esa-sceva/satcom-chunk-collection huggingface dataset:
# Future Scenarios for Software-Defined Metro and Access Networks and Software-Defined Photonics
## Reasons for Software Defined Metro and Access Networks
### Data Centers Interconnect and Edge Cloud  
Simultaneously with diffusion of broadband access services, the metro traffic is expected to grow in the coming years even at a faster and faster pace driven by IP video and cloud services. In other words, since client-server applications require ever more frequently access to distributed databases intercommunicating one to each other, the 'horizontal' (or east-west) traffic between servers is progressively increasing together with the traditional'vertical' (or north-south) traffic between client and server. According to a Bell Labs study, data center traffic will have increased more than 440% from 2012 by 2017 and total metro traffic will have increased 560% during the same time period, i.e., about two times faster than the backbone traffic [17], as shown in Figure 3a. Moreover, according to Cisco forecasts, metro traffic will represent the 66% of total IP traffic by 2019 [18], as shown in Figure 3b.  
It is also worth noting that, by 2017, 75% of total metro traffic will be terminated within the metro network [17]. This is also due to the increasingly significant role of content delivery networks (CDNs), i.e., distributed networks of proxy servers in data centers for caching content closer to the end user within the metro network. The diffusion of CDNs is contextualized in the so-called 'edge cloud' trend (i.e., small, highly-distributed data centers) which can be implemented by telecommunication operators through a strategy that will move the data center to the central office (CO), i.e., central office re-architected as a datacenter (CORD). The main goals of CORD strategy is to make the COs integral parts of the cloud strategies and to enable them to support new networking services. Other side goals are the optimization for east-west traffic and the commoditization of connectivity to the access network (in coherence with the SDN principles) [5]. The maximum distance between the edge cloud and the end users will be approximately 20 to 40 km according to latency requirements, possibly through a fully passive network without locally powered repeaters or amplifiers [6]. Moreover, processing of data in an edge cloud will be the key also to meet the new real-time latency requirements of the IoT [6].  
All the technological trends described above will result in a traffic tremendous growth in metro networks which are already under pressure and will risk to become the bottleneck of the entire telecommunication system. In order to mitigate this risk, the adoption of innovative reconfigurable SDN-based converged metro-access networks will allow an optimization in traffic management to avoid congestion.
## 3 Analysis of the Inter-Satellite Measurement Situation  
### Analysis of the Link Establishment Situation for Inter-Satellite Links  
According to the constellation configuration, there are three kinds of visible relationships among BDS-3 MEO satellites, namely, continuously visible, non-continuously visible, and invisible, which are related to the continuity of inter-satellite measurement, the number of established links, the orbital positions of satellites, and time slot route planning, etc. [PERSON], [PERSON] et al. analyzed the inter-satellite observation conditions [30]. Figure 1 shows the 30-day time series of the number of satellites with established links for the PRN36 satellite. The fluctuation is considerable, with a minimum of 0 and a maximum of up to 20. Table 1 shows the statistics for the number of established links. In general, the minimum number of established links is 0, the maximum is 19.96, and the average number of established links is 14.45.  
Figure 1: The 30-day link-tracking number of PRN36 for BDS-3.
## 4 Discussion  
Our experimental results demonstrate that the proposed PDCNER method effectively utilizes pig disease feature information by seamlessly integrating it into the BERT architecture. Specifically, the Lexicon Adapter is inserted between the first and second transformer layers within BERT, facilitating the infusion of pig disease lexicon knowledge into the model's representations.  
In addition to the lexicon-enhanced BERT's contribution to improvement, contrastive learning has also played a significant role. The lexicon-enhanced contrastive learning method improves the model's capacity for semantic representation of text based on the normalized temperature-scaled cross-entropy loss function. This loss function enhances the model's ability to identify similar entities by minimizing the distance between positive samples (i.e., the same type of entities) while simultaneously maximizing the distance from negative samples (i.e., different types of entities). Consequently, this approach reduces the risk of the model incorrectly classifying different entities. Furthermore, it enables the model to adjust the weighting of distance measurements, allowing it to focus more on distinguishing subtle differences between different entities during the training process.  
