esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Gas Turbine
### Evolution of Combustion Technology in Modern Gas Turbine
#### 2.1.4 Dry-Low Emission and NanoSTAR  
In the fourth generation, the DLE gas turbine and NanoSTAR are introduced. The DLE gas turbine combines all the good aspects in the previous combustion system to reduce NOx without increasing the bottleneck COx and unburned hydrocarbon. A DLE gas turbine combustion technology operates a clean operation based on lean pre-mixed (LPM), which adapts the RQL \"rich-burn\" method and MILD to reduce NOx [32]. As reported in [33], the emission can be reduced up to 97% using this technique. The pilot fuel valve introduction is based on the TVC system as a cavity stabilizer for the flame. The DLE turbine temperature lies in the desired range that is not too low, as this will increase the COx formation, and not too high, as this will increase NOx production [34; 35]. The turbine is also flexible in fuel selection, either gas or liquid. The last advanced technology is NanoSTAR. Since all reviewed combustion technologies are based on turbulent flow and non-premixed/premixed, combustion instabilities are inevitable. Thus, the technology utilized the high thermal intensity laminar surface stabilizer from a porous-metal fiber for the injection. The full-scale test of NanoSTAR exhibited high emission reduction, robust ignition and significantly less pressure drop (2-4%) from the system pressure. However, the system is still in the proof-of-concept stage, which is limited to the natural gas application and holds complexity in manufacturing.  
The comparison between features of the combustion systems is summarized in Table 1. The factors considered here are the performance of these technologies for emission reduction and power efficiency. The table shows that the three combustion technologies, COSTAIR, NanoSTAR and DLE, share an essential feature of very low COx and NOx emission. However, only NanoSTAR and DLE gas turbines exhibit high power efficiency (significantly less pressure drop), but NanoSTAR is still in the proof-of-concept stages. Thus, the DLE combustion technology selection for this study is justified after considering all the turbine features.  
Even though the DLE gas turbine meets emission and pressure drop requirements, it is still susceptible to frequent trips, especially LBO faults, during disturbances. The plant trips release unplanned exhaust gas during faults and further increase the pollutants emission.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **TVC** & **RQL** & **COSTAIR** & **MILD/Flameless** & **NanoSTAR** & **DLE** \\ \hline \multirow{2}{*}{**NOx Emission**} & Medium (60\% & Low (72\% & \multirow{2}{*}{Very Low} & Low (90\% & \multirow{2}{*}{Very Low} & **Very Low (97\% & \multirow{2}{*}{**reduction**)} \\  & reduction) & reduction) & & reduction) & \\ \hline
**CO Emission** & Medium [15] & Medium [27] & Very Low [31] & Medium [24] & Very Low [33] & **Very Low [36]** \\ \hline \end{tabular}
\end{table}
Table 1: Summary of combustor technologies in modern gas turbines.
###### Abstract  
In this letter, we present the first attempt of active light-emitting diode (LED) indexes estimating for the generalized LED index modulation optical orthogonal frequency-division multiplexing (GLIM-OFDM) in visible light communication (VLC) system by using deep learning (DL). Instead of directly estimating the transmitted binary bit sequence with DL, the active LEDs at the transmitter are estimated to maintain acceptable complexity and improve the performance gain compared with those of previously proposed receivers. Particularly, a novel DL-based estimator termed index estimator-based deep neural network (IE-DNN) is proposed, which can employ three different DNN structures with fully connected layers (FCL) or convolution layers (CL) to recover the indexes of active LEDs in a GLIM-OFDM system. By using the received signal dataset generated in simulations, the IE-DNN is first trained offline to minimize the index error rate (IER); subsequently, the trained model is deployed for the active LED index estimation and signal demodulation of the GLIM-OFDM system. The simulation results show that the IE-DNN significantly improves the IER and bit error rate (BER) compared with those of conventional detectors with acceptable run time.  
