esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Grassmann MTS Clustering
### Intrinsic Grassmann Clustering  
The core idea of the first intrinsic approach is to constrain the MTS clustering completely within the manifold itself [20]. It is thus clear that the existing clustering algorithms in the literature are not relevant unless they are altered according to the geometric characteristics of the Grassmann manifold [51]. Since we cannot define the Gaussian density function on the Grassmann manifold, for instance, the Gaussian mixture model (GMM) [1] algorithm is inappropriate in Grassmann MTS clustering. Another counter-example is the \(k\)-means clustering because taking the mean of MTS is unclear. Hence, the simplest and yet the most intuitive resolution form of its kind is to replace the similarity measure with geodesic distances in accordance with a considered clustering algorithm. With these observations in mind, the spectral clustering [52; 53] algorithm, which is suitable in this context, is considered.  
In the spectral clustering algorithm, the clustering problem is regarded as an eigenanalysis problem of the Laplacian matrix. The first step is to construct an affinity matrix \(\mathbf{A}\in\mathbb{R}^{n\times n}\) by calculating the distance for all the pairs of the MTS data \(\mathbf{U}_{i}\). Since MTS data do not lie in the vector space, as aforementioned, the geodesic distances shown in (5) and (6) are utilized instead of the usual distance measures. According to [53], the affinity matrix can be generated in several ways, including a fully connected graph (FC), an \(m\)-nearest neighbors (\(m\)NN) graph, etc. [53]. The next step is to convert the affinity matrix into a normalized Laplacian matrix \(\mathbf{L}_{norm}\). Lastly, the conventional spectral clustering algorithm shown in Algorithm 2 is then performed.  
```
1:Input:\(n\) points \(\{\mathbf{U}_{i}\}_{i=1}^{n}\) on the Grassmann manifold, number of clusters \(K\).
2:Compute the affinity matrix \(\mathbf{A}_{(i,j)}=\exp(\frac{-d^{2}(\mathbf{U}_{i}\mathbf{U}_{j})}{\sigma^{2}})\) where \(i\
eq j\), \(\mathbf{A}_{(i,i)}=0\), and \(d(\mathbf{U}_{i}\mathbf{U}_{j})\) is a dissimilarity measurement function such as distance defined in (5) and (6), and \(\sigma\) is a spread factor.
3:Compute the normalized Laplacian matrix \(\mathbf{L}_{norm}=\mathbf{D}^{-\frac{1}{2}}(\mathbf{D}-\mathbf{A})\mathbf{D}^{- \frac{1}{2}}\) where \(\mathbf{D}_{(i,i)}=\sum_{j=1}^{n}\mathbf{A}_{(i,j)}\).
4:Compute the lowest \(K\) eigenvectors of \(\mathbf{L}_{norm}\).
5: Form a matrix \(\mathbf{V}\in\mathbb{R}^{n\times K}\) containing the \(K\) eigenvectors.
6: Normalize the rows of \(\mathbf{V}\) to have unit length by \(\mathbf{Z}_{(i,j)}=\mathbf{V}_{(i,j)}/(\Sigma_{j}\mathbf{V}_{(i,j)}^{2})^{ \frac{1}{2}}\).
7: Let every row of \(\mathbf{V}\) be the corresponding point in the Euclidean space, the new set of points become \(\{\mathbf{z}_{i}\}_{i=1}^{n}\) where \(\forall\mathbf{z}\in\mathbb{R}^{K}\).
8:Output: Clusters \(\{C_{1},...,C_{K}\}\) with \(C_{k}=\{i|\mathbf{z}_{i}\in C_{k}\}\) obtained by the \(k\)-means clustering on \(\{\mathbf{z}_{i}\}_{i=1}^{n}\).
