esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The 3D A2A MIMO Channel Model
### Specular and Diffuse Components
#### 2.2.1 Lengths and Angles of the Rays
The horizontal distances between the \(n\)th scatterer and the Tx and Rx array centers are obtained as  
\[\tilde{d}_{1,n}=-h_{t}\tan(\theta_{n}) \tag{37}\]  
\[\tilde{d}_{2,n}=\sqrt{\tilde{d}_{1,n}^{2}+D^{2}-2\tilde{d}_{1,n}D\cos(\phi_{n })}. \tag{38}\]  
**Proposition 2**. : _The time-varying travel distance between the Tx and Rx via the nth ray, incorporating the effects of UAV movements and antenna vibrations, can be calculated as_  
\[d_{n}(t) \approx d_{n}-v_{r}t\sin\vartheta_{n}\cos\alpha_{r}-v_{t}t\sin \theta_{n}\cos\alpha_{t}\] \[-a_{r}\sin(2\pi f_{r}t)\cos\gamma_{r}^{(n)}-a_{t}\sin(2\pi f_{t}t )\cos\gamma_{t}^{(n)} \tag{39}\]  
_where \(\gamma_{t}^{(n)}\) is the angle between \(\mathbf{a}_{t}\) and the departure direction of the nth ray and \(\gamma_{r}^{(n)}\) is the angle between \(\mathbf{a}_{r}\) and the arrival direction of the nth ray. These angles are determined as follows:_  
\[\cos\gamma_{t}^{(n)}=\frac{\mathbf{a}_{t}^{T}\mathbf{\Phi}_{n}}{|\mathbf{a}_{ t}|\cdot|\mathbf{\Phi}_{n}|} \tag{40}\]  
\[\cos\gamma_{r}^{(n)}=\frac{\mathbf{a}_{r}^{T}\mathbf{\Psi}_{n}}{|\mathbf{a}_{ r}|\cdot|\mathbf{\Psi}_{n}|} \tag{41}\]  
_where \(\mathbf{\Phi}_{n}\) and \(\mathbf{\Psi}_{n}\) are the departure and arrival angle unit vectors of the nth ray. Additionally, \(d_{n}\) is the travel distance at the initial time instant. For the specular ray, the travel distance is calculated as_  
\[d_{0}=\sqrt{(h_{t}+h_{r})^{2}+D^{2}}. \tag{42}\]  
_Furthermore, the travel distance of the nth diffuse ray is calculated as_  
\[d_{n}=\frac{-h_{t}}{\cos(\theta_{n})}+\frac{-h_{r}}{\cos(\theta_{n})} \tag{43}\]  
_where \(n=1,...,N\)._  
The proof of (39) follows a similar procedure to the proof of (10) and is omitted for brevity. By substituting (39) into (27), the CIR of the NLoS components of the A2A model between \(A_{p}^{t}\) and \(A_{q}^{r}\) is expressed as  
\[h_{qp}^{\text{NLoS}}(t,\tau) = \sum_{n=0}^{N}\sqrt{P_{n}}\cdot e^{-j\frac{2\pi}{\lambda}d_{n}} \cdot e^{j\xi_{n}}\cdot e^{j2\pi(\gamma_{t}^{(n)}+\gamma_{t}^{(n)})t} \tag{44}\] \[\tilde{d}_{1,n}=-h_{t}\tan(\theta_{n}) \tag{37}\]0 \[\tilde{d}_{1,n}=-h_{t}\tan(\theta_{n}) \tag{37}\]1where \(v_{r}^{(n)}\) and \(v_{r}^{(n)}\) are the Doppler frequencies associated with the \(n\)th ray due to the motions of the Tx and Rx, respectively, which are given as  
\[\tilde{d}_{1,n}=-h_{t}\tan(\theta_{n}) \tag{37}\]2 \[\tilde{d}_{1,n}=-h_{t}\tan(\theta_{n}) \tag{37}\]3  
It can be observed from (44) that the time variation in the NLoS components results from the UAV movements and antenna vibrations at both ends of the link.