visible light communications; deep learning; bit error rate; orthogonal frequency division multiplexing; index modulation
# DTS-Net: Depth-to-Space Networks for Fast and Accurate Semantic Object Segmentation
## 3 Proposed Method
### Feature Extractor Architectures
Another branch features global average pooling and a fully connected layer is added for multi-label classification. (**a**) Training architecture of the proposed method and (**b**) inference architecture, which features one additional step, that is, the nearest label filtration (NLF). NLF is a patch-by-patch image filtration based on the global labels of the image and the patch labels. The noisy pixels with labels that are not included in the global image labels are mapped to the nearest neighbor in the intersection between the patch and global labels using a simple iterative difference operation. lower number of filters with the same factor \(t\)). In [36], \(t=6\) was employed as the expansion factor. Each of the three modules features patch normalization and ReLU6 activation (i.e., ReLU with the maximum value clipped to 6). The inverted residuals form a skip-connection between the bottleneck start and end, which have the same channel depth. This gives the network access to earlier features before entering the bottleneck block. MobileNetV2 originally consisted of seven sequential MobileNet blocks (a MobileNetV2 block is shown in detail in Figure 3b), followed by a \(1\times 1\times 1280\) convolution. In our proposed approach, we add a \(1\times 1\times 1024\) convolution to reduce the depth to \(1024\) (\(32\times 32\)) in order to construct a dense map using the DTS layer. We also add a global average pooling layer after the \(1\times 1\times 1280\) convolution, which is followed by a fully-connected layer with sigmoid activation for multi-label classification. As noted above, we employ MobileNetV2 as the feature extractor of the lightweight version of the proposed method, that is, DTS-Net-Lite. We believe that our choice of depth-wise separable convolution-based architecture is key to the high accuracy and speed achieved in this work. The depth-wise convolution (DW-Conv) is perfectly compatible with depth-to-space image construction as DW-Conv extracts information from each channel separately, allowing small details in each channel (depth-axis) to be captured. Then, the DTS layer reconstructs the image from the aggregation of the information in each channel, as discussed in detail in the next section. This explains the high accuracy of the depth-wise separable convolution-based architecture combined with the DTS dense map reconstruction.
###### Abstract
A subset of tools with available descriptions or links was selected for detailed evaluation, forming the shortlist. This final comparison highlights the diverse capabilities of these tools, which vary in their ability to address ESs, provide management support, and cater to different geographic and operational contexts. Several DSSs in the shortlist reveal key gaps in their capacity either to support multifunctional forest management or to align with the FSC ES procedure. Many tools analysed, such as ESC, OpTimber-LP, VDDT-Path, and GISCAME, lack the ability to estimate ESs, which limits their utility for evaluating and enhancing ES provision. Tools like iTree and Collect Earth focus narrowly on specific applications, such as urban tree species or land use, limiting their applications in forest management. While some tools, such as Sim4 Tree and SORTIE-ND, provide spatial explicitness, their functionality is constrained to stand-level management or by the significant amount of data input needed. Furthermore, global tools like InVEST and Costing Nature lack integration for forest-specific ES modelling and management or require external mapping software, complicating their application for practical forest management. Several region-specific tools, including IPTIM and Forest Management Optimization, address only a subset of ESs (e.g., carbon and timber), leaving gaps in assessing trade-offs among multiple ESs. These limitations underscore Multisilva's unique position to fill these gaps by providing Europe-wide applicability, explicit modelling of actions to enhance ESs, comprehensive ES indicators for trade-off analysis, and integrated spatial explicitness tailored to forest ecosystems. Multisilva's ability to support standardised methodologies across countries aligns closely with the FSC ecosystem service procedure, which requires robust and consistent frameworks for ES management and monitoring. The basis of the system presented in this study is ecosystem services and how forest management can affect their provision. While this has also attracted the attention of other researchers [22; 23], the novelty of our system is the focus on multifunctional forest management and ecosystem services by computing and comparing the provision of various ESs, as well as the economic outcome deriving from different alternative management scenarios. Furthermore, with our knowledge of European policy, silvicultural practices, and social context, the presented tool is especially suited to the European market.
