esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison of Simulation Performance Across Climate Zones
#### 3.5.2 Gross Primary Productivity (GPP)  
In contrast to ET, the GPP simulations demonstrated statistically significant positive correlations with the observations across all five climate regions (\(p<0.01\) for all). However, the model's ability to capture the correct magnitude and variability of GPP is highly climate-dependent.  
The model systematically overestimates GPP, a bias most pronounced in the Arid (53.11%) and Temperate (+26.80%) zones. In the Arid zone, the large positive mean bias contrasts with a near-zero median bias (Figure 13b), indicating that extreme overestimation  
Figure 13: Distribution of annual simulation biases for (**a**) evapotranspiration (ET) and (**b**) gross primary productivity (GPP) across five principal climate zones. The bias is calculated as the simulated value minus the observed value. In the box plot, the central horizontal line indicates the median, while the bottom and top edges of the box represent the 25 th (Q\({}_{1}\)) and 75 th (Q\({}_{3}\)) percentiles, respectively. The whiskers extend to the most extreme data points within 1.5 \(\times\) IQR (where IQR = Q\({}_{3}-\) Q\({}_{1}\)) of the box. The triangle symbol (\(\triangle\)) indicates the mean value. Individual points beyond the whiskers are marked as outliers.  
at a subset of sites skews the average. This suggests a conditionally flawed water-stress parameterization that inadequately suppresses photosynthesis during drought [41; 42; 43]. The consistent overestimation in the Temperate zone, reflected by its positive median bias and wide interquartile range, points to an overly optimistic parameterization of photosynthetic capacity (e.g., \(\mathrm{V_{cmax}}\)) within the model's PFT scheme, possibly compounded by phenological inaccuracies [44; 45].  
Furthermore, the fidelity with which the model captures GPP variability is low in climates with complex, interacting limiting factors. The correlation is notably poor in the Arid (R = 0.31) and Continental (R = 0.24) zones. The Continental region is unique in exhibiting a mean GPP underestimation (\(-6.99\%\)). Visually, its box plot in Figure 13b displays the largest interquartile range among all climates, indicating the highest degree of simulation uncertainty. This high site-to-site variability, coupled with the very weak correlation, underscores the model's struggle to consistently simulate physiological responses to the wide temperature extremes of this climate. This could involve over-penalizing productivity during summer heat stress while inaccurately modeling the rapid onset of photosynthesis following cold-period dormancy [46; 47].  
By contrast, the stronger correlations in the Tropical (R = 0.74) and Polar (R = 0.74) zones, supported by the more compact and centered bias distributions in Figure 13b, suggest greater model skill where GPP dynamics are governed by a single, dominant environmental driver, such as radiation or a temperature-defined growing season.
# Preparations for Galileo PRS in Poland
## 5 Polish Contribution for the PRS
### GNSS Jamming Tests in a Controlled Laboratory Environment
Moreover, in most cases, the Sony had the highest C/I cut-off and degradation levels. On the other hand, the best resistance to the jamming signals during static tests was featured by the Huawei smartphone, especially for the Galileo case. It can be concluded that the Broadcom BCM47755 chip that the Huawei Mate 20 Pro was equipped with has, as stated by its manufacturer, a high immunity to interference and jamming [12]. On the other hand, in the case of the dynamic tests, and especially in the presence of one of the most sophisticated jamming signals, the sweep, the resistance of the Huawei smartphone was worse than the most vulnerable smartphone in the static tests, i.e., Sony. It is worth noting that position determination algorithms in smartphones normally use all the available GNSS signals, and the various sensors that the smartphones might be equipped with (e.g., the accelerometer, gyroscope and magnetometer) may also support the inertial navigation system (INS). The performed jamming tests, conducted in a static scenario in the laboratory environment, demonstrate that in many cases, the GNSS smartphones receivers were still able to determine the position, as indicated by the saved NMEA logs. Only in the case of INT3, i.e., the wideband AWGN noise covering all 60 MHz of the L1/E1 frequency band, which affects all GNSS systems, did the smartphone receivers lose (in most cases) their ability to determine their position.
## 3 Results
### Climatic Impacts on Vegetation Asymmetry Indices
#### 3.3.2 Climatic Effects on Vegetation Asymmetries  
To estimate climatic controls on seasonal difference in plant growth, the relative importance (RI) of the temperature and precipitation asymmetries on vegetation growth asymmetries was calculated using a linear regression model (Equation (8)). For the entire region, this analysis confirms that AsyTemp and AsyPrcp account for 37.1% and 37.5% of the change in AsyV, respectively. The corresponding proportion of variance explained by the model is 74.6%. The equivalent relative contribution of AsyTemp and AsyPrcp to AsyR (AsyL) are 37.5% (12%) and 41.8% (23%), respectively. These RI results suggest that asymmetry of precipitation provides a slightly larger contribution to the asymmetry of vegetation growth. Conversely, the difference in growing season length between green up and senescence is not strongly influenced by AsyTemp and AsyPrcp, which together only explain 35% of the variations in AsyL.  
