esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition
## 4 Experimental Results
### Experiments on SCface
Despite the fact that the recorded images for distance 1 have low resolution, MS-LZM achieved significant progress, especially for cameras 3, 4, and 5. The results obtained for SCface NightTime protocol are given in Table 4 with the best known results in the literature. In order to calculate these results, the moment degrees used in the first and second LZM transformations were 1 and 4, respectively, and the filter size was 7 in both transformations. With the proposed method, it is obvious that the best performance was obtained for the average success for the NightTime protocol. In addition, the best results were achieved in all probe sets. In experiments for this protocol, both Daytime and NightTime gallery sets were used together. To learn PCA matrices, only the Fa set was used as in the DayTime protocol. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Probe** & **PCA**[56] & **ELBP**[79] & **LPOG**[46] & **MS-LZM** \\ \hline cam6\_1 & 1.5 & 9.2 & 13.1 & 21.5 \\ cam6\_2 & 3.1 & 15.4 & 23.9 & 38.5 \\ cam6\_3 & 3.9 & 25.4 & 31.5 & 33.1 \\ cam7\_1 & 0.7 & 13.1 & 17.7 & 27.7 \\ cam7\_2 & 5.4 & 13.1 & 20.0 & 34.6 \\ cam7\_3 & 4.6 & 13.9 & 19.2 & 25.4 \\ \hline Average & 3.2 & 15.0 & 20.9 & 30.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results on the SCface NightTime Protocol with state-of-the-art results. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Probe** & **PCA**[56] & **SR**[77] & **DSR**[78] & **ELBP**[79] & **LPOG**[46] & **MS-LZM** \\ \hline cam1\_1 & 2.3 & & & 43.1 & 69.2 & 72.3 \\ cam1\_2 & _7.7_ & & & 56.2 & 73.
## 5 Results and Discussion
### Contextual Optimisation Results
The proposed model changes improved R\({}^{2}\) of the short-term model by 5% (1 h) to 11% (3 h) for SVM and 7% for FBP. The MAE for the FBP model decreased by 39 W/m\({}^{2}\) and by ca. 25 W/m\({}^{2}\) for SVM. RMSE was also decreased by 17 to 24 W/m\({}^{2}\) for SVM and 18 W/m\({}^{2}\) for FBP. The overall R\({}^{2}\) improvement associated with model changes for the long-term forecast is 20% for SVM and 8% for FBP, as shown in Table 5. MAE decreased by 42 W/m\({}^{2}\) for FBP but only by 16 W/m\({}^{2}\) for SVM. For SVM, however, RMSE decreased by 41 W/m\({}^{2}\), whereas for FBP, it decreased by 20 W/m\({}^{2}\). The insights of the individual model results for different horizons were taken to determine which algorithm to use for which horizon in the final DCF. For DCF, the highest accuracy for the 1- and 2-hour predictions was achieved using SVM with extraterrestrial radiation as an additional input feature, using the dynamic night-time adjustment and version 3 of the seasonal adaptation. Figure 11 shows that the 1-hour prediction SVM displayed a compact trend line with only a few normally distributed errors. For FBP, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Initial Features**} & \multicolumn{3}{c}{**Initial + Additional Features**} \\ \cline{2-7}  & **Sunset and** & **Seasonal** & **Improvement** & **Sunset and** & **Seasonal** & **Improvement** \\  & **Sunrise** & **Adaptation** & **Sunrise** & **Adaptation** & **Improvement** \\ \hline R\({}^{2}\) & 80.56\% & 83.35\% & 2.79\% & 83.22\% & 83.97\% & 0.74\% \\ MAE & 70.1 & 61.51 & 8.60 & 62.42 & 57.81 & 4.61 \\ RMSE & 126.4 & 117.01 & 9.42 & 117.46 & 114.83 & 2.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of influence on seasonal adaptation on FBP models with different features. Figure 10: SVM (**a**) before and (**b**) after seasonal adaptation. most values were on a line that was slightly too steep, indicating an overprediction for those values. However, there were also many points below the dense line, signalling underprediction. For the 3-hour and long-term predictions, the FBP using V1 of the seasonal adaptation outperformed all the other versions and algorithms. It can be concluded that the SVM model should be used for 1- and 2-hour ahead predictions, while beyond that, the FBP model should be utilised in the final DCF. The performance of FBP suffered less from an increase in horizon than the SVM model. This is due to the underlying characteristics of the algorithm; FBP is specifically designed for time-series prediction [30]. An advantage is that the performance declines less over time. However, inputting the whole past time series into the model did not allow emphasising values that had a higher correlation and were more relevant to the particular prediction. For SVM, this could be differentiated.
