esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Average Photon Number inside the Cavity  
From the qubit absorption spectra reported in Figure 4, it is possible to extract the average number of photons inside the cavity as a consequence of the readout process. The qubit absorption spectra reflect the coherent photon distribution inside the cavity. As a consequence, the intensity of each peak expressed as a function of the relative Fock state number must follow a Poisson distribution [27]:  
\[P(n)=A\frac{e^{-\bar{N}}}{n!} \tag{1}\]  
where \(A\) is an arbitrary scale factor, \(n\) is the cavity Fock state number, and \(\bar{N}\) is the average photon number inside the cavity. Fitting the intensity distributions of the absorption peaks reported in Figure 4, we managed to extract the average number of photons inside the cavity, which are \(\bar{N}\) = 1.8 \(\pm\) 0.1 (P\({}_{probe}\) = \(-\)102 dBm) and \(\bar{N}\) = 4.1 \(\pm\) 0.1 (P\({}_{probe}\) = \(-\)98 dBm).
# Listeners' Spectral Reallocation Preferences for Speech in Noise
## 3 Expt. 1: Spectral Reallocation Preferences for Full-Bandwidth Speech
### Stimuli
#### 3.2.2 Spectral Band Filtering  
The three spectral band conditions had cut-off frequencies forming octave intervals corresponding to the frequency ranges 0.5-1 kHz, 1-2 kHz and 2-4 kHz. We will refer to these as the low, mid and high bands. In total, 21 energy adjustment steps were available to listeners. These were determined according to the function \(y=a-b(c^{x-1})\) for \(a=15\), \(b=100\), \(c=0.8\) and for step \(x\) taking on all integer values in the range 1-21, with the level difference \(y\) expressed in decibels. Constants \(a\)-\(c\) were chosen to provide a range that spanned an attenuation of 85 dB to a boost of nearly 15 dB. The lower value provided the complete removal of speech energy in the band, while the upper value was chosen to avoid clipping in the output. Band energy changes were implemented by multiplying 1024-point Fourier amplitude spectra computed in successive 30 ms Hann windows with 50% overlap, followed by inverse Fourier transformation, with the phase component unmodified.
# Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges
###### Abstract  
The increasing population across the globe makes it essential to link smart and sustainable city planning with the logistics of transporting people and goods, which will significantly contribute to how societies will face mobility in the coming years. The concept of smart mobility emerged with the popularity of smart cities and is aligned with the sustainable development goals defined by the United Nations. A reduction in traffic congestion and new route optimizations with reduced ecological footprint are some of the essential factors of smart mobility; however, other aspects must also be taken into account, such as the promotion of active mobility and inclusive mobility, encouraging the use of other types of environmentally friendly fuels and engagement with citizens. The Internet of Things (IoT), Artificial Intelligence (AI), Blockchain and Big Data technology will serve as the main entry points and fundamental pillars to promote the rise of new innovative solutions that will change the current paradigm for cities and their citizens. Mobility-as-a-service, traffic flow optimization, the optimization of logistics and autonomous vehicles are some of the services and applications that will encompass several changes in the coming years with the transition of existing cities into smart cities. This paper provides an extensive review of the current trends and solutions presented in the scope of smart mobility and enabling technologies that support it. An overview of how smart mobility fits into smart cities is provided by characterizing its main attributes and the key benefits of using smart mobility in a smart city ecosystem. Further, this paper highlights other various opportunities and challenges related to smart mobility. Lastly, the major services and applications that are expected to arise in the coming years within smart mobility are explored with the prospective future trends and scope.  
smart mobility; sustainability; smart cities; smart services +
Footnote †: journal: Sensors
## 3 Methodology of Reconstructing the 3D Foot Shape
### Segmentation Foot Image
#### 3.5.3 Loss Function  
For each region of interest (ROI), a multi-objective loss function consisting of classification losses (1,2), localization losses (3_A_), and mask segmentation (5) is used.  
\[L_{cls}=\frac{1}{N_{cls}}{\sum_{i}{L_{cls}(p_{i},p_{i}^{*})}} \tag{1}\]  
\[L_{cls}(p_{i},p_{i}^{*})=-p_{i}^{*}log{p_{i}}-(1-p_{i}^{*})*log(1-p_{i}) \tag{2}\]  
where \(L_{cls}\) is the classification loss function; \(N_{cls}\) is a normalizing term, which has a value equal to mini-batch (\(\sim\)256); \(L_{cls}(p_{i},p_{i}^{*})\) is the logarithmic loss function, which is used to convert the multi-class classification into a binary one, which allows one to determine whether an object is a target or not; \(p_{i}^{*}\) is a label reference value (binary) used to determine if the anchor box is an \(i\)th object; and \(p_{i}\) is the probability that an \(i\)th anchor box is an object.  
