esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Effects of Process Parameters on the Performance of the Printing Layer  
### Laser Power  
In order to optimize the process parameters, it is necessary to explore the influence of each parameter on surface quality. The level values of laser power P\({}_{\rm I}\), sweep speed v\({}_{\rm s}\), powder feeding speed v\({}_{\rm r}\), and ultrasonic power percentage P\({}_{\rm s}\) were determined through single-factor experiments. The single-factor experimental design and result analysis under different laser powers are shown in Table 3.  
From Figure 3a, it can be seen that as the laser power gradually increases, the melt width B and the melt height H both increase, which indicates that the higher the laser power, the deeper the matrix melts. This indicates that as the laser power increases, the heat-affected zone gradually expands. In Figure 3b, it can be seen that the dilution ratio is around 5%, and the shape coefficient \(\gamma\) is also greater than 2, which indicates that the combination of the printing layer is relatively good.  
When the laser power is higher, the sintering temperature has already exceeded the stable limit of CBN, which will inevitably lead to a decrease in the quality of the formed part. After observation and analysis, it was found that the cladding layer had a relatively regular shape in the experiments with a laser power of 200 W and 235 W, and the shape coefficient of the 235 W parameter was higher. Therefore, it can be determined that the most appropriate laser parameter level is around 235 W. The final determined laser power levels are 220 W, 235 W, and 250 W.  
Figure 3: Influence of laser power on printing layer: (**a**) The melt width, melt height and melt depth; (**b**) the shape factor and the dilution rate.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{1}{c}{P\({}_{\rm I}\)} & \multicolumn{1}{c}{v\({}_{\rm s}\)} & \multicolumn{1}{c}{v\({}_{\rm r}\)} & \multicolumn{1}{c}{P\({}_{\rm s}\)} & \multicolumn{1}{c}{B} & \multicolumn{1}{c}{H} & \multicolumn{1}{c}{h} \\  & **(W)** & **(mm/s)** & **(r/min)** & **(\%)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** \\ \hline
1 & 200 & 6 & 1.8 & 10 & 0.749 & 0.263 & 0.016 & 2.85 & 5.6 \\
2 & 235 & 6 & 1.8 & 10 & 0.873 & 0.303 & 0.018 & 2.88 & 5.6 \\
3 & 280 & 6 & 1.8 & 10 & 0.960 & 0.348 & 0.021 & 2.76 & 5.7 \\
4 & 315 & 6 & 1.8 & 10 & 1.052 & 0.364 & 0.022 & 2.89 & 5.7 \\
5 & 350 & 6 & 1.8 & 10 & 1.278 & 0.415 & 0.026 & 3.08 & 5.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Laser power single-factor experiment design and results.
## 2 Unit Cell Design Configurations  
The design layout of the anticipated band-reject FSS unit cell is shown in Figure 1. The unit cell consists of two independent resonant structures, CMSL-FSS and MJCL-FSS, imprinted on a single-layer dielectric. The FSS is designed on a low-loss RT/droid 5880 substrate having a thickness of t = 0.787 mm, \(\epsilon_{r}\) = 2.2, and tan8 = 0.0009. Thus, the unit cell has the overall dimensions of \(7\times 7\) mm\({}^{2}\).
# TrendFlow: A Machine Learning Framework for Research Trend Analysis
## 2 Related Work
### Topic Modeling  
Topic modeling is a technique used in natural language processing and information retrieval to discover the underlying themes or topics within a collection of documents. Topic modeling algorithms such as latent dirichlet allocation (LDA) [26] and non-negative matrix factorization (NMF) [27] can automatically identify and group words that frequently co-occur, thus revealing the hidden semantic structure of the text corpus.  
**LDA:** LDA, first introduced by [PERSON], [PERSON], and [PERSON] (2003) [26], is a generative probabilistic model that assumes each document is a mixture of a predefined number of topics, and each topic is a distribution over words. The model learns these topic distributions and document-topic proportions using a Bayesian framework, incorporating Dirichlet priors to capture uncertainty and smoothness. LDA has been widely used and acclaimed for its ability to discover coherent and interpretable topics in large text corpora [28].  
