esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Another recent analytical framework for the mapping and assessment of ecosystem condition [29] proposed indicators related to environmental pressures (physical and chemical quality) and ecosystem attributes (biological quality) based on a combination of individual metrics. In October 2010, Japan and the EU member states at the Nagoya Biodiversity Summit signed the Convention on Biological Diversity. In order to achieve its EU biodiversity policy goals and to align with international commitments in the Convention on Biological Diversity, in May 2011, the EU adopted the Biodiversity Strategy to 2020. To achieve Target 2: \"Maintain and restore ecosystems and their services\", i.e., the restoration of at least 15% of the degraded ecosystems by 2020, the document proposes that by 2014 each member state should develop a strategic framework for establishing the priorities for the restoration of ecosystems at a national level. In order to respond to the development needs and to contribute to the EU 2020 Strategy, in 2014, the Large Infrastructure Operational Program (LIOP) strategy was elaborated in Romania. Within LIOP, priority axis 4 was established for the promotion of ecological reconstruction projects [30]. Therefore, an assessment and mapping of Romanian degraded ecosystems was necessary. Given the need for composite indicators on ecosystem condition that can reflect the overall quality of an ecosystem asset in terms of its characteristics, the mapping and evaluating of degraded ecosystems in Romania were done. The results of this paper are based on research started in April 2016, comprising detailed assessment for each type of ecosystem. The databases used were provided by various Romanian and European institutions. These were mainly spatial data, statistical data and satellite images. In the first phase, the degradation criteria and indicators for each type of ecosystem, the degradation classes and the sustainability thresholds were established, as well as the limits and the methodology for ecosystem mapping. In the second phase, the mapping and evaluation of the natural ecosystems were performed, as well as the validation of the results. The integration of all data was achieved and completed in May 2021. EU Biodiversity Strategy to 2020 set objectives toward mapping and assessing the state of ecosystems from each member state. The target of this strategy was to restore 15% of degraded ecosystems [30] and the current EU-wide Biodiversity Strategy to 2030 aims to protect at least 30% of land and 30% of sea in Europe [31]. The main objectives of our study were as follows: to define and identify the types of natural and semi-natural ecosystems existing in Romania; to develop a nationally applicable methodology for the evaluation of each type of natural and semi-natural ecosystem; and, in this paper, to assess the complete ecosystem's condition across Romanian territory in order to outline directions for their conservation status.
## 3 Numerical Simulation Analysis
### Establishment and Validation of a Micro-Perforated Finite Element Model
We created finite element simulation models for four foam placement schemes with a thickness of 20 mm, solving the vibration and acoustic radiation performance of the  
Figure 19: Schematic diagrams of foam placement schemes. micro-perforated laminated cylindrical shell in the frequency range of 1-1000 Hz under free conditions. The computed results are displayed in Figure 20.  
From the graph, it can be observed that the trends in structural radiation acoustic power levels for the four placement schemes are essentially the same. Moreover, in the 400-800 Hz frequency range, the curve for Scheme 2 is the lowest, the curves for Scheme 1 and Scheme 3 coincide, while the curve for Scheme 4 is the highest. This is attributed to the fact that the internal point force is located at the center of the inner shell, and the point source is positioned at the center of the inner acoustic field. Therefore, centrally and concentratedly distributed schemes are more effective in attenuating sound waves. Scheme 1 and Scheme 3 are symmetrically placed, and they have the same effect for symmetric external loads. Scheme 4 is symmetrically dispersed, and in this scheme, some foam has no effect on the middle region of sound waves, resulting in a higher structural radiation acoustic power level. From the perspective of overall radiation acoustic power level, in the 1-1000 Hz frequency range, the overall structural radiation acoustic power levels for Schemes 1-4 are 126.9 dB, 126.81 dB, 126.9 dB, and 126.91 dB, respectively. The maximum difference in overall radiation acoustic power level among the four schemes is only 0.1 dB. Therefore, changing the placement location has a relatively small impact on the structural radiation acoustic power level.
