esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Analysis
### Experiment Setup and Performance Metrics  
In the simulation experiments, we have created connected inter-actor topologies that contain varying numbers of nodes (20-100). Nodes are deployed randomly in an obstacle-free area of 1,000 m \(\times\) 600 m. To observe the impact, we have changed the transmission range of actors (50-200). The following performance metrics were used to assess the performance:  
* The _total distance moved_ by all nodes involved in the recovery: This reflects the efficiency of PCR in terms of energy efficiency and movement overhead incurred during recovery.
* The _number of nodes_ engaged during the recovery: This metric indicates the scope of the recovery process.
* The _number of messages_ required to coordinate recovery among nodes: Again this metric gauges the communication overhead during recovery and ultimately affects energy dissipation.
* The _percentage of coverage reduction_ relative to the pre-failure level: Although the prime concern of PCR is to restore connectivity, however, node coverage is important for many setups. In addition to coverage loss due to node failure, the recovery process may negatively impact coverage. This metric assesses the effectiveness of PCR in terms of coverage reduction.
* The _degree of connectivity_ maintained after recovery: This metric indicates the availability of node independent paths and is measured by averaging the number of neighbors for each node.  
The following parameters were used to vary the WSAN configuration in the experiments:  
* The _number of actor nodes (N)_ in the network affects the node density and the inter-actor connectivity.
* The _node communication range (r)_ influences the network connectivity and highly affects the recovery overhead in terms of movement distance and scope of recovery.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Schema Name** & **Syntax Type Check** & **Domain Check** & **Reduction** & **Proof** \\ \hline _Topology_ & Y & Y & Y & Y \\ _Sensor_ & Y & Y & Y & Y \\ _Detected_ & Y & Y & Y & Y \\ _Neighbour_ & Y & Y & Y & Y \\ _Actor_ & Y & Y & Y* & Y \\ _WSAN_ & Y & Y & Y* & Y \\ _CriticalalsIdentification_ & Y & Y & Y* & Y \\ _BackupsAssigning_ & Y & Y & Y* & Y \\ _CriticalalsBackups_ & Y & Y & Y* & Y \\ _Failure_ & Y & Y & Y* & Y \\ _Recovery_ & Y & Y & Y* & Y \\ \hline \end{tabular}
\end{table}
Table 1: Results of model analysis.
## 3 Designs
IM2 calibration is verified through circuit simulations. Figure 6 shows simulation results. The desired RF input of \(-\)30 dBm at 402 MHz is injected, and the OOB two-tone blockers of \(-\)10 dBm at (420, 421 MHz) in one simulation and (650, 651 MHz) in the other simulation are injected. With the LO frequency set to 404 MHz, the wanted IF signal appears at 2 MHz, while the IM2 distortion appears at 1 MHz. The IM2 distortion power with respect to the wanted tone power is examined with the gate bias voltage V\({}_{\text{gp}}\) tuned from 0.5 to 1.0 V, while the other gate bias V\({}_{\text{gm}}\) is fixed at 0.7 V. As can be seen, the IM2 distortion level is drastically reduced when the gate bias voltage V\({}_{\text{gp}}\) is set to an optimal point, which is 0.65 V for 420/421 MHz blocker and 0.7 V for 650/651 MHz blocker. It clearly implies that any unwanted mismatches that are inevitable during the circuit fabrication processes can be successfully compensated by the proposed IM2 calibration circuit, thus guaranteeing a satisfactory IM2 distortion level in the receiver. Figure 5: Circuit schematic of the RF front end. Meanwhile, it is found that the proposed IM2 calibration circuit creates an unwanted dc level change at the mixer output during the IM2 calibration. Since the signals from mixer output to the TIA, the first VGA, and the first biquad are all dc-coupled, the dc level changes at the mixer output will directly lead to a significant dc offset at the first biquad output of the complex BPF. The dc offset must be cancelled out even though the down-converted signal resides in the low-IF band and not in dc. It is because the residual dc offset created by the preceding IM2 calibration will harmfully reduce the dynamic range of the following stages. In the conventional low-IF receiver designs, for example, that reported in [12; 29], the signals after the mixer output are ac-coupled. Then, the dc offset can be cancelled out by directly controlling the gate biases of the input FETs of a subsequent stage. However, in this design, that approach cannot be taken because the gate bias tuning will adversely alter the optimum IM2 calibration condition obtained at the previous mixer stage. Thus, in this work, we tune the body bias voltages of the input FETs of A\({}_{1}\) to cancel out the dc offset. This approach successfully cancels the dc offset at the biquad output, while avoiding the adverse interaction with the IM2 calibration condition at the mixer.
