esa-sceva/satcom-chunk-collection huggingface dataset:
# Future Renewable Energy Communities Based Flexible Power Systems
## 4 The Role of Prosumers in Smart Cities  
Recent advances in information and communication technology (ICT) have paved the way for small-scale energy end-users to play more active roles in energy sectors [24]. These end-users whose previous roles were limited to energy consumers, are now able to monitor and manage their consumption and produce energy by their own renewable resources. For example, a household can be equipped with a rooftop PV panel integrated with a battery which produces energy. In this way, not only does the end-user fulfil its own electricity need, but it can also sell the surplus energy to the grid and make profits. In fact, the role of this smart end-user changes from a passive consumer to a pro-active consumer or a \"prosumer\".  
In addition to producing energy, smart prosumers with energy management systems (EMS) can help the grid become more flexible [25]. Smart prosumers have the ability to reshape their consumption based on the grid's flexibility needs. The grid operator activates the flexibility potential of prosumers using dynamic pricing, or designing different local markets and various programs. These programs are called \"demand-response programs\". Demand response programs act as a motivation for small-scale prosumers to reshape their consumption based on the utility and power distribution system requirements.  
The recent technologies that are integrated into EMSs enable a smart prosumer to follow certain targets [26]. For example, the prosumer's targets can be to minimize its electricity costs or maximize the profits obtained from participating in various demand-response programs or marketplaces. EMSs help the prosumers to achieve these objectives. However, they need to consider the comfort level of prosumers along with their economic profits. For instance, the EMSs should manage air conditioning devices so that it does not disturb the thermal comfort of household occupants [27]. Figure 9 shows a smart prosumer equipped with the EMS, as well as the interaction between the EMS and the household appliances; energy management tasks include monitoring uncontrollable devices, controlling household controllable appliances, and forecasting PV generation.  
In general, EMSs coordinate several tasks to manage prosumer's energy consumption and production. The efficient management procedure requires adequate meters and sensors as well as the bi-directional communication capability between the smart prosumer and the other utilities that request flexibility. A prosumer may have its own EMS and individually manage its consumption and production. On the other hand, prosumers can be aggregated by an aggregator or in an energy community environment. In this way, there is a central EMS that manages the consumption and production of the community's members to benefit the community as a whole [28].  
Figure 8: Hybrid AC-DC architecture for smartgrids.
## Materials and Methods
### Materials Characterization
#### 2.1.2 Ion-Exchange Characterization  
The Cation Exchange Capacity (CEC), which indicates the maximum amount of cations (expressed as meq) exchangeable by 1 g of tuff, was assessed using the Cross-Exchange Method (CEM) [43], which permits the estimation of the amounts of exchangeable cations removed by elution from the specimen, using two different solutions: one with high content of K\({}^{+}\), for which most natural zeolites exhibit a valuable selectivity; the other rich in Na\({}^{+}\), for which natural zeolites are generally weakly selective. Subsequently, two 0.5 g zeolite samples, placed on Gooch filters, were percolated drop by drop, at about 60 \({}^{\circ}\)C, up to exhaustion by 1 M NaCl (CAS: 7647-14-5; M = 58.44 g/mol; Sigma Aldrich; assay: >99.0%) or KCl (CAS: 7447-40-7; M = 74.55 g/mol; Sigma Aldrich; assay: >99.0%) solutions (purity of the chemicals: 99.5%). The monocationic (Na\({}^{+}\) or K\({}^{+}\)) sample forms, after suitable washing, were then cross-exchanged in the equal way as aforementioned with K\({}^{+}\) or Na\({}^{+}\) solutions, respectively. The CEC was determined after the cationic content evaluation via ICP-OES.  
In addition to the CEC evaluation, the External Cationic Exchange Capacity (ECEC)--i.e., the maximum amount of cations exchangeable on the zeolite surface--was determined by contacting 0.5 g of the starting material and 50 mL of surfactant solution (100 mM Hexadecyltrimethylammonium bromide, HDTMA-Br (CAS: 57-09-0; M = 364.45 g/mol; Sigma Aldrich; assay: >97.0%)) under continuous stirring and at room temperature, replacing the total volume of the exhausted solution with a fresh one every 12 h [32]. The obtained supernatants were analyzed for the exchanged cations (Na, K, Ca and Mg) using ICP-OES.  
