esa-sceva/satcom-chunk-collection huggingface dataset:
# BIM and GIS Applications in Bridge Projects: A Critical Review
## 4 Discussion and Future Work
### Operation and Maintenance  
At present, the existing bridge transportation network models based on GIS usually adopt deterministic [71; 72] or static traffic flow patterns [76], without considering the influence of other factors, such as wind intensity [70]. The benefits of GIS technology in the management of major transportation facilities have not been verified [13; 74]. BIM/GIS models for bridge management have poor compatibility with other models or modules such as the finite element model [15; 39; 40], damage model [16], and disaster model [14; 77; 78] and, therefore, influences data mining. There is a growing trend using UAV and other detection technologies for bridge detection based on BIM/GIS [94]. Although these technologies have many benefits, there are some shortcomings, including the lack of overall perspective [41; 42] and the impact of weather changes on data quality [14; 34; 37; 38; 77; 78]; these can affect bridge evaluation outcomes [73]. Therefore, the existing evaluation models may not be directly applicable to other projects [48; 49; 52; 81], given that they may negatively affect maintenance decisions of other bridges.  
In the future, researchers could consider introducing uncertainty [71; 72] or a dynamic traffic flow [76] and other factors to improve bridge transportation network models based on GIS to enhance model utility and expand the scope of application. BIM/GIS software, including API, should be further developed to improve their compatibility with other models or modules and enhance data mining for bridge management systems [15; 39; 40; 95]. It is suggested to improve the quality of bridge detection by adding acquisition methods such as omnidirectional acquisition [41; 42] and improving data processing methods. In terms of bridge maintenance decision making, more researchers and decision makers need to study and verify different bridge maintenance decision-making objectives and constraints [51], and combine with BIM/GIS technology to improve decision making.
## 4 Materials and Methods
### Experimental Data
#### 4.3.7 Products **4B**  
Isolated yield of two stereoisomers of **4B** was 19 mg (9%). Clear oil.  
Major Isomer of Product **4B**:  
\({}^{1}\)H-NMR (500 MHz, CDCl\({}_{3}\)): \(\delta\) (ppm) = 6.86 (d, \(J\) = 8.1 Hz, 1 H H-5), 6.82 (dd, \(J\) = 8.1, 1.7 Hz, 1 H, H-6), 6.67 (d, \(J\) = 1.7 Hz, 1 H, H-2), 6.58 (s, 1 H, H-2\({}^{\prime}\)), 6.21 (s, 1 H, H-5\({}^{\prime}\)), 4.12 (dd, \(J\) = 10.5, 9.7 Hz, 1 H, H-8), 3.90 (s, 3 H, OMe), 3.87 (d, \(J\) = 9.7 Hz, 1 H, H-7), 3.86 (s, 3 H, OMe), 3.82 (s, 3 H, OMe), 3.78 (s, 3 H, OCOMe), 3.59 (s, 3 H, OMe), 3.17 (dd, \(J\) = 16.2, 12.4 Hz, 1 H, H-7\({}^{\prime}\)a), 3.10 (dd, \(J\) = 16.2, 5.2 Hz, 1 H, H-7\({}^{\prime}\)b), 2.99 (ddd, \(J\) = 12.4, 10.5, 5.2 Hz, 1 H, H-8\({}^{\prime}\)).  
\({}^{13}\)C-NMR (125 MHz, CDCl\({}_{3}\)): \(\delta\) (ppm) = 175.0 (9\({}^{\prime}\)), 149.3 (3), 148.3 (4\({}^{\prime}\)), 147.7 (3\({}^{\prime}\)), 134.9 (1), 129.8 (6\({}^{\prime}\)), 126.1 (1\({}^{\prime}\)), 122.3 (6), 112.4 (5\({}^{\prime}\)), 112.1 (2), 111.3 (5), 110.5 (2\({}^{\prime}\)), 74.6 (8), 56.0 (OMe), 56.0 (OMe), 56.0 (OMe), 56.0 (OMe), 53.9 (7), 52.2 (COOMe) 47.6 (8\({}^{\prime}\)), 32.0 (7\({}^{\prime}\)).  
