esa-sceva/satcom-chunk-collection huggingface dataset:
## 11 Discussion  
There is a significant research gap regarding high-bandwidth wireless communications for hard rock stope environments. Solutions have been proposed for tunnels and soft rock mines such as coal mines, but there is currently no mainstream high-bandwidth wireless solution for underground hard rock mine slopes, such as underground gold and platinum mines stopes, and little research has been conducted in these areas. This work provides real-world practical performance metrics of Wi-Fi6 in a tunnel and stope, and it was found that the Wi-Fi6 performance is greater in the stope as compared to the tunnel. This can be attributed to Wi-Fi6's newly introduced features which mainly focus on spectral efficiency which allowed it to utilize the waveguide nature of the stope to its advantage. This work also emphasizes the minimal feasible requirements for a mesh system in the stope, and it was found that the 5 GHz channel with 80 MHz channel bandwidth and two spatial streams provided good results even with a multi-hop scenario in the stope. This work also outlines the expected performances of using two of the most widely used open-source mesh routing protocols, namely B.A.T.M.A.N.-adv and HWMP alongside the IEEE 802.11s mesh standard.  
Figure 14: Power consumption for transmitting 10 MB of data.  
This work emphasizes a wireless mesh solution for slopes. A wired connection should be used to interconnect the first node of the mesh to a gateway/sink node, which would be installed at a safe distance away from the slope. This work does not specify the routing method to the surface above the mine. In terms of high-bandwidth solutions, IEEE 802.15.4 and its variants cannot be used due to its modulation scheme throughput limitations. COFDM is a great solution for harsh environments, and [5] showcases its signal robustness. For critical mine safety applications, COFDM is still desirable; however, the technology is not available to the mainstream public as most vendors are of the broadcasting and military nature. Due to COFDM not being mainstream, there are wide fluctuations in price, and the technology is expensive. Wi-Fi6 is mainstream, and Wi-Fi has matured enough to be a good solution for slopes.
## 4 Results and Discussion
Figure 15: Dimensionless amplitude (\(W_{\text{max}}/W_{0}\)) – excitation amplitude (\(W_{0}\)) curves for linear/nonlinear vibration and different porosity coefficients (\(\kappa_{0}\)) (\(m_{s}=0\), \(R=1k\Omega\), \(P=0\) for distribution 1). dimensionless RMS voltage output is processed, so that when the PE is considered only, the dimensionless RMS voltage output is kept constant with the variation on the total thickness of the beam. Figure 17 shows the effect of the total beam thickness on the dimensionless root mean square voltage output with or without considering the flexoelectric effect (FE) (\(m_{s}=0\), \(P=0\), \(R=1k\Omega\), \(\kappa_{0}=0.5\), \(W_{0}=0.01h_{t}\) for distribution 1). It can be seen that the dimensionless RMS voltage output is independent of the total beam thickness when only piezoelectric effect (PE) is considered. While the dimensionless RMS voltage output decreases with the increase in the total beam thickness when the piezoelectric and flexoelectric effects are considered simultaneously, which indicates that the flexoelectricity has a significant effect on the performance of the small-scale energy harvester. When the structure size is smaller, the flexoelectric effect is more pronounced. Figure 18 shows the effect of the ratio of the piezoelectric layer thickness to the total beam thickness on the dimensionless root mean square voltage output (\(h_{t}=3\times 10^{-4}m\), \(m_{s}=0\), \(P=0\), \(R=1k\Omega\), \(\kappa_{0}=0.5\), \(W_{0}=0.01h_{t}\) for distribution 1). It can be seen that the RMS voltage output increases with the increase in the thickness ratio of the beam, and when the thickness ratio increases to a certain value, the RMS voltage output changes very little. This is because the total beam thickness is certain, and the thickness of the piezoelectric layer no longer changes when the thickness of the piezoelectric layer increases to a value comparable with the total beam thickness. In addition, under the same conditions, the RMS voltage output considering both piezoelectricity and flexoelectricity is greater than the RMS voltage output considering only piezoelectricity. Figure 17: Effect of the total beam thickness on the dimensionless RMS voltage output with or without considering the flexoelectric effect (\(m_{s}=0\), \(P=0\), \(R=1k\Omega\), \(\kappa_{0}=0.5\), \(W_{0}=0.01h_{t}\) for distribution 1).