# Detection of Cavities from Dental Panoramic X-ray Images Using Nested U-Net Models
## 4 Conclusions  
Dental cavities are one of the most prevalent and chronic diseases worldwide. The early detection of dental caries lesions can prevent tooth damage and save expensive healthcare costs. U-Net is one of the CNN architectures that has become most popular and widely applied in medical image segmentation. However, there are a few studies on applying the U-Net or CNN-based U-Net architecture in dental caries detection.  
Although the dental panoramic image is clinically helpful for the diagnosis of dental diseases such as caries, it is difficult to interpret because it contains such a large area of anatomy with numerous superpositions of the hard and soft tissue structures of the region. A limited number of datasets with their ground truth labeling are available for deep learning training and validation. Thus, in this work, we extended the Ivisonlab DNS Panoramic Dataset by annotation of the cavity in X-ray images and used them as ground truth to train the U-Net modules.  
Moreover, for cavity detection, this work utilized the U-Net 3+ model, which is a modified version of U-Net and U-Net++ by combining the multiscale features by redesigning the skip connections and using full-scale deep supervision for more accurate medical image segmentation. The results of cavity segmentation was compared with those of different U-Net architectures and different data-splitting ratios. According to these extensive experiments, data splitting did not affect or enhance the performance of the model. U-Net 3+ performed better compared to U-Net and U-Net ++, with 95% segmentation accuracy. The reason for this is that U-Net 3+ utilizes full-scale skip connections, and deep supervisions were used; the use of dense and nested skip connections in the contract prevented U-Net and U-Net++ from detecting enough information from the full scale (i.e., cavity).
## 5 Simulation Results and Analysis
### Performance Comparisons under Different Parameters
of successful transmission of the V2V links in the proposed scheme decreases. This is because compared to the low-speed state, when the vehicle is in the high-speed state, the environmental state changes more significantly, leading to higher observation uncertainty and reducing the learning efficiency. Therefore, when the vehicle speed gradually increases, the V2V links' successful transmission probability declines. However, the proposed scheme can still maintain a higher probability of successful transmission, which shows that the proposed algorithm has better stability in a highly dynamic environment. In order to more clearly explain the reason why the solution in this article is better than the random solution, we randomly selected an episode in the test and plotted the changes in the remaining load of the V2V links over time. Among them, the delay constraint T = 100 ms, and the payload size is 3 \(\times\) 1060 bytes. Since in the randomly selected episode, all V2V links in the proposed scheme and the random scheme have completed the transmission task within 50 ms, we only show the data within 0-50 ms. Comparing Figure 5a,b, it can be seen that although all V2V links of the proposed scheme and the random scheme have completed data transmission within the delay constraint, the transmission time required by the scheme of this paper is much shorter than that of the random scheme. As shown in Figure 5a, all vehicles in the scheme in this paper are transmitted within 15 ms, while the random scheme in Figure 5b is completed within 42 ms. This shows that the solution in this paper is more suitable for transmitting delay sensitive services and meeting the delay requirements of the V2V links. Figure 4: V2V payload transmission success probability with varying vehicle speeds. Figure 5: _Cont_. Figure 6 reflects the impact of V2I power changes on V2I throughput and the probability of successful V2V link transmission. It can be seen that with the increase in V2I power, the throughput of the V2 link increases, and at the same time, the interference to the V2V links increases, so the probability of successful transmission of the V2V links decreases. Therefore, it is necessary to reasonably set the power of the V2I links to meet the throughput requirements of the V2I links and the delay requirements of the V2V links. Figure 5: Remaining V2V payload comparisons between proposed and random schemes. (**a**) The remaining payload of the proposed. (**b**) The remaining payload of the random. Figure 6: Performance comparisons with varying power. (**a**)Sum capacity performance of V2I links with varying power. (**b**) V2V payload transmission success probability with varying power.