HRMS: found 425.1584 (M\({}^{+}\)Na). C\({}_{22}\)H\({}_{26}\)O\({}_{7}\)Na\({}^{+}\) requires 425.1571.  
**Supplementary Materials:** The following are available online, \({}^{1}\)H-NMR, \({}^{13}\)C-NMR and HSQC spectra of the major isomers or diastereomeric mixtures of **1A, 1B, 2A**, **2B, 3A**, **3B**, and **4B**.  
**Author Contributions:** Conceptualization, D.A. and P.C.E.; methodology, D.A. and P.A.R.; formal analysis, P.A.R.; investigation, P.A.R.; resources, D.A. and P.C.E.; writing--original draft preparation, P.A.R.; writing--review and editing, D.A. and P.C.E.; supervision, P.C.E. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Academy of Finland, project number 265726, and the Swedish Cultural Foundation in Finland (Svenska kulturfonden), grant number 136293.  
**Acknowledgments:** [PERSON] at the technical service unit at Abo Akademi University is thanked for the HRMS data.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Topology Abstraction Implementation
### Topology Abstraction-Based Key Relaying Strategy  
After obtaining the QKD relaying path from the routing process on the abstracted topology, we use the classic channel (e.g., laser channel) to relay the global secret keys following the QKD relaying path. Due to the relatively high data rate over the classic channel, there can be a large number of classic channels between two sites with the time-division-multiplexing (TDM) technique. In the hybrid GEO/LEO network, due to the large coverage of the GEO satellite, there commonly exist classic channels to send the encrypted global secret keys to the corresponding site (in theory, three GEOs can fully cover the entire earth). Even if a GEO does not exist in the network, due to the large number of LEOs in most of the satellite constellations, it still can find a path to send the encrypted key to any site in most cases. As shown in Figure 2a, the QKD relaying path is from Node-a to Node-2, then, it is from Node-2 to Node-3, and finally, it is from Node-3 to Node-c. Since LEO-2 and LEO-3 cannot establish the link at this time (they are not visible to each other), we first send the encrypted global secret key (i.e., key\({}_{\text{a-2}}\oplus\) key\({}_{\text{2-3}}\)) from LEO-2 to GEO; then, the GEO forwards this key to LEO-3 to decrypt for the global secret key (i.e., key\({}_{\text{a-2}}\)), as shown in Figure 2b. Finally, LEO-3 send the encrypted global secret key (i.e., key\({}_{\text{a-2}}\oplus\) key\({}_{\text{c-3}}\)) to Ground station-c to decrypt for the global secret key; then, Ground station-a and Ground station-c can successfully share the secret key.
## 4 Precoding Schemes
### Introduction to Precoding Schemes
#### 4.1.1 Fundamentals
Zero forcing (ZF) precoding is subject to completely cancelling the multi-user interference (see (21)), i.e., \(\mathbf{h}^{[k]}\mathbf{w}^{[j]}=0,\forall j\
eq k\), considering specific conditions such as maximizing the sum-rate [33; 137], guaranteeing the fairness among users [135] or optimizing a specific objective function [138]. The precoding vectors can also be determined by minimizing the mean squared error (MSE) between the received symbol of each user and the legitimate transmitted symbol [139; 140]. In this context, maximum ratio combining (MRC) on the transmitter side can also be considered [141]. However, there are still some issues to solve before considering the implementation of these transmission schemes in VLC.