**NMF:** NMF, initially proposed by [PERSON] and [PERSON] (1999) [27], is a linear algebra-based technique that decomposes a non-negative data matrix into two lower-dimensional nonnegative matrices. One matrix captures the underlying topics, while the other represents the weights of the topics for each document. NMF is widely applied in domains where data sparsity and non-negativity play a crucial role, such as text mining and image processing [29]. By imposing non-negativity constraints, NMF results in a parts-based representation, which can lead to more interpretable topics [30].  
Traditional topic modeling techniques have certain drawbacks when compared to deep-learning-based methods. For example, they may have limited semantic understanding] as they rely on word co-occurrence statistics, making it challenging to capture more profound semantic relationships between words and phrases. Additionally, most traditional topic modeling techniques necessitate specifying the number of topics in advance, which may not be optimal or straightforward to determine [31].
## 3 Results
### Assessment the Effect of Hypoxia on Nitric Oxide Production  
To examine whether hypoxic conditions promote nitric oxide production, DAF-FM staining in the course of myogenic differentiation as an indicative parameter for nitric oxide synthase activity was carried out. The data observation demonstrated a gradual increase in NO staining intensity up to 3 days in the growth medium compared to the early time point after 3 h. Under hypoxia induction, there was a marked increase in NO staining at all time points compared to the cells cultivated under normoxia (Figure 7a-h). In the myogenic differentiation medium, NO staining was weaker on day 7 for both experimental conditions compared to day 3 (Figure 7i-p). To confirm this observation, quantification of the NO staining was performed. The data analysis revealed a significant increase in NO production in the proliferating cells under hypoxia (\(p<0.01\)) compared to cells cultured under normoxic conditions (Figure 7q). After differentiation induction, a significant decrease in NO production was detected at day 7 under hypoxia (\(p<0.05\)) compared to those cells cultivated under normoxia. The analysis revealed a tendency for variable interaction (\(p=0.07\)), indicating that hypoxia modulates the reduction in the NO production (Figure 7r).  
Figure 7: (**a–r**) Assessment of the effect of hypoxia on nitric oxide (NO) production indicative of nitric oxide synthase (NOS) activity in the C2C12 cells under normoxic and hypoxic conditions. (**a–h**) DAF-FM-stained images show NO-positive cells (green) after cultivation in the growth medium for 3 h, 24 h, and 72 h under hypoxic (HX) compared to normoxic (NX) conditions. (**i–p**) DAF-FM-stained images demonstrate NO-positive cells (green) after cultivation in myogenic differentiation medium for 3 h, 3 d and 7 days under HX compared to NX conditions. Cells processed in parallel without staining served as a negative control (NC). (**q**) Quantification of NO in proliferating cells (\(n=6\))under NX and HX conditions. (**r**) Quantification of NOS in differentiating cells (\(n=6\)) under NX and HX conditions. Data are presented as means \(\pm\) SEM. * = \(p\) < 0.05, ** = \(p\) < 0.01. Variable interaction (VI). Scale bar = 20 \(\upmu\)m.
# Electricity Losses in Focus: Detection and Reduction Strategies--State of the Art
## 3 Methods for Identifying Power Losses
### Technical Energy Losses
#### 3.1.2 Energy Losses Due to Conversion and Control Processes  
The technical energy losses that are generated by the control and conversion processes are quite high, as a significant amount of energy is lost through heat and corona effects (see Figure 1).  
In [22], a methodology for calculating technical losses was also implemented, wherein the authors placed particular emphasis on the determination of Joule losses, Corona losses, and insulation losses. To facilitate this computation, the researchers developed a special computational software based on adaptive computational models. Joule energy losses constitute a substantial proportion of unutilized energy; therefore, this study highlights their importance. The authors directed their attention toward the values of the external temperatures of the substations for different transmission lines with the objective of more accurately estimating this category of losses. The implementation of the software design has yielded substantial contributions through the obtained results, thereby facilitating its integration into the planning of dispatching services and the operation of power lines. The long-term benefits of this approach are instrumental in regulating power losses within the transmission system.  
