esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Influence of the Feature Variables on the Classification Accuracy  
#### 3.2.1 Importance Scores of the Variables Used in the RF Classification Algorithm  
Using the \"explain\" function provided by the GEE cloud platform, the importance of each of the feature variables of the random forest (RF) classifier when participating in the classification was determined. The contribution of the variables to the classification results was greater if they had a higher importance score [55]. Figure 4 shows the importance score distribution of the 31 input feature variables involved in the classification. As can be seen from the figure, terrain features and radar features had higher importance scores, while the lower importance scores were for spectral bands, spectral indices, and texture features. Specifically, among the terrain features, the importance of the elevation feature was the highest (801.24), followed by slope feature (789.51), and the importance of the aspect and hill shade were relatively low. Radar features including VV and VH had high importance scores, with average values of 769.23 and 681.5, respectively. Spectral bands B1, B11, B5, B12, B3, and B2 of the Sentinel-2 images had generally higher importance scores during the classification, and their average values were all >560. Among the six spectral indices, the LSWI had the highest importance scores, with an average value of 603.5, which indicated that they were very important for water recognition. The importance scores of the texture features were generally low, among which the NDVI_idm, NDVI_corr, NDVI_asm, and the NDVI_ent were the lowest, with their values all <560.
## 5 Results
For example, we did not consider CondBERT [30] as the authors mention \"Reranks\" replacement words suggested by BERT based on the similarity of suggestions with the word to be replaced, and we consider this a retrieval method. We also left out ParaGeDi [30] since it makes use of a pre-trained paraphraser model, which we consider to be a large generalized retrieval model. Both ParaGeDi and CondBERT outperform our system, given that they use an additional component that enhances their performance. STRAP [29] uses a paraphrase as well, so itwas not considered. Finally, DLSM [27] uses a very different approach (probabilistic-based rather than framework-based) from all other methods. However, both methods perform much worse than MultiLexiconGST: DLSM generates very nonfluent text and STRAP has very weak style transfer. This collection of State of the Art (SOTA) methods is shown in Table 5, where we show how our best model, MultiLexiconGST with a dataset-specific lexicon and an average top-three similarity function, not only outperforms by a wide margin the baseline BlindedGST but also has the best performance among all known competitor models. In addition, MultiLexiconGST also generates the most fluent (FL) and similar sentences to the input (SIM). Lastly, although MultiLexiconGST is behind Mask and Infill [21] or SST [28] in style transfer strength (ACC), these models perform worse in terms of fluency (FL). We attribute this to the abrupt addition of tokens associated with the target style without maintaining fluency. Figure 2: Performance as K increases on the validation set of both Jigsaw and ParaDetox: (**a**) ParaDetox. (**b**) Jigsaw.
## 3 Analysis of Test Results
### Analysis of the Maximum Spreading Radius of the Slurry  
After the termination of the grouting test for 25 s, the maximum diffusion radius R\({}_{\text{max}}\) for different water-cement ratios in the x+, x-, y+, and y\(-\) directions were obtained by organizing the diffusion radius data of the grouting test, as shown in Figure 6.  
As illustrated in Figure 6, the maximum diffusion radius in the x+, x-, and y- directions demonstrated an increase with the expansion of the crack width throughout the grouting time (0 to 25 s). In contrast, the y+ direction exhibits a fluctuating pattern, initially rising and then declining, due to the combined influence of viscosity and gravity.  
At 25 s of grouting, the maximum diffusion radius was observed for a water-cement ratio of 1.0 and a viscosity of 1.8 mPa-s. The maximum diffusion radius in the x+, x-, and y- directions were 18.3 cm, 18.4 cm, and 31.2 cm, respectively, while the y+ diffusion radius was 13.1 cm. As the water-cement ratio changes from 0.5 (viscosity, 33.6 mPa-s) to 0.6 (viscosity, 20.2 mPa-s), the maximum diffusion radius R\({}_{\text{max}}\) increases in each direction. Between 0.6 and 0.8, the maximum diffusion radius value exhibits a gradual growth in each direction. However, between 0.8 and 1.0, the maximum diffusion radius value experiences a decline in the direction of y+ due to the significant influence of viscosity and the effect of gravity.  
