esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Hilbert-Huang Transform for Vibration-Based Damage Detection of Utility Timber Poles
## 6 Conclusions  
In this study, vibration-based NDT using HHT with WPT as pre-processor are adopted for determining features (natural frequencies) of timber poles with a view to assess its condition. In the proposed method, raw vibration signals are captured from field using impact hammer and portable data acquisition system equipped with 12 accelerometers. The signals are multicomponent and nonstationary and use of FRF alone cannot satisfactorily  
Figure 23: Captured time domain acceleration data and their frequency spectra at A0R1 and A0R2 for Unserviceable Pole 1.  
Figure 22: Instantaneous amplitude of IMF1 for A0R1 considering 3 serviceable poles and 3 unserviceable poles.  
determine the natural frequencies. Therefore, WPT along with a mother wavelet selection method is employed as a pre-processor to decompose the captured raw signal into several narrow bands. Then EMD is utilised on these narrow band signals to obtain IMFs. After that, a simple screening technique is applied to retain the relevant IMFs that are related to the raw signal. Both original HHT and improved HHT have been used to analyse the vibration data of serviceable/healthy and unserviceable/unhealthy in situ timber poles. From the analysis, it is found that improved HHT has better computational efficiency than the original one for the vibration data analysis of timber utility poles. Natural frequencies have been identified which is vital for the health state determination of timber poles. Additionally, condition of the pole can be considered healthy if slight variation in instantaneous frequency is observed for dominant IMFs. From the comparison between serviceable and unserviceable pole, it is found that instantaneous amplitude of dominant IMFs rises according to the severity of damage. Since the condition of the pole vary with time, routine inspection is essential. If amplitude of instantaneous frequency and variations are increased with time, pole condition is referred to have deteriorated and sufficient measures should be undertaken accordingly.  
Conceptualization, M.S.; methodology, I.D. and M.S.; validation, M.S., M.T.A. and I.D.; formal analysis, I.D.; investigation, M.S. and I.D.; funding, A.M.T.O. and M.S.; writing--original draft preparation, I.D.; writing--review and editing, M.S. and M.T.A.; supervision, M.S., M.T.A. and A.M.T.O.; project administration, A.M.T.O. and M.S.; funding acquisition, A.M.T.O. All authors have read and agreed to the published version of the manuscript.  
The research was funded by Ausnet Services, Victoria, Australia.  
Not applicable.  
The authors gratefully acknowledge [PERSON] and [PERSON] from Ausnet Services for helping with the data collection of in-service poles. Authors would also like to express their gratitude to [PERSON] and [PERSON] their supports on packaging the device.  
The authors declare no conflict of interest.
# Underwater Image Super-Resolution via Dual-aware Integrated Network
###### Abstract  
Underwater scenes are often affected by issues such as blurred details, color distortion, and low contrast, which are primarily caused by wavelength-dependent light scattering; these factors significantly impact human visual perception. Convolutional neural networks (CNNs) have recently displayed very promising performance in underwater super-resolution (SR). However, the nature of CNN-based methods is local operations, making it difficult to reconstruct rich features. To solve these problems, we present an efficient and lightweight dual-aware integrated network (DAIN) comprising a series of dual-aware enhancement modules (DAEMs) for underwater SR tasks. In particular, DAEMs primarily consist of a multi-scale color correction block (MCCB) and a swin transformer layer (STL). These components work together to incorporate both local and global features, thereby enhancing the quality of image reconstruction. MCCBs can use multiple channels to process the different colors of underwater images to restore the uneven underwater light decay-affected real color and details of the images. The STL captures long-range dependencies and global contextual information, enabling the extraction of neglected features in underwater images. Experimental results demonstrate significant enhancements with a DAIN over conventional SR methods.  
underwater image; super-resolution; transformer; multi-scale +
Footnote †: journal: Applied
# A Spectral-Aware Convolutional Neural Network for Pansharpening
## 4 Experiment Results
### Experimental 3--Quickbird Sundarbans Dataset  
This dataset covers a forest area of Sundarbans in India. Similar to the previous setting, a sub-scene with \(256\times 256\) pixels was used for testing.  
