esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Setup
### Dataset Description  
The publicly available PHM2010 dataset is acquired from the milling cutter machining experiment, the schematic diagram of the test bench used in the milling experiment is shown in Figure 5[57]. The data can be accessed through their official website, [[https://phmsociety.org/phm_competition/2010-phm-society-conference-data-challenge](https://phmsociety.org/phm_competition/2010-phm-society-conference-data-challenge)]([https://phmsociety.org/phm_competition/2010-phm-society-conference-data-challenge](https://phmsociety.org/phm_competition/2010-phm-society-conference-data-challenge)), accessed on 15 February 2024.  
The experiment was processed with one tool on a high-speed Computer Numerical Control (CNC) machine, the milling cutter was a three-flute ball nose cutter, and the workpiece was a stainless-steel block. The milling parameters were 10,400 rpm spindle speed, feed rate of 1555 mm/min, 0.125 mm radial cut depth, and 0.2 mm axial cut depth. A 3-component platform dynamometer, three accelerometers, and an acoustic emission (AE) sensor were mounted to collect the force and vibration in three directions (x direction, y direction, and z direction) and the high-frequency stress wave. All these sensor signals were sampled at a frequency of 50 kHz. An optical microscope was used to measure three flutes' wear after a complete 108 mm cutting distance. The dataset consists of three sub-datasets of the tool run-to-failure degradation process, and each sub-dataset includes 315 tests. The three run-to-failure sub-datasets are named C1, C4, and C6, which are used in this study. The tool wear measurements are presented in Figure 6, and the average wear value of three cutting edges is selected as the tool wear label in this study.  
Figure 5: The experiment equipment and configuration.
## 2 Materials and Methods
### Principal Component Analysis of the Physicochemical Characteristics and Scores of the Wheat Samples  
Principal Component Analysis (Figure 4) was carried out to underline the correlations between the physicochemical characteristics of wheat samples and wheat varieties and how these varieties were influenced by their physicochemical characteristics. The first two principal components explained 54.55% of the total variance (PC1 = 19.79% and PC2 = 34.77%). The two plots underlined a good correlation between wheat varieties _Triticum spelta_, _Triticum monococcum_ and _Triticum aestivum_, which were grouped close to each other. Generally, the ancient species _Triticum spelta_ and _Triticum monococcum_ were placed on the right part of the graph, whereas the _Triticum aestivum_ was placed on the left. On the right part of the PCA, _Triticum monococcum_ clustered together on the top, whereas _Triticum spelta_ was on the bottom. However, some wheat samples of _Triticum monococcum_ were placed in the same area as _Triticum monococcum_ and _Triticum spelta_, indicating that some of those show some similarities with these varieties. The quality of ancient varieties was strongly characterized by the wheat physicochemical characteristics wet gluten, protein, lipid and ash content, all of them being placed alongside the PC2 component. However, _Triticum monococcum_ was more related to the wet gluten and protein content, whereas the ash and lipid content of _Triticum spelta_ were underlined by the first component PC1. _Triticum monococcum_ was more related to the wheat physicochemical characteristics of damaged starch, falling number and starch, all of which were alongside the PC2 component. The physicochemical variable lipid content was positively correlated with the ash content (r = 0.970, \(p\) < 0.05) and protein with wet gluten (r = 0.914; \(p\) < 0.05), and there was also a positive correlation between damaged starch and falling number (r = 0.672; \(p\) < 0.05). These data are in agreement with those reported by others [25; 41; 42]. The position of starch in the space of main components indicated a negative correlation with wheat physicochemical characteristics lipid, fat, protein and wet gluten content. Similar data have also been reported by [PERSON] et al. in their study [25].  
Figure 3: Dendrogram showing the genetic similarity among the 31 genotypes of _Triticum_, obtained by the UPGMA method and Jaccard coefficient, forming 5 clusters. Cut-off point of approximately 50% (dotted line).
