esa-sceva/satcom-chunk-collection huggingface dataset:
# Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing
## 5 Conclusions  
In this study, the SM method was applied to MODIS GPP data in order to estimate phenological events of start of season (SOS) and end of season (EOS) for six tree species in mixed forests in Slovenia for several years between 2004 and 2019. The results reinforce the importance of temporal resolution over spatial resolution in modelling tree phenology, due to the importance of seasonality, particularly for the SM method. It also appeared clearly that the usual threshold of 0.5 is not best in most cases, especially for estimating the EOS. Finally, this study showed that despite the difficulty in modelling the phenology of different tree species in a mixed forest using remote sensing, it remains possible with moderate errors. It was possible to estimate SOS and EOS with errors as low as \(<\)8 days (_Fagus sylatica_ and _Tilia_ sp.) and \(<\)10 days (_Fagus sylatica_ and _Populus tremula_), respectively.  
**Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10.3390/rs13153015/s1](https://www.mdpi.com/article/10.3390/rs13153015/s1)]([https://www.mdpi.com/article/10.3390/rs13153015/s1](https://www.mdpi.com/article/10.3390/rs13153015/s1)), Figures S1-S15: similar to Figures 6-8 of this article, reproduced for five vegetation indices (MODIS LAI, MODIS EVI, MODIS NDVI, Landsat 7 EVI, and Landsat 7 NDVI).  
**Author Contributions:** Conceptualisation, K.D.N. and U.V.; methodology, K.D.N. and U.V.; software, K.D.N.; validation, K.D.N., G.O., A.Z. and U.V.; formal analysis, K.D.N.; investigation, G.O. and A.Z.; resources, U.V.; data curation, G.O. and K.D.N.; writing--original draft preparation, K.D.N.; writing--review and editing, K.D.N., G.O., A.Z. and U.V.; visualisation, K.D.N.; supervision, U.V.; project administration, U.V.; funding acquisition, U.V. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Slovenian Research Agency within the core funding for the Program Group \"Forest biology, ecology and technology\", grant number P4-0107 and the FORGENIUS project that has received funding from the European Union's Horizon 2020 research and innovation program, grant number 862221. The APC was funded by the FORGENIUS project.  
**Data Availability Statement:** The data presented in this study are openly available in FigShare at [[https://doi.org/10.6084/m9.figshare.14829081.v1](https://doi.org/10.6084/m9.figshare.14829081.v1)]([https://doi.org/10.6084/m9.figshare.14829081.v1](https://doi.org/10.6084/m9.figshare.14829081.v1)), reference number 14829081, accessed on 30 July 2021.  
**Conflicts of Interest:** The authors declare that no conflict of interest exists.
###### Abstract
* We propose the method to segment trajectories and split the data set by MMSI (Maritime Mobile Service Identity). The latter avoids the data leakage problem during the classifier training process. * The proposed MFELCM, fusing the static and dynamic information, is suitable for the global wide space-based AIS data, which can update the type prediction with the continuous input of AIS data and achieve near real-time online classification. MFELCM can be applied in detecting the abnormal behaviors of ships and, thus, can enhance the capability of maritime supervision. * The model parameters of MFELCM are determined by experiments, and it is verified that MFELCM outperforms the base classifiers. Moreover, when there are insufficient samples for a certain base classifier (e.g., dynamic feature distribution samples), the degraded MFELCM, integrated with the remaining base classifiers, can also achieve acceptable classification accuracy, which extends the application scope of MFELLCM. The rest of this paper is organized as follows. In Section 2, the data from the ocean satellite HY-2B is taken as an example to present a basic introduction of space-based AIS data, including data pre-processing, data volume, and ship type distribution. Section 3 illustrates the detailed implementation of MFELCM, including static and dynamic features extraction, samples construction for different base classifiers, data set splitting, and the implementation of base classifiers. In Section 4, MFELCM is applied to the real AIS data, and the performance of the model is evaluated. In addition, we discuss the effectiveness of degraded MFELCM without a certain base classifier. Section 5 concludes the full paper and presents an expectation for future work.
