esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability Enhancement Driven by ANN for Lighting Control System in Highway Tunnels
## 5 Model Training of Tunnel Lighting ANN
### Process of Model Training
#### 5.1.3 Training Results  
Based on above findings, this neural network is determined to be composed of one input layer (three neurons), one hidden layer (ten neurons) and one output layer (three neurons), hence the topology of this network is defined as in Figure 10.  
This training algorithm achieved the regression results as Figure 11.  
For this trained network model, partial network parameters are listed below as examples.  
(1). _Net.IW[1, 1]_, weigh matrix [10 \(\times\) 3 array] denotes that the 10 branch extends from the input layer to the hidden layer.  
\[Net.LW\{1,\ 1\}=\begin{vmatrix}5.3781&1.0433&-2.0605\\ 5.7211&-1.0239&-1.9557\\ -1.4488&-0.4322&5.6562\\ 2.6351&4.0524&3.3387\\ -2.1650&3.0634&-4.6949\\ 0.2097&-5.7442&-1.8147\\ 2.5098&3.6840&4.2226\\ -1.1691&-5.1603&-2.8807\\ -2.3365&-3.8057&-3.6902\\ -2.6272&-2.5205&-4.7722\\ \end{vmatrix}\]  
Figure 10: Structure of lighting neural network.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Order** & **Hidden Layers** & \
\begin{tabular}{c} **Neuron Number in** \\ **Hidden Layers** \\ \end{tabular} &
\begin{tabular}{c} **Comparison** \\ **(Regression, Luminance and CT Fitting)** \\ \end{tabular} \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{1} & \multirow{3}{*}{5} & \multicolumn{3}{c}{\(R>0.995\) for training, test and validation sets.} \\  & & & \multicolumn{3}{c}{\(L_{\text{man}}\)curve fits well, but bigger deviations take place for several lower O Luminance labels.} \\ \cline{3-5}  & & & \multicolumn{3}{c}{CT achieves a 100\% recognition rate.} \\ \hline \multirow{3}{*}{2} & \multirow{3}{*}{1} & \multirow{3}{*}{10} & \multicolumn{3}{c}{\(R>0.997\) for training, test and validation sets.} \\  & & & \multicolumn{3}{c}{\(L_{\text{man}}\)curve fits well, and bigger deviations take place for several lower O Luminance labels.} \\ \cline{3-5}  & & & \multicolumn{3}{c}{CT achieves a 100\% recognition rate.} \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{1} & \multirow{3}{*}{20} & \multicolumn{3}{c}{\(R>0.997\) for training, test and validation sets.} \\  & & & \multicolumn{3}{c}{\(L_{\text{man}}\)curve fits well, and bigger deviations take place for several lower O Luminance labels, similar to Test-1.} \\ \cline{3-5}  & & & \multicolumn{3}{c}{CT achieves a 100\% recognition rate.} \\ \hline \hline \end{tabular}
\end{table}
Table 18: Test-2: Extend the dimension of hidden layer (number of neurons).  
(2). _Net.LW_[2, 1], weigh matrix [3 \(\times\) 10 array] denotes the 10 branch extends from the hidden layer to the output layer,  
\[\begin{array}{c}\begin{array}{c}\begin{array}{c}\begin{array}{c}Net.LW \{2,\ 1\}\\ \end{array}\\ \begin{array}{c}\begin{array}{c}-0.5353&-0.3432&0.5064&0.1871&0.5203&-1.299  
for 12 h continuously from 6:00 to 18:00 in normal weather, with a sampling period of 10 min, and then these labels are used to verify the validity of this lighting neural network.
## 2 Fundamental Models and Literature Search
### System Model
#### 2.1.3 Clustering or Layering Network  
The final approach divides the network nodes into distinct clusters, thereby empowering the AMV to exclusively gather data from cluster heads, see Figure 5. This innovative  
Figure 4: Data collection with fixed path.  
Figure 3: Distributed network.  
scheme yields a substantial reduction in the travel time of the AMV, contributing to enhanced operational efficiency and data collection speed. However, the effectiveness of this method is intricately tied to the clustering or layering algorithms. Cutting-edge algorithms capable of efficiently grouping nodes based on diverse criteria, such as spatial position, communication patterns, or resource utilization, can foster seamless coordination among cluster members and facilitate data exchange. Conversely, if the chosen algorithms are not meticulously designed or optimized, network performance may suffer. Suboptimal clustering may lead to imbalanced data distribution, increased communication overhead, or inefficient travel routes.
