esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 4 Comparison of Forest AGB Maps over Large Regions
### Southeast Asia  
[PERSON] et al. [19], [PERSON] et al. [18], [PERSON] et al. [103] and [PERSON] and [PERSON] [77] provided similar forest AGB spatial distributions across Southeast Asia, while [PERSON] et al. [99] and [PERSON] et al. [100] provided similar results especially in the tropical regions, where AGB was considerably higher than the other datasets (Figure 7). In southern China, AGB was less than 60 Mg/ha, the lowest among the eight forest datasets ([PERSON] et al. [54]; Figure 7). Across southeast Asia, the majority of AGB estimates in [PERSON] et al. [54] ranged from 30 to 120 Mg/ha, which was far from realistic (Figure 8). Additionally, there were substantial discrepancies in the spatial and frequency distribution between AGB from this dataset and from other datasets (Figures 7 and 8). Similarly, differences between the [PERSON] et al. [105] dataset and the others were also quite significant.  
Figure 8 showed that among the eight forest AGB maps over Southeast Asia, the [PERSON] et al. [103] and [PERSON] and [PERSON] [77] datasets were quite similar, with the majority of AGB estimates from about 50 to 230 Mg/ha. The [PERSON] et al. [103] dataset was generated by the fusion of the [PERSON] et al. [19] and [PERSON] et al. [18] datasets using the bias removal and weighted linear averaging method, and should represent a more precise AGB map across the pan-tropical regions. The use of a similar method, also suggests more precise AGB estimates from the [PERSON] and [PERSON] [77] dataset for Southeast Asia. The percentage curves of [PERSON] et al. [99] and [PERSON] et al. [18] showed two peaks corresponding to AGB of about 150 Mg/ha and 300 Mg/ha, respectively. For the [PERSON] et al. [100] dataset, most pixels had AGB of around 250 Mg/ha, and compared with other datasets, more pixels had AGB higher than 350 Mg/ha.  
Figure 6: The average value of the fuzzy numerical index in eight forest biomass maps of evergreen needleleaf forest, deciduous broadleaf forest, mixed forest, and woody savannas over the conterminous U.S.A.  
For evergreen broadleaf forests, AGB maps were spatially in agreement expect for the [PERSON] et al. (2015) and [PERSON] et al. (2010) datasets (Figure 9). However, in the mixed forest and especially in woody savannas, spatial differences in these AGB maps were evident. AGB for mixed forest in [PERSON] et al. (2018) was quite different from other mixed forest maps, and there were large discrepancies between the [PERSON] et al. (2010) map and other datasets for woody savannas.  
Figure 7: Remote sensing-based forest biomass maps across Southeast Asia.
# Coproduction as an Approach to Technology-Mediated Citizen Participation in Emergency Management
## 3 Design Case Study: The Participative Roles of Citizens in EM
#### 3.1.1 First Study: The Challenges of Integrating Social Computing in EM  
In August 2011 a study involving 36 emergency workers and decision-makers from British Columbia and Washington State was carried out in the city of Vancouver as part of a series of workshops about the use of social networks in EM organized by the City of Richmond and Simon Fraser University. The goal of the study was to find out the main challenges EMAs faced to integrate social computing within their working protocols. Participants belonged to very different agencies, with different sizes and resources what guaranteed a varied perception of the real situation. After a short introduction and discussion on the use of social media in EM, participants filled a questionnaire made up of closed questions that explored the utility and potential problems of using them in their daily practice. A detailed description of the study and its findings can be found in [34] but, as a summary, there were three main obstacles identified: a) they had to make sense of a huge quantity of messages, not all of them equally informative and reliable, since most citizens lack the technical knowledge required to analyze the situation and provide an accurate picture useful for the professionals; b) they had to give an immediate answer to the messages generated by the citizens, for which not all of the agencies had enough resources; and c) they had to deal with a) and b) whilst being efficient in their response. During the workshop discussions participants highlighted the need for democratizing EM by involving citizens, but the results of the survey also confirmed what the literature consistently points out about lack of trust and the quality of the information generated by citizens [1; 7; 9]. A second questionnaire was filled after a short presentation of a specific software prototype called remerWeb that was used to illustrate with a real example how to integrate a community of volunteers into the EM process as a first step to move to a coproduction service. In this case, the closed questions explored how the prototype would improve communication, coordination, and integration and also the technologies and approaches that could be used to engage communities in EM. As reported in [35], the main barriers identified were about trust and reliability, information processing and integrity, and interaction support. We will revisit these issues in the findings of both studies. We will revisit these issues in the findings of both studies.