# Leveraging Blockchain Usage to Enhance Slag Exchange
## 3 Materials and Methods
### Use Case  
The organizations described above interact with each other exchanging information which is altered through the operations shown in Figure 2. These operations are performed sending transactions to the smart contract deployed in the underlying blockchain network.  
The steelmaking process is held in the ladle furnace. To avoid impurities to obtain the desired steel quality, the ladle is covered with reducing slag. The steel company creates basic information of a slag in the blockchain network using a _register_ operation. Afterwards, the composition of this slag can be measured and registered with a _composition_ operation.  
Once the heat has finished, the slag is poured to a cone, and this fact is recorded in the system using the _to-cone_ operation. The cone can store a slag of up to three heats, so once it is full, it is grouped with slag batches of similar qualities (i.e., compositions) in a slag stock.  
Cement companies can bid for any amount of slag, providing a price per the desired volume and using the _bid_ operation. Thanks to blockchain and its properties, any participant can know when a slag was generated, modified or grouped and who made each change. Furthermore, the composition of each stock is expected to give them a good indicator of the  
Figure 2: Proposed use case.  
quality of the slag (i.e., better slag optimizes the cement-making process). This information should help both parties to adjust the price of the bids.  
At any point, steel companies can either accept (_bid-accept_) or reject (_bid-reject_) any of the bids. After acceptance, the appropriate volume is disaggregated from the stock and sent to the cement company. Steel companies can also get rid of a certain volume of stock if they do not meet an appropriate purchaser with the _dispose_ operation to mark that they were sent to the landfill.  
The proposed auction scheme does not have time restrictions. Any cement company can see the composition of a slag when they are in the _stocked_ status and bid for it. Similarly, the steel company can accept or reject bids anytime. However, the logic of the smart contract could be easily extended to define the period when the bids can be received and another time to start the decision process. In fact, this decision process could be coded in the smart contract if the steel company has a predefined acceptance criteria, e.g., the bid with the best unitary price or the bid with the larger volume of slag.  
Finally, public administrations can use the _stats_ operation to obtain indicators of the amount of slag recycled by each company.
# Voxel-Based Digital Twin Framework for Earthwork Construction
## 4 Implementation of the Proposed System
### Graphical Simulation Environment for Model-Equipment Interaction
#### 4.1.2 Digital Twin Models Creation  
The digital twin models for the graphical simulation were based on the as-is condition. The ground, geotechnical, design, and the current state of the data were processed to develop the twin models. The procedure for creating the digital twin models is presented in Figure 8.  
Firstly, a ground model was generated, representing the original ground condition before construction, and serving as a base for the model registration in the downstream stages. A 3D point cloud was generated by processing images through image registration, which involved merging and aligning the images using feature extraction, feature matching, and bundle adjustment. A TIN surface was created from the point cloud data.  
Then, geotechnical modeling was performed using borehole investigation data, using the algorithm proposed in a previous study [24].  
The next step was to create an as-is condition model. Since the scenario was created before the construction started, practically on site, the design model, along with ground and geotechnical, was used to represent the as-is condition. It was modeled using Autodesk Civil 3D, providing target information and quantities. The ground and target models are shown in Figure 9a. A solid was extracted using the ground and target TIN surfaces, which shows the as-planned excavation model. The earthwork models from Autodesk Civil 3D were imported into Rhino 3D software using the Drawing (dwg) format. The extracted solid was converted into a mesh, which was then converted into voxels. Before converting the design mesh into voxels, the voxel size was determined based on the allowable accuracy and computation, site material, and bucket information, such as width. A voxel size of 1 cm was selected, considering the site material and bucket size, as it resulted in minimal error and efficient computation. The voxel-based as-planned model is shown in Figure 9b.  
For equipment modeling, a customizable library was used in the implementation. Users can manipulate the equipment in the simulation, such as translating the machine, rotating mechanical parts, and digging voxels with a bucket. Keyboard keys were used to control the equipment commands. Each mechanical component had a lever attached to perform its functions. The lever had an input setting controlled by keyboard commands. The details of the input settings for the excavator and dump truck are described in Table 2.  