The ECEC evaluation was performed only with HDTMA-Br since previous experience [42] demonstrated, in the evaluation of the ECEC of clinoptilolite, phillipsite and chabazite samples that the influence of the selected surfactant (HDTMA-Br, HDTMA-Cl, CP-Cl and BC-Cl) on the results was negligible.
## 4 Simulation and Experimental Results
### Simulations
#### 4.1.1 Case 1 (A Static Target)  
In case 1, the initial poses of five robots and the speed of the target are, respectively, \(\mathbf{q}_{1}(0)=[3.5,-3,\pi/2]^{T}\), \(\mathbf{q}_{2}(0)=[4,-2,\pi]^{T}\), \(\mathbf{q}_{3}(0)=[3.5,1,-\pi/2]^{T}\), \(\mathbf{q}_{4}(0)=[-0.5,0.5,0]^{T}\), \(\mathbf{q}_{5}(0)=[0,-3.5,0]^{T}\), \(\mathbf{v}_{0}(t)=[0,0]^{T}\).  
Figure 4: Simulation results of case 2. (**a**) Trajectories of five robots and a moving target with a constant speed in a plane. (**b**) Error between current phase and the desired phase of robot 1. (**c**) Distance errors between five robots and their desired circles centered on the target. (**d**) Errors between current angular spacing and the desired angular spacing of all adjacent inter-robots. (**e**) The linear speeds of five robots. (**f**) The angular speeds of five robots.  
Figure 5: Simulation results of case 3. (**a**) Trajectories of five robots and a moving target with time-varying speed in a plane. (**b**) Error between current phase and the desired phase of robot 1. (**c**) Distance errors between five robots and their desired circles centered on the target. (**d**) Errors between current angular spacing and the desired angular spacing of all adjacent inter-robots. (**e**) The linear speeds of five robots. (**f**) The angular speeds of five robots.  
The motion trajectories from Figure 3a show that the robots can form desired circular formation with three different circles radii and four different angular spacings, and avoid obstacles effectively. Moreover, it can be seen from Figure 3b-d that the initial phase of robot 1 on its circle, the relative distance between the robots and the target, and the angular spacing between the adjacent robots can converge to the desired values. In addition, the output linear and angular speeds of the designed controller from Figure 3e,f can meet the actual speed requirements of the robots, and the maximum linear and angular speeds of the robots are set to \(v_{\text{max}}=1.5\) m/s and \(\omega_{\text{max}}=1.5\) rad/s in order to prevent excessive input speeds.
# A Multi-Objective Path-Planning Approach for Multi-Scenario Urban Mobility Needs
###### Abstract  
With the development of smart cities and intelligent transportation systems, path planning in multi-scenario urban mobility has become increasingly complex. Traditional path-planning approaches typically focus on a single optimization objective, limiting their applicability in complex urban traffic systems. This paper proposes a multi-objective vehicle path-planning approach tailored for diverse scenarios, addressing multi-objective optimization challenges within complex road networks. The proposed method simultaneously considers multiple objectives, including total distance, congestion distance, travel time, energy consumption, and safety, and incorporates a dynamic weight-adjustment mechanism. This allows the algorithm to provide optimal route choices across four application scenarios: urban commuting; energy-efficient driving; holiday travel; and nighttime travel. Experimental results indicate that the proposed multi-objective planning algorithm outperforms traditional single-objective algorithms by effectively meeting user demands in various scenarios, offering an efficient solution to multi-objective optimization challenges in diverse environments.  
Keywords:path planning; urban travel; smart transportation; multi-objective optimization
## 3 Proposed Method
### System Architecture
#### 3.1.1 Local-End Learnings and Intelligence  
In this part of the framework, each \(k\) client (\(k\in[1,\,\ldots,\,K]\)) at the local end trains the data acquired from their separate IIoT devices with the local models shared by the server, while the IDS at the client end detects any unwanted attacks. Additionally, an analyzer is employed to keep track of their network data for subsequent analysis. This kind of smart learning on the device protects the independence of local intrusion detection by requiring local training, tweaking of parameters, and improving inference procedures.