# Measurement of Sub-Surface Microstructures Based on a Developed Ultrasonic Atomic Force Microscopy
## 3 Results and Discussion
### Experimental Results
Three different lines show the displacement of the piezo scanner in Z direction required to keep oscillation frequency of cantilever constant and here, use such curves to show the results of subsurface topography. It can be seen from Figure 8 that the raster-like lines are obtained in the surface scanning of the probe. Figure 8 studies the impact of different buried depths on the measurement results under the same grating height. The measured grating depths in three scans are 452 nm, 37.5 nm and 14.6 nm, respectively. According to the result, The HOPG film thickness difference between the first sample and the second sample is 365 nm, and the measured subsurface grating depth difference between that is 414.5 nm. Meanwhile, the HOPG film thickness difference between the second sample and the third sample is 33.664 um, but the measured depth difference is only 22.9 nm. It means that the measured grating depth and the buried depth do not show a simple linear relationship. With the increase of the buried depth of the microstructure, the value of the measured subsurface depth gradually decreases, which verify the aforementioned simulation results. Then, we focused on the result of measuring the width of the subsurface microstructure. It is known that the width of the silicon wafer grating is 2 um, but the measured slot width of the subsurface microstructure is 3.08 um that is a little bit larger than 2 um according to both the simulation and the experimental results. This can be interpreted as the relatively small separation distance between the subsurface microstructures, at this time, the stiffness of the surface location without subsurface microstructure between the intervals of the locations with subsurface microstructure will also be affected by the subsurface microstructure. This means that two adjacent defects will be coupled to each other and act on the stiffness of the surface between them, and finally change the contact resonance frequency of the probe on its surface. It is worth noting that although the three samples in Figure 8 have the same microstructures with different buried depths of, the positions of the detected subsurface microstructures are almost the same, demonstrating the capability of the CR-AFM to detect the subsurface microstructures. Even if the thickness of the HOPG film (buried depth of the subsurface microstructure) reaches a large value of 34.469 um, it is possible to detect the location of the subsurface microstructures by the developed CR-AFM. Figure 8: The measurement results of subsurface microstructures using the CR-AFM. (**a–c**) show the subsurface line scan results of the samples with 485 nm, 805 nm, and 34.469 μm HOPG film thicknesses, respectively.
## 5 Performance Evaluation
### Numerical Results
In addition to that, the effect of node speed on the control overhead is illustrated in Figure 16c. It can be seen that when the node speed and the number of nodes increases, the control overhead increases. The reason is that the network density increases when nodes increase, resulting in more frequent packets to establish routes. Therefore, increased node speed or increased number of nodes requires more resources to establish a multicast route; consequently, the control overhead will be increased. Despite these challenges, it can be proved that the proposed DLSMR+TD-PSO protocol with RPGM mobility model has good scalability and effectively manages to improve the PDR, routing delay, and control overhead as the number of nodes increases. Figure 15: Comparison of packet loss ratio and number of packet losses as a function of node speed.
# Temporal EEG Imaging for Drowsy Driving Prediction
## 3 Approach
### Classification Using the CNN Model  
This study applies a CNN including six convolution layers, three max-pooling layers, and a layer to the classification of the input EEG image, as shown in Figure 8. A popular open source deep learning framework named Caffe is employed for implementing the CNN model [40]. The parameter setting of the overall CNN architecture is presented in Table 1. A set of filters is used to convolve the input EEG images for feature extraction. The convolved images are then subsampled by the max-pooling layer to derive compacted features. The convolution and pooling progress are repeated several times through CNN layers. The lower-level features of the input data are extracted via the early layers, and those features are collected in the later layers to hierarchically learn the higher-level features. Finally, the acquired high-level features are concatenated and passed into the fully-connected layer for the classification. The final prediction result is determined according to the output of the fully-connected layer. We only use the alert and drowsy classes in this study, so the output size of the fully-connected layer is \(2\times 1\).
# A Reinforcement Learning Approach to Speech Coding
## 7 The Value Function  
The goal of the overall control problem is to reproduce speech at the Decoder that sounds as close to the original input samples as possible. This performance evaluation can only be determined by human listening tests or objective performance criteria that are representative of human listening tests as captured by ITU-T standard software that compares the input speech to the reconstructed sequence [30], after the entire utterance has been reconstructed. We use the ITU-T standard software [30] as the final performance indicator (Value Function) in this work, which generates the PESQ-MOS, a quantity between 0 and 5, higher values being preferred. We call the PESQ-MOS the Value Function. This objective comparison of the full input utterance with the full reconstructed utterance is a strong indicator of perceptual quality and intelligibility similar to what would be obtained with large-scale human listening tests. The latter are too expensive and too difficult to organize and too complicated to conduct except in the validation of a final design for establishing an International Standard. We note that all of the speech samples used in this paper are taken from the ITU-T P.501 standard [31].  
