esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussion
### Ablation Experiment
#### 6.3.3 Advantage of Using a Transformer  
Ref. [43] has demonstrated that transformers may perform poorly on some small datasets, specifically those with several million entries. This raises a pertinent question for our study: Why is the transformer used for the reason generation task in GCLTE instead of Recurrent Neural Networks, which are more commonly employed in NLP tasks? In response to this query, we have replaced the transformer module in GCLTE with a GRU, known for its simplicity and effectiveness in RNNs, while keeping the encoder unchanged. We introduce the variant, GCLTEGRU, utilizing the encoder's hidden state as the initial state for the GRU.  
As shown in Table 7, the performance of GCLTEGRU in the rating prediction task remains nearly identical to that of GCLTE, as anticipated. There is a noticeable degradation in text generation quality, indicating that the complex hidden states of GCN-based neural networks are not ideal inputs for GRUs. In contrast, transformers are more effective in decoding embeddings with complex relationships than GRUs. Research earlier in this paper demonstrated that complex embeddings positively impact the reason generation capabilities of transformers. Consequently, GCLTE achieves superior results by utilizing a transformer as the decoder.
## 3 Methodology
### Pyramid Self-Learning Fusion Module  
To effectively capture multi-scale contextual information in images and enhance the segmentation ability for complex objects, we propose an adaptive pyramid pooling fusion module, as shown in Figure 4. This module optimizes feature fusion through multi-scale context aggregation and dynamic weight learning. The specific design is as follows:  
(1) Pyramid Pooling Operation: The module first applies the pyramid pooling operation to both local and global feature channels to extract contextual information at different scales. Specifically, for the input feature map \(x_{k}\), we perform pooling at different scales:  
\[pyramid\_feat_{k}=AvgPool(x_{k})\quad w.r.t\quad k\in\{1,2,3,\dots\}, \tag{11}\]  
where \(x_{k}\) denotes the feature maps at different pooling scales.  
(2) Feature Dimension Matching: To reduce the number of channels and match the feature dimensions, the multi-scale features obtained from pooling are processed through a \(1\times 1\) convolution:  
\[pool\_feat_{k}=Conv2d(pyramid\_feat_{k},channels_{out}), \tag{12}\]  
where \(channels_{out}\) is the output number of channels after the convolution.  
(3) Feature Concatenation and Weighted Fusion: The pooled multi-scale features are fused through concatenation:  
\[fused\_feat=Concat(pool\_feat_{1},pool\_feat_{2},\dots). \tag{13}\]  
Subsequently, we design a weighted fusion mechanism, which explicitly fuses local and global features through a \(1\times 1\) convolution and learnable normalized weights:  
\[fused\_output=Conv2d(fused\_feat)\cdot Softmax(weights), \tag{14}\]  
Figure 3: The architecture of the global feature extractor based on Swin transformer in the parallel feature encoder.  
where _weights_ are the normalized weights learned to dynamically adjust the contribution ratios from different feature sources. This mechanism can dynamically adjust the contribution of different feature sources based on the task requirements, thus improving the expression ability of fused features, enhancing the network's perception of features at different scales, and ultimately improving the accuracy and robustness of semantic segmentation for remote sensing images.
###### Abstract  
Water-use efficiency (WUE) is not only an important indicator to connect the carbon and water cycles of a terrestrial ecosystem, but also a key parameter for an ecosystem to respond to climate change. It is crucial for understanding the mechanism of regional ecosystem response to environmental change by researching the influences of vegetation and climate change on WUE variation and revealing its drivers. Based on trend analysis, grey relational analysis, and ridge-regression analysis, this study analyzed the spatiotemporal variation characteristics of WUE in Inner Mongolia (IM) from 2001 to 2018 and determined the dominant influencing factors of WUE variation. The results showed that the annual mean WUE in IM was 1.39 g C m\({}^{-2}\) mm\({}^{-1}\) and it generally presented a rising trend, with an increasing rate of 0.0071 g C m\({}^{-2}\) mm\({}^{-1}\) yr\({}^{-1}\). Leaf-area index (LAI) and precipitation were the most important factors influencing WUE in IM, followed by relative humidity and wind speed. Temperature, water vapor pressure and sunshine duration slightly influenced WUE and they were relatively less important. According to the ridge-regression analysis, LAI, precipitation and relative humidity had a positive contribution to WUE variation, while the wind speed had a negative contribution. Regionally, LAI was the dominant cause of WUE variation. The contribution and relative contribution rate of LAI to WUE variation were 0.008 g C m\({}^{-2}\) mm\({}^{-1}\) yr\({}^{-1}\) and 44.57%, which were significantly higher than those of precipitation, relative humidity, and sunshine duration. Thus, vegetation primarily dominated WUE variability during the study period. The relative contribution rate of LAI varied across the different vegetation types and ranged from 25.26% in swamps to 51.29% in meadows. Our results improve the understanding of the effects of driving factors on WUE, which can help policymakers with water resource management and ecological restoration.  