## 4 Experiment and Discussion  
### Description of Dataset  
Our experiments utilized the DailyDialog dataset [50] to evaluate our models. The dataset consists of a wide variety of dialogues from daily communications and is divided into three main categories: Work (14.49%), Ordinary Life (28.26%), and Relationship (33.33%). The dataset was built to cover various topics in our daily lives, and it contains 13,118 multi-turn dialogues. To ensure the dataset's consistency with real-life experiences,Li et al. [50] invited individuals to engage in social activities (Relationship), discuss recent events (Ordinary Life), and talk about work-related topics (Work).  
The DailyDialog dataset serves multiple purposes, including enhancing social bonding. The dataset contains rich emotions and is manually labeled to ensure high quality. Additionally, the dialogues cover various daily scenarios such as holidays, shopping, restaurants, and so on. In contrast to social media datasets such as Twitter Dialog Corpus [18] and Chinese Weibo [51], the language used in DailyDialog is written by humans and often focuses on a specific topic. So, we prefer the conversations in this dataset because of its formal writing style. The primary objective of this dataset is to develop a high-quality multi-turn dialogue dataset, which distinguishes it from most existing dialogue datasets.  
As discussed before, reinforcement learning is applied to each given multi-turn dialogue as a Markov Decision Process, in which the agent learns to determine the following action to take in the environment to complete the conversation based on specific criteria. Based on these characteristics, we have chosen the dataset for the proposed model.
# A Multi-Scale Attention Fusion Network for Retinal Vessel Segmentation
## 4 Experiments and Results
### Results  
#### 4.3.1 Comparison with Baseline Models  
We conducted experiments on U-Net [10], Unet++ [33], and attention U-Net [34] as baseline models.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset** & **Total Images** & **Training Set** & **Testing Set** \\ \hline DRIVE dataset & 40 & 20 & 20 \\ CHASE\_DB1 dataset & 28 & 20 & 8 \\ STARE dataset & 20 & 10 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The specific information of each database.  
Table 2 displays the sensitivity, F1-score, specificity, accuracy, and AUC values of the proposed model and the baseline model on the DRIVE dataset. It can be observed that compared with the baseline models, although the specificity value of Unet++ was higher than that of the proposed model, the proposed model achieved the highest values in terms of sensitivity, F1-score, accuracy, and AUC.  
Tables 3 and 4 display the sensitivity, F1-score, specificity, accuracy, and AUC values of the proposed model and the baseline model on the CHASE_DB1 dataset and STARE dataset, respectively. It can be observed that compared with the baseline models, all evaluation metrics of the proposed model achieved the highest values on the CHASE_DB1 dataset and the STARE dataset.  
The ROC curves for the proposed model and the baseline models on three datasets are depicted in Figure 4. It is evident that the proposed model achieved higher ROC values on all three datasets compared with the baseline models, indicating superior segmentation performance of the proposed model over the baselines.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **SE** & **F1** & **SP** & **ACC** & **AUC** \\ \hline U-Net [10] & 0.8452 & 0.8287 & 0.9798 & 0.9521 & 0.9738 \\ Unet++ [33] & 0.8510 & 0.8319 & 0.9865 & 0.9542 & 0.9741 \\ Attention U-Net [34] & 0.8427 & 0.8294 & 0.9807 & 0.9534 & 0.9726 \\ The proposed model & 0.8611 & 0.8383 & 0.9851 & 0.9629 & 0.9760 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The segmentation results on the DRIVE dataset.  
Figure 4: The ROC curves of the proposed model and baseline models. (**a**) The ROC curves on the DRIVE dataset. (**b**) The ROC curves on the CHASE_DB1 dataset. (**c**) The ROC curves on the STARE dataset.
