esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### NEE, Reco, and Their Interactions with Environmental Variables  
The results for Reco showed a strong positive correlation with Tc and Rn. It is worth noting that these variables are strongly and positively related (R\({}^{2}\) = 0.84). According to previous studies, Tc is an important factor in CO\({}_{2}\) emissions from agricultural ecosystems (30; 31); the same applies to Rn. As Rn and Tc intensify throughout the day, root and microbial activity emits CO\({}_{2}\) into the atmosphere. The maximum Reco occurs at 12:00 p.m. Nevertheless, photosynthetic activity is higher than Reco. In comparison with [PERSON] et al. (27), it was observed that Reco had a weak negative correlation with Ts, possibly influenced by soil texture, ecosystem type, and water regime. In contrast, the seasonal variation in NEE was negatively related to Tc and Rn. Rn plays a crucial role as the primary energy source for plant metabolism. Consequently, when there is an increase in available energy, plants will absorb more CO\({}_{2}\) (Figure 9c). These findings line up with the research conducted by [PERSON] et al. (31).  
Regarding Tc, the results are consistent with those of [PERSON] et al. (29) and [PERSON] (32). They found an inverse relationship between temperature and CO\({}_{2}\) assimilation after surpassing 34 \({}^{\circ}\)C. This is because the rubisco enzyme, which is essential in CO\({}_{2}\) fixation, is susceptible to thermal stress. Thus, the temperature inside the chamber exceeded this threshold at 82 DPS between 10:00 a.m. and 3:00 p.m. This may be a factor in why NEE is at its maximum throughout the season. Additionally, between 2:00 p.m. and 6:00 p.m., there is a 3.9-fold increase in CO\({}_{2}\) emissions (Figure 9a). There is a weak positive correlation between NEE and Ts. Ts, in turn, has a weak negative correlation with Tc. This partially agrees with [PERSON] et al. (31), as they did find a significantly high effect between Tc and Ts.
# Survey: Using Augmented Reality to Improve Learning Motivation in Cultural Heritage Studies
## 2 The Reality-Virtuality Continuum  
[PERSON] and [PERSON] [10] define what AR, VR, and MR are, based on the [PERSON] and [PERSON] Reality-Virtuality Continuum scale they developed (Figure 1). The real world is represented on the left and the virtual world on the right. Thus, AR is closer to the real world, VR to the virtual world, and MR in between as it is the mixture of both AR and VR.  
One of the most accepted definitions for AR is given by [PERSON], who defines AR as a system that [11]:  
1. Combines or overlays virtual objects in the real or physical world
2. Executes in real-time
3. Allows user-interaction with the virtual and real objects  
Although when talking about AR it is common to mention the use of virtual objects like texts, images, and videos, it is also possible to expand the contents to human senses (sight, smell, taste, hearing, and touch) and it can be used to improve or replace senses that the user may have lost [12].  
At first, AR was used only in professional applications such as medical visualizations or repairing complex equipment because initially, it was very expensive in terms of money, implementation, and development, and the only users who were able to invest in this technology were some companies and governments [13]. Nowadays, a number of companies have invested in developing applications for AR (for example, ARToolKit, ARCore, Vuforia, Layar, etc.) which has, in turn, simplified its access for all parties [13; 14] and ensured that AR technology would be even bigger than VR in the future [13].  
Unlike AR, VR creates totally computer-generated digital environments generated in which a user is immersed, and the environment has similarities to reality [15]. In this case, the users are immersed  
Figure 1: The Milgram and Kishino Reality-Virtuality Continuum.  
inside a virtual environment that allows them to manipulate what they perceive as real objects--just as they would in the real world [15].  
MR, on the other hand, refers to the mixture of the two technologies (AR and VR) mentioned above, i.e., there is an interaction between virtual and real objects [10]. To understand the difference between AR and MR, AR overlays virtual objects onto the real world, whereas in the case of MR, the user can not only interact with virtual objects but also with those from the real world as well.  
Although these three technologies are available, from here on, the article will only discuss AR approaches.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### ICT for in-Home and Building Energy Management
#### 4.3.4 eESEH  
The eESEH project, which involved participants from six countries, offered mainly energy awareness and energy management services to 5000 tenants through ICT solutions. For this purpose, energy awareness and management systems have been created in 10 sites in order to provide with a feedback on the energy consumption and reduce consumption peaks respectively. In addition, load management has also been implemented, so as the energy distribution system operator could remotely adjust the customers' load. The project offered the possibility to have data visualization for electricity, gas, heating, cold and hot water. Regarding the telecommunication technologies used,  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Project** & **Aim** & **Characteristics** & **SM Technologies Used** & **Other Technologies** \\ \hline E2 SG & Smart Grid & PMUs used for network monitoring, & Smart Meters with & \\  & Monitoring and & Smart Meters application, optimum & wireless transceiver & \\  & Control & power transfer to the grid & (some cases) & \\ \hline NOBEL & Grid Monitoring & Smart Meters form a wireless sensor & Wireless for & \\  & & network & communication among & \\ \hline Mirubee & Grid Monitoring & Identifying electricity consumption of & & \\  & meters & each device connected to grid & PLC (first link) & Wi-Fi for transmitting \\  & & & & device consumption to \\  & & & & a cloud server \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of projects for Grid Monitoring and Control through Smart Metering and their main characteristics.  
