esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Optimization Model
### Set of Constraints
#### 2.2.4 Integration of FACTS  
The optimal integration of the FACTS is divided into two parts, which include their proper location in the electrical grid as well as their size. The following constraints are defined to address these challenges:  
\[0\leq q_{k}^{\text{FACT}}\leq z_{k}q_{\max}^{\text{FACT}},\;\forall\;k\in \mathcal{N}, \tag{13}\]  
\[-q_{k}^{\text{FACT}}\leq q_{kh}^{\text{FACT}}\leq q_{k}^{\text{FACT}},\;\forall \;k\in\mathcal{N},\;\forall\;h\in\mathcal{H} \tag{14}\]  
\[\sum_{k\in\mathcal{N}}z_{k}\leq\eta, \tag{15}\]  
\[z_{k}\in\{0,1\},\;\forall\;k\in\mathcal{N}, \tag{16}\]  
where \(q_{k}^{\text{FACT}}\) is the variable used to define the size of the FACTS to be installed, and \(q_{\max}^{\text{FACT}}\) is its maximum capacity. This study's capacity goes from 0 Mvar to 2 Mvar at the distribution level, a value usually employed for electrical networks [18]. \(z\) is a vector of the binary variable that denotes a FACTS location at node \(k\), i.e., if \(z=1\) in position \(k\), this indicates that a device will be located there (otherwise, \(z=0\)). Finally, \(\eta\) is the maximum number of FACTS to be installed.
# Durability of High Volume Glass Powder Self-Compacting Concrete
###### Abstract  
The transport characteristics of waste glass powder incorporated self-compacting concrete (SCC) for a number of different durability indicators are reported in this paper. SCC mixes were cast at a water to binder ratio of 0.4 using glass powders with a mean particle size of 10, 20 and 40 \(\upmu\)m and at cement replacement levels of 20, 30 and 40%. The oxygen permeability, electrical resistivity, porosity and chloride diffusivity were measured at different ages from 3 to 545 days of curing. The amount and particle size of the incorporated waste glass powder was found to influence the durability properties of SCC. The glass incorporated SCC mixes showed similar or better durability characteristics compared to general purpose (GP) and fly ash mixes at similar cement replacement level. A significant improvement in the transport properties of the glass SCC mixes was observed beyond 90 days.  
glass powder; self-compacting concrete; durability +
Footnote †: journal: Applied Sciences
# Analysis of Hydrological Characteristics of Blue Nile Basin, Nishe Watershed
## 2 Materials and Methods  
### Description of the Study Watershed  
The Nashe catchment is located in Oromia regional state, Ethiopia at about 300 km north-west of Addis Ababa. Geographically, the watershed is located at 9\({}^{\circ}\)35\({}^{\prime}\) N and 9\({}^{\circ}\) 52\({}^{\prime}\) N latitude, and 37\({}^{\circ}\)00\({}^{\prime}\) E and 37\({}^{\circ}\)20\({}^{\prime}\) E longitude (Figure 1). The watershed is a part of the Finchaa-Amerti-Nashe Project which is the sub-basin of Upper Blue Nile River Basin covering 94,578 ha. The Nashe river valley starts on the highland plateau with valley elevation above 2200 m and the surrounding ridges extend to over 2500 m. The elevation of the catchment varies from 1600 m in the lower plateau under the escarpment to hills and ridges of the highland climbing to over 2500 m. The average annual rainfall of the Nashe catchment varies from 1200 mm to 1600 mm, and June to September is the main rainy season of the watershed.
## 3 Results
### Comparison of the Sentinel-2 Irrigation Mapping to PROBA-V  
The coarser spatial resolutions of the different data sets, which were used to investigate the scaling behavior, are generated by spatially averaging the reflectance values from Sentinel-2 data before further processing the data. This ensures that the spectral sensitivity with which the red and NIR bands reflectance is measured is the same for all resolutions and that resulting NDVIs are derived in a consistent manner.  
Operational irrigation monitoring relies on coarse resolution sensors such as PROBA-V. It is, therefore, important from a monitoring point of view to investigate whether this downscaling approach leads to irrigated areas, which are comparable to those which are monitored operationally with PROBA-V. For one, the spatial resolution of 300 m of the downscaled Sentinel-2 data geometrically closely resembles that of VEGETATION on PROBA-V. Nevertheless, there are differences in the spectral characteristics of the red and NIR spectral bands, the time of overpass and, thereby, the illumination condition and related bi-directional reflectance effects during recording and the temporal coverage between the two sensors. To explore the influence of the different sensor systems on irrigation mapping, the results of the 300 m Sentinel-2 irrigation maps are compared to the results using the identical approach and NDVI time series derived from PROBA-V.  