The results of the reduced resolution quality assessment on the Quickbird dataset are shown in Table 4. As the previous dataset, our SA-PNN still keeps being largely preferable. In the traditional methods, PCA and CNMF present worse pansharpening qualities while MTF-GLP-HPM gets the best result. PNN, DRPNN, and DiCNN1 achieve close improvements, which far exceed the traditional methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Q4** & **Q** & **SAM** & **ERGAS** & **SCC** \\ \hline Reference & 1 & 1 & 0 & 0 & 1 \\ \hline EXP & 0.4654 & 0.4528 & 6.2444 & 6.0977 & 0.4811 \\ PCA & 0.6726 & 0.6689 & 6.2384 & 4.9672 & 0.7551 \\ GSA & 0.8094 & 0.8009 & 5.9050 & 4.2201 & 0.7643 \\ PRACS & 0.7715 & 0.7594 & 5.7972 & 4.3321 & 0.7652 \\ ATWT & 0.7625 & 0.7512 & 5.9945 & 4.4026 & 0.7649 \\ MTF-GLP-HPM & 0.7798 & 0.7698 & 5.9840 & 4.2576 & 0.7755 \\ CNMF & 0.6958 & 0.6565 & 5.9772 & 5.0231 & 0.7358 \\ PNN & 0.9273 & 0.9238 & 3.9546 & 2.6630 & 0.9214 \\ DRPNN & 0.9233 & 0.9188 & 4.0627 & 2.7367 & 0.9166 \\ DiCNN1 & 0.9246 & 0.9204 & 4.0473 & 2.7168 & 0.9183 \\ \hline SA-PNN & **0.9348** & **0.9313** & **3.6563** & **2.5401** & **0.9296** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quality indexes at reduced resolution on a \(256\times 256\) sub-scene of the IKONOS dataset (best result is in bold).  
Figure 3: Pansharpening results for the WorldView-2 dataset: (**a**) ground truth; (**b**) polynomial kernel (EXP); (**c**) principal component analysis (PCA); (**d**) Gram–Schmidt adaptive (GSA); (**e**) partial replacement adaptive component substitution (PRACS); (**f**) _à trous_ wavelet transform (ATWT); (**g**) Generalized Laplacian Pyramid with High-Pass Filtering (MTF-GLP-HPM); (**h**) coupled nonnegative matrix factorization (CNMF); (**i**) pansharpening CNN (PNN); (**j**) deep residual pansharpening neural network (DRPNN); (**k**) detail injection-based pansharpening CNN (DiCNN1); and (**l**) spectral-aware pansharpening neural network (SA-PNN).  
Visual inspection is shown in Figure 6. It is obvious that CNMF suffers from severe spectral distortion. Furthermore, the result of traditional methods are spatially blurred, such as the area in the upper right of the images in Figure 6. Analogously, CNN-based methods achieve better visual results and our SA-PNN still attained the best performance in terms of spatial detail reconstruction and spectral fidelity.
# Forward-Looking Element Recognition Based on the LSTM-CRF Model with the Integrity Algorithm
## 2 Related Work
### Deep Learning Methods  
With the development of word-distributed representation, deep learning is a powerful tool for sequence modeling. Typically, a word is expressed by word embedding and then input into the neural network model. In this process, the hidden layer of the multilayer structure is used to extract features and predict the label. [PERSON] et al. (2015) combined a convolutional neural network (CNN) with a CRF to achieve better results for named entity recognition tasks. [PERSON] et al. (2016) presented a bidirectional LSTM-CRF model (Bi-LSTM-CRF model) for NLP benchmark sequence tagging data. The model yielded an F-value of 88.83% for the CONLL2003 corpus. [PERSON] et al. (2017) used the Bi-LSTM framework to automatically learn orthographic features and investigate the named entity recognition problem. The proposed approach performed excellently in \"segmentation and categorization\" and \"segmentation only\" subtasks. [PERSON] et al. (2018) constructed a LSTM-CRF model using word representations and character-based representations of captured morphological and orthogonal information, and the model performed well in named entity recognition (NER) tasks in four languages.  