###### Abstract  
A new research scheme for a full-scale/model test comparison is proposed for effectively validating the wind tunnel pressure measurement technique in this article. With the new research scheme and using quantifiable data, future full-scale/model test comparisons are expected to reasonably disclose the main problem with the traditional atmospheric boundary layer wind tunnel pressure measurement technique with regard to the Reynolds number (Re) effects, the aero-elastic effects and the flow characteristic effects. Based on the engineering background of the Peng-cheng cooling tower, it was quantified by using the new research scheme that the average relative errors of the mean wind pressure coefficients are 52.39%, 1.87% and 35.91% for the Re effects, the aero-elastic effects and the flow characteristic effects, respectively. In view of the fluctuating wind pressure coefficients, the average relative errors are 97.58%, 21.14% and 20.20% for the Re effects, the aero-elastic effects and the flow characteristic effects, respectively. These suggest that Re effects are extremely significant for pressure measurement model tests of circular cylindrical structures, and flow characteristic effects are noticeable too. They should be mitigated using effective approaches, i.e., increasing the model surface roughness and placing the actively controlled devices at the beginning of the wind tunnel's working section, respectively. Additionally, the key field measurement technique adopted (i.e., the calculation of the reference static pressure) and details of manufacturing the aero-elastic pressure measurement model for the present study are also expounded in the article for reference.  
comparison; full-scale measurement; wind tunnel test; pressure measurement; cooling tower; Reynolds number; flow characteristic; aero-elasticity Article
# Simulating Resource Management across the Cloud-to-Thing Continuum: A Survey and Future Directions
## 3 Methods and Review Framework
### Identification of Research  
Research was identified from the following electronic databases--Springerlink, ScienceDirect, IEEEeXplore, ACM Digital Library, Wiley Interscience, and Taylor & Francis Online. The search string _\"(resource management' OR'resource scheduling' OR'resource provisioning', 'QoS-based resource scheduling', 'QoS-based resource provisioning') AND cloud AND (fog OR edge) AND (IoT OR 'internet of things') AND ('simulation framework' OR simulator OR'simulation tools' OR'simulation engine')\"_ was used to query these databases. To keep a focus on the quality of works, we limited the search to journal publications only, excluding symposiums, workshops, book chapters, and conference papers. In the case of _ScienceDirect_, the query did not produce sufficiently accurate results. As such, the query for _ScienceDirect_ was rerun to include the keywords \"simulation framework\", \"simulator\", \"simulation tools\", and \"simulation engine\".  
The initial search returned a total of 317 research articles related to the research topic. In line with [PERSON] (2004) [39], two researchers independently screened titles, abstracts and conclusions, meeting regularly to resolve differences. Title-based exclusion reduced the number of papers to 82; subsequent exclusion based on abstract and conclusions reduced the number of works to 60. In the next pilot phase, we started reading papers fully.  
Following the pilot phase, a number of works were removed because they were outside the scope of the C2T continuum, e.g., grid and cluster computing. Additionally, we removed works that used mathematical analytical models referred to as simulations. As a number of quality issues were identified, it was decided to limit the survey to Q1 and Q2 journals only. The final inclusion and exclusion criteria are described in Table 1.  
Following the application of these criteria, 35 research articles were included in the final review. These are listed in Table 2.  
While our search was across all years, the selected articles were all published in the period from January 2015 to March 2019 as shown in Table 2. Note that Data were only collected for the first quarter of 2019; thus, only seven publications are reviewed for that year. An analysis of journals suggests that Future Generation Computer Systems is the leading journal publishing on the topic with six publications followed by Computers & Electrical Engineering, and Simulation Modelling Practice and Theory, each with three publications. Notwithstanding this, the most cited article (508 citations), [PERSON] et al. (2017) [28] (iFogSim), features in Software: Practice and Experience, a journal edited by one of the leading authorities in cloud simulation and the originator of CloudSim, followed by [PERSON] et al. (2015) [41] (433 citations) in Future Generation Computer Systems, and [PERSON] et al. (2016) [42] in Information Sciences (178 citations).
