esa-sceva/satcom-chunk-collection huggingface dataset:
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### ICT for in-Home and Building Energy Management
#### 4.3.8 Ginseny  
Another project dealing with energy management for buildings through ICT solutions has been the \"FINSENY\" project, in which participants from 12 countries took part. The project addresses ICT solutions for the operation of smart distribution networks as well as issues related to monitoring and control of microgrids and their interconnection to the overall grid. Regarding the distribution networks, the project intended to design an ICT solution for distribution system automation in order to enhance the grade of integration of Distributed Energy Resources. For this purpose, access to electrical data of the MV network has been planned by the control center. With respect to the energy management in buildings, smart metering solutions have been used. In general, the PLC technology has been preferred for smart meter communication with the data concentrator. In cases, PRIME technology has been also used for smart meters. Normally, substations are considered as the sensors and smart metering aggregation points in the LV or MV network. From that point, data is communicated with the central operation center through GPRS technology. For the Home Network and communication between devices inside the house, the ZigBee protocol is used [88].
# Analysis of Urban Residents' Travelling Characteristics and Hotspots Based on Taxi Trajectory Data
## 3 Experiments, Results, and Analysis
### Analysis of Residents' Travel Time Characteristics
#### 3.2.3 Comparative Analysis  
To gain a comprehensive understanding of residents' travel patterns, a comparative analysis of taxi travel duration on both weekdays and weekends is essential. This involved calculating the passenger carrying time of taxis \(i\) at different times of the day, denoted as \(T_{1}\), \(T_{2}\), \(T_{3}\), \(T_{4}\),... \(T_{i}\), with the cumulative duration for an entire day recorded as \(\sum_{i=1}^{n}T_{i}=1\). Using the OD point matching algorithm, the analysis focused on the statistical travel time of residents on taxis on both weekdays and weekends, as exemplified in Figure 12.  
By comparing the passenger flow during various time periods on both weekdays and weekends, the following conclusions are obtained:  
1. On weekdays, peak hours are predominantly in the time periods of 7:00-9:00, 11:00-13:00, and 19:00-21:00. The uniformity in residents' travel purposes on these days, primarily for work or school, results in more regular and concentrated traffic flow. Time-bound obligations, such as office check-ins and school start times, contribute to this pattern.
2. Weekends exhibit more varied peaks and troughs in passenger volume, and the timing of taxi usage tends to be later compared with weekdays. This variation stems from the lack of uniform travel purposes on weekends, giving residents the freedom to choose their destinations and travel times without strict time constraints. Consequently, weekend travel is characterized by more rest time and flexibility.
3. Combining the findings from Sections 3.2.1 and 3.2.2 with Figure 12, it is observed that weekday travel times predominantly range from 19 to 40 min, while weekend travel times are mostly between 16 and 35 min. The longer travel times on weekdays could be attributed to longer commutes to work or school. In contrast, weekend  
Figure 12: Statistics of residents’ travel time on taxis: (a) statistics of residents’ travel time on weekdays; (b) statistics of residents’ travel time on weekends.  
travels are typically shorter city trips. Additionally, there are some variations in travel times, influenced by factors such as the proximity of destinations to residential areas or traffic conditions. On weekdays, shorter travel times might occur when schools or workplaces are closer to homes, while traffic congestion could extend travel durations. On weekends, travel times vary due to the flexible nature of leisure travel, but they generally represent shorter distances.
