esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Research Limitations  
Besides these practical findings that can contribute to expanding knowledge on the use of resilience engineering approaches in the management of complex medical devices, some limitations of the current study must be outlined as well. From the methodological point of view, the use of FRAM alone can provide a qualitative description and analysis of the functions' coupling mechanisms, but it lacks quantitative analysis and assessments of the coupling and correlation relationships between the system's components [81; 82; 83]. Moreover, as observed by [PERSON] et al. [84], even though FRAM is one of the few systems thinking accident analysis tools allowing safety managers to account for normal performance variability, the outcomes of the analysis can be complex and difficult to interpret. Hence, the use of supporting tools such as the Resonance Analysis Matrix (RAM) [85], the Montecarlo Analysis [86; 87], or the Bayesian Network (BN) and Failure Modes and Effects Analysis (FMEA) used by [PERSON] et al. [88] can certainly augment the effectiveness of the FRAM application. Also, the use of Fuzzy Logic and Rough Set Theory (RST) was investigated to better characterize the internal variability of a FRAM function [89]. Similarly, several augmentations of FRAM were proposed in the healthcare sector to investigate performance variability in complex operations [32]. Additionally, the use of descriptive statistics tools to prioritize variability sources as in [PERSON] and [PERSON] [90], as well as to evaluate functions' coupling as in [PERSON] et al. [79], can certainly augment the effectiveness of the FRAM use.  
Another criticality is related to the fact that when analyzing the accident that occurred in the HBOT room, the system and accident analysis were based on accident reports only. Hence, although this case study shows the effectiveness of FRAM in illustrating how different types of failures have stimulated each other, amplifying the consequences of the accident, field research and interviews with the involved parties could have certainly allowed us to apply the FRAM more holistically. Therefore, this case study should be regarded mostly as an illustrative example of retrospective analysis.  
Finally, it must be noted that the analysis of the MRI room considered the system per se, rather than in connection with other hospital facilities, staff, and equipment. On the one hand, this allowed us to focus on the MRI room as a single sociotechnical system for its detailed analysis. On the other hand, in this way, the influence of the above-mentioned connections was not taken into account.
# Design and Verification of a Modular Reconfigurable Test Platform for Electric Tractors
## 3 Framework Design of the Test Platform
### Test Platform Configuration
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Parameters** & **Values** & **Pcs** \\ \hline \multirow{4}{*}{Diesel engine} & Type & Four-stroke & \\  & Rated power (kW) & 36.8 & \\  & Rated speed (r/min) & 2400 & 1 \\  & Idle speed (r/min) & 750 \(\pm\) 25 & \\  & Maximum torque & \(165/\leq 1800\) & \\  & (N\(\cdot\)m)/speed (r/min) & \(165/\leq 1800\) & \\ \hline \multirow{4}{*}{Generator} & Type & PMSM & \\  & Rated power (kW) & 30 & \\  & Rated voltage (V) & 280 & 1 \\  & Rated current (A) & 66 & \\  & Rated speed (r/min) & 3000 & \\ \hline \multirow{4}{*}{Drive motor} & Type & PMSM & \\  & Rated power (kW) & 22 & \\  & Rated voltage (V) & 250 & \\  & Rated torque (N\(\cdot\)m) & 70 & 2 \\  & Rated speed (r/min) & 3000 & \\  & Maximum power (kW) & 30 & \\ \hline \multirow{4}{*}{Load motor} & Type & AM & \\  & Rated power (kW) & 30 & \\  & Rated speed (r/min) & 1500 & 3 \\  & Rated torque (N\(\cdot\)m) & 195 & \\  & Maximum speed (r/min) & 4500 & \\ \hline \multirow{4}{*}{Battery pack} & Type & Ternary lithium-ion & \\  & Rated capacity (Ah) & 132 & \\  & Nominal voltage (V) & 318.2 & \\  & Operating voltage range (V) & 258–356.9 & \\  & Sigle