# A Programmable ROADM System for SDM/WDM Networks
## 3 Design of Programmable SDM/WDM ROADM System
### Programmable Virtual topology  
To demonstrate the promotion of optical network performance of core switch, 6-node topology optical network which is shown in Figure 3 has been used for analysis. There are four three-degree nodes and two two-degree nodes with 8 multicore fibres used in the whole network. The number of cores in every MCF was assumed as 13 and the fibre-core switch can select different numbers which can be controlled by a centralized controller. Due to there being only six nodes in this optical network, the pairs of transmitting and receiving nodes are including three types: neighbor pairs, one-hop pairs, and two-hop pairs. As mentioned above, the programmable ROADM can dynamically distribute the number of bypass connection cores and WDM RS cores, which can be seen as different virtual topologies. Three different topologies with different bypass connections are used to illustrate the virtual topology. The difference between these topologies is the number of bypass links and where to add them. One-hop core switch was proposed to explain the nondirect two-node whose shortest physical path needs two MCFs and goes through one internode, such as node 0 and node 2. Furthermore, two-hop core switch can be seen as the nondirect two-node whose shortest physical path needs three MCFs and goes through two internodes. Due to there being only six nodes in the proposed optical network, the maximum hop is two.  
Figure 2: Detailed node structure including bypass connection and Route-and-Select ROADM.  
In Figure 3, the three different virtual topologies come from using different cores to set up the bypass connection. Topology 1 shows that one core is used for one-hop pair nodes with one core for two-hop pair node bypass connection, while topology 2 presents two cores that are used for one-hop pair-nodes and one core is used for two-hop pair nodes. As for topology 3, only two cores are used for one-hop pair nodes bypass connection.
# Link Prediction Based on Data Augmentation and Metric Learning Knowledge Graph Embedding
## 3 Methodology
### Metric Learning Module
#### 3.2.1 Shared Parameters  
In a specific graph scenario, the entity features and relation features are constrained. As a consequence of this, a KG can be represented as two dimensions: _de_ and _dr_. _de_ represents the dimension of all entities in the graph, while _dr_ represents the dimension of all relations. Moreover, the entities, relations, and the head and tail inhabit distinct feature spaces. The KG features are critical to embedding quality. Utilizing feature information poses a challenge because the available data often lack a comprehensive representation of these features.  
Referencing the literature [23,35], this paper expresses relation features in a vector space of dimensionality _dr_ and entity features in a vector space of dimensionality _de_.  
We adopt an implicit feature representation approach, utilizing a three-dimensional tensor \(W\in\mathbb{R}^{de\times de\times dr}\) to capture feature interactions. Compared with previous methods, our approach avoids explicitly expressing the eigenvalues and embedding dimensions of entities and relationships, mitigating the exponential growth of parameters while the  
Figure 2: The overall architecture of the metric learning module.  
various relations and entities scale. Considering the potential disparity in vector spaces between head and tail entities, directly measuring their distance lacks practical relevance. As a result, a projection matrix must be obtained in order to map them in a specified space before performing measurements. The projection matrix in this work is relation-based-embedded.  
The entity's final embedding is dynamically determined by relational embedding to achieve unique representations of the same entity across various relations and locations.  
Unlike previous works, we propose a new method: the relation \(r\) in this paper is split into two components: \([r^{H},r^{T}]\), with \(r=r^{H}||r^{T}\), where \(||\) denotes vector concatenation. By multiplying each pair of relation vectors with the shared parameter tensor, we derive the projection matrix for both the head and tail entities, illustrated in Figure 3.  
\[M^{H},M^{T}=r^{H}\times W,r^{T}\times W^{T} \tag{3}\]  
where \(M^{H},M^{T}\in\mathbb{R}^{de\times de}\) denote the projection matrices for the head and tail entities, and \(W,W^{T}\in\mathbb{R}^{de\times de\times dr}\) represents the shared parameter tensor, with \(W^{T}\) being the transposed form of \(W\). The goal is to maintain relational consistency by providing different representations for a given entity at the head and tail positions. After obtaining the projection matrix, we compute the final entity representation in the context of the current relation.  
\[h_{\perp},t_{\perp}=hM^{H},tM^{T} \tag{4}\]  
The method here does not employ distance metrics such as Euclidean distance because the head and tail entities may not lie on the same plane. Directly using distance metrics would not make sense in such cases. Therefore, the method of distance measurement after projection was chosen.