## 3 Experimental Setup and Output Characterization  
### Experimental Setup  
The experimental setup for the single-pass simultaneous sum frequency generation of orange laser and second harmonic generation of blue laser is illustrated in Figure 1.  
As a pump (P\({}_{1}\)), we used a laser diode (LD) fixed at 974 nm (\(\lambda_{1}\)) of 0.20 nm spectral width, whereas the signal (P\({}_{2}\)) was a C-band (1525-1565 nm) ASE source (\(\lambda_{2}\)). A 50 mm long uniform MgO:PPLN was used for collinear single-pass SFG and SHG processes. The output of C-band ASE was combined with \(\lambda_{1}\) inside a 50:50 wavelength division multiplexer (WDM) coupler and collimated with a C-lens before focusing at the center of the crystal using a coupling lens. We varied the temperature of the MgO:PPLN crystal to obtain the phase matching points for SFG and SHG. The temperature of the device was stabilized by a temperature controlled oven (CTL photonics) with an accuracy of \(\pm\)0.1 \({}^{\circ}\)C. The output power and bandwidth were evaluated using a laser power meter (PM100D, Thorlabs, Newton, NJ, USA) and a fiber spectrometer (BIM-6001, Brolight, Hangzhou, China).
# Device-To-Device Communication in 5G Environment: Issues, Solutions, and Challenges
## 2 Device Discovery
### Recent Advances in Device Discovery  
[PERSON] et al., in [24], proposed an energy-efficient device discovery for a public safety scenario. The proposed solution works in D2D networks where the authors considered a simultaneous user access of resources and major constraints on overlay interference. The results produce a great number of energy-efficient discovered devices and increased the number of discovered devices over static or random back-off patterns. The authors in [25] suggested a D2D neighborhood device discovery by a device where, out of network, static device discovery is discussed, and the times of discovery are examined. The authors developed a mathematical model that can be used by moving D2D devices. The results were verified by Monte-Carlo simulations. [PERSON] et al., in [26], suggested an authentication method for D2D and privacy-preserving device discovery in a 5G heterogeneous network. The authors proposed an authentication scheme using identity-based and integrated privacy protection for device discovery using Elliptic-Curve Diffie-Hellman (ECDH) methods. The performance analysis showed that the proposed scheme protects the privacy with an ideal efficiency. The authors in [27] proposed a public safety full duplex device discovery for the D2D system that is empowered time-efficiently using the IB-FD framework. The proposed framework emphasizes the aim to increase the spectral efficiency and minimize the delay in device discovery on public safety devices. The transmission mode of the proposed framework is able to switch to a full-duplex from half-duplex. The results from the simulation are then verified and compared with the typical random-access technique. The result shows that around 37 percent of the device discovery time is saved using the public safety priority method instead of the random-access technique. [PERSON] et al., in [28], suggested a device discovery and localization in public safety systems that is supported by UAVs. The direct discovery used a proximity service, and in the case of core network failure such as in a disaster situation, the discovery signal to the device link is transmitted over UAVs. The MUSIC algorithm is applied to achieve the optimum accuracy in simulation results. Furthermore, the throughput and the packet error rate are calculated for the performance results. The authors in [29] analyze various device discovery algorithms for D2D communication to produce performance analysis where the accuracy and RMSE metrics are defined. Results from the simulation show that all the algorithms studied maximize the accuracy and minimize the discovery error and complexity of the algorithm. [PERSON] et al., in [14], suggested a public safety full-duplex device discovery for a D2D system that is based on priority. The results showed that in-band full-duplexing performs well on the radio resources in comparison to the half-duplex. The results also showed that the proposed scheme decreases the discovery time by about 37 percent.
