esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Carbon Emissions in the Whole Construction Process of Prefabricated Floor Slab
## 3 Calculation Model of Carbon Emissions in the Whole PFS Construction Process
### Calculation of Carbon Emissions in the Transportation Stage  
The transportation stage refers to the process from the prefabrication plant to the construction site after the PFS production is completed. The transport vehicles consume a certain amount of fuel oil, which releases greenhouse gases like carbon dioxide. Since there are often one-two people on the transport vehicle and the transportation time is longer depending on the distance and road restrictions, artificial carbon emissions cannot be completely ignored. In the calculation, the vehicle load, the transport distance, and the no-load data must be considered when the vehicle returns from the construction site. The specific carbon emissions are calculated as follows:  
\[E_{t}=\sum_{i=1}^{2n}\left(T_{t}\times F_{p}+F_{q}\times C_{f}\times D\times C _{e}\right) \tag{3}\]  
where \(i\) represents the \(i\)th transport vehicle, with a total of \(n\) vehicles; \(2n\) indicates that the vehicle is considered to go back and forth; \(E_{t}\) represents the total carbon emissions in the transportation stage; \(T_{t}\) represents the total working hours of each person on the vehicle; \(F_{p}\) indicates the carbon emission factor consumed by artificial respiration; \(F_{q}\) represents the carbon emission factor of energy consumption of vehicles; \(C_{f}\) represents the fuel consumption when the transport vehicle is fully loaded, in L/100 km; \(D\) represents the distance from the prefabricated plant to the construction site; and \(C_{e}\) represents the empty car return coefficient. In fact, the last vehicle is often not fully loaded, but in order to simplify the calculation process, we assume that the vehicles from the prefabrication plant are all fully loaded to the construction site with \(C_{e}\)=1.0 and empty when returned with \(C_{e}\) = 0.67.
## 2 Materials and Methods  
### Site Description and Experimental Setup  
A field trial of 1 ha was set up at a former dredged sediment disposal site located in Fresnes-sur-Escaut (Hauts-de-France, France; 50\({}^{\circ}\)25\({}^{\prime}\)41.1\({}^{\prime\prime}\) N 3\({}^{\circ}\)35\({}^{\prime}\)01.9\({}^{\prime\prime}\) E). The disposal site was created in 1978 and received canal sediments until 1989; it was no longer exploited by human activities after this date. The area is characterized by a temperate climate, with a mean temperature between June and September of 18.7 \({}^{\circ}\)C and cumulative precipitation of 194 mm [38]. In April 2019, an experimental setup was devised to assess different species' applicability for phytoattenuation. Three zones were defined, according to previous studies [39], which have different levels of contamination: area 1, area 2, and area 3 (Figure 1). The study on these three areas allowed the examination of sediment parameters' heterogeneity and their potential impacts on plant performance ( agronomic parameters, tolerance, and accumulation potential/accumulator behavior of the different cultivars). To reduce herbivy risk, the plots were protected by a 2 m high fence, which was buried down by 50 cm.  
In this study, two sorghum cultivars used in Europe were selected, based on their biogas production potential. Seeds certified for agricultural use were bought at a seed company [40; 41]. Sorghum biomass 133 is a monoharvest cultivar, characterized by a high production of harvestable biomass (20 t ha\({}^{-1}\) of dry weight (DW)) 125 days after the sowing. Trudan Headless (HL) sorghum is a multicut cultivar with a total production ranging between 10 and 15 t ha\({}^{-1}\) DW. In each area, one plot of 25 m\({}^{2}\) was devoted to each cultivar (Figure 1). Before manually sowing (June 2019), tillage, plowing, and weeding were carried out. In order to maximize the number of plants, sowing at a higher density than the one recommended in agriculture (250,000 seeds ha\({}^{-1}\) and 25 kg ha\({}^{-1}\), for Biomass 133 and Trudan HL, respectively) [40; 41] was performed (between 1518 and 1656 seeds for 25 m\({}^{2}\), i.e., a sowing density between 600,200 and 662,400 seeds ha\({}^{-1}\)). No weed treatment other than mechanical was performed during the experiment, due to the constraints imposed by the sediment site manager.
# Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey
## 3 Physical Layer in 5G
### Signaling Techniques for 5G
These intelligent approaches provide digital assistance, hazardous investigation, 24/7 availability, reliability, minimum error, and high customer experience [243]. Moreover, with ML and DL-based approaches, we can optimize the performance of real-time applications and handle repetitive jobs adequately. Therefore, it is necessary to optimize the existing solutions and reduce the challenges of 5G to reap the benefits of these technologies [244]. Recent studies have mentioned the requirement of high data rate (in Gbps) links for the eMBB, uRLLC, and mMTC use cases and service implementation, \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Waveform** & **Filter** & **Time** & **Frequency** & **Spectral** & **uRLLC** & **PAPR** & **Reference** \\  & **Granularity** & **Orthogonality** & **Orthogonality** & **Efficiency** & **uRLLC** & **PAPR** & **Reference** \\ \hline UFMC & Subband & Orthogonal & Quasi-orthogonal & High & Better & High & [232] \\ OFDM & Whole Band & Orthogonal & Orthogonal & Medium & Better & High & [233] \\ F-OFDM & Subband & Non-orthogonal & Quasi-orthogonal & Medium & Better & Medium & [234] \\ GFDM & Subcarrier & Non-orthogonal & Non-orthogonal & Medium & Better & Low & [235] \\ FBMC & Subcarrier & Orthogonal & Orthogonal & High & Bad & Medium & [236] \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison between 5G waveform candidates. since 4G LTE-A sub-6 GHz bands will be highly overcrowded [245]. The mmWave bands (10-300 GHz) will lead to obstructions in the form of high path loss and penetration loss. At the same time, the beamforming, directed transmission, and higher distribution deployment density of base stations are the best countermeasures to tackle these problems. These state-of-the-art solutions allow mmWave to work with the modernized wireless technologies and architecture [246]. Nevertheless, this revamp also introduces many new challenges in the system design, such as accurate beam alignment between the base station and the users, while signals are prone to blockages (i.e., buildings). Hence, the performance of mmWave may be critically retarded by selecting an inaccurate beam. ML-based methodologies are well capable of tackling the dynamic traffic patterns and blockage issues perfectly [247]. After autonomous exploration and learning the environmental behavior, the ML-based techniques will help existing solutions to select the beam efficiently while providing a reliable connection so that performance degradation issues can be handled [248].
# Selection of Grid Road Networks Based on Raster Data
## 4 Experiments and Evaluations
### Experimental Results and Analysis
The main parameters of the proposed method include the angle threshold (AT) and ratio threshold (RT). We tested the road selection results under different parameter values of the AT and RT. The results can be found in Figure 13, from which we can see the following:  
1. When the AT is too small, it can result in too few matched road intersection superpixels and remove some important main road segments after selection. For example, as shown by the blue arrows in Figure 13a-e, the main road can be preserved well when AT = 20\({}^{\circ}\), while the same main roads are removed when AT \(<\) 10\({}^{\circ}\)(AT = 2\({}^{\circ}\), AT = 4\({}^{\circ}\), AT = 6\({}^{\circ}\), AT = 8\({}^{\circ}\)). When AT \(>\) 30\({}^{\circ}\), the result of the road selection will basically not change; \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Level**} & **Original** & **Selected** & **Original Segment** & **Selected** & & & \\  & & **Length** & **Length** & **/Name of** & **Segment** & **Segment of Superpixels** & **GS** & **TC** & **PD** \\ \hline ArcGIS & 1 & 487,520 & 361,849 & 1832 & 1040 & 0.74 & 0.99 & 0.69 \\  & 2 & 487,520 & 251,237 & 1832 & 638 & 0.52 & 1.00 & 0.51 \\ \hline AWRS & 1 & 524,371 & 479,841 & 46,852 & 36,063 & 0.92 & 1.00 & 0.78 \\  & 2 & 524,371 & 359,744 & 14,363 & 9697 & 0.69 & 1.00 & 0.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative evaluation of road network selection using the ArcGIS and AWRS methods. Figure 12: Density contrast of road network selection results using the proposed AWRS and ArcGIS methods. (**a**) Distribution density of original road networks. (**b**) Level 1 using ArcGIS. (**c**) Level 1 using AWRS. (**d**) Level 2 using ArcGIS. (**e**) Level 2 using AWRS. 2. When the RT is too small, it is possible to remove some main and important roads, which need to be selected. For example, as shown by the black rectangles in Figure 14a-f, the distribution of main roads can be preserved well when RT = 0.8, while the same main roads can not be maintained well when RT \(<\) 0.4 (RT = 0.1, RT = 0.2, RT = 0.3). When RT \(>\) 0.8, some redundant roads will be generated. Thus, we recommend that the ranges of parameters AT and RT are \(10^{\circ}\leq\) AT \(\leq\) 30\({}^{\circ}\) and 0.4 \(\leq\) RT \(\leq\) 0.8. Figure 14: The influence of parameter RT on the road selection results. (**a–f**) Road selection results with different RT values. Figure 13: The influence of parameter AT on the road selection results. (**a–e**) Road selection results with different AT values.