voltage range (V) & 2.8–4.15 & 1 \\  & State of charge (SOC) & 15–100 & \\  & operating range (\%) & & \\  & BOL maximum continuous & 90 & \\  & charging current (A) & & \\  & BOL maximum continuous & 100 & \\  & discharging current (A) & & \\ \hline \multirow{4}{*}{Torque-speed sensor 1} & Torque range (N\(\cdot\)m) & –200–200 & \\  & Speed range (r/min) & 0–5000 & \\  & Rated voltage (VDC) & 24 & \\  & Rated current (mA) & 300 & \\ \hline \multirow{4}{*}{Torque-speed sensor 2} & Torque range (N\(\cdot\)m) & –500–500 & \\  & Speed range (r/min) & 0–4000 & \\  & Accuracy & 0.1\% F.S. & 4 \\  & Rated voltage (VDC) & 24 & \\  & Rated current (mA) & 300 & \\ \hline Differential reducer & Transmission ratio & 22:1 & 1 \\ \hline Fixed gearbox & Transmission ratio & 1:14 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main technical parameters of the components. can be implemented in real time. Further, the measurement data acquisition system is safe, reliable, and stable. To reduce the energy consumption of the platform, the power generated by the regenerative braking of the three load electric machines is fed back to the power grid through a grid-connected inverter. In the following subsections, the reconfigurable framework of the control and measurement system for the test bench is described first. Then, the host control framework design and communication protocol are illustrated using a combination of dSPACE and MATLAB/Simulink.
## 3 Multiple AUVs-UAVs Control Design
### Flocking and Boids Mode
* Alignment: The matching average velocity of the agents is defined as follows: \[\overset{\rightharpoonup}{M}_{\alphalig}(m)=\frac{1}{n}\sum_{j=1}^{n}\overset{ \rightharpoonup}{v}_{s,j}(m)=\frac{1}{n}\sum_{j=1}^{n}(\overset{ \rightharpoonup}{v}_{j}(m)-\overset{\rightharpoonup}{v}_{s}(m)),\] (49) where the velocity of agent \(j\) with respect to agent \(s\) is represented by \(\overset{\rightharpoonup}{v}_{s,j}\) (\(m\)) and the velocity of agent \(j\) and agent \(s\) is represented by \(v_{j}\) and \(v_{s}\), respectively. * Cohesion: The cohesion force is utilized to calculate the AUVs attraction to the desired position based on the UAVs leader's position, which is defined as follows: \[\overset{\rightharpoonup}{C}_{coh}(m)=\frac{1}{n}\sum_{j=1}^{n}k_{coh}\Big{(} \overset{\rightharpoonup}{p}_{d}(m)-\overset{\rightharpoonup}{p}_{j}(m) \Big{)},\] (50) where the desired agent position is represented by \(p_{d}\) and a constant gain is represented by \(k_{coh}\). * Obstacle Avoidance: The obstacle avoidance force is utilized for avoiding collision between the obstacles and the agents (UAVs and AUVs), which is defined as follows [34]: \[\overset{\rightharpoonup}{S}_{obs}(m)=\frac{1}{n}\sum_{j=1}^{n}k_{o}\xi_{o}(m )\frac{\overset{\rightharpoonup}{p}_{j}(m)-\overline{p}_{o}(m)}{\left\lVert \overset{\rightharpoonup}{p}_{j}(m)-\overline{p}_{o}(m)\right\rVert},\] (51) with \[\xi_{o}(m)= \begin{cases}r_{0}-\left\lVert\overset{\rightharpoonup}{p}_{j}(m) -\overset{\rightharpoonup}{p}_{o}(m)\right\rVert,\\ if\left\lVert\overset{\rightharpoonup}{p}_{j}(m)-\overset{\rightharpoonup}{p}_{ o}(m)\right\rVert\leq r_{0}\\ 0,\ \ \ if\left\lVert\overset{\rightharpoonup}{p}_{j}(m)-\overset{\rightharpoonup}{p}_{ o}(m)\right\rVert>r_{0},\end{cases} \] (52) where the position of agent \(j\) and obstacle \(o\) are represented by \(p_{j}\) and \(p_{o}\), respectively, \(n\) represents the agent's number, \(k_{o}\) represents a positive constant, \(\left\lVert\overset{\rightharpoonup}{p}_{j}(m)-\overline{p}_{o}(m)\right\rVert\) represents the Euclidean distance between agent \(j\) and obstacle \(o\), the separation gain is represented by \(\xi_{o}\), and \(r_{0}\) represents the constant distance between agent \(j\) and the obstacle \(o\). The total control law is a combination formula of all the forces, which will be given as follows:  
\[U_{r}=S.S_{sep}+M.M_{align}+C.C_{coh}+S_{o}.S_{obs}, \tag{53}\]  
where the control law \(U_{r}\) is affected by positive constants \(S\), \(M\), \(C\), and \(S_{o}\).