WUE; LAI; grey relational analysis; ridge regression; Inner Mongolia 2022
# Thermo-Optic Numerical Research on Segmented Circular LD Arrays Side-Pumping a Nd:YAG Laser Rod
## 3 Results and Discussion
### Thermo-Optic Effects  
Based on the temperature-dependent variation in the refractive index, we investigated the optical distortions caused by the thermal effects of laser rods in the three different side-pumping schemes. The wave-fronts, after a planar wave passed through the laser rods, are illustrated in Figure 13.  
Figure 12: The average temperature profile of the laser rod in the compensated side-pumping scheme.  
Figure 13: The wave-fronts after a planar wave passed through the laser rods in the (**a**) segmented, (**b**) liquid-cooling, and (**c**) compensated schemes.  
The optical distortion in the segmented side-pumping scheme shown in Figure 13a is close to that in the annular liquid-cooling scheme as shown in Figure 13b; both are a near-spherical wave, from which the influence of heat is interpreted as the behavior of a thick lens. The optical distortion in the compensated scheme shown in Figure 13c has an asymmetrical wave-front that is regarded as formed by the two spherical waves of different curve radii in the \(x\) and \(y\) directions. The curve radius in the \(x\)-direction is close to that in Figure 13a,b but that in the \(y\)-direction is rather long, which implied that the thermal lens of the laser rod is a bifocal lens with different focal lengths in the \(x\) and \(y\) directions.  
According to the numerical results above, the performance of the laser rods in the three side-pumping schemes is summarized in Table 4.
# Poly(lactic acid)-Based Electrospun Fibrous Structures for Biomedical Applications
## 4 Electrospinning of PLA Fibers
### Solvent Systems
The mechanical properties of the electrospun fibers are also affected by the solvent system (Figure 5). Figure 5: SEM images of electrospun yarn structures from PLA solutions using different solvents; **(a)** TFE (unpublished original picture by the authors), **(b)** DCM (unpublished original picture by the authors), **(c)** CHCl\({}_{3}\) (unpublished original picture by the authors), **(d)** Stress–strain curves of electrospun structures (unpublished original picture by the authors). The difference in surface morphologies as well as in crystallinity influenced by solvent vapor pressure and dielectric constant can influence the mechanical properties of the electrospun structures. [PERSON] et al. [37] found that the electrospun PLLA yarns produced from the solvent with a low evaporation rate like TFE showed a higher degree of molecular orientation and crystallinity, and smaller diameter, and consequently higher modulus and tensile strength (Figure 5).
## 6 Application of the Optimization Techniques: Routing Protocols
### Comparison  
Hierarchical and geographic routing protocols are considered scalable solutions. Keeping a hierarchical structure demands the coordination of nodes by means of transmitted messages. In dense networks, the use of the cluster-based structure makes up for this cost. However, this benefit does not hold in small networks. A similar behavior is observed for geographic approaches. When the network is composed of a significant number of nodes in an extended area, the exchange of messages to establish the location of neighbors becomes negligible compared to the reduction of transmissions that the geographic algorithm achieves. In these two approaches, the topology of the network must be stable. On the contrary, the cluster structure and the geographic information must be frequently updated which leads to additional costs. For instance, in stable networks, PEGASIS is usually more efficient than LEACH. However, the construction of the chains in PEGASIS could lead to significant resource consumption in highly dynamic topologies.  
