esa-sceva/satcom-chunk-collection huggingface dataset:
# Distribution and Deposition of Cylindrical Nanoparticles in a Turbulent Pipe Flow
## 6 Conclusions  
Transport and deposition of cylindrical nanoparticles in a turbulent pipe flow were studied. The equations of mean momentum and turbulent kinetic energy and turbulent dissipation rate of fluid including the effect of particles were solved together with the mean equations of particle number density and the probability density function for particle orientation including the combined effect of Brownian diffusion and turbulent diffusion. Distributions of particle volume concentration and particle orientation on the cross-section at different axial positions were given and analyzed. The penetration efficiencies for different parameters were calculated and discussed. The main conclusions were summarized as follows:  
Distribution of the particle volume concentration on the cross-section becomes non-uniform along the flow direction. The particle volume concentration first decreased and then increased from the pipe center to the wall, and there was a minimum around \(\eta\)/\(R\approx 0.95\)  
Figure 12: Penetration efficiency as a function of pipe length-to-diameter ratio (Re = 20,000, \(\lambda=8\)).  
Figure 13: Penetration efficiency as a function of related synthetic parameter. \(\bullet\): numerical data and \(\bullet\): expression (15).  
The particles with uniform distribution of mean orientation angle on the cross-section at inlet will change their orientation angles along the flow direction. The distributions of orientation angle became non-uniform and more and more particles aligned with their major axis near to the flow direction. The particles in the near-wall region were aligned with the flow direction obviously, and only a slight preferential orientation was observed in the vicinity of the pipe's center.  
Distribution of particle volume concentrations becomes more uniform on the cross-section with increasing the particle aspect ratio and Reynolds number. The particles tend to align in the flow direction and this phenomenon becomes more obvious with the increasing particle aspect ratio and with decreasing Reynolds number. The particle orientation distribution is not sensitive to the aspect ratio for the particles with an aspect ratio larger than 5.  
The penetration efficiency of cylindrical particles decreased with an increasing particle aspect ratio, Reynolds number and pipe length-to-diameter ratio. Finally, the relationship between the penetration efficiency and related synthetic parameters was established based on the numerical data.  
Conceptualization, J.L. and W.L.; methodology, W.L. and R.S.; software, W.L. and R.S.; validation, R.S. and W.L.; writing, W.L. and R.S.; resources, W.L. and J.L.; review, J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant no. 91852102).  
Not applicable.  
The area no conflicts of interest regarding the publication of this paper.
## 4 Experimental Results
### Performance Metrics
#### 4.2.1 Time Required for Similarity Search  
The run-time computation of a particular algorithm when performing the all-pairs similarity search is calculated as:  
\[T_{req}=\frac{T_{cal}}{n_{cal}^{2}}\times n_{new}^{2} \tag{47}\]  
where \(T_{req}\) is the time spent for the performance computation of \(T_{cal}\) on every given hardware configuration, \(T_{cal}\) is the computing time in one calibration run, \(n_{cal}\) is the length of one calibration run, and \(n_{new}\) is the new length of the time series \(T\).  
In Figure 4a, we show the time required for the similarity search. We generated the list of queries by attempting to extract a random data series. Random time series data set queries of lengths 18,000, 45,000, 90,000, and 135,000 were used. In Figure 4a, we observe that STOMP and STAMP show different times for time-series query detection, which is 115 for STOMP and 118 for STAMP milliseconds. Thus, it is observed that STAMP and STOMP have a certain time complexity in determining the length of the query. On the other hand, the proposed NMP appears to be compatible with the interval of the query. Based on the results, it is observed that NMP takes 104.1 milliseconds to detect the maximum 18,000 time series-length queries.  
In Figure 4b, the maximum 45,000 time series-length query is used. The result demonstrates that NMP takes 236.3 milliseconds to complete the 45,000 time series-length query. However, the contending methods STAMP and STOMP take 331.3 and 302.9 milliseconds, respectively. It is also observed that the proposed NMP rapidly increases the time after 600 time series lengths. The compared STAMP and STOMP increase the time exponentially.  
