esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this paper, a semi-Markov model for determining the optimal time for preventive replacements according to the age of technical objects is presented. In the analyzed system of transportation, due to its specific characteristics, the basic type of renewal process carried out is minimal repair. Minimal repairs of technical objects in semi-Markov models have been analyzed in the literature to date. In the system studied, the technical objects (sets of agricultural tractors with trailers), due to the continuous operation of combine harvesters, should carry out the assigned tasks of transporting agricultural crops without interruption. The damage to agricultural tractors that arises during the implementation of transport tasks should be repaired in the shortest possible time. The repairs to damaged tractors are carried out primarily by the Technical Emergency Service and, due to their purpose and scope, may be considered minimal repairs. The effectiveness of the function of the tested technical objects is analyzed by two criteria functions, which are very important for the system managers. These are profit per unit of time and availability. In the analyzed case, it is the availability to carry out the assigned transport tasks. The conditions for the existence of a maximum of criterion functions have been written for the assumptions. The analyzes carried out, which are presented in the work, are illustrated with sample calculations. It has been proven that, under general assumptions, the criterion functions considered in the paper have exactly one maximum. On the basis of the conducted analysis, sufficient conditions for the existence of a maximum of these functions were formulated. In the analyzed transport system, it is possible to increase the efficiency of the function of the technical facilities in use as a result of planning additional preventive replacements (increasing the frequency of these replacements). This is especially important for a system where transport units must have high availability.  
minimal repair; perfect repair; age-replacement; preventive maintenance; corrective maintenance; availability; semi-Markov processes +
Footnote †: journal: Applied Sciences  
1 Faculty of Mechanical Engineering, Bydgoszcz University of Sciences and Technology,  
85-795 Bydgoszcz, Poland 1
# Practical Application of Mesh Opportunistic Networks
## 5 Discussion
### Mesh OppNets in One-to-One Communication  
The biggest benefit of mesh OppNets is the fact that it does not use commercial data networks and there is no need for telephone coverage in order to communicate to anyone. This type of collaborative mesh communication without a mobile data plan, Wi-Fi or satellite can revolutionize one-to-one communication in local areas. However, the fact that the mobile app was unable to share photos and videos in electronic one-to-one communication flow is one of the most important limitations in the actual context, with dozens of options to do so. Note that some of the weaknesses raised here about the communication flow are not related to the design of an app, but to the frequency band used by the system. Improvements in this technology may be able to solve this issue, however it does not seem to be an option now.  
Figure 6: Map of the flow (connections, relevance and nuclearity) and the approximate situation of the participants at 8:15 p.m.
# Advances in Bio-Hydrogen Production: A Critical Review of Pyrolysis Gas Reforming
## Conclusions
Similarly, increasing the steam-to-biomass ratio leads to increasing hydrogen yield in the two-stage reforming of pyrolysis volatiles. Space velocity can also affect hydrogen yield. This literature review shows that lower space velocity and longer residence time have beneficial effects on tar reforming and gas production. The analysis of these studies, mostly performed at the lab scale, shows that feedback type and composition strongly affect hydrogen production in the two-stage process of reforming pyrolysis volatiles. Also, catalyst deactivation is another important factor influencing hydrogen production, and implementing methods to reduce or avoid this effect can significantly increase hydrogen output. This issue is one of the most important technical challenges that has to be resolved for scaling this approach to the commercial scale. The regeneration strategies of catalysts are another important factor that can be taken into account when developing industrial-scale plants. Also, it is important to note that mixing plastic waste with biomass feedstock allows for a significant increase in hydrogen production; this is another interesting approach that has attracted attention in the past few years and it should be carefully evaluated in detail in future studies. It can be concluded that reforming pyrolysis volatiles with a two-stage approach can significantly enhance the potential for hydrogen production from the pyrolysis process. Clearly, optimizing operating conditions is key to producing high hydrogen yield. Increasing pyrolysis and reforming temperatures increases hydrogen yield, although energy requirements may be carefully considered in the overall balance. The possibility of using commercial, low-cost, metal-based catalysts and biochar-based materials offers a chance to find appropriate solutions and ensure that the process is economically feasible. Overall, the availability of a wide range of choices for all effective factors provides an opportunity for the optimization of the techno-economic design of reforming pyrolysis volatiles, allowing the production of hydrogen from biowaste feedstock. K.Z.K. : conceptualization, investigation, writing--original draft preparation; M.P. : methodology, writing--review and editing; D.C.: validation, supervision, project administration, funding acquisition. All authors have read and agreed to the published version of the manuscript. This research is part of the H2 Steel project, funded by the European Union under the grant agreement 101070741. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript:  
GHG Greenhouse gas  
PEM Polymer electrolyte membrane  
SOE Solid oxide electrolyzer  
TRL Technology readiness level  
HTL Hydrothermal liquefaction  
S/B Steam to biomass  
RDF Refuse-derived fuel
# Gaussian Process Surrogates for Modeling Uncertainties in a Use Case of Forging Superalloys
## 2 GP based Surrogate Model  
In this section, we first introduce briefly the general idea behind our surrogate modeling approach. We familiarize in Section 2.1 the reader with the general functionality of GP regression to provide an appropriate foundation for the content that follows. In Sections 2.2 and 2.3 we provide more detailed descriptions of each individual GP of our surrogate model. After describing our surrogate model, we move on to uncertainty propagation analysis with FEM simulation in Section 2.4, where we present the procedure for obtaining aleatoric uncertainties.  
GP regression is already well researched for surrogate modeling, replacing expensive target labellers (e.g., numerical simulations, expensive manually labelling, conducting real physical experiments, etc.). One reason is their ability to work with low-dimensional data. Another big advantage of using GP regression is that predictions are made in a probabilistic way, i.e., a prediction is represented by a posterior distribution. Thus, a prediction of GP regression is described by a mean and a covariance. The covariance of a prediction can be used as a metric of prediction confidence, i.e., epistemic uncertainty. We specify that outputs of GP regression describe a distribution with mean \(m\) and epistemic uncertainty \(\sigma\).  
The proposed surrogate model consists of two individual GPs and takes manufacturing process-specific parameters \(x_{m}\), part-specific parameters \(x_{p}\) and aleatoric process uncertainty \(\bar{\Sigma}_{al}(Z)\) as input and predicts the mean manufacturing result \(\mu\) and aleatoric uncertainty \(\sigma_{al}\) of the manufacturing result, see Figures 0(b), 2 and 3. A similar simulation approach using FEM is shown in Figure 0(a). We define \(Z\) as a parameter that describes the manufacturing process characteristics, e.g., velocity profile of a forming tool. Our model assumes that \(\bar{\Sigma}_{al}(Z)\) can be efficiently obtained for every \(x_{m}\). This assumption is justified in our running example, where we focus on the first of two directly successive forging strokes. That means that measurements of the manufacturing process are available (i.e., velocity profile of the forging tool), but measurements in respect to the forged part are not possible due to the short time span between the first and second stroke.  
Figure 1: Simulation of Manufacturing Processes with Uncertainties: (**a**) FEM simulation scenario and (**b**) GP based surrogate model with manufacturing process-specific parameters \(x_{m}\), part-specific parameters \(x_{p}\) and distribution \(Z\) that describes a manufacturing process-specific characteristic by  
mean \(m(Z)\) and aleatoric manufacturing process uncertainty \(\Sigma_{al}(Z)\) where \(\bar{\Sigma}_{al}(Z)\) is an aggregated form of \(\Sigma_{al}(Z)\). Outputs are the mean of the manufacturing process result \(m(\mu)\) and mean of the aleatoric uncertainty \(m(\sigma_{al})\), each with corresponding epistemic uncertainties \(\sigma(\mu)\) and \(\sigma(\sigma_{al})\) in the GP based surrogate model.
###### Abstract  
Radiative transfer models (RTMs) provide reliable information about crop yield and traits with high resource efficiency. In this study, we have conducted a systematic literature review (SLR) to fill the gaps in the overall insight of RTM-based crop yield prediction (CYP) and crop traits retrieval. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 76 articles were found to be relevant to crop traits retrieval and 15 for CYP. China had the highest number of RTM applications (33), followed by the USA (13). Crop-wise, cereals, and traits-wise, leaf area index (LAI) and chlorophyll, had a high number of research studies. Among RTMs, the PROSAIL model had the highest number of articles (62), followed by SCOPE (6) with PROSAIL accuracy for CYP (median R\({}^{2}\) = 0.62) and crop traits (median R\({}^{2}\) = 0.80). The same was true for crop traits retrieval with LAI (CYP median R\({}^{2}\) = 0.62 and traits median R\({}^{2}\) = 0.85), followed by chlorophyll (crop traits median R\({}^{2}\) = 0.70). Document co-citation analysis also found the relevancy of selected articles within the theme of this SLR. This SLR not only focuses on information about the accuracy and reliability of RTMs but also provides comprehensive insight towards understanding RTM applications for crop yield and traits, further exploring possibilities of new endeavors in agriculture, particularly crop yield modeling.  
leaf area index; PROSAIL; crop growth model; integration approach; meta-analysis 2022
# Mixer U-Net: An Improved Automatic Road Extraction from UAV Imagery
## 4 Experiments, Results and Discussion
### Evaluation Metrics  
Some of the common evaluation metrics for semantic segmentation are _pixel accuracy (PA)_, _Precision_, _Recall_, _F1 score_, and _Jaccard index_. However, as mentioned above, the Massachusetts roads dataset suffers from a severe class imbalance problem, i.e., the number of pixels representing the background exceeds the number of pixels denoting road areas excessively. Thus, using a pixel accuracy metric for the purpose of evaluation would be inappropriate. The following equations are the mathematical expressions of the above-mentioned metrics, except _PA_:  
\[Precision=\frac{TP}{TP+FP} \tag{13}\]  
\[Recall=\frac{TP}{TP+FN} \tag{14}\]  
\[F1\ score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{15}\]  
\[Jaccard(A,B)=\frac{|A\cap B|}{|A|\cup|B|}=\frac{TP}{TP+FP+FN} \tag{16}\]  
where _TP_, _FP_, and _FN_ stand for the number of true positive, false positive, and false negative predictions, respectively. Precision is the ratio of true positives to the prediction, while recall is the ratio of true positives to the ground truth. The _F1 score_ is the harmonic mean of _precision_ and _recall_. The Jaccard index, also known as mean intersection over union (mean IoU), is the ratio of the intersection and union for the two sets \(A\) and \(B\). All metrics are calculated on each class separately, and their average is reported as the final score.
# Novel Motion Sickness Minimization Control via Fuzzy-PID Controller for Autonomous Vehicle
###### Abstract  
In terms of vehicle dynamics, motion sickness (MS) occurs because of the large lateral acceleration produced by inappropriate wheel turning. In terms of passenger behavior, subjects experience MS because they normally tilt their heads towards the direction of lateral acceleration. Relating these viewpoints, the increment of MS originates from the large lateral acceleration produced by the inappropriate wheel's turn, which then causes greater head movement with respect to the lateral acceleration direction. Therefore, this study proposes the utilization of fuzzy-proportional integral derivative (PID) controller for an MS minimization control structure, where the interaction of the lateral acceleration and head tilt concept is adopted to diminish the lateral acceleration. Here, the head movement is used as the controlled variable to compute the corrective wheel angle. The estimation of the head movement is carried out by an estimation model developed by the radial basis function neural network (RBFNN) method. An experiment involving a driving simulator was conducted, to verify the proposed control system's performance in regard to the autonomous vehicle's passengers. The results show that the averages of motion sickness incidence (MSI) index can be lowered by 3.95% for single lap and 11.49% for ten laps.  
autonomous vehicle; fuzzy-PID; head roll angle; lateral acceleration; motion sickness minimization; radial basis function neural network
## 3 O-RAN with 200 m FSO Link
### The 200 m FSO Link Backhaul for O-RAN with 10 km Fronthaul for Outdoor Network
This integration has the capacity to enhance network performance reliability and optimize resource allocation, thereby improving the cost-effectiveness of upcoming network applications. Notably, the 10 km fiber-optic fronthaul incorporated in this framework could be substituted with FSOs, given the open architecture of O-RAN. Consequently, this substitution would significantly benefit the future provisioning requirements of networks, allowing for a more flexible layout configuration and achieving an elevated level of resource allocation.
## 5 Multi-Giga-Hertz Agile Radio Front-End Design
### Potential Front-End Design Options  
Scheme 1  
Frequency sweeping: like a spectrum analyzer, too slow.  
Scheme 2  
With multiple narrowbands and working in a hybrid parallel/serial fashion: too many RF analog branches including a filter bank, and band switching takes too much time.  
Scheme 3  
With a few wide bands and working in a hybrid parallel/serial fashion: less RF analog branches and less band switching time; taking advantage of the power of digital signal process to achieve flexibility and agility.  
The scheme 3 seems the most attractive and it is considered in the following.
###### Abstract  
In this paper, we design constant modulus waveforms for dual-function radar-communication (DFRC) systems based on a multi-input multi-output (MIMO) configuration of sensors for a far-field scenario. At first, we formulate a non-convex optimization problem subject to waveform synthesis for minimizing the interference power while maintaining a constant modulus constraint. Next, we solve this non-convex problem, iteratively, using the alternating direction method of multipliers (ADMM) algorithm. Importantly, the designed waveforms approximate a desired beampattern in terms of a high-gain radar beam and a slightly high gain communication beam while maintaining a desired low sidelobe level. The designed waveforms ensure an improved detection probability and an improved bit error rate (BER) for radar and communications parts, respectively. Finally, we demonstrate the effectiveness of the proposed method through simulation results.  
dual-function radar communication; MIMO communication; waveform design; ADMM
# Dynamic Dark Channel Prior Dehazing with Polarization
## 3 Experimental Setup
### Polarization Imaging System
#### 3.1.3 Circuit Module  
This paper uses an FPGA chip as the circuit module of the polarization imaging system because FPGAs can process logic signals in parallel. The FPGA used in this paper is Xilinx Spartan-6 series FPGA chip, which is modeled as shown in Figure 4a. The goal of this paper is to independently acquire the specific polarization state (\(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\), \(135^{\circ}\)) information of the same transient scene in different imaging channels. Therefore, by means of the FPGA, the data in the four polarization channels can be collected in real time, which enables the polarization imaging system to output the linear polarization elements of the dynamic Stokes vector.  
Design a synchronous trigger circuit based on the Xilinx Spartan-6 series FPGA chip, the corresponding Synchronization control unit is shown in Figure 4b. The different voltages required for the normal operation of the FPGA chip are obtained by converting the 5 V voltage. A linear regulated power supply is selected as the board-level power supply module, which has low ripple and can ensure the stability and reliability of the synchronous control circuit. The LM1117S series LDO buck chip is used to reduce the 5 V to 3.3 V and 1.2 V, and with decoupling capacitors, it can achieve stable and high-quality power output. Based on the FPGA, a synchronous pulse signal generator is designed. In the synchronous control circuit, a 50 MHz crystal oscillator is used to provide a clock signal with a clock cycle of 20 ns. The program download adopts the JTAG communication protocol and uses a Flash chip with a capacity of 16 Mbit to store the program file, which is M25 SP16. When the single-board circuit is connected to the power supply, the FPGA chip reads the program from Flash and runs by itself. A counter is defined inside the FPGA, which counts according to the clock frequency. By adjusting the parameters, signals with different pulse widths and different pulse periods can be realized. Since the FPGA is a parallel processing logic device, four pulse signals can be output simultaneously.
## 4 Simulation Analysis
### Simulation Results
#### 4.2.2 Data Transmission and Threshold Value Evaluation  
Next we analyze: (i) total number of transmissions; (ii) the number of data transmissions received by the base station; (iii) number of data values sensed but unsent as they are beneath the threshold value; (iv) number of data values between the base and critical threshold; and (v) number of data values that are sent immediately as they are above the critical value for the three routing protocols under consideration. For this experiment, we again simulated 100 different 200 m \(\times\) 200 m network topologies. From Table 3, we can see that the total number of data transmissions received by the sink is much less in our protocols. Because a separate cluster is formed in S-EERP for every floor, the number of data transmissions in S-EERP is more than R-EERP.  