# _remote sensing_
## 3 Results
### Orbit Error  
The precision ephemeris products of COD were used as true values to evaluate the accuracy of the D1 and B-CNAV navigation messages of the 24 MEO satellites of BDS-3. According to a one epoch per hour BDS-3 broadcast ephemeris, the orbit position errors of the 16-parameter broadcast ephemeris of 2022 are plotted as a time series in Figure 2. The figure shows the annual evolution of the orbit position's accuracy in the radial, cross, and along directions. The D1 navigation message of the BDS-3 MEO constellation exhibits mean orbit errors of \(-0.032+/-0.057\) m, \(-0.001+/-0.273\) m, and \(0.037+/-0.293\) m in the three directions, respectively, where the preceding values represent the mean value, and the specified uncertainties reflect the standard deviation. For the entire MEO satellite constellation, the mean values of the orbit errors were very small. This also shows that the antenna offset correction used was effective and reasonable. Overall, for the entire year of 2022, the orbit position accuracy of BDS-3 MEO's broadcast ephemeris was the highest and the most stable in the radial direction and was slightly worse and fluctuating in the cross and along directions. For the C19 satellite, there was a significant change in orbit accuracy in 2022, especially in the radial direction.  
Figure 2: BDS-3 orbit differences (EPOCH 1-8760, 2022) between D1 navigation message and precise products. Each satellite has its own color.  
The annual RMS statistics of the orbit errors from the D1 navigation message are listed in Table 1, and the accuracies of the orbit errors in 3D, along, cross, and radial directions are listed. Generally, the radial accuracy was the best, and cross accuracy was better than that of the along direction. In order to clearly analyze the orbit position's accuracy, the annual RMS accuracy statistic of every satellite was calculated and is shown in Figure 3. It can be observed that the orbit position accuracies of the 24 MEO satellites have similar variations in the three directions and in 3D.  
Table 2 shows the RMS statistic and comparison of the orbit error between the D1 and B-CNAV navigation messages. Moreover, the accuracy improvement ratio of B-CNAV messages compared to D1's is shown. It can be observed in Table 2 that compared with the D1 navigation message, the orbit position accuracy of the B-CNAV message had no difference in the cross and along directions but had significant improvements in the radial direction, and its accuracy improvement ratio was 19.06%. This also improved the orbit accuracy in 3D directions. The accuracy improvement ratio of the 24 satellites of the B-CNAV navigation message is plotted in Figure 4. For each satellite, the orbit accuracy was hardly improved in the along and cross directions, but it was improved to varying degrees in the radial direction.
# Approaches for Dealing with Seasonality in Clinical Prediction Models for Infections
## 3 Materials and Methods
### Dataset Description
#### Data Extraction  
It should be noted that the aim of this work is not to develop a precise clinical model; rather, it is to test the performance of the different approaches employed to deal with seasonality in this context. Therefore, we designed a query for MIMIC-III in order to extract a dataset containing generic data related to infections that might be suitable for our study.  
The query was specifically designed to retrieve the first positive microbiology test for each microorganism and sample type in every admission. It collected various demographic data (age, gender, insurance, marital status, ethnicity), data related to microbiology tests (microorganism found, type of sample, date of the test), and hospital stay data (admission type and location, previous ICU stays, current service at the time of test ordering), as well as the mean, maximum, and minimum values of the white blood cell count and lactate within a 24 h time windows on the day the sample was obtained. The code of the query is depicted in Appendix A.  
We generated two datasets from the results of the query, each of which was focused on a different species of bacteria. The target outcome was to predict whether the microorganism isolated in each test belonged to the species of bacteria being studied.  
In the _Acinetobacter dataset_, we focused on bacteria belonging to the _Acinetobacter_ species. These are responsible for many healthcare-associated infections (HCAIs), and multiple studies suggest the existence of clear seasonal variations in these infections [3]. In this dataset, we assumed that those microbiology tests that were positive for _Acinetobacter sp._, _Acinetobacter baumannii_, or _Acinetobacter baumannii complex_ belonged to the _positive_ class and that the others were _negative_.  
Another bacteria species with clear seasonal variations is _Streptococcus pneumoniae_, for which infections are known to occur more frequently in cold seasons [39]. Using a similar strategy, we generated an _S. pneumoniae dataset_ in which those microbiology tests in which _S. pneumoniae_ were detected were considered as _positive_ and the others where considered as _negative_.  
The time when the microbiology sample was obtained was considered as the temporal reference in these datasets. The data in the MIMIC-III database have been de-identified in order to protect the patients' confidentiality, which implies that the available date is not the real one. The de-identification procedure randomly shifts the real date into the future, sometime between the years 2100 and 2200. However, the season is preserved (i.e., an observation made during winter will be shifted to a winter month in the future), making these data appropriate for our work.
