esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### BIM Integration  
As an application example the SAVAS prototype system was developed and adapted for renovation projects during the intelligent, self-learning and adaptive buildings (SAVAS) project. This integration of the Building Information Model (BIM) subsystem with the intelligent multi-criteria decision support (SAVAS) system helps professionals make the most rational renovation decision. This solution takes advantage of BIM technology's most significant benefit: creating, storing, and managing comprehensive information about the renovated building. The DALUX software platform was selected as the basis of the BIM subsystem, in which the common data environment (CDE), 3D model (according to the principles of BIM methodology) and information/data management interfaces are combined. This information integration with BIM and possibility of the customisation for SAVAS needs was the most suitable solution with extensive configuration, data/information management functions and face/emotion recognition system application (connection with emotional data). Another benefit of the DALUX software platform is cloud-based storage and accessibility by every project participant. In the SAVAS prototype's common data environment (CDE), a dynamic BIM integration subsystem represents multimodal data obtained and synchronised from SAVAS project participants or data from the analysis of emotion sources as described in previous Sections 3 and 4 (see Figure 11).  
In the SAVAS prototype's common data environment (CDE), a dynamic integrated BIM subsystem represents multimodal data obtained and synchronised from SAVAS project participants or data from the analysis of emotion sources.  
For example, Figure 12 shows the interface window for image recording and processing of detected faces. Figure 13 shows how the video recording system does not identify the face covered with the mask and does not capture the data. Figure 14 shows an example of the collected SAVAS emotional data fragmentation.  
Figure 11: SAVAS view in BIM integration subsystem (plan layout, 3D BIM model and the photo of the face/emotion recognition equipment).  
Figure 14: Example of fragmentation of collected SAVAS emotional data by separate users.  
Figure 12: Interface window for recording video and processing detected faces.  
Figure 13: The video recording system does not identify the face with the mask and does not record data (highlighted in the red circle).  
The parameters of the emotion indicators (such as happiness, sadness, angry surprised, scared and disgusted) collected and analysed in specific zones (considering the structure and interrelationships of the data layers) are linked to the 3D/geometrical elements of the BIM model shown in Figure 15. The DALUX platform enables detailed filtering and analysis of the BIM model parameters of the building, as well as updating and multimodal data after the analysis procedures for renovation decision.
## 4 Experimental Results and Discussion
The intensity of out-coupling can be equalized along the direction of beam propagation by using an out-coupling grating with a variable DE which increases with distance from the in-coupling. Second, in practice, the DE of the out-coupling grating can vary across its area in the case of energy inhomogeneity of the recording beams. Thus, the central part of the grating will have a higher DE than the regions near the edges. Moreover, a strong influence of the rigidity of the \"glasses frame-waveguide\" structure on the sharpness of the out-coupled images was experimentally observed. Deformation of the waveguide leads to aberrations of the images in the form of loss of sharpness or doubling. So, the structure of the glasses frame and its interface with the waveguides should either completely eliminate deformations of the waveguides during the operation of the glasses, or redistribute the stress on the waveguides in such a way as to ensure their minimum value in the central zone of visibility [16]. This is planned to be taken into account in future development. Further, the experimentally obtained images demonstrate the presence of scattering, which has small level, but reduces the contrast of the observed out-coupled virtual images in our system. One of the reasons for its occurrence may be the intrinsic scattering of the used holographic material. In addition, the quality of the surfaces of manufactured PMMA waveguides can also give an impact. The power of a propagating wave is transferred from one waveguide mode to another on the waveguide's imperfect surfaces. For example, small-angle scattering of waveguide modes causes local noise in the virtual image. The intermode distance used in the waveguide holographic AR displays is about several arcmins and, as shown, for example, in [17], even waveguide waviness of about 10-20 \(\upmu\)m per cm of length leads to a very large intermode power transfer and corresponding aberrations of the image. Also, high-frequency spatial power transfer between modes leads to a strong decrease in contrast of the image. Thus, the experimental implementation of the calculated optical system demonstrates the possibility of transmitting an image through a curved concentric waveguide with aberrations comparable in order of magnitude to the case of image transmission through a plane-parallel waveguide.
# Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review
## 4 Remote Sensing-Based Proxies for DRM in Urban Areas
### Economic RS-based Proxies
#### 4.2.1 Macro, Regional, and Urban Economics  
The macro, regional, and urban economics category includes proxies that are associated with the performance of the economy as a whole and its associated changes (Table 4). Land use data provides information about locations and types of economic activities. In the urban context it is one of the most popular proxies used to identify disaster-related economic damage for different economic sectors [47], as well as recovery [49] and vulnerability [108]. For example, [PERSON], et al. [47] applied the building-based land use proxy to identify damages to commercial and industrial buildings when assessing economic damages from the Wenchuan earthquake in China. Similarly, it has been also used for recovery assessment by extracting reconstructed buildings that are associated with economic activities [34; 49]. The land use proxy and corresponding economic activities, their location, public infrastructure, economic assets and economic capacity, has also been used for economic vulnerability evaluations of urban areas [109]. Although land use is a crucial source of information with respect to direct impact on economic activities, it is salient on the detailed economic information, such as type of businesses and operationally of the impacted buildings. Moreover, while land use proxies can capture direct economic damages to physical assets, it provides little information about direct damage to building content (e.g., equipment and furniture) and about indirect damage due to business interruption. Nightlight satellite imagery has been utilized to assess the economic impacts of hurricanes [110], typhoons [111], and tropical cyclone [41]. Nightlight images give an opportunity to compare changes in night light intensity, which relates to electricity availability, for before and after a disaster. It has been shown that a strong correlation exists between night light intensity and GDP [112; 113]. Furthermore, it has also been suggested as a potential proxy for economic resilience assessment [114].
## 2 Results
### Effects of Different Postharvest Calcium on Metabolomics of Fresh-Cut Apples During Storage
Lipoxygenase (_LOX_) oxidizes fatty acids, producing aldehydes and alcohols for scents in apples. Alcohol dehydrogenase (_ADH_) converts aldehydes to alcohols, influencing aromas in apples. Hydroperoxide lyase (_HPL_) cleaves hydroperoxides, generating aldehydes for scents in apples. These genes regulate metabolic pathways, shaping apple aroma profiles. As shown in Figure 6, the expression levels of aroma-related genes _AAT1_, _AAT2_, _LOX_, _HPL_, and _ADH_ in differently treated fruits during storage were considerably different. The overall trend initially exhibited an increase and subsequently decreased. The expression levels of _AAT1_, _AAT2_, and _LOX_ in the T2 treatment during storage were significantly higher than those in other treatments. _AAT1_ and _AAT2_ exhibited significant peak expression on day 6, which were 2.01 and 2.20 times that of the CK group, respectively, while _LOX_ was 1.74 times that of the CK group on day 9. The expression levels of _HPL_ and _ADH_ in the T2 treatment were significantly higher than those in the other treatments, reaching a maximum on days 6 and 9, which were 1.61 times and 1.53 times those of the CK group, respectively. This indicates that the effect of sorbitol-chelated calcium on the expression of fruit aroma substances _AAT1_, _AAT2_, _HPL_, and _ADH_ was remarkable, while the observable effect of calcium nitrate was on the expression of _LOX_. As shown in Figure 7 and Table 4, the total aroma substance content had a significant positive correlation with the expression level of _AAT1_. The ester content was significantly positively correlated with the expression levels of _AAT1_ and _AAT2_. No significant positive correlation existed between the alcohol content and the expression levels of _AAT1_, _AAT2_, _LOX_, and _ADH_. The aldehyde content was positively correlated with _LOX_ and _HPL_, especially _HPL_. Figure 6: Effects of different calcium preparations after harvesting on the expression of volatiles-related genes in apples during storage. CK, T1, T2, and T3 represent the different treatments of control, calcium chloride, sorbitol-chelated calcium, and calcium nitrate. The 0 MCK represents the 0 th day CK group, 3 MCK represents the 3 rd day CK group, and so on.
## 5 Comparison of Different Topologies  
This section compares the different topologies introduced above, namely flat, cluster-based, chain-based and tree-based topologies. They will be compared using the performance metrics that is described in Section 3.
## 1 Introduction
The existing results on control and estimation under information constraints are briefly recalled in Section 2, where the minimum necessary data rate for the estimation and control of Linear Time Invariant (LTI) systems in the form of the data rate theorem is specified and various coding/decoding schemes are described. Section 3 is devoted to the dynamics of two satellites' relative motion in a near-circular orbit. The main result is concentrated in Section 4. This section starts with designing the control law, which ensures the asymptotic regulation of the satellites relative motion (Section 4.1). The design is based on the linearized dynamics model without taking into account the control signal saturation. The classical modal control approach based on the pole-placement technique is employed [36]. The behavior of the system with the saturation in control is studied in the subsequent sections by the simulations. The next stages of the present study are dedicated to the evaluation of the proposed scheme of the inter-satellites data transmission over a digital communication channel, aimed for reducing the necessary channel capacity. To this end, the adaptive coding procedure for the position transmission between the satellites in the formation, employing the kinematics process description, is introduced in Section 4.2. It is worth mentioning that the application of this procedure makes it possible to avoid measuring the time derivatives of the satellites relative position (these derivatives are needed for control) due to the state observer, which is embedded into the adaptive \(\mathrm{coder}/\mathrm{decoder}\) pair. Then in Section 4.3, the model of the erasure communication channel, adopted in the present research, is described. Results of the numerical study of the closed-loop system performance and accuracy of the data transmission algorithm dependence on the communication channel bitrate and erasure probability obtained based on the extensive simulations are presented in Section 4.4. Concluding remarks and the future work intentions given in Section 5 finalize the paper.
## 3 Results and Discussion
### Shear Strength  
Table 1 shows laminated woven bamboo mat boards' internal bonding (IB) strength. Five-ply laminated boards had the highest dry IB strength of 0.89 N/mm\({}^{2}\), while 9-ply laminated boards had the lowest dry IB strength (0.59 N/mm\({}^{2}\)). When the ply number was increased from 5-ply to 9-ply, dry shear strength decreased by 33.7%. In terms of moisture resistance shear strength, after 24 h of immersion in water, laminated boards showed varying degrees of bonding quality degradation. Shear strength values of 0.51 N/mm\({}^{2}\) and 0.53 N/mm\({}^{2}\) were recorded in 5- and 7-ply samples, respectively. However, 9-ply samples had the most severe bonding degradation, with only 0.14 N/mm\({}^{2}\) recorded, a 76.3% reduction from its shear strength in dry state. Shear strength was severely reduced in all ply samples under cyclic boiling conditions, ranging from 0.11 to 0.16 N/mm\({}^{2}\).  
In terms of shear strength, 5-ply and 7-ply laminated boards performed better than 9-ply laminated boards. This could be due to the thickness of the samples, which affects heat transfer to the board's core layer. According to [PERSON] et al. [9], the time required for the core layer of 5-, 7-, and 9-layer plywood to reach 100 \({}^{\circ}\)C was 80 s, 150 s, and 250 s, respectively. Simultaneously, the duration of the first constant temperature range, or moisture vaporization, increased from 360 s to 700 s to 1240 s for 5-, 7-, and 9-ply plywood, respectively. The 9-ply laminated board is very thick and has a negative effect on heat transfer efficiency. Furthermore, the 9-ply laminated board has more glue lines than the 5-ply and 7-ply laminated boards, so its moisture content is higher. The adhesive was unable to flow freely as the time for moisture vaporization increased, preventing effective adhesion. As a result, 9-ply laminated boards had lower bonding qualities than 5- and 7-ply laminated boards.
## 3 Materials and Methods
### Embryotoxicity Potential  
Zebrafish maintenance and spawning have been described in our recent paper [43]. The zebrafish embryotoxicity test (OECD 236, 2013) was conducted to determine the negative potential of tested F3 and F4 fractions. A wide range of serial dilutions of F3 (115-3.59 \(\mathrm{\SIUnitSymbolMicro m}\)) and F4 (62-2.88 \(\mathrm{\SIUnitSymbolMicro m}\)/mL) was tested, at the same time taking care that the concentration of the solvent (MeOH and DMSO) did not exceed 1%. As a negative control artificial water was used, while MeOH and DMSO (1%) were used as a solvent control. Upon 96 h of exposure to tested fractions, mortalities and abnormalities were inspected using an inverted microscope Olympus CKX41, equipped with a Leica EC3 digital camera and LAS EZ 3.2.0 digitising software. The concentrations of fractions that showed no negative impact on zebrafish embryonal development were further used.
## Appendix A
### Quant GAN  
The QGAN model [12] uses a TCN for the discriminator and generator networks. This section describes training for the unconditional QGAN model using notation and conventions from the original paper where possible. The RSQGAN model in Sections 5.1.3 and 5.1.4 uses the same notation for consistency.  
Quant GAN Model  
QGAN adversarially trains a generator TCN against a discriminator TCN:  
\[D:\mathbb{R}^{N_{X}\times T_{RS(D)}}\rightarrow\mathbb{R};D(X;\theta_{d}) \mapsto[0,1] \tag{14}\]  
\[G:\mathbb{R}^{N_{Z}\times T_{\{RS(G)+RS(D)-1\}}}\rightarrow\mathbb{R}^{N_{X} \times T_{RS(D)}};G(Z;\theta_{g})\mapsto\widehat{X} \tag{15}\]  
where \(N_{X},N_{Z}\) are the dimensions of the input data and noise prior distributions; the input data distribution is an \(N_{X}\)-variate time series of RFS length \(T_{\{RS(D)\}}\); the noise prior distribution is an \(N_{Z}\)-variate time series of RFS length \(T_{\{RS(G)+RS(D)-1\}}\); the receptive field sizes of the discriminator and generator are \(T_{\{RS(D)\}}\) and \(T_{\{RS(G)\}}\). The required time dimension of the noise prior used as the input into \(G\) is \(T_{(RFS(G)+RFS(D)-1)}\), so that the output of synthetic data \(\hat{X}\) is of time dimensionality \(T_{RFS(D)}\) for discriminator evaluation.  
The aim is to determine \(\theta_{g}\) such that \(\mathbb{P}_{g}=\mathbb{P}_{r}\), i.e., \(\hat{X}\sim\mathbb{P}_{g},X\sim\mathbb{P}_{r}\), and that  
\[G\big{(}Z;p_{Z}(z),\theta_{g}\big{)}=p_{X}(x)=\mathbb{P}_{r} \tag{111}\]  
Parameters for \(\theta_{g}\) can be learned through an alternating optimisation of generator and discriminator loss functions:  
\[\min_{\theta_{g}}\mathbb{E}\Big{[}\log\Big{(}1-D_{\hat{\theta}_{d}}\Big{(}G_{ \theta_{g}}(Z)\Big{)}\Big{)}\Big{]} \tag{112}\]  
\[\max_{\theta_{d}}\mathbb{E}\big{[}\log D_{\theta_{d}}(X)\big{]}+\mathbb{E} \Big{[}\log\Big{(}1-D_{\theta_{d}}\Big{(}G_{\hat{\theta}_{g}}(Z)\Big{)}\Big{)} \Big{]} \tag{113}\]  
This is equivalent to optimising the _vanilla GAN_ adversarial loss as originally described in [PERSON] et al. [6]:  
\[\min_{\theta_{g}}\max_{\theta_{d}}\mathbb{E}\big{[}\log D_{\theta_{d}}(X) \big{]}+\mathbb{E}\Big{[}\log\Big{(}1-D_{\theta_{d}}\Big{(}G_{\hat{\theta}_{g }}(Z)\Big{)}\Big{)}\Big{]} \tag{114}\]  
Expectations are estimated by sampling a minibatch of size \(M\in\mathbb{N}\).
# UCLAONT: Ontology-Based UML Class Models Verification Tool updates
## 4 UCLAONT Architecture  
There are many tools available for the verification of the UML class model. However, none of them focus on XOR constraints and dependency relationships. They also face scalability issues: their performance goes down when they deal with large and complex models. UCLAONT tool uses the ontology-based approach to verify large and complex association constraints, XOR constraint and decadency constraint of the class model. Additionally, it provides the formalization and verification of various XOR constraints and different types of dependency relationships. The implemented approach in UCLAONT is very prompt and can check the correctness of large models within a few milliseconds. The tool has been implemented in Apache JENA, which is an open-source JAVA-based API. JENA is used for extracting and writing OWL and RDF. It has many inference engines and also provides support for many third-party inference engines. The core of JENA is a JAVA library that manipulates the ontology graph.  
The proposed tool takes the UML class model in the XML Metadata Interchange (XMI) format, that is, XII 2.41. The XII provides the facility of exchanging UML models among the CASE tools in XML format. It provides a common format of UML models for sharing them among different CASE tools. The architecture of the prototype tool is shown in Figure 4. The UML class model in the XII format is given as an input to the tool. The transformation component is responsible for transforming the UML class model into the ontology, so the proposed method can be implemented. The reasoning component is in charge of the verification of the UML class model. Finally, the feedback component generates feedback on the verification result.
# A Denoising Method for Seismic Data Based on SVD and Deep Learning
## 2 Method
### Denoising Method Based on SVD and Deep Learning
#### 2.2.3 MobileNetV2 Network Training  
The data for the production dataset comprised three synthetic seismic data with 267 channels of 2048 sampling points per channel and 31 channels of 4200 sampling points per channel of field seismic data. SVD processed the seismic data, and then the right singular vector was transformed into a graph with a picture size of 224 \(\times\) 224 pixels. Finally, the generated images were divided into two categories, effective signal and noise. It was built into a dataset of 10,468 training photos and 3416 validation images, representing approximately 75 and 25% of the training and validation sets, respectively.  
The network model was trained on the computer using the PyTorch deep learning framework and by setting the batch size to 16. The training network was divided into two phases: the freezing phase and the thawing phase, and the backbone part extracted the generic network features. Freeze training can speed up the training speed and prevent the weights from being destroyed early in training. We set the learning rate to 0.01, and the epoch to 50 in the freeze phase. The learning rate and epoch of the thawing phase were set to 0.0001 and 50, respectively. The accuracy of the training set did not significantly increase after epoch 70 over the 100-epoch training process, and the final accuracy of the validation set was 99.94%.  
The rate of convergence of the MobileNetV2 network for image categorization is shown in Figure 5. The training set had large initial fluctuations and gradually decreased after 20 epochs. The loss of the training set curve after smoothing decreased and gradually smoothened out close to 0 at 70 epochs. According to the validation set's loss curve, the training of the validation set was initially unstable, and the loss value increased for a while. The loss value gradually and steadily decreased after running for about 5 epochs. Finally, at 70 epochs, the loss value approached 0 gradually and smoothly.  
After training the MobileNetV2 network, the network model was used to predict the input images. The input images' prediction results are shown in Figure 6a-c, which are effective signals represented by the right singular vector. The test results are consistent with the manual prediction results. Figure 6d-f are the noise represented by the right singular vector, which presents a cluttered and random fold. The test results are consistent with the manual prediction results, and the network can accurately determine the effective signal and noise.  
Figure 5: Convergence speed of MobileNetV2 during the training process.
# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 2 Intelligent Transportation Systems (ITS)  
ITS is a combination of cutting-edge information and communication technologies for the advancement of traffic management. Examples include traffic signal control, smart parking management, electronic toll collection, variable speed limit, route optimization, and, more recently, connected and automated vehicles. Regardless of the specific application, ITS is typically composed of five major components: traffic sensing, data pre-processing, data pattern analysis, information communication, and control. Other components, such as traffic prediction, may also be necessary for certain tasks. In this section, we review the ITS components and their functions.