# Energy Trading Strategy for Heat and Electricity-Coupled Microgrid Based on Cooperative Game
## 5 Nash Bargaining Game
### Bargaining-Based Cooperation Model  
The paper assumes that MGO and energy users who participated in energy bargaining are independent and rational. The premise of the MGO and users participating in energy bargaining is that they can improve their operational utilities by strategically choosing the volume and price of both electricity and heat, otherwise they will not participate in energy bargaining. Therefore, the following conditions need to be met:  
\[U_{MGO}\geq B_{MGO} \tag{23}\]  
\[U_{i}\geq B_{i} \tag{24}\]  
where, \(B_{MGO}\) and \(B_{i}\) are respectively the utility of the MGO and user utility \(i\) under original electrical and thermal prices. Thus, the bargaining-based optimal problem between the MGO and users can be realized by solving the following cooperative game optimization problem.  
\[\begin{array}{l}\max\left(U_{MGO}^{h}-B_{MGO}^{h}\right)\prod\limits_{i=1} ^{n}\left(U_{i}^{h}-B_{i}^{h}\right)\\ s.t.\;U_{MGO}^{h}\geq B_{MGO}^{h}\cup U_{i}^{h}\geq B_{i}^{h}\\ \left(1\right)-\left(5\right),\left(7\right)-\left(12\right),\left(18\right)- \left(22\right)\end{array} \tag{25}\]  
where \(U_{MGO}^{h}-B_{MGO}^{h}\) is the benefit increment of MGO, and \(U_{i}^{h}-B_{i}^{h}\) is the profit increment of user \(i\). \(B_{MGO}^{h}\) and \(B_{i}^{h}\) can de redefined as the disagreement points of the MGO and user \(i\), respectively. The constraints of variables \(\{p_{M,gas}^{h}\), \(p_{B,gas}^{h}\), \(e_{l}^{lh}\), \(t_{i}^{lh}\), \(p_{i}^{h}\), \(\lambda_{i}^{h}\}\) and constraints of variables \(\{e_{l}^{lh}\), \(t_{i}^{lh}\), \(p_{i}^{h}\), \(\lambda_{i}^{h}\}\) can be regarded as the strategy sets of the MGO and user \(i\), respectively. The equilibrium of bargaining can ensure that the benefits of cooperation are shared fairly by each stakeholder. Furthermore, according to the logarithm of the problem (25), the objective function can be further transformed into as follows:  
\[\begin{array}{l}\max\;\ln(U_{MGO}^{h}-B_{MGO}^{h})+\sum\limits_{m\in M}\ln( U_{i}^{h}-B_{i}^{h})\\ s.t.\;U_{MGO}^{h}>B_{MGO}^{h},U_{i}^{h}>B_{i}^{h}\\ \left(1\right)-\left(5\right),\left(7\right)-\left(12\right),\left(18\right)- \left(22\right)\end{array} \tag{26}\]  
By solving the NBS of (26), the following theorem is obtained.  
**Theorem 1**.: _The NBS of the bargaining problem (26) is also the solution to the social welfare maximum problem._  
Proof.: Please see Appendix A. \(\square\)  
To illustrate the point, the social welfare maximization model is analyzed in the next subsection.
# Efficient and Effective Unsupervised Entity Alignment in Large Knowledge Graphs
## 5 Experiments
### Parameter Study  
In this section, we further investigate the stability of _MPGT-Align_ by examining the effects of three key hyperparameters: the number of Transformer layers \(L\), the perturbation ratio \(\gamma_{q}\), and the temperature \(\tau\). Specifically, we conduct experiments on _MPGT-Align_ with varying hyperparameter settings using the dataset _DBP-15K_.  
**On the number of Transformer layers \(L\).** We fix the number of propagation steps and vary the number of Transformer layers \(L\) from 1 to 7 across each dataset. Figure 5  
Figure 4: Ablation study of _MPGT-Align_’s components on _DBP-15K_.  
presents the performance of _MPGT-Align_. Generally, a smaller \(L\) yields a higher accuracy, whereas a larger \(L\) tends to degrade performance due to an increased likelihood of overfitting. However, as illustrated in Figure 5, there is no significant decline in _MPGT-Align_'s performance, even when \(L\) exceeds 7. For instance, the performance of _MPGT-Align_ on both the _ZH-EN_ and _FR-EN_ datasets improves with increasing values of \(L\). This finding indicates that _MPGT-Align_ demonstrates considerable stability and robustness.  
**On perturbation ratio \(\gamma_{q}\).** In graph enhancement experiments, it is common practice to adjust the perturbation rate in one view while keeping the other view constant. In this experiment, we set five ratios for the perturbation ratio \(\gamma^{q}\), ranging from 1.0 to 0.5, while maintaining the other perturbation ratio \(\gamma^{k}=0.3\) unchanged. Generally, a higher perturbation ratio introduces more noise into the model. As illustrated in Figure 6a, the overall performance of _MPGT-Align_ exhibits a degree of stability and tends to improve with increasing noise levels. This suggests that _MPGT-Align_ is not particularly sensitive to noise; rather, an appropriate level of noise can enhance performance.  