A PESQ-MOS value of 3.5 or greater is desired, even when the entire utterance including the VAD/CNG, which is used for silence intervals, is compared to the original utterance. See Appendix D for a discussion of VAD/CNG. A PESQ-MOS value of 3.5 or better is associated with good quality, intelligible speech and is about the performance obtained in general use by cell phones; however, since our Reinforcement Learning method is only used for Voiced speech, the most important Value Function is the PESQ-MOS over the Voiced segments only. Both Value Functions are used for performance evaluation in the following. Note also that the PESQ-MOS scores are coupled with the bit rate, equivalent to the control sequence energy, in order to obtain a meaningful Value Function. This coupling is necessary because an higher bit rate constraint, which corresponds to an higher constraint on control sequence average energy, should always lead to better performance, reflected here as PESQ-MOS.
# Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11
## 3 Materials and Methods
### Model Architecture  
YOLOv11, the latest version of the YOLO series, is a state-of-the-art real-time object detection model which is designed to surpass its predecessors in both accuracy and speed. An upgraded backbone network for better feature extraction is one of the innovations new innovations in YOLOv11. Along with that, to improve precision, optimized anchor boxes and refined loss functions are used. It concentrates on the important areas in an image and uses a new path aggregation network by using a transformer-based attention mechanism which enhances multi-scale feature fusion. Architecture of YOLOv11 (You Only Look Once) in Figure 6 is a modern deep learning model for real-time object detection. This model is used to increase accuracy and efficiency based on the context of infrastructure fault detection.  
In this study, we utilize YOLOv11 [12], an object detection model developed by Ultralytics, as the core of our fault detection pipeline. We clarify that YOLOv11 is not proposed or developed by the authors; rather, it is an existing deep learning framework that has demonstrated strong performance in real-time object detection tasks. We contribute by implementing and evaluating this model, specifically when it comes to detecting infrastructure defects. Our goal is to evaluate the model's effectiveness and pinpoint its flaws in real-life infrastructure defect inspection scenarios by modifying it to our personalized dataset. The model starts with input images of infrastructure faults as broken manholes, damaged structures, faulty staircases, etc. Then, the feature extraction process initializes where these standard convolutional layers are that extract features from the input images. To process the input image as well as decrease the spatial dimensions and increase the depth of the feature map with each layer, multiple convolutional layers are used. Additionally, complex convolutional layers are used to achieve deep feature extraction. Shortcut = False and n = 3x or n = 6x parameters are used as the configuration of the layers. The concatenation feature maps are there to combine information from different  
Figure 6: Proposed YOLOv11-based architecture.  
levels of the network at different stages. The C2 PSA module uses spatial information in feature maps that combines multi-scale features. It is utilized before passing the data to the detection layers. The detection layers create bounding boxes and class probabilities for object detection by using final convolutions and post-processing. The model produces the detected results with bounding boxes and labels overlaid on the images, indicating the identified infrastructure faults.
## 3 Experimental Results and Discussion
### Basic Physical Parameters
#### 3.1.1 Effect of High Temperature on Mass  
Based on the high-temperature treatment, the mass loss of the ordinary concrete and the basalt fiber-reinforced concrete is presented in Tables 3 and 4.  
The high-temperature treatment test results show that the mass of the two kinds of concrete decreases with the increase of the temperature. The mass loss of concrete is mainly due to the evaporation of free water before the temperature reaches 200 \({}^{\circ}\)C. When the temperature reaches 400 \({}^{\circ}\)C, the bound water in the concrete begins to precipitate, while the CaCO\({}_{3}\) in the concrete will resolve into CaO and CO\({}_{2}\) when the temperature reaches 800 \({}^{\circ}\)C.
## 5 International Standards and Current Regulations  
This section explores the relationship between AW applications in RC elements and existing international standards and regulations. While the use of AW in construction has gained attention for its sustainability potential, current codes and standards (e.g., BS8110 [25], ASTM C330 [37], ACI 318 [72]) often fail to comprehensively address the integration of these innovative materials. This limitation presents challenges in ensuring compliance and optimizing structural performance in practical applications.
## 5 Feasibility and Evaluation
### Architectural Comparison
#### 5.5.9 Simplicity  
As a CBA, EDAS separates concerns into functional components. At the component level, separating concerns make it easier for developers to isolate, understand, verify and develop functionalities with fewer complexities [28]. However, at the system level and size of the monitored environment, its complexity is increased as more components are added. This notion also affects agent-based, centralized and distributed architectures. End-user architectures are comparatively simple to design, develop and implement due to the fewer number of components involved.
## 3 Results
The classification results obtained for the speckles collected at laser wavelengths  
Figure 5: (**a**) Speckle intensity correlation as a function of wavelength drift bandwidth; (**b**) Classification accuracy for datasets collected for the two cases discussed in the text: DNN trained at 800 nm wavelength and tested at data collected at different wavelengths (red circles) and DNN trained with data that include the perturbation (blue circles); (**c**) Classification accuracy for a DNN trained in the presence of 12 nm bandwidth drift and tested at data collected outside this range. All the results are obtained through the same GRIN fiber (10 cm, CIF625, Thorlabs GmbH, Germany). outside the 12 nm range follow the trend observed in the first case of experiments discussed before, as shown in Figure 5c. Based on these results, it is found that training DNNs to infer the inputs from speckle patterns in the presence of wavelength drift cannot assist their performance outside this range. In other words, the trained model does not generalize to new wavelength values which lie outside the bandwidth used for training. Once crucial parameter usually suggested for improving the performance of the DNNs is the number of available samples in the dataset. At the same time, this parameter is considered to be one of the drawbacks of deep learning, because there are cases for which the number of available data is limited. In order to assess whether the dataset used to train the VGG classifier in the case of wavelength drifting is sufficiently large, we investigate the classification accuracy for training datasets of different sizes. The dataset of 10,000 speckle images is shuffled in a random way and each time only a part of the total dataset is used for training and testing the DNN. The classification results are presented in Figure 7. The classification accuracy is observed to vary with the size of the training dataset. It is apparent from the values reported in Figure 7 that the more severe the wavelength drift, the more samples are needed for achieving higher classification accuracies. Nevertheless, for all the tested wavelength bandwidths, a saturation of the classification improvement is observed while increasing the size of the dataset; for the largest bandwidth (96 nm), this happens at about 6000 samples, as suggested by Figure 7. Interestingly, the accuracies measured for each bandwidth saturate at values of total available samples that are below 10,000, which implies that an increase in the dataset size would not significantly improve the results. Figure 6: Normalized confusion matrices showing the classification results for the case that the drifting wavelength is: (**a**) not part of the training set and; (**b**) is included in the drifting wavelengths for which the VGG classifier is trained. Figure 7: Classification accuracy of the VGG-type DNN classifier used in the experiment for increasing the number of available samples in the training dataset.
## 2 Materials and Methods
### Expression of Recombinant MlongTPS29 in Escherichia coli and Enzyme Assays  
The coding sequence of _MlongTPS29_ was cloned into the prokaryotic expression vector pET28a using the homologous recombination method. Briefly, _MlongTPS29_ was amplified with primers containing homology arms. The forward primer was 5'-_CAAAATGGGTCCGGTTCAAAGTGTTTAGTG-3', and the reverse primer was 5'-GGCCGCAAGC _TTGTCAGACTCATGCAAAGGGCTCGAAT-3'_ (Italic indicates homology arms). The pET28a vector was digested with the restriction endonuclease _Bam_HI and _Sal_I. Then, the homologous recombination was performed with a Trelief(tm) SoSoo Cloning Kit Ver.2 (Tsingke, Beijing, China) according to the manufacturer's instructions. The recombinant vector was transformed into _E. coli_ BL21 (DE3), and the expression of recombinant MlongTPS29 was induced by addition of isopropyl-\(\beta\)-D-thiogalactoside (IPTG) to a final concentration of 1 mM. After cultured at 16 \({}^{\circ}\)C for 20 h, the cells were collected by centrifugation and washed twice using reaction buffer (50 mM HEPES, pH 7.5, with 5 mM MgCl\({}_{2}\), 2 mM MnCl\({}_{2}\), 200 mM KCl, 5 mM dithiothreitol, and 10% (\(\
u\)/\(\
u\)) glycerol). Then, the cells were resuspended in reaction buffer and disrupted by sonication. After centrifugation at 16,000\(\times\)\(g\) at 4 \({}^{\circ}\)C for 15 min, the supernatant was collected and used for further enzyme assays.  
The enzyme activity of MlongTPS29 was detected according to an earlier report with minor modification [42]. Briefly, the supernatant of _E. coli_ with recombinant MlongTPS29 was added to a 200 \(\upmu\)L reaction mixture, and then 10 \(\upmu\)M of GPP was added to initiate the reaction. The reaction mixture was incubated at 30 \({}^{\circ}\)C for 1 h. Products of the reaction were extracted with dichloromethane and then detected by an Agilent 8860/597/B GC-MS system equipped with a DB-5 MS column (30 m \(\times\) 0.25 mm i.d.). The oven temperature was isothermal at 45 \({}^{\circ}\)C, then increased at a rate of 10 \({}^{\circ}\)C/min to 220 \({}^{\circ}\)C, and maintained at 220 \({}^{\circ}\)C for 2 min.