esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Magnifications** & **Precision** & **Recall** & **F-Score** \\ \hline \multirow{4}{*}{ResNet50 + KWELM [39]} & 40\(\times\) & 87.2 & 86.2 & 86.6 \\  & 100\(\times\) & 85.2 & 88.0 & 86.1 \\  & 200\(\times\) & 88.6 & 89.2 & 88.7 \\  & 400\(\times\) & 82.1 & 84.2 & 82.8 \\ \hline \multirow{4}{*}{Xception-SVM [41]} & 40\(\times\) & 96.0 & 96.0 & 96.0 \\  & 100\(\times\) & 96.0 & 96.0 & 96.0 \\  & 200\(\times\) & 95.0 & 95.0 & 95.0 \\  & 400\(\times\) & 95.0 & 93.0 & 93.0 \\ \hline \multirow{4}{*}{Deep semantic-GLCM [42]} & 40\(\times\) & 97.5 & 96.9 & 97.2 \\  & 100\(\times\) & 97.5 & 96.0 & 96.7 \\  & 200\(\times\) & 97.9 & 97.4 & 97.7 \\  & 400\(\times\) & 96.4 & 94.0 & 95.1 \\ \hline \multirow{4}{*}{Proposed method 1} & 40\(\times\) & 89.3 & 84.5 & 86.8 \\  & 100\(\times\) & 87.7 & 79.2 & 83.3 \\  & 200\(\times\) & 88.0 & 78.5 & 83.0 \\  & 400\(\times\) & 78.4 & 81.2 & 81.2 \\ \hline \multirow{4}{*}{Proposed method 2} & 40\(\times\) & 93.0 & 93.9 & 93.4 \\  & 100\(\times\) & 91.3 & 89.8 & 90.5 \\  & 200\(\times\) & 92.3 & 91.4 & 91.9 \\  & 400\(\times\) & 92.1 & 92.7 & 92.7 \\ \hline \multirow{4}{*}{Proposed method 3} & 40\(\times\) & 95.4 & 95.8 & 95.6 \\  & 100\(\times\) & 94.4 & 96.2 & 95.3 \\ \cline{1-1}  & 200\(\times\) & 95.2 & 93.2 & 94.2 \\ \cline{1-1}  & 400\(\times\) & 94.2 & 93.9 & 93.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between the precision, recall, and F-Score obtained by the proposed method and the state-of-the-art methods. Figure 10: Comparison among the classification accuracy achieved by the proposed method and the state-of-the-art methods at a magnification factor of 100\(\times\). Figure 11: Comparison among the classification accuracy achieved by the proposed method and the state-of-the-art methods at a magnification factor of 200x. Figure 12: Comparison among the classification accuracy achieved by the proposed method and the state-of-the-art methods at a magnification factor of 400x. Figure 13: Comparison among the average image classification accuracy achieved by the proposed method and the state-of-the-art methods at all magnification factors.
# Defect Detection for Wear Debris Based on Few-Shot Contrastive Learning
## 3 Method
### Multi-Scale Fusion  
The statistical bounding box area information in each category of the wear debris defect dataset shows that the size of defect instances has greater diversity. Even instances in the same category vary greatly in size, perspective and possible occlusion, as shown in Figure 4.  
Figure 4: The size of cutting in wear debris defect dataset varies widely.  
Figure 3: InfoNCE Loss with alignment module.  
Feature fusion is one of the most useful methods to detect multi-scale defect. But the previous two-branch object detection method didn't use FPN, because it's difficult to select the most representative features from FPN. To address this issue, we propose Multi-Scale Fusion not only to avoid the selection, but also to fuse features from different resolution, as shown in Figure 5.  
When fusing features with different resolutions, a common way is to first resize them to the same resolution and then sum them up. However, we observe that different input features usually contribute to the output features unequally since they are at different resolutions. We notice that the support branch hasn't been exploited in the previous methods. The information of support branch was idle. So, we propose an attention module to utilize the information of support branch to learn fusion weights. The attention module is a bottleneck network consisting of 3*3 convolution, 1*1 convolution and sigmoid operation. The value of each normalized fusion weights falls between 0 and 1.  
The support branch should provide diverse features and perspectives of different resolutions so that attention module can learn more effective fusion weights. So, we utilize multi-scale training, random flipping, color jitter and other augmentations for the images in support branch.