Figure 8: Process of creating voxel-based as-planned and as-built models.
## 3 Methodology
### Multi-Channel Traffic and Weather Scene Image
#### 3.1.3 Construction of Channels  
Based on the above channel visualization results, nine image data channels are constructed. These include inbound flight volume channel, inbound flight average flight speed channel, inbound flight average flight altitude channel, departing flight volume channel, departing flight average flight speed channel, departing flight average flight altitude channel, mild convective weather channel, moderate convective weather channel, and strong convective weather channel. In order to eliminate the image features that do not have any significance to the airport delay prediction model, this paper adopts the approximate ablation experiment to screen the channels with various combinations of the channels, and the image resolution is set to 25 \(\times\) 25, and the model performance is evaluated by the Accuracy, Precision, Recall, and F1 metrics. Given that the severity of convective weather is a key factor affecting airport delays, all channel combinations must include the three convective weather channels. In total, 145 combinations are tested, and Table 2 lists 8 combinations that achieved better prediction results.  
Figure 4: Convective weather channel. (**a**) Shows the weak convective weather within the terminal area, (**b**) Shows the moderate convective weather within the terminal area, (**c**) Shows the strong convective weather within the terminal area.  
Figure 3: Flight state channel. (**a**) Shows the flight volume channel, (**b**) Shows the average altitude channel, and (**c**) Shows the average speed channel.
# In Vitro Cultures and Volatile Organic Compound Production in _Chiliadenus montanus_ ([PERSON].) [PERSON]
## 3 Results and Discussion
### Volatile Organic Compound Analysis
For instance, the number of different constituents identified in microshoot extract was higher compared to the wild plant and in vitro callus, which produced the lowest number of compounds (Table 4). In addition, fluctuations in the concentration of common compounds were observed, where, for instance, \(\alpha\)-Caryophyllene was higher in wild plant extract (6.77%) when compared to microshoot extract (1.73%). Ref. [15] identified \(\alpha\)-Caryophyllene as a major compound in the VOCs of _C. montanus_ from the Sinai region in Egypt and this is in general agreement with the findings of this study. Interestingly, \(p\)-Cymene was identified only in callus and microshoot extracts, although it was previously identified in wild _C. montanus_ plants [44] and other related species [42; 43; 44; 45; 46; 47; 48]. In general, several identified compounds in this study were reported previously in wild _Chiliadenus spp._ from the region. For instance, \(\alpha\)-Caryophyllene was detected previously in _C. iplionoides_[46], _C. lopadusanus_[47], and _C. antiatlanticus_[45]. Ref. [45] reported 15.5% \(\alpha\)-pinene in the aerial parts of _C. antiatlanticus_, while in this study, 13.78% were detected in _C. montanus_ callus extract. They also detected \(\gamma\)-Terpinene (0.8%) that was identified in the _C. montanus_ microshoot and callus extracts (0.74% and 1.53%, respectively) of this study. Finally, the qualitative and quantitative variations in VOCs between intact plants and in vitro cultures were reported previously in _Artemisia spicigera_ in vitro cultures [43] which is in general agreement with the results of this study. Such variations in VOCs between _C. montanus_ samples used in this study could be attributed to environmental factors affecting their production in natural habitats such as light, temperature, water, and nutrients. For most plants, while under in vitro conditions, such factors are controlled continuously and adjusting any individual factor will result in altering their levels [48].
## 7 Performance Evaluation
### Numerical Results and Discussion
#### 7.2.3 Comparison with Related Works  
As we introduce some modifications to the maximum waiting time expression, as presented in [13], the related comparison has to be considered here. The comparison is performed under complete isolation among windows, as shown in Table 1. In [13], the presented algorithm dramatically reduced the worst-case latency bounds compared with the results in [11]. Hence, our proposed model (FWOS) is compared with that in [13] (STNet) and [11] (STNode) based on two different scenarios, as shown in Figure 17a,b.  