Similarly, a relative importance (RI) analysis was performed at the pixel level to detect any geographic pattern of climatic impacts on vegetation asymmetries (Figure 11). Geographically, the RI of AsyTemp to AsyR and AsyV tend to show an inverse pattern, as shown by the contrasting yellow and green colors in central and northern NEC. Across the entire area, the magnitude of the RI for AsyTemp on AsyR and AsyV is nearly identical (25% vs. 24%). AsyPrcp has more influence on differences in the growth rate (AsyR) than in greenness (AsyV) during senescence (31% vs. 24%). Spatially, asymmetries in temperature and precipitation do not supply a significant contribution to changes in vegetation asymmetries across the eastern broad-leaved forest. Growing season length (AsyL) is weakly affected by AsyTemp and AsyPrcp, as indicated by the corresponding small RI values widespread across the NEC (total mean value of 3.8% for AsyTemp and 8.7% for AsyPrcp). Their complex spatial heterogeneities (Figure 11e,f) are consistent with the average annual AsyL (Figure 4) and long-term trends in AsyL (Figure 5).  
Figure 10: Maps of asymmetry for temperature and precipitation (1982–2015 multi-years average).
## 2 Materials and Methods
### Materials and Method  
To collect the demographic and subjective data, a questionnaire was created using the questionnaire software Unipark Tivian XI GmbH [68]. The questionnaire was also used to structure the course of the study. This ensured that the study was conducted in a standardized manner. Figure 2 shows the hardware components from the study management perspective. The presentation of the questionnaire was on an Apple iPad Pro 12.9. Participants were sitting in front of a computer monitor. The iPad was placed to their left and used via the built-in touchscreen. The data resulting from the questionnaire was analyzed using the software IBM SPSS Statistics Version 29. In the main part of the study, the participants were equipped with an eye tracker. For the eye-tracking, Dikablis Glasses 3 from the company Ergoneers (Egling, Germany) were used [69]. A series of data was recorded using the eye tracker. The most significant parameter for this evaluation was the area of interest (AOI) attention ratio. The AOI attention ratio in eye tracking is a metric that quantifies the amount of visual attention a specific region of interest receives relative to the total attention given to the entire visual stimulus [40; 48]. It is typically expressed as a percentage. The data collected by the eye tracker was recorded, stored, processed and analyzed using the DLab software 3.0 Manual [47]. The data resulting from DLab was then further processed in Microsoft Excel and R. The multiple paired comparison test method was used in conjunction with the eye-tracking [70; 71]. There were always two buttons presented at the same time. These two buttons on each presentation were designated as the two AOIs. The participants' task was to compare the pair of buttons and select the preferred element from the pair (forced choice). It was not possible for participants to abstain. This process was repeated for all possible pairs of buttons. In order to rule out a reading direction effect the pairs were also presented mirrored. A total of 25 pairs of buttons were evaluated per button category. In total, the participants had to choose between 50 pairs of buttons. The pairwise comparison was presented with the help of a 27\" BenQ EW2780 LED monitor and implemented using the PsychoPy software version 2021.2.3 [72]. PsychoPy is a library for the Python programming language that offers tools for reliable study control and automatic logging of important time points and inputs made by the study participants. In the context of this study, the choice between the presented buttons could be saved directly through the input in PsychoPy. With the help of a trigger program by [PERSON] et al. [73], direct communication between PsychoPy and the DLab eye-tracking software was achieved via an Ethernet connection. This made it possible to trigger special PsychoPy triggers in DLab based on the information displayed by PsychoPy. As a result, the randomized representations of the buttons and the inputs from the pairwise comparison were reliable and traceable in real time.  
Figure 2: Hardware components from the perspective of the study management.