## 3 Tests, Results, and Discussion
### BSSD PPP/INS/ODO TCI Positioning
The visible differences emerge during the periods of re-convergence caused by satellite outages, while the differences are invisible in periods with sufficient available satellites. Figure 17 shows the average maximum positioning drifts calculated by the odometer-aided PPP/INS TCI based on the BSSD model and the undifferenced PPP model in the satellite outage periods from 1500 s to 2500 s. The position drifts of the solutions based on the BSSD model are 66.29 cm, 45.84 cm, and 49.17 cm, which are smaller than those solutions based on the undifferenced model (82.38 cm, 50.66 cm, and 52.30 cm). It means that the BSSD model can provide visible enhancements in demanding user environments. Figure 16: Positioning differences between undifferenced PPP/INS/ODO TCI and BSSD PPP/INS TCI/ODO. Figure 17: Mean maximum position drifts of different GNSS for BSSD PPP/INS/ODO TCI and undifferenced PPP/INS/ODO TCI. Figure 15: Positioning errors of undifferenced BSSD PPP/INS/ODO TCI with different GNSS using PPP-B2b correction.
## 2 SORGETINA RF Rotating Target  
The thermo-mechanical demonstrator to be tested in the first phase of the project is the rotating target assembly shown in Figure 1. The outer side surface of the disk-shaped evaporator intercepts D+ and T+ 300 keV ions coming from the ion source. The evaporator has a perfectly cylindrical shape characterised by the major radius.  
On the other hand, the condenser has a conical shape and is connected to the external water-cooling system. The whole scaled system, containing water, dissipates the power load due to the impinging ion beam by means of a heat transfer mechanism similar to a heat pipe system. The cooling fluid is at sub-atmospheric pressure due to its well-known properties in worldwide usage and experience as a coolant in power plants.  
More in detail, the cooling system is designed to safely remove a thermal power of 250 kW concentrated over a circular hot spot of about 150 mm in diameter, and thus an average heat flux of about 14 MW/m\({}^{2}\).  
The concept of the target cooling system was oriented to a rotating component working with a closed and localised thermal cycle to avoid any external hazard. The cooling fluid selected is water at sub-atmospheric pressure due to its well-known properties in the literature and worldwide usage and experience as a coolant in power plants.  
The cooling system identified is a prototypical out-of-scale rotating heat pipe (RHP) [11; 12; 13]. The RHP mainly consists of three regions: the evaporator where a fluid is heated up to its evaporation/boiling, a conical condenser region where the vapour previously generated finds a cold surface to condensate and then moves towards the evaporator thanks to the centrifugal effect of the rotation, an adiabatic region between the two previous ones where the condensate moves back to the evaporator (Figure 1).  
In between the evaporator and condenser, vacuum seals and bearings are located allowing the machine operation.  
