esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Current Limitations and Future Trends
While previous reviews have primarily focused on specific aspects of MaaS, such as its conceptual foundations [24], early adoption models [13], and framework architectures [14], this study provides a comprehensive bibliometric and systematic review, offering quantitative insights into research trends, citation patterns, and keyword networks. Unlike prior works that analyze MaaS through case studies or theoretical models [2; 19], our study integrates statistical methods to identify research gaps, key influencing factors, and emerging themes. Additionally, by categorizing MaaS research into theoretical, architectural, and data-driven innovations, this review structures the fragmented literature, allowing for a more holistic understanding of the field [37]. These contributions position this study as a valuable reference point for both academics and industry practitioners, guiding future research and MaaS implementation strategies. Future MaaS research should explore new theoretical frameworks that integrate system-of-systems theory, digital ecosystems, and servitization models to enhance scalability and interoperability. Methodologically, adopting hybrid approaches--combining bibliometric analysis with machine learning--can improve trend forecasting and research gap identification. Additionally, longitudinal case studies and experimental simulations can provide empirical validation, strengthening the theoretical foundation of MaaS in Industry 4.0. These approaches will ensure a more comprehensive understanding of MaaS evolution and its impact across industries. Bibliometric trends reveal key research gaps, influential contributors, and emerging topics, helping funders and policymakers prioritize underexplored areas like MaaS security and AI-driven optimization. Identifying leading institutions and collaborations can also drive strategic partnerships and accelerate innovation.
# Lightweight Infrared and Visible Image Fusion Based on Nested Connections and Res2 Net
## 4 Training Strategy
### Training in the Second Stage  
In the fusion layer, the multimodal multi-scale feature fusion module R2 FN is designed to replace the manually designed fusion strategies typically used in autoencoder-based fusion networks. In the second stage of training, the focus is on training the R2 FN module to enhance its capability of extracting multimodal multi-scale features. The R2 FN module is trained using multimodal images, aiming to optimize its performance in effectively fusing multimodal multi-scale features. The parameters of the encoder and decoder obtained from the first stage are kept fixed to ensure consistency in the features extracted and reconstructed by these networks. Subsequently, a loss function tailored for R2 FN is designed to train the multi-scale depth feature fusion network.  
The fixed encoder network is employed to extract multi-scale features from the source images, with the features at each scale being fused by the corresponding R2 FN. The fused multi-scale features are then used as inputs to the decoder network to reconstruct the fused image. We define a loss function \(L_{\text{R2 FN}}\) as the training loss for R2 FN. \(L_{\text{R2 FN}}\) consists of two components: detail loss (\(L_{\text{detail}}\)) and feature enhancement loss (\(L_{\text{feature}}\)), defined as follows:  
\[L_{\text{R2 FN}}=\beta L_{\text{detail}}+L_{\text{feature}} \tag{11}\]  
where \(\beta\) represents the balancing parameter between \(L_{\text{detail}}\) and \(L_{\text{feature}}\).  
In infrared and visible image fusion networks, the visible image typically contributes texture details in the background. Therefore, we define the detail preservation loss by computing the structural similarity loss of the visible-light image. It is defined as follows:  
\[L_{\text{detail}}=1-\text{SSIM}(O,I_{\text{vi}}) \tag{12}\]  
In infrared images, more salient object features are typically present. Therefore, a feature enhancement loss function is designed to enhance salient feature information. It is defined as follows:  
\[L_{\text{feature}}=\sum_{m=1}^{M}\omega_{1}(m)\cdot\left[\left(\phi_{f}^{m}- \phi_{ir}^{m}\right)^{2}\cdot\omega_{ir}+\left(\phi_{f}^{m}-\phi_{vi}^{m} \right)^{2}\cdot\omega_{vi}\right] \tag{13}\]  
where M represents the number of multi-scale features obtained through downsampling, \(\omega_{1}(m)\) represents the balancing parameter for the m-th multi-scale feature, and \(\omega_{\text{ir}}\) and \(\omega_{\text{vi}}\), respectively, represent the balancing parameters controlling the ratio of visible-light depth features and infrared features.