In the above studies, the rule methods are effective, but they require manual construction and domain knowledge. Machine learning models require large numbers of corpus-based and manual features, and the recall rate is not high. Although deep learning models provide satisfactory recognition effects, they ignore the meaning of the language itself and cannot be further improved. Analyses of financial domain corpora have shown that these methods cannot be directly applied to the financial field. Notably, the openness and integrity of elements must be considered.  
In recent years, dependency grammar has performed well in identifying tasks. [PERSON] (2019) and others used the rules of dependency syntax to recognize emotional words. [PERSON] et al. (2019) used the rules of syntactic dependency to formulate rules and identify evaluation objects and evaluation word pairs. [PERSON] et al. (2018) used dependency syntax to build evaluation objects and candidate sets of evaluation words and obtain evaluation objects and evaluation words. Then, the candidate set was reselected using a maximum entropy model to obtain the final evaluation object and evaluation word pair. Therefore, we can rely on the syntax integrity algorithm to correct the element boundaries and combine it with the LSTM-CRF model to propose the LSTM-CRF model with the integrity algorithm. This method has the following three advantages over the conventional method:  
1. The dependency syntax can capture the long-range collocation and modification relations between the internal components of a sentence. This feature can be used to solve the problem of integrity;
2. Open-element and new entity issues can be solved with the superior recognition performance of the LSTM-CRF model;
3. The proposed model meets the requirement of simultaneously identifying different types of elements.
# Phtyoremediation and Bioremediation of Pesticide-Contaminated Soil
## 7 Remediation of Pesticides
### In Situ Bioremediation and Phtoremediation
#### 7.3.1 Biodegradation of DDT  
The insecticide 1,1,1-trichloro-2,2-bis(p-chlorophenyl) ethane (DDT) is known to be persistent in the environment, and its use is banned in many locations [289]. However, it is still in use in Africa and India. It has very low solubility in water, about 0.001 mg/L and is more soluble in lipids [289; 290]. The log of the octanol/water partition coefficient is 7.48 [292]. [PERSON] et al. have reported that DDT is taken up and transformed by several different plants [283]. Zucchini and pumpkin are two garden plants that take up DDT [254; 255; 256]. DDT can be removed from soil and transported into alfalfa, rye grass and tall rescue also [139]. [PERSON] et al. [289] have reported their research to isolate a bacterium that biodegrades DDT and uses DDT as a carbon and energy source. The mineralization pathway is reported [289]. Nano-scale zero valent iron has been found to enhance the biodegradation rate of DDT [290]. Adding these nano particles several times resulted in biodegradation of the DDT [290]and further biodegradation of some of the initial degradation products. Spent mushroom waste (with white-rot fungus), and biosolids have been reported to be effective for degradation of DDT [137; 198]. There are many plants that take up DDT and its metabolites and are beneficial in the remediation of DDT-contaminated soil [1; 44; 79; 139; 155; 254; 255; 256; 257; 258; 259; 260; 254; 256; 257; 258; 259; 274; 283]. Recently, [PERSON] et al. [172] have reported their work on phytoremediation of soil containing DDT and metabolites of DDT. Table 1 contains data from their research [172]. In the table, DDE refers to 1,1-dichloro-2,2-bis(p-chlorophenylenyl) ethylene, and DDD refers to 1,1-dichloro-2,2-bis(p-chlorophenylenyl) ethane. When both chlorines on the benzene rings are in the para positions, 4 is used to show this. The two indicates that one of the chlorines is in the ortho position. MAC refers to the maximum allowable concentration, which is 0.1 mg/kg or 100 mg/kg. In this soil, the average total concentration of DDT and the metabolites was 6.181 mg/kg, which is 62 times the MAC.
# A Review of the High-Mix, Low-Volume Manufacturing Industry
## 9 Discussion
### The Application of Digital Tools and The Extent of Simulation Testing in the Industry  
Question 3: \"How are digital tools being used in HMLV for production management?\"  
The production planning work has progressed over two decades from mathematical modelling and algorithms. The new Industry 4.0 concept advances towards using decision support systems for the production planning of HMLV. Digital twin and big data enabled data collection and reactive planning, based on real-time factory data. At the same time, algorithms can self-learn and optimise reactively, increasing the flexibility of the decision-making process against variations and changes.  
More real-world industrial user testing is expected in coming years to include comprehensive evaluations of their developed work. This is a continuation of the simulation and mathematical research for industrial validation.  