The geometrical dimensions of the structures are here summarised: the total height is 2400 mm, the diameter of the evaporator is 1000 mm, and the diameter of the condenser varies from 200 to 400 mm.  
Working conditions foresee the target rotating at a constant speed under irradiation of 833 mA (total electric current) of deuterons and tritons ions at 300 keV, thus releasing a thermal power of 250 kW in the target. Thermal power deposited by alpha particles into the target from fusion reactions is negligible (<100 W). The area enlightened by the ion beam is circular, with a diameter of about 100-120 mm. The thermal power deposited onto the target surface diffuses into the device through conduction and represents the main potential cause of system failure. The frequency rotation of the rotating target is equal to 5.3 Hertz. A schematic representation of the rotating target and the intercepted ion beam is depicted in Figure 2.  
As can be seen from Figure 2 the ion beam is inclined with respect to the external surface of the evaporator. Due to the projection onto it, the footprint of the ion beam (150 mm) is different from its diameter (about 100-120 mm).
# Fusion of Different Image Sources for Improved Monitoring of Agricultural Plots
## 3 Results
### Validation
#### 3.4.3 Similarity of the Sentinel 2 Images during the Overlapping Period
It must be recalled that the series of synthetic images obtained by merging the August image covered the period 1 August 2019-30 September 2019; therefore, they did not overlap during August with the series of synthetic images obtained by merging the September image (1 September 2019-31 October 2019). Likewise, this last one did not overlap the previous one in October. Comparing two NDVI, GNDVI and GCI images obtained on different days of August values of _SSIM_ were, respectively, 0.993, 0.993 and 0.985. In the same way, in October, values of _SSIM_ were, respectively, 0.995, 0.994 and 0.991, very close to those obtained directly from Sentinel 2 images during the overlapping period (Section 3.4.4).
## 2 Materials and Methods  
### Electroless Plating of MWCNT  
#### 2.1.1 Electroless Plating of Copper on MWCNT  
Multi-walled carbon nanotubes (60-100 nm diameter, 1-2 \(\upmu\)m) purchased from Tokyo Chemical Industry Co. Ltd. were boiled in 1:3 mixed acid solution of HNO\({}_{3}\) and HCl at 100 \({}^{\circ}\)C for 10 min and subsequently rinsed with distilled water until the pH approached neutral (pH = 7). The chemicals used in the electroless deposition were purchased from Daejung, Korea. The surface activation of MWCNT was performed using a palladium activation solution. The purpose of surface activation was to incorporate the Pd nuclei on the MWCNT surface, which initiates copper nucleation during coating. The MWCNT was stirred in solution containing 0.055 MF tin II chloride (SnCl\({}_{2}\)), 0.003 M palladium II chloride (PdCl\({}_{2}\)), and 0.5 M hydrochloric acid (HCl) for 12 h at 40 \({}^{\circ}\)C. The MWCNT were washed with distilled water, rinsed in sodium hypophosphite (NaPO\({}_{2}\)H\({}_{2}\)) for 10 min, and subsequently washed with distilled water. Copper coated MWCNT were obtained by stirring the surface activated MWCNTs in a copper electroless bath for 1 h. The bath was composed of 0.042M of copper II sulfate pentahydrate (CuSO\({}_{4}\cdot\)5H\({}_{2}\)O), 0.0029 M of nickel II sulfate heptahydrate (NiSO\({}_{4}\cdot\)7H\({}_{2}\)O), 0.242 M of sodium hypophosphite (Na\({}_{2}\)H\({}_{2}\)PO\({}_{2}\cdot\)H\({}_{2}\)O), 0.060 M of sodium citrate (Na\({}_{3}\)C\({}_{6}\)H\({}_{5}\)O\({}_{7}\)), 0.51 M of boric acid (H\({}_{3}\)BO\({}_{3}\)) with minor poly ethylene glycol (PEG 6000) addition, and few drops of sodium hydroxide were added to maintain a pH of 9. The solution was maintained at 50 \({}^{\circ}\)C. The copper coated MWCNT were filtered and washed with distilled water and dried in the oven at 60 \({}^{\circ}\)C. After coating, the powders were weighed using analytical balance and the weight gain (8) percent was calculated [(8% = ((W\({}_{2}-W\))/W\({}_{1}\)) \(\times\) 100], where W\({}_{1}\) is the initial weight of the MWCNT powders and W\({}_{2}\) is the weight of the powders after copper coating (grams).