Figure 4c shows that the proposed NMP takes 520.7 milliseconds to process the maximum 90,000 time series-length queries, whereas the compared algorithms STOMP and STAMP take 631.8 and 668.2 milliseconds, respectively. The proposed NMP shows constant performance in time series detection after 50,000 time series lengths. On the other hand, the compared algorithms increase the time exponentially, and the variable performance of the compared algorithms can affect the accuracy.  
Figure 5a shows that the proposed NMP takes 959.2 milliseconds to process the maximum 135,000 time series-length queries, while the compared algorithms STOMP and STAMP take 1040.6 and 1074.9 milliseconds, respectively. The proposed NMP shows variable performance for time series detection. However, the proposed NMP takes less time than the compared algorithms. The compared algorithms increase the time exponentially despite having extended time series length.
## Appendix A State Space Equations for FG-GPLRC Arch with Different End Conditions
### S-S Arch  
The discrete end conditions can be written as:  
\[\begin{array}{ll}W_{1}=0,\ Y_{1}=-\frac{c_{33}}{c_{13}}\frac{k_{11}}{c_{0}} \frac{\lambda}{\eta}\sum\limits_{k=1}^{N}g_{1k}^{(1)}U_{k},&\text{ at }\theta=0\\ W_{N}=0,\ Y_{N}=-\frac{c_{33}}{c_{13}}\frac{k_{11}}{c_{0}}\frac{\lambda}{\eta} \sum\limits_{k=1}^{N}g_{Nk}^{(1)}U_{k},&\text{ at }\theta=\theta_{0}\end{array} \tag{16}\]  
The state equations give:  
\[\begin{array}{ll}\frac{\partial U_{i}}{\partial\zeta}=\frac{\lambda}{\eta}U_ {i}-\frac{\lambda}{\eta}\sum\limits_{k=2}^{N-1}g_{ik}^{(1)}W_{k}+\frac{c_{0}}{ c_{55}}\Gamma_{i}&(i=1,2,\cdots,N)\\ \frac{\partial Y_{i}}{\partial\zeta}=\frac{\lambda^{2}}{\eta^{2}}\frac{k_{11 }}{c_{0}}\sum\limits_{k=1}^{N}g_{ik}^{(1)}U_{k}+d\frac{\lambda}{\eta}\Big{(} \frac{c_{13}}{c_{33}}-1\Big{)}Y_{i}-\frac{\rho}{\rho_{0}}\Omega^{2}W_{i}+ \frac{\lambda^{2}}{\eta^{2}}\frac{k_{11}}{c_{0}}W_{i}&(i=2,3,\cdots,N-1)\\ -\frac{\lambda}{\eta}\sum\limits_{k=1}^{N}g_{ik}^{(1)}\Gamma_{k}&\\ \frac{\partial W_{i}}{\partial\zeta}=-\frac{c_{13}}{c_{33}}\frac{\lambda}{\eta} \sum\limits_{k=1}^{N}g_{ik}^{(1)}U_{k}+\frac{c_{0}}{c_{33}}Y_{i}-\frac{c_{13}} {c_{33}}\frac{\lambda}{\eta}W_{i}&(i=2,3,\cdots,N-1)\\ \frac{\partial\Gamma_{i}}{\partial\zeta}=-\frac{\rho}{\rho_{0}}\Omega^{2}U_{i }+\frac{\lambda^{2}}{\eta^{2}}\frac{k_{11}}{c_{0}}\sum\limits_{k=1}^{N}\Big{[} g_{i1}^{(1)}g_{1k}^{(1)}+g_{iN}^{(1)}g_{Nk}^{(1)}-g_{ik}^{(2)}\Big{]}U_{k}&(i=1,2, \cdots,N)\\ -\frac{c_{13}}{c_{33}}\frac{\lambda}{\eta}\sum\limits_{k=1}^{N-1}g_{ik}^{(1)} Y_{k}-\frac{\lambda^{2}}{\eta^{2}}\frac{k_{11}}{c_{0}}\sum\limits_{k=2}^{N-1}g_{ik}^{(1)} W_{k}-\frac{2\lambda}{\eta}\Gamma_{i}&(i=1,2,\cdots,N)\end{array} \tag{17}\]
# FPGA Implementation of a Higher SFDR Upper DDFS Based on Non-Uniform Piecewise Linear Approximation
## 2 Non-Uniform PWL Approximation Method
### PWL Approximation of the Sin Function  
Let's consider the first quadrant of the sin function:  
\[f(x)=sin(\frac{\pi}{2}x)\quad 0<x<1 \tag{12}\]  
Set \(E_{c}=0.001\), \(Q=10\). The segmentation results can be obtained through MATLAB R2021a as shown in Table 1.  