# Influence of Yeast Strain on Odor-Active Compounds in Fiano Wine
## 3 Results and Discussion
Whilst previous research has shown that all yeasts have some glycosidic hydrolyzing activity, their activity might be different, according to the different chemical structure of the sugar and the aglycon moieties [30]. A study on Fiano wine specifically investigated the release of free and bound volatile compounds as affected by the enzymatic or acid hydrolysis. The authors showed that linalool, geraniol, leptinen-4-ol,1,1,6-trimethyl-1,2-dihydronaphtalene (TDN), \(\beta\)-damascenone, (E)-1-(2,3,6-trimethylphenyl)buta-1,3-diene (TPB), ethyl cinnamate, and 4-vinylguaiacol were the most abundant odorous compounds in Fiano wine originated from the hydrolysis of odorless precursors. It was also shown that the formation of linalool and geraniol is mostly attributed to the enzymatic activity [31]. In the present case, a statistically significant effect of the yeast strain used was verified for VL1 compared to all other treatments and the control, which is likely due to the lower hydrolytic activity of this strain toward the odorless precursor of Fiano wine to release linalool. In general, the results show that \"signature\" volatile compounds exist, as for the majority of compounds analyzed, we verified that the change in concentration is significant but not yeast-specific. However, a few compounds were particularly more abundant or were absent in some selected strains, compared to the others and to the control, e.g., 3-hydroxy-2-pyranone was particularly abundant in QA23, while 2-furanmethanol was not detected in this same strain, or, for example, hexyl acetate was found in all yeast strains except VL1. Based on the state-of-the-art of the literature, the combined use of biotechnical and chemical methods can help in improving the final aroma of wines, for example taking advantage of yeast strains enhanced for their \(\beta\)-glycosidases activity. However, often an empirical approach based on testing a range of yeast strains for specific wine varieties has shown that their suitability depends on the wine style, or the target that the winemaker has set.
### Main Work and Contributions  
Based on the mobility and group information of UAV swarms, this paper proposes a distributed clustering algorithm that considers communication link stability and task attributes. Our work employs the coalition game theoretical framework to solve the group-oriented clustering problem in multi-UAV networks. Extensive simulations show that our algorithm outperforms other clustering algorithms for group-oriented clustering problems in communication link stability and load balance. In addition, our method can ensure that UAVs in the same group are divided into one cluster, which is better than comparison algorithms. The main contributions are as follows:  
* Defining a compound correlation combines multiple indicators of the clustering problem into a single indicator, thus easing the analysis of cluster rewards. The compound correlation combines communication link stability and task group information by designing the adaptive weights based on the information of cluster members.
* A distributed clustering algorithm is designed based on the coalition game for the network management of large-scale UAV swarms. UAV nodes are the game participants, and each cluster refers to one coalition. The total compound correlation value of the cluster is the coalition reward. Through a series of switch operations of nodes, a stable coalition structure is achieved.
* A distributed parallel clustering mechanism is designed based on cellular automata. UAVs switch between idle and busy states, and only idle nodes can participate in new switch operations, which improves clustering efficiency.
## 4 Results
### Two-layered Head Model  
In a realistic scenario, the structure of the head is more complex than a two-layered semi-infinite geometry. Therefore, data were simulated using the same tissue properties as shown in Table 1 on two-layered (cerebral and extra-cerebral tissues) head models using NIRFAST corresponding 10 different subject head models, Figure 7, with their scattering parameters (both Sa and Sp) randomly varying with 10% standard deviation from the reference values shown in Table 1 and the cerebral TOI varying from 50% to 80%, and subsequently the two-step parameter recovery algorithm was utilized, initially, using a two-layered semi-infinite model for the recovery. This was to consider the case where data is from a human subject, but the model utilized for parameter recovery is based on a simple two-layered model. The parameter recovery results are shown in Figure 8a, Figure 9, and Table 3. As some measurement data did not converge to a stable solution, from these results it can be observed that the parameter recovery is accompanied by high standard deviation which can be attributed to the mismatch between the assumed semi-infinite model and the true head-model. Figure 8: Homogenous and two-layered recovery of TOI for two-layered head model based on: (**a**) semi-infinite recovery model, (**b**) head-based recovery model. The shaded region shows the standard deviation over different head models with different scattering parameters. Figure 7: Schematic