## 4 Discussion and Conclusions
### Discussion
#### 4.1.2 Explanations for Further Research  
There are some limitations in this study. The first disadvantage of this paper is that whether the results of our study are valid in other regions is still in doubt. As the study area was the main developed area of Guangzhou city, the POI data were sufficient to support the implementation of the proposed method. However, the applicability of the multi-source geospatial data-driven method in other regions still needs to be studied, especially in non-urban areas, where auxiliary data are relatively sparse. Hence, the proposed method may be more suitable for developed areas with abundant geospatial data.  
Second, the data sources for prompting the fine-scale spatial decomposition research need to be enriched. Dynamic geo-data, such as mobile phone communication data [74], social media check-in data [64; 67; 68], GPS trajectory data [66], and so on, can effectively improve the accuracy of the proposed approach and provide the spatial-temporal information on population distribution on the fine scale, which is very important for issues in urban governance, such as emergency management, public service facility configuration, and so on. In future research, we could focus on revealing the spatial-temporal dynamics of population distribution with more individual-scale trajectory data, such as mobile phone signaling data, non-floating bicycle trajectory data, online car trip hailing data, and so on.  
Finally, although this article discusses twelve commonly used linear regression models and non-linear regression models that were used to perform the experiments, the popular neural network model was not involved. However, studies of the spatial decomposition of demographic data based on neural networks are relatively rare. These issues are worthy of further exploration.
# On the Thermal Stresses Due to Weathering in Natural Stones
## 2 Fundamentals and Methodology
### Heat Flux  
Solar radiation flux \((q_{I})\) is expressed as:  
\[q_{I}=I_{T}\cdot a_{s}, \tag{18}\]  
where \(a_{s}\) is the solar absorptivity.  
Gray-body radiation flux \((q_{R})\) is defined by the Sfetan-Boltzmann law as:  
\[q_{R}=\sigma\epsilon t_{w}^{4} \tag{19}\]  
where \(\sigma\) is the Stefan-Boltzmann constant (\(5.67\cdot 10^{-8}\ \mathrm{W/m^{2}\cdot K^{4}}\)), \(\epsilon\) is the surface emissivity, and \(t_{w}\) is the wall surface temperature.  
The superficial heat exchange due to convection flux \((q_{c})\) can be calculated by [PERSON]'s law of cooling:  
\[q_{c}=h_{o,i}(t_{o,i}-t_{w}), \tag{20}\]  
where \(h_{o,i}\) is the convection heat-transfer coefficient for the outside or inside surface and \(t_{o,i}\) is the outdoor or indoor dry-bulb temperature.  
The convection heat-transfer coefficients for the outside \((h_{o})\) and the inside \((h_{i})\) surfaces are defined as:  
\[h_{o,i} = \frac{Nu\cdot k_{a}}{L_{eq}} \tag{21}\] \[L_{eq} = min\bigg{[}\frac{B}{\sin(\theta_{w})},\frac{L_{b}}{\cos(\theta_{w })}\bigg{]}, \tag{22}\]  
where \(Nu\) is the Nusselt number, \(k_{a}\) is the air thermal conductivity (0.025 W/m\(\cdot\)K ), \(L_{eq}\) is the equivalent length, \(B\) is the building plan width, \(L_{b}\) is the building plan length, and \(\theta_{w}\) is the wind direction.  
The influence of forced air velocity on the Nusselt number \((Nu)\) can be represented by [35]:  
\[Nu=0.036 Pr^{0.43}(Re^{0.8}-9200)\bigg{(}\frac{\mu_{\infty}}{\mu_{w}}\bigg{)}^{ 0.25}, \tag{23}\]  
where \(Pr\) is the Prandtl number and \(Re\) is the Reynolds number. However, the correction of the viscosity \((\mu)\) in Equation (23) could be neglected for gases [36].  
For the natural condition in which the wind velocity is set as zero, the Nusselt number \((Nu)\) is calculated considering natural convection [37]:  
\[Nu=c(Gr\cdot Pr)^{n}, \tag{24}\]  
where \(Gr\) is the Grashof number, \(c\) is a constant, and \(n\) depends on the flow regime (\(1/4\) for laminar flow and \(1/3\) for turbulent flow).  