The test data demonstrate that the x+ and x\(-\) diffusion laws are consistent, indicating that the diffusion laws in the horizontal directions are approximately equivalent, except for incidental factors such as the crack surface. As the water-cement ratio increases, the maximum diffusion radius also rises, indicating a positive correlation between the slurry water-cement ratio and the diffusion radius in the second lining cracks. This suggests that an increase in slurry viscosity has an inhibitory effect on slurry diffusion.  
The laws of slurry diffusion in the y+ and y\(-\) directions are distinct. In the y\(-\) direction, diffusion is impeded by gravity and the viscosity of the slurry. The penetration and diffusion mechanisms are influenced by the water-cement ratio, viscosity, and diffusion radius. As the water-cement ratio decreases and viscosity increases, the blocking effect becomes more pronounced, mobility declines, and the maximum diffusion radius dimin  
Figure 6: Maximum diffusion radius in different water–cement ratios: (a) horizontal direction; (b) vertical direction.  
ishes; for the y+ direction, a very different pattern from the other directions appeared, and it was found that at water-cement ratios of 0.5 to 0.8, the larger the water-cement ratio, the larger the maximum radius of diffusion of the slurry, and at water-cement ratios exceeding 0.8, the larger water-cement ratio corresponded to smaller values of the maximum radius of diffusion. The variability in the y+ direction suggests that the critical water-cement ratio \(\xi\) = 0.8.
###### Abstract  
Monitoring the relationship between the urban heat island (UHI) effect and land use/land cover (LULC) is of great significance in land use planning to adapt to climate change. However, the dynamic response of the UHI effect to LULC change over space and time has not been deeply studied. In this study, a transfer matrix method was carried out to monitor the class-to-class transitions between different LULC types, as well as those between different NLST (normalized land surface temperature) levels over space and time. The spatiotemporal correlation and dynamic coupling between UHI variation and LULC change from 1987 to 2016 were simulated based on multi-temporal remote sensing data in Wuhan, China. The results showed that high temperature (level V) and sub-high temperature (level IV) were mainly concentrated in construction land, while the majority of low temperature (level I) was distributed in water bodies. During the study period, the most notable changes were the rapid increase in construction land, as well as the continuous shrinkage of farmland and water bodies. The inward transfer of construction land was mainly from farmland and water bodies, with the transferred area of 218.3 km\({}^{2}\) (69.2%) and 78.9 km\({}^{2}\) (25.0%). These transitions were mainly responsible for the thermal deterioration in the study area. The transition of farmland to construction land contributed the most (66.3% and 81.6%) to thermal deterioration in the original medium temperature area (level III). The transition of water bodies to construction land was the main driving force in rapidly upgrading NLST level I into level IV (55.8%) and level V (58.6%). These findings provided detailed information for decision support in optimizing land use structure to fight against the thermal deterioration caused by urbanization.  
**Keywords:** thermal characteristic; spatiotemporal variation; urbanization; contribution; thermal deterioration
# On Edge Pruning of Communication Networks under an Age-of-Information Framework
## 8 Simulation
### Experimental Results
#### 8.2.1 The High-Clustered Topology  
Starting from the simulation results of the high-clustered topology, we see from Figures 10 and 11 the demonstration of the trade-off between the average AoI value and the number of duplicate messages (after a significant time, which is defined as the time until the first copy of the next message is received at the destination node \(d\)). This trade-off is shown when we apply the algorithm that prunes the least significant link of the network \(k\) times shown on the \(x\)-axis.  
We find that as the number of links pruned from the network increases, the total average AoI value increases so that the messages are not fresh enough anymore. However, this pruning process leads to avoiding duplicating the messages received at the destination node \(d\) within a significant time of the arrival of the first message. The figures in the simulation show that the correlation between the number of pruned links and the average AoI of the network is very strong and near the exponential in the high-cluster topology case. In contrast, the number of duplicate messages decreases in a near-linear proportion.  