\[L_{box}=\frac{\textbf{1}}{N_{cls}}\sum_{i}{p_{i}^{*}*L_{1}^{smooth}(t_{i}-t_{ i}^{*})} \tag{3}\]  
\[L_{1}^{smooth}(t_{i}-t_{i}^{*})=\left\{\begin{array}{ll}0.5*(t_{i}-t_{i}^{ *})^{2}&if\Big{|}t_{i}-t_{i}^{*}\Big{|}<1,\\ \big{|}t_{i}-t_{i}^{*}\big{|}-0.5&\text{otherwise}\end{array}\right. \tag{4}\]  
where \(L_{box}\) is the localization loss function; \(\textbf{2}\) is the balancing parameter for the importance of the classification and localization loss function; \(N_{box}\) is the normalizing term associated with the location, and this has a value equal to the anchor location (\(\sim\)2400); \(L_{1}^{smooth}\) is the loss function, which is used for box regression; \(t_{i}\) is the predicted coordinate value; and \(t_{i}^{*}\) is the true coordinate value.  
\[L_{mask}=-\frac{1}{m^{2}}{\sum_{1\leq i,j\leq m}{[y_{ij}\log{\hat{y}_{ij}^{k} }+(1+y_{ij})\log(1-{\hat{y}_{ij}^{k}}]}} \tag{5}\]  
where \(L_{mask}\) is the mask segmentation loss function, \(\hat{y}_{ij}^{k}\) is the label of cell \((i,\ j)\) in the true mask, \(y_{ij}\) is the predicted value for the cell in the mask, and \(m\) is the value of the selection contour dimensionality \(m*m\).  
Thus, the final formula for the multi-task loss function is represented by the following (6):  
\[L=L_{cls}+L_{box}+L_{mask} \tag{6}\]
# Effect of Different Drying Methods on the Quality of _Oudemansiella raphanipes_
## 3 Results and Analysis
### Antioxidant Activities Analysis of PS-V, PS-H and PS-I  
As a stabilizing free radical, DPPH is widely used to evaluate the free-radical scavenging ability of different compounds. As shown in Figure 6A, the scavenging rates of DPPH free radicals by PS-V, PS-H, and PS-I all gradually increase with their increasing mass concentrations. It can be observed that the scavenging rate of PS-I is consistently higher than the other two, ultimately reaching 70.14%, while PS-V and PS-H reach 68.78% and 63.81%, respectively. These rates are lower than the free-radical scavenging rate of the positive control, Vc, which was 99.65%. The IC50 values are 1.802, 1.306, and 0.712, respectively. The experimental results showed that the scavenging ability of DPPH radicals of IR radiation-dried polysaccharides was stronger than that of hot air drying and vacuum freeze drying, which showed good antioxidant ability.  
Figure 5: Effect of different drying methods on the microstructure of _O. raphanipes_ polysaccharide, (**A**) PS-H, (**B**) PS-I, (**C**) PS-V. (**1**) The magnification was set as \(\times\)200; (**2**) the magnification was set as \(\times\)2000.  
Hydroxyl radicals, as harmful reactive oxygen species, are associated with oxidative damage to biomolecules, such as carbohydrates, proteins, lipids, and deoxyribonucleic acid in cells, leading to tissue damage and even cell death. Therefore, the removal of hydroxyl groups is very important for the conservation of organisms. As shown in Figure 6B, the scavenging rates of hydroxyl free radicals by PS-V, PS-H, and PS-I all increase with their increasing mass concentrations, and the scavenging rate of PS-I is consistently higher than the other two. When the polysaccharide mass concentration is 4.0 mg/mL, the scavenging rates under the three drying methods reach 27.61%, 31.30%, and 33.05%, respectively, which are lower than the free-radical scavenging rate of VC, 98.71%. The IC50 values of PS-I, PS-V, and PS-H are 1.14, 0.715, and 0.268, respectively, indicating that the polysaccharide dried by infrared radiation has the strongest ability to scavenge hydroxyl free radicals. This may be due to the higher carboxyl group content of PS-I, which helps to reduce the production of hydroxyl radicals by chelating ferrous ions.
## 4 Discussion
On the contrary, stress avoidance and coping subscales clearly had worse predictions than those of attachment, which could be due to the lack of variety in either the features (only 12 available) and/or the SG situations related to the underlying psychological need. Despite the promising results, some limitations of the present study should be considered. The main limitation is related to the limited number of subjects for model validation. None of the models were tested with new or external data. Future studies should include a more extended sample in order to test the models and improve the validity of the study. The SML models were validated using as ground truth the psychological questionnaires presented in the introduction. Even though they are commonly used questionnaires, an assessment performed by a psychologist could have been preferred. Another limitation of this study is represented by the categorization of predicting classes of psychological needs (low and high) rather than continuous data scores. The categorization was generated around the fixed central tendency estimate (mean) and previous research on personality has shown that the distribution of trait scores tends to be Gaussian, so that most individuals will fall into the central tendency estimate of the scale. The binary classification imposes a constriction on the mean, distancing trait scores from reality. Future studies should take into consideration the use of psychological construct data for continuous scores. Regarding the SG contents, our results support our initial hypothesis on attachment, self-esteem, self-efficacy, and not on stress avoidance. Indeed, the virtual content definition of stress avoidance could not predict the related psychological construct, which highlights, on the one hand, the importance of validating the virtual construct-related content and SML as an effective technique to check consistency. Future works, according to this result, should modify the actual virtual situations related to this construct, test it, and validate as a final step. Despite these limitations, the results of this study show that by using SML techniques on psychological dimensions with a VSG, it may be possible to enhance, first, the assessment of behavioral-based psychological needs, overcoming biases presented by traditional assessment, and second, to validate the appropriateness of virtual contents based on psychological needs.
## 3 Methods
### Evaluation Metrics  
The performance of the proposed multistage network was evaluated by conducting experiments on the multimodal datasets. The evaluation was based on volumes (three-dimensional images and segmentations). The degree of overlap between the anatomical segmentations of the moved image (\(S_{M}\)) and the fixed image (\(S_{F}\)) was determined using the Dice coefficient.  
Additionally, we evaluated the level of image folding due to the deformable transformation by calculating the Jacobian determinant (\(\det(J)\) or \(|J|\)) of each point of the deformation field. In cases where the Jacobian determinant is equal to 1, the volume remains unaltered, if it exceeds 1, there is volumetric expansion, and if it falls between 0 and 1, there is volumetric shrinkage. A Jacobian determinant of \(\leq\)0 means that folding has occurred here. Image folding is anatomically impossible in medical images [22]. Therefore, the aim is a registration with a deformation field where the Jacobian determinant for each point is \(>\)0. For rigid and affine transformations, no folding can occur. The number of Jacobian determinants \(\leq\)0 is therefore 0.  
We applied a paired \(t\)-test (for normal distribution) or a Wilcoxon signed-rank test (for non-normal distribution) to determine the significance of the improvement in the Dice coefficient and number of Jacobian determinants \(\leq\)0 after registration compared to the data before applying the registration (sub)network. The null hypothesis, which states that registration by the neural networks does not improve the two metrics, is rejected if \(p<0.05\).  
For visual evaluation of the spatial alignment before and after registration, the fixed, moving and moved image were examined. Additionally, the moving/moved and fixed images were color overlaid to form a \"Composite Image\". If available, the segmentations of the moving/moved and fixed images were also color-overlaid to form a \"Composite Segmentation\". In addition, the deformation field was displayed in RGB colors. The three color channels represent the deformation in the three spatial directions of three-dimensional images and transformations. The color red corresponds to a displacement on the y-axis (height), green on the x-axis (width) and blue on the z-axis (slice direction). This enables the identification of areas with strong deformation and a check on whether the deformation is smooth within the individual organs and structures. For areas that are displayed in gray values, the same deformation is applied in all three spatial directions.
## 2 Multi-Body Constellation Design
### Possible Cislunar Orbits
[17] have suggested a solution with an Elliptical Lunar Frozen Orbit (ELFO): ELFOs minimise sensitivity to external perturbations and provide persistent stable coverage to either the north or south pole with no requirements for station keeping for up to 10 years. However, given the proximity to the surface, insertion costs can increase exponentially. Even ref. [18] developed an LLO for small-sat constellation-exploiting areas of partial stability of the lunar environment, but their polar communication analysis makes use of over a hundred satellites: problems related to such numerous navigations and communication constellations have yet to be solved and may not be applicable. These include, for example, the proper design of efficient deployment schemes and the development of effective end-of-life disposal scenarios. Still, concerning the high number of orbiting satellites, a numerical analysis of the Flower Constellation provides its best performance, increasing the number of petals and consequently that of the orbiting spacecraft. Therefore, at least at this early exploratory stage, it might be prohibitive to push for such impactful solutions, risking compromising access to the lunar surface: furthermore, building a multi-body architecture in the immediate vicinity of the Moon requires larger beamwidths and/or constellation sizes to ensure horizon-to-horizon coverage [19]. After evaluating different possibilities to design a multi-body infrastructure, the Earth-Moon Lagrange points appear to be more viable locations to deploy these constellations.
# IPN HandS: Efficient Annotation Tool and Dataset for Skeleton-Based Hand Gesture Recognition
###### Abstract  
Hand gesture recognition (HGR) heavily relies on high-quality annotated datasets. However, annotating hand landmarks in video sequences is a time-intensive challenge. In this work, we introduce IPN HandS, an enhanced version of our IPN Hand dataset, which now includes approximately 700,000 hand skeleton annotations and corrected gesture boundaries. To generate these annotations efficiently, we propose a novel annotation tool that combines automatic detection, inter-frame interpolation, copy-paste capabilities, and manual refinement. This tool significantly reduces annotation time from 70 min to just 27 min per video, allowing for the scalable and precise annotation of large datasets. We validate the advantages of the IPN HandS dataset by training a lightweight LSTM-based model using these annotations and comparing its performance against models trained with annotations from the widely used MediaPipe hand pose estimators. Our model achieves an accuracy that is 12% higher than the MediaPipe Hands model and 8% higher than the MediaPipe H holistic model. These results underscore the importance of annotation quality in training generalization and overall recognition performance. Both the IPN HandS dataset and the annotation tool will be released to support reproducible research and future work in HGR and related fields.  
IPN Hand dataset; skeleton-based hand gesture recognition; landmark annotation tool; MediaPipe H holistic; MediaPipe Hand
# Optimal Task Offloading Strategy for Vehicular Networks in Mixed Coverage Scenarios
## 5 Simulation and Performance Evaluation
### LSTM for Load Estimation  
First, based on the temporal traffic flow model, we divided the appropriate time period and reasonably constructed the relevant training dataset. Then, we construct the LSTM neural network, which makes it possible to estimate the current BS load based on the current moment.
## 4 Case Study
### Influence of Heat Storage Device on Operation Results  
Based on Scenario 6, the influence of the heat storage device on the operating results of the system is analyzed. The operating results are shown in Table 4 and Figure 9, where the heat storage device is not considered in Figure 9a but is considered in Figure 9b.  
As can be seen from Figure 9a, during the wind abandonment period, the heating output of the electric boiler has been full, and it cannot absorb more wind power. As can be seen from Table 3, after addition of the heat storage device, the CHP thermal output at 3 h in Figure 9b further decreases, thus reducing the penalty cost of wind abandonment. In the rest of the time, the use of heat storage devices can make the electrical and thermal output of the CHP unit more flexible and avoid the rigid coupling of thermoelectric to a certain extent reducing the total cost of the system by 1.4%.
# Construction of Air Quality Level Prediction Model Based on STEPDISC-PCA-BP
## 1 Introduction
Therefore, this study collected the real data of 1771 sets of day-by-day air pollutant evaluation factors and air quality level classification labels in Bayannur city during 2015-2020, combined the meteorological data such as air pressure, temperature, and wind speed in the same period, tried to retain the evaluation factors with significant discriminative ability through STEPDISC, and on this basis, used PCA to reduce the dimensionality of factor vector and calculated the score vector of the principal components with large contribution rates and input them into BP neural network for air quality level prediction modeling which named STEPDISC-PCA-BP model. The purpose of this study is to provide a comprehensive approach to air quality modeling to improve the accuracy, and also to provide a new perspective for air quality level forecasting.