```  
**Algorithm 2** Grassmann spectral clustering
## 2 Related Work  
### Unimodal Sarcasm Detection  
Early sarcasm detection primarily concentrates on text modality [15; 16; 17]. Moreover, on the basis of the textual data, the early works extract paragraph-level, sentence-level, or word-level fine-grained features. The research on text sarcasm detection mainly includes three categories: deep learning-based, machine learning-based, and rule-based. [PERSON] et al. [18] bring up a rule-based algorithm that iteratively expands positive and negative phrases and then uses these learned words to predict irony label, but it lacks sufficient adaptability. With the widespread use of machine learning algorithms, researchers have begun to extract manual textual features and design machine learning algorithms to carry out sarcastic detection work. [PERSON] et al. [19] use SVM as their classifier to solve this problem. These methods rely on the quality of features and are not friendly to data with similar features. However, since features can automatically be extracted through deep learning architecture, a large amount of work on deep learning has been used for sarcastic detection. [PERSON] et al. [20] leverage mixed CNN-SVM to encode emotional and personal features for sarcastic detection. [PERSON] et al. [21] use an attention mechanism to capture word differences to detect sarcastic discourse. [PERSON] et al. [22] employ ELMo to extract a word's character-level representation to express contextual satire. These deep learning-based methods have achieved excellent performance. However, due to the increase in multimodal data, researchers are paying more attention to multimodal sarcasm detection.
# A Data Augmentation Method for Skeleton-Based Action Recognition with Relative Features
## 4 Experiment
### Discussion  
Although our method achieves competitive results, it still has shortcomings. Figures 6 and 7 describe the recognition results of each action on Florence-3D and UTkinect-3D using our method separately. As shown in Figure 6, answer phone and read watch are the two actions with the lowest recognition, at only 88.06% and 86.89%, respectively. We consider the reason for this phenomenon to be that without considering external objects, some actions are so similar that they will be misclassified. For example, answer phone may be classified as drink from bottom because both movement processes are raising one hand to the head. The key poses are exhibited in Figure 8. For the action, the read watch swings one hand to the chest, similar to the swing of two hands on the chest when clapping, so the test result is not ideal. Similarly, in Figure 7, the action throw is incorrectly detected as pull. From the perspective of skeleton sequence, both actions push one hand far away, but judging from the images, things fly out of the hand during the throw actions.  
In summary, the recognition of some actions is not good enough due to a lack of consideration of the influence of external objects on human motions. Consequently, to further improve the recognition accuracy of our method, we detect external object information that is related to the action and encode it into an image as a feature.
# Vegetation Growth Response and Trends after Water Deficit Exposure in the Loess Plateau, China
## 3 Results
### Sustainable Development of Vegetation Growth in the Loess Plateau
#### 3.3.2 Dominant Environmental Factors of Vegetation Growth  
The restoration of vegetation is mainly influenced by human beings, especially regarding the conversion of farmland to forests and grasses. However, environmental factors, such as temperature, precipitation, soil moisture, and solar radiation, also play a crucial role in vegetation growth [56]. To identify the relatively more important factors, standardized regression coefficients were used. As illustrated in Figure 10, temperature, precipitation, root-zone soil moisture, and solar radiation accounted for 2.78%, 27.48%, 51.56%, and 18.18% of vegetation growth, respectively. Soil moisture was the dominant factor in most areas, followed by precipitation. However, the dominant factor of vegetation growth varies by climatic zones. In the arid and semi-arid climate zones, soil moisture is the most critical factor, while solar radiation is the dominant factor in the semi-humid climate zones. The effect of soil moisture on vegetation growth is more direct than that of precipitation, especially in areas where vegetation growth is limited by moisture, such as arid and semi-arid regions [57; 58]. Furthermore, the response of vegetation to environmental factors such as temperature and precipitation differs among regions due to differences in hydrothermal conditions and vegetation types [59; 60]. Each climate zone has a unique ecosystem structure, and different ecosystems have varying water use efficiencies [61], resulting in different sensitivities to water surplus and deficit [17] and different response characteristics in each climate zone [13]. Hence, understanding the impacts of environmental factors on vegetation growth is crucial for scientific management and sustainable forestry development.  