## 6 Extended Evaluation: Mobile Scenario  
We extend the evaluation of our prototype system across mobile environments. The mobility considerations in these evaluations refer to the the case when the VLC transmitter can potentially change its spatial position while transmitting data. The receiver is kept stationary in all these experiments. When an actively transmitting LED changes its position, the receiver must be able to actively identify that the movement has happened and that it needs to adapt its reception area. Using our pixelated shutter approach, we hypothesize that when the LED changes position, the receiver will identify the movement event based on its periodic SNR measurements across each pixel and the automated shutter control protocol (Algorithm 1) shifts control to the appropriate pixel over which the LED signal is now being received. The goal of these evaluations is to verify the feasibility of our system to adapt its reception when the LED changes position.
# Fast Support Vector Machine for Power Quality Disturbance Classification
## 3 Design of the Fast SVM (FSVM)
### Training Patterns Creation  
In this paper, harmonics (harm) are defined as the situation where the branch currents and node voltages are polluted by the harmonic sources. Besides harmonics, we have the following PQD conditions as recommended in [32]:  
\begin{tabular}{c c c} \hline \hline PQD type & Voltage \% & Duration \\ \hline sag & 10-90\% & 0.5 cycle-secs \\ \hline swell & \textgreater{}110\% & 0.5 cycle-1 min \\ \hline harm + sag & 10-90\% & 0.5 cycle-secs \\ \hline harm + swell & \textgreater{}110\% & 0.5 cycle-1 min \\ \hline normal & 95-105\% & - \\ \hline interruption & severe sag/blackout & \textless{}1 min \\ \hline \hline \end{tabular}  
With the PQD defined, harmonic power flow [33] was used to create training patterns for FSVM and checked with [34]. Running the power flow, regular power models were used including  
1. transmission line model: pi-equivalent model,
2. transformers: simplified R + jX,
3. shunt magnetizing components: ignored,
4. capacitors: capacitances varied with frequencies,
5. generators: sub-transient model with reactance,  
Figure 2: Structure of the FSVM at observation location.  
linear loads: series impedances R + jX,  
7. nonlinear loads: harmonic current sources from field data. The procedure of running the harmonic power flow is  
1. read system data,
2. execute power flow with fundamental data,
3. obtain fundamental voltages and harmonics,
4. change linear loads into impedance,
5. find equivalent current injection from non-linear load,
6. change system components into equivalent circuit model,
7. build Y_h_ Matrix, with harmonic order \(h\) = 2, 3, 5, 7, 9... 50,
8. for an \(h\), calculate harmonic voltages _V_h_ = (Y_h_)\({}^{-1}\)\(\times\)I,
9. if \(h\) < 50 go to 6,
10. calculate total voltage distortion _V_td_%,
11. end.
## 2 Beam Dynamics Design
### Simulation Results of Beam Transport  
The simulation results generated by PARMTEQM are plotted in the figures below, utilizing 10,000 macro particles with a 4D water-bag distribution. Figure 3 displays the transverse and longitudinal beam envelopes along the RFQ, revealing focused beams within a confined region of the cavity and achieving a remarkable 100.0% transmission efficiency. In Figure 4, the phase space projection at the entrance and exit of the RFQ demonstrates well-controlled emittance growth in both transverse and longitudinal planes. The full width at half maximum (FWHM) phase spread and energy spread at the RFQ exit are approximately 15\({}^{\circ}\) and 0.05 MeV, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameters** & **This Design** & **INFN-SRFQ1** & **INFN-SRFQ2** \\ \hline Frequency (MHz) & 162.5 & 80 & 80 \\ Particles & proton & \({}^{238}U^{28}+\) & \({}^{238}U^{28}+\) \\ Inter-vane voltage \(V\) (kV) & 240 & 148 & 280 \\ Average aperture \(r_{0}\) (mm) & 8.18 & 8.00 & 15.30 \\ \(E_{sp}\) (MV/m) & 41.8 & 24.0 & 25.5 \\ \(B_{sp}\) (mT) & 27.5 & 25.0 & 30.0 \\ \hline \end{tabular}
\end{table}
Table 2: Parameters comparison between this design and INFN SC RFQs [22; 34].  