showing an example of the head model with source-detectors placed on the forehead. To overcome this instability for parameter recovery using a semi-infinite or slab-model approximation, a second head-model that is different to that as used for the data simulation is introduced and incorporated in the inverse problem to improve the accuracy and consistency of the parameter recovery. The geometrical information for the external boundary of this head model (obtained from the original subject-specific head model) is used for the inverse problem, therefore instead of the semi-infinite model, a medium with similar boundary as the true head model is considered and by building a model corresponding to this geometry, NIRFAST was used to generate the model-data for the inverse problem, and also for calculating the sensitivities of layer-1 and 2. A dense mesh is built for the recovery model (with an average element size around 0.1 mm\({}^{3}\) which 10 times smaller than the average element size of 1.7 mm\({}^{3}\) considered for head models that are used to simulate data) to compute the sensitivity values corresponding to tissue thickness as shown in Equation (12). Individual layers are assigned to this model structure based on the distance from the
## 4 Intelligent Weather-Conscious AMC Scheme for Global Satellite-to-Ground Communications
### DL-Based Estimation Model
#### 4.2.2 Input Gate  
The input gate is used to determine which new information is saved in the cell state of the gate. The input gate is divided into two parts, where one is a control signal consisting of a sigmoid function to control the \(\hat{\mathbf{C}}_{t}\) input, and the other is the estimated cell state \(\hat{\mathbf{C}}_{t}\) at the current moment generated by a \(\tanh(\cdot)\) function:\[\mathbf{i}_{t}=\sigma\big{[}\mathbf{W}_{i}\mathbf{\beta}(t-1)+ \mathbf{U}_{i}\mathbf{h}^{\prime}(t-1)+\mathbf{b}_{i}\big{]} \tag{8}\] \[\widetilde{\mathbf{C}}_{t}=\tanh\big{[}\mathbf{W}_{c}\mathbf{\beta }(t-1)+\mathbf{U}_{c}\mathbf{h}^{\prime}(t-1)+\mathbf{b}_{c}\big{]}\]  
where \(\mathbf{W}_{i}\) and \(\mathbf{W}_{c}\) are the weights between the input gate and state \(s(t)\), while \(\mathbf{U}_{i}\) and \(\mathbf{U}_{c}\) are the weights between the precious hidden layer \(\mathbf{h}^{\prime}(t-1)\) and the input gate. \(\tanh(\cdot)\) is a hyperbolic tangent function. The cell state vector is updated as follows:  
\[\mathbf{C}_{t}=\mathbf{f}_{t}\odot\mathbf{C}(t-1)+\mathbf{i}_{t}\odot \widetilde{\mathbf{C}}_{t} \tag{9}\]  
where \(\odot\) represents Hadamard product operator.
# Optimum Slot and Pole Design for Vibration Reduction in Permanent Magnet Synchronous Motors
## 3 Results
### Analysis of the Influence of the Number of Slots and the Number of Poles on the Vibration Response
It is also linked to the slotting harmonic, which is the second harmonic in 24p8 whereas it is the first in 48p8, the latter being of higher amplitude. For this reason, the amplitude of vibration plotted in Figure 8 is lower for the 24p8 machine than for the 48p8 in the 6 th harmonic and its multiples. Furthermore, other harmonics present larger amplitudes for 24p8, since the second main mode excited is the 8 th instead of the 16 th. Similar results are observed for 12p8; however, in this machine, the mode of order 4 may also be significant, excited by the second pressure harmonic and its multiples. The 18p8, 30p8, and 42p8 machines have high LCM values, hence, vibrations of spatial order 0 may not be significant. However, their GCD values are low (2), and thus, the pressure harmonics that excite that mode have to be taken into account. These are the 2 nd, 4 th, and 16 th harmonics, and give the largest vibration amplitudes. Finally, the number of poles was changed to observe their influence on vibration response. The machines compared are listed in Table 7. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Machine** & **12p8** & **18p8** & **24p8** & **30p8** & **42p8** & **48p8** \\ \hline \(\gcd(Q_{s},2p)\) & 4 & 2 & 8 & 2 & 2 & 16 \\ \(\mathrm{lcm}(Q_{s},2p)\) & 48 & 144 & 48 & 240 & 336 & 48 \\ \(k_{min}|_{n=\gcd}\) & 2 & 2 & 2 & 4 & 16 & 4 \\ \(v_{min}|_{n=\gcd}\) & 1 & 1 & 1 & 1 & 3 & 1 \\ \(k_{min}|_{n=0}\) & 6 & 18 & 6 & 30 & 42 & 6 \\ \(v_{min}|_{n=0}\) & 4 & 8 & 2 & 8 & 8 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Characteristics of the machines of 8 pole pairs. Figure 8: Comparison of the vibration response of machines of several numbers of slots and 8 pole pairs. The vibrations obtained are compared in Figure 9. The rotational speed is the same for all the machines; hence, the frequencies of the harmonics are different for 8 and 10 pole pairs. The main harmonics of the machines of 10 pole pairs are, for the most part, accurately predicted. The difference is significant between the 12p10 and 15p10 machines due to the lowest mode order excited, which are the 4 th and 5 th, respectively. Therefore, exciting a mode of higher order makes the vibration amplitudes much lower. Regarding the comparison of the 8 and 10 pole pair machines, the amplitudes in the machines with the greater number of poles were generally found to be bigger, as were their frequencies. However, as expected, in the same frequency range, a lower number of harmonics was obtained.