Figure 10: Spatial distribution of dominant environmental factors for vegetation growth. (**a–d**) represent the standardized regression coefficients of precipitation, temperature, root-zone soil moisture,and net solar radiation, respectively; (**e,f**) represent the dominant environmental factors for vegetation growth (Pre = precipitation, Tmp = temperature, SM = root-zone soil moisture, and Rad = net solar radiation).
# Role of Restored Underwater Images in Underwater Imaging Applications
## 4 Results
### Results for Image Classification Application  
For investigating the performance of the restored images using the proposed restoration methods for image classification applications, four general classifications are made: fish, ships, status, humans. The supervised machine-learning approach is used for designing the classification algorithm.  
For the training purpose, 890 raw underwater images have been chosen from the [PERSON] et al. [30] dataset; with one model trained using the raw underwater images and another model trained using restored underwater images using the proposed restoration method. Sensitivity, specificity, and accuracy are used as performance measures. Sensitivity is the ability of a test to correctly classify an image, while specificity is the ability of the test to classify the incorrect objects. Accuracy is defined as the capability of the test to produce a result closer to the original correct classification. In total, 75 underwater images have been used for testing the trained model.  
Unprocessed images and restored images using the proposed restoration method are used for testing; with results tabulated for both SVM and KNN separately, in Table 2. From the table, it can be seen that the proposed method is able to improve sensitivity, specificity,and accuracy compared to using the raw unprocessed underwater images. Using the proposed restoration method improves accuracies from 72% and 82%, to 83% and 89% for SVM and KNN, respectively.
## 5 Core Structure of Ses Transmission  
SES is based on three voltage levels (+A, 0, \(-\)A). Therefore, SES belongs to a bipolar format to represent the binary digits. SES presents bit 1 with a high voltage amplitude, +A. In the case of consecutive 1 s, SES represents the second 1 with a level 0 voltage. The 0 is represented in SES as a \"\(-\)A\" waveform amplitude; in the case of repeating 0 s, the second 0 bit is shifted over the 0 voltage level. The logic 0 has a high probability because it represents both logics in consecutive cases. The basic signal modulation pattern against digital bits (0, 1) and the mapping of the digital bitstream (11110000101) modulation in SES into the waveform is shown in Figure 3.  
The basic structure of signal modulation in SES is shown in Figure 4. It has been observed that there is no synchronization and double bandwidth usage for bit representation in the waveform.  
Figure 4: Shadow Encoding Scheme (SES) waveform.  
Figure 3: The basic structure of SES.
# Towards a Low-Cost Precision Viticulture System Using Internet of Things Devices
## 5 Hardware Infrastructure
### Radio Unit
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**MCU** & **Protocol** & **Frequency** & **IEEE Standard** \\ \hline MKR 1010 & Wi-Fi & 2.40–2.50 GHz & 802.11 b/g/n \\ \hline MKR 1010 & BLE v4 & 2.40–2.48 GHz & 802.15.1 \\ \hline MKR 1300 & LoRaWAN & 433/868/915 MHz & 802.11 ah \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Wireless protocol overview. LoRa board is lower than the other two boards. The energy consumption of the boards without the sensors was also measured, shown in Figure 8(b). It is clear that LoRa technology is promising and performs better in terms of energy requirements in both the monitoring and the relay nodes. The low data rate of LoRa is not a drawback for our system since the sensor nodes are expected to transmit only a few data over an hour of monitoring.