## Appendix A Calculation of Dynamic Track Compliances by 3-Dimensional FEBEM
### Slab Track in a Tunnel (on a Layer over Bedrock)  
The slab track in the tunnel has a 0.6 m thick concrete plate which lies on a 1.5 m layer of softer soil material. The base is the tunnel invert which is assumed to be rigid. Medium soft rail pads of \(k_{P}\) = 60 kN/m are used to compensate the stiff track plate. The compliance of the rail is ruled by these soft rail pads and is constant if the stiffness of the sub-soil is varied between \(v_{S}\) = 100 and 300 m/s. Moreover it does not change its static value of \(u/F\) = 3.7 \(\times\) 10\({}^{-9}\) m/N with increasing frequency (see Figure 11d). The influence of the sub-soil can be clearly seen for the plate compliance under the axle load (Figure 11c). The massive plate yields low resonance frequencies of \(f\) = 20; 30; 40 and 60 Hz with the compliances of the different sub-soils. The amplitudes of the plate are much smaller than those of the rail. The amplitude ratio is 20 or 10% for the soft sub-soils and even less for the stiff sub-soils. Such strong differences between rail and sleeper displacements are only found for the tunnel track. The sleeper to rail displacement ratio of the ballasted tracks is at 33 to 67% for the bridge track line and at 50 to 77% for the surface line. The differences increase with a stiffer ballast material; but also with a softer rail pad.  
Figure 11d shows the rail compliances of the three tracks, surface track with soft ballast, bridge track with soft ballast, and tunnel track with a thick plate over a stiff sub-soil. The rail compliance of the tunnel is increasing at 150 Hz, the end of the frequency range. The resonance of the rail on the soft rail pads is expected at about 200 Hz. Whereas the ballasted tracks show a phase drop below 100 Hz, the rail of the tunnel track shows almost no phase delay.  
Figure 11: Compliances of different tracks: (**a**) ballasted track on the soil, (**b**) ballasted track on the bridge, (**c**) slab track on a sub-soil layer (compliance of the plate) variation in the ballast or subsoil \(v_{S}\) = \(\square\) 100, \(\circ\) 150, \(\triangle\) 200, +300 m/s in (**a**–**c**). Comparison of the rail compliances (**d**) for the ballasted track on the soil \(\square\), the ballasted track on the bridge \(\circ\), and the slab track \(\triangle\).
# Supervised Dynamic Correlated Topic Model for Classifying Categorical Time Series
## 4 Application for Promoter Sequence Identification in _E. coli_ DNA Sub-Sequences
### Comparison with Other Methods  
We compared our sDCTM model with existing classification techniques, such as SVM, k-NN, and classification trees [33]. We used the k-NN classification technique, which is a popular approach used for sequence classification [10] on the observed data. We compared our approach to popular machine learning techniques like SVM and classification trees on the extracted features from the DNA sequence. We used the Haar wavelet transform and the simplest discrete wavelet transform (DWT) to extract features from the observed time series and build a classification model based on these features. We implemented the k-NN classification technique using the R package _class_[34], which identifies \(K=5\) nearest neighbors (in Euclidean distance) to classify the train and test data. We used the R package _wavelets_[35] to extract the DWT (with Haar filter) coefficients. The classification tree was implemented using the R package _party_[36], and the SVM was implemented using the R package _e1071_[37]. We ran each of these methods using an 80-20 train-test split to assess model performance. The train and test accuracies are shown in Table 7. In comparison with the the other classification techniques, sDCTM performed better on both the train and test data.
# Modeling and Simulation of IGCC Considering Pressure and Flow Distribution of Gasifier
###### Abstract  
The integrated gasification combined cycle (IGCC) is a power generation technology which combines clean coal technology with a combined cycle. The system modeling is significant for design, operation and maintenance of the IGCC power plant. However, the previous IGCC modeling methods only contained a simplified compartment gasifier model, which is useful to consider the heat transfer and chemical reaction inside the gasifier, but cannot analyze the pressure and flow distribution. In order to obtain a more accurate model of IGCC system, the volume-resistance technique and modular modeling method are utilized in this paper. The new model can depict the dynamic response and distribution characteristics of the gasifier, as well as their influence on the IGCC system. The simulation result of the gasifier and IGCC system shows an obvious delay after considering pressure and flow distribution. Therefore, the proposed IGCC system model can obtain a more reliable result when considering the distribution characteristics of the gasifier.  
gasifier; IGCC; volume-resistance; pressure distribution +
Footnote †: journal: Applied Sciences
# An Integrity Monitoring Method for Navigation Satellites Based on Multi-Source Observation Links
## 2 Systematic Integrity Design for BDS-3 Satellite
### Limitation of the Integrity Monitoring System  
Compared with BDS-2, the service area and timeliness of integrity monitoring for BDS-3 have been greatly improved. Nevertheless, the generation of integrity parameters is still limited by valid observation data. The limitations are mainly reflected as follows:  
1. Layout of ground monitoring stations
2. BDS-3 has deployed dozens of ground monitoring stations in China and surrounding areas. The daily tracking and monitoring arc rate of the medium earth orbit (MEO) satellite is less than 40%. Most integrity parameters could not be generated as the satellite leaves the monitoring area. The current layout of ground monitoring stations could not support the real-time calculation of integrity parameters.