## 4 Numerical Model Set-Up
The water level at lake rest (0 m a.l.l. at \(z\)-axis in the domain system) is used as the initial condition for all model runs. Recording data intervals of 0.5 and 1 s are set for the medium-size and large-size lakes, respectively. The model set-ups are chosen to allow completion of each simulation in a reasonable amount of time (between 2 and 24 h per simulation) and to optimize the balance between output accuracy and output file size (4 to 109 Gb per simulation). The model environments have been designed to ensure that output files are within the processing capabilities of FlowSight, while also producing results suited for inter-model comparisons. The computational resource and hardware components used for numerical modeling are the following:  
Figure 4: Examples of theoretical lakes and model set-ups showing simulations of a landslide-induced impulse wave. (**a**) Small elongated medium-size lake; in this example, the boundary conditions are shown (S-symmetry; W—wall; O—outflow). (**b**) Equidimensional medium-size lake. (**c**) Narrow elongated medium-size lake. (**d**) Elongated large-size lake (dotted white lines represent lines probes (L.P. 1–12) used to record water-surface elevations in FlowSight). (**e**) Example of longitudinal section for the theoretical lake in (**c**) along the slide direction (dotted white line); boundary conditions are shown. The yellow dot represents the origin of the system in the model domain. * Processor: Intel(r) CoreTM i7-3820 CPU 3.60 GHz;
* RAM: 32 GB;
* System type: 64-bit operating system;
* Number of core license tokens: 8 (Flow-3D parallel license code);
* Graphics card: GeForce GTX 6602 (Integrated RAMDAC, total available memory 4096 MB).
# A Multi-Dimension Spatial Method for Topology Awareness and Multipath Generating
## 3 Constructing Multi-Dimensional Topology View  
### Constructing Independent Views  
We prefer to use centralized network positioning methods [21] to generate coordinates for RSes in SDSON, e.g., Vivaldi [22], MDS [21; 23] and GNP [24]. We obtain required inputs by measuring [18]. The coordinate of \(RS_{i}\) is \(C_{i}\). We use D(i,j), MR(i,j) and MM(i,j) as the distance, measure relation and metric between \(RS_{i}\) and \(RS_{j}\).
## 5 Measurement Details and Results
### CA Service  
The standalone use of CA service is typical today in the so-called Day1 scenarios or deployments. In some cases, Day1 use cases also include DENM and IVIM messages in highway scenarios; however, these message types were not in the scope of this work, since they would only change the results marginally.  
Figure 6 shows that the CBR values will remain low if we only use the CA service. This means that the maximum value of the CBR remains around 6% even with the assumption of 100% penetration--this is approximately half of the value measured with both CP and CA services. We can also see in Figures 6 and 7 that the data are growing nearly linearly.  
Figure 7 shows that the average CBR values reported by V-ITS-S nodes are slightly higher than those reported by R-ITS-S nodes. The clusterization is more or less similar to the CAM and CPM scenarios.
# Polynomial Cancellation Coded DFT-s-OFDM for Low-PAPR Uplink Signaling
## 5 Conclusions  
As 5G services bring more diverse communication scenarios, low-PAPR LTE-legacy uplink signaling, DFT-s-OFDM, requires greater flexibility in its PAPR performance for adapting miscellaneous 5G devices. We have proposed a generalized PCC technique to solve the inflexibility of regulating the PAPR of DFT-s-OFDM uplink signals by reducing the PAPR in a controllable manner. By deriving the SS function for PCC-DFT-s-OFDM, we have proved that the proposed method can be considered as a novel SS technique for DFT-s-OFDM. Based on the cost of spectral efficiency, which might be a relatively tolerable requirement for URLLC, mMTC, and D2D ProSe in LTE/NR, the simulation results show that our PCC-DFT-s-OFDM outperforms the conventional DFT-s-OFDM in terms of PAPR, PSD, and BER, the last especially with the inter-user interference. Hence, our method is the most suitable to support the various D2D transmissions and URLLC/mMTC services.  
Owing to the low complexity of PCC, future work could evaluate the combination of PCC and current uplink/downlink signaling options (i.e., DFT-s-OFDM/CP-OFDM) as potential waveform  
Figure 9: Two-user bit error rate (BER) performances of polynomial cancellation coded (PCC) discrete Fourier transform-spread orthogonal frequency division multiplexing (DFT-s-OFDM) over the Rayleigh channel for different values of \(d\) for **(a)** User 1 (with medium bandwidth) for \(\pi/2\)- binary phase shift keying (BPSK) and quadrature PSK (QPSK), **(b)** User 2 (with narrow bandwidth) for \(\pi/2\)-BPSK and QPSK, **(c)** User 1 for 16-quadrature amplitude modulation (QAM), and **(d)** User 2 for 16-QAM.  
candidates for 5G and beyond. Such combinations can include other advantages of PCC-OFDM, such as the reduction of ICI [30], enhancement for MIMO systems [31; 32], and increasing the robustness of asynchronous communications [33; 34], forming a highly flexible PCC-/DFT-s-/OFDM system against the complicated 5G environment.  