## 4 Results
### Suarm and CSES-01 Magnetic and Electron Density Data Analysis
Figure 17: Swarm-A (from left to right panels) first differences (MASS method for magnetic data) of: X, Y, Z, and F; and map with the location of the epicentre of the earthquake (green star), Dobrovolsky area (yellow circle), and analysed track (red line) for 29 August 2017 (i.e., 1987 days before the earthquake). Coloured squares indicate the anomalies automatically detected by MASS. In order to verify the origin of this finding on CSES-01 data, we made a comparison with a different spacecraft (i.e., Swarm) measuring the same parameter, on the same day and in close orbit. We have carried out this comparison with the Ne data from Swarm shown in Figure 19, on 15 December 2022, in order to see if this phenomenon is something related to the measurement acquisition system by CSES-01 or if it has a physical reason. We found that, for similar local times, tracks do not show jumps or other particular features (Figure 19a,b) so we can conclude that these particular features in CSES-01 Ne are likely an artefact. Incidentally, we find an anomaly from Swarm-B that could be related to the EQ (anomaly evidenced by a red square in Figure 19b). Figure 19: _Cont._  
Figure 18: CSES-01 (from left to right panels): electron density data with respect to geomagnetic latitude; first differences (NeAD method for Ne data); electron density data with respect to geographic latitude; electron density data with respect to time in hours; and map with the location of the epicentre of the earthquake (green star), Dobrovolsky area (yellow circle), and analysed track (red line) for 15 December 2022 (i.e., 53 days before the earthquake). Coloured squares indicate the anomalies automatically detected by NeAD. In this case this anomaly is an artefact due to the jumps seen around 20\({}^{\circ}\)-10\({}^{\circ}\) geomagnetic and geographic latitudes, and it is outside of the Dobrovolsky area.
# Cooperative Position Aware Mobility Pattern of AUVs for Avoiding Void Zones in Underwater WSNs
## 2 Related Work
### Mobility Focused Protocols
The coverage hole problem is addressed in [20]. Taking the benefit of the redundant overlapping of the ranges of sensor nodes, the coverage hole is repaired during network operation. This protocol reduces the energy consumption, resulting in a high network throughput and lifetime of the network \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Technique** & **Features** & **Achievements** & **Limitations** \\ \hline AURP [12] & Long data transmissions are minimized using AUVs as relay nodes & High delivery ratio and low energy consumption & High end to end delay \\ \hline
3D-SM [13] & Use of MS and courier nodes to transmit data & Energy consumption of normal nodes is minimized & High end to end delay \\ \hline Distributed data gathering [14] & Routing using clusters and collection of data from path nodes using AUVs & Adjustment in transmission power & Overall transmission power is not minimized \\ \hline AEDG [15] & Association of sensor nodes with gateway nodes & Reliability of data delivery at the destination & High end to end delay \\ \hline EBRP (Energy Balanced Routing Protocol) [16] & Depth, residual energy and density of sensor nodes are considered as routing metrics & Energy of sensor nodes is saved and consumed in a balanced way & Low network lifetime \\ \hline LAFR [17] & Link-state-based adaptive feedback routing and link state information is used for adaptive feedback & Utilization of asymmetric links and routing tables is maintained & Low network lifetime \\ \hline CARP [18] & Selection of relay nodes that exhibit the latest history of successful data transmission & Loops are avoided, and data are routed around void and shadow zones & High end to end delay and more energy consumption \\ \hline VAPR [19] & Void-aware pressure routing and sonobuoy propagates the control information & Avoidance of void zone & High end to end delay \\ \hline Coverage hole avoidance [20] & Repairing of the coverage hole during network operation & Low energy consumption, high throughput and network lifetime & High end to end delay \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance tradeoffs in existing mobility-based routing protocols. AURP, AUV-aided underwater routing protocol; SM, sink mobility; AEDG, an efficient data-gathering; CARP, channel-aware routing protocol. at the cost of high end to end delay. The performance tradeoffs of mobility-based schemes are given in Table 2.  
The discussed existing schemes [13; 14; 15; 16; 17; 18; 19] have limitations in terms of high end to end delay. Therefore, in this paper, PAMP is proposed to minimize the end to end delay.