Figure 3: Evolution of beam envelopes along the RFQ. From the top to bottom are the beam envelope in the x and y planes and phase and energy spectrums, respectively.  
The normalized rms transverse emittances of the output beam are \(\varepsilon_{x}=\varepsilon_{y}=0.024\) cm-mrad, both of which represent a 20% increase from the input values. Additionally, the output normalized rms longitudinal emittance is \(\varepsilon_{z}=0.058\) cm-mrad, equivalent to 0.106 MeV-deg. Illustrated in Figure 5, the small emittance growth along the RFQ suggests effective beam focusing and bunching. Notably, the initial rapid emittance growth of transverse planes in the first few cells is attributed to the rise of \(B\) from a minimal to maximum value in these cells. The insufficient focusing force at the beginning of the RFQ fails to effectively constrain the high-intensity proton beam, resulting in a rapid increase in beam emittance. However, in the subsequent cells, \(B\) is kept at a maximum value of 13, which is sufficient to limit the growth of the beam emittance and thereby achieve a small transverse emittance at the end of the RFQ. In the longitudinal plane, due to the slow change of the synchronous phase from \(-90^{\circ}\) to \(-30^{\circ}\), its emittance only increases significantly in the accelerator section when the synchronous phase reaches \(-30^{\circ}\), but it can still achieve a good bunching effect and obtain low emittance growth.  
Figure 4: Particle phase-space projection at the entrance and exit of RFQ.  
Figure 5: Emittance growth along RFQ.
# Internet of Bikes: A DTN Protocol with Data Aggregation for Urban Data Collection
## 5 Simulation Settings  
IoB-DTN protocol was evaluated in the area of Lyon, France. It is in east central France belonging to the Auvergne-Rhone-Alpes region and is the second largest urban city of France. The self-service bicycles in Lyon is called Velo'v ([[https://velov.grandlyon.com/](https://velov.grandlyon.com/)]([https://velov.grandlyon.com/](https://velov.grandlyon.com/))) and it has been managed by JCDecaux since May 2005. The metropolis of Lyon has provided about 4000 bicycles, which are available 24/7, distributed in more than 300 bike stations scattered in the cities of Lyon and Villeurbanne. This mobility service offers the possibility to make local trips mainly in the urban areas. It allows citizens to rent a bike to move from one zone to another. One of the bike stations in Lyon is presented in Figure 2.  
The platform \"Data Grand Lyon\" provides open data regarding the Velo'v bike stations ([[https://data.grandlyon.com/](https://data.grandlyon.com/)]([https://data.grandlyon.com/](https://data.grandlyon.com/))). These inputs are combined with the map of the city of Lyon that is generated from Open StreetMap ([[https://www.openstreetmap.org](https://www.openstreetmap.org)]([https://www.openstreetmap.org](https://www.openstreetmap.org))). The area considered for our simulations is in the city center of Lyon, as depicted in Figure 3. The fusion of those data is imported to the open source road traffic simulator \"SUMO\" [46]. It is designed to handle bicycle routes, road networks and obstacles. The open source framework for running vehicular network simulations \"Veins\" ([[http://veins.car2x.org/](http://veins.car2x.org/)]([http://veins.car2x.org/](http://veins.car2x.org/))) is used to connect the road traffic simulator \"SUMO\" to the network simulator \"OMNeT++\" ([[https://omnetpp.org/](https://omnetpp.org/)]([https://omnetpp.org/](https://omnetpp.org/))) via a TCP socket. Veins includes a real radio propagation and 802.11p models.  
In our simulated scenario, each mobile bike generates a data packet every second. The travel time of bikes corresponds to the trip duration of each bike in the simulation. The longest travel time of bicycles is 1418 s while the average one is about 550 s, as illustrated in the histogram in Figure 4.  
We simulate four sets of parameters as shown in Table 1 by varying the buffer size and the duty cycle:  
* **SB-SDC**: Small Buffer-Short Duty Cycle;
* **SB-LDC**: Small Buffer-Long Duty Cycle;
* **LB-SDC**: Large Buffer-Short Duty Cycle; and
* **LB-LDC**: Large Buffer-Long Duty Cycle.  