###### Abstract  
Cu/Zr-modified TiO\({}_{2}\) photocatalysts were prepared in the form of nanopowders and characterized by photothermal spectrometry, UV-Vis spectrophotometry and X-ray diffraction (XRD) to investigate the effect of Cu/Zr content on their thermo-optical and transport properties. Adding Cu (0.05%) caused a change in the light absorption range limit, which reduced from 3.25 eV for pure TiO\({}_{2}\) to 2.85 eV for Cu-modified TiO\({}_{2}\). The decrease in energy band gap was accompanied by a 19.5% decrease in the charge carrier lifetime, which is not favorable for photocatalysis. The decrease in charge carrier lifetime can be minimized by additional modification of TiO\({}_{2}\) with Zr (1%), which showed insignificant effects on the energy band gap of the investigated materials. Furthermore, modification of TiO\({}_{2}\) with Zr affected the material's structure and increased its specific surface area, which improved the adsorption of degraded compounds as well as the absorption of light. Altogether, these effects resulted in higher photocatalytic degradation rate constants of the investigated TiO\({}_{2}\)-based photocatalyst. It was also found that modification of TiO\({}_{2}\) with Cu and/or Zr increases both the material's thermal diffusivity and conductivity due to changes in the band gap and structure of material. Beam deflection spectrometry (BDS) has demonstrated high potential in materials' characterization which stems from its high sensitivity and precision.  
photocatalysts; TiO\({}_{2}\); Cu/Zr; photodegradation; material characterization; photothermal spectrometry +
Footnote †: journal: Applied Sciences
## 3 Measurement Setup
### Metrics
#### 3.2.3 Download Throughput via HTTP  
For download throughput, we compare the transfer time with and without Tor while downloading 50 KB and 1 MB of data via HTTP. Pre-tests showed that 50 KB and 1 MB are sufficient in order to have a good throughput indicator. Furthermore, these are also steps chosen by the Tor metrics project [21]. By choice, the target URL for this part of the experiment was fixed throughout the experiment, since download of arbitrary files from multiple sites could lead to practical problems and could cause random errors. The downloaded file was located on Google's servers ([[http://dl.google.com/picasa/picasa3-setup.exe](http://dl.google.com/picasa/picasa3-setup.exe)]([http://dl.google.com/picasa/picasa3-setup.exe](http://dl.google.com/picasa/picasa3-setup.exe))). Since this file is larger then required, the download was canceled after 1 MB. In this paper, throughput is used in the sense of effective \"goodput\", referring to application-layer throughput and not the actually larger amount of bytes sent via the network. Transmission overhead such as a TCP header is not being considered according to the definition of goodput [41]. The Perl script was executed twice an hour in order to measure the traffic being downloaded. This was once done directly and once using Tor via the Socks proxy Privoxy.
# Lifting Activity Assessment Using Kinematic Features and Neural Networks
## 3 Results
### Mapping of Kinematic Features on Li Levels  
The detection performances are reported in Figure 3; they are expressed as the detection percentage P (mean \(\pm\) SD) obtained by using the nine different network topologies (in terms of HL and N) when using the eight different training sets. Furthermore, Figure 3 shows also the best confusion matrix for each SET (confusion matrix with highest value of P). The training set, as in the outcome of the three-way ANOVA, (df = 7; F = 174.01; \(p<0.001\)), N (df = 2; F = 6.07; \(p=0.002\)) and HL (df = 2; F = 5.2; \(p=0.01\)) resulted to have a significant effect on the ANN performance. Particularly, post-hoc analysis outlined the following significant differences (\(p<0.05\)):  
* SET\({}_{1}\) vs. all SET\({}_{1}\);
* SET\({}_{2}\) vs. SET\({}_{3}\), SET\({}_{4}\), SET\({}_{5}\), SET\({}_{6}\);
* SET\({}_{3}\) vs. SET\({}_{1}\), SET\({}_{2}\), SET\({}_{7}\), SET\({}_{8}\);
* SET\({}_{4}\) vs. SET\({}_{1}\), SET\({}_{2}\), SET\({}_{7}\), SET\({}_{8}\);
* SET\({}_{6}\) vs. SET\({}_{1}\), SET\({}_{2}\), SET\({}_{7}\), SET\({}_{8}\);
* SET\({}_{7}\) vs. all SET\({}_{1}\) excepted SET\({}_{2}\);
* SET\({}_{8}\) vs. all SET\({}_{1}\) excepted SET\({}_{2}\). As regards N, from post-hoc analysis the following significant differences (\(p<0.05\)) showed up: N = 12 vs. N = 50 and N = 20 vs. N = 50. As regards HL, post-hoc analysis didn't show significant differences (\(p>0.05\)). In general, SET\({}_{2}\), SET\({}_{7}\) and SET\({}_{8}\) showed higher mean values of performance with lower standard deviations (Figure 3). Table 2 shows the sensitivity and specificity (mean \(\pm\) SD) values obtained when the nine architectures of the ANNs and the different training sets. Figure 2: Jerk square mean (JSM) values. Mean and standard deviation values of the JSM values evaluated considering angles of trunk, elbow and knee while performing manual material lifting tasks in the three different conditions (LI = 1, LI = 2 and LI = 3) and statistical significance (* means \(p\)-value \(<\) 0.05; ** means \(p\)-value \(<\) 0.001). Figure 3: ANNs performance. The performance are reported on the basis of the different training sets (SET\({}_{i}\), i = 1,..., 8) and the nine different architectures (see Section 2.7 for details). The best mean confusion matrix was reported for each SET\({}_{i}\).