# Counter-Terrorism Video Analysis Using Hash-Based Algorithms
## 3 Datasets and Methodology  
**Datasets.** We tested our detector with videos obtained by the web crawler described in Section 4 and comparing them against a fictitious propaganda video. The detector contains 2215 videos of different duration. The total size of this dataset is 34 GB and it contains 5,834,570 frames. Both videos are based on the same images. However, the colors, brightness, watermark, logos, resolution, and aspect ratio are different. Figure 2 shows some examples.  
**Methodology.** We selected a fixed number of frames/second (f/s) and then extracted the corresponding frames from the videos. The hash database contains about 1,100,000 elements. The hash of each frame of the new video was compared with the whole database, and matching frames have been stored. This process was repeated several times, each time at different frame extraction rate to identify at which rate the system performs best. Databases were implemented using MongoDB and algorithm implementation was carried out in Python; an i7 7700 processor was used. Parallel computing is implemented for the CPU.  
Figure 1: Method preview.
###### Abstract  
Three-dimensional (3D) reconstruction techniques are increasingly used to obtain 3D representations of buildings due to the broad range of applications for 3D city models related to sustainability, efficiency and resilience (i.e., energy demand estimation, estimation of the propagation of noise in an urban environment, routing and accessibility, flood or seismic damage assessment). With advancements in airborne laser scanning (ALS), 3D modeling of urban topography has increased its potential to automatize extraction of the characteristics of individual buildings. In 3D building modeling from light detection and ranging (LIDAR) point clouds, one major challenging issue is how to efficiently and accurately segment building regions and extract rooftop features. This study aims to present an investigation and critical comparison of two different fully automatic roof segmentation approaches for 3D building reconstruction. In particular, the paper presents and compares a cluster-based roof segmentation approach that uses (a) a fuzzy c-means clustering method refined through a density clustering and connectivity analysis, and (b) a region growing segmentation approach combined with random sample consensus (RANSAC) method. In addition, a robust 2.5D dual contouring method is utilized to deliver watertight 3D building modeling from the results of each proposed segmentation approach. The benchmark LIDAR point clouds and related reference data (generated by stereo plotting) of 58 buildings over downtown Toronto (Canada), made available to the scientific community by the International Society for Photogrammetry and Remote Sensing (ISPRS), have been used to evaluate the quality of the two proposed segmentation approaches by analysing the geometrical accuracy of the roof polygons. Moreover, the results of both approaches have been evaluated under different operating conditions against the real measurements (based on archive documentation and celerimetric surveys realized by a total station system) of a complex building located in the historical center of Matera (UNESCO world heritage site in southern Italy) that has been manually reconstructed in 3D via traditional Building Information Modeling (BIM) technique. The results demonstrate that both methods reach good performance metrics in terms of geometry accuracy. However, approach (b), based on region growing segmentation, exhibited slightly better performance but required greater computational time than the clustering-based approach.  
LIDAR point clouds; 3D urban model; rooftop modeling; segmentation; reconstruction; 3D building +
Footnote †: journal: Applied Soft. 2019, 9, 4674; doi:10.3390/app9214674
# Fast Prediction and Optimization of Building Wind Environment Using CFD and Deep Learning Method
## 1 Introduction
However, previous studies of building wind environment on the integration of CFD and deep learning have mainly focused on the feasibility of the methods, with primary emphasis on the predictive performance of surrogate models, without leveraging the advantages of deep learning for optimization problem. Therefore, this paper aims to demonstrate the practical applicability of integrating CFD and deep learning in optimizing building wind environments to advance the field and promote its wider adoption and practical implementation in real-world engineering scenarios. In this study, a deep learning-based surrogate model is proposed for the rapid and accurate prediction and optimization of building wind environments. Initially, the surrogate model is trained using CFD simulation data. Subsequently, this surrogate model is employed to achieve rapid prediction of wind environments, and the optimization of building wind environments is realized through evaluation of the predicted results.
## Materials and Methods  
### Substrate and Seeding Inoculum  
As substrates, this study used the excess sewage sludge (ESS) and food waste slurry (FWS) collected from a wastewater treatment plant Gryaab AB (Gothenburg, Sweden) and a solid waste treatment company Renova AB (Gothenburg, Sweden), respectively. The food waste slurry was prepared from food wastes collected from households and retailers within Gothenburg municipality, was diluted using 20% water, and screw pressed through a 10 mm mesh. The substrates were stored in a freezer and thawed in a cold room (4-5 \({}^{\circ}\)C) prior to use as feed in the bioreactors. The main characterization data for these substrates are provided in Table 1.  
The granulated bacteria used as inoculum for the batch reactors were collected from an upflow anaerobic sludge blanket reactor treating municipal wastewater (Hammarby Sjostad, Stockholm, Sweden). The fermentation broth from another study by our group [27] was used as inoculum seed for the immersed membrane bioreactors (iMBRs).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **Excess Sewage Sludge** & **Food Waste Slurry** \\ \hline pH & \(6.0\pm 0.10\) & \(4.1\pm 0.04\) \\ TSS (g/L) & \(61.6\pm 0.92\) & \(131.9\pm 1.09\) \\ VSS (g/L) & \(38.3\pm 0.71\) & \(125.5\pm 1.02\) \\ Total COD (g/L) & \(80.5\pm 2.50\) & \(217.0\pm 1.00\) \\ Soluble COD (g/L) & \(5.35\pm 0.15\) & \(86.00\pm 0.50\) \\ NH\({}_{4}\)\({}^{+}\)-N (g/L) & \(0.86\pm 0.02\) & \(0.49\pm 0.01\) \\ PO\({}_{4}\)\({}^{3-}\)-P (g/L) & \(0.04\pm 0.00\) & \(1.02\pm 0.00\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the excess sewage sludge and food waste slurry.
## 5 Conclusions and Future Scope  
In the present work, a PTC collector with inner helical axial fins as a swirl generator or turbulator is considered and analyzed. This work consists of two parts. In the first part, four various geometries of the collector are appraised. In the second part, the selected schematic of the collector from the first sector is selected and four various pitches of inner helical fins including 250, 500, 750 and 1000 mm are studied. Results presented that the thermal performance improvement by 23.1% could be achieved by using one of the proposed innovative parabolic trough solar collector compare to the simple one. Additionally, the utmost and least thermal performance improvement (compare to the case without fins) belong to the case with P = 1000 mm by 21.53% (at V\({}_{\text{inlet}}\) = 0.314 m/s) and the case with P = 250 mm by 14.1% (at V\({}_{\text{inlet}}\) = 0.314 m/s), respectively.  
Although present work has presented new insight into the flow and thermal characteristics of a parabolic trough solar collector combining innovative inner helical axial fins as a swirl generator, additional research is required to determine the capability of this design in more realistic engineering situations by the development of an experimental prototype.  
Investigation, Software, Validation, Writing--original draft, M.Z.; Formal analysis, Methodology, Validation, Writing--review & editing, S.S.M.A.; Supervision, Writing--review & editing, S.S.; Funding acquisition, Writing--review & editing, M.S.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data available on request due to restrictions eg privacy or ethical.  
The authors declare no conflict of interest.  
Data available on request from the authors: The data that support the findings of this study are available from the corresponding author upon reasonable request.