## 4 CW Micro-Doppler MMW Radar
### Advantages of Using MMW in Radar Fall Detection  
MMW or extremely high frequency (EHF) refers to the band of frequencies in the electromagnetic spectrum from 30 to 300 gigahertz (GHz). Our radar system operates at 94 GHz. There are several advantages to using MMW in radar fall detection in comparison to the approaches used in other published studies. First, MMW allows higher frequency resolution and more accurate results. Second, it enables the utilization of small-aperture directive antennas, resulting in smaller systems. Finally, at 94 GHz, the atmospheric attenuation is low (i.e., within the atmospheric transmission window). Therefore, this radar can perform in places where the atmosphere is not optimal for camera sensors, such as saunas.
## 3 Results
### Stray Light  
FIREFLY demonstrated very little influence of stray light across its spectral range (Figure 13), suggesting the characterization of FWHM by estimation of ASR (Figure 4) was appropriate. Investigation of far-field stray light in wavelengths ranging from 800 nm to 1000 nm found that the FIREFLY sensor response was quite similar to dark current, with dark current adjusted DN remaining close to zero, with a standard deviation of 0.1 DN.
## 2 Materials and Methods
### Data Collection  
#### 2.2.1 NDVI Dataset  
The Normalized Difference Vegetation Index (NDVI) data obtained from satellite images is an important proxy to show the dynamics of vegetation production in response to climate variability and drought conditions [38]. NDVI data obtainable from NOAA-AVHRR sensors is used in various studies to analyse the dynamics and trends of vegetation production in different regions [10; 39; 40]. NDVI is computed using Near-InfraRed (NIR) and the visible RED spectral bands because healthy vegetation highly reflects in the NIR and absorbs in the RED bands, respectively [41].  
The values range from \(-1\) to +1, with high values representing healthy/active vegetation while non-vegetated surfaces such as water bodies, bare land/rocks are represented by negative NDVI values [8].  
NDVI satellite data from the AVHRR NOAA weather satellites ([[https://www.ncei.noaa.gov/data/avhr-land-normalized-difference-vegetation-index](https://www.ncei.noaa.gov/data/avhr-land-normalized-difference-vegetation-index)]([https://www.ncei.noaa.gov/data/avhr-land-normalized-difference-vegetation-index](https://www.ncei.noaa.gov/data/avhr-land-normalized-difference-vegetation-index)), accessed on 23 June 2021) were used in this study. The data contains gridded daily NDVI from the NOAA Climate Data Record (CDR) of AVHRR Surface Reflectance. While NDVI derived from the Global Inventory Monitoring and Mapping Studies (GIMMS) database is widely used [10], the NDVI derived from the NOAA CDR database was used due to limited access to the GIMMS dataset. Furthermore, NOAA CDR NDVI data has a 5 km spatial resolution unlike GIMMS, which has an 8 km spatial resolution. Moreover, the NOOA CDR dataset has better geolocation accuracy and use the centre of each grid as reference for consistency with other heritage records [42]. The study acquired NDVI data from 1990 to 2019 at 0.05 (5 km) spatial resolution, 1-day temporal resolution and projected on a 0.05-degree \(\times\) 0.05-degree global grid [42]. NDVI from the growth season (October to April) was used in this study as it represents vegetation growth status and is highly correlated with the growing season rainfall [8; 43; 44]. The NDVI data was then weighted by number of days acquired for each growing season.
