHS system proves to be an efficient approach for horizontal replica scaling in Kubernetes environments, especially in the context of edge computing. By using fuzzy logic, FHS dynamically adjusts the number of replicas, thereby reducing CPU consumption and requiring fewer replicas to meet load demands. Our experimental results show that FHS outperforms the standard Kubernetes HPA scaling mechanism regarding resource consumption and maintaining SLAs, demonstrating its capability to handle dynamic and unpredictable scenarios typical of edge computing. Furthermore, the ability of FHS to operate without requiring large historical datasets or extensive training phases makes it a practical and applicable solution for various applications. The integration of edge computing enables optimization of resource usage close to the data source, thereby reducing latency and improving operational efficiency. These characteristics make FHS a scalable tool for next-generation computing infrastructure, potentially benefiting intelligent city applications and areas such as connected healthcare, autonomous vehicles, and smart factories in which real-time computing and resource optimization are essential.  
A limitation of the FHS system is related to the precise definition of fuzzy rules and control parameters. Fuzzy systems require manual configuration of rules based on specialized domain knowledge, which can be time consuming and prone to error. Additionally, adjusting these parameters to achieve optimal performance can be a complex and iterative process that does not guarantee the system's generalization to different operational conditions. A promising future approach for this work could be the implementation of machine learning techniques for the automatic definition and adjustment of fuzzy rules. Such integration would significantly improve the system's efficiency and accuracy, allowing for dynamic adaptation to changing operational conditions. Consequently, the reduced need for manual intervention would make the system more robust and autonomous.  
All authors have contributed in various degrees to ensure the quality of this work. S.N.S. and M.A.C.F. conceived the idea and experiments; S.N.S. and M.A.C.F. designed and performed the experiments; S.N.S., M.A.S.d.S.G., L.M.D.d.S. and M.A.C.F. analyzed the data; S.N.S., M.A.S.d.S.G., L.M.D.d.S. and M.A.C.F. wrote the paper; and M.A.C.F. coordinated the project. All authors have read and agreed to the published version of the manuscript.  
Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brasil (CAPES)--Finance Code 001.  
The raw data supporting the conclusions of this article will be made available by the authors on request. The data are not publicly available due to privacy concerns and the sensitive nature of the data used in the experiments, which may include proprietary information and detailed configuration of the infrastructure setup used in the study.  
The authors would like to express their gratitude to the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES) for providing financial support.  
The authors declare no conflicts of interest.
## 5 Experiment Analysis
### Efficiency Test  
As one of the significant features of the normal end hopping, hopping rate determines the transmission rate of our end spreading sequences to maintain the non-detectability in terms of timing attribute. To test the efficiency of the CCES scheme, we set \(e=4\) and the hopping rate from \(1\) (hops/s) to \(200\) (hops/s); the final test results are shown in Figure 8a.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Grades:** & **Level 0** & **Level 1** & **Level 2** & **Level 3** & **Level 4** \\ \hline DoS & 0 & (0,20] & (20,40] & (40,60] & (60,80] \\ (Mbps): & PLR (\%): & 0 & (0,10] & (10,20] & (20,30] & (30,40] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Network environment grading.  
Figure 7: BER under different PLR, BER under DoS attack. (**a**) BER under different PLR. (**b**) BER under DoS attack.  
It is clear that as the hopping rate increases, so too does efficiency. However, the efficiency is greatly affected by the environment, such as the efficiency under Level 4 is 450 (bits/s), which is merely about half of that under Level 0.  
Efficiency and End Spreading Degree Test  
According to the novel definitions of robustness and efficiency, we learn that the end spreading degree is closely but contradictorily related to them. The larger end spreading degree makes the end spreading sequences more robust against network attacks, but meanwhile, makes them less efficient. To describe it intuitively, we fixed the hopping rate at 100 (hops/s) and gained the following results.  
In Figure 8, the efficiency is inversely related to \(e\) and tends towards stability to a larger degree, even though the environment gets worse. On the contrary, the accuracy rate (AR, \(AR=1-BER\)) is getting better with the increase in \(e\) and also tends towards stability at the larger \(e\). In other words, we gain great communication quality at the expense of efficiency.  
Summary: Based on the evaluation metrics proposed in Section 4, we tested the performance of the CCES scheme, and the results show that the CCES scheme has great non-detectability with appropriate \((n,e)\) pairs and performs well in terms of robustness tests. With the increase in the end spreading degree, efficiency and robustness become steady, which is little-affected by the environment. While our CCES scheme improves synchronization through asynchronization and end spreading, the reliance on distributed nodes for sequence reception may pose scalability challenges in large-scale environments. Furthermore, although our design is robust against delays, decryption across decentralized receivers may require secure key distribution and channel coordination, which we acknowledge as potential areas for future improvement.
# Application of Artificial Neural Network (ANN) in Predicting Box Compression Strength (BCS)
## 1 Introduction
ML offers significant advantages for modernizing traditional packaging practices by increasing automation, improving precision, and supporting data-driven decision-making [11]. Among various ML techniques, artificial neural networks (ANNs) have attracted considerable interest from researchers for their ability to support decision-making and prediction in packaging property evaluations. For instance, [PERSON] et al. employed ANNs to predict the compressive strength of various cardboard packaging types by accounting for key influencing factors such as material properties, box dimensions, and the presence of ventilation holes or perforations that affect wall load capacity [12]. Similarly, [PERSON] et al. developed an ANN model to estimate box compression strength using a limited dataset while considering the effects of box dimensions, temperature, and relative humidity [13]. [PERSON], [PERSON] et al. conducted a comparative analysis to examine the relationships between key ANN architectural factors and model accuracy in the evaluation of BCS [2]. As interest in ANN applications continues to grow, an increasing number of studies have explored its use in packaging evaluation. However, limited research has focused on predicting box compression strength for the diverse box dimensions commonly used in industrial settings. Additionally, it has not been directed toward real-world industrial applications. This study aims to address that gap by developing an ANN model capable of predicting BCS at an industry-applicable level. Using a real-world dataset covering 90% of commonly used box dimensions in the industry, a generalized ANN model was trained for BCS prediction with a focus on practical industrial applications. During model development, four key ANN parameters--the number of hidden layers, the configuration of neurons per layer, the number of training epochs, and the number of modeling cycles--were optimized using various optimization techniques while balancing the computational efficiency and prediction accuracy of the ANN model. Finally, the model's reliability was validated using experimentally tested data obtained in the lab, demonstrating the model's feasibility and potential to significantly advance BCS evaluation methods in the packaging industry.