For the yellow region highlighted in the plots of the simulation, the highlighted area spawns the region of an empirical balance between the number of repeated messages and the total average AoI that the network supports. If the chosen number of the pruned links is within the range of 6-12% of the total number of links in the network.  
Even though the shape of the relationship is nearly the same between different values of \(\sigma\) and \(\mu\), we observe that the higher the value of the standard deviation \(\sigma\), the more sensitive the AoI and number of duplicate messages become for the pruning of the network. Furthermore, the sensitivity increases slightly with the increase in the value of \(\mu\).  
Figure 9: The pruning rate in the deterministic case where \(n=20\), \(s=0\%\). For the left plot \(p=50\%\), for the right plot \(L=1.5\).
## 3 Case Studies
### Sarajevo 5D
#### 3.4.3 Workflow  
The application design process began with consulting historians to select the most interesting cultural monuments which do not exist on their original locations, or their appearance has changed. After selection of 6 monuments we created 3D virtual reconstructions, upon advices of experts for cultural heritage. The graphics designer created the visual appearance of the application and the corresponding map with tracker images (Figure 9). The application can be used in any location, but if the user is not on the location of a monument, the navigation to the place is offered, as well as additional digital content as motivation for physical visit. After tracking by mobile device camera the tracker images on the map, 3D models of selected monuments appear on the screen. The info button opens the panel with basic historical information about the monuments. The application creation workflow is shown in Figure 11.  
Figure 10: Sarajevo 5D application structure.  
Figure 9: The map with AR trackers—Sarajevo5D project.
# Frequency-Dependent Acoustic Reflection for Soil Classification in a Controlled Aquatic Environment
## 5 Conclusions
### Future Research Directions  
To further develop and refine this methodology, future studies should focus on:  
* Expanding the Spectral Database: Establishing a comprehensive reference dataset of spectral signatures for different sediment types to facilitate machine learning-based classification models.
* Analyzing the Effect of Incidence Angle: Investigating how different transducer orientations influence spectral characteristics to improve robustness across diverse seabed geometries.
* Testing in Diverse Marine Environments: Conducting field studies in coastal and deep-sea environments to validate performance under real-world conditions.
* Machine Learning Integration: Developing AI-driven classification systems to automate spectral-based sediment identification and improve real-time processing capabilities.
* Hybrid Approaches: Combining coherent spectral analysis with traditional backscatter methods to develop an integrated multimodal classification framework.  
Addressing these research areas could make acoustic spectral analysis a standardized, high-resolution, and real-time tool for seabed classification. The ability to characterize marine soils based on their unique spectral signatures represents a significant advancement in marine geotechnical science, opening new possibilities for environmental monitoring, offshore engineering, and aquatic habitat mapping.  
Conceptualization, M.G., U.K. and V.F.; methodology, M.G., U.K. and V.F.; software, M.G. and U.K.; validation, U.K. and V.F.; formal analysis, M.G., U.K. and V.F.; investigation, M.G., U.K. and V.F.; resources, M.G., U.K. and V.F.; data curation, U.K.; writing--original draft preparation, M.G., U.K. and V.F.; writing--review and editing, M.G., U.K. and V.F.; visualization, M.G. and U.K.; supervision, U.K. and V.F.; project administration, U.K. All authors have read and agreed to the published version of the manuscript.  
[PERSON] is highly thankful for SCE for the Master fellowship. [PERSON] acknowledges support from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie RISE project EffectFact, grant agreement No. 101008140.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in this article; further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare that this research was conducted without commercial or financial relationships that could be construed as a potential conflicts of interest.
## 2 Model Introduction
### RNN Neural Network  
An RNN is a neural network architecture designed for processing sequential data and finds extensive application in tasks involving sequence modeling, such as language modeling, sequence generation, and time series prediction. Unlike standard neural networks, RNNs may connect the past with the present task. The fundamental idea is that recurrent connections in the network give it memory capacity, which allows it to record dependencies in the time series. The traditional neural network structure comprises the input, output, and hidden layers. The input and output layers handle data input and output, while the hidden layer processes data computationally and predictively. Figure 1 illustrates the structure of a traditional neural network.  