## 3 The Influence of Soiling on Photovoltaic Energy
### Soiling by Pollen on PV Modules: A Review
Contrarily, neither such transmittance loss nor such pollen grains were observed in the unshetletered glasses, obviously due to the cleaning effect of the rainfall. Simultaneously, a layer of smaller dust particles (2-10 \(\upmu\)m in size) remained on both sheltered and unshetletered glasses, meaning that the rainfall had little effect on removing such small particles. This observation leaves the question of whether the rainfall would manage to effectively remove pollen grains having about 10 um in size. When coping with soiling, some passive cleaning strategies could be attempted. There is evidence of both organic degradation and adherence reduction of the pollen deposited on a photocatalytic surface [93], which suggests that PV modules having a TiO\({}_{2}\)-based nanocoated solar glass might have some advantage in areas exposed to high airborne pollen levels. In addition, some tests performed with electrodynamic screens (EDS) showed a lower removal efficiency of pollen with respect to dust [94]. Concerning the impact of a given soiling agent (broadly referred to as \"dust\"), an empirical relationship between the amount of deposited dust (\"dust density\") and the light transmission loss [7; 95; 96; 97] or the PV performance reduction [7; 98; 99] could be established. Such experimental correlations are not suitable for universal application, as they differ depending on the nature and size of the soiling particles [7; 99]. This implies that a given dust density will unlikely have an identical soiling impact neither when comparing different pollutants having the same particle size, nor when comparing the same pollutant with different particle sizes. Either way, for a given pollutant, smaller particles are certainly known to have a more detrimental effect on both the light transmission and the PV performance than bigger ones [98]. Unfortunately, experimental correlations for the specific case of pollen acting as the soiling agent have not been previously published.
## 3 Results and Discussion
### Effect of External Environment Condition on the NOx Removal Rate  
Based on the results obtained from Sections 3.2 and 3.3, the textile fibers were coated with coconut shell activated carbon using 98% deionized water for the dilution solution and 2% epoxy. Figure 11 shows the NOx removal rate of the textile fibers coated with coconut shell activated carbon after exposure to external environmental conditions. The washing and freeze thawing procedures were identical to those in Section 3.3. Before washing, the NOx removal rate was 19.8%. This value was maintained after one and after 10 washings in which the removal rates were 16.9% and 16.6%, respectively. In particular, after 10 washings and one freeze-thaw cycle, the NOx removal rate was 16.3%, which was similar to that after one washing. This implies that the coating conditions of the solution type and epoxy content determined from the coating procedure of textile fibers with zeolite were effective in maintaining a stable NOx removal rate in textile fibers coated with coconut shell activated carbon in external environments.  
Figure 10: SEM images and EDS spectra of textile fabric coated with zeolite. (**a**) Before washing. (**b**) After 10 washings and one freeze–thaw cycle.  
Figure 9: Typical SOx removal rate of textile fabrics coated with zeolite after exposure to external environmental conditions.  
Figure 12 shows the SEM image and EDS spectra of the textile fibers coated with coconut shell activated carbon. As shown in Figure 12a, before washing, C, which is the main components of coconut shell activated carbon, was present at the proportion of 84.7%. After 10 washings and one freeze-thaw cycle, the proportion of C was 99.9%. This implies that the coconut shell activated carbon was well attached to the surface of the textile fabrics after exposure to external environmental conditions such as washing or freeze thawing. The attachment of the coconut shell activated carbon was also confirmed in the SEM image in Figure 12b.