# Efficient Algorithms for Computing the Inner Edit Distance of a Regular Language via Transducers
###### Abstract  
The concept of edit distance and its variants has applications in many areas such as computational linguistics, bioinformatics, and synchronization error detection in data communications. Here, we revisit the problem of computing the inner edit distance of a regular language given via a Nondeterministic Finite Automaton (NFA). This problem relates to the inherent maximal error-detecting capability of the language in question. We present two efficient algorithms for solving this problem, both of which execute in time \(O(r^{2}n^{2}d)\), where \(r\) is the cardinality of the alphabet involved, \(n\) is the number of transitions in the given NFA, and \(d\) is the computed edit distance. We have implemented one of the two algorithms and present here a set of performance tests. The correctness of the algorithms is based on the connection between word distances and error detection and the fact that nondeterministic transducers can be used to represent the errors (resp., edit operations) involved in error-detection (resp., in word distances).  
Keywords:algorithms; automata; complexity; edit distance; implementation; transducers; regular language +
Footnote †: journal:
## 1 Introduction
Semiconducting materials can be excited by light irradiation and photoelectron-hole pairs can be generated in the lattices, which can improve both the internal conductivity for enlarged charge density and the oxidation kinetics on the surface [23]. [PERSON] et al. first demonstrated the positive relationship between electroactive microorganisms and semiconducting iron/manganese oxides minerals at Karst areas in natural environments [24]. Moreover, enhancing the electron transfer process between a hematite/birnessite photoelectrode and different microorganisms under visible light was demonstrated, and the interaction between microbes and semiconducting minerals is becoming a hot spot in the interdisciplinary field of geomicrobiology [25; 26; 27; 28]. Due to the abundant production, environmentally friendly attributes, and convenient access, abundant natural semiconducting minerals may be considered as alternatives for photocathode catalysts. Although natural rutile was successfully utilized in MFCs [21], their wide band gap was a limiting factor. Exploring alternative semiconducting minerals with suitable bandgaps is crucial in the development of MFC photocatalysts with the efficiency required for large-scale field conditions. Very recently, tungstate was demonstrated to have a promising narrow band gap with fascinating applications in photocatalytic processes and optical fibers, owing to its low price, appropriate band gap, and strong redox capability [29; 30; 31]. Notably, natural wolframite is a common tungstate in nature, is an anionic catalyst. and is abundantly distributed in China. Natural semiconducting minerals are often doped with a variety of different elements, thus influencing their band gap. In this study, natural wolframite was used as a catalyst in a photocathode MFC system under near-neutral conditions. Both the mineralogical characteristics and the semiconducting properties of natural wolframite were systematically studied. The cell performances with different cathodes under dark/light conditions were discussed. Given the improvement of power output in this wolframite-MFC system, natural wolframite was demonstrated as an efficient and low-cost catalytic photocathode material in MFC, which had great potential applications in environmental treatment and large-scale field conditions in the future. Moreover, our study makes contributions to the rational and efficient use of mineral resources.
# Methacrylate Polymer Monoliths for Separation Applications
## 2 Methacrylate Polymer Monoliths
### Functionalization Routes and Functional Groups
#### 2.4.2 Nanoparticle Functionalization  
Reports of methacrylate monoliths functionalized with nanoparticles have been made by a number of groups. The attention has generally been focused on gold [84; 85; 86; 25; 87; 88], silver [89; 90; 91], functionalized gold nanoparticles [92; 93], and iron oxide [94; 95]. Gold is routinely used due to its well established chemistry [96; 97] and silver nanoparticles are also used in Capillary Electrochromatography (CEC) and in Surface Enhanced Raman Spectroscopy (SERS) [98; 99]. The abundance of contributions using gold nanoparticles is not unexpected; it lends itself to numerous functionalization reactions, due to its favorable binding to thiol from cysteamine [25; 93], amine, and di-sulphide groups. The most commonly utilized source of the gold nanoparticles is HAuCl\({}_{4}\). The drawback to using cysteamine (Figure 6) is that both the thiol and amine groups can be involved in the functionalization reactions with epoxide groups. The thiol can react to form a less reactive thioether, while cross-linking can occur between the amine groups and the epoxide, reducing the binding of the gold nanoparticles due to steric interactions. Gold has also been used as a means to attach moieties such as cyclodext in order to facilitate chiral capillary electro-chromatography [93]. In this case, the thiol groups from cysteamine provided anchoring points for the nanoparticles on the monolith surface.  