Finally, the sol-air temperature \((t_{e})\) can be expressed as:  
\[t_{e}=t_{o}+\frac{I_{\gamma}\alpha_{s}}{h_{o}}-\frac{\epsilon\Delta R}{h_{o}}, \tag{25}\]  
where \(t_{o}\) is the outdoor dry-bulp temperature, \(\epsilon\) is the surface emissivity, and \(\Delta R\) is the infrared radiation correction factor. For vertical surfaces, \(\epsilon\Delta R=0\).
# Noise Prediction Using Machine Learning with Measurements Analysis
## 2 Materials
### Methods
#### 2.2.2 Machine Learning Model  
Artificial intelligence has recently attracted considerable attention, and various machine learning approaches have been extensively implemented to model data in numerous applications [19; 20]. For example, a travel time prediction model based on gradient boosting decision tree (GBDT) has been proposed to improve the prediction accuracy of traffic flow [21]. A new extreme gradient boosting (XGBoost) model with weather similarity analysis and feature engineering was proposed for short-term wind power forecasting [22]/ Air quality prediction in smart cities was undertaken using machine learning technologies based on sensor data [23]. This paper presented an innovative gradient boosting decision tree (GBDT) model to explore the joint effects of comprehensive factors on the traffic accident indicators [24]. A method was presented for predicting the broadband noise spectra of horizontal axis wind turbine generators [25] as well as a study on noise sensitivity by machine learning algorithms [26].  
We used the gradient boosting model (GBM) to predict future Leq levels. This model combines fitting functions, loss functions, a decision tree, and gradient descent analysis [9]. The decision tree, error function \(L(F(X_{t}),Y_{t+1})\)[27; 28], fitting function \(F(X_{t})\)[29], and gradient descent analysis were applied to train the model. Specifically, the decision tree algorithm was used to generate a series of fitting functions \(F(X_{t},\beta^{\prime})\).
## 3 Used Datasets and Training Tricks
### Existing Datasets  
The first considered dataset is Mobility Aids, which was created and used by [PERSON] et al. [47]. It contains roughly 17,000 RGB, Depth, and DepthJet images grouped into sequences. The images are split into a training set containing around 11,000 images and two test sets, the first one having around 4000 images and being used for testing the object detection algorithm. The rest of the images (1801), grouped into four sequences, form the second test set, which is used exclusively for testing tracking algorithms, as it contains annotations for both visible and occluded people. The dataset features five different 'objects' of interest that are represented by people with different disabilities (1. person without any handicap; 2. person using crutches; 3. person in a wheelchair; 4. person in a wheelchair that is pushed by another person; 5. person using a walking frame). The sequences are recorded at 15 frames per second (FPS) using both a fixed and a mobile camera placed on a robotic platform at eye level. Figure 5 shows image samples from this dataset. The annotations contain information about the position of the top-left and bottom-right corners of the bounding boxes, the class of the person, and the depth.  
Another dataset that features images recorded at eye level is MOT17 Det [48; 49; 50], which features 14 sequences of images (for a total of 11,235 frames) split into training and test sets at a ratio of around 50-50%. MOT15 and MOT16 have also been considered, but the sequences of interest in MOT15 are already contained in this dataset and, as stated in [PERSON] et al. [50], MOT17 contains all the sequences of MOT16 with more accurate ground truth. Out of the 14 sequences, only seven contain ground truths (which are necessary for training/testing), two of which feature an indoor (or close to indoor) environment. This means that five of the seven sequences with annotations can be used for training and two for testing, MOT17-09 and MOT17-11, both of which were recorded at 30 FPS with a camera positioned at eye-level; however, while MOT17-09 uses a static camera, MOT17-11 has a mobile one. All MOT_Challenge datasets have a single 'object' of interest and that is people. The annotations contain information about the bounding boxes (top-left corner, width, and height), the identity of the detected person, and a visibility ratio in case of occlusion. Samples from this dataset can be seen in Figure 6. The difference between MOT17 and MOT17 Det is that MOT17 Det also provides the ground truth annotations as opposed to MOT17, whose annotations are the output of an object detection algorithm. This makes this dataset suitable for training and testing a people detection algorithm.  