## 1 Introduction  
Physical-layer security is of utmost importance in unmanned aerial vehicle (UAV)-enabled communication networks. Nevertheless, UAV-enabled air-to-ground communication networks are susceptible to various malicious attacks. Spectrum jamming is particularly concerning and may result in wireless interference with unmanned aerial vehicles (UAVs), disrupting authentic data transmissions. Commonly used approaches to mitigate spectrum jamming, including frequency hopping and random selection techniques, depend on carrier frequency adaptation. Nonetheless, sophisticated spectrum sensing and learning techniques enable jammers to detect changing frequency patterns and make more effective jamming decisions. Consequently, the development of intelligent anti-jamming methods capable of allowing UAVs to adaptively select carrier frequencies that suppress malicious jamming-induced channel interference is essential.  
In light of the prevailing context, there is a pressing need to craft sophisticated anti-jamming strategies. These strategies should empower UAVs to dynamically choose center frequencies, effectively countering jamming from adversaries. At the heart of this initiative lies adaptive frequency modulation. In scholarly circles, a comprehensive, integrated approach is often advocated. This merges various methods and techniques, summarized as follows: (1) Spectrum Sensing and Dynamic Spectrum Access: UAVs utilize spectrum sensing to identify interference or vacant frequency bands [1; 2; 3]. Through Dynamic Spectrum Access (DSA), UAVs adapt in real-time to less crowded channels, reducing interference [4; 5]; (2) Game Theory Approaches: Game theory models UAV interactions with wireless entities. Strategies are devised for UAVs to select frequencies adaptively, considering other users' behaviors to target optimal interference suppression [6; 7; 8]; (3) Machine Learning and AI: Algorithms predict and counteract interference. UAVs discern interference patterns, adjusting frequencies based on real-time insights and environmental factors, enhancing interference mitigation [9; 10; 11].  
However, due to the inherent limitations of these methodologies, they cannot be directly applied to UAV-assisted A2G networks. In light of this, we introduce an innovative model for selecting central frequencies in UAV-jammer interactions within A2G networks. Unlike traditional studies, ours utilizes the Stackelberg game framework to enable UAVs to adaptively respond to dynamic jamming. By transforming this game into a concise linear programming approach, the study identifies optimal mixed-equilibrium strategies with polynomial time complexity, offering a novel solution for countering jamming attacks in UAV communication networks.
# Zernike Coefficient Prediction Technique for Interference Based on Generation Adversarial Network
## 4 Discussion and Conclusions  
The paper proposes a novel prediction technique to predict Zernike coefficients based on the GAN structure with the interference fringe image. Moreover, the GAN becomes an image-to-number translation network, which is a novel usage of GAN. After the testing and transfer learning, the RMSE is about 0.0182 \(\pm\) 0.0035\(\lambda\) with the ideal image and about 0.0586 \(\pm\) 0.0183\(\lambda\) with the simulated image.  
In the paper, we achieved four significant points: the prediction accuracy is better than that in our previous research, transfer learning is used to help the model adapt the quality of the simulated image, GAN can predict more or fewer coefficients but only needs to adjust the designed image, and GAN becomes a flexible network for predicting pictures or values.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **GoogLeNet [25]** & **GAN** \\ \hline RMSE(\(\lambda\)) & 0.055 \(\pm\) 0.021 & 0.0182 \(\pm\) 0.0035 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The performance of two models—ideal image.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **GoogLeNet [25]** & **GAN \\ \hline RMSE(\(\lambda\)) & 0.055 \(\pm\) 0.021 & 0.0182 \(\pm\) 0.0035 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The performance of two models—simulated image.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **GoogLeNet [25]** & **GAN** \\ \hline RMSE(\(\lambda\)) & 0.055 \(\pm\) 0.021 & 0.0182 \(\pm\) 0.0035 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The time consumption of two models.  
In the future, we will improve and retrain the prediction technique with the Spatial Light Modulator (SLM) to expand the generalization for the actual fringe images from the interferometer. Additionally, the new GAN usage can increase the possibility of applications in other different research fields.  