**On temperature \(\tau\).** Temperature serves as a standard hyperparameter in contrastive learning and correlates with the attention given to challenging samples. Drawing on related work, we conduct relevant experiments on _ZH-EN_, with the results presented in Figure 6b. The findings indicate that larger values of \(\tau\) lead to more stable performance, with a value of 0.08 providing an optimal balance consistent across most studies. Overall, these hyperparameter experiments demonstrate that _MPGT-Align_ exhibits reduced sensitivity to hyperparameters and maintains model robustness even when there are variations in hyperparameter settings.
## 3 Results and Discussion
### Residual Mechanical Properties of NC and LC after High Temperature Exposure
At 500-700\({}^{\circ}\)C, LC28 shows a sharp decrease compared to LC41 and LC33, where chemical decomposition of the cement matrix occurs actively in the temperature section, and it is judged that the ultrasonic pulse velocity decreases rapidly due to matrix damage in LC28 with a large amount of cement mixture. At 500 \({}^{\circ}\)C, NC41, NC33, and NC28 showed residual ultrasonic pulse velocities of 0.53, 0.51, and 0.56, respectively, while LC41, LC33, and LC28 exhibited 0.60, 0.60, and 0.66, respectively. At 700 \({}^{\circ}\)C, NC showed a residual ultrasonic pulse velocity of approximately 0.31 at all levels, while LC exhibited approximately 0.49. It appears that the influence of the coarse aggregate on the ultrasonic pulse velocity increases as the temperature increases, in a similar manner to its influence on the residual compressive strength. Figure 7 shows the residual modulus of elasticity of NC and LC after high temperature exposure. As the temperature increased, the residual modulus of elasticity showed a tendency to continuously decrease in a similar manner to that of the ultrasonic pulse velocity [27]. In addition, the residual modulus of elasticity showed a lower residual rate at the same temperature compared to the other mechanical properties [25; 28]. At 100 \({}^{\circ}\)C, the  
Figure 6: Residual ultrasonic pulse velocity of normal concrete (NC) and lightweight concrete (LC) after high temperature exposure. residual modulus of elasticity was approximately 0.88 at all levels. At 200 \({}^{\circ}\)C, it significantly decreased to 0.69 at a W/B ratio of 0.41 and approximately 0.76 at the remaining levels. At 300 \({}^{\circ}\)C, it was 0.70 for NC28 and approximately 0.59 at the remaining levels. The sharp decrease in the modulus of elasticity at higher temperatures can be attributed to the chemical decomposition of C-S-H gel, as was the case for the residual compressive strength. At temperatures higher than 500 \({}^{\circ}\)C, the residual modulus of elasticity of LC tended to be higher than that of NC under the same W/B ratio. At 500 \({}^{\circ}\)C, NC41, NC33, and NC28 showed residual moduli of elasticity of 0.20, 0.21, and 0.29, respectively, while LC41, LC33, and LC28 exhibited 0.30, 0.29, and 0.28, respectively. At temperatures below 700 \({}^{\circ}\)C, NC28 showed higher or similar residual moduli of elasticity compared to LC at all levels, but it exhibited a lower residual rate compared to LC at 700 \({}^{\circ}\)C. At 700 \({}^{\circ}\)C, NC41, NC33, and NC28 showed residual moduli of elasticity of 0.03, 0.05, and 0.09, respectively, resulting in very low residual rates, while LC41, LC33, and LC28 exhibited 0.14, 0.15, and 0.20, respectively. Although LC showed higher residual rates compared to NC, it appears that most of the stiffness disappeared at all levels at temperatures above 700 \({}^{\circ}\)C.
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 6 Current Challenges and Future Research Directions
### Technical Challenges  
#### 6.1.1 Data Processing Capabilities  
With the comprehensive deployment of ETC systems nationwide and the continuous expansion of urban application scenarios, data volumes are experiencing explosive growth. The real-time processing of massive data has become a critical bottleneck, constraining system performance and application effectiveness.  
Technical challenges related to real-time requirements manifest across multiple levels. Traffic management decisions require second-level or even millisecond-level data processing response capabilities, but traditional centralized data processing architectures struggle to meet these requirements. The introduction of edge computing technology provides new technical pathways for addressing this challenge. [PERSON] et al. noted that the combination of intelligent transportation system sensing with edge computing offers considerable benefits for transportation systems in terms of efficiency, security, and sustainability [65]. By shifting partial computing tasks to roadside devices and edge nodes, data transmission delays can be significantly reduced, and system response speeds can be improved. However, edge computing deployment also introduces new technical challenges, including computing capability limitations of edge devices, distributed data consistency assurance, and edge-cloud collaborative scheduling optimization, requiring in-depth research in system architecture design, algorithm optimization, and resource management.  