Conceptualization, L.C. and Y.-S.W.; Software and Validation, L.C. and Y.-M.K.; Formal Analysis, L.C.; Writing--Original Draft Preparation, Review and Editing, L.C and C.-Y.H.; Supervision, C.-Y.H.  
This work was supported in part by Industrial Technology Research Institute, Taiwan, under grant number V1-107001.  
The authors would like to thank the Editor and the anonymous reviewers for their valuable comments and suggestions.  
The authors declare no conflict of interest.
## 1 Introduction
The classical Fenton reaction involves the reaction of hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) with iron salts (Fe(II)) to produce hydroxyl radicals, while the photo-Fenton variant incorporates ultraviolet radiation (UV) or sunlight to regenerate Fe(II) from Fe(III), thus improving the process efficiency [21; 22]. Recent studies have shown that the photo-Fenton process is particularly effective in the degradation of emerging pollutants due to its ability to generate a higher amount of hydroxyl radicals and its applicability in ambient conditions [23; 24]. Despite the progress made in the application of the photo-Fenton process for the removal of emerging contaminants, significant gaps in knowledge remain regarding the intermediate transformation of contaminants and their actual degree of mineralization [25; 26; 27]. Most studies have focused on the degradation of the parent compound [28; 29], but have not delved into the persistence of intermediate aromatic structures or the kinetic evolution of indicator properties such as water color or loss of aromaticity, which are directly linked to the degree of oxidation and detoxification of the treated system. Furthermore, although the catalytic role of ferrous ions in the generation of hydroxyl radicals has been recognized [30,31], the impact of catalyst dosage on side effects such as the formation of iron-reaction intermediate complexes, which can stabilize partially oxidized compounds, impairing iron regeneration and reducing overall process efficiency, has not been comprehensively evaluated. In this context, identifying operating conditions that prevent radical recombination and the formation of colored or poorly biodegradable products is key to treatment optimization. Likewise, there is little research evaluating the mineralization of heteroatomic elements contained in pharmaceuticals, such as nitrogen and sulfur, by monitoring inorganic species like nitrates and sulfates. These species can be used as mineralization markers, allowing stoichiometric balances to be established and complete oxidation pathways to be analyzed. This study proposes a detailed kinetic approach that considers not only the disappearance of the parent contaminant, but also the loss of aromaticity, water color change, and the evolution of inorganic species (NO\({}_{3}^{-}\) and SO\({}_{4}^{2-}\)) as indicators of the degree of mineralization. Furthermore, the influence of ferric catalyst concentration on the formation of complexes with aromatic intermediates and its impact on treatment efficiency is analyzed. This comprehensive analysis allows for the identification of optimal conditions to maximize the generation of oxidative radicals, avoid inhibitory effects, and facilitate future integration with biological processes to achieve the complete and sustainable removal of emerging contaminants.
## 3 Detailed Description of the Existing Protocols
### Co-Improved Hydrocast  
In Co-Improved Hydrocast, the aforementioned problem is tackled by the diminishing of transmissions (via opportunistic routing cooperation technique). The working of the routing protocol is two folded, i.e., in the first fold, some fixed sensor nodes are conveyed in the UWSN (at vital areas) and the remaining nodes are deployed independently. In the second fold, the idea of opportunistic routing is implemented to upgrade the UWSN throughput. Every node finds the neighbor nodes in its transmission range to advance the data and compute the Euclidean distance with neighbors. The priority to advance the information is based on how a sensor node is close to a sink node. In the proposed work, void nodes are avoided in two distinctive ways, i.e., if a source node itself becomes a void node, then the protocol follows recovery mechanism and starts back transmission (by selecting high pressure nodes to advance the information towards sink); otherwise, the low-pressure node is elected as the next potential node. The number of fixed nodes and their deployment benefits are:  
1. Twelve static nodes are conveyed in the UWSN to diminish the number of transmissions [27].
2. Four sets including three fixed nodes are conveyed to cover the maximum area of the UWSN [27].
3. These fixed nodes reduce the retransmission in the UWSN and increase the PDR with minimum E2E delay [27].  
This strategy enhances the network throughput using the short recovery path as in Figure 4. In this figure, the source node sends the DP to sink via relay nodes (by avoiding the void hole region)  
Figure 3: System model of hydrocast protocol.  