## 3 Methodology of FSACM
### Hybrid Signature- and Anomaly-Based Attack Detection Technique
#### 3.2.1 Signature Based Model  
The signature-based model, which is the first step of the proposed intrusion detection technique, was developed in our previous study and updated within the scope of this study. This new algorithm can detect attacks as well as attack types [Algorithm 2]. The updated algorithm was created as a result of the arrangement given in Figure 2 (due to the high number of determined rules and parameters, each of them is presented as basic functions rather than algorithmic details).  
In this context, the features commonly used within many datasets in the literature are set out and determined as basic features. Parameters for normal situations such as network connection time, the number of network logins, and minimum and maximum values were determined. Based on these parameters, rules were issued. Attacks and their types were determined by comparing possible attack situations with these normal situations. For instance, the maximum/minimum usage times of a network in a normal state and attack state were specified. By comparing these times in both cases, a rule was derived.  
```
Input : \(f\) : Selected Based Features Output : \(s\) : Determined Signatures Definitions \(\beta\) : corresponding state in the network \(NDP\) : Normal state parameters \(ADP_{1}\) : Attack type1 state parameters \(ADP_{2}\) : Attack type2 state parameters \(ADP_{3}\) : Attack type3 state parameters \(r\) : extracted rules \(f_{1}\leftarrow\mathit{feature1}\), \(f_{2}\leftarrow\mathit{feature2}\),... \(f_{n}\leftarrow\mathit{featureN}\), \(n\gets u(f(i))\) for\(i\gets 1\) to do if\(\beta(f(i))==\mathit{normal}\)then \(\mathit{write.NDP}()\) else if\(\beta(f(i))==\mathit{type_{i}}\)then \(\mathit{write.ADP_{1}}()\) elseif\(\beta(f(i))==\mathit{type_{2}}\)then \(\mathit{write.ADP_{2}}()\) elseif\(\beta(f(i))==\mathit{type_{3}}\)then \(\mathit{write.ADP_{3}}()\) else \(\mathit{write.ADP_{n}}()\)  end if  end for\(\mathit{compare}(NDP,ADP_{1},ADP_{2},...,ADP_{n})\)  determine.r ()  end for
```  
**Algorithm 2**Signature Based Model  
In general, we recorded both known attack signatures from the literature as well as signatures identified based on common fundamental features found in well-known datasets. Our aim with this model was to enhance accuracy rates through detailed feature analysis and improve the speed of attack detection via rules that lead to direct results.
# From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
## Appendix A Compendium of Mappings and Compilations
### XY Mixers
#### a.3.2 Graph Partitioning (Minimum Bisection)  
**Problem:** Given a graph \(G\) such that \(n\) is even, find a subset \(V_{0}\subset V\) satisfying \(|V_{0}|=n/2\) such that the number of edges between \(V_{0}\) and \(V\setminus V_{0}\) is minimized.  
**Prior AQO work:** Studied in the context of AQO for constrained optimization [6].  
**Approximability:** An efficient \(O(\log^{1.5}n)\)-approximate algorithm is known [80].  
**Configuration space, domain, and encoding:** Bit strings \(\mathbf{x}\) of Hamming weight \(n/2\).  
**Objective:**\(\min\sum_{\{uv\}\in E}\mathsf{NEQ}(x_{u},x_{v})\).  
**Initial state:** Any \(\mathbf{x}\) of Hamming weight \(n/2\).  
**Phase separator:**\(U_{\mathrm{P}}(\gamma)=\exp(-i\gamma\sum_{\{uv\}\in E}Z_{u}Z_{v})\).  
**Partial mixing Hamiltonian:** Qubit ring XY mixer \(H_{\mathrm{ring}}^{\mathrm{(enc)}}=\sum_{u=1}^{n}\left(X_{u}X_{u+1}+Y_{u}Y_{u+1 }\right)\).  
**Resource count:**  
* **Number of qubits:**\(n\).
* **Parity ring mixer:**\(n\) two-qubit (XX+YY) gates, with depth at most \(2\) (\(n\) even) or \(3\) (\(n\) odd).
* **Phase separator:**\(m\) two-qubit (ZZ) gates. Depth at most \(D_{G}+1\).
* **Initial state:**\(n/2\) single-qubit \(X\) gates. Depth \(1\).