## 4 Discussion
### Similarity Metrics  
Another finding of this analysis is the correspondence between parametric (Pearson correlation coefficient, \(\rho\)) and nonparametric (mutual information, MI) methods of quantifying statistical similarity and information content. This consistency strengthens the findings of the VI intercomparison, as both methods show strong relationships between [F\({}_{\rm v}\)] and [DVI, NIRv, EVI, and EVI2] but much weaker relationships between [F\({}_{\rm v}\)] and [SR, NDVI]. Reduced MI values for the NDVI suggest that the limitations of the index are yet more pernicious than simply being due to a nonlinear, heteroskedastic relationship (which should be better captured via MI). This is supported by the broader dispersion of the NDVI distribution than is evident in DVI, NIRv, EVI, or EVI2, as seen in Figure 3. Note also that NDVI and SR have, by far, the largest value of MI (11.33) of any pair of indices, consistent with their demonstrable nonlinear relationship (Figure S1, lower right). This is due to a functional dependence between the two indices, further explored in Figure S1 and Analytical Exercise S1. Such behavior is notably absent for the NDVI:Fv and SR:Fv relationships, which do not markedly deviate above \(\rho\):MI loglinearity, suggesting that even nonlinear regression is unlikely to be effective in capturing this variability.
# A BiGRU Model Based on the DBO Algorithm for Cloud-Edge Communication Networks
## 4 Simulation Experiment and Result Analysis
### Performance Evaluation Indicators  
In this chapter, we chose to use four classical neural network model evaluation metrics for the experimental results to assess the effectiveness of short-term temporal flow prediction. They are Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Population Size** & **Number of Iteration Rounds** & \(k\) & \(\beta\) & \(S\) & **Dimensionality of the Problem** \\ \hline
30 & 50 & 0.1 & 0.3 & 0.5 & 4 \\ \hline \hline \end{tabular} (2) Parameter Settings for The BiGRU Part of The BO-BiGRU Model and for The Comparison Model  
After first setting the number of iteration rounds of BiGRU to 50, the optimisation range of each parameter is set, as shown in Table 2. Subsequently, it is handed over to the DBO algorithm for iterative optimisation, and finally the DBO algorithm will return the best parameters of the BiGRU model.  
\end{table}
Table 1: DBO algorithm parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Width of** & **Number of Hidden** & **Dropout Rate** & **Neural Network** \\
**Observation Window** & **Layer Neurons** & **Learning Rate** \\ \hline
[12; 27] & [30; 80] & [0.01, 0.4] & [0.001, 0.01] \\ \hline \hline \end{tabular}
\end{table}
Table 2: BiGRU parameter optimization range.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \
\begin{tabular}{c} **Width of** \\ **Observation** \\ **Window** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Hidden Layer** \\ **Neurons** \\ \end{tabular} & **Dropout Rate** & **Learning Rate** \\ \hline RNN & 24 & 32 & 0.05 & 0.003 \\ LSTM & 24 & 32 & 0.05 & 0.003 \\ TCN & 24 & 32 & 0.05 & 0.003 \\ DBO-BiGRU & 12 & 46 & 0.01 & 0.0024 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter settings for each model.  
Absolute Percentage Error (MAPE), and R2 coefficient of determination (R2). The specific formulas are shown in Equations (14)-(17):  
\[MAE =\frac{1}{n}\sum|(Y_{pred}-Y_{real})|, \tag{14}\] \[RMSE =\sqrt{\frac{1}{n}\sum\Big{(}Y_{pred}-Y_{real}\Big{)}^{2}},\] (15) \[MAPE =\frac{100\%}{n}\sum\Big{|}\frac{Y_{pred}-Y_{real}}{Y_{real}}\Big{|},\] (16) \[R2 =1-\frac{\Sigma\Big{(}Y_{pred}-Y_{real}\Big{)}^{2}}{\sum\big{(}Y_{ real}-\overline{Y_{real}}\big{)}^{2}}, \tag{17}\]  
where \(\overline{Y_{real}}\) denotes the average of the true values. Among the above evaluation indicators, MAE, RMSE, and MAPE are used to indicate the prediction accuracy of the model. The smaller their values are, the more accurate the prediction results are and the stronger the model performance is. R2 is used to assess the regression model fitting goodness of fit, and its value range is [0, 1]; the closer it is to 1, the better it symbolises the model fitting effect.