Figure 5: The decrease in irrigated area at coarser resolutions at the study site in Sudan. The left image shows the area classified as irrigated at the resolution of 10 m. The middle shows the same area at 300 m and at the right at 1000 m.  
Figure 6 shows the irrigation map derived from PROBA-V NDVI data in 2016 with the same approach used for the Sentinel-2 series of spatial resolution data sets. The patterns shown in Figure 6 closely resemble those in Figure 3. Table 2 shows that the mapping results of approx. 300 m PROBA-V are very close to the results at the aggregated 300 m Sentinel-2 results in all three regions. PROBA-V overestimates the area in all three regions by approx. 6% in Sudan, 1.4% in China and 0.7% in the USA compared to Sentinel-2.  
We thus conclude that our irrigation mapping method using annual NDVI courses is transferable between Sentinel-2 and PROBA-V data. On the other hand, our analysis of the mapping results at different spatial resolutions shows that Sentinel-2, at a resolution of 10m, is able to detect additional irrigated areas which are lost at the coarser resolution. On the other hand, the large data volume involved would be a large obstacle for an operational global irrigation monitoring system based on Sentinel-2. By possibly using scaling relations that would, depending on the geographical setting, allow us to correct for the lost irrigated area in the coarse resolution operational irrigation monitoring system, the use of 10m-resolution Sentinel-2 data would largely enhance the monitoring result. Here, we propose a framework which would allow global PROBA-V irrigation monitoring to profit from sample Sentinel-2 irrigation mapping by allying appropriate scaling relations.
## 4 BIM in the Facility Management of Infrastructure
BIM technologies and open standards provide new opportunities for integrating the FM data, allowing a better management and integration of information for FM needs. The potential use of BIM, in the FM scenario, was realised since the early years of the development of the IFC schema. Some studies [153, 154, 155], e.g., in the case of buildings, show how the open standards can be used for FM applications. In this FM scenario, to date, building applications are more advanced and diffused than infrastructure ones. Indeed, one of the reasons is due to the lack of an infrastructure-specific version for some open standards (e.g., IFC). Recent developments in the building SMART scenario will enable advances in the FM application as well. However, the application of BIM methodology in infrastructure is still quite complex due to its limitations despite the technological advances that the construction industry is making. These limits are the lack of specialised software, the sector's resistance to change, the interoperability between systems and the processing of the information necessary for infrastructure management [10, 91, 156, 157]. Italso entails the risks, both legal and technical, associated with collaboration between the teams involved [158]. Below, there are some case studies focusing on the O&M of high infrastructure. For instance, the authors [159] investigate the BIM application areas for FM in urban rail transit in order to obtain efficient O&M activities. Most of the articles focus on building, with much fewer publications dedicated to transport infrastructure. These BIM investigations for an asset's FM focus on the use of a 3D model as a DB to be used during corrective or preventive maintenance. Moreover, the BIM model assists in the location of the maintenance operator and allows quick access to element documentation, usually in 2D plans. This process is further limited by the level of BIM model detail and the capacity of the equipment used in the operation.
## 3 Results
### Clustering  
#### 3.2.1 Irreproducibility Issues  
As described in the Reproducibility section, for each output metric and NN, we implemented the UMAP + clustering algorithm pipeline \(T=100\) times to highlight how independent runs of UMAP + clustering yield inconsistent outputs in terms of variability of the N\({}_{c}\) and ARI. In Figure 3, for each clustering technique, we report the absolute frequency of the estimated N\({}_{c}\) over the \(T\) iterations for each NN. The Euclidean metric shows the least variability in estimating N\({}_{c}\) over the \(T\) runs for each NN: the mode of N\({}_{c}\) is equal to 2 for HDBSCAN, 4 for DBSCAN, and 3 for OPTICS. The hyperbolic space is slightly more variable than the Euclidean one: the mode of N\({}_{c}\) is equal to 2 for HDBSCAN, except for NN equal to 10 and 20, 4 for DBSCAN, and it varies between 3 and 4 for OPTICS. On the contrary, the spherical surface has the highest variability in estimating N\({}_{c}\): the mode of N\({}_{c}\) varies between 3 and 4 for HDBSCAN, it ranges from 2 to 5 for DBSCAN, and it is equal to 3 for OPTICS.  
The inconsistency in results arises even when considering the clustering quality, which is quantified by means of the ARI score, as described in the Methods section. To assess its irreproducibility, we evaluated the interquantile range of the ARI returned by each implementation of the clustering method applied on T embeddings. Looking at Figure 4, except for HDBSCAN and OPTICS applied on Euclidean and hyperbolic spaces, the interquartile range of the ARI is quite large for each NN. Despite this high variability, if we consider the median of the ARI value, the Euclidean and hyperbolic metrics have higher  
Figure 2: A 3D visualization of UMAP projection (NN = 65). The pictures show 1084 samples grouped by five BC subtypes on a spherical surface (**a**), Euclidean space (**b**), and hyperboloid (**c**). The input RNA-seq data comprised 20,530 genes. We reduced it to three dimensions for Euclidean space and two dimensions for spherical and hyperbolic embedding.  
performances than the spherical one on three (DBSCAN, OPTICS, and agglomerative clustering) clustering algorithms out of four.  
Figure 3: Variability of the number of clusters (N\({}_{c}\)) estimation. Absolute frequency of the N\({}_{c}\) obtained from implementing HDBSCAN, DBSCAN, OPTICS in \(T=100\) iterations on UMAP embeddings. NN ranging from 10 to 100, for Euclidean, spherical, and hyperbolic space. A lighter color (yellow) indicates a higher frequency value, while a darker color (blue) indicates a lower, tending to zero, frequency value. We did not report N\({}_{c}\) of the agglomerative clustering since it is fixed a priori.
## 3 Method of Prediction and Optimization
### Multi-Objective Particle Swarm Optimization (MOPSO)
#### 3.2.2 Improved Parts and Scheme  
MOPSO designed in this paper could tackle the problem mentioned above precisely and efficiently. In order to better deal with the preceding optimization problem, this paper mainly modified frontier update and \(gbest\) selection. For frontier update, MOPSO adopts double refinement of frontier in each generation. Non-inferior solutions can enter Pareto frontier in the first refinement, then frontier needs out some inferior solutions with respect to other solutions in the second refinement. For \(gbest\) selection, MOPSO adopts changing tactic that allocates particles to each \(gbest\), which have two periods of time. It starts with allocating particles evenly to each \(gbest\) in order to keep the diversity of swarm. It would select \(gbest\) randomly from frontier for each particle after several generations, thus, particles will swarm to the concentrated area by reason of possibility. In all, this method pursues diversity in the early period and seeks high speed of convergence in later period [9].  
MOPSO can be split into three parts: main function, fitness function, constraint function. Main function manages the logic of each evolution of swarm. Fitness function calculates objectivesvalue such as cost of operation and the degree of users' satisfaction about power quality. Constraint function ensures the three equality constraints and two inequality constraints. Detailed scheme outlines as Figure 4.
## 5 Results and Analysis
### Analysis and Discussion
#### 5.4.1 \(Esoc_{s}\) Variations  
Figure 11 demonstrates the test results of \(ESOC_{S}\) for the selected four days. In the case of the spring day, \(ESOC_{S}\) increased from 0:00 to 7:30. Over the period, the energy demand is low while the wind power maintains a relatively high output. Most of wind energy is used to charge the storage system (hydrogen generation via electrolysis). Afterwards, the wind power and \(ESOC_{S}\) decrease while the energy demand is met by both the wind power and the fuel cell, as shown in Figure 11a.  
In Figure 11b, \(ESOC_{S}\) presents a continuous growth from 0:00 to 5:00 until reaching its upper limit (0.9). From 6:30 onwards, it dramatically decreases until 14:00. \(ESOC_{S}\) reached its upper limit at 20:00 again after some fluctuations. In the summer day, a fast and continuous growth is seen as the wind is strong while a decrease is seen due to the demand over the wind energy supply.  
The autumn day in Figure 11c also presented some fluctuations. \(ESOC_{S}\) kept growing over the night until 6:30 when the energy demand was higher than the wind energy. From 9:30 onward, \(ESOC_{S}\) recovered and grew to its upper limit from 12:00 to 17:00 before meeting a peak demand in the afternoon. Afterwards, \(ESOC_{S}\) declined until reaching its low limit (0.2).  
\(ESOC_{S}\) on the winter day maintained higher than 0.5, as shown in Figure 11d. The main decline occured at 9:00 and 17:00 when both morning and evening peak demands start. After peak demands, \(ESOC_{S}\) started to rise as more power from wind turbines was used to charge the storage system.  
Figure 11: ESOC\({}_{S}\) profiles of the storage system in the four days. (**a**) spring day; (**b**) summer day; (**c**) autumn day (**d**) winter day.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 5 Integrating Cognitive Radio in LPWAN for the Internet of Things
### Network Architecture to Support CR-LPWAN
#### 5.3.2 CR at Gateway  
In CR at gateway (CRGW) scheme, CR technologies are embedded in the GW. In this case, the CR-LPWAN GW conducts all CR functions (particularly the spectrum and decision making functions) and communicates the acquired white spaces to all end nodes in the network. Then, end nodes only conduct spectrum access/sharing. This may present certain challenges for adapting the CRGW scheme, for example, in the Sigfox and LoRaWAN class A brands since both technologies imply ALOHA-like access methods. Thus, the GW can only communicate with an end node after the end node has sent some data in the uplink and only in a one-to-one mode. Additionally, Sigfox has very strict limits for the number of downlink packets sent. Thus, it may be challenging to implement the CRGW scheme, since this may require modifications to the existing respective protocols, for example, the need to depart from LoRaWAN Class B/C technologies. Nevertheless, it is envisaged that the CRGW scheme will reduce the design complexities of the end nodes, which will reduce the device costs of CR-LPWAN systems and the power consumption rates of end nodes. However, network message overheads may increase owing to the need for continuous negotiations between the GW and the large number of end nodes in the network. These issues are potential research issues to be  
Figure 5: A simple CR-LPWAN-based network architecture (adapted from [84]).  
addressed towards developing functional CR-LPWAN systems. An example of the CRGW scheme can be found in [10].
## 4 Conclusions  
In this study, PEMS were compared with laboratory-grade instruments measuring the exhaust gas of four N3 Euro VI step E vehicles (three diesel and one CNG), all equipped with particulate filters. The tests covered different test cycles, different ambient temperatures, cold and hot starts, and different fuels. The differences between PEMS and the reference instruments were typically below 40% or 10\({}^{11}\) #/kWh at a wide range of conditions and emission levels. The PEMS operated normally from \(-\)7 \({}^{\circ}\)C up to 35 \({}^{\circ}\)C. Two issues were identified, though, that resulted in differences of 50-100% at the 10\({}^{11}\) #/kWh range: the AVL MOVE had a sensitivity to pre-charged particles originating from urea. This was solved by activating the electrofilter. The Horiba OBS had pressure fluctuation issues with one vehicle. The reasons are not clear, but we assume a different orientation of the probe would minimize these effects. We recommend always using the sampling probe without facing the flow in order to protect the instruments from big flakes or exhaust condensates and minimize pressure pulsation impact. Furthermore, the pressure fluctuations were measured by the device and could trigger a warning in the future. Although this study demonstrated the robustness of PEMS under a wide range of conditions, more tests in the two cases identified are recommended to confirm that in the future, such cases will not take place.  
Conceptualization, B.G.; formal analysis, B.G.; writing--original draft preparation, B.G.; writing--review and editing, A.M., S.B., R.G. and R.S.-B. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Figure 9: Difference of AVL’s MOVE PEMS from laboratory reference instruments. Results for 10 nm instruments, unless otherwise specified. All tests with the D2 vehicle and EF were done at \(-\)7 \({}^{\circ}\)C. CNG = compressed natural gas; EF = electrofilter; HVO = hydrotreated vegetable oil; SPN = solid particle number.  
**Acknowledgments:** The authors would like to acknowledge the technical staff ([PERSON], [PERSON], [PERSON], and [PERSON]) and AVL's resident engineer, [PERSON], for the execution of the tests. The authors also would like to acknowledge Horiba ([PERSON]) and AVL ([PERSON] and [PERSON]) for providing the 10 nm PEMS instruments used in this study.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
**Disclaimer:** The information and views set forth are those of the author(s) and do not necessarily reflect the official opinion of the European Commission. Neither the European Union institutions and bodies nor any person acting on their behalf may be held responsible for the use that may be made of the information contained therein. Mention of trade or commercial products does not constitute endorsement or recommendation by the authors or the European Commission.