Conceptualization, A.J.-W.W. and Y.-Y.C.; methodology, T.-H.Y. and C.-T.L.; software, T.-H.Y.; validation, C.-T.L.; formal analysis, Y.-Y.C.; investigation, Z.-J.J.; resources, C.-H.C.; data curation, T.-H.Y.; writing--original draft preparation, T.-H.Y.; writing--review and editing, Y.-Y.C.; visualization, C.-T.L.; supervision, A.J.-W.W.; project administration, Y.-Y.C.; funding acquisition, Y.-Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology, Taiwan, grant number 109-2221-E-011-030-.  
The authors declare no conflict of interest.
## 2 Virtual SMA Extrapolation Method
the accuracy of the regularization solution can be improved by modifying the regularization matrix in the Tikhonov regularization process, and the regularization matrix is defined, which is also called the beamforming regularization matrix \(\mathbf{L}=\text{diag}(|\mathbf{G}^{H}\mathbf{p}|/||\mathbf{G}^{H}\mathbf{p}| |_{\infty})^{-1}\). Therefore, the solution of source strength on the envelope surface can be written as [14; 15]:  
\[\mathbf{q}_{BF}=\frac{\mathbf{G}^{H}\mathbf{p}}{\mathbf{G}^{H}\mathbf{G}+ \lambda^{2}\mathbf{L}^{H}\mathbf{L}} \tag{6}\]  
Based on the above theoretical analysis, if the virtual source strength \(\mathbf{q}_{BF}\) on the envelope surface \(S\) can be obtained, the sound pressure value of any field point inside the envelope surface can be approximately calculated, that is, the approximate sound pressure data on any spherical surface inside \(S\) can be obtained without changing the array aperture, thus realizing the SMA extrapolation.
## 3 Results and Discussions
### The Effect of the Heat Exchanger Wall Temperature on the Flow Field
#### 3.2.1 Study of Flow Using the Adiabatic Model  
The investigation of flow across the parallel plate structure starts with a model where the walls of the heat exchangers are treated as adiabatic. The resulting velocity profiles plotted at a location of 1 mm from the joint above the cold plate are shown in Figure 16 for comparison to the experimental results of [PERSON] et al. [11]. The agreement with the experimental data is very good. Figure 17 shows the contours of vorticity, \(\omega_{z}\), plotted for selected phases of the oscillatory flow, obtained as:  
\[\omega_{z}=\frac{\partial v}{\partial x}-\frac{\partial u}{\partial y}, \tag{9}\]  
where \(u\) and \(v\) are the velocity components in the \(x\) and \(y\) directions of the flow. The contours are plotted within the same \"viewing area\" as in the experiment [11]--cf. also Figure 2d. For phases \(\phi 1\) to \(\phi 10\), the fluid is flowing into the channel from the left; for phases \(\phi 11\) to \(\phi 20\) from the right. At the beginning of the cycle, a pair of small vortices appear at the entry to the channel followed by a pair of stronger counter-rotating vortices, which appear to \"leap-frog\" the original pair around phase \(\phi 4\). However, both vortex structures dissipate by about phase \(\phi 8\). A similar feature is present at the other end of the channel when the flow changes direction. The laminar model captures the features of the oscillatory flow at 0.3% drive ratio very well.
# Automated 3D Image Processing System for Inspection of Residential Wall Spalls
## 2 Materials and Methods
### Randla-Spall  
#### 2.3.1 Network Architecture  
RandLA-Net is recognized as a network architecture capable of providing efficient semantic segmentation [21]. Its encoder layer consists of random sampling and Local Feature Aggregation (LFA) modules. The random sampling module progressively downsamples the input point cloud, thus expanding the network's receptive field and maintaining low computational complexity, which enables it to handle large-scale point cloud data. Through the LFA module, the encoder layer extracts feature vectors that capture the relationships between each point and its surrounding points, thereby aggregating both local spatial and feature relationships. Even following random sampling, the LFA module effectively preserves local features, thus enabling RandLA-Net to achieve precise semantic inference while maintaining computational efficiency.  