Both datasets have been converted to COCO [51] format to use the same data loader.
# Driver Behavior Classification System Analysis Using Machine Learning Methods
## 2 Driver Behavior Overview
### Driving Behavior Classification Methods  
Several methods and techniques have been used in the literature to detect abnormal driver behavior, such as artificial neural networks, gradient boosting machine [36], dynamic Bayesian network, logic regressions, support vector machine and various machine learning methods: decision tree, random forest, k-NN, SVM and Naive Bayes [37; 38; 39; 40]. They have been used to detect the physiological and visual attributes to identify and to propose distraction reduction techniques. Some of these methods were used to detect the vehicle dynamics to determine abnormal driving situations, such as the double-class classifier based on Gaussian mixture model [41] and hidden Markov models to simulate driver behavior based on car following, lane changes and pedal orientation [42]. Naive Bayes (BN) has also been used to predict driver behavior based on preliminary collected data [43]. Driver behavior is detected based on vehicle velocity and steering wheel data using many sensors including a GPS unit, cameras and ranging sensors (ultrasonic and laser sensors) to monitor the turning patterns of the steering wheel and recognize lane and acceleration patterns. The data are compared to reference data reflecting a normal driving condition to detect abnormal driver behavior [44; 45; 46].
# Microalgae as Potential Sources of Bioactive Compounds for Functional Foods and Pharmaceuticals
## 3 Results and Discussion
### Evaluation of Bioactivities of Microalgal Extracts
#### 3.1.4 Anti-Inflammatory Activity
Two-way Anova and multiple comparisons were achieved with the Dunnett’s test and presented relatively to the control untreated cells in **(A)**, and LPS-stimulated cells in **(B)**. Statistical significance was defined as \(p\leq 0.05\) (*), \(p\leq 0.01\) (**), and \(p\leq 0.001\) (***).
# Topology-Embedded Temporal Attention for Fine-Grained Skeleton-Based Action Recognition
## 2 Related Work
### Fine-Grained Classification  
Fine-grained classification is a popular task, particularly in image classification. Relational research on the task could be divided into the following directions: (1) using deep convolution networks with strong representation abilities, [PERSON] et al. firstly proposed bilinear models [26]. With two deep convolution networks as feature extractors, the model performed strongly because of the representation ability of the deep convolution networks. (2) Localization and recognition methods. In the work by [PERSON] et al. [27], they proposed an NTS-Net, consisting of a navigator agent, a teacher agent, and a scrutinizer agent. The navigator agent finds the most informative regions, and the teacher agent evaluates the regions and then provides feedback, finishing the localization. Final regions are then put into the scrutinizer to extract fine-grained features for recognition; [PERSON] et al. [28] shared a similar idea with [PERSON] et al. [27]. In their work [28], they propose part-based R-CNNs. The network firstly proposes and scores the regions by a detector, and then extracts features on the localized regions for fine-grained recognition, as do [PERSON] et al. in their work [29]. (3) Approaches based on visual attention mechanisms. In the work by [PERSON] et al. [30], RA-CNN is proposed. In their paper [31], [PERSON] et al. proposed a novel attention-based convolutional neural network. With a one-squeeze multi-excitation (OSME) module, the network first learned about the multiple attention region features of each input image, and then the features were utilized for recognition. A diversified visual attention network (DVAN) was proposed by [PERSON] et al. [32]. The network contains four parts: attention canvas generation, CNN feature learning, diversified visual attention, and classification. The first three parts are for localization and the last part is for recognition. In conclusion, attention mechanisms are for localization to some extent.  
To summarize the methods discussed above. It is easy to see that they all share the same goal: finding and magnifying fine-grained features to make data samples more discriminative; this concept has also been applied in our work. However, due to the different data forms, fine-grained features of skeleton-based data samples with higher dimensions than image data not only show intra-frame action differences in spatial dimensions but also show inter-frame action differences in the temporal dimensions. Thus, to solve this, our method can be used to model the topology relationship between bone points in the local area as embedded attention, which can simultaneously pay attention to action differences between and within frames.