## 3 Background
### Blockchain
* Decentralization and Distributed Ledger: as shown in Figure 2 the decentralized nature of blockchain eliminates single points of failure by distributing data across multiple nodes [26]. This ensures that no single entity can alter the ledger. Consensus Mechanisms:  
* Proof-of-Work (PoW): Requires computational effort to validate transactions, as seen in Bitcoin, ensuring that adding new blocks is costly and difficult to manipulate. Figure 2: Decentralized network. * Proof-of-Stake (PoS): Selects validators based on their stake in the network, reducing energy consumption and incentivizing honest participation. * Delegated Proof-of-Stake (DPoS): Introduces a voting mechanism to elect trusted validators, enhancing scalability and transaction speed. * Transparency and Immutability: Transactions recorded on the blockchain are visible to authorized participants and cannot be altered once recorded, preventing fraud and enhancing trust [27]. **Applications of Blockchain**  
Blockchain technology finds applications in various domains:  
* Decentralized Identity Management: Blockchain enables the efficient management of digital identities without relying on centralized authorities. In a decentralized or \"self-sovereign\" identity framework, individuals retain full control over their personal data, thereby minimizing the risks of identity theft and unauthorized access. Recent research has explored the advantages and challenges of blockchain-based identity systems [28]. * Supply Chain Tracking: In supply chain management, blockchain's transparency and immutability offer an effective means to track products from origin to destination. Every transaction or transfer is permanently recorded, which enhances traceability, helps prevent fraud or counterfeiting, and improves overall operational efficiency [29]. * Smart Contracts: Smart contracts are self-executing programs that automatically enforce the terms of an agreement once predefined conditions are met. By removing the need for intermediaries, they lower transaction costs and reduce the chances of human error or fraud [30].
## 3 Photocatalytic Materials for Water Treatment
### Nanostructured Photocatalysts
Nanotubes, represented as hollow cylindrical forms, offer confined spaces  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  
that enhance the reaction efficiency and reduce the charge recombination by promoting rapid carrier migration [107; 108]. The core-shell structures, composed of a central core enveloped by a distinct shell layer, illustrate how combining different materials can optimize the charge separation, band alignment, and structural stability. Collectively, these engineered nanostructures aim to increase the surface area, improve the charge carrier dynamics, and enhance light-harvesting capabilities, thereby significantly improving the photocatalytic efficiency in water treatment and other environmental applications. However, while many photocatalysts demonstrate promising results under controlled conditions, their long-term stability in real water matrices remains a concern. For example, ZnO is more susceptible to photocorrosion and leaching than TiO\({}_{2}\), particularly in acidic environments, which limits its field applicability despite its high electron mobility [24; 25]. Moreover, the activity of BiVO\({}_{4}\) and similar bismuth-based materials has been shown to deteriorate due to surface instability and poor charge separation in complex water matrices [32; 33].
## 3 Results
### The Regional and Seasonal Contribution to the Global NEP IAV  
#### 3.2.1 Regional Contributions  
We adopted the contribution index to quantify the contributions to the NEP IAV over the globe at the grid-cell scale (Figure 5). The results indicated that 63.7% and 34.1% of the areas showed positive and negative contributions to NEP IAV over the globe, respectively. Furthermore, the remaining 2.3% of global areas contributed zero to the global NEP IAV. The grid cells with high positive contributions were mostly located in some regions of eastern North America, northern South America, and central Africa, where the contribution values were larger than 0.01%. However, the grid cells with negative contributions were mostly located in southeast Asia, the south of the Sahara Desert, and some areas of Australia, with contributions less than zero.  