Silver nanoparticles not been used as much for methacrylate monolithic separations. However, advantage has been made of their good conductivity and electrochemical properties in capillary electrophoresis. Given these excellent properties of silver relative to other functional groups, it is thought that this functionality deserves further investigation. It is potentially the additional reactive nature of gold nanoparticles that has seen the latter gain greater interest. Gold nanoparticles have been used to functionalize methacrylate monoliths for the separation of peptides [25; 100], and chiral drugs [93], while silver functionalized monoliths have been used in capillary electrophoresis of sterols, fatty acid methyl esters, tocopherols, and polyaromatic hydrocarbons, as well as in the reverse phase separation of radiolabeled pharmaceutical compounds [89; 98].  
Figure 6: Chemical structure of cysteamine (**16**) crosslinked with glycidyl methacrylate and Au nanoparticle.
## 2 Research Progress in In Situ Road Health Monitoring Technology
### Wireless Sensing Elements  
With the development of network communication technology, wireless sensing technologies came into being. Their low price, small size, high reliability wireless sensor, and their network have become a research focus in recent years [85]. [PERSON], H.A. [12] designed and studied self-sensing powered wireless sensors (SWSs), whose structural and encapsulating forms are shown in Figure 10. This piezoelectric sensor consists of a piezoelectric transducer, a sensor piezoelectric electronic device, a terminal board, etc. Under the action of traffic load, the piezoelectric transducer is pressurized to generate an electrical signal to supply power to the sensor piezoelectric electronic device through the circuit board, and the signal of the sensor piezoelectric electronic device is used for the diagnosis of structural damage. In the encapsulation process, the spherical packaging shell was prepared using 3D-printed technology with openings reserved at its top and six fixed anchors in 3 directions. The wireless sensor was sealed by injecting polyurethane glue TU-981, since the stiffness of glue TU-981 is similar to that of asphalt concrete. The final encapsulated sensor had a similar size with asphalt concrete aggregates. Communication between the wireless sensor and the vehicle is performed using radio frequency identification (RFID) technology. It was shown [86] that data from such unembedded sensors can be read from 16 inches using the RFID method. For the sensing system embedded in the asphalt sample, the thickness of the asphalt layer affected the accuracy of the transmission and recovery signal. In this case,the sensor and the RFID scanner commutated at a distance of approximately 12 inches. Therefore, they proposed a sustainable long-term health monitoring and analysis system using the developed SWSs and verified the system with finite element simulations and the three-point bending-beam experiment method, respectively. With the wireless sensor signal output values as the basic data, the system obtained the probability density function (PDF) from the strain distribution provided by each sensor in the system. Thus, the cumulative density distribution (CDF) was calculated by the characteristic factors \(\upmu\) and \(\sigma\) so as to predict the damage rate and damage location of road damage. However, instead of directly monitoring the absolute values of the strain, the system uses the relative strain distribution to infer the strain trend. [PERSON] et al. [87] also explored network communication protocols, network designs, and integration technology in wireless monitoring technology. However, wireless sensing technology for monitoring the mechanical state of asphalt pavements is still in its early stages.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Chemical Composition** & \multirow{2}{*}{**OPC**} & \multirow{2}{*}{**Bentonite**} & \multicolumn{2}{c}{**ASTM C618 Class N**} \\