The core of an RNN lies in its hidden state vector, which dynamically updates at each time step of the input sequence, enabling the seamless transfer of information across different time steps. This intrinsic mechanism equips RNNs with the ability to effectively capture short-term dependencies within sequences, as demonstrated in Figure 2. In the figure, \(x\), \(h\), and \(y\) represent the input state, hidden state, and output state of the RNN neuron, respectively. \(u\), \(v\), and \(w\) are the matrices that linearly transform the vectors \(x\), \(h\), and \(y\). The RNN neural network is unfolded, as shown in Figure 3.  
Figure 1: Conventional neural network.  
Figure 2: RNN neural network.  
At time step t, the inputs received by the neurons in the RNN are computed as a combination of the current input \(x_{t}\) and the hidden state \(h_{t-1}\) from the previous time step. The hidden state \(h_{t}\) and the output \(y_{t}\) at time step t are computed as follows:  
\[h_{t}=\sigma(z_{t})=\sigma(Ux_{t}+Wh_{t-1}+b) \tag{1}\]  
\[y_{t}=\sigma(Wh_{t}+c) \tag{2}\]  
Although the RNN has a recurrent structure that captures the sequential nature of the data, it does not perform well when more long-term contextual information is required to solve the current problem. As illustrated in Figure 4, if the prediction of \(h_{t}\) relies on information from distant inputs like \(x_{0}\) and \(x_{1}\), the RNN struggles to associate the relevant information due to the long gap between the target and the required context.
# A New SDN-Based Routing Protocol for Improving Delay in Smart City Environments
## 2 Background and Related Work
### Machine Learning-Based Routing Algorithms
In another type of networks, [PERSON] et al. [30] introduced a Q-learning based multi-objective Unmanned Aerial Vehicles (UAV) routing algorithm to reduce energy consumption and delay. Moreover, [PERSON] et al. [31] presented a range of Deep Learning algorithms used in routing solutions. However, the \"No Free Lunch\" theorem [32] states that there is not just one ML algorithm that can solve any problem, and that always performs better than all other algorithms because it depended on many factors. Furthermore, several factors are involved in choosing the ML algorithm that provides a given situation's proper learning model. We mention the main ones which are the dataset dimension and structure, the type and complexity of the problems, the prediction speed, the precision of the forecasts, the duration of the training step, the size of trained data, the straightforward implementation, and the storage space of training data. Indeed, the works presented in this section opt for various ML solutions, except that these solutions do not meet the real-time requirements of the routing problem in the intelligent environment. Because they take a long time to compute the prediction, and they require massive storage resources due to the network's dynamic scalability. Whereas The Naive Bayes algorithm provides fast training and prediction speed, which meets real-time operating, it performs well with small and big instances; it can handle lots of features and easy to implement. Moreover, it can provide high accuracy predictions using different types of datasets [33, 34]. Thus, this candidate algorithm is a suitable approach that fulfills our expectations to resolve the smart cities' routing issue.
## 3 Marine-Derived Carotenoid Screening  
### In Vitro Carotenoid Screening  
#### 3.1.1 Culture-Dependent Approaches  
Microbial screening refers to the detection and isolation of microorganisms present in a certain sample. Culture-dependent methods in the analysis of microbial communities for further screening purposes, such as identification of specific secondary metabolic properties, rely on the in vitro cultivation of microorganisms obtained from a certain sample [58]. Owing to the immense diversity within the physiological properties of microorganisms, various types of media have been developed in order to support the growth of different microorganisms--which thrive in different natural environments--under laboratory conditions.  
[PERSON] et al. [59] described one method for screening marine-derived carotenoids-producing microorganisms, which involves serial dilution of bacterial samples and 48 h incubation of said samples at 30 \({}^{\circ}\)C in a dish containing seawater yeast peptone agar [59]. In general, culture-dependent screening methods provide several advantages: (1) the ability to assess living microorganisms, (2) the ability to screen viable cells within a sample, and (3) the high sensitivity demonstrated by microorganisms when cultured within appropriate growth media [60]. Despite these advantages, culture-dependent methods would still be insufficient in representing the entirety of the microbial community present in a marine-derived sample, as marine microorganisms have been regarded as notoriously hard to cultivate in vitro [61].  