We counted the mean absolute NEP difference by latitudes (Figure 6a) and different vegetation types (Figure 6b) to explore their contributions to the global NEP interannual variability. The results show that the largest contributions occurred in tropical regions, with contributions larger than 0.005%. Furthermore, the smallest contributions occurred in the latitude around 30\({}^{\circ}\)N, 10\({}^{\circ}\)N, and 30\({}^{\circ}\)S, where the contributions were smaller than 0.002% (Figure 6a). These results suggested that the contribution to global NEP IAV decreased from the equator to the poles. We also analyzed the contributions of different vegetation types to global NEP IAV (Figure 6b). The results showed that EBF contributed most (31.1%) to NEP IAV, followed by CRO (21.7%) and GRA (20.8%). Deciduous forests (DNF and DBF) contributed the smallest to NEP IAV at a global scale. These results suggested that EBF played a vital role in regulating the global NEP IAV.
# Review of Recent Advances in the Application of the Wavelet Transform to Diagnose Cracked Rotors
## 4 Results Presentation  
Studies about the dynamics of cracked rotors usually do not involve the inverse process of crack detection. Therefore they do not include quantitative results of detection methods. On the other hand, when using intelligent classification systems, it is common to present the results in the form of a success rate percentage.  
Recently, the Probability of Detection (POD) curves have been imported from Non Destructive Evaluation (NDE) methods for presenting the results of crack detection in rotors. In NDE methods, POD curves are a normalized parameter used to evaluate the capability of a specified inspection method under specified conditions [70]. Usually, POD is represented versus crack size \"\(a\)\" (length, depth, or, more recently the reflecting area [71]).  
The confidence level of these POD curves is a matter of great importance. Usually, the lower 95% confidence bound is shown, which means that 95% of the POD curves constructed would be better than that represented. An NDE crack size, \(a_{NDE}\), may be defined as the crack size that reaches a certain probability of detection with a given confidence level. Figure 5 shows the mean POD(\(a\)) curve together with the 95% confidence bound, and the 90/95 crack size (the probability of detection is 90% and the confidence bound is 95%) [72].  
Recently, several works regarding crack detection in rotors have used POD curves to present diagnosis results, examples of these can be found in [33; 73; 74; 75].
# How Will Rwandan Land Use/Land Cover Change under High Population Pressure and Changing Climate?
## 4 Discussion
### Analysis of Future Land Use Change  
In the next 30 years, LULC changes in Rwanda will mainly consist of reductions in forest area and increments in grassland and cropland area, followed by urban expansion. This is closely related to the gradual increase in the population of Rwanda. Given a certain unit agricultural output, reclamation of forest becomes the best approach to ensure food security [48]. Rwanda's post-war recovery was achieved in this manner in the past, and this approach is expected to continue for the foreseeable future.  
Spatially, the future LULC of Rwanda exhibits 'grassland moving eastward' and 'cropland moving westward' trends. The 'westward movement' of cropland is the result of a combination of several factors. The coupling of precipitation, topography, and farming patterns is an important factor. The precipitation and topography of Rwanda, which is located in the interior of the East African Plateau, are high in the west and low in the east (Figure 3i). Moreover, the agricultural system consists primarily of rain-fed agriculture. Most food crops need to be planted and harvested during the two rainy seasons. However, heavy rainfall and frequent floods occur during the rainy seasons, necessitating the implementation of agricultural drainage systems in Rwanda [49; 50]. However, the agricultural drainage system in Rwanda is not well developed, and therefore, the natural advantages of the topography are employed to solve this problem. In addition, the western region consists mainly of forest, and cropland that has been converted from forest has more abundant soil nutrients and produces higher yields.  
The 'eastward movement' of grassland is caused by natural and socio-economic factors. In terms of natural conditions, the road network in the ES and EP regions in the east is not well developed (Figure 3e,f), and the vegetation consists mainly of sparse grassland, which is not suitable for reclaiming high-quality cropland. With the rapid development of society and economy, the diet of the population has changed, and the demand for meat, dairy products, and animal husbandry products has increased [51; 52]. The eastern region has flat topography and numerous pastures. The water system in this region is well developed and the water supply is adequate, which favors the development of animal husbandry.