# SNR Analysis for Quantitative Comparison of Line Detection Methods
## 5 Experimental Results and Discussion
This observation indicates the consistency of SNR measures with respect to completeness and correctness under various line conditions. However, as shown in Figure 15, the SD-based line detection was observed to produce low completeness and correctness as compared to its SNR when SNR \(>3.0\). This is caused by frequent bifurcations of the SD-based line detection. When the line width was not relatively large, but the contrast was relatively large, the calculated value of SNR was relatively large. Meanwhile, in the experiments, the shape of many line profiles with such conditions deviated from that of the ideal line profile and the SD-based line detection could not overcome this problem and produced low completeness  
Figure 9:3 Completeness and correctness against SNR for the Lena image under varying \(\sigma_{n}\). Top row shows completeness and bottom row correctness. Figure 9:4 Completeness and correctness against SNR for the Lena image. The second and fourth plots show the zoom-in versions of the first and third plots with SNR range [0, 10], respectively. and correctness. However, the SGAD-based line detection was observed to overcome this problem upto some extent and produce high completeness and correctness, when the value of SNR was high. Figure 9:5 Natural images. (**Top**) From left to right: Baboon, Barbara, Boats, Cablecar; (**Bottom**) From left to right: Flowers, Goldhill, Monarch, and Yacht. Figure 9:6 _Cont._
## 3 Results
### Pathological and Long-Term Oncological Outcomes  
Pathological examination of the resected specimens showed comparable T stages (\(p\) = 0.315), N stages (\(p\) = 0.397), number of retrieved lymph nodes (26.0 vs. 24.2; \(p\) = 1.000), and number of positive nodes between groups (\(p\) = 0.388). Furthermore, R0 resection was achieved in 100% of cases in both groups (\(p\) = 1.000). Finally, post-preoperative TNM stage was comparable between the groups (\(p\) = 0.774). Results are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Responders** & **Non-Responders** & \(p\) \\  & **\(n\) = 6** & **\(n\) = 9** & **\(p\)** \\ \hline
**Age (years, mean \(\pm\)SD)** & 59.9 (\(\pm\)12.6) & 64.8 (\(\pm\)15.2) & 0.224 \\ \hline
**Gender F/M** & 3/3 & 3/6 & 0.519 \\ \hline
**BMI (mean, \(\pm\)SD)** & 23.8 (\(\pm\)2.2) & 24.8 (\(\pm\)3.1) & 0.639 \\ \hline
**ASA (n, \%)** & & & 0.174 \\
**1** & 0 (0.0\%) & 1 (11.1\%) & \\
**2** & 3 (50.0\%) & 2 (22.2\%) & \\
**3** & 3 (50.0\%) & 6 (66.6\%) & \\
**4** & 0 (0.0\%) & 0 (0.0\%) & \\ \hline
**Comorbidities (n, \%)** & 3 (50.0\%) & 4 (44.4\%) & 0.408 \\ \hline
**Histotype** & & & 0.274 \\
**Non poorly cohesive** & 3 (50.0\%) & 6 (66.6\%) & \\
**Poorly cohesive** & 2 (33.3\%) & 0 (0.0\%) & \\
**Poorly cohesive**, **signet-ring cell** & 0 (0.0\%) & 1 (11.1\%) & \\
**Mixed** & 1 (16.7\%) & 2 (22.2\%) & \\ \hline
**Tumor Location** & & & 0.255 \\
**Cardias** & 1 (16.7\%) & 0 (0.0\%) & \\
**Subcardial** & 1 (16.7\%) & 1 (11.1\%) & \\
**Fundus** & 0 (0.0\%) & 0 (0.0\%) & \\
**Body** & 0 (0.0\%) & 1 (11.1\%) & \\
**Angulus** & 2 (33.3\%) & 1 (11.1\%) & \\
**Attrum** & 1 (16.7\%) & 6 (66.7\%) & \\
**Pylorus** & 1 (16.7\%) & 0 (0.0\%) & \\ \hline
**Tumor size (cm, mean \(\pm\)SD)** & 2.2 (\(\pm\)0.8) & 3.4 (\(\pm\)1.1) & 0.073 \\ \hline \end{tabular}
\end{table}
Table 1: Baseline characteristics of the included patients.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Responders** & **Non- Responders** & \(p\) \\  & **\(n\) = 6** & **\(n\) = 9** & **\(p\)** \\ \hline
**T-stage (n, \%)** & & & 0.315 \\
**ypT1** & 3 (50.0\%) & 2 (22.2\%) & \\
**ypT2** & 1 (16.7\%) & 3 (33.3\%) & \\
**ypT3** & 1 (16.7\%) & 4 (44.4\%) & \\
**ypT4a** & 1 (16.7\%) & 0 (0.0\%) & \\
**ypT4b** & 0 (0.0\%) & 0 (0.0\%) & \\ \hline
**N-stage (n, \%)** & & & 0.397 \\
**ypN0** & 5 (83.3\%) & 4 (44.4\%) & \\
**ypN1** & 0 (0.0\%) & 1 (11.1\%) & \\
**ypN2** & 0 (0.0\%) & 2 (22.2\%) & \\
**ypN3** & 1 (16.7\%) & 2 (22.2\%) & \\ \hline
**M-stage (n, \%)** & & & 1.000 \\
**ypM0** & 6 (100\%) & 9 (100\%) & \\
**ypM1** & 0 (0.0\%) & 0 (0.0\%) & \\ \hline \end{tabular}
\end{table}
Table 2: Oncological outcomes including spTNM stage, resection margin, node details, and lymphovascular/perineural invasion in responder vs. non-responder patients.