Figure 2: The process of seeking for the \(i\)th segment that meets the error requirements.  
The first quadrant of the sin function is divided into 10 segments, the first segment starts at 0, the end point of the previous segment is the start point of the next segment, and the end of the last segment is 1. Using the symmetry of the sin function, we make a PWL approximation of one period, as shown in Figure 3. The sin function realized by PWLMMAE fits well with the sin function in MATLAB, and MAE is within the preset range, which verifies the effectiveness of the algorithm.
## 4 Experimental Results and Discussion
### Model Testing
#### 4.4.2 Comparison between the mAP of M4 and the Other Controls  
In M2, M3, and M4, the images were first divided into Class A and Class B using YOLOv7, and then Class A was further subdivided into Class A_30, Class A_40, Class A_50, Class A_60, Class A_70, Class A_80, Class A_90, Class A_100, and Class A_110. In M1, the images were all divided into Class B, Class A_30, Class A_40, Class A_50, Class A_60, Class A_70, Class A_80, Class A_90, Class A_100, and Class A_110 using YOLOv7. Because M1 had a different number of categories than the other models (10 for M1 as opposed to 11 for the other three models), for the sake of impartiality, we calculated the overall weighted mAP (hereinafter denoted as the model mAP) of M2, M3, and M4 using the following equation:  
\[\text{Weighted mAP}\ (\text{denoted as model mAP})=\frac{(\text{mAP of Mask R - CNN})\times 9+\text{AP of Class B}}{10} \tag{1}\]  
The model mAP of each model derived through (1) is presented in Table 20. Even though the model mAPs of M2, M3, and M4 were lower than the mAP of their respective Mask R-CNN, the model mAP of M4 was still above 88% and was the highest among all models. This value is 8.6 percentage points higher than the mAP of M1, in which only YOLOv7 was used. The model mAP results further demonstrate the excellence of M4 in terms of detection accuracy.
# A Comparative Analysis of Wi-Fi Offloading and Cooperation in Small-Cell Network
## 3 System Model
### Transmission Model for Wi-Fi Offloading in Small Cells
The achievable data rate of a UE \(j\) using a RB \(k\) of LTE band of a SBS \(i\) is represented as:  
\[R_{ij}^{LTE,k}=\log_{2}(1+\gamma_{ij}^{LTE,k}), \tag{8}\]  
and the data rate achieved over all SBSs over LTE band using all RBs is represented as:  
\[R_{j}^{LTE}=\sum_{i\in I}\sum_{k\in K}y_{ij}^{k}R_{ij}^{LTE,k}, \tag{9}\]  
where \(y_{ij}^{k}\) is an association indicator of a UE \(j\) of a SBS \(i\) on LTE band \(k\) as in (5). The achievable data rate by a UE \(j\) on unlicensed band is represented as:  
\[R_{j}^{U}=\sum_{i\in I}z_{ij}R_{ij}^{U}, \tag{10}\]  
where \(z_{ij}^{k}\) is an association indicator of a UE \(j\) on LTE-U band of a SBS \(i\) as defined in (6). The achievable data rate by a UE \(j\) while using both bands LTE and LTE-U of a SBS is represented as:  
\[R_{j}^{o}=R_{j}^{LTE}+R_{j}^{U}. \tag{11}\]  
The overall system performance is depicted by finding the overall data rate achieved by all the cellular UEs in Wi-Fi offloading SC network as:  
\[R^{o}=\sum_{j\in I}R_{j}^{o}, \tag{12}\]  
where \(R_{j}^{o}\) represents the rate achieved by a UE \(j\) while simultaneously using the LTE band and Wi-Fi offloading. Thus, the cellular UE initially tries to satisfy its rate requirement through LTE link, failing to do so it offloads to Wi-Fi AP to leverage its unused capacity.