It is interesting to note that the copies of a packet stored in a buffer are virtual. We just increment a counter and each packet occupies only one slot of the buffer. In other words, the buffer size is given as the number of slots in bytes. The duty cycle is a period defined in seconds, after which each node forwards the data packets stored in its buffer.  
Figure 3: Considered area of Lyon.  
Figure 2: Vélo’v bicycle sharing system in Lyon.
## 3 Results and Discussion
### Manual Design of Model: Effects of Layers and Hyperparameters
#### 3.2.1 Layers  
In designing the proposed model, the appropriate layers were selected in such a way that the model exhibited better classification accuracy. The proposed model was also tested with fewer layers and more layers. The model did not learn the necessary root rot disease features when designed with fewer layers, resulting in lower accuracy, whereas the model learned more features but showed overfitting when it was designed with more layers. In our design, combinations such as (Conv2, Pool2), (Conv3, Pool2), (Conv4, Pool2), (Conv4, Pool3), (Conv5, Pool2), (Conv5, Pool3), (Conv6, Pool2) and (Conv6, Pool3) were evaluated. The high accuracy and low error was achieved when the number of convolution layers was increased from two to four and the number of pooling layers used was three. However, when the number of layers was increased, the accuracy was reduced. Hence, a further increase in the number of layers did not have a significant effect on the accuracy. [PERSON] and [PERSON] [34] studied the effects of the number of layers in the performance of their model. They investigated the layer depth of the CNN model and analyzed the effect of the number of layers in their model for pest classification by varying the number of convolution layers from three to seven. They observed that the accuracy decreased with an increase in the number of layers, and lower error was observed for six convolution and five pooling layers, avoiding the occurrence of degradation. It can be observed from Table 2 that the proposed model performed better for four convolution and three pooling layers and effectively reduced overfitting.
# Secure Transmission for Buffer-Aided Relay Networks in the Internet of Things
## 2 System Model and Relay Selection Policy
### System Model
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Description** & **Symbol** & **Description** \\ \hline \(M\) & Number of relay sensors & \((\cdot)^{T}\) & The transpose operation \\ \(R_{m}\) & The \(m\)-th relay sensor & \(M_{1,n}\) & The number of available links in the first hop \\ \(K\) & Number of eavesdroppers & \(M_{2,n}\) & The number of available links in the second hop \\ \(E_{k}\) & The k-th eavesdropper & \(\alpha\) & The power allocation factor \\ \(L\) & Buffer size & \(P_{out}\) & The overall secrecy outage probability \\ \(h_{ab}\) & The channel coefficient of link \(a\to b\) & \(P_{out,n}\) & The secrecy outage probability at state \(s_{n}\) \\ \(\mathbf{h}_{ab}\) & The channel vector of link \(a\to b\) & \(\boldsymbol{\pi}\) & The stationary probability vector \\ \(E[\cdot]\) & The expectation operation & \(\boldsymbol{\pi_{n}}\) & The stationary probability vector at state \(s_{n}\) \\ \(d_{ab}\) & The distance between \(a\) and \(b\) & \(R_{s}\) & The predefined secrecy rate \\ \(\kappa\) & The path loss factor & \(\tau_{th}\) & The secrecy outage threshold \\ \(C_{ab}\) & The achievable secrecy rate of link \(a\to b\) & \(N\) & The number of all the buffer states \\ \(\sigma^{2}\) & The variance of AWGN & \(\mathbf{A}\) & The state transition matrix \\ \(P_{S}\) & The maximum transmit power of \(S\) & \(\mathbf{A}_{\varphi,n}\) & The (\(\varphi\),\(n\))th entry of \(\mathbf{A}\) \\ \(P_{R}\) & The maximum transmit power of relay sensor & \(\mathbf{I}\) & The identity matrix \\ \(P_{Total}\) & The total power & \(\mathbf{Q}\) & The all-ones matrix \\ \(s_{n}\) & The n-th buffer state & \(T\) & The average secrecy throughput \\ \(\varphi_{n}\) (\(m\)) & The number of data packets in \(B_{m}\) at state \(s_{n}\) & \(D_{total}\) & The average end-to-end delay \\ \(\left\|\cdot\right\|\) & The Euclidean or \(L_{2}\) vector norm & \(Q_{m}\) & The average queuing length at \(R_{m}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of the main notations and parameters.