In a subsequent study, [PERSON] and his co-authors underscored the issue of energy dissipation, emphasizing its primary causes, namely, energy transportation and distribution [23]. The paper introduces a new modified computational methodology for calculating technical losses. This methodology utilizes the concept of reduced equivalent networks, models the analyzed power grid in the Open Distribution System Simulator software, and later defines equivalent operational impedances. The study incorporated customer energy bills and power factor at the TS, along with appropriate profiling according to load curves, as input data. The implemented method demonstrated good accuracy in terms of calculation and identification of technical energy losses in a power network.
# Modern Technologies for Waste Management: A Review
## 2 Overview of Technologies
### Smart Waste Bins
Overall, the choice of communication technology for smart waste bins depends on various factors such as coverage, energy consumption, the cost of the technology, and the requirements of the application (Figure 1). GSM is a commonly used technology for smart waste bins, but other options such as MQTT or LoRaWAN may also be suitable.
# Generating Activity-Based Mobility Plans from Trip-Based Models and Mobility Surveys
## 3 Materials and Methods
### Approach
#### 3.3.1 Initialize the Population  
The car population contained static information about all simulated cars and thereby provided the foundation for generating mobility plans. The static information comprised everything that was assumed to not change throughout the simulation period for which mobility plans were generated [57]:  
Figure 4: Definition of an activity-based mobility plan and outcomes of the methodology’s steps. Red and green elements belong to general and geospatial-specific mobility, respectively.  
* A car's home location;
* The driver's static activities, such as work and education. When performed, they were always assumed to be at the same place and at the same time of day;
* The driver's work status.  
The simulated car population was created by randomly sampling the desired number of cars from the synthetic car population known from Section 3.2 and assigning information on the static activities of the cars' households' inhabitants.  
The data structure for the activity-based mobility plans was initiated by creating a table for each simulated car, comprising columns for activity type, time, duration, and location. Each car of the population started with a dummy activity to initialize the mobility plan. This fictitious \"0 th\" activity set the start time and location of the mobility plan. The location of the 0 th activity was set to the home location of the car. A second table was initiated to store the static information associated with the car.  
Whether a car was used to get to work was based on whether the person drawn from the household had work information provided. This decision was thereby carried over from the trip-based model MITO, which is based on a realistic synthetic population. Hyperparameters \(p_{\text{no\_worker}}\) and \(p_{\text{worker}}\) were used to calibrate the share of the working population in case mobility plans were generated for samples small enough to skew the initially calibrated proportion of workers in the input population. If adjustments were not needed, then both were set to \(p=0\). \(p_{\text{no\_worker}}\) is the probability of replacing a working person with a non-working person, and vice-versa for \(p_{\text{worker}}\).
# New Stable Non-Vector Control Structure for Induction Motor Drive
## 9 Conclusions  
The designed new stable control method can be applied generally for any continuous nonlinear system. In this article, a controlled AC drive with an induction motor was chosen for verification of its properties, where the IM represents a highly oscillating nonlinear system and its parameters (e.g., the rotor resistance and moment of inertia) can vary during the motor operation.  
Both simulation results and experimental measurements performed for basic operating states of the IM drive have confirmed advantages of the proposed controller concerning simplicity at the design and implementation and the excellent performance of the controlled system. Application of the new control method considerably contributes to improving dynamic properties and simultaneously ensures the drive stability, invariance against disturbances and robustness against the motor parameters variations. Compared to the control structures of the vector control of IM, the new control structure is significantly simpler; it exhibits features of an optimal linear system and simultaneously it achieves almost identical dynamic control performance. When comparing with the classical vector control structure of the IM, the designed control structure is much more robust against changes of the important motor parameters.  
The main advantage of the novel control structure consists of the fact that it does not require any knowledge of controlled system parameters. On the other side, the precise control at the majority of the used control methods strongly depends on precise knowledge of system parameters. The control quality is also guaranteed by the independence of the Park transformation calculation from the exact knowledge of the IM parameters. The control structure ensures high quality of the IM speed control within the whole control range, up to zero speeds.  
The proposed control structure is suitable for the control of continuous nonlinear systems with unknown and time-varying parameters. The controller is suitable especially for any drive system including control of robotic systems control having a precise hierarchical control structure. Therefore, its broad utilization in industrial applications can be assumed.  
**Author Contributions:** Conceptualization, P.F. and D.P.; methodology, P.F. and D.P.; writing--original draft preparation, P.F.; writing--review and editing, D.P.; formal analysis, P.B.; software, P.B. and M.F.; validation, P.B. and M.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Slovak Research and Development Agency under the contract No. APVV-19-0210.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 7 Conclusions  
The unified F-O and S-O performance analysis of an FSO RIS-ACs over F-S \(\mathcal{F}\) TI fading channels under different TI fading severity conditions was investigated. Namely, novel C-F expressions for \(p_{\gamma}(\gamma)\) and \(F_{\gamma}(\gamma)\) of the received SNR in terms of Beta and Gauss-hypergeometric functions were successfully derived. The \(F_{\gamma}(\gamma)\) was obtained as a finite series expression and, as such, was only valid for integer values of \(a_{1}\). Capitalizing on the obtained analytical expressions for \(p_{\gamma}(\gamma)\) and \(F_{\gamma}(\gamma)\), the novel C-F expressions of the end-to-end SNR for \(\mathcal{M}_{MGF}\), \(P_{\gamma}(\gamma_{th})\), \(P_{BER}(\gamma)\) and \(C_{\gamma}\) in terms of the Meijer G function were derived and numerically evaluated for different F-S \(\mathcal{F}\) TI fading system model parameters. Moreover, the paper provided the mathematical framework for the derivation of S-O statistical measures such as \(N_{\gamma}(\gamma_{th})\) and \(A_{\gamma}(\gamma_{th})\) of an FSO RIS-ACs system over F-S \(\mathcal{F}\) TI fading channels under different TI fading severity conditions. The obtained results pointed out that a significant performance improvement of SISO FSO RIS-ACs links could be achieved by shifting from more severe to less severe TI fading conditions. Moreover, the system performance improvement in terms of \(P_{\gamma}(\gamma_{th})\) could be further achieved by increasing the \(\tilde{\gamma}\) dB value. The provided analytical and numerical results could be useful for designing SISO FSO RIS-ACs systems over TI fading channels. Further works will consider the experimental validation of the obtained results.  
The authors equally contributed to the presented work. All authors have read and agreed to the published version of the manuscript.  
This research was funded by funding from UC3M and the European Union's Horizon 2020 programme under the [PERSON] grant agreement no. 801538 and by project IRENE-EARTH (PID2020-115323 RB-C33/AEI/10.13039/501100011033).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The authors confirm that the data supporting the results of this work are provided within the paper.  
Figure 7: Average fade duration versus \(\gamma_{th}\) observed for different TI fading severity conditions and different \(\gamma_{th}\).  
**Acknowledgments:** The authors would like to acknowledge the CONEX-Plus project. CONEX-Plus received research funding from UC3M and the European Union's Horizon 2020 programme under the Marie Sklodowska-Curie grant agreement no. 801538. The authors would also like to acknowledge the Spanish National Project IRENE-EARTH (PID2020-115323 RB-C33/AEI/10.13039/501100011033) and the Cost Actions CA19111 and CA16220.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
The 3D reconstruction of asphalt pavement macrotexture holds significant engineering value for pavement quality assessment and performance monitoring. However, conventional 3D reconstruction methods face challenges, such as high equipment costs and operational complexity, limiting their widespread application in engineering practice. Meanwhile, current deep learning-based monocular image reconstruction for pavement texture remains in its early stages. To address these technical limitations, this study systematically prepared four types of asphalt mixture specimens (AC, SMA, OGFC, and PA) with a total of 14 gradations. High-precision equipment was used to simultaneously capture 2D RGB images and 3D RGB-D point cloud data of the surface texture. An innovative multi-scale feature fusion CNN model was developed based on an encoder-decoder architecture, along with an optimized training strategy for model parameters. For performance evaluation, multiple metrics were employed, including root mean square error (RMSE = 0.491), relative error (REL = 0.102), and accuracy at different thresholds (\(\delta\) = 1/2/3: 0.931, 0.979, 0.990). The results demonstrate strong correlations between the reconstructed texture's mean texture depth (MTD) and friction coefficient (\(f_{8}\)) with actual measurements (0.913 and 0.953, respectively), outperforming existing methods. This confirms that the proposed CNN model achieves precise 3D reconstruction of asphalt pavement macrotexture, effectively supporting skid resistance evaluation. To validate engineering applicability, field tests were conducted on pavements with various gradations. The model exhibited excellent robustness under different conditions. Furthermore, based on extensive field data, this study established a quantitative relationship between MTD and friction coefficient, developing a more accurate pavement skid resistance evaluation system to support maintenance decision-making.  
Keywords:road pavement; convolutional neural network; monocular vision; 3D reconstruction
# A Police and Insurance Joint Management System Based on High Precision BDS/GPS Positioning
## 5 Actual Accident Cases
On the remote accident handling platform, the traffic police can obtain more evidence: such as the vehicle vibration location when the accident happened, velocity, collision angle and time, videos during a period of 10 s before and after the accident and so on, as shown in Figure 12.  
Figure 11: The situation of two ordinary GPS-based cars after an accident. Figure 12: Views from the driving record video. As shown in Figure 13, this is a car rear-end collision that occurred on the 2 nd Ring Rd in Wuhan. The collision point, braking point, the speedometer, and the simulated video, etc., of the accident are shown in the figure. The traffic police can know the whole process of the accident, determine the authenticity of the accident and judge the fault and liability accurately according to the relevant evidence available on the remote platform. 4 Realize rapid responsibility identification  
Finally, the responsibility can be identified and the authenticity of the accident can be judged according to the data analysis of the whole accident and communication analysis between the traffic police and the relevant drivers of an accident. A location-based service application is also applied during the whole process. With this result, the owners can supplement the relevant evidence by mobile App. It also ensures that the accident scene can be restored; evidence can be collected and law can be applied.
## Security Analysis  
### Resilience against Some Attacks  
In this section, we analyze the security of the two-hop reporting scheme, and discuss its resilience against some potential attacks.  
* Attacks against the key management system: if an adversary overhears exchanged messages during the key establishment phase, only random numbers, which are encrypted with a preloaded initial key, are exchanged among nodes. As the adversary is not preloaded with the initial key, it cannot compute the master keys. Because \(K_{IN}\) and \(RN_{i}\) are erased before the expiration of \(T_{min}\), the capture of one node does not reveal the master keys of other nodes. The adversary can only access the two-hop broadcast keys stored on the capture node.
* Attacks against the authentication mechanism: one-way hash chain is largely used in authentication. An adversary can only extract the hash chain of the captured node, and hence it cannot generate observations using the identity of other nodes.
* Attacks using Fabrication: as a node does not have the two-hop broadcast keys of its neighbor, it cannot alter the observations it has to relay to the monitoring node. However, an adversary can make a captured node generate false observations. To deal with this problem, we use the solutions that are presented in Sections 5.2.4 and 5.2.5.
* Beacon dropping attack: a malicious node might drop the beacon packets originated from honest nodes. As long as there is one path with only honest and well-behaved nodes, the fair rating will eventually reach the monitoring node.