**(wt.\%)** & & & **Requirements (\%)** \\ \hline Na\({}_{2}\)O & - & 1.39 & 5 maximum \\ MgO & 2.31 & 2.70 & - \\ Al\({}_{2}\)O\({}_{3}\) & 9.78 & 18.32 & - \\ SiO\({}_{2}\) & 18.8 & 56.6 & - \\ K\({}_{2}\)O & - & 0.67 & - \\ CaO & 59 & 3.1 & - \\ TiO\({}_{2}\) & - & 0.98 & - \\ Fe\({}_{2}\)O\({}_{3}\) & 3.44 & 6.1 & - \\ SO\({}_{3}\) & 2.85 & - & - \\ (SiO\({}_{2}\) + (Al\({}_{2}\)O\({}_{3}\)) + & & 80.93 & 70 minimum \\ (Fe\({}_{2}\)O\({}_{3}\)) & - & 7.1 & 10 maximum \\ Loss on ignition (LOI) & - & \multicolumn{1}{c}{} & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Chemical composition of OPC and bentonite clay. mechanically activated bentonite (MAB) and thermo-mechanically activated bentonite (TMAB) (at the levels of 10%, 15%, 20%, 25%, 30%, and 35% by mass of OPC), in terms of compressive strength, split tensile strength, water absorption, sorptivity, and acid attack resistance up to 180 days. The results concluded that 15% mechanically-activated and 25% thermo-mechanically activated bentonite can be used (significantly) as substitutes of OPC, with good mechanical and even better durability properties for concrete.
# RESEMBLE: A Real-Time Stack for Synchronized Mesh Mobile Bluetooth Low Energy Networks
## 6 Experimental Assessment
### End-to-End Delay Assessment  
The assessed scenario, which is shown in Figure 12, consists of four stationary nodes connected in a mesh topology. To realize the mesh topology, the node visibility is artificially limited using a whitelist filter in the BLE interface. The filter drops the transmissions arriving from the nodes that are not in the whitelist.  
Each node generates a periodic message with period 2 s. The Nodes 1, 2, and 3 transmit the messages to Node 0. The Node 0 transmits its messages to Node 3. The flooding algorithm defined in Mesh RT BLE is used. In this scenario all messages are assigned the same priority. As the nodes are synchronized and the MaxAbsSkew in this scenario is 6 ms, the results have a \(\pm\)6 ms synchronization accuracy.  
The parameters are presented in Table 2.  
The obtained e2 eDelays are compared with the analytical maximum e2 eDelay of 1280 ms obtained when applying the worst-case response-time analysis for TDMA-based  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Node count & 4 \\ Timeslot count & 8 \\ Timeslot duration (\(T_{\text{ts}}\)) & 40 ms \\ Max. hop count & 2 \\ Transmission period & 2 s \\ Message size & 31 bytes \\ Experiment duration & 30 min \\ Result sample count & 900 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Testbed parameters.  
Figure 11: Maximum absolute clock skew.  
Figure 12: Scenario for end-to-end delay assessment.  
networks, as explained in Section 4.3. A stochastic response-time analysis [41; 42] to obtain less pessimistic results will be addressed in future work.  
Figure 13 shows the e2 eDelay distribution for the flows generated by Nodes 1 and 2. In all the graphs, the vertical red line indicates the analytical e2 eDelay bound.  
The messages transmitted by Node 1 and Node 2 are always received before the analytically obtained maximum e2 eDelay (1280 ms), which is an upper bound. The e2 eDelay difference between Node 1 and Node 2 is because the application is not synchronized with the Bearer Layer.  
Figure 14 shows the e2 eDelay distribution for the flows generated by the Nodes 3 and 0.  
In this case, the e2 eDelay of the messages transmitted by the Node 3 are lower than the theoretical maximum e2 eDelay (i.e., 1280 ms). In this case, the messages follow a two-hop path; therefore, the e2 eDelays are higher than those of the messages transmitted by Nodes 1 and 2. The same holds for the messages transmitted by Node 0 delivered to Node 3.