Judging from the recognition results of different entities, despite the sample sizes of type, disease, and medicine entities being only about 10%, their F1 values are all above 90%. In contrast, although symptoms have the largest sample size, accounting for 39.55%, their F1 value is the lowest. This disparity arises primarily because the boundaries of pig type and disease entities are very clear, whereas the boundaries of symptom entities are more ambiguous. For instance, type entities typically end with terms such as \"pigs (\")\" (e.g., sick pigs (\")\"), conservation pigs (\")\"), fattening pigs (\")\"), and disease entities usually end with terms like \"disease (\"),\" \"inflammation (\"),\" and \" plague (\")\" (e.g., porcine blue ear disease (\")\"), necrotic enteritis (\")\"), African swine fever (\")\"). In contrast, symptom entities are often composed of complex verbs, conjunctions, and modifiers, such as \"feed intake continuing to decrease (\")\" and \"continuous spasmodic cough (\")\". Moreover, the average length of complex symptom entities is approximately eight Chinese characters, which contributes to a lower overall recognition rate.
## 3 Results and Discussion
### Predictive Analysis  
Figure 9 shows the BMEP comparison among the proposed models over the transient cycle considered. To estimate the accuracy of the engine models running NRTC, the Y1 (left) axis in the second row of Figure 9 shows the instantaneous errors, whereas the Y2 (right) axis shows the average errors compared with the NRTC target BMEP. In particular, the instantaneous error for the Map-based model shows some spikes, reasonably due to an incorrect interpolation of the performance map running the simulation. Nevertheless, for all of the considered models, the average errors over the transient cycle are within 6%.  
From the emission point of view, for a more accurate prediction of pollutant emissions and to employ detailed chemical kinetics, more complex models are necessary and out of the scope of the work. However, this is not the purpose of the present work. Since the detailed Zeldovich mechanism is already implemented, and the in-cylinder temperature with a predictive model is quite well predicted, the NO\({}_{x}\) emissions are considered instead.  
First, a comparison between the detailed model and the fast-running model is made. As can be seen in Figure 10, the Simplified 1D Model overestimates the NO\({}_{x}\) emissions. The differences lay in the fact that the in-cylinder conditions are slightly different between the Simplified 1D Model and the detailed model, because, in the first case, the intake thermodynamic conditions are constant for all of the cylinders, while, in the second case, they are calculated for each cylinder.  
Figure 9: BMEP comparison.  
This trend is confirmed by comparing cumulative NO\({}_{\rm x}\) emission results over the NRTC, as shown in Table 4. This step is necessary to also compare the map-based model, due to its cumulative NO\({}_{\rm x}\) emission result.  
As can be seen, the map-based model, via interpolation, gives a cumulative NO\({}_{\rm x}\) result of 267 g, which is 12% higher than the value obtained via simulation with the detailed model. The result can be considered satisfactory, considering that a map-based model includes no chemical kinetics relationships. In terms of fuel consumption comparison, no relevant difference can be noticed between the simplified 1D Model and the detailed one. The difference in terms of fuel consumption for the map-based model is within 5% compared to the detailed one. Thus, it can be stated that lower accuracy models, such as the proposed map-based model, are suitable for the trends analyses, in terms of energy, NO\({}_{\rm x}\) emissions, fuel consumption, global and subsystems outputs, of complex powertrain systems.