Question 4: \"What is the extent of simulation being tested on the industry?\"  
Categorisation of the industrial validations, with industrial testing and dataset, carried out by the literature, according to the industrial sector. Most of the validations were on production and operation management solutions for production planning. As the electronic and semiconductor industry were the two dominating industries for validations, this highlighted the gap in research in other HMLV manufacturing industries.
## 7 Conclusions  
In this final section of the paper, we present an answer to the main research question. The main research question was: Do the perceived ability, motivation, and opportunity to procure in a sustainable manner affect the implementation of the various types of SPP? To answer this question, a survey was administered amongst Dutch public procurers, measuring the impact of ability (knowledge and skills), motivation (affective commitment to SPP), and opportunity (organisational learning capacity) on various types of SPP (GPP, ISC, CIE, BBPP, IPP and SROI).  
In short, the answer to the research question is no, it does not. Although the various types of SPP did correlate with each other, the different types of SPP are distinct from each other; furthermore, the SEM analysis showed that each type of SPP has its own unique antecedents. This refers to ability (operationalised as knowledge and skills), knowledge and any (albeit an indirect) relationship to GPP, but not to any of the other types of SPP. Skills had no significant relationship with any type of SPP. Motivation (operationalised as affective commitment) did affect GPP, but none of the others types of SPP. Opportunity had an influence on most types of SPP, as there is a direct relationship between opportunity (operationalised as OLC), and GPP, IPP and CIE. We thus conclude that ability, motivation, and opportunity affect GPP but not all types of SPP. We were unable to identify an antecedent for more social types of SPP in this research. This conclusion highlights the importance of a clear operationalisation of the different types of SPP, and therefore that findings based on GPP cannot be directly generalized to other types of SPP. Moreover, it highlights the overall need for further research into the antecedents of social types of SPP.  
**Author Contributions:** Conceptualization, J.G. and D.V.; Formal analysis, J.G. and D.V.; Investigation, D.V.; Methodology, J.G. and D.V.; Project administration, D.V.; Supervision, J.G.; Writing-original draft, J.G.; Writing-review & editing, J.G.  
**Funding:** The data was collected during a paid internship of the second author at PIANOo (an expertise centre for public procurement). The research itself however did not receive funding.  
**Acknowledgments:** The authors wish to thank NEVI and PIANOo for allowing them to administer the survey amongst the subscribers of their newsletters and the three anonymous reviewers for their comments.  
**Conflicts of Interest:** The authors declare no conflict of interest. The internship organisation (PIANOo) was not involved in the design of the study or data collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# A Distance-Vector-Based Multi-Path Routing Scheme for Static-Node-Assisted Vehicular Networks
## 5 Evaluation
### Evaluating Delay Metrics  
We compared the performance of two routing metrics, i.e., the traffic volume metric and the delivery delay metric, under the same evaluation procedure as described above. We fix the routing scheme to MP-RDV and see the effects of the routing metrics and flush ratio.  
Figure 14 shows the results. First, we focus on the case of 0%, and find that both traffic volume and delivery delay metrics have no difference in all of those three performance criteria. This is because  
Figure 12: Delivery ratio with various data loads: delivery ratio under variation of data load is shown for each flush rate 0%, 1%, and 2.5%.  
Figure 13: Performance in failure scenario: delivery ratio sensitively degrades as flush rate increases.  
traffic volume has relatively high correlation with delivery delay so that almost the same set of paths has been selected as a result.  
Second, when we see the difference in flush ratio, we find that the delay metric significantly reduces its performance as flush ratio increases in delivery ratio shown in Figure 13(a), whereas the volume metric remains at 99%. This trend is the same in the load of packet copies shown in Figure 13(c). The reason why this occurs is that delay metric has a large fluctuation due to traffic signals and vehicle traffic state. Thus, statistically the delay metric is less stable than the volume metric especially when flush ratio increases, which impacts the performance. When routing metrics fluctuate in a large range, routing paths also change frequently. This causes instability of networks and then the delivery ratio as well as the load of copies get worse as shown in Figure 13(a),c.  
In Figure 15, we show the performance under variation of generated data rate. The trend seen here is that performance gradually reduces as the data load increases, while performance of delay metric is lower than that of volume metric. This is also caused by the instability of the delay metric. Figure 16 shows the results of failure scenario, in which again the performance of the delay metric is lower than the volume metric.  
Figure 16: Metric Comparison in Failure Scenario.  
Figure 14: Comparing two routing metrics: delivery ratio, delivery delay, and the number of packet copies are compared with both delay and volume metrics.  
Figure 15: Metric Comparison under Variation on Data Rate.
# Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses
## 3 Methods
### Conventional Regression Methods  
The conventional regression models are included for the thoroughness of this work. The list of models considered is not exhaustive, but it includes a variety of methods. Some are selected for their simplicity and others are chosen for their capability. For example, the dummy method will always output the same value and provides a lower bound that any useful method should exceed.  
The conventional regression methods used in this paper include:  
* _Linear Regression:_ A common numerical approach where each feature contributes linearly to the regressed output [22];
* _Two-Layer Neural Network:_ The two-layer fully connected neural network has a capability of approximating any function [23];
* _K-Nearest Neighbors:_ _K-Nearest Neighbors_ (KNN) uses the data as the model and uses the nearest \(K\) points in the feature space to determine the regressed value [24];
* _Dummy:_ This is a simple method that ignores input data and creates outputs based only on the training data; the version used for this work outputs the median value of the BER observed in the training data [25];
* _Decision Tree:_ Decision trees are well studied graph-based methods where each node splits based on feature values. The specific decision tree utilized in this work is the _Classification and Regression Tree_ (CART) algorithm [26] implemented in _Scikit-learn_[25].  
Figure 4: Diagram of the proposed CNN architecture.
# Review of Unmanned Aerial Vehicle Swarm Communication Architectures and Routing Protocols
## 4 Conclusions and Discussions
**Funding:** This research was funded by the Natural Science Foundation of Hunan Province, China, grant number 2019 JJ20021, NUDT Scientific Research Project, China, grant number ZK18-02-12, Hu-nan Science and Technology Innovation Plan Project, China, grant number 2018 RS3079, Young Talents Lifting Project, China, grant number 17 XXXT048 and Complex Situational Cognitive Technology, China, grant number XXX050202. **Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. **Abbreviations**  
AODV Ad hoc On Demand Distance Vector  
APAR Ant colony optimization-based Polymorphism-Aware Routing  
B.A.T.M.A.N. Better Approach to Mobile Ad hoc Networking  
BeeAdhoc Bee colony algorithm based Ad hoc network  
CAS Close Air Support  
DCR Data Centric Routing  
DOLSR Directional Optimized Link State Routing  
DREAM Distance Routing Effect Algorithm for Mobility  
DSDV Destination-Sequenced Distance Vector  
DSR Dynamic Source Routing  
GGF Greedy Geographic Forwarding  
GLS Grid Location Services  
GLSR Geographic Load Share Routing  
GPMOR Geographic Position Mobility Oriented Routing  
GPSR Greedy Perimeter Stateless Routing  
G-T-G Group-to-Group  
HLS Hierarchical Location Services  
HRP Hybrid Routing Protocol  
IAFSA Improved Artificial Fish-swarm Algorithm  
IP Internet Protocol  
LCAD Load Carry and Deliver Routing  
MLHR Multilevel Hierarchical Routing  
ML-OLSR Mobility and Load-aware Optimized Link State Routing  
MPGR Mobility Prediction based Geographic Routing  
MPR Multiple Point Relay  
OLSR Optimized Link State Routing  
PRP Proactive Routing Protocol  
QoS Quality of Service  
RE-DSR Restrict Dynamic Source Routing  
RGR Reactive-Greedy-Reactive  
RLS Reactive Location Services  
RREP Route Reply  
RREQ Route Requests  
RRP Reactive Routing Protocol  
SI Swarm Intelligence  
SPOF Single Point of Failure  
TC Topology Control  
TORA Temporarily Ordered Routing Algorithm  
UAV Unmanned Aerial Vehicle  
UE-DSR UAV Energy Dynamic Source Routing  
U-T-I UAV-to-Infrastructure  
U-T-U UAV-to-UAV  
ZRP Zone Routing Protocol