## 2 Related Works
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Main Topic and Novelty** & **Corpus** & **Result** \\ \hline \multirow{4}{*}{[19]} & They used contrastive learning and & & The model reached the highest \\  & proposed two models, BERT with & PPI (AIMed) & F\({}_{1}\)-score of 82.9\% on the DDI \\  & contrastive pre-training on the training set & DDI & datasets and an F\({}_{1}\)-score of 82.7\% of the human-labeled dataset and BERT & ChemProt \\  & with contrastive pre-training on the data & ChemProt & and 78.7\% on the PPI (AIMed) and \\  & from the external knowledge base. & & ChemProt datasets, respectively. \\ \hline \multirow{4}{*}{[20]} & This study proposed a lexically aware & & It achieved a remarkable \\  & transformer-based BERT model, which & PPI (AIMed) & performance, with an F\({}_{1}\)-score of \\  & explored both local- and global-context & PPI (BiolInfer) & 74.00\% and 72.80\% on the most \\  & representations for sentence-level & & significant PPI corpus, AIMed, and \\  & classification tasks. & & BioInfer datasets. \\ \hline \multirow{4}{*}{[21]} & They used cross-entropy as the loss & & The overall performance on PPI (AIMed) & (AIMed) and PPI(BiolInfer) datasets \\  & function and applied it to drop out before & PPI (BiolInfer) & can obtain F\({}_{1}\)-scores of 72.10\% and \\  & each fully connected layer during training. & & 73.60\%, respectively. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._patterns [6]. Traditionally, pattern/rule-based methods involve intensive feature engineering; however, our model can automatically converge shallow semantic representations into structurally invariant linguistic patterns [22]. Earlier, a study by [PERSON] et al. [3] used features such as the number of words in a sentence, the number edges in the shortest path between interacting entities, the number of words before an entity mention, etc., as part of semantic representation with decision trees. We aimed to understand the relevance of entity-associated semantic contexts, order of context mentions, and how these features can be employed to improve the identification of bio-entity associations.
## 3 Results and Discussion
### Hierarchical Cluster Analysis (HCA) and Volatile Profiles Development  
The dendrogram obtained from the HCA analysis was used to further differentiate the various dehydration stages (Figure 3). The aroma profiles of the nine samples were split into two big branches, of which branch 1 (S-0) does not cross over with any other eight samples, while S-30 to S-240 were under branch 2. However, among the eight samples, further differential analysis was conducted, whereby S-30 to S-120 were attributed to one subclass, and S-150 to S-240 assigned to another subclass. Each of the subclasses was further divided into another two branches, S-30 to S-90, S-120 and S150-210, S240. Apparently, most of the endogenous volatiles from sugarcane juice disappeared within the first 30 min, so the evaporation or further consumption of the aldehydes in the consequent Maillard reaction could be responsible for the phenomenon. The aroma of NCS developed thereafter, especially from S-150 to S-240, during which the brix increased from 45 to 98 \({}^{\circ}\)Bx, and the boiling temperature varied from 100 to 108 \({}^{\circ}\)C, while the pH decreased from 5.64 to 5.50. An overall dynamic change of the volatile compounds over the dehydration process is shown in Figure 4. Dynamic variations of typical volatile compound classes at each of the nine time intervals are discussed in the following section.  
Figure 2: PCA score plot (R\({}^{2}\)X = 0.977, Q\({}^{2}\) = 0.881) of interval samples from the NCS formation process over time.  
Figure 3: Dendrogram obtained from HCA (Hierarchical Cluster Analysis) of the 111 identified volatile compounds during the dehydration of sugarcane juice to NCS process. (The colour box for each compound indicates the abundance of the compound, the red colour annotates higher abundance while the blue colour annotates the lower abundance).  
Figure 4: Changes of Brix, sucrose, glucose, fructose and water content at different stages during the dehydration of sugarcane juice to NCS products, and the consequent dynamic changes of volatile compounds throughout the process.