and fixed sensor nodes (deployed strategically). This strategy results in short recovery paths, i.e., (from node I to node IV). However, the next hop forwarder node selection raises the problem of local optimal solution (in which the current source node considers the higher pressure level node as the next forwarder node instead of the low-pressure level node) and back transmissions (see Figure 5). These aforementioned issues show that this protocol needs to be enhanced to minimize the EC and E2E delay by avoiding the void hole problem.
## 2 System Synthesis
### Condition Monitoring Subsystem  
There must be sensors used along the SailREC System to track the health of critical subsystems. This can be monitored for safe operation and predictive maintenance of the system. The data acquired by the **Condition Monitoring Subsystem** is sent out to the control centre through the Communication Subsystem. The SailREC System could alsohave on-board sensors to monitor the weather, which can be used for trajectory planning, apart from using these data for research and science.
## 3 Results
### Regional Assessment of _I_SBACH-Modelled Start of Season against Satellite Observations
We observed an early anomaly in the start of season in ENF in 2007, both from satellite observations and JSBACH simulations (Figures 10 and 11c,f). The yearly anomaly for ENF_SOS\({}_{\text{sat}}\) was larger than \(-\)20 days in south-western coastal areas and in central Finland (Figure 11c). The ENF_SOS\({}_{\text{mod}}\) anomaly agreed in many parts of Finland with the satellite-observed anomaly, but not for western areas at Bothnian Bay and areas in the northern boreal zone (Figure 11f). Figure 9: **(a–f)** Scatterplots of satellite-observed and JSBACH-simulated start of season for individual years 2005–2007. Upper panel: evergreen needle-leaf forest (ENF_SOS); Lower panel: deciduous broad-leaf forest (DBF_SOS). The dotted line gives the 1:1 line and the black line the linear fit between satellite- and JSBACH-modelled estimates. Figure 11: (**a–f**) Spatial distribution of yearly anomalies, the deviations (in days) from grid cell mean for 2003–2010, of start of season in evergreen needle-leaf forest. Upper panel: remote sensing estimate (ENF_SOS\({}_{\text{sat}}\)). Lower panel: model-derived estimate (ENF_SOS\({}_{\text{mod}}\)). Figure 10: Interannual variations of the start of season in evergreen coniferous forest and deciduous broad-leaf forest in Finland derived from the JSBACH model and from remote sensing observations. Furthermore, agreement was found for parts of central and northern Finland regarding the early anomaly of DBF_SOS in 2006 (Figure 12b,e); although DBF_SOSmod indicated a stronger early anomaly than shown by DBF_SOSsat. Some disagreement between the anomalies was observed in 2007; an early anomaly (up to 15 days) occurred in the south-eastern parts of Finland, according to DBF_SOSsat, but this was not the case for DBF_SOSmod (Figure 12c,f).
# Spatial Data-Based Automatic and Quantitative Approach in Analyzing Maintenance Reachability
## 3 Materials and Methods  
### Generation of the Arm's Maximum Solution Set  
#### 3.1.1 Arm Model Establishment  
From the viewpoint of kinematics, arm movements are enabled by joints that drive the bones and this process can be regarded as the motion of spatial linkage. Hence, the model of the arm can be simplified as a spatial linkage mechanism. In the model, joints are equivalent to kinematic pairs, whereas bones are equivalent to links.  
The methodology presented in this study evaluates reachability on the basis of hand fixation on the maintenance spot. Generally, once a hand is placed on the repair site or has grabbed the maintenance tool, the fingers' degrees of freedom (DOF) will barely affect reachability. In the proposed methodology, the fingers' DOF can be ignored and the hand can be simplified as a point. The established arm model includes seven DOFs, two of which are for the wrists, one is for the elbow, three are for the shoulders, and one is for the forearms. In this model, each DOF is equivalent to one link, in which the lengths of the forearm and upper arm are their actual values, whereas the lengths of other links are zero.