## Appendix A Operation Phase  
This subsection describes the framework's applicability during the Operation Phase, with each stage elaborated sequentially according to the corresponding rows in Table 7.  
The diagram shows a Kaizen-driven process for optimizing maintenance, system performance, and space utilization through the integration of BIM, IoT, Digital Twins, and Big Data Analytics [88].  
The process begins with problem identification, where IoT sensors and data analytics monitor real-time performance (e.g., temperature, energy, and vibration), while BIM and Digital Twins visualize issues for team review. In analyzing and planning solutions, simulations test root causes and proposed improvements, such as optimized maintenance schedules, which are integrated into BIM for team coordination. Implementation and testing involve applying these solutions, reconfiguring workflows, and validating results through Digital Twin simulations and real-time IoT tracking. Monitoring and continuous improvement follow, using ongoing data analysis and Digital Twin updates to refine processes and standardize successful practices in BIM-based protocols. KPI impacts include improved process optimization through predictive analytics and IoT monitoring, enhanced productivity through reduced downtime, and cost savings from fewer equipment failures. Stakeholder collaboration is strengthened by BIM-integrated feedback loops, while waste reduction is achieved by streamlining maintenance and improving energy efficiency.  
The diagram presents the application of the PDCA (Plan-Do-Check-Act) cycle for asset monitoring, which can also support emergency management by integrating BIM,IoT, and Blockchain technologies to improve decision making, operational efficiency, and data security [86]. The process starts with Plan, where BIM is used to map assets, define specifications, and deploy IoT sensors for monitoring conditions such as temperature and vibration. Blockchain is established to securely record asset data. In Do, IoT sensors stream real-time data to BIM for visual asset monitoring, with Blockchain ensuring secure, tamper-proof records. In Check, Big Data Analytics evaluate IoT data to detect trends and risks, while BIM benchmarks asset conditions and Blockchain verifies data integrity for accountability. Act involves applying predictive maintenance, updating BIM models with revised schedules, and logging actions on Blockchain to support continuous improvement. KPI outcomes include enhanced process optimization through automated monitoring and secured data management, improved productivity by reducing downtime, cost savings from preventive maintenance, and strengthened quality and safety through early issue detection. Stakeholder satisfaction benefits from improved transparency and reliable data secured by Blockchain and BIM-integrated dashboards.
## 6 Conclusions  
In this paper, we proposed a new location-based forwarding strategy that can be a very good candidate to enable the efficient and effective future use of the NDN architecture in LEO satellite networks. The proposed forwarding strategy takes advantage of the knowledge of the relative positions of the satellite nodes, together with the grid structure formed by the inter-satellite links, to perform the forwarding of the NDN interest packets without the need of interchanging information between nodes, as is the case with conventional adaptive routing protocols, thus avoiding the scalability problems related to them. In addition, our forwarding strategy performs load balancing from the producer between the two paths around the diagonal toward each consumer, and then uses a single path toward it. The reason to use this alternative is that load balancing is much more necessary close to the producers, while using a single path close to the consumers could benefit from the eventual use of caches at the NDN nodes.  
The simulation experiments we have conducted show that the proposed forwarding strategy works properly, and it is able to achieve an average link usage at the producer of 95%. Moreover, our experiments show that an NDN-based constellation with our proposal makes even better use of network capacity when consumers request the same content from the producer. The usage improves even more if caches are deployed in the NDN nodes.  