## 4 Results and Discussion
### Assessment of Monthly Marine Gravity Field Recovered a Single GNSS-R Satellite
Additionally, it is worth noting that the coverage rate and average revisits of spaceborne GNSS-R observations increase as the grid resolution decreases, resulting in improved monthly marine gravity field inversion accuracy (RMS). Furthermore, at a grid resolution of 25\({}^{\prime}\), spaceborne GNSS-R achieves a coverage rate of over 90%, specifically 91.14%, with an average of 14.68 revisits. Compared to radar altimeters, spaceborne GNSS-R offers superior global coverage due to its ability to conduct multiple track observations over the ocean. This is more efficient than traditional radar altimeters, which can only observe at nadir points. Additionally, the wide-swath observations of spaceborne GNSS-R overcomes \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Grid Resolution** & **Scenarios** & **Mean (mGal)** & **RMS (mGal)** & **Coverage Rate** & **Revisits** \\ \hline \multirow{3}{*}{\(10^{\prime}\)} & IOD & \(-0.06\) & 28.37 & \multirow{3}{*}{72.62\%} & \multirow{3}{*}{2.34} \\ \cline{2-3} \cline{5-6}  & Operational & 0.05 & 13.22 & & \\ \hline \multirow{3}{*}{\(15^{\prime}\)} & IOD & \(-0.06\) & 14.34 & \multirow{3}{*}{84.31\%} & \multirow{3}{*}{5.27} \\ \cline{2-3} \cline{5-6}  & Operational & \(-0.07\) & 8.09 & & \\ \hline \multirow{3}{*}{\(20^{\prime}\)} & IOD & \(-0.08\) & 8.98 & \multirow{3}{*}{89.83\%} & \multirow{3}{*}{9.39} \\ \cline{2-3} \cline{5-6}  & Operational & \(-0.08\) & 6.15 & & \\ \hline \multirow{3}{*}{\(25^{\prime}\)} & IOD & \(-0.09\) & 6.77 & \multirow{3}{*}{91.14\%} & \multirow{3}{*}{14.68} \\ \cline{2-3} \cline{5-6}  & Operational & \(-0.09\) & 5.36 & & \\ \hline \multirow{3}{*}{\(30^{\prime}\)} & IOD & \(-0.10\) & 5.70 & \multirow{3}{*}{91.91\%} & \multirow{3}{*}{21.14} \\ \cline{2-3} \cline{5-6}  & Operational & \(-0.11\) & 4.93 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation of monthly gravity anomaly simulation for single PARS IOD and Operational satellite with different grid resolution. the limitations of repetitive orbits in non-geodetic measurements, a common issue with traditional radar altimeters. Therefore, spaceborne GNSS-R interferometric altimetry has a natural advantage in high spatial resolution time-variable gravity fields compared to radar altimeters.
# Influence of Footwear Features on Oxygen Consumption and Running Economy: A Review
## 4 Discussion
### Shoe Mass  
The weight of the shoe was one of the most studied features by the scientific community, as the large number of articles found regarding this characteristic shows. In order to study this feature, two types of studies may be differentiated. One type of study compared barefoot running and/or minimalist footwear with running with standard footwear [29; 30; 31; 46; 48]. A second type compared barefoot running with standard footwear and also added weights in barefoot running and footwear to observe the difference that existed in this type of running when the mass was modified [32; 33; 47].  
In the studies that compared barefoot running and/or minimalist footwear with running in standard footwear, there was a consensus that lower weight led to lower oxygen consumption. [PERSON] et al. compared barefoot running with 350 g footwear and found that there was a 3.8% greater chance of injury with the shoes (\(p\) = 0.034) [30]. Conversely, [PERSON] et al. found differences in favor of minimalist footwear of 148 g when compared with standard footwear of 341 g (\(p\) < 0.05) [29]. Likewise, [PERSON] et al. found differences (\(p\) = 0.04) between barefoot running and standard shoes [46]. At the same time, although they found a difference between barefoot running and minimalist footwear, the difference was not significant (\(p\) = 0.15). [PERSON] et al. compared barefoot running with a 590 g shoe at different speeds and they found differences (\(p\) = 0.018) only when the subjects ran at 85% VO\({}_{\text{2 max}}\)[48]. [PERSON] et al. were also able to find differences in favor of minimalist footwear after 4 weeks of familiarization with this footwear (\(p\) = 0.016) [31].  
Considering the studies that compared barefoot running with standard footwear and added weight to both conditions, the increase in mass influenced oxygen consumption [32; 33; 47]. At this time, it is worth highlighting the study by [PERSON] et al. who used a material in the midsole that was different from the one used in footwear in the rest of the studies [32]. [PERSON] et al. also found differences according to the weight gain of the shoe. Nevertheless, when the shoe weight was less than 220 g and was provided with a material in the midsole to provide the necessary cushioning, there were no differences between this condition and barefoot running.