## 3 Results and Discussion
### Effect of Aluminum Dross on Free Radical Generation during Catalytic Pyrolysis
#### 3.1.2 Effect of Aluminum Dross on Free Radical Concentration in Pyrolysis Residue  
The free radical concentration in pyrolysis residue, char, formed with increasing temperature, was analyzed by EPR and is presented in Figure 3.  
Biomass pyrolysis undergoes an activation process, generation of free radical fragments, and coupling of these radicals. The free radical concentration in biomass pyrolysis residue was about 10\({}^{19}\) spins/g and increased to a maximum value of 10\({}^{20}\) spins/g at 500 \({}^{\circ}\)C during pyrolysis. It is interesting to note that the temperature for maximum free radical concentration was higher than the terminal pyrolysis temperature. Free radical concentration decreased after 500 \({}^{\circ}\)C due to condensation reactions where the weight loss reached a plateau, as shown in Figure 2. During the co-pyrolysis of AD and pine, the biomass components, especially hemicellulose, were melted and actively combined by hydrogen bonds with AD at about 300-350 \({}^{\circ}\)C. Then, C-C bonds and C-H bonds were activated to form C-O-M (M is the metal) intermediates. The free radicals were trapped in the molten cluster [37]. The weight loss rate was delayed and the area percentage of peak 1 decreased, as shown in Figure 2. The free radical concentration in PADC increased from 2.28 \(\times\) 10\({}^{19}\) to 10.97 \(\times\) 10\({}^{19}\) spins/g at 300 \({}^{\circ}\)C, as illustrated in Figure 3. The maximum  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Peak**} & \multicolumn{3}{c}{**DTG-Pine**} & \multicolumn{3}{c}{**DTG-Pine+AD**} \\ \cline{2-7}  & \(\mathbf{T_{p}}\)/\({}^{\circ}\)C & \(\mathbf{W_{p}}\)/\({}^{\circ}\)C & \(\mathbf{A_{p}}\)/\(\mathbf{\%}\) & \(\mathbf{T_{p}}\)/\({}^{\circ}\)C & \(\mathbf{W_{p}}\)/\({}^{\circ}\)C & \(\mathbf{A_{p}}\)/\(\mathbf{\%}\) \\ \hline
1 & 320 & 79 & 44.03 & 317 & 79 & 41.10 \\
2 & 349 & 41 & 23.69 & 348 & 44 & 25.95 \\
3 & 369 & 23 & 28.32 & 369 & 23 & 29.22 \\
4 & 430 & 54 & 3.96 & 429 & 52 & 3.73 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fitting results of the DTG curve.  
Figure 3: Variation on free radical concentrations in pyrolysis residue from pine and a mixture at different temperatures of 200–700 \({}^{\circ}\)C.  
free radical concentration of 22.82 \(\times\) 10\({}^{19}\) spins/g was reached at 500 \({}^{\circ}\)C, correlating to the cleavage of C-O and C-C bonds in C-O-M intermediates; the value was similar to that in pure pine pyrolysis residue. It demonstrates that AD accelerates the free radical generation during pine pyrolysis. The results agree well with the DTG behavior in Figure 2 and Table 2. The areas of peaks 2 and 3 increased with AD addition, demonstrating faster reaction. The free radical coupling reaction was also promoted by AD, indicated by the steeper decline in free radical concentration, as shown in Figure 3.