# Electric-Vehicle Routing Planning Based on the Law of Electric Energy Consumption
## 4 The Electric Vehicle Routing Problem with Time Windows Based on Driving Cycles (EVRPTW-DC)  
### Formulation of EVRPTW-DC  
The electric vehicle routing problem in our work is constructed using the energy consumption models established in Section 3, also with consideration of customer time windows, the safe battery level, and vehicle load capacity. We define EVRPTW-DC with the following assumptions:  
* Only one distribution center exists and all EVs depart from the distribution center at the same time with a full battery, and return to the same distribution center after completing the delivery tasks;
* EVs are homogeneous;
* When visiting a recharging station, an EV is always recharged to full battery capacity;
* Only delivery tasks for a customer are considered, i.e., no pickup tasks;
* A customer must be visited only once, and by only one vehicle;
* The locations of the distribution center, customers, and recharging stations, and the demands of customers, are known;
* The number of chargers is sufficient at any recharging station, and an EV can be serviced whenever it arrives. With the above assumptions, we now present the notation of our model in Table 2. \begin{table}
\begin{tabular}{l l} \hline \hline
**Sets** & \\ \hline \(N\) & Set of all nodes \\ \hline \(N_{0}\) & Distribution center \\ \hline \(N_{C}\) & Set of customers \\ \hline \(N_{R}\) & Set of recharging stations \\ \hline \(K\) & Set of EVs \\ \hline
**Parameters** & \\ \hline \(T_{lim}\) & The maximum time span of a delivery route, in hour units \\ \hline \(Q\) & The maximum battery level of an EV, in kW-h \\ \hline \(Q_{low}\) & The lowest battery level of an EV, in kW-h \\ \hline \(r\) & Recharging rate at a charger, in kW \\ \hline \(\phi\) & Unit price of recharging, in Yuan/(kW-h) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Notation of variables and parameters in the EVRPTW-DC model. The objective for EVRPTW-DC is to minimize the total costs of distance, recharging, and time window punishment. 1. Cost of traveled distance \(C_{d}\): \[C_{d}=c_{1}\sum_{i\in N}\sum_{j\in N}\sum_{k\in K}x_{ijk}d_{ij}\] (32) (2) Cost of recharging \(C_{r}\): \[C_{r}=\left[c_{2}+\phi\left(Q-p_{jk}^{a}\right)\right]\sum_{i\in N}\sum_{j\in N }\sum_{k\in K}x_{ijk}\] (33)  
\(C_{r}\) consists of two components: the fixed cost for using a recharging facility, and the variable cost depending on the amount of recharged energy. To linearize \(C_{r}\), we introduce new nonnegative variables \(y_{ijk}\).
# Proof-of-Concept of a Millimeter-Wave Integrated Heterogeneous Network for 5G Cellular
## 3 mmWave Beamforming Antenna and Backhaul
### Passive Reflectarray Antennas  
Our implementation of a static mmWave backhaul link was based on the use of passive reflecting structures [20]. These so-called reflectarrays comprise an array of specially-designed dipole elements on a printed circuit board (PCB), with a ground metallization layer on the opposite side [60]. Through the proper design of the dipole structures, the phase of the locally-reflected electromagnetic wave can be influenced, and a collimating effect similar to that of a parabolic dish can be obtained. The dipole elements can be made polarization dependent, allowing the reflectarray to use different focal points for the orthogonal polarizations. Furthermore, the shape of the collimated beam can be controlled by the phase configuration. The aperture and gain of a reflectarray can be adapted to match specific needs, in the same way as a parabolic dish. The application of well-understood PCB technology allows both the cost and weight to be kept low.  
We combined an integrated mmWave transceiver with on-chip antennas in a reflectarray structure, with the goal of increasing the antenna gain and therefore the link distance that could be achieved. Figure 5 shows our design. The integrated transceiver chip was installed at the position of the feed antenna and facing towards the array.  