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 2 Literature Review
### Deployment  
The deployment of network edges within city environment is a multi-factor dependent problem, since it relies on static components such as roads layout, segments length, and the number of lanes, in addition to dynamic factors including mobility patterns, driving behaviors, accident frequently occurring areas and vehicles distribution [7]. Fixed RSUs can be equipped with edge computing capabilities to act as network edges and provide edge-based services. However, the employment of flying RSUs that are carried by unmanned aerial vehicles (UAVs) would enable supporting traffic safety and convenience applications dynamically in accordance with traffic events and congestion conditions. The key benefit of this dynamism is to enable edge computing environment with a constrained budget.  
Instead of equipping fixed RSUs with MEC servers, UAVs can be utilized to present mobile network edges. Many new ITS applications could be enabled by realizing UAVs to improve traffic safety and enhance travel comfort. Before being able to utilize UAVs in such applications, there are some serious issues to address including limited energy, processing capabilities, and signal transmission range. Taking into account the technology revolution in the past decades, there is a great potential that these limitations in UAV utilization will be resolved in the near future. Challenges to enable UAVs in the next-generation ITS are described in [34].
# A Multi-Parameter Comprehensive Optimized Algorithm for MPTCP Networks
## 5 Result Analysis
### UNIS to HiN  
In this scenario, we examined an inter-city transmission between UNIS and HiN in the same country (see Table 1). The distance between the site of HiN and the site of UNIS is around 1089 km. UNIS is located in Longyearbyen, on the remote island of Spitsbergen, about 1200 km from the North Pole. Uninett is the Norwegian research network ISP (fiber), while BKK is a commercial ISP providing a business-grade fiber connection. Telenor provides a consumer-grade fiber connection. For FullMesh and PCDC path management, according to Table 1, the total number of effective subflows is six and the names of subflows are Uninett-Uninett, Uninett-Broadnet, Uninett-PowerTech, Telenor-Uninett, Telenor-Broadnet, and Telenor-PowerTech. See Table 2 for the values of the IF \(\Omega\).  
Based on the results from Table 2, according to Equation (6), the optional subflow subset is obtained for each CC, as shown in Table 3. Then, the throughput is remeasured. Taking the CC, PM and BS as input, the prediction model of them with recalculated throughput can be established by multiple regression analysis. Following step 7 in Section 3, we find the minimum BS in a certain range of maximum throughput. By using Equation (8), the relationship between BS and throughput for each CC and PM is obtained, as shown in Equation (14). The calculation results are presented in Table 4. The R-square value is 0.94, which is very close to 1, indicating that Equation (14) is a good fit. The \(p\)-values are less than 0.00001. Overall, this output shows that Equation (14) is a good model.  
\[tp=\left\{\begin{array}{l}85.02+10.58 bs+43.3 CC_{1}+8.51 CC_{2}+11.99 CC_{3}+13. 36 CC_{4}+22.73 CC_{5}-14.87 CC_{6}-\\ 0.29 bs^{2}+0.5 bs*CC_{1}+2.58 bs*CC_{2}+2.9 bs*CC_{3}+2.96 bs*CC_{4}+2.37 bs*CC_{5}- \\ 0.73 bs*CC_{6}+43.3 CC_{1}^{2}+8.51 CC_{2}^{2}+11.99 CC_{3}^{2}+13.36 CC_{4}^{2}+22. 37 CC_{5}^{2}-14.87 CC_{6}^{2}\\ 107.82+9.14 bs+46.5 CC_{1}+5.39 CC_{2}+25.84 CC_{3}+15.24 CC_{4}+35.37 CC_{5}-20.51 CC_{6 }-\\ 0.27 bs^{2}+0.49 bs*CC_{1}+3.11 bs*CC_{2}+2.28 bs*CC_{3}+3.03 bs*CC_{4}+1.6 bs*CC_{5}- \\ 1.37 bs*CC_{6}+46.5 CC_{1}^{2}+5.39 CC_{2}^{2}+25.84 CC_{3}^{2}+15.24 CC_{4}^{2}+35. 37 CC_{5}^{2}-20.51 CC_{6}^{2}\end{array}\right\}\text{ if }PM=\text{FullMesh} \tag{14}\]  
From Table 4, we find the maximum throughput is \(tp_{\text{max}}\) = 286.73 Mbit/s, and the corresponding BS is \(bs\) = 17.83 MiB when PM is PCDC and CC is Cubic. Considering Equation (10), by sacrificing \(\delta\) = 7% throughput, the final output of the MPCOA algorithm is OBS = 9.22 MiB, OPM = PCDC, OCC = Cubic and OTP = 266.66 Mbit/s. Figure 7 presents the throughput test results on NorNet Core testbed for both path managers and all CC algorithms without considering MPCOA algorithm, where the buffer size is set to be 30 MiB. The test results show that the combination of PCDC and Cubic with the maximum throughput of 274.2 Mbit/s is the best choice, which is consistent with the MPCOA output.  