## 4 Discussion
### Issues with Crop Yield Estimation in Areas with Mixed Cropping System  
It is observed that EVI2 extracted using the general cropland mask was correlated with yields of the three annual crops positively in summer and negatively in spring and fall. This reveals the following two aspects of the reality. First, yields of the three crops were highly correlated, with a much stronger correlation between corn and soybean (\(R^{2}\) = 0.71) than that between corn and winter wheat (\(R^{2}\) = 0.30). This echoes similar yield limiting factors of the three crops related to environmental conditions. The relatively weaker correlation between winter wheat and the two summer crops could be due to the difference in their growing cycles and the experienced different meteorological conditions. In the study region, the peak growth stage for winter wheat is in late May, whereas for corn and soybean it is in August. Second, the negative correlation in spring could be due to the typical phenological cycles and the area proportion of soybean and corn. A negative correlation was observed between county-level EVI2 during the spring and the areal proportions of corn and soybean (Figure 8a). This was understandable because these two crops are seeded in spring when the corresponding EVI2 is low; thus, the larger areal proportion of these two crops corresponded to the lower average EVI2. On the contrary, the county-level areal proportion of these two crops was positively correlated with the county level grain yield (Figure 8b). This suggests that soybean and corn are less likely to be seeded in counties where yield of the two crops is low. According to the reported crop yields and harvested areas, the counties distributed to the east and north of the study area have relatively lower yields, and this tendency is persistent from year to year. The negative correlation indicates that the effect associated with area proportions of different crops has not been completely eliminated by using crop-specific masks (Figure 4).  
The inferior performance for yield estimation in Central Ontario (larger MRAE) was because crop yields in this region are relatively low. Another reason is that annual crops are not extensively distributed in the region, thus coarse resolution MODIS EVI2 cannot effectively capture annual crop growth conditions. Higher resolution remote sensing data would be useful in improving the performance in this case.  
As shown in Figure 2, large confusion existed between classification of corn and soybean and between winter wheat and hay, but not between the two groups. This confusion would not have a big impact on yield estimation from EVI2 at peak growing stage extracted using the crop-specific masks, as during their peak growing stages, corn and soybean had about the same EVI2, and winter wheat and hay had about the same EVI2. Further improvement of crop-specific masks might improve yield estimation, but this will rely on higher resolution data to reduce pixel mixing effects.
## 2 UAV-Based Data Collections and Processing  
To investigate the feasibility of detecting water surfaces using GNSS-based passive radar carried on small UAVs, we implemented a GNSS-R sensor complying with the strict requirements of a UAV payload in terms of weight, size and power consumption. In post-processing, we targeted water surfaces detection, and for that we optimized all the parameters needed in order to increase the reliably of water detection from GNSS signals. GNSS signals are transmitted with RHCP, and the carrier frequency is in the order of 1 GHz ( 1.150-1.620 GHz). Their polarization is reversed when a smooth surface reflects them becoming LHCP. If the reflecting surface is non-specular, the reflected signal will be a mixture of LHCP and RHCP signals. Thus, it is necessary to use antennas capable of discriminating between the two kinds of polarization, and selecting the desired one. The synchronous recording of both the direct and the reflected GNSS signals acts as an accurate geo-referencing mechanism for the GNSS-R measurements, which is important for post mission assessments. Figure 1 shows the sensor (left) and a photo from one of the data collection campaigns showing the sensor mounted on-board a UAV (right). In the following subsections, we will describe the sensor used for data collection, then we will explain the data collection campaigns, after that we will detail the signal processing procedure, and finally we will introduce the data sets processed in this paper.
## 2 Literature Review
### Emerging Trends and Novel Techniques  
In exploring novel approaches to fake news detection, [PERSON] et al. (2019) [23] introduced an unsupervised method utilizing a generative model. Acknowledging the rapid dissemination of news on social media and the challenges posed by traditional supervised learning methods, which require extensive labeled datasets, this study employed a Bayesian network to treat news truths and user credibility as latent variables. By utilizing user engagement data to infer the authenticity of news without labeled data, the authors demonstrate a notable improvement over existing unsupervised methods across two datasets.  
Building on this idea of early detection, [PERSON] et al. (2020) [24] developed FNED, a deep neural network designed for the timely identification of fake news on social media. They address the challenge of limited early-stage data by proposing a model with the following three innovative components: (1) a feature extractor that combines user text responses and profiles, (2) a position-aware attention mechanism to prioritize significant user responses, and (3) a multi-region mean-pooling mechanism for effective feature aggregation. Their experiments show that FNED achieves over 90% accuracy within five minutes of news propagation, significantly outperforming state-of-the-art baselines while requiring only 10% labeled samples.  