In Figure 17a, the comparison is performed under different window offsets between adjacent nodes (\(OS_{4}^{h,h-1}\)). FWOS gives the lowest WCDs compared with others. For a three-hop connection, FWOS reduces the WCD by 55% on average and 60.6% maximum compared with STNode and 3.03% on average and 3.4% maximum compared with STNet. The WCD enhancement of the FWOS algorithm increases under large use cases, as illustrated in Figure 17b. Compared with STNet, FWOS reduces the WCD by 9%, 11.5%, and 12.3% for connection with 10, 20, and 30 hops, respectively. The interpretation of that is our latency improvement depends linearly on the number of selected nodes, as can be noticed from Equations (28) and (38). Our model reduces end-to-end latency bound by a value equal to the propagation and technical switch delays in each link. Note that the  
Figure 17: Comparison between the proposed flexible window-overlapping scheduling (FWOS) algorithm and related works: (**a**) FWOS, STNode ([11]), and STNet ([13]) under different relationships between offsets for same-priority windows among three nodes. (**b**) FWOS and STNet ([13]) with constant relationships between offsets in connections with 10, 20, and 30 hops.  
STNode model is excluded in Figure 17b as it produced non-comparable latencies. The STNode model is implemented based on separate latency computations among nodes without considering the relative positional offsets between adjacent nodes. Undoubtedly, it has colossal WCDs compared with others.
## 2 Materials and Methods  
### Experimental Materials and Preparation  
In this study, the commonly used grade HRB400 steel was utilized as the material for fabricating the working electrodes, the preparation process of which is shown in Figure 1a. A chemical composition analysis on the HRB400 steel sample was also performed, indicating 0.24% C, 0.77% Si, 1.57% Mn, 0.036% P, and 0.041% S by weight. The chemical composition of this batch of HRB400 steel complies with the composition control requirements of GB/T 1499.2-2018 for HRB400 steel [31]. The steel reinforcements were first polished to a smooth finish using a grinder and then cut into 1 cm-long sections, with one end serving as the working surface and the other end connected to a copper wire via welding. The welded steel reinforcements were then encapsulated using a layer of polytetrafluoroethylene (PTFE) to prevent any possible contact between the electrolyte and the copper wire. Prior to use, the working electrodes required sanding to prepare their surfaces. The working surfaces of the electrodes were gradually polished using sandpaper of grades 1000#, 1200#, 1500#, and 2000# until the surfaces were clean, uniform, and scratch-free. These surfaces were then cleaned using ultrasonic cleaning to remove any residual particles or contamination. The oxide layer on the polished working electrode surface was completely removed, leaving a mirror-like finish. To prevent moisture and other atmospheric components from rusting the bright working electrodes, they should be stored in a dryer for future use.  
To enhance the practical engineering significance of studying the passivation characteristics for steel reinforcements in concrete, this study used Ca(OH)2 solution and deionized water to prepare an electrolyte in which the working electrode of the steel reinforcement was immersed to simulate the environment in which the steel reinforcement is located in concrete in actual engineering applications. The pH value of the prepared electrolyte was around 12.5, which is similar to that of a concrete solution. After standing for 24 h to ensure that the solution was stable, the mixture was filtered using quantitative filter paper to obtain the electrolyte, which was then sealed with clingfilm for future use. The concrete simulation solution was prepared by following the process in Figure 1b. The electrolyte solution prepared in this study can simulate the environment of steel reinforcements in concrete in actual engineering applications, providing a more accurate prediction of the passivation of steel reinforcement in concrete.
# Interference Alignment for Cognitive Radio Communications and Networks: A Survey
## 6 Open Research Challenges
### CR Network Synchronization and Organization  
IA in CR networks by means of linear precoding is a transmission strategy for the coherent interference channel. Thus, IA requires tight synchronization to remove any timing and carrier frequency offsets between cooperating CRs [133]. In the absence of sufficient synchronization, additional interference terms are introduced to the signal model, rendering the IA solution ineffective. Synchronization strategies that could help fulfil this requirement are a vital future research topic. CRs cooperating via IA must not only synchronize, but also negotiate physical layer parameters.
# Carrier-to-Noise-Threshold Filtering on Off-Shore Wind Lidar Measurements
## 4 Discussion
[26] presented a novel post-processing algorithm that performs a correction for the instruments background noise. The algorithm which still is in a trial stage has been developed for a Halo Doppler wind lidar and successfully applied to measurements carried out in Finland. It is not clear how the CNR threshold filtering will affect the wind speed climatology when a post-processing algorithm is applied, but it likely will shift the bias towards lower CNR threshold values.
# A Robust and Energy-Efficient Weighted Clustering Algorithm on Mobile Ad Hoc Sensor Networks +
## 3 Description of RE2 WCA Algorithm
### Fault Detection Algorithm
Node 6 is normal. (**c**) Nodes (3, 9) send message to _CH_ respectively. Node 6 still fails to send messages. Figure 5: (**a–c**) is the practical example of Backup _CH_ mechanism process. (**a**) In the period of _time_fn2_, nodes (3, 9) receive the message from _CH_ (5) but node 6 not. Node 6 marks _CH_ possibly failed. (**b**) Backup _CH_ launch the status enquiry message for cluster. (**c**) Backup _CH_ receive the messages from inner- cluster nodes and judge _CH_ is faulted or not. ```
After the packet transmission is finished, the fault detection begins.\(CH\) is elected and the degree (\(dv_{j}\)) of each \(CH\) is ensured. Initialization: \(k_{i}\) = 0, \(\sum i\) = \(N\), \(\sum j\) = \(H\), \(S_{fault}\) = \(\phi\), \(S_{prob\_fault}\) = \(\phi\), \(S_{prob\_fault}\)(\(j\)) = \(\phi\). #Round m (period time_ft2)
1. Initialization(\(k_{i}\), S);
2. \(whit(v_{i}\) \(\in\) Cluster\({}_{j}\) and \(j\) <= \(H\) and \(i\) = \(N\))
3. for\(i\) = 1 to \(dv_{j}\)
4. bool flag = Send (\(v_{i}\) \(\rightarrow\) ID, \(v_{i}\))
5.if (flag = = false)
6. Append ((\(v_{i}\) \(\rightarrow\) ID),\(S_{prob\_fault}\)(\(j\)))
7.\(k_{i}\)++;
8. end;
9.end;
10. j++;
11.end;
12.if(\(k_{i}\) >= 2)
13. Append ((\(v_{i}\) \(\rightarrow\) ID), \(S_{fault}\))
14.else
15.\(S_{prob\_fault}\) = \(\cup_{j=1}^{H}S_{prob\_fault}\)(\(j\));
16.return\(k_{i}\), \(S_{prob\_fault}\) and \(S_{fault}\);
```  
**Algorithm 3** Fault detection algorithm description (run between inner-cluster and intra-cluster)
## 4 Conclusions  
We have proposed and experimentally demonstrated a proof-of-concept of integrating fiber communication system and BOTDR sensor system using a single 25 km optical fiber at two different dedicated wavelengths. The communication system EVM performance was investigated using different modulation formats of QPSK, 16-QAM, 64-QAM in the presence of various BOTDR sensor system optimized power levels. The EVM performance with and without BPF before the photodetector of the data system receiver is demonstrated. Using a QPSK modulation and 18 dBm sensing power and at 10 dBm data power the measured EVM values with and without BPF are 3.5% and 14.5%, respectively. While the BOTDR temperature sensing performance has been investigated in the presence of data system signal. As a result, the measured temperature error is \(\pm\)0.49 \({}^{\circ}\)C when the applied temperature is 50 \({}^{\circ}\)C at the end of the sensing fiber., Therefore, the proposed system demonstrates a low temperature measurement error ( \(\pm\)0.49 \({}^{\circ}\)C) and acceptable EVM values, which were within the 3 GPP requirements, hence demonstrating the feasibility for practical applications. For further study, the proposed system configuration can be investigated for narrow wavelength spacing between data and sensor system laser wavelengths. The proposed system can be further exploited into DWDM and CWDM based communication system to improve the system performance in terms of larger bandwidth/higher data rate [15]. Furthermore, the BOTDR sensor system can be integrated with other emerging techniques, such as Raman amplification, pump pulse coding and wavelength diversity techniques [19] for further improvement in sensor system performance.  
Conceptualization, W.P.N.; Data curation, Q.W.; Formal analysis, N.L.; Funding acquisition, W.P.N.; Investigation, N.L.; Methodology, N.L.; Supervision, W.P.N. and X.D.; Validation, Q.W., [PERSON], N.J.G. and C.L.; Writing - original draft, N.L.; Writing - review & editing, W.P.N, X.D., Q.W., Y.Q.F., P.H. and N.J.G. All authors have read and agreed to the published version of the manuscript. This work is supported by Royal Society Kan Tong Po International Fellowship KTP\(\backslash\)R1\(\backslash\)181005.  
The authors declare no conflict of interest