# Assessment of Daytime HONO Emission Source from Asphal Surface to Urban Air
## 2 Experimental  
### Experimental Setup  
A dynamic flow-through acrylic chamber system (45 cm \(\times\) 45 cm \(\times\) 20 cm) was implemented to measure HONO production on asphalt shingles (CLASSIC \({}^{\text{\textregistered)}}\) SUPER, Owens Corning, Toledo, OH, USA) with NO\({}_{2}\), toluene, hexane, and UV light sources. Teflon (Polytetrafluoroethylene, PTFE) sheets were attached to each surface of the chamber to prevent unnecessary chemical reactions, and the chamber had a quartz window on top to allow UV-A (315-400 nm) and UV-B (280-315 nm) to enter the system. Compressed zero gas was used as the carrier gas to deliver the reactant gases through the chamber into the HONO measurement instrument described below. A dynamic dilutor connected to a standard NO2 gas cylinder (11.3 ppm) and a VOCs permeation tube was used to prepare the precursor gas mixtures. Humidity inside the chamber (50% RH) was controlled by injecting the main flow of zero gas through an impinger filled with distilled water. The total air flow rate and residence time to the chamber was approximately 15.5 LPM and 2.6 min, respectively. Light intensities were controlled by changing the distance of the xenon lamp (XG-100B, Seric., Ltd, Tokyo, Japan) from the chamber. The spectral irradiance emitted by the xenon lamp was measured with an Eppley Lab model TUVR (295-385 nm) and an Ocean Optics USB4000 spectrometer (280-950 nm). The experimental setup is presented in Figure 1.
## 2 Materials and Methods
### Measurement Stands  
The SJ-210 stylus profilometer from Mitutoyo (Mitutoyo Corporation, Kawasaki, Japan) was used for surface roughness measurements. It is a portable system that allows measurement of surface roughness profiles for various types of products directly at the workstation. The measurement was carried out by means of a powered arm placed on an adjustable tripod. The radius of the measuring tip was 2.0 \(\upmu\)m. The device allows measurement according to many standards, including DIN, ISO, ANSI, and JIS, and the determination of a wide set of roughness parameters [35]. A view of the device during measurement is presented in Figure 3.  
Measurement of the dimensional and shape accuracy of the manufactured parts was realized using a CrystAApex V544 coordinate measuring machine (CMM) from Mitutoyo (Mitutoyo Corporation, Kawasaki, Japan). This system allows measurements in the range of 500 \(\times\) 400 \(\times\) 400 mm, using interchangeable measuring probes. The measurement resolution of the CMM is 0.0001 mm, which makes the obtained measurement results very precise. The manufacturer's software designed for the CMM allows the measurements to be programmed, making it possible to automate the measurement process in industrial or laboratory conditions [36]. Figure 4 shows the CMM during measurement.  
Figure 5 graphically illustrates the logical connections between the main stages of the work carried out in the scope of the described research, together with information summarizing the described methodology.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Characteristics** \\ \hline Working space & 220 \(\times\) 220 \(\times\) 250 mm \\ Platform calibration & Automatic nozzle height, automatic platform calibration \\ Layer height & 0.1-0.4 mm \\ Filament diameter & 1.75 mm \\ Compatible materials & PLA (polylactide), TPU (thermoplastic polyurethane), \\  & PETG (Polyethylene Terephthalate Glycol-modified) \\ Connectivity & Micro 50 card \\ Software & Cura Ultimaker 3 \\ Overall dimensions & 475 \(\times\) 470 \(\times\) 620 mm \\ Mass & 7.8 kg \\ \hline \end{tabular}
\end{table}
Table 2: Technical parameters of the Ender-3 V2 device [34].  
Figure 3: View of the Mitutoyo SJ-210 surface microtopography measurement system equipped with a stylus profilometer: (**a**) general view; (**b**) view of the system during measurement.  
Figure 4: Mitutoyo’s CrystApex V544 coordinate measuring machine in the Metrology and Measurement Systems Laboratory of the Department of Production Engineering at Koszalin University of Technology: (**a**) general view; (**b**) view of the measuring head; (**c**) view of the measuring probe during measurement of one of the cylindrical openings in the model.  
Figure 5: Flow chart of logical connections between the main stages of the work carried out in the scope of the described research.
## 3 Results and Discussion
### Monitoring of Airborne Asbestos Fibers
Although fiber concentrations are considered to be a more relevant parameter than dust mass concentration, the fiber-to-dust concentration ratio is often used as an indicator of occupational exposure to asbestos [59]. The values of the calculated fiber/dust ratio for samples taken at four sampling sites (B2; B3; C1; C2) with the measured highest concentrations during the demolition work ranged from \(0.745\times 10^{-3}\) to \(1.41\times 10^{-3}\). These values are three orders of magnitude lower than those found during processing in chrysotile mines [59]. It should be noted that numerous asbestos concentrations during the ACM demolition activities in our case study were lower in comparison to the measured fiber concentrations in workplaces involving asbestos mining and processing. As illustrated in Figure 9, there is a relationship between mean values of number and mass concentration of asbestos dust. It confirms some dependence of the fiber concentration on the measured dust levels. However, the conclusion stated in [59] was not confirmed, i.e., that the fiber-to-dust ratio increased with a decreasing mass concentration of asbestos dust.