Attribute-based techniques become relevant when the data sensed by the nodes are not usually of interest to the rest of the nodes. Under these circumstances, the algorithms could greatly reduce the network overhead. The decision about which algorithm should be selected mainly depends on the data delivery model that the application forces. When the communication should be triggered by events, SPIN is the most suitable attribute-based algorithm. However, Directed Diffusion, Rumor, COUGAR and ACQUIRE are query-driven protocol. They roughly differ in how the query is propagated and resolved in the network.  
Concerning the multipath routing protocols, their main disadvantage lies on the cost of maintaining the paths. This cost comprises memory resources as well as network overhead. Therefore, they are not appropriate for networks critically constrained by their reduced batteries. However, they become necessary when reliability is a strong requirement in the application business.
## 2 Materials and Methods
### Training Process of the Obtained Images  
The newly obtained images were used to train a k-NN (k-nearest neighbors) classifier [32]. During the training process, specific points around the follicular region (brown, white, and purple areas) were labeled to develop a model based on these annotations. The images were scanned to gather the necessary data for training. The k-NN algorithm was used to train the model, with the parameter k set to 3 based on experimental evaluation. This value was chosen as it provided the best balance between model accuracy and computational efficiency during preliminary testing (k = 3) [33]. Manual labeling of specific regions was essential for developing the machine learning model. Once the classes were defined, the model automatically applied them to all images.
# Large Scale Implementations for Twitter Sentiment Classification
## 4 Sentiment Analysis Classification Framework
### Algorithmic Description  
In this subsection, we describe in detail the sentiment classification process as initially implemented in the Hadoop framework. We adjust an already implemented MapReduce A&NN classifier to meet the needs of opinion mining problem. Our approach consists of a series of four MapReduce jobs, with each job providing input to the next one in the chain. These MapReduce jobs are summarized in the following subsections Pseudo-codes are available in a technical report in [49].  
Furthermore, as the next step in the specific subsection, we consider the implementation of the sentiment classification algorithm in the Spark framework. The approach consists of a single Spark program that runs in parallel. The logical flow of our solution can be divided, as previously, into the abovementioned four consecutive steps:  
* **Feature Extraction:** Extract the features from all tweets in \(T\) and \(TT\),
* **Feature Vector Construction:** Build the feature vectors \(F_{T}\) and \(F_{TT}\), respectively,
* **Distance Computation:** For each vector \(u\in F_{TT}\) find the matching vectors (if any exist) in \(F_{T}\),
* **Sentiment Classification:** Assign a sentiment label \(\forall t\in TT\).  
The records provided as input to our algorithm have the format \(<\)tweet_id, class, text \(>\), where class refers either to a sentiment label for tweets in \(T\) either to a no-sentiment flag for tweets in \(TT\). In the following subsections, we describe each MapReduce job separately and analyze the Map and Reduce functions that take place in each one of them.
## 3 Results
### LDA Topic Classification  
#### 3.2.1 Grid Search of the Optimal Number of Topics  
LDA requires modelers to specify the number of topics. In this study, an optimal topic number should result in the model having the highest topic coherence score and the largest cosine similarity distance between topics. A two-stage grid search was used to find the optimal number of topics in the 3743 articles. In the first stage, the model set was calculated in steps of 10 in the range of 3-93 (i.e., 3, 13, 23... 93), and each topic was repeated 30 times to avoid the influence of random resampling in the LDA. Each model was evaluated by topic coherence measures. As shown in Figure 3, the first-stage grid search program shows that when the number of topics is 33, the coherence score is the largest (0.3680), and the second largest value is achieved with 23 topics, with a score of 0.3667. Based on the two largest topics obtained in the first stage, we carried out the grid search process in the second stage with a topic range of 13-43 and step size of 1 (i.e., 13, 14, 15,..., 42, 43). Figure 4 shows that in the second stage of the grid search process, when the number of topics is 29, the topic coherence score reaches the maximum (0.387), and the optimal numbers of topics seem to be 29, 31, and 34.  