Conceptualization, S.H.-A. and M.R.-P.; methodology, R.F.R.-R.; software, P.I.-S.; validation, S.H.-A. and A.S.-G.; investigation, M.R.-P. and P.I.-S.; writing--original draft preparation, J.C.L.-A.; writing--review and editing, J.C.L.-A., R.F.R.-R. and A.S.-G.; supervision, R.F.R.-R. and A.S.-G.; funding acquisition, M.R.-P. and S.H.-A. All authors have read and agreed to the published version of the manuscript.  
This work has received financial support from grant PID2020-113240 RB-I00, financed by MCIN/AEI/10.13039/501100011033, and by the Xunta de Galicia (Centro singular de investigacion de Galicia accreditation 2019-2022) and the European Union (European Regional Development Fund--ERDF).  
**Data Availability Statement:** Not applicable, the study does not report any data.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 7: Total amount of data exchanged (normalized) through the network when a set of consumers requests the same content from a producer at their maximum attainable rate for different cache sizes.
## 5 Experimental Evaluation  
This section presents the evaluation of the Edge-FLGuard+ framework in terms of detection performance, communication efficiency, and scalability under federated learning (FL) conditions. We compare it with centralized and local baselines and conduct an ablation study to assess the influence of model size, compression, and training configuration.
# Comparison Study on Coarseness Index and Maximum Diameter of Rock Fragments by Linear Cutting Tests
## 5 Comparison of the Correlation with Cutting Results  
### Correlation with Cutting Force  
The cutting force is an important factor in determining the \(SE\). Similar to the \(CI\) and particle size parameters, this variable increases as the penetration depth and spacing increase [2]. In this context, the relationship between \(CI\) and mean cutting force (\(F_{c}\)) and the relationship between \(d_{\rm max}\) and mean cutting force were comparatively analyzed. The mean cutting forces for each cutting condition are listed in Table 6.  
Figures 11 and 12 show the relationship between the mean cutting force and \(CI\) and between the mean cutting force and \(d_{\rm max}\), respectively. Both \(CI\) and \(d_{\rm max}\) increased with an increase in the mean cutting force. \(CI\) formed an exponential function relationship with the mean cutting force, whereas \(d_{\rm max}\) formed a linear relationship. In addition, the correlation with \(d_{\rm max}\) (R\({}^{2}\) > 0.79) was larger than that with \(CI\) (R\({}^{2}\) < 0.79).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(p\) (mm)} & \multirow{2}{*}{\(s\) (mm)} & \multirow{2}{*}{\(s\)/\(p\)} & \multicolumn{6}{c}{**UCS (MPa)**} \\ \cline{3-10}  & & & & \multicolumn{2}{c}{**20**} & \multicolumn{2}{c}{**30**} & \multicolumn{2}{c}{**40**} \\ \cline{3-10}  & & & \(F_{c}\) & \(V_{c}\) & \(SE\) & \(F_{c}\) & \(V_{c}\) & \(SE\) & \(F_{c}\) & \(V_{c}\) & \(SE\) \\  & & & **(kN)** & **(mm\({}^{3}\))** & **(MJ/m\({}^{3}\))** & **(kN)** & **(mm\({}^{3}\))** & **(MJ/Figure 11: Relationship between _CI_ and mean cutting force: (**a**) rock of UCS 20 MPa; (**b**) rock of UCS 30 MPa; (**c**) rock of UCS 40 MPa.  
This difference occurred because the penetration depth and spacing limit the size of rock chips [9]. An increase in the cutting force up to a certain level increases the size of \(d_{\text{max}}\), but when the size of \(d_{\text{max}}\) reaches its limiting value, the increase in the cutting force increases the amount of rock chips of the \(d_{\text{max}}\) size.