3. Communication capacity of inter-satellite links  
BDS-3 has constructed a dynamic inter-satellite network to realize rapid distribution and return of information throughout the whole constellation. A limitation of communication capacity is that navigation messages could not be returned to the main control station except for the parameters of SIF. Furthermore, the inbound rate of inter-satellite observation data within inter-satellite planning was only about 95% [13], and the average age of data (AOD) for MEO satellites was 1.07 h [14], meaning that there was a certain absence of observation data through the inter-satellite links and cannot guarantee real-time transmission of all information correctly.
4. Monitoring content of systematic integrity  
Systematic integrity for navigation satellites mainly relies on pseudo-range and regional navigation messages through satellite-to-ground observation links. Some data that can effectively identify the service status of a satellite, such as full-arc returned navigation messages, autonomous navigation messages [15], and inter-satellite observation data, are not fully applied in integrity monitoring.
# Effects of Drying Process on the Volatile and Non-Volatile Flavor Compounds of _Lentinual edodes_
## 3 Results and Discussion
### HS-SPME-GC-MS Analysis of Volatile Compounds of L. edodes
This interesting finding could be helpful to further understand the biosynthesis of C8 compounds in mushrooms. Volatile sulfur compounds are important aroma-active compounds in vegetables and processed meats, and they can offer unique aroma in _L. edodes_ differing from many other commonly consumed mushrooms [22; 32]. As shown in Table 1, the contents of sulfur compounds in mushrooms decreased after the drying process. However, more types of sulfur compounds were detected from dried mushrooms compared with fresh mushrooms. which was similar to that reported by [PERSON] et al. [14]. Carbon disulfide from mushrooms had little influence in relation to the overall aroma [33]. The straight chain sulfur compounds and cyclic sulfur compounds were reported to greatly contribute to the unique aroma of _L. edodes_[34]. As shown in Table 1, the straight chain sulfur compounds, dimethyl disulfide and dimethyl trisulfide, were not detected in fresh and ND mushrooms, while dimethyl disulfide was detected in HAD mushrooms, and dimethyl trisulfide was detected in both FD and HAD mushrooms. The results showed that 1,2,4-tritiholane, 1,2,4,5-tetrathidine and lenthiionine were the main cyclic sulfur compounds detected in the four kinds of mushroom samples. HAD mushrooms had the highest level of cyclic sulfur compounds (56.55 \(\upmu\)g/g) among the four mushroom samples, and the content of cyclic sulfur compounds in fresh shiitake mushroom, FD and ND were 7.24 \(\upmu\)g/g, 3.90 \(\upmu\)g/g and 0.04 \(\upmu\)g/g, respectively. [PERSON] et al. [1] reported that HAD increased the content of cyclic sulfur compounds compared with fresh shiitake mushrooms. The formation of these cyclic sulfur compounds included two steps: the enzymic reactions of lenthiinic acid catalyzed by cysteine sulfoxide lyase and \(\upgamma\)-glutamyl transpeptidase; and the nonenzymatic polymerization of methylene disulfide [8]. In addition, lenthiionine can decompose to dimethyl disulfide and dimethyl trisulfide [35]. Therefore, the differences of sulfur compounds in FD, HAD, and ND mushrooms might be attributed to enzyme activities and drying temperature during drying processes. In sum, different drying processes significantly affected the volatile profiles of _L. edodes_, especially the C8 and sulfur compounds. HAD mushrooms had weaker \"mushroom flavor\"  
Figure 2: Contents of different types of volatile compounds in fresh and different dried _L. edodes_; F: fresh sample; FD: freeze drying sample; HAD: hot-air drying sample; ND: natural drying sample. and strong shiitake flavor. In fact, the volatile content of _L. edodes_ was different in each stage (fresh, early, middle and late stage) of HAD [21]. The above complicated changes of volatile compounds in mushrooms may be attributed to typical drying conditions, such as higher temperature for HAD, long-time exposure in the sunlight for ND and vacuum evaporation for FD, which could affect volatile compounds formation and precursor degradation.