###### Abstract  
Germany (DE) and China (CN) have different political approaches in supporting the biogas sector. Three German and three Chinese large-scale biogas plants (BGPs) were evaluated as part of a year-round monitoring program. Laboratory methods were utilized to analyze the chemical indicators. Results showed a stable anaerobic digestion process without system failures in all BGPs. The methane yield had a range of 0.23-0.35 m\({}^{3}\)CH\({}_{4}\)/kg\({}_{\text{GDM}}\) for DE BGPs and 0.11-0.22 m\({}^{3}\)CH\({}_{4}\)/kg\({}_{\text{GDM}}\) for CN BGPs, due to different substrates and working temperatures. Financial analyses indicated that DE BGPs are viable under their current feed-in tariffs contracts. Their financial internal rate of return (IRR) ranged between 8 and 22%. However, all CN BGPs had negative IRRs, indicating that they are financially unfeasible. Risk analyses illustrated that DE BGPs will face financial nonviability if benefits decrease by 9-33% or costs increase by 10-49%, or if a combined worse case (benefit decrease and cost increase) of 5-20% occurs. Incentives to BGP operations are particularly important in China, where the government should consider switching the construction-based subsidy to a performance-based subsidy system to motivate the operators. BGP monitoring is necessary to understand the performance, in addition to briefing policymakers in case a policy reform is needed.  
anaerobic digestion; biogas; performance monitoring; financial analysis; policy +
Footnote †: journal: Appl. Sci. 2021, 11, 1271. [[https://doi.org/10.3390/app11031271](https://doi.org/10.3390/app11031271)]([https://doi.org/10.3390/app11031271](https://doi.org/10.3390/app11031271))
# Electrical Monitoring under Transient Conditions:
## 4 Results
### Results in Field Motors
#### 4.2.2. Cage Motor in a Sewage Treatment Plant (30 kW, 400 V)  
Several cage induction motors of a sewage treatment plant were diagnosed by applying the transient-based current methodology. Figure 9 depicts the time-frequency analyses for one of these motors (considering two different frequency ranges in the \(y\)-axis for a better visualization of the fault components). After a careful analysis of this map, it is observed that not only the components linked with rotor damages are present in that map (note the existence of the V-shaped pattern caused by the transient evolution of the \(f\)-\((1-2\cdot s)\) component), but also other evolutions corresponding to harmonics caused by other factors. In this regard, we note the presence of the evolutions linked with eccentricity components (\(f_{\text{ecc}}\)), as well as some other harmonics that are caused by problems in the coupling system (\(f_{\text{cupl}}\)). The presence of these harmonics enabled diagnosing, on the one hand, the existence of a slight eccentricity in the motor and, on the other hand, the presence of a certain level of degradation in the coupling system (which in this motor was based on gear reducers).
# A Two-Stage Local Path Planning Algorithm Based on Sampling and Optimization Methods
## 4 Simulations and Experiments  
### Simulations  
To validate the performance of the PSF algorithm, simulations were conducted using the Gazebo \(11\) virtual simulation software in Ubuntu 20.04. The TurtleBot3 Waffle model was employed as the mobile robot. A straight global path of 8 m in length was utilized, with two obstacles placed along its trajectory. The initial coordinates of the robot relative to the starting point of the global path were \((0.5,0.2)\). The simulation was performed on a system with the following specifications: Ubuntu 20 operating system, Intel i5-12 processor, and 16 GB of RAM. The simulation environment is illustrated in Figure 5. The parameters of the PSF algorithm are shown in Table 1.  
The simulation results of the exploration stage of the PSF algorithm are shown in Figure 6. It can be seen that the exploration stage of the PSF algorithm can easily find a preliminary path that is closest to the global path and does not collide with the obstacles.  
The optimization process during the PSF algorithm optimization stage is depicted in Figure 7. Upon the first iteration of preliminary path optimization, deviations from constraints cause the initial path to deform towards the global path, while obstacle constraints prevent the deformation of the preliminary path around obstacles. As the number of optimization iterations increases, the influence of velocity constraints on the preliminary path gradually intensifies. By the fifth iteration, the optimization algorithm has converged, yielding the optimal local path. It can be observed that due to the PSF algorithm's op  
Figure 5: Gazebo simulation environment.