Figure 7: Throughput test results on NorNet Core testbed for UNIS to HiN with different PM and CC, without MPCOA.
# PRG: A Distance Measurement Algorithm Based on Phase Regeneration
## 3 Simulation and Analysis
### Distance Measurement Performance of PRG Method  
Because PRG method adopts carrier phase to realize distance measurement, its precision is completely dependent on the tracking precision of carrier loop. The tracking accuracy of carrier loop mainly depends on thermal noise, oscillator frequency oscillation error, Allan mean square error and dynamic stress error. In this paper, the simulation experimental parameters are set follows as Table 1, and then the carrier loop tracking error analysis is as follows [10; 16].  
1. The mean variance of thermal noise error can be expressed as: \[\sigma_{tPLL}=\frac{\lambda}{2\pi}\sqrt{\frac{B_{n}}{C/N_{0}}\left(1+\frac{1} {2T_{c}C/N_{0}}\right)}=0.0951\text{ m}\]
2. The mean square error of Allan can be expressed as: \[\sigma_{A}=cT_{c}\sigma_{A}(\tau)=3\times 10^{-3}\text{ m}\]
3. Phase jitter caused by the mechanical vibration of user movement and the receivers of the frequency of oscillation is \(2^{\circ}\), and then \[\sigma_{V}=\frac{\lambda}{180}=0.167\text{ m}\] Then, the \(1\sigma\) distance measurement error caused by the tracking of carrier loop can be expressed as: \[\sigma_{PLL}=\sqrt{\sigma_{tPLL}^{2}+\sigma_{A}^{2}+\sigma_{V}^{2}}=0.1992 \text{ m}\]  
Under the static condition of the user, the parameter setting is the shown in Table 1, and the we use Matlab software to simulate the tracking performance of carrier loop. We can be seen from Figure 12 that after 100 ms, the standard deviation of the tracking phase error is 0.043 rad, and the corresponding tracking error is 0.2055 m. The simulation result is very close to the theoretical analysis above. In addition, the corresponding \(1\sigma\) range finding error is 0.411 m in the PRG method.
# BCS_YOLO: Research on Corn Leaf Disease and Pest Detection Based on YOLOv11n
## 3 Experiments
### Results and Analysis
#### 3.2.2 Detection Performance Comparison  
To comprehensively evaluate the effectiveness and superiority of the proposed BCS_YOLO model over the mainstream object detection models in corn leaf disease and pest detection tasks, we designed and implemented a series of comparative experiments using the first (public) dataset described in Section 2.1. The mainstream models, participating in the experiments, were the classic Faster R-CNN, RT-DETR, and different versions of YOLO, including YOLOv5n [36], YOLOv8n [37], YOLOv10n [38], and YOLOv11n [37].  
Figure 6 shows the _precision-recall_ curves of all models, obtained on the test subset, which intuitively display the detection accuracy performance of each model at different _recall_ levels. From this figure, it can be observed that the _precision-recall_ curve of BCS_YOLO is overall at the outermost position. This indicates that the proposed model maintains high _precision_ levels across the full _recall_ range, especially showing more significant performance advantages in the high _recall_ region (>0.6). In contrast, classic lightweight models, such as YOLOv5n and YOLOv8n, show obvious _precision_ drops at high _recall_, reflecting insufficient feature expression capability.  