## 3 Results and Discussion
### Morphological and Surface Analysis  
Figure 3 shows FE-SEM images of niobium oxide samples with different concentrations of manganese addition. Nb\({}_{2}\)O\({}_{5}\) samples obtained at 700 \({}^{\circ}\)C are presented. It is possible to evidence slight changes in the morphology of the samples with the addition of manganese. For Mn concentrations of 1.0, 2.5 and 5.0 wt.%, it was possible to identify the presence of particles with a size of about 90 nm and a grain densification tendency; specifically at 5.0 wt.% Mn content, it was possible to distinguish particles with minor size. On the other hand, the 10.0 wt.% Mn content promoted the production of smaller nanoparticle agglomerates. Mn atoms may have disturbed the growth process, hence, the incorporation of Mn influences the morphology of Nb\({}_{2}\)O\({}_{5}\).  
Figure 4 shows %, subjected to 700 \({}^{\circ}\)C. These isotherms have a type-III form, which indicates that there is a low adsorbate-adsorbent interaction, and it is associated with solids that contain large pores. In cases (a) and (b) of Figure 4, a hysteresis at partial pressures higher than 0.2 due to the presence of mesoporous can be distinguished. Samples obtained at 5.0 wt.% and (b) 10.0 wt.% showed similar behavior.  
Figure 3: FE-SEM images of (**a**) Nb\({}_{2}\)O\({}_{5}\), Mn-doped Nb\({}_{2}\)O\({}_{5}\) (**b**) 1.0, (c) 2.5, (**d**) 5.0 and (**e**) 10.0 wt.% samples, submitted at 700 \({}^{\circ}\)C. Guide bar: 200 nm; Mag = 80.00 KX.  
Table 2 presents the results of the \(N_{2}\) physisorption analysis on the samples subject to 700 \({}^{\circ}\)C for various concentrations of Mn, these results are in agreement with the literature [12]. The specific surface area, \(S_{BET}\), was determined by taking data at relative pressures between 0.1 and 1.0. The oxides modified with Mn at 1.0 and 2.5 wt.% are macroporous, whereas the ones modified with Mn at 5.0 and 10.0 wt.% are mesoporous. \(S_{BET}\) data showed that an increase in Mn concentration is reflected by an increase in the specific surface area. These results suggest that the presence of manganese on the surface of niobium pentoxide does not achieve an effective incorporation. The increasing trend in \(S_{BET}\) indicates that there is not a partial blockage of some pores in the niobium metal oxide. \(S_{BET}\) of the Nb\({}_{2}\)O\({}_{5}\):Mn 10.0 wt.% sample would cause it to be the most effective in the photocatalytic process, but structurally and vibrationally this does not have the desired characteristics. The Nb\({}_{2}\)O\({}_{5}\):Mn 2.5 wt.% sample \(S_{BET}\) is low; however, its pore size is suitable by the system action over large molecules of pollutants. The \(S_{BET}\) results and information obtained from the FE-SEM images show that small \(S_{BET}\) measurements are linked to larger particle sizes, as is the case of the Nb\({}_{2}\)O\({}_{5}\) sample. The average pore diameter also decreased from 52 to 42 and 26 nm due to the presence of the manganese, except for the sample obtained at 2.5 wt.%.
# Optimal Design of Droplet Ejection for PZT Printhead Based on Surrogate Model
## 5 Results  
In this study, the result objective of the optimization is to achieve stable droplet ejection. Therefore, during optimization, the length of the liquid column, Hd, was taken as the optimization target value, and the KRG model was taken as the adaptive function model. According to the forty generated samples, utilizing the genetic algorithm (GA) was used for data optimization.  