they vary depending on the site. So, NB-PLC communications are chosen for some cases for the data transmission from the meter to data concentrator or wireless solutions for other cases (ZigBee). For data transmission to the control center, wireless solutions like GPRS can be found [84].
## 7 Conclusions  
The proposed PODESCA and SBDMT have been implemented in this paper to tackle the OPSC problem in different-scale RDSs, where the system's total annual cost has been minimized considering different operational constrains. The effectiveness of the proposed methods has been validated by testing them on the 69-bus, 85-bus, and large-scale 118-bus RDSs. Moreover, several comparisons have been performed with different previous methods from the literature, where those comparisons have signified that, comparing to most of the other methods, a lower system's annual cost as well as higher annual savings can be achieved by less amount of total injected reactive power, especially for the large-scale 118-bus system. In addition, the consistency and robustness of the proposed PODESCA have been verified by indicating the small standard deviations and fast convergence rates. Furthermore, the proposed SBDMT has extensively reduced the OPSC problem's search space while keeping the most suitable locations for SC placement, which led to attain better results by the PODESCA with smaller standard deviations and faster convergence rates especially for the large-scale 118-bus system. Thus, the proposed PODESCA and SBDMT are recommended as robust methods for solving the OPSC problem. However, more investigations are suggested to check PODESCA's performance on other engineering applications, and further tests might be explored on the SBDMT to effectively reduce the search space of the optimal planning of DGs problem as well.  
[PERSON] and N.F.A. did the methodology, simulation and validation. [PERSON] and N.F.A. did the analysis and wrote the paper. Conceptualization, R.J.M., N.F.A. and Y.S.; software, R.J.M., N.F.A. and Y.S.; investigation, R.J.M. and N.F.A.; resources, R.J.M., N.F.A. and Y.S.; data curation, R.J.M., N.F.A. and Y.S.; writing--original draft preparation, R.J.M.; writing--review and editing, N.F.A., Y.S., H.H.A., P.S. and M.P.; visualization, R.J.M., N.F.A., Y.S., H.H.H.A., P.S. and M.P.; supervision, Y.S. and P.S.; project administration, Y.S.; funding acquisition, Y.S. and H.H.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 61673161, and in part by Six Talent Peaks High Level Project of Jiangsu Province under Grant 2017-XNY-004.  
The authors declare no conflict of interest.
## Materials and Methods  
To investigate the effects of the drivers flooding duration, wave forcing, competition, and Creek formation on spatial patterns in salt marshes we looked at it at three levels. We (1) examined which vegetation properties are affected by these drivers, we (2) investigated how this translates to reflectance, and (3) used satellite data to map the large scale effects of the drivers to see how large scale spatial patterns are affected.  
To examine which vegetation characteristics are affected by the mentioned drivers (1) we compared the influence of these drivers with in situ measurements using linear regressions. To translate between vegetation properties and reflectance (2) we used the radiative transfer model ProSail [42]. This model was calibrated with detailed in situ vegetation measurements, and validated with independent vegetation measurements. The linear regressions of step 1 were not used in this model. We used this model to evaluate the effect of plant and canopy characteristics on vegetation reflectance, by simulating spectra using the range of the in situ measured values and examining the size of their effect on the simulated spectra. Finally, (3) we applied the model to a satellite image and combined it with the estimated effects of the drivers behind spatial patterns to examine their impact at a large scale.  
For the first analysis (1), we used the entire range where the study species (_S. anglica_) occurs, as this does not depend on optical data. For the other analyses (2 and 3), where optical data is used, we selected only plots where the study species is dominant, to avoid mixing reflectance of multiple species.
# HERMES CubeSat Payload Thermal Balance Test and Comparison with Finite Volume Thermal Model
###### Abstract  
Scientific payloads onboard CubeSats usually have complex geometries and occasionally narrower allowed temperature ranges with respect to the rest of the spacecraft. In these cases, the capability to correctly predict the thermal behaviour of the payload once in orbit is mandatory. To achieve this ability, a thermal balance test is required to correctly identify the thermal model of the payload. The test consists in the application of different external thermal boundary conditions together with the addition of heat dissipation to simulate the thermal load produced by active electronics during operation. Those experimental data are fundamental to validate the numerical thermal model and make its predictions reliable. This paper presents the configuration and procedures of the thermal balance test performed on the Demonstration Model of the payload to be embarked on each satellite of the HERMES constellation. The test data is compared with the results of a finite volume thermal model of the payload, proving the application of this method to be reliable for space thermal analyses. The obtained test results show the temperature jumps caused by the heat dissipation applied to active components. A weak correlation between the payload interface and internal equipment has been observed, thus proving that the payload is almost decoupled from the Service Module temperature variations. Based on test outcomes, some modifications in the payload design have been implemented, with the aim to lower the operative temperature on critical, temperature-sensitive equipment.  
CubeSat; thermal balance test; Gamma Ray detector; OpenFOAM; thermal model 22 March 2023  
22 April 2023
## 2 Materials and Methods
### Experimental Design
#### 2.3.1 Training Phase  
In the first part of each session, subjects performed 20 trials. Each trial consisted of a sequence of three mental tasks: MI of the gait, idle state and regressive count. For idle state, participants were asked to be as relaxed as possible. The regressive count was randomly changed every trial and consisted of a number between 300 and 1000 and a subtrahend between 1 and 9. For example, if they were given the count 500-4, they had to compute the series of subtractions of 496, 492, 488... until they had to perform the following task. This task aims to focus the subject on a demanding mental task very different to MI in order to assess a low level of attention to gait. The protocol can be seen in Figure 2a. There was a voice message that indicated the beginning of each task: 'Relax', 'Imagine', '500-5'. The message for the regressive count indicated a different mathematical operation  
Figure 1: Experimental setup.  
each time. In order to avoid evoked potentials, the 4 s period after auditory cues was not considered for further analysis.  
During the session, subjects used crutches to maintain stability. In addition, a member of the research staff softly held the exoskeleton to prevent any possible loss of balance or fall. Ten of the training trials were performed in a full no-motion status and the other ten in a full motion status assisted by the exoskeleton. These trials were employed to train two different BMI classifiers: StandClassifiers (with non-motion trials) and GaitClassifiers (with full motion trials).
## 2 Materials and Methods
### System Design
#### 2.1.2 Swarm Aggregation
The initial threshold is 100% agreement directly following the initial process of assigning watchers to presenters and taking a vote of all presenting agents. If this threshold is met, the prediction is considered \"Very High Confidence\", the prediction and confidence are returned and the swarming period ends. When there is no immediate agreement, agents perform an interaction period with all of the \"watchers\" assigned to the \"presenter\" and with the \"presenting\" agent. This interaction step follows the same protocols outlined in Section 2.1.1. This step facilitates additional information dispersal between agents. Following the interaction step, a new group of presenting agents is randomly selected and the agents go through the same steps as previously outlined. This process can run for an additional 100 iterations with the threshold for agreement lowered to 90%; if the 90% threshold is met at any point the prediction is considered \"High Confidence\" and the swarming period ends. When neither of the above thresholds are met, the decision threshold is further reduced to 75% for an additional 50 iterations of selection, voting, and interaction. If the 75% threshold is met the prediction is \"Medium Confidence\". However, if the threshold has still not been met, a weighted vote is taken from all presenters and watchers, and the prediction is \"Low Confidence\". The weighting is based on the agent's certainty score, with agents more certain in their opinion given more weight than those who are less certain. The confidence thresholds were initially selected using the breast cancer dataset described in Section 2.2.1 based on experimentation with the goal of 100% accuracy for the \"Very High Confidence\" interval. The number of iterations for each threshold was selected to balance classification performance with runtime. A future goal is to automate the selection of threshold values for each dataset and classification constraints. For example, some usage scenarios may prefer more samples captured in the \"Very High Confidence\" interval while having a lower average accuracy for that category. The confidence categories can be summarized as:  
* Very High Confidence: All presenting agents agree on a prediction class immediately following fitness proportionate selection,
* High Confidence: 90% of presenting agents agree on a prediction class within 100 iterations of the swarming process,
* Medium Confidence: 75% of presenting agents agree on a prediction class after 100-150 additional iterations of the swarming process,
* Low Confidence: Weighted vote of presenters and watchers if above thresholds are not met.
###### Abstract  
Day-ahead forecasting of solar radiation is essential for grid balancing, real-time unit dispatching, scheduling and trading in the solar energy utilization system. In order to provide reliable forecasts of solar radiation, a novel hybrid model is proposed in this study. The hybrid model consists of two modules: a mesoscale numerical weather prediction model (WRF: Weather Research and Forecasting) and Kalman filter. However, the Kalman filter is less likely to predict sudden changes in the forecasting errors. To address this shortcoming, we develop a new framework to implement a Kalman filter based on the clearness index. The performance of this hybrid model is evaluated using a one-year dataset of solar radiation taken from a photovoltaic plant located at Maizuru, Japan and Qinghai, China, respectively. The numerical results reveal that the proposed hybrid model performs much better in comparison with the WRF-alone forecasts under different sky conditions. In particular, in the case of clear sky conditions, the hybrid model can improve the forecasting accuracy by 95.7% and 90.9% in mean bias error (MBE), and 42.2% and 26.8% in root mean square error (RMSE) for Maizuru and Qinghai sites, respectively.  
WRF (Weather Research and Forecasting) model; kalman filter; hybrid model; clearness index; solar radiation forecasting +
Footnote †: journal: Applied Sciences
# Research on Crack Width Measurement Based on Binocular Vision and Improved DeeplabV3+
## 2 Crack Region Segmentation Algorithm  
### Improved DeeplabV3+ Algorithm  
In this paper, we choose to improve on the DeeplabV3+ semantic segmentation model to achieve higher segmentation accuracy for crack regions. The improvements include the following four areas:  
1. Modify the Xception feature extraction network to the L-MobileNetV2 network structure.
2. According to the HDC (Hybrid Dilated Convolution) strategy, an improved DenseA-SPP module (IDAM) is designed to replace the ASPP (Atrous Spatial Pyramid Pooling) structure in the original model.
3. ECA (Efficient Channel Attention) mechanism is introduced to modulate the weight of channel information before splicing 1/4 shallow feature layers and 1/16 deep semantic information.
4. Introducing Focal loss and Dice loss functions to optimize the loss function.  
The improved DeeplabV3+ model network structure is shown in Figure 1.
## 3 Experimental Results and Analysis  
### Experiment Setup  
#### 3.1.1 Benchmark Datasets  
In this study, the ICVL [39] hyperspectral dataset serves as the benchmark for experiments. The ICVL dataset contains 31 spectral bands and was captured by the Specim PS Kappa DX4 hyperspectral camera (manufactured by Specim, Spectral Imaging Ltd., Oulu, Finland) with a spatial resolution of 1392 \(\times\) 1300, comprising a total of 201 images. To construct the training data, overlapping patches are extracted from the original images, with each patch treated as an independent training sample. Each sample has a spatial size of 128 \(\times\) 128 and a spectral dimension of 31. For data splitting, 100 images are allocated for training, 5 for validation, and the remaining images for testing. To improve the network's generalization capability, data augmentation techniques, including rotation and scaling, are applied, generating approximately 10,000 training samples in total. During testing, 128 \(\times\) 128 \(\times\) 31 patches are cropped from the main regions of each test image to balance computational cost with evaluation performance.  
Additionally, to thoroughly evaluate the robustness and generalization capability of the network, systematic experiments were conducted on several representative remote sensing hyperspectral datasets. These datasets span various sensor types, spatial resolutions, and land cover categories, enabling comprehensive validation of the network's performance across different real-world scenarios. Four representative hyperspectral datasets were selected for the experiments: the Pavia University dataset [40], the Washington DC Mall dataset [41], the Urban dataset [42], and the Indian Pines dataset [43].
# ODIN112-AI-Assisted Emergency Services in Romania
## 4 Results  
### Automatic Speech Recognition  
We have trained and tested several models on various subsets of presented datasets (see Table 6. The most basic training dataset was SWARA [42]; the second training dataset was Romanian Read-Speech Corpus (RSC) [4]; the third one was the one collected by us using Echo, whereas the final one was trained using all available data. For the test experiments, we used the evaluation partition of RSC and selected 10% of the other datasets with no transcript overlap between train and test sets. For all experiments, we used the same language model to reduce its impact.  
The metric used for evaluation is the Word Error Rate (WER) derived from the Levenshtein distance, which measures the difference between two sequences of words. It represents the errors in a transcript over the total number of words spoken. It can be computed as:  
\[WER=\frac{S+D+I}{N}=\frac{S+D+I}{S+D+C}\]  
where \(S\) is the number of substitutions, \(D\) is the number of deletions, \(I\) is the number of insertions, \(C\) is the number of correct words, and \(N\) is the number of words in the reference (\(N=S+D+C\)).  
WER is not a perfect metric, but it is an objective way of measuring the accuracy of various speech systems. WER does not account for the type or cause of the error, whereas the transcript can still be understood by a human even if the WER is high. For example, in certain conditions, a misspelled or misrecognized word is a better output than a missing word.