## 3 Results
### sRAGE Levels
#### 3.2.3 Environment  
Mean plasma sRAGE levels were significantly decreased in children exposed to tobacco smoking during pregnancy: 1478 \(\pm\) 296 pg/mL versus 1870 \(\pm\) 422 pg/mL, \(p\) = 0.007 (Table 1). In contrast, current passive tobacco exposure had no impact on sRAGE levels (1772 \(\pm\) 479 pg/mL versus 1837 \(\pm\) 397 pg/mL, \(p\) = 0.547) (Table 1). Association of antenatal and current exposure was associated with decreased levels of sRAGE, but not significantly (\(p\) = 0.053) (Figure 2). In children with asthma, sRAGE levels were lowered in those exposed to tobacco during pregnancy (1257 \(\pm\) 96 pg/mL versus 2099 \(\pm\) 303 pg/mL in non-exposed, \(p\) = 0.004) unlike those currently exposed to tobacco, with 1686 (\(\pm\)271 SD) pg/mL versus 1805 (\(\pm\) 388 SD) pg/mL in non-exposed, \(p\) = 0.550. Lower levels were observed in children living in apartments (1557 \(\pm\) 372 pg/mL) than in those living in houses (1863 \(\pm\) 427 pg/mL) (\(p\) = 0.031) (Table 1). The presence of animals did not modify sRAGE levels at age 7 (\(p\) = 0.322), specifically not cats (\(p\) = 0.579).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Mean Plasma sRAGE (pg/mL) \(\pm\) SD** & & & \\ \hline  & **Yes** & **\(N\)** & **No** & **\(N\)** & **\(p\)** \\ \hline Asthma 7 years & 1875 \(\pm\) 472 & 15 & 1794 \(\pm\) 418 & 53 & 0.525 \\ Patient with inhaled treatment & 1893 \(\pm\) 453 & 23 & 1771 \(\pm\) 414 & 45 & 0.271 \\ \hline Personal atopic status & & & & \\ Aeroallergen sensitization & 1908 \(\pm\) 494 & 16 & 1783 \(\pm\) 406 & 52 & 0.309 \\ Eosinophilia & 1821 \(\pm\) 380 & 30 & 1819 \(\pm\) 465 & 37 & 0.981 \\ \hline Environmental exposures & & & & \\ Tobacco smoking in utero & 1478 \(\pm\) 296 & 10 & 1870 \(\pm\) 422 & 58 & 0.007 * \\ Passive tobacco smoking & 1772 \(\pm\) 479 & 26 & 1837 \(\pm\) 397 & 42 & 0.547 \\ Apartment & 1557 \(\pm\) 372 & 11 & 1863 \(\pm\) 427 & 56 & 0.031 * \\ Cat & 1774 \(\pm\) 311 & 25 & 1834 \(\pm\) 485 & 43 & 0.579 \\ \hline \hline \end{tabular} Data are presented as \(N\) and mean \(\pm\) standard deviation (SD). * means \(p\)\(\leq\) 0.05.  
\end{table}
Table 1: Mean serum sRAGE levels.
## 4 Steady-State Performance Index and Optimal Gains  
We derive the indices for the tracking performance (steady-state bias error) and the smoothing performance (steady-state variance of random errors) of the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter. Furthermore, the optimal gains are calculated using the derived indices to analytically investigate the optimal performance of the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter.