esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data Acquisition and Processing
### UAV Data Acquisition and Processing  
A SenseFly eBee with Canon S110 NIR camera (R, G, NIR bands) was used to acquire image mosaics every two weeks (synchronously with field measurements) [26]. Flight plans were optimized to cover the 48 fields located in the Northern part of the study area (Figure 1) in 7 flights, with each flight surveying a cluster of fields. Image acquisition settings ensured a 70% lateral and 75% longitudinal overlap and a target pixel resolution of approximately 10 cm \(\times\) 10 cm on the ground.  
All the pictures of a given flight were acquired with the same camera settings optimized shortly before takeoff. The shutter speed was set to maximum, _i.e._, 1/2000; and the sensitivity (ISO) and focal length settings were adjusted in order to slightly under-expose the picture (avoiding any saturation in the images).  
The Canon camera records images in a raw format (.CR2). They are first converted to TIF format using eMotion software. Then, AgiSOFT Photoscan Pro was used to generate a Digital Surface Model (DSM) and an orthomosaic. For accurate image geolocation, the set of 50 GCPs distributed throughout the entire area of the 7 clusters was used. These GCPs were manually geo-located for each  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Millet/Sorghum** & **DAP** & **15-15-15** & **Urea** & **Kg N/ha** & **Kg P/ha** & **Kg K/ha** \\ \hline B & & & & 0.0 & 0.0 & 0.0 \\ C & & & 50 & 23.0 & 0.0 & 0.0 \\ D & 75 & & 50 & 36.5 & 15.1 & 0.0 \\ E & 150 & & 50 & 50.0 & 30.1 & 0.0 \\ F & & 150 & 50 & 45.5 & 9.8 & 18.7 \\ \hline
**Maize** & **DAP** & **15-15-15** & **Urea** & & & \\ \hline B & & 100 & 150 & 84.0 & 6.5 & 12.5 \\ C & 100 & & 150 & 87.0 & 20.1 & 0.0 \\ D & & 200 & 300 & 168.0 & 13.1 & 24.9 \\ E & 200 & & 300 & 174.0 & 40.1 & 0.0 \\ \hline
**Peanut** & **DAP** & & & & \\ \hline B & & & & 0.0 & 0.0 & 0.0 \\ C & 50 & & & 9.0 & 10.0 & 0.0 \\ D & 100 & & & 18.0 & 20.1 & 0.0 \\ E & 150 & & & 27.0 & 30.1 & 0.0 \\ \hline
**Cotton** & **Cotton Complex** & **Profeba** & **Urea** & & & \\ \hline B & & & & 0.0 & 0.0 & 0.0 \\ C & 150 & & 50 & 44.0 & 11.8 & 22.4 \\ D & 200 & & 100 & 74.0 & 15.7 & 29.9 \\ E & 200 & 10,000 & 100 & 109.6 & 43.1 & 6.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fertility levels (kg/ha) for each plots of each crop type (five plots (B; F) for millet and sorghum field; four plots (B; E) for maize, peanuts, and cotton). DAP stands for Di-Ammonium Phosphate with 18% N, 46% P\({}_{2}\)O\({}_{5}\) and 0% K\({}_{2}\)O; “15-15-15”, locally called “_complex cérales_” is also a NPK fertilizer with 15% N, 15% P\({}_{2}\)O\({}_{5}\) and 15% K\({}_{2}\)O; “Coton complex”, locally called “_complex céton_” is a NPKSB fertilizer with 14% N, 18% P\({}_{2}\)O\({}_{5}\), 18% K\({}_{2}\)O, 6% S, and 1% BO\({}_{3}\); Profeba is a compound organic fertilizer, locally called “_sabunyuman_” [23] containing cattle manure, poultry manure and 14% N, 5% P, and 1% K in addition to various other micronutrients; Urea contains 46% N.  
image to ensure an accurate point cloud for DSM generation and geometric correction as required for image mosaicking.
## 2 Results and Discussion  
Scheme 1 summarizes the reaction mechanism of the two vinyl bond insertions in the copolymerization of propylene with the _bis_-styrene monomers (DVB and BVPE), as proposed by [PERSON] [14,15]. DVB or BVPE was inserted into the intermediate [Zr]-PP\({}_{1}\), obtained via propylene polymerization, to form [Zr]-DVB-PP\({}_{1}\) or [Zr]-BVPE-PP\({}_{1}\), respectively. As a result of the spatial blocking effect in [Zr]-DVB-PP\({}_{1}\) or [Zr]-BVPE-PP\({}_{1}\), a high energy barrier for the subsequent propylene insertion ensued. Therefore, propylene cannot be further inserted into [Zr]-DVB-PP\({}_{1}\) or [Zr]-BVPE-PP\({}_{1}\) to inactivate the catalytic system. The sequence of insertions is similar to the copolymerization mechanism of propylene and \(p\)-methylstyrene [16]; the data are included in the Supplementary Materials (Figures S1-S6).  
However, hydrogen can easily be inserted into [Zr]-DVB-PP\({}_{1}\) and [Zr]-BVPE-PP\({}_{1}\). In contrast to \(p\)-methylstyrene, the _bis_-styrene molecules (DVB and BVPE) contain two vinyl bonds. When inserting the second vinyl bond of DVB or BVPE, it is inserted into the polymer in a rare 1,2-way, and the chain reinsertion introduces a regional irregularity. The irregularity begins with the introduction of hydrogen.  
Figure 1: Structures of DVB and BVPE.
## 2 Materials and Methods
### Photocatalytic Performance Evaluation  
The photocatalytic activity performance of the as-prepared materials was assessed by the photocatalytic degradation capability for MB dye simulated textile wastewater. In brief 0.05 g of the photocatalyst was added to 100 mL of a 5.0 mgL\({}^{-1}\) aqueous solution of MB dye and photocatalytic degradation efficiency was investigated. The solution was stirred for 60 min. in dark to achieve adsorption-desorption equilibrium between the photocatalyst and the dye. Afterwards the MB dye solution with photocatalysts is subjected to light illumination generated by 500 W xenon lamp for 120 min. An aliquot of 5.0 mL of the solution was recovered from the solution every 30 min., during the illumination period, centrifuged (6000 rpm for 5 min, Pro Research K241, Centurion Scientific, Chichester, UK), and MB dye degradation analyzed using UV-Vis absorption spectrometer (Biobase, BK-UV1900 PC, Jinan, China).  
The formula for calculating the photocatalytic efficiency (%) is presented in Equation (2):  
\[\text{Photocatalytic efficiency}\left(\%\right)=\left(\frac{\left(\text{ Co}-\text{ Ct}\right)}{\text{Co}}\right)\times 100 \tag{2}\]  
where, Co (mg L\({}^{-1}\)) is the initial concentration and C\({}_{\text{t}}\) (mg L\({}^{-1}\)) is the concentration at time t, after light irradiation.
# A Bird Vocalization Classification Method Based on Bidirectional FBank with Enhanced Robustness
## 7 Conclusions  
To classify bird vocalizations, this study proposed a method involving iWAVE denoising with iFBank and BiFBank feature extraction. Compared with traditional methods, the iWAVE method produced good results for both simulated and real signals at various noise settings. In most cases, it can obtain the highest objective SNR values and generate better waveforms. BiFBank integrates the advantages of FBank and iFBank, which was combined with the proposed denoising method to create a comprehensive feature set for constructing an effective bird vocalization recognition method. The random forest classifier was used to evaluate the classification performance of both traditional and proposed features. In the classification of the vocalization of 16 bird species, the classification accuracy of iWAVE-BiFBank reached 94.00%, outperforming MFCC and FBank by 16.02% and 10.96%, respectively, and its other evaluation indicators were over 93%. The experimental results showed that the proposed method has better bird vocalization classification performance with enhanced stability and robustness.  
Figure 14: Boxplot of various features based on accuracy.  
**Author Contributions:** Conceptualization, C.P.; methodology, C.P., Y.Z. and Y.X.; software, C.P.; validation, C.P. and J.L.; formal analysis, C.P.; investigation, C.P. and J.L.; resources, Y.Z.; data curation, J.L.; writing--original draft preparation, C.P.; writing--review and editing, C.P., Y.Z. and J.L.; visualization, C.P. and J.L.; supervision, Y.Z., D.L. and Y.X.; project administration, Y.Z. and D.L.; funding acquisition, Y.Z. and D.L. All authors have read and agreed to the published version of this manuscript.  
**Funding:** This study was supported by the National Natural Science Foundation of China (grant No. 32360388, grant No. 31860332, grant No. 31960142).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data included in this study are available from the corresponding author or by accessing Xeno-canto.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Experimental Results and Discussion
### The Result of Newstest 2014 EN-DE and EN-FR  
As shown in Table 4, the X-Transformer obtained the best BLEU scores of 46.63 and 55.62 in the WMT 2014 English-German (EN-DE) and English-French (EN-FR) corpora. The baseline Transformer [4] had BLEU scores of 27.3 and 38.1, which are about 19 and 17 points of BLEU score lower than our proposed model. The comparison with the BERT-fused NMT model revealed that, although the performance improved, it did not surpass that of the X-Transformer. As shown, the obtained BLEU scores of the BERT-fused NMT model are 30.75 and 43.78 for EN-DE and EN-FR, respectively, which are about 15 points in terms of BLEU score lower than the X-Transformer.  
In Table 4, the training time experienced for each model per epoch in average under the same environment is also presented. For the convenience of observation, it is presented in multiples and the original Transformer is set as the baseline 1\(\times\). The training time of the X-Transformer is only 0.35 that of the original Transformer. In addition, the BERT-fused Transformer requires a longer training time, mainly because BERT is a pre-trained model with twelve layers of encoder and decoder stacked. Therefore, the BERT-fused Transformer has to wait for the output of BERT during each training process and takes a long time for training. The experiment results reveal that the training time of the BERT-fused Transformer is around 6.9 times higher than that of the proposed X-Transformer. The proposed X-Transformer not only significantly reduced the training time but also obtained a better BLEU score compared to that of the BERT-fused Transformer.  
Analyzing the experimental results obtained from two different language pairs, we can conclude that the X-Transformer has a streamlined encoder and decoder that sped up the training speed and simultaneously improved accuracy.
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 6 Blockchain Potential in V2X Networks
### Integrating Blockchain with Mec and 5G Technology in V2X Networks
#### 6.3.3 Integration of Blockchain with 5G-Based Edge Computing in V2X
The edge layer consists of 5G network infrastructures, like the Macro BS (MBS), small BS (SBS), and RSUs, which are geo-distributed and installed with MEC servers and blockchain platforms. MEC reduces latency and prevents back haul congestion by offloading computation and caching content in distributed manner. Each BS or RSU supports blockchain to allow untrustworthy vehicles to communicate securely for content caching or storing critical messages as transactions. The cloud layer has a global view of the network that consists of high computing servers, but long distance between vehicle and cloud causes delay. The advantage of this hierarchical scheme is that it intelligently and securely manages the resources in an optimal way by leveraging the new paradigm such as AI and blockchain technology. The AI based on DRL effectively evaluates the topology, channel distribution, and existing wireless network congestion effectively. MEC incorporated with blockchain and AI facilitate reliable network diagnosis and enable dynamic orchestration. This scheme helps to reduce the block generation time as well as block propagation delay as the transaction messages are exchanged near the edge nodes. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & **Ref. No. ** & **Description** & **Blockchain/Consensus** & **Edge Comp. ** & **Cellular /5G** & **System Characteristics** \\ \hline [PERSON] et al. (2018) & \multirow{4}{*}{[79]} & Presented Content Centric Networking (CCN) based on BC for secure vehicular communications & Permiss-ioned BC & No & \multirow{4}{*}{Yes} & Used CCN instead of TCP/ IP \& allowed BC to monitor source confidentiality, quality \& authenticity of shared data \\ \cline{1-1} \cline{3-6} \cline
# Research into Image Point Positioning Compensation of a High-Orbit Thermal Infrared Staring Camera
## 3 Experimental Results and Analysis
### Temperature Installation Angle Experiment  
According to the image point positioning error compensation process designed above, two schemes are used to compensate the image point positioning under different temperature conditions; that is, only external parameter compensation and internal and external compensation are performed. At different times of the day, under the temperature condition, the same target trajectory is staring imaging, and the camera distortion is the same each time. The specific distortion parameters are shown in Table 1. The external relative installation angle parameters of each experiment are calculated, and a difference is made with the original relative installation angle to obtain the corresponding curve of the installation angle error related with time and compare it with the original added relative installation angle error. The compensation results of the two schemes under different temperature conditions are compared with the installation angle of the original simulation, and the results are shown in Figure 11.  
Although temperature has a great effect on the relative installation angle, the change rule of the relative installation angle and time can be recovered after compensation, and the compensation scheme of \"the internal first and then the external\" is better than that of the external only. It can be found that even when the relative installation angle error is large, the two schemes can better calculate the true relative installation angle, and this can also verify the correctness and effectiveness of the two compensation process schemes designed above.
# Model Predictive Control Algorithm for Converter Based on a Convolutional Neural Network
## 3 Load-Type Identification Method Based on CNN
### Load-Type Identification  
In this article, the four load types designed in Figures 4 and 6 are defined as follows: NL refers to no load connected to the inverter output terminals. HPFRIL refers to a resistive-inductive load connected to the output of the inverter, and the power factor of the load is higher than a certain value, which is set to 0.85 in this article. LPFRIL refers to a resistive-inductive load connected to the output of the inverter, and the power factor of the load is lower than a certain value, which is set to 0.6 in this article. NLL load refers to an uncontrolled rectifier followed by a resistive-capacitive load, connected to the output of the inverter.  
For the 2L-VSI, this article collects a total of 600,000 data comprising four load types, NL, HPFRIL, LPFRIL, and NLL, and each type contains 150,000 sample data. The dataset is split in a 4:1 ratio, with 80% allocated as training data and 20% as test data, ensuring class balance. The ADAM optimization algorithm is employed for model training, with hyperparameter settings detailed in Table 2.  
Figure 7 shows the curves of accuracy and loss function changes versus iteration number during the training process. The loss function exhibits exponential decay and the load identification accuracy increases rapidly from the 1 st to the 200 th iteration and then stabilizes, with the final overall identification accuracy reaching 96.4%.  
The constructed CNN is employed to identify load types of the 2L-VSI. Based on test data, the confusion matrix shown in Figure 8 is obtained. The results indicate that the CNN achieves 100% identification accuracy for both HPFRIL and LPFRIL, representing the best performance. For NL, the accuracy reaches 95.5%, and for NLL the accuracy is identified with 90.3% accuracy. The overall metrics are as follows: 96.42% accuracy, 96.49% precision, 96.44% recall, and F1-score of 96.44%.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Parameter Value** \\ \hline Max epochs & 50 \\ Initial learning rate & 0.001 \\ L2 regularization & \(1\times 10^{-4}\) \\ Learning rate drop factor & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the CNN training process.  
Figure 6: CNN structure diagram.
## 3 Path Loss Models
### COST-231 Hata Model  
This model is an extension of the Okumura-Hata model to a higher frequency (2 GHz) [40,41]. The COST 231 model relies on empirical data acquired through measurements of signal propagation in diverse urban and suburban environments. It considers the effects of building density, street width, and antenna height, among other factors. This model offers a formula for path loss computation between a transmitter and receiver, taking into account factors such as signal frequency, the transmitter-receiver separation distance, and multiple environmental parameters [25]. The formula includes separate terms for line-of-sight and non-line-of-sight propagation, and it can be adapted for use in different frequency bands and environments [36]. As seen in (7), the formula can thus be written as follows [42]:  
\[PL=46.33+33.9\log f-13.82\log h_{tx}+C_{m}-a(h_{rx})+(44.99-6.55\log(h_{tx})) \log d, \tag{7}\]  
where \(f\), \(d\), \(h_{tx}\), \(h_{rx}\), and \(C_{m}\) are the frequency in MHz, distance from the transmitting station to the receiving antenna, transmitting antenna height, and receiver antenna height, respectively. The correction factor is thus written as follows [39]:  
\[a(h_{rx})=\big{(}1.1\log_{10}(f)-0.7\big{)}h_{rx}-\big{(}1.56\log_{10}(f)-0.8 \big{)}\quad for\ 1500\ MHz\leq f\leq 2000\ MHz. \tag{8}\]  
The COST-231 Hata model has been widely used in the telecommunications industry for path loss predictions and for designing mobile communication networks in urban areas. However, it is important to note that the model has limitations and may not accurately predict path loss in every specific scenario [39].
# Flaw Size Quantification for Cable Flaw Inspection System with Inductive Search Coil Sensor
## 5 Discussion and Conclusions
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methodology** & **Advantages** & **Limitations** & **Reference** \\ \hline Dye Penetrant (DP) testing & Simple method for detecting shallow cracks & Costly for assessing quality at multiple locations & [5; 6] \\ \hline Optical Fiber Sensing & Stable and precise for cable force monitoring & Complex detection process and high sensitivity & [7; 8; 9; 10] \\ \hline Radio-graphic Testing (RT) & Capable for internal structure examination & Costly and potential health risks due to radiation exposure & [11] \\ \hline Ultrasonic Testing (UT) & Quick detection of defects with strong penetration ability & Poor intuitiveness of detection results & [12; 13; 14; 15; 16] \\ \hline Magnetic Particle Testing (MT) & Analysis of magnetic flux leakage for surface or near-surface flaws & Shallow depth measurement capabilities & [17; 18; 19] \\ \hline Magnetic Flux Leakage (MFL) & Highly effective and easy to achieve quantitative testing & Sensitivity to lift-off height and excitation intensity & [20; 21; 22; 23] \\ \hline Acoustic Emission (AE) testing & Monitoring of cable force and vibration frequency during dynamic processes & Decreased accuracy with increasing distance & [24; 25; 26] \\ \hline Vibration-based method & Remote sensing & Complex data analysis & [27; 28; 29; 30; 31; 32; 33] \\ \hline The proposed method & Less data processing, real-time responsiveness, and lightweight for robots to carry & Applied to small-sized cables currently \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative analysis of cable flaw detection methodologies.
## 1 Introduction
Research on VOCs has been increasing because of their biotechnological/industrial applications, where volatiles are involved in the biocontrol of plant pathogens [20; 21], clinical infections [22; 23], biomarkers of diseases, and diagnosis [24; 25; 26; 27]. Moreover, the roles of VOCs in biological and interactional processes have been described [28; 29; 30]. Currently, a VOC microbial database contains nearly 2000 volatile compounds registered as either bacteria or fungi. However, compounds from _Malassezia_ are not available in this database [31]. Certainly, it is important to determine the volatile profile for _Malassezia_ species and open the gate for further research on their implication in metabolism or biological interaction, as has been described for other microorganisms [32]. Currently, there are only two studies on VOCs produced by _Malassezia_. In 1979, the authors reported that these yeasts could produce VOCs on lipid-supplemented media; only 11 volatiles were identified [33]. Recently, 61 VOCs from _Malassezia furfur_ were identified in growth media supplemented with oleic and palmitic acid, suggesting that fatty acid consumption determines VOC production in this species [12]. Nevertheless, the biological function of volatile compounds and their relationship with lipid metabolism and other metabolic processes remains unclear because there is no information concerning this topic [12; 32]. This study aimed to identify the different VOCs produced by _M. globosa_, _M. restricta_, and _M. sympodialis_ in exponential and stationary growth phases to clarify the metabolism of these yeasts. We found differences in the volatile profiles of these three species and identified at least one unique compound in each growth phase. These findings suggest metabolic changes in _Malassezia_ associated with phylogeny and developmental phases. These novel results will allow species recognition based on a volatile profile and set a precedent for future studies about the role of VOCs from _Malassezia_ yeasts in biological interactions with the skin microbiota or the host. Moreover, the volatile profiles help to understand why these yeasts could transition from being commensal to pathogenic.
# A Novel Multipath Transmission Scheme for Information-Centric Networking
## 5 Performance Evaluation
### Chunk Completion Time  
In the ICN scenario, a chunk needs to be completely received before it can be delivered to the application layer. Therefore, the average chunk completion time is an important indicator showing the performance of the transmission method. The simulation steps are the same as those described in Section 5.3, and we measured the average chunk completion time under different bandwidths. Figure 7 shows the experimental results. It can be seen that as the bandwidth increases, the average CCT of each method decreases. The average CCT of the single path method is the highest, roughly 2.16 s, 1.75 s, 1.41 s, and 1.28 s under different bandwidths. It can also be observed that both ECMP and our proposed method can reduce the average CCT, and our proposed method is significantly more effective. Compared with the other two methods, our proposed method can reduce the average CCT to 1.44 s, 1.1 s, 0.74 s, and 0.56 s, respectively, under different bandwidths. This also shows that our proposed method can speed up the transmission, thus ensuring timely delivery of data to upper-layer applications.  
Figure 6: The average FCT under different link bandwidths.  
Figure 7: The average CCT under different link bandwidths.  
In addition, as shown in Figure 8, we obtained the CCT cumulative distribution of data source node S and receiver node C1 when transmitting 100 chunks under 200 Mbps bandwidth. We can see that the proportions of CCT less than 1 s for the single path method, ECMP, and our proposed method are 0.74, 0.67, and 0.91, respectively, and the proportions of less than 2 s are 0.86, 0.98, and 0.98, respectively. Therefore, we can conclude that the single path method does not perform as well as the multipath method in reducing the CCT. Moreover, compared with ECMP, our proposed method can maintain the CCT at a lower level.