Next, we evaluated the quality of the topic modeling to select the optimal number of topics. We used K for 29, 31, and 34 to draw the pyLDAvis plots of the LDA model for comparison. As shown in Figure 5, each bubble in the figure represents a topic, and the larger the bubble is, the more common the topic is. A good topic model will display quite large non-overlapping bubbles throughout the chart, rather than being clustered in one quadrant. Models with too many topics usually have a lot of overlap, and small-sized bubbles are concentrated in one area of the chart. The words and bars on the right represent the most salient terms in the corpus. When K = 29, pyLDAvis bubbles are more evenly distributed in the quadrant with less overlap, which shows that 29 is the optimal number of topics we are looking for.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Outlets** & **Percentage** \\ \hline IEEE Transactions on Power Systems & 8.58\% \\ IEEE Transactions on Smart Grid & 7.16\% \\ International Journal of Electrical Power \& Energy Systems & 6.81\% \\ IEEE Transactions on Sustainable Energy & 5.82\% \\ IEEE Transactions on Power Electronics & 5.56\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The five most frequent outlets in the field.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Research** & **Percentage** & **Counts of Topics Covered** \\ \hline Engineering & 100.00\% & 29 \\ Energy \& Fuels & 13.01\% & 29 \\ Computer Science & 9.30\% & 28 \\ Science \& Technology—Other Topics & 8.79\% & 28 \\ Telecommunications & 8.68\% & 27 \\ Automation \& Control Systems & 7.10\% & 26 \\ Instruments \& Instrumentation & 5.98\% & 23 \\ Physics & 2.83\% & 23 \\ Operations Research \& Management Science & 1.52\% & 19 \\ Transportation & 1.26\% & 17 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Top ten Web of Science (WoS) research areas.
# Path Planning of Inspection Robot Based on Improved Ant Colony Algorithm
## 3 Improved Ant Colony Algorithm
### Improved Pheromone Update Rule and Mathematical Analysis of Convergence  
#### 3.4.1 The Pheromone Update Formula in the Original Ant Colony Optimization Algorithm  
In the classical ant colony optimization algorithm, the pheromone update formula is as follows:  
\[\omega_{ij}(t+1)=(1-\sigma)\cdot\omega_{ij}+\omega_{ij}(t)+\Delta\omega_{ij}(t) \tag{14}\]  
where \(\omega_{ij}(t)\) represents the pheromone concentration between node \(i\) and node \(j\) at time \(t\). \(\sigma\): the pheromone evaporation rate, denoted as \(\sigma\), satisfies (0 < \(\sigma\) < 1). \(\Delta\omega_{ij}(t)\): the additional pheromone quantity accumulated by all ants on the path (\(i\), \(j\)) during this iteration is defined as:  
\[\Delta\omega_{ij}(t)=\sum_{k=1}^{m}\Delta\omega_{ij}^{k}(t) \tag{15}\]  
\[\Delta\omega_{ij}^{k}(t)=\frac{Q}{L_{k}} \tag{16}\]  
where \(Q\) is the pheromone intensity coefficient, and \(L_{k}\) represents the path length found by the k-th ant.
# Optimal Meshing Degree Performance Analysis in a mmWave FWA 5G Network Deployment
## 5 Experimental Simulation and Results
In Equation (1), \(MAD\) is the median absolute deviation defined by Equation (2), where \(x_{i}\) is the set with the simulated values of a performance metric and \(m\) is its median value. \[CV=\left(1.4826\times\frac{MAD}{m}\right)\times 100 \tag{1}\]\[MAD=median(|x_{i}-m|) \tag{2}\]  
Each network topology was simulated 10 times, and we calculated the median (i.e., median gain) and the \(CV\) (risk) of each metric for each topology across all the iterations. Note that the performance of 10 independent iterations was sufficient for obtaining realistic and statistically meaningful values for the scenario considered in this paper. The results in Table 1 summarize the medians and \(CV\)s, denoted as \(M\) and \(C\), respectively, for each metric and in each topology, denoted as \(T\), in the table. Moreover, in the next subsections, we use box plots to show the distribution of the resulting values over all simulations for each metric, where the bottom and top edges of the plotted boxes (representing the metric distributions) indicate the 25 th and 75 th percentiles, respectively, whereas the central marks are the medians. The outliers were plotted individually and represented with the '+' symbol.