# Biomass Fly Ash Self-Hardened Adsorbent Monoliths for Methylene Blue Removal from Aqueous Solutions
## 3 Results and Discussion
### MB Adsorption Tests
#### 3.3.3 Adsorption Isotherm Model Studies  
Experimental data were fitted by the four different linearised forms of the Langmuir isotherms and by the Freundlich isotherms. The better fitting was achieved by the type 2 Langmuir linearisation. The adsorption parameters (\(q_{max}\), \(K_{L}\) ) and their corresponding R\({}^{2}\) values are presented in Table 3. After calculating the \(K_{L}\) constant (Equation (7)), the dimensionless separation factor (\(R_{L}\) ), which is generally used to express the feasibility of adsorption and affinity between the adsorbent and adsorbate, was determined. For all the samples the \(R_{L}\) values, independently of the initial MB concentration, were between 0 and 1 which means that the adsorption was favourable. The calculated values for \(q_{max}\), which represents the maximum adsorbed capacity in a monolayer, increased with the porosity, from 0.302 to 0.623 mg/g for the samples without PLC, and from 0.219 to 0.657 mg/g for the samples with PLC. For samples with the same Al content, the obtained \(q_{max}\) values are very similar, due to the fact that, for the same amount of AP, the total and open porosity values of the samples (with and without PLC) are very similar. As stated above, PLC shows negligible activity for MB adsorption. The calculated values are close to the ones obtained experimentally, Figure 7, meaning that after 25 h the system was close to the equilibrium.  
The Freundlich constant \(K_{F}\) and \(n\) parameter, and their corresponding linear regression coefficients, were calculated for the different monolith samples analysed, and are shown in Table 4. The R\({}^{2}\) values obtained from the isothermal Langmuir equation were higher than [PERSON]'s, although all \(n\) values are between 1 and 10, which means that the adsorption is favourable.  
Thus, the equilibrium data is better characterised by a type 2 Langmuir isotherm model, with a monolayer sorption capacity \(q_{max}\) that ranged from 0.219 to 0.657 mg/g. As the use of cement does not significantly alter the adsorption capacity of the monoliths, it should be used only when the experimental layout requires higher mechanical strength.  
Figure 9: Effect of contact time on the MB adsorption over samples prepared (**a**) without PLC (0C:9A) and (**b**) with PLC (5C:9A).
# A Novel Cross-Layer Routing Protocol Based on Network Coding for Underwater Sensor Networks
## 3 Background
### Overview of NCHARQ
Meanwhile, the estimation of sending window \(W_{i}\) can be used as an important evaluation parameter which can lead to routing update and maintenance. So frequent broadcasting beacon messages are not needed any more and packet collisions are reduced.
## 3 On-Site Measurements
### Comparison with Other In Situ Studies  
When comparing the force density spectra obtained during this study, as shown in Figure 16, the following trends can be observed: Amplitudes in the mid-frequency range, around 32 Hz, appear similar for each train class, independent of the site. At low frequencies (one-third octave bands below 32 Hz), the force density spectra were greater on the ballasted track, which illustrates the effect of track structure, soil dynamics, track irregularities and the quasi-static effect of the moving loads, which appear to be more dominant on the embankment than on tunnel tracks. On the other hand, the effect of rail roughness--manifested at frequencies above 32 Hz--is more dominant on slab tracks, as also indicated by theoretical considerations in [5,7]. Furthermore, the vehicle-track resonance caused by soft rail pads on slab track must be considered [18].  