## 5 Experiments  
### Corpus and Evaluation Metric  
We used Chinese Corpus CEC [16] as the corpus ([[https://github.com/shijiebei2009/CECCorpus](https://github.com/shijiebei2009/CECCorpus)]([https://github.com/shijiebei2009/CECCorpus](https://github.com/shijiebei2009/CECCorpus))). CEC is an event ontology corpus developed by the Semantic Intelligence Laboratory of Shanghai University. It has 332 articles. Compared with the ACE and TimeBank corpus, CEC is smaller, but more comprehensive in the annotation of events and event arguments. Therefore, we used CEC as the experimental data for the event extraction. The text was divided into five categories: earthquake, fire, traffic accident, terrorist attack, food poisoning. In the experiment, the word segmentation and grammar analysis tools adopted the module provided by the LTP language technology platform of Harbin Institute of Technology. After pretreatment, there were 8804 sentences, 5853 trigger words, 1981 event participants, 1633 event locations, and 1388 event times. For the experiment, we divided the corpus into 20% test set, 64% training set, and 16% verification set.  
This experiment mainly recognized the event trigger, event participants, event time, and event location. We used recall (R), precision (P) and the F-measure (F) to evaluate the results.
# Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review
## 8 ML and Cyberattack Detection
### ML Challenges in WSN
#### 8.3.2 Challenges Related to Applications and Routing Algorithms  
Developing a suitable ML security model to detect attacks for diverse WSN applications is challenging, especially for mission-critical, highly sensitive, real-time, and adversarial environment applications. It is preferable that anomaly detection be performed locally at the local sensor nodes to avoid any communication with other nodes, the BS, or the IoT cloud due to high security requirements, which are not feasible for resource-scarce nodes. Another cyberattack detection challenge is the attacker's ability to exploit the routing algorithm and compromise its individual forwarding steps to attack the network. The purpose of these actions is to disrupt the routing and communication process by misdirecting or alternating the routing information or broadcasting fake information. The IDSs, on the other hand, can take advantage of the known behavior associated with the routing algorithm to build models of legitimate operation and compare them to the real exchange of routing messages between the nodes. Routing attacks belong to the network layer, and include sybil, 'Hello' flooding, sinkhole, blackhole, grayhole, and wormhole attacks [88; 89]. Using secure routing protocols as a prevention technique and deploying proper ML-based IDS should be considered when securing these networks.
# Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest
## 1 Introduction
[42] developed an ANN algorithm to predict the deflection of drilled shafts based on SPT data and the shaft geometry. In addition, [PERSON] et al. [43] developed a model to predict the pile settlement using ANN, based on SPT data using 12 input factors. Last but not least, [PERSON] et al. [44] presented an ANN model to predict the shaft and tip resistance of concrete piles. In general, ML algorithm could be considered as an efficient tool for predicting the mechanical behavior of piles. However, no general argument has yet been reached on the selection of the best model to predict the bearing capacity of piles. Moreover, the database is an important factor that highly affects the accuracy of ML algorithms and crucial to provide a reliable modeling tool. The existing database in the literature related to the bearing capacity problem was still limited due to the lack of experiments or on-field measurements. Therefore, the main objective of this study is dedicated to investigating the prediction capability of ANN and random forest (RF) algorithms, using all possible factors that might affect the bearing capacity of driven piles. To this aim, a considerable effort in collecting 2314 pile load test reports from the construction site of Ha Nam--Vietnam was devoted, which, to the best of our knowledge, is the largest database in the available literature. The database was then divided into the training and testing subsets, dedicated to the learning and validation phases of the two proposed ML models. Various error criteria--namely, the mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination (R\({}^{2}\))--were applied to evaluate the prediction capability of the algorithms. In addition, 1000 simulations taking into account the random splitting of the dataset were conducted for each model in order to finely evaluate the accuracy of RF and ANN. Proved to outperform ANN, the RF algorithm was next used to compare with five empirical formulas in the literature as well as classical multivariable regression (MVR) in predicting the bearing capacity of piles. Finally, the feature importance analysis was proposed to find out the important factors affecting the prediction capability of the RF model.
# A Novel Physics-Guided Neural Network for Predicting Fatigue Life of Materials
## 1 Introduction
Through a specially designed neural network structure and constraints on the bias and weight range of some neurons, the fatigue life prediction results conform to the characteristics of the S-N curve and can also output its predicted confidence interval [19]. [PERSON] et al. proposed a hybrid neural network, which successfully predicted the fatigue life of the main bearing of the wind turbine through the empirical formula of bearing life combined with the recurrent neural network [20; 21; 22]. The machine learning methods mentioned above are all direct predictions of the fatigue life of materials, some of which add some constraints to make the prediction results conform to the S-N curve characteristics or other natural facts. This study proposes a physics-guided neural network (PGNN) for predicting fatigue life. Different from the above published methods, the load and fatigue life of the material under the same loading environment follow a certain power-law relationship, that is, the Basquin equation. Instead of directly predicting the fatigue life of the material, the neural network predicts the fatigue life parameters under the corresponding loading environment, and the corresponding fatigue life is determined by the fatigue life parameters combined with the loading stress. The proposed method does not consider the influence of the numerical change of the load on the fatigue life, so the load does not participate in the parameter update of the neural network, which simplifies the dimension of the function that the neural network needs to approximate. The simplification of the process means that the number of training data required can be effectively reduced. The ratio of the number of training samples to the number of validation samples used in this study was 1:3 (the number of training samples was 26, and the number of validation samples was 78) to verify the model's ability to predict fatigue life under different loading environments (such as notch and stress ratio). Due to the randomness of the training path of the neural network, the prediction result is the optimal result after repeated training for 5 times [23]. The remainder of this paper is organized as follows. Section 2 details the physical model and the operating mechanism of the proposed PGNN. In Section 3, the collected experimental data are used to verify the predictive ability of the PGNN. Section 4 presents the conclusions drawn from the study.
## 3 Results and Discussion
### Structural Properties of RD-DF
#### 3.1.3 FTIR Spectra  
In the infrared spectrum of RB-DF at various treatment stages (Figure 3), absorbance at 3323 cm\({}^{-1}\) and 2921 cm\({}^{-1}\) were assigned to hydroxyl groups and C-H bonds, respectively [28]. The absorption intensity of rice bran was decreased after being treated with amylase and protease, and the absorption intensity of RB-DF was increased after enzymatic hydrolysis and high-pressure treatment. This result indicates that the infrared absorption peak intensity of RB-DF is greatly reduced with the dissociation of starch from fat-removing D-RB. The widening and smoothing absorption peak of EH-DF and HPH-DF indicates that cellulase hydrolysis and HPH treatment exposed hydroxyl groups promotes disintegration of glycosidic bonds in DF structure [29]. Although the intensity of the characteristic absorption peak of carbonyl of cellulose uronic acid at 1743 cm\({}^{-1}\) was weakened, the characteristic absorption peaks of cellulose and oligosaccharidesaccharide at 1647 cm\({}^{-1}\) and 1018 cm\({}^{-1}\)[30] were enhanced in EH-DF and HPH-DF. This phenomenon also indicates that cellulase hydrolysis and HPH treatment dissociate the non-crystalline regions in rice bran DF, promoting the degradation of small-molecule saccharides, and effectively changing RB-DF microstructure.
# Improving Adversarial Robustness of CNNs via Maximum Margin
## 4 Experiments
### AT-SVM in SVHN  
We also used ResNet-18 to conduct experiments on the SVHN dataset [47] to verify that our AT-SVM method is effective on different datasets. The initial learning rate is set to  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \(k\) & **Clean** & **FGSM** & **MIM** & **PGD** & **PGD-b** & **Robustness** \\ \hline
1 & 81.37 & 56.92 & 54.99 & 51.13 & 59.42 & 51.13 \\
5 & 84.64 & 58.42 & 56.42 & 54.17 & 61.06 & 54.17 \\
10 & 84.02 & 58.83 & 57.22 & 54.59 & 60.26 & 54.59 \\
20 & 83.27 & 60.30 & 58.91 & 56.11 & 59.62 & 56.11 \\
30 & 83.51 & 59.35 & 58.54 & 56.38 & 59.76 & 56.38 \\
40 & 82.25 & 58.14 & 56.94 & 53.28 & 58.20 & 53.28 \\
50 & 81.99 & 58.55 & 57.26 & 53.99 & 58.38 & 53.99 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test accuracy of models training with different batch size radios.  