Table 4 further quantitatively compares the proposed BCS_YOLO model with mainstream object detection models, showing that BCS_YOLO is superior to all these models according to all evaluation metrics used. Specifically, BCS_YOLO reached a _precision_ value of 0.784, a _recall_ value of 0.737_, a _F1 score_ of 0.760, and an _[EMAIL_ADDRESS] value of 0.820. Compared to the baseline (YOLOv11n), these values are higher by 0.030, 0.033, 0.032 and 0.046, respectively. Moreover, BCS_YOLO achieves this outstanding detection performance with  
Figure 6: A _precision–recall_ curve comparison of the corn leaf disease and pest detection performance of the proposed BCS_YOLO model and mainstream object detection models, achieved on the first (public) dataset.  
only 6.7 M parameters and 10.1 G computational load. The obtained results demonstrate the effectiveness of the proposed feature enhancement mechanism in improving the model object detection performance by achieving a very good balance with computational complexity.  
To more intuitively show the detection performance improvement of BCS_YOLO compared to the baseline (YOLOv11n), visual result comparison analysis was performed on the test subset for both models, as illustrated in Figure 7.
# Readiness of Enterprises in Czech Republic to Implement Industry 4.0: Index of Industry 4.0
## 4 Results
### Verification and Evaluation of Industry 4.0 Index
#### 4.3.3 Relation of the Index to Intensity of Technology and Index Weighting  
The relation to the intensity of technology in the industry was tested in the first wave of the research only (Table 6). The Mann-Whitney test determined the null hypothesis at the sample significance level of \(\alpha=0.05\), where X = high-technology sector difficulty and Y = low-technology sector difficulty. The hypotheses were tested, providing H3\({}_{0}=x0.50-y0.50=0\) and H3\({}_{\rm A}=x0.50>y0.50\), as viewed from VPi4. As shown in the table below, the null hypothesis of both samples was rejected when the \(p\)-value was close to zero, and a positive \(Z\) confirmed the alternative hypothesis, claiming that the higher-tech enterprises have a higher level of Industry 4.0 (VPi4).  
Interestingly, it was not possible to reject this hypothesis for the perception of the enterprises from the perspective of Industry 4.0, with a \(p\)-value of 0.2298; therefore, this hypothesis could not be rejected, and we can further assume that the high- and low-tech enterprises saw themselves in a similar way. The hypothesis testing also failed to reject the null H3\({}_{0}\) hypothesis at levels 1 and 3 of Industry 4.0 implementation, as the \(p\)-values were greater than \(\alpha\). On the other hand, for the second phase of Industry 4.0 implementation, it was possible to prove the differences between the two groups. The enterprises with higher technological demands were often more successful.  
The VPi4 index was adjusted for comparing enterprises with different intensities of technology. The Mann-Whitney test was used to compare more independent samples where the \(p\)-value did not indicate statistically significant sectoral differences from the entire sample (Table 7), except for LTS, where a \(p\)-value (0.0184) indicated a difference at the 0.05 level of significance. For this reason, it was necessary to adjust the index (index obtained by the median difference of 0.0899) for LTS companies, so that their results could be compared with the values of other groups. This fact was logical in terms of the lower use of technologies that were included in VPi4 for enterprises belonging to the low-technology sector. After adjusting the index for LTS enterprises, the Mann-Whitney test was re-conducted, where no significant difference between the whole sample and LTS was found (\(p\)-value = 0.0967).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Sum of HTI** & **Sum of LTI** & **U** & **Z** & \(p\)**-Value** \\ \hline VPi4\% total & 7672.5 & 5857.5 & 2617.5 & 2.4410 & 0.0146 \\ VPi4\% level 1 & 7178.5 & 6351.5 & 3111.5 & 0.8159 & 0.4146 \\ VPi4\% level 1 & 7751.5 & 5778.5 & 2538.5 & 2.7009 & 0.0069 \\ VPi4\% level 2 & 7354.5 & 6175.5 & 2935.5 & 1.3949 & 0.1631 \\ Enterprise perception & 7295.5 & 6234.5 & 2994.5 & 1.2001 & 0.2298 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Intensity of technology levels (HTI—high-tech intensity; LTI—low-tech intensity), based on Mann–Whitney test.