The integrated transceiver can be, for example, based on the IEEE 802.11 ad standard, operating at 60 GHz. RF feeding structures and their associated losses can be minimized, as the on-chip antenna requires only a low gain. As the proposed solution is intended for static links, a mechanical fine alignment can be implemented to reduce the installation cost.  
A prototype of the static backhaul is shown in Figure 6. The transceiver was based on an IEEE 802.11 ad modem and was connected to an embedded PC acting as both the controller and router. A standard Gigabit Ethernet connection was available. Average and peak power consumption of the prototype was 14 Watts and 20 Watts, respectively. Since the IEEE 802.11 at power over Ethernet (PoE) standard allows up to 25.5 Watts (0.6 A \(\times\) 42.5 V) of power delivered at power devices [54], the entire device could be supplied with PoE.  
The achievable net TCP/IP throughput of the point-to-point prototype was around 1.7 Gbps, and the round trip IP latency was below 1 ms. The achievable range was in the order of several hundred meters using a reflector with a 20-cm edge length. The data rate could be partitioned arbitrarily between uplink and downlink, as the underlying protocol operated in TDD mode. However, the wired Ethernet interface limited the speed in each direction to around 1 Gbps. Full throughput was therefore achieved when traffic was symmetrical in both directions.
## 4 Results  
### Mechanical Tests  
After healing, the recovery of mechanical features was the first indication of a successive intervention. The load is plotted in Figure 5 against the crack opening as measured by the DIC horizontal displacement of two points fixed at both crack faces at the bottom of the beam. In contrast to other studies that report on load-deflection curves, herein the DIC online and continuous displacement tracking permits accurate tracking of the crack onset and progress. Three cases are illustrated in Figure 5 and their mechanical results are summarized in Figure 6:  
* **Full crack healing (Beam 4):** The early compliance was fully recovered (96%) and the ultimate load reached after healing was up to 80% of the original value. The initial crack reopened after healing; however, significant resistance to fracture was foreseen (Figure 5a), as proven by the significant toughness recovery (71%, Figure 6).
* **Partial healing (Beam 11):** Figure 5b demonstrates the case of a beam at which partial healing and moderate restoration was reported (i.e., recovery of mechanical features by approximately 60%, see Figure 6). In this case, unexpectedly the crack was located at the limits of the bending zone. This phenomenon was reported for the majority of beams that carried 3D printed PET reservoirs. It was shown that the healing system design impacted both healing efficacy and crack formation. Since the PET reservoirs were standing only 30 mm away from the end of the limits of the bending zone, the cracks were attracted by the boundary effect at the reservoir-concrete interface. It should be noted that a clear distinction between the fully and partially healed samples could not be made with confidence; however, this classification was chosen to underline the healing potentials of the systems under design.
* **No healing (Beam 9):** In a few cases, the crack was formed out of the healing zone; therefore, the agent delivery could not be obtained. This case reports the limited recovery (Figure 6). Indicatively, the ultimate load at re-loading stage was equal to the load reached at the end of the loading cycle. Additionally, considering the limited toughness measured after healing and the absence of compliance recovery, this case will be considered further in this study as the unhaeled scenario.  
Figure 5: Load-crack opening curves and strain maps at ultimate force for: (**a**) healed (Beam 4); (**b**) partially healed (Beam 11); and (**c**) unhaeled beam (Beam 9).  
By implementing DIC analysis, the crack position and propagation could be obtained, a result that demonstrates the potential of this technology for the online inspection of healing phenomena in the laboratory.  
Effective crack re-healing was not achieved in any beam under study, as illustrated with the red plot in Figure 5a. The failure of the vascular network after healing will be further discussed in the following sections. It is concluded that the presence of 3D printed PET reservoirs in the beam affects the crack formation since in all cases, the crack deviates from the middle bending zone.
# Multi-IRS-Assisted mmWave UAV-BS Network for Coverage Extension
## 4 Proposed IRS Selection and Access Link Connection Approach with 3D Simulation Environment
### Access Link Connection  
As shown in Figure 4, some UEs may experience NLoS conditions with UAVs due to obstructions. This is particularly problematic for millimeter wave communication, where signal attenuation can be significant. Coordination with IRSs or the utilization of neighboring BSs is essential to address this issue.  