Figure 3: Sample images from the CIFAR-10 dataset, with corresponding adversarial examples. The first row represents the adversarial examples and predicted values obtained by the normal model, the second row represents the SAT model, and the third row represents the AT-SVM model. We show the original image and adversarial examples for epsilons of 0.03 and 0.3.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Methods** & **Clean** & **FGSM** & **MIM** & **PGD** & **PGD-b** & **Robustness** \\ \hline Natural & 95.63 & 0 & 0 & 0 & 0 & 0 \\ SAT & 86.91 & 61.39 & 59.10 & 55.66 & 55.66 & 55.66 \\ GAIRAT & 82.70 & 62.24 & 62.19 & 60.65 & 56.71 & 56.71 \\ AT-SVM & 86.87 & 62.82 & 60.70 & 58.70 & 63.24 & 58.70 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test accuracy of WideResNet-34-10 on the CIFAR-10 dataset.  
0.01, and the other experimental settings are the same as the basic model. The results are shown in Table 4. We can achieve the same conclusion as CIFAR-10, so AT-SVM is suitable for different datasets.
## 3 Results and Discussion
### Spectroscopic Analysis  
The average NIR and MIR spectra of thirteen different Zingiberaceae fruits are shown in Figure 4. The average NIR spectra show some similar characteristic peaks at 8330, 6803, 5696, 5179, and 4693 cm\({}^{-1}\), but there are significant differences in their absorption intensities (Figure 4A). The absorption peak at 8330 cm\({}^{-1}\) was related to the C-H second overtone stretch in -CH\({}_{3}\) and -CH\({}_{2}\); the wave peak at around 6803 cm\({}^{-1}\) was the first overtone of O-H; the 6000-5400 cm\({}^{-1}\) region was associated with the first overtone of C-H stretching vibration in -CH\({}_{2}\); the combination of O-H and C-O stretching resulted in a peak at 5179 cm\({}^{-1}\)[37]; and the peak at around 4693 cm\({}^{-1}\) was related to combination bands involving C-H, C=C, and C=O stretching vibrations of lipids, phenols, and other aromatic groups [38].  
The MIR spectra of different Zingiberaceae fruits showed a total of nine obvious common absorption peaks at 3289, 2921, 2854, 1623, 1425, 1369, 1315, 1240, and 1010 cm\({}^{-1}\) (Figure 4B). The wide absorption peak around 3289 cm\({}^{-1}\) was due to the stretching vibration of O-H contributed by saccharides, glycosides, and water molecules. The absorption peak at 2921 cm\({}^{-1}\) was related to the C-H asymmetric stretching vibration of -CH\({}_{2}\) and -CH\({}_{3}\)[39]. The 1800-1500 cm\({}^{-1}\) region was associated with C=O stretching vibration, C=C stretching vibration, asymmetric stretching vibration of the carboxyl group involved in the hydrogen bonds, and hydrogen-bond scissoring vibration of the free carboxyl group in pectin, fatty acids, flavonoids, saccharides, and steroid saponin. The 1500-650 cm\({}^{-1}\) region as the MIR fingerprint region could reflect the subtle changes in molecular structure, and the vibration types in this region were complex and overlapping, but they were highly sensitive to changes in molecular structure, which could be used to distinguish small differences in the structure of different compounds. The absorption peaks at 1425 and 1369 cm\({}^{-1}\) were related to C-H stretching vibration and bending vibration of -CH\({}_{2}\) and -CH\({}_{3}\). The highest absorption peak appeared in the 1200-950 cm\({}^{-1}\) region, which was related to the C-C stretching vibration, C-O stretching vibration, and C-O-H bending vibration [40].  
Using NIR and MIR spectra data, it can be seen that the spectra of these thirteen Zingiberaceae spices show similar trends, which is because there are similar functional groups between the samples, which indirectly indicates the existence of similar metabolic components. However, there are also significant differences in some absorption peaks and intensities, indicating significant differences in compounds between different species, as confirmed by GC-MS data results. Therefore, we can classify different species based on the significant differences in this part.  
Figure 4: The average near-infrared (NIR) spectra (**A**) and mid-infrared (MIR) spectra (**B**) of thirteen different Zingiberaceae spices.