###### Abstract  
The detection of skin diseases is becoming one of the priority tasks worldwide due to the increasing amount of skin cancer. Computer-aided diagnosis is a helpful tool to help dermatologists in the detection of these kinds of illnesses. This work proposes a computer-aided diagnosis based on 1D fractal signatures of texture-based features combining with deep-learning features using transferred learning based in Densenet-201. This proposal works with three 1D fractal signatures built per color-image. The energy, variance, and entropy of the fractal signatures are used combined with 100 features extracted from Densenet-201 to construct the features vector. Because commonly, the classes in the dataset of skin lesion images are imbalanced, we use the technique of ensemble of classifiers: K-nearest neighbors and two types of support vector machines. The computer-aided diagnosis output was determined based on the linear plurality vote. In this work, we obtained an average accuracy of 97.35%, an average precision of 91.61%, an average sensitivity of 66.45%, and an average specificity of 97.85% in the eight classes' classification in the International Skin Imaging Collaboration (ISIC) archive-2019.  
computer-aided diagnosis; fractal signatures; CNN; Densenet-201
## 7 Conclusions  
Emergencies and natural disasters may affect all sectors in the supply chain and disrupt domestic and global supply chains. For example, the earthquake and tsunami damaged factory equipment and caused suppliers to cut off parts and raw materials in the supply chain [66]. Once faced with an uncertain global crisis (such as the outbreak of the COVID-19 epidemic), multinational companies can no longer rely on low-cost production factors in the process of adjusting global supply chains. How to quickly establish an adaptive production strategy and find alternative solutions is the main key to avoiding losses. In this case, many companies may have to learn to conduct business digitally, and blockchain with smart contract technologies do provide smart solutions that meet the requirements. In particular, companies need agility to quickly find new suppliers and open auxiliary sales channels to meet customer needs and remain competitive. Therefore, it is necessary to integrate the event-monitoring system into the supply chain in order to share emergency information in the early stage for preemptive management in the supply chain.  
The use of blockchain technology can improve the transparency of the traceability system to ensure that the supply chain system provides high-quality products. In this work, we developed a hybrid approach of blockchain platform system and an event monitoring method using a developed real-time event detection and tracking technique so that each supply chain member has enough information and agility to quickly make appropriate decisions. The contribution of the study is that we have demonstrated the implementation of a sensible solution, including two parts: (1) building a resilient blockchain with the smart contract approach for achieving smart supply chain management; (2) using smart contracts to incorporate an event-monitoring system model to share emergent information by detecting events for preemptive measures in supply chains. We have evaluated and carefully analyzed the performance of smart contracts in order to ensure that the proposed system is robust and efficient. The experiments show some interesting results and a couple of potential paths for future work in the field of agile supply chain management.  
Conceptualization, C.-H.L. and H.-C.Y.; methodology, C.-H.L. and Y.-C.W.; software, H.-C.Y.; validation, Y.-C.W. and W.-K.H.; formal analysis, Y.-C.W.; investigation, C.-H.L. and W.-K.H.; resources, W.-K.H.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Kaohsiung University of Science and Technology, grant number 109E9010P02.  
Not applicable.  
The study did not report any data.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Estimate of NH\({}_{3}\) Emission Factor at Biogas Production Facilities
#### 3.2.2 NH\({}_{3}\) Emission Factors Based on Biogas Production at Biogas Production Facilities  
The NH\({}_{3}\) emission factor based on the biogas production was determined and presented in Table 6. The results showed that the NH\({}_{3}\) emission factor from the biogas production facility was 0.150 kgNH\({}_{3}\)/10\({}^{3}\) Nm\({}^{3}\)-Biogas. The NH\({}_{3}\) emission factor for each biogas production facility was 0.023 kgNH\({}_{3}\)/10\({}^{3}\) Nm\({}^{3}\)-Biogas from biogas production facility A, 0.166 kgNH\({}_{3}\)/10\({}^{3}\) Nm\({}^{3}\)-Biogas from biogas production facility B, and 0.192 kgNH\({}_{3}\)/10\({}^{3}\) Nm\({}^{3}\)-Biogas from biogas production facility C. The emission factor was largest from biogas production facility C, followed by biogas production facility B and biogas production facility A. In the case of NH\({}_{3}\) emission factors based on biogas production, it was difficult to compare with values from other studies because there were no studies developed or studied with the same base.