# ADLBiLSTM: A Semantic Generation Algorithm for Multi-Grammar Network Access Control Policies
## 4 Experiments
### Model Structure and Evaluation Metrics  
In this paper, four experimental groups were set up to demonstrate the superiority of the proposed model. The first group was the baseline group; that is, the BiLSTM-CRF model. The second group added another layer of BiLSTM to the baseline to confirm the performance improvement of the double-layer BiLSTM in extracting contextual features of security policies. The third group added a self-attention mechanism to the baseline to verify the performance improvement of the self-attention mechanism in generating semantic representations of security policies. The fourth group was the model proposed in this paper, ADLBiLSTM, which adds a self-attention mechanism and uses double-layer BiLSTM to extract contextual features of security policies. The specific model structure is shown in Table 3.  
The models in the four experimental groups were all trained using Data_mix in Table 1, and then the semantic representations of security policies from three vendors, Cisco, Huawei, and Ruijie, were generated respectively. To evaluate the performance of the models, this paper adopted common evaluation metrics, namely precision (_P_), recall(_R_), and \(F\)1 score(_F_1). We defined _TP_ as the number of correctly generated semantics, _NP_ as the number of incorrectly generated semantics, and \(N\) as the total number of semantics that should be generated. Therefore, these three metrics can be defined as follows in Equations (8)-(10)  
\[P=\frac{TP}{TP+NP} \tag{8}\]  
\[R=\frac{TP}{N} \tag{9}\]  
\[F1=\frac{2 PR}{P+R} \tag{10}\]  
In the experiments, the dataset used was simulated based on usage frequency, resulting in a small amount of data for some types, which may lead to inaccurate evaluation of the model's performance. Therefore, we conducted multiple experiments and calculated the metrics to improve their stability and reliability.
# The Usage of Virtual and Augmented Reality in Underwater Archeology
## 5 Use of Augmented and Virtual Reality in Underwater Archeology--Example Solutions  
### Virtual Science  
The modeling of the interior of the \"Pepper Wreck\" took place mainly in the period 1996-2000 ([PERSON], 2008). This wreck was a Portuguese merchant ship that sank at the mouth of the Tagus River in September 1606. It was named because of the amount of pepper found in its hull. The wreck currently lies at a depth of 9 m ([PERSON], 2011; [PERSON], 2011).  
The main recipients of the created virtual application were scientists. The app was supposed to help in understanding how the internal space of this ship was arranged and used, particularly in terms of the likely configuration of the cargo carried ([PERSON], 2015). It helped in understanding a complex environment that was very difficult to study using conventional 2D imaging techniques. The visualization helped to obtain data that are much closer to reality and allowed users to examine the reconstructed artifact. The resulting environment dramatically improved the ability to understand spatial relationships, including cargo distribution and nuances of rigging, as well as other ship elements ([PERSON], 2011; [PERSON], 2011). The created application could be used on a specially designed simulation station equipped with large screens. This was similar to the idea in the CAVE configuration ([PERSON], 2015). After testing by scientists, the system was also made available for educational purposes ([PERSON], 2015).
## 3 Results and Discussion
### The Influence of Various Factors on the Dynamic Performance of RFR
#### 3.2.3 The Effect of the Parallel Relationship between the Tie Rods and the Rotor  
The tie rods and the rotor are in a parallel relationship in the structure, and the tie rods are subjected to pre-tension. It is necessary to study the effect of the flexural stiffness of the tie rods on the natural frequency of the rotor. The rate of change of natural frequencies of the rod-fastened rotor considering the effect of the parallel relationship of the tie rods and the rotor with the increase of the preload, corresponding to situation D, is given in Figure 15.  
It can be seen from Figure 15 that the parallel relationship of the tie rods and rotor slightly increases the natural frequencies of the rotor system. The parallel relationship between the tie rods and the rotor has a relatively larger influence on the first-order natural frequency of the rod-fastened rotor. With the increase of preload, the effect of the parallel relationship between the tie rods and rotor on each order natural frequency gradually decreases. In addition, the influence coefficient of the preload of the tie rods \(\beta^{\prime}\) has little influence on each order natural frequency of the rotor system. Only when the preload is larger than \(5\times 10^{4}\) N does it have a relatively larger influence on the first-order frequency of the rotor system.