Algorithm efficiency optimization represents another critical pathway for enhancing data processing capabilities. Traditional batch processing algorithms can no longer accommodate real-time processing requirements for streaming data, necessitating the development of efficient algorithms specifically designed for streaming data. These algorithms must not only achieve an optimal balance between accuracy and real-time performance but also possess excellent scalability and fault tolerance. Deep learning algorithms demonstrate enormous potential in traffic data processing, but their characteristics of high computational complexity and substantial resource consumption place higher demands on system design. How to enhance algorithm efficiency through technical approaches such as model compression, quantization acceleration, and parallel computing, while ensuring algorithm effectiveness, represents an urgent technical challenge that requires resolution.
## 3 Results  
The initial search yielded 104 studies; two studies [22; 25] were added through additional bibliography exploration in another article [13]. Subsequently, after reviewing the titles and abstracts, 86 articles were excluded. Following the full-text reading of the remaining 20 studies, 14 were deemed eligible, all in English and meeting the predefined inclusion criteria. The PRISMA Flow Diagram (Figure 2) depicts the entire research pathway, detailing the number of studies analyzed and the reasons for their exclusion.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Article** & **Sample** & **Objective** & **Diagnostic Method** & **Methods** & \multicolumn{1}{c}{**Outcome**} \\ \cline{5-6}  & & & & & & \(\pm\)**DS (Range)** \\ \hline [PERSON] et al. & 34 climbers with & & & & & & (a) CR A2: \\
[21] & injury; 20 normal & TBD & US 10 MHz & & & & Rest: 3.1 \(\pm\) 0.05 mm \\
2002 & (control group) & & & (gel pad) & active flexion & & AFF: 5.1 \(\pm\) 0.15 mm \\  & 32 patients & & & & & (a) CR A2: \\ [PERSON] et al. & (29 climbers, & injury with dynamic & & & & Rest: 3.1 (3.1-4.5) mm \\
[22] & 3 non-climbers); & ultrasound compared & (silicon pad) & (silicon pad) & TBD at rest and with & & AFF: 5.1 (3.9–7) mm \\
1999 & control group & & & & & (b) FR A2: \\  & (10 non-climbers) & & & & & (b) FR A2: \\  & & & & & & (a) CR A2: \\  & & & & & & & 2.8 \(\pm\) 0.7 (1.1-3.3) mm \\  & & & & & & AFF: 4.6 \(\pm\) 0.6 (3.0-4.9) mm \\ [PERSON] et al. & 64 climbers with & & Diagnosis of pulley & & & (b) FR A2: \\
[21] & injuries to pulleys A4 & & injury with dynamic & US 12 MHz & & & Rest: 0.9 \(\pm\) 0.7 (0–2.2) mm \\
2002 & and A2 & & ultrasound compared & (gel pad) RMN & & & (c) CR A4: \\  & & & & & & Rest: 1.5 \(\pm\) 0.4 (1.1–2.0) mm \\  & & & & & & AFF: 3.1 \(\pm\) 0.5 (2.5–3.7) mm \\  & & & & & & (a) CR A2: \\ [PERSON] et al. & & & & & & (b) FR A2: \(<\)2 mm \\
[19]. 2003 & 604 climbers & Pulley injury score & US 7.5 MHz & & & (b) FR A2: \(<\)2 mm \\  & & & & (water tube) RMN & & & (c) CR A4: \(>\)2 mm \\  & & & & & & (d) PR A4: \(<\)2 mm \\  & & & & & & (a) CRA2 AFF: 1.9 mm \\ [PERSON] et al. & & & & & & (b) CR A2 AFF: 3.7 mm \\
[23] & 14 cadaver hands & Diagnosis of A2, A4, & & & & (c) CR A4 AFF: 1.8 mm \\
2017 & & and A3 challenging & & & & (10 = N) & (d) CR A4 AFF: 2.7 mm \\ [PERSON] et al. & 30 fingers in vitro [24] & & Diagnosis of partial & & & (10 = N) & (d) CR A4 AFF: 2.7 mm \\
2023 & 5 groups (G1–G5) & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Methods and results of the reviewed studies.