###### Abstract  
Fault diagnosis models based on machine learning are often subjected to degradation in performance when dealing with data that are differently distributed than the training data. Such an occasion is common in reality because machines usually operate under various conditions. Transfer learning is a solution for the performance degradation of cross-condition fault diagnosis problems. This paper studies how transfer learning algorithms transfer component analysis (TCA) and joint distribution alignment (JDA) improve the cross-condition fault diagnosis accuracy of an aircraft environmental control system (ECS). Both methods work by transforming the source and target domain data into a feature space where their distributions are aligned to allow a uniform classifier to act accurately in both domains. This paper discovered that both TCA and JDA produce significantly more accurate results than traditional methods on target domains with unlabelled ECS data taken at different operating conditions than the source domain. Additionally, when dealing with unlabelled data from unknown conditions bearing a different composition of classes in the target domain, TCA is found to be more robust and accurate, generating an average predictive accuracy of 95.22%, which demonstrates the ability of transfer learning in solving similar problems in the real-world application of fault diagnosis.  
environmental control system; fault diagnosis; machine learning; maintenance; transfer learning 2023  
[PERSON]; [PERSON]; Jennions, I.K. Cross-Condition Fault Diagnosis of an Aircraft Environmental Control System (ECS) by Transfer Learning _Applied Sci._**2023**, _13_, 13120. [[https://doi.org/10.3390/app132413120](https://doi.org/10.3390/app132413120)]([https://doi.org/10.3390/app132413120](https://doi.org/10.3390/app132413120))
## 2 Experimental Methodology
In other words, the principle of generating a flow of groundwater in the underground aquifer was applied to generate a difference in the operation level of the two SCW geothermal heat exchangers. In this study, the experiment was conducted using the cross-operating condition supply and return of 80% and 120%, respectively, as depicted in Figure 2a. If the circulation water supplied simultaneously from both the ground heat exchangers is returned after the heat exchange, 120% of it is recovered to the ground heat exchanger (#1 zone), and 80% of the remaining circulation is recovered to the other ground heat exchanger (#2 zone) and the cross-operating mains, which are set to activate the aquifer by forming a difference in the groundwater level. The system energy efficiency is calculated as the ratio of the resultant value of the production heat obtained by applying the liquid enthalpy test method of the heat source and load sides to the coefficient of performance (COP) of the system to compare it with the existing SCW geothermal system. In the liquid enthalpy method, the temperature, flow rate, and electricity consumption of circulating water are recorded through the data logger at the entrance and exit of the heat exchanger on the heat source and the load side of the heat pump. The produced heat of the geothermal system is calculated using the following equations. Where, total power consumption means the total consumption power used in the geothermal system. The COP of the actual geothermal system was calculated using Equations (5) and (6), and these results were configured to be calculated by the system [21; 22]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Actual Flow (LPM)** & **Calibration Flow (LPM)** & **Error (\%)** \\ \hline
3.316 & 3.292 & 0.73 \\
15.87 & 16.032 & 1.01 \\
100.90 & 102.99 & 2.03 \\
350.62 & 355.41 & 1.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results from the flowmeter calibration. Cooling mode:  
\[\varnothing_{\text{tco}}=w_{f}c_{pf}\big{(}t_{f4}-t_{f3}\big{)}-\varnothing_{t} \tag{5}\]  
Heating mode:  
\[\varnothing_{\text{tho}}=w_{f}c_{pf}\big{(}t_{f3}-t_{f4}\big{)}+\varnothing_{t} \tag{6}\]  
where,  
\(\varnothing_{\text{tco}}\): Total cooling capacity of the heat pump (W)  
\(\varnothing_{\text{tho}}\): Total heating capacity of the heat pump (W)  
\(\text{t}_{\text{f3}}\): Heat pump inlet temperature (\({}^{\circ}\)C) of circulating water on the source side  
\(\text{t}_{\text{f4}}\): Heat pump outlet temperature (\({}^{\circ}\)C) of circulating water on the source side  
\(\text{c}_{\text{pf}}\): Specific heat of heat source circulation water (J/(kg\(\cdot\)K))  
\(\text{w}_{\text{f}}\): Mass flow rate of circulating water on the heat source side (kg/s)  
\(\varnothing_{\text{t}}\): Total power Consumption (W)