## Appendix B
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(P_{2}\)} & \multicolumn{2}{c}{**Lower Bond HS\(-\)**} & \multicolumn{2}{c}{**DH Method**} & \multicolumn{2}{c}{**MS Method**} & \multicolumn{2}{c}{**Upper Bond HS+**} & \multicolumn{2}{c}{**Errors \%**} \\ \cline{2-11}  & \(k^{HS-}/k_{1}\) & \(\mu^{HS-}/\mu_{1}\) & \(k^{off}/k_{1}\) & \(\mu^{off}/k_{1}\) & \(\mu^{off}/k_{1}\) & \(\mu^{off}/\mu_{1}\) & \(k^{HS+}/k_{1}\) & \(\mu^{HS+}/\mu_{1}\) & \(Err^{k}\) & \(Err^{\mu}\) \\ \hline
0.05 & 1.6029 & 1.7075 & 1.6036 & 1.7221 & 1.6036 & 1.7226 & 46.7585 & 61.2569 & 0 & 0.0345 \\ \hline
0.08 & 1.6877 & 1.8171 & 1.6887 & 1.8410 & 1.6887 & 1.8421 & 49.7160 & 64.2878 & 0 & 0.0596 \\ \hline
0.12 & 1.8108 & 1.9759 & 1.8122 & 2.0173 & 1.8122 & 2.0190 & 53.7008 & 68.3538 & 0 & 0.0845 \\ \hline
0.15 & 1.9115 & 2.1057 & 1.9132 & 2.1648 & 1.9132 & 2.1669 & 56.7208 & 71.4220 & 0 & 0.0955 \\ \hline
0.22 & 2.1801 & 2.4517 & 2.1825 & 2.5701 & 2.1825 & 2.5726 & 63.8747 & 78.6441 & 0 & 0.0977 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The two methods, DH and MS, normalized effective properties comparison for different volume fractions \(P_{2}\) and the contrasts \(C_{1}=80\), \(C_{2}=10\). The values of the normalized properties must be multiplied by \(10^{-2}\). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\(P_{2}\)} & \multicolumn{2}{c}{**LDH Method**} & \multicolumn{2}{c}{**NMS Method**} & \multicolumn{2}{c}{**Errors \%**} \\ \cline{2-9}  & \(k^{eff}\)/\(k_{1}\) & \(\mu^{eff}\)/\(\mu_{1}\) & \(k^{eff}\)/\(k_{1}\) & \(\mu^{eff}\)/\(k_{1}\) & \(\mu^{eff}\)/\(\mu_{1}\) & \(Err^{k}\) & \(Err^{\mu}\) \\ \hline
0.05 & 1.6043 & 1.7879 & 1.6055 & 1.7909 & 0.0803 & 0.1673 \\ \hline
0.12 & 1.8129 & 2.1433 & 1.8168 & 2.1542 & 0.2155 & 0.5074 \\ \hline
0.15 & 1.9140 & 2.3209 & 1.9140 & 2.3249 & 0.0007 & 0.1718 \\ \hline
0.22 & 2.1834 & 2.8037 & 2.1840 & 2.8094 & 0.0256 & 0.2045 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: The results of the NDS and NMS normalized effective properties comparison for different volume fractions \(P_{2}\) and the contrasts \(C_{1}=80,\ C_{2}=100\). The values of the normalized properties must be multiplied by \(10^{-2}\).
# Oral and Dental Spectral Image Database--ODSI-DB
## 1 Introduction
Our dental and oral spectral image database--ODSI-DB--contains 316 spectral images from human test subjects. The front-view and the occlusal surfaces of lower and upper teeth, oral mucosa, and face surrounding the mouth were imaged from all test subjects. Other features of interest were imaged on a case-by-case basis. When possible, additional medically relevant information was collected from the test subjects--pulse, blood pressure, and blood oxygen saturation--both before and after the imaging session. The database comes with annotations marking the features of interest. The annotations were made by dental experts, who used an annotation tool built for the project. We have successfully used the database to develop a prototype optical imaging system based on partially negative filters derived from principal component analysis of healthy and diseased tissue [9], and in automatic image segmentation and classification based on a convolutional neural network [11]. To the best of our knowledge, our oral and dental spectral image database is the first of its kind to be made publicly available under a permissive license for the research community. The database is available online at the following address: [[https://sites.uef.fi/spectral/odsi-db/](https://sites.uef.fi/spectral/odsi-db/)]([https://sites.uef.fi/spectral/odsi-db/](https://sites.uef.fi/spectral/odsi-db/)). Figure 1: (**a**) A single-channel grayscale image, (**b**) Red/Green/Blue (RGB) color image and its Red/Green/Blue channels as grayscale images, (**c**) spectral image consisting of several bands as a stack of grayscale images.
## 3 Circuit Analysis and Operation Modes
### Operation Mode II (\(t_{1}-t_{2}\))  
The equivalent circuit of Mode II is shown in Figure 10b. This mode starts as soon as the resonant current, \(i_{r}\) (via the center-tapped transformer) decreases to zero, and the primary winding then induces a back electromotive force (emf) and result in the conduction of power switch \(S_{1}\), along with the turn-off of switch \(S_{2}\). The current \(i_{LPFC}\) reaches a peak value at the beginning of Mode II. Since \(S_{2}\) is off, the current \(i_{LPFC}\) diverts from \(S_{2}\) to \(D_{S1}\) to charge the dc-bus capacitor \(C_{bus}\). The voltage across \(L_{PFC}\) is equal to \(V_{clus}\) minus \(v_{rec}\), which is negative, meaning that \(i_{LPFC}\) decreases linearly.  
\[i_{LPFC}(t)=\frac{V_{m}|\sin(2\pi\,f_{L}\,t)|DT_{s}}{L_{PFC}}-\frac{V_{clus}-V _{m}|\sin(2\pi\,f_{L}\,t)|}{L_{PFC}}(t-t_{1}) \tag{25}\]  
In this mode the current \(i_{r}\) is negative. Simultaneously, the resonant tank releases energy to the load.
## 1 Introduction
The TDMA part of O-MAC is also improved because the nodes are not required to synchronize with all of the 1-hop neighborhood nodes but rather only with the particular nodes that must be used for forwarding the data to the next hop of the network. In addition, ODFF includes the security portion to avoid the attacks encountered at the transceiver of the radio. To handle the collisions in accessing the