Based on the RandLA-Net network architecture, the RandLA-Spall network is proposed, which enables model training with fewer labeled data items. The proposed network has the following three key components: (1) a dual-branch structure inspired by self-supervised learning [35] to augment the data inputs. To constrain features generated by perturbations in the two input modules, consistency constraints [36] are employed to enhance model stability; (2) the inclusion of a lightweight attention mechanism, CBAM [37], to further enhance the model's feature extraction capability within the performance range; (3) to address the issue of class imbalance in labels, a weighted cross-entropy loss function and a Lovasz-softmax loss function [38] are employed to enhance the model's performance in small object segmentation. The network architecture is depicted in Figure 8.  
Figure 7: Dataset structure.
# A Review on Anaerobic Digestion of Lignocellulosic Wastes: Pretreatments and Operational Conditions
## 6 Conclusions  
In summary, the results compiled in this review clearly demonstrated that lignocellulosic wastes can be important sources of renewable methane through anaerobic digestion. Among all the proposals used to overcome the problems of these wastes in this process, which are mainly related to the hydrolysis step, pretreatments and codigestion seems to be the more suitable strategies. Regarding the operational conditions, inhibition must be considered, but solid-state anaerobic digesters have proved to be a great alternative to classical AD stirred reactors. It is evident that the state-of-the-art of this approach is still not completely developed, but it seems to be the correct approach for lignocellulosic wastes. Finally, it is clear that these wastes are the most abundant sources of organic matter on earth, and therefore, all approaches to convert them into renewable energy are a step toward sustainability and climate change mitigation.  
Writing--original draft preparation, T.S.; writing--review and editing, A.S.  
Authors wish to thank the financial support of Spanish Ministerio de Economia y Competitividad (Project CTM2015-69513-R).  
**Acknowledgments:** [PERSON] would like to thank the Palestine Technical University for the administrative support.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multicolumn{1}{c}{**Lignocellulosic**} & \multicolumn{1}{c}{**Co-Substrate**} & \multicolumn{1}{c}{**Mixing Ratio**} & \multicolumn{1}{c}{**Temp (\({}^{\circ}\)C)**} & \multicolumn{1}{c}{**CH\({}_{4}\) (L/kg VS)**} & \multicolumn{1}{c}{**Ref**} \\ \multicolumn{1}{c}{**Biomass**} & & & & & & \\ \hline Yard waste & Food waste & 4:1 & 36 & 120 & [46] \\ Swine manure & Corn stover & NA & 35 & 350 & [170] \\ Hay & Soybean processing waste & 1:1.3 & 37 & 258 & [117] \\ Agricultural wastes & Chicken manure & 7:3 & 55 & 502 & [163] \\ Cattle manure & Palm pressed & 1:3 & 37 & 346.2 & [171] \\ Cattle manure & Silage of cardoon & 85:15 & 37 & 308 & [172] \\ Cow manure & Rice straw & 1:1 & 37 & 383.5 & [173] \\ Cattle manure & Corn stover & NA & 37 & 228 & [167] \\ Cow manure & Kitchen waste & 1:1 & 35 & 179.8 & [174] \\ Cattle manure & Wheat straw & 95:5 & 55 & 351 & [175] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Codigestion of lignocellulosic biomass with other feedstock for methane production under ss-AD.
## 5 Mitigation of Pharmaceutical Pollutants by Nanomaterials
### Biochar-Based Adsorbents
#### 5.1.4 Silica-Based Nano-Sorbents  
In recent years, silica particles have been increasingly employed as an adsorbent for the removal of environmental contaminants. Their performance as an adsorbent is attributable mostly to their high adsorption capacity due to high porosity (2-50 nm), greater surface areas (up to 2370 m\({}^{2}\)/g), and excellent mechanical and thermal stabilities [113]. Furthermore, these adsorbents are well-defined and have a homogeneous structure, as well as a low cost of manufacture, quick recovery, and low toxicity, making them superior to  
Figure 7: Characterizations of MOF using (**a**) BET, (**b**) PXRD, (**c**) SEM, (**d**) TEM, and (**e**-**h**) SEM-EDS for elemental mapping of Al, C, O, and total, respectively, and (**i**) zeta potential adapted with permission from [111].  