Figure 14: Examples of the immission spectra for different train categories recorded 12 m from the embankment track.  
In addition to the force density spectra measured during this study, the results for an ICE train from the H2020 project SILVASTAR [11] were included in Figure 16. These spectra were converted from their original RMS vibration velocities to slow-weighted values using factor 1.7 (see Table 3), which stems from empirical correlations described in [17]. In terms of the overall profile, the ICE shows a very similar spectrum to the Westbahn trains and correlates very well with regard to amplitude and trend to the author's experimental findings.
# An Accurate Probabilistic Model for TVWS Identification
## 5 Experimental Results and Discussion
### Spectrum Sensed Results  
In this sub-section, the obtained results are described. Figure 17 illustrates five locations, i.e., \(Lab\), \((a)\), \((b)\), \((c)\), and \((d)\), where data were sensed and represented. In \(Lab\), we found the existence of PU in 23 channels, so it shows 72% of TV spectrum used and 28% idle spectrum. The later may be considered as potentially TVWS allowing DSA as SU. In Figure 18a, the results are plotted for data taken in location (a), where we observe seventeen channels for which signal was detected (in cyan), so that this is 53% of TV spectrum used, and 47% idle spectrum. Figure 18b corresponds to location (**b**) to the South of the city, where there are 16 channels for which signal was detected (in cyan), representing 50% of used spectrum, and 50% of TVWS. In Figure 18c, which is the Middle-east part of the city, there are 15 channels for which signal was detected, which represents 50% of the used spectrum, and 50% of TVWS. Finally, in Figure 18d, which shows the East limit of the city, there are 13 channels for which signal was detected by the DAI (in cyan) so that this represents 40% of used spectrum and 60% of TVWS.
# Secure Utilization of Beacons and UAVs in Emergency Response Systems for Building Fire Hazard
## 5 Performance Evaluation
Table 3 shows the computation time required to execute encryption and decryption algorithms for secure communication to the size of the messages. When the size of the transmitted text message was 256 bytes, the smart sensor took 1.79 ms and 1.77 ms to complete the protocol with encryption and decryption, respectively. Even for a large text size such as 10,240 byte, the smart sensor encrypted and decrypted the message relatively quickly. It took only 164 ms to perform the encryption or decryption operation.
# Next-Generation Dual Transceiver FSO Communication System for High-Speed Trains in Neon Smart City
## 3 System Description
### Handover Process  
During the train's journey along the track according to Figure 1, a line-of-sight connection between the HST and the BS is consistently maintained. This is achieved by utilizing the geometrical properties illustrated in Figure 1. To ensure uninterrupted connectivity between the BS and the train, the two transceivers placed on the train work together. As the train moves through the coverage distance of a BS, the cooperative behavior of these transceivers guarantees that at least one of them remains connected to the BS. Although the two laser beams of the BSs do not cover the entire railway seamlessly, continuous G2T FSO communication remains unaffected. Here is how it works:  
* Once the transceiver at the front of the train is within range of the BS, it forms a connection link with the BS.
* When entering the coverage gap are \(\mathrm{L_{2}}\), the CTC promptly transfers all network traffic to the back transceiver, which is already within. Despite the front transceiver passing through the coverage gap area for a specific period of time, the BS maintains service to the train using the FSO link of the back transceiver.  
Figure 1: Dual transceiver G2T communication link model.  
Once the front transceiver reaches, which is within the range of other transceiver of the BS, it establishes a connection link with the BS. Subsequently, the CTC allocates the network traffic back to the front transceiver. In order to maintain uninterrupted communication between the BS and the train, the back transceiver must also reach before the front transceiver leaves that particular area.  
By employing this approach between the train's front and back transceiver, continuous communication between the BS and the train is maintained throughout the journey. Then the total coverage distance for the dual transceiver BS can be expressed as:  
\[\mathrm{L_{C_{total}}}=\mathrm{L_{1}}+\mathrm{L_{2}}+\mathrm{L_{3}}=2\mathrm{ L_{c}}+2\mathrm{L_{H}} \tag{2}\]