Numerous obstacles exist when it comes to cultivating marine microbial communities in the laboratory: (1) the imbalance of growth between different microbial species within a community when they are removed from their natural environment, (2) the faulty cellular processes that occur within these microorganisms when placed under laboratory conditions, (3) the probability of viral invasion, which may inhibit bacterial growth, (4) the incompatibility between microorganisms and the substrates present in culture media, and (5) the toxicity induced by high concentrations of media required for detectable growth [61]. Furthermore, even in culturable marine microorganisms, secondary metabolite production may be hindered by the axenic conditions associated with in vitro culturing methods [58]. In general, these factors render culture-dependent methods incapable of accurately portraying a microbial community.  
Figure 4: Variations in marine bacteria carotenoid gene clusters. _Brevundimonas_ sp. carotenoid gene cluster consists of _crtW_, _crtY_, _crtI_, _crtB_, _crtE_, _idi_, and _crtZ_ genes to synthesize 2-hydroxyastaxanthin; _Agrobacteriumium anuntiaum_ consists of _crtW_, _crtZ_, _crtY_, _crtU_, and _crtB_ genes; _Paracoccus zaxanthiniliensis_ consists of _crtZ_, _crtY_, _crtI_, _crtB_, and _crtE_ genes; _Brevundimonas_ consists of _crtB_, _crtI_, _crtK_, _crtU_, _crtYd_, _crtYc_, _crtE_, and _idi_ genes; _Brevundimonas_ scallop_ consists of _crtG_, _crtZ_, _idi_, _crtE_, _crtB_, _crtI_, _crTY_, and _crtW_ genes; and _Algoriphagus_ sp. consists of _crtI_, _crtB_, _crtYcd_, and _crtW_ genes.
## 1 Introduction
[PERSON] and [PERSON] (2006) [40] also used a three-mass vocal fold model for voice simulation in adult males to study the role of individual CT and TA muscle activities on \(f_{o}\). They found that largest \(f_{o}\) change with CT activation was observed at low TA levels. Coming to the posturing features, [PERSON] et al. (2010) [21] used an in vivo canine model to study the role of intrinsic laryngeal muscle activations on vocal fold length. At lower superior laryngeal nerve (SLN) stimulation levels, they observed linear change in length, and at higher levels, the strain reached a plateau. [PERSON] et al. (2012) [30] also included glottal distance at the vocal processes as one of the posturing features. They observed that LCA/IA activation primarily closed the cartilaginous glottis and TA activation closed the mid-membranous glottis. [PERSON] et al. (2017) [41] used an in vivo canine hemilarynx model to study the role of paired intrinsic laryngeal muscles on three-dimensional vocal fold postural changes. They found that combined TA and CT activation yields a rectangular glottal surface, and further addition of TA yields a divergent glottis. The current study focuses on the role that TA and CT muscle activations play in controlling the four acoustic variables (\(f_{o}\), SPL, spectral content, and aperiodicity) and two postural variables (length and tissue fiber stress in the three vocal fold layers). To the best of our knowledge, there is no previous study that focused on the combination of these variables for self-sustained vocal fold oscillation in a finite-element computational model. In this study, we used the fiber-gel finite element model, a muscle activation-based model that produces flow-induced oscillation. The model accepts five intrinsic laryngeal muscle pairs as inputs to set up prephonatory glottal shapes. Lung pressure then drives the vocal folds into self-sustained oscillation. The outputs of the model are many time-varying signals, including oral pressure, oral flow, glottal flow, glottal flow derivative, and transglottal pressure. The findings from the fiber-gel model are compared to the results from human electromyography (EMG) studies ([PERSON] et al. 1989) [32], and in vivo canine model studies ([PERSON] et al. (2012) and [PERSON] and [PERSON], 2016) [30; 31].