Algorithm 1 determines the communication path from the UAV to each UE. It notices which UE can create an LoS. We assume that four IRSs are deployed on each side of a building and that there is a single neighboring BS. The determination of the access link communication path follows the procedures below. For a given UE, if the path between the UAV and the UE is in the LoS, direct communication is considered feasible. Otherwise, when it is in the NLoS, if the path from the UAV to the IRS is in the LoS, the incident angle of the radio wave at the IRS is within the threshold \(\zeta\), the path from the IRS to the UE is the LoS, and the reflection angle of the radio wave is within the threshold \(\zeta\); then, this UAV-IRS-UE link is established. If multiple IRSs satisfy these conditions, the shortest total path length is selected. Finally, if none of the above conditions are met, and the path from the neighboring BS to the UE is in the LoS, then this communication path is established. If the neighboring BS-UE link is in the NLoS, then the NLoS UAV-UE link is chosen.
# A Text-Generated Method to Joint Extraction of Entities and Relations
## 6 Discussion
### Comparison of the Multiple Relational Triples Extraction Performance  
We further divide the NYT11 test set and classify test set into 7 subclasses according to the relation number of the entity pairs in each sentence. We test the extraction capability of our model and MultiDecoder on each class which contains 1, 2, 3, 4, 5, 6 and \(>=7\) relational triples, respectively. The results are shown in Figure 4, we can see that as the number of relations contained in a sentence increases, the performance of MultiDecoder decreases. However, when the sentence has one to four relational triples, the effect of our model is gradually increasing, and achieves the best performancewhen the number of relations is 4. When the number of relations between the same entity pair is greater than 4, the extraction effects will gradually decrease.  
As the number of relations increases, extraction becomes more difficult, so the performance of MultiDecoder decreases gradually. For our model, as mentioned earlier, if there are multiple relations between entities, it will be more likely to be noticed when decoding and thus extracted, but this is within a certain threshold range (<=4), when the number of relations continues to increase, our model will also have a performance degradation.  
We will analyze why F1 score is the highest when there are 4 relational triples in a sentence from the perspective of the proportion of entity pair overlap in each subclass. From Figures 2 and 3, we can see that our model is more suitable for dealing with entity pair overlap class than single entity overlap class. If there are more than two relations in the sentence, there will usually be one of two types of overlapping situations. Therefore, we analyze the proportion of entity pair overlap situation in these sentences which contain more than two relations. We count the number of relations that an entity pair contains when there are 2, 3, and 4 relations in the sentence respectively, if the entity pair contains 2 or more relations, it means there is entity pair overlap.  
Table 4 shows the statistical results. From the table, we can see that in the subclass containing 4 triples in the sentence, has the largest proportion of entity overlap, reaching 80%, which is beyond the other subclasses, so we think this is the reason makes the model perform best on this subclass. The more relations are contained in sentences, the more complex the extraction is. When sentences contain more relations (>4), we consider the following two reasons leading to the performance degradation of the model. First, since our model generates relatively independent words rather than sentences with  
Figure 4: Results of Precision, Recall and F1 score of sentences that contains different number of triplets of our model and MultiDecoder on NYT11 dataset.  
contextual contexts, it is relatively weak for LSTM to generate such a long sequence without coherent semantics. Secondly, because the training set contains less than 3% of the sentences with more than 4 relations, the model is not sufficient to learn this situation.
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 7 Case Study and Results
### Results and Discussion  
This section presents a comparative analysis between the baseline Earliest Due Date (EDD) scheduling method and our proposed Predictive Fault-Aware Scheduling (PFAS) method. The analysis is conducted using the job definitions from Table 5, examining both fault-free and fault-present scenarios to demonstrate the effectiveness of fault-aware scheduling optimization.
# A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images
## 2 Materials and Methods
### Data and Experiment Details
#### 2.5.2 False Color Data  
A typical false color image using a 4-3-2 band blends to illustrate UGS characteristics. Select three bands from the HRS data and put them into RGB color in the order of bands 4, 3, and 2, then finally, obtain a false color image.