other adsorbents, (e.g., graphene oxide). The surface chemical functionalization of silica with inorganic and organic compounds gives the silica particles good physicochemical properties, which aids in the efficient utilization of these particles as adsorbents. The silanol and amino groups contained in silica-based adsorbents in particular, offer high adsorption energy and charge association to these adsorbents, resulting in high adsorption efficiency [114]. Silica adsorbents are frequently utilized for the adsorption of carbamazepine, diclofenac, ibuprofen, ketoprofen, acetaminophen, and other pharmaceuticals [115; 116]. For instance, in a study by [115], Individual pharmaceutical removal rates were high in acidic media (pH 3-5) and reached 85.2% for carbamazepine, 88.3% for diclofenac, 93.0% for ibuprofen, 94.3% for ketoprofen, and 49.0% for clofibric acid at pH 3 but started decreasing with increasing pH. SBA-15 also demonstrated high efficiency for the removal of a mixture of five pharmaceuticals. The removal of pharmaceuticals in the mixture ranged from 75.2 to 89.3%, with the exception of clofibric acid at 35.6%. The mechanism of the selected pharmaceuticals was discovered to be a hydrophilic interaction based on adsorption and desorption results, providing valuable information for future studies to design materials for the purpose. As shown in Figure 8, low desorption percentages were confirmed, implying that pharmaceuticals are not easily detached from adsorbents and returned to the treated water. The pseudo-second-order equation and the Freundlich isotherm model were used to best describe the kinetics and equilibrium of pharmaceutical adsorption.
# An Energy Balanced and Lifetime Extended Routing Protocol for Underwater Sensor Networks
## 5 Simulations
### Random Node Distribution
The node with the lowest energy consumption per effective distance is chosen as the best forwarder. When the residual energy of the best forwarder is relatively low, EBLE selects the suboptimal node with higher residual energy for relaying data instead of long range one-hop direct transmission to the sink node. So BTM performs worse than our proposed EBLE in the random node distribution case. From the 10 groups of results in Figure 15, we can calculate that EBLE can send 62.79% more packets than BTM on average before a first node is dead. Figure 14: Percentage of dead nodes at different sending rounds. To explain the performance fully, we present the 50%_Dead_ network lifetime performance as shown in Figure 16. In this case, EBLE no longer performs better than BTM. This is because EBLE balances the network energy consumption to ensure that all the sensor nodes can work. In BTM, some nodes may only have a few data loads, therefore these nodes can still work for a long time when other nodes are dead. We believe that this unbalanced energy consumption is unhealthy for underwater sensor networks. So we mainly focus on the optimization of \(FirstNodeDead\) network life performance. Next, we consider the end-to-end delay performance as shown in Figure 17. It can be seen that the average time for transmitting one packet in EBLE is prolonged. This is because EBLE is more likely to divide long range direct transmissions into multi-hop transmissions. In our settings, each relay needs 0.1 s to handle the received data packets and change its transducer state from receiving mode to transmitting mode. So the more relays that are used, the longer time a data transmitting process needs. The irregular paths also induce some propagation delay to some extent. So our design may not be suitable for some delay sensitive applications. However, as the induced delay is not significant and is about 1.8 times that of the direct transmissions, EBLE can be used in most long time data transmission cases. The energy consumption per transmitted packet for different protocols is given in Figure 18. The energy consumption for direct transmission protocol is much higher than other protocols because long-range direct transmissions are not energy efficient and the sender has to send data at a higher power to reach the sink node far away. The performance of BTM and EBLE is similar and they are both better than BEAR and direct transmission. This is because EBLE selects the next hop forwarders by comparing their energy consumption per effective distance. The node with the lowest energy consumption per effective distance is chosen as the best forwarder. So long range direct transmissions can be divided into multi-hop transmissions effectively. Figure 15: The number of transmitted packets for different distributions when a first node is dead. Figure 16: The number of transmitted packets for different distributions when 50% nodes are dead. Figure 17: The average end-to-end delay per transmitted packet for different distributions.