After optimization convergence, the optimal value of Hd = 0.968 was obtained, and the corresponding optimization parameters were: liquid density \(\rho\) was 1500 kg/m\({}^{3}\), viscosity \(\eta\) was 11.73 cp, surface tension \(\sigma\) was 0.058 N/m and driving waveform period T was 10.92 \(\upmu\)s. In order to verify the optimization results, the obtained optimization parameters were substituted into the CFD model for simulation calculation again. The simulation results of optimized parameters and initial parameters are shown in Figure 15. Compared with the original Model 1 (\(\rho\), \(\eta\), \(\sigma\) and T are 1350 kg/m\({}^{3}\), 12 cp, 0.07 N/m and 8 \(\upmu\)s, respectively), the droplet velocity was basically unchanged, and it can be obviously observed that there were no satellite droplets in the optimized model, which achieved the objective of stable droplet ejection. Compared with the original Model 2 (\(\rho\), \(\eta\), \(\sigma\) and T are 1500 kg/m\({}^{3}\), 12.5 cp, 0.065 N/m and 8 \(\upmu\)s, respectively), the droplet velocity of the optimized model was increased by 20.84%. From the above comparison results, it can also be found that the ink characteristics, such as viscosity, density and surface tension, directly affect the droplet stability ejection state, which is consistent with the previous parameter sensitivity analysis results and verifies the feasibility of this study.  
Figure 14: The design parameters influence trend. (**a**) Effect of design parameters on Hd. (**b**) Effect of design parameters on the number of satellite droplets.
## 2 Figure of Performance for Analysis
### Navigation Error  
Navigation performance is directly affected by GDOP and URE. In SSV, the use of multi-GNSS and additional first side lobe signals is beneficial for increasing GDOP but may be harmful in range accuracy due to the use of the signals in low \(C/N_{0}\), i.e., the trade-off between these two different things should be taken into account. We assume that range error performance can be improved by employing a high-sensitivity receiver processing technique, such as narrow bandwidth and correlator spacing, especially for static receivers of GEO mission.  
In this study, for simplicity, navigation error is computed by using the covariance propagation rule of least-squares estimation in each time epoch via Equations (4) and (11) assuming zero-mean, uncorrelated, and non-identically distributed UERE. Inter-system bias that is an offset in time scale between different satellite navigation systems should be properly taken into account but is not considered in this study. This is because the inter-system bias at the user end is stable and can be calibrated well as an additional parameter provided by satellite navigation systems as part of the broadcast message [25].
## 4 System Architecture
### Software Performance  
The speed at which the processor computes the phases for each element is a crucial aspect of the system that directly impacts its performance. The highly dynamic environment introduced by a vehicle necessitates fast beam control.  
The arm processor present in the RFSoC contains four cores, which enables the capability of parallel calculation of the different beam phases, therefore increasing performance.  
To test the processing capabilities, a loop was made where all phases were calculated for all 16 elements over 50 times in a quick succession while constantly changing the [\(\phi_{0}\), \(\theta_{0}\)] of said beams, forcing the change of the complex delays of the IP core.  
Table 2 contains the average time and corresponding frequency achieved when calculating the channel phases. Our system can perform beamforming of 20 beams at over 400 Hz (2.5 ms), which is significantly more than what is required to use current GNSS receivers. However, it is important to consider that these results are largely due to the simple control algorithm present and may worsen when more complex algorithms are used.
## 7 Discussion and Limitation
### Devices with Different Performance  
In addition, the current application scenario of this defense scheme mainly focuses on the anonymous communication network of Tor as an example. Most of the defense operations can be processed by the server of the anonymous communication network. The minimum requirement for the device is to have the ability to process the coding operation. For example, narrow-bandwidth IoT networks and low-power devices have wider coverage, but do not have the processing ability to integrate complex security detection or protection mechanisms, and are often easy victims of network attacks. For example, the WebUI-based IoT device identification proposed by [PERSON] et al. [53]. Therefore, for the security research of this type of equipment, we will further explore more applicable defense methods.