As in Table 3, the total number of data transmissions received by the base station in our protocols is less than the LEACH protocol. Figure 4 shows that use of a threshold value is both important in terms of urgent measures and energy efficiency. The critical threshold value and above are sent to the base station without waiting them in cluster heads affects the energy efficiency in a negative way. However, network life time is significantly prolonged by ignoring the values beneath the base thresholds values and preventing duplicated values.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameters** & **LEACH** & **R-EERP** & **S-EERP** \\ \hline Total number of data transmission in the network & 232.426 & 330.002 & 349.237 \\ Total number of data transmission received by the sink & 114.333 & 5.612 & 10.551 \\ Number of data values sensed but unsent (\textless{}Base threshold) & - & 11.310 & 11.759 \\ Number of data values kept waiting at the cluster heads as they & & & \\ are between the base and critical thresholds & - & 127.821 & 131.096 \\ Number of data values sent immediately (\textgreater{}critical threshold) & - & 21.295 & 22.078 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Total number of data transmission at the whole network and received at the sink.  
Figure 3: Network lifetime comparison between LEACH and our protocols.
## 3 The Proposed Method
(**a**) Impact signal; (**b**) the impact signal with noise; (**c**) average coding length of Huffman coding (ACLHC); (**d**) protrugram; (**e**) sparsogram; (**f**) genetic algorithm sparsogram (GA-sparsogram). As shown in Figure 4b, due to the noise interference, the impact signal was completely submerged in the noise in the time-domain waveform. Besides this, the obvious characteristic frequency of the impact signal also could not be found in the frequency spectrum. The additional frequency component of 40 Hz was introduced by the noise. Although the obvious impact component cannot be found in the time-domain waveform by ACLHC in Figure 4c, the obvious characteristic frequency of the impact signal can already be found in the frequency spectrum. However, the characteristic frequency of the impact signal could not be found by protrugram, sparsogram, and GA-sparsogram, as shown in Figure 4d-f. Besides this, the high-frequency part of the frequency spectrum was also lost, while the frequency spectrum of ACLHC contained the whole frequency components. The information after 400 Hz of the frequency band processed by protrugram and GA-sparsogram was lost, as shown in Figure 4d,f. After 600 Hz, the frequency band processed by Sparsogram was lost, as shown in Figure 4e. Figure 4: The waveforms and frequency spectrums at the SNR of 3 dB. (**a**) Impact signal; (**b**) the impact signal with noise; (**c**) average coding length of Huffman coding (ACLHC); (**d**) protrugram; (**e**) sparsogram; (**f**) genetic algorithm sparsogram (GA-sparsogram). Therefore, the ACLHC not only highlights the impact components of the signal, but also retains the whole frequency components of signal. Altogether, compared with protrugram, sparsogram, and GA-sparsogram, the ACLHC can effectively restore the location of impact in the signal when the SNR is high. Even if the ACLHC cannot obviously restore the location of impact in the signal when the SNR is low, it can also effectively reflect the characteristic frequency of the impact in the frequency spectrum.
## 5 Experimental Results and Discussions
### Evaluation Measures  
#### 5.2.1 Network Lifetime analysis (\(N_{l}\))  
The ratio of the active node (\(A_{Node}\)) to the availed node (\(T_{Node}\)) is referred to as network lifetime.  
\[N_{l}=\frac{A_{Node}}{T_{Node}} \tag{28}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline WMSN node-1\(\rightarrow\)a1, WMSN node-2\(\rightarrow\)a2, \\ Remaining energy of a1\(\rightarrow\)z1, Remaining energy of a2\(\rightarrow\)z2 \\ Initialize \(\tau\) \\ confirm whether the Euclidean distance (EC) between a1 and a2 is EC \textgreater{} 2 M \\ If (EC \textgreater{} 2 M) \\  No overlap found in the FOV \\ End If \\ If (EC \textless{}= 2 M) \\  If the shape is a polygon, then \\  Redundancy is present \\ End If \\  If shape is either quadrilateral or triangle \\  Compute the area of either the quadrilateral or triangle \\  End If \\  If (Area \textgreater{} \tau) \\ \hline \multicolumn{2}{c}{Redundancy present} \\ Else & \\ \multicolumn{2}{c}{Redundancy absent} \\ End If \\ End If \\ If (z1 \textgreater{} z2) \\  Then a1 is responsible for the items FoV and the CH transmits the data \\ Else \\  Then a2 is responsible for the items FoV and the CH transmits the data \\ End If \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters employed for simulation.