In a related effort, [PERSON] et al. (2023) [25] focused on toxic fake news classification in the context of COVID-19 misinformation. Recognizing the detrimental effects of toxic fake news on society, they collected datasets from various social media platforms, labeling instances as toxic or nontoxic through toxicity analysis. The study employed both traditional machine learning techniques (such as linear SVM and random forest) and transformer-based methods (including BERT) for classification. Their findings reveal that the linear SVM method achieved an accuracy of 92% alongside impressive F1, F2, and F0.5 scores. This research indicates that their toxicity-oriented approach effectively distinguishes toxic fake news from non-toxic content, suggesting a promising direction for future investigations into combating misinformation.
## 3 Efficient Hierarchical Co-Occurrence Graph Convolution Networks
### Channel Attention Module  
The hierarchical co-occurrence framework can effectively extract the local and global information of joint features, implicitly incorporating an attention mechanism in the joint dimension. To further consider the relationships between channel features, this paper implements an attention module based on the SENet [34] structure to automatically learn the importance of each channel feature. As shown in Figure 6, the spatial dimensions are first compressed through an adaptive average pooling operation to obtain a global receptive field. Then, the channels are dimensionally reduced and expanded through fully connected (FC) layers, which is performed to apply nonlinear transformations to the network and reduce the computational load. Finally, the obtained scores for each channel are applied to the corresponding channels through a multiplicative weighting process.  
Figure 5: Comparison between standard convolution and separable convolution.  
Figure 6: Channel attention module.
###### Abstract  
Crop rotation affects soil pH by disturbing H\({}^{+}\) production and consumption within soil-crop systems, primarily through fertilization, irrigation, cropping, and harvest. Studies have shown that crop rotation improves soil organic matter prediction. However, simply incorporating crop rotation may not significantly improve soil pH prediction, because the spatial variability in soil pH is lower and the way crop rotation influences pH is different. To quantify the extent to which crop rotation improves soil pH mapping, we introduced the strategy of grouping soil samples by crop rotation and modeling separately. We chose a typical multiple-cropping region suffering soil acidification in Southern China, where the complex crop rotation was mapped by Sentinel-1/2 time series and a legend featuring three main systems (i.e., paddy, vegetable, and orchard) and nine subsystems. This crop rotation map was then combined with other variables to derive multiple combinations and predict soil pH. Based on the best combination, we further assessed the grouping strategy. The results showed that simply incorporating crop rotation in one joint model was useful but could not obtain the expected accuracy, with a root mean squared error (RMSE) of 0.66 and an R\({}^{2}\) of 0.36. The individual statistical accuracies were quite low for the vegetable and orchard rotations, with an RMSE of 0.77/0.70 and an R\({}^{2}\) of 0.30/\(-\)0.04. Grouping soil samples by crop rotation significantly enhanced soil pH predictability with a decrease in the RMSE of 15% and an increase in the R\({}^{2}\) of 53%. The results proved that grouping by crop rotation can fit and optimize the sub-models after learning the characteristics of the rotation subsamples, offering a way for improving digital mapping of soil pH over heterogeneous agricultural landscapes.  
soil pH; Sentinel-1/2 images; cropland soil; crop rotation; soil sample grouping; machine learning
## 3 Results
### Antioxidant Activity  
DPPH and ABTS antioxidant assays were used to assess the radical scavenging activities of the _Acer_ samples, expressed as IC\({}_{50}\) values (\(\upmu\)g/mL), where lower values indicate higher antioxidant activity. Sample A3 exhibited the highest antioxidant activity in both assays, with IC\({}_{50}\) values of 10.88 \(\pm\) 3.39 \(\upmu\)g/mL for DPPH and 10.39 \(\pm\) 2.86 \(\upmu\)g/mL for ABTS. Sample A2 also showed strong antioxidant potential, with IC\({}_{50}\) values of 22.61 \(\pm\) 3.28 \(\upmu\)g/mL (DPPH) and 15.68 \(\pm\) 3.31 \(\upmu\)g/mL (ABTS). In contrast, sample A7 had the weakest antioxidant activity, with IC\({}_{50}\) values of 290.2 \(\pm\) 39.2 \(\upmu\)g/mL (DPPH) and 271.2 \(\pm\) 60.7 \(\upmu\)g/mL (ABTS). Moderate antioxidant activities were observed in samples such as A9 (54.25 \(\pm\) 10.59 \(\upmu\)g/mL for DPPH, 37.75 \(\pm\) 9.7 \(\upmu\)g/mL for ABTS) and A10 (60.62 \(\pm\) 12.72 \(\upmu\)g/mL for DPPH, 57.58 \(\pm\) 13.61 \(\upmu\)g/mL for ABTS). These results are highlighted in Table 2.  
Figure 1: TPC of the analyzed _Acer_ (A1–A10) samples. GAE: gallic acid equivalents; TPC: total phenolic content.  
FRAP assay, measured in umol Fe\({}^{2+}\)/L, revealed significant differences in antioxidant capacity among the _Acer_ samples. Sample A2 demonstrated the highest reducing power at 1119 \(\pm\) 164.6 umol Fe\({}^{2+}\)/L, followed closely by sample A3, with 1158 \(\pm\) 48.98 umol Fe\({}^{2+}\)/L. Samples A9 (471 \(\pm\) 12.87 umol Fe\({}^{2+}\)/L) and A10 (378.1 \(\pm\) 5.64 umol Fe\({}^{2+}\)/L) exhibited moderate reducing capacities. In contrast, samples A1 (117.8 \(\pm\) 7.659 umol Fe\({}^{2+}\)/L) and A7 (139.8 \(\pm\) 5.361 umol Fe\({}^{2+}\)/L) displayed the lowest reducing power. These findings underscore substantial variability in the antioxidant potential of the _Acer_ samples, as shown in Table 2.
## 3 Results
The results indicate a consistent trend toward the overrepresentation of those born in the first quarter of the year (Q1) compared to those born in later quarters, particularly against the third (Q3) and fourth (Q4) quarters. This pattern is prominently observed in several categories, especially in the Q1/Q4 comparisons in the U-10 category (OR = 1.856; 95% CI = 1.32-2.60; Z = 3.579; \(p\) < 0.001), F-18 (OR = 1.483; 95% CI = 1.14-1.93; Z = 2.936; \(p\) = 0.003), F-20 (OR = 1.549; 95% CI = 1.12-2.14; Z = 2.636; \(p\) = 0.008), and all male categories except M-16, where no statistically significant differences are observed (OR = 1.139; 95% CI = 0.95-1.37; Z = 1.396; \(p\) = 0.163). On the other hand, in some specific categories, such as F-E, negative associations (95% CI < 1) were identified in those born in Q2 compared to Q1, suggesting a lower probability of participation at this competitive level for those born in the first quarter. When performing OR analyses to compare quartiles, the fourth quartile is typically used as the reference group--i.e., the relatively youngest individuals [1]. In this regard, Figure 4 represents the ORs and 95% CIs for birth quartile Q1 compared to Q4 for all populations (and subgroups) and age groups studied. OR and CI values to the left of one, as observed in the elite subgroups and female elite subgroup (F-E), indicate no significant differences between birth quartiles Q1 and Q4, as the confidence interval includes the value one. Conversely, in the other evaluated population groups, significant differences are identified, increasing proportionally with the rightward displacement in the graph. For example, in the male elite subgroup (M-E), the ORs and their 95% CIs reflect a significant effect favoring those born in the first quarter of the year (Q1) compared to those born in the fourth quarter (Q4). Regarding age groups (Figure 5), following the same reasoning as above, no significant differences between birth quartiles Q1 and Q4 are observed in the F-E, F-14, F-16, and M-16 categories, as the value one is included in their confidence intervals. However, the U-10, F-20, and F-18 categories present the highest OR values, indicating a more pronounced birth quartile effect in these categories. Regarding the effect trend as athletes age, the effects are more pronounced at younger ages (U-10, F-12, M-12), weaken (M-14), or even disappear (F-14 and F-16) between ages 13 and 17, reappearing in junior ages (F/M-18 and F/M-20) and persisting in adulthood for men (M-E) while disappearing for women (F-E). Figure 4: Odds ratio (OR) and confidence interval (CI) for the relationship between birth quartiles in Q1/Q4 of athletes according to population (subgroup and sex). (---) no association limit (OR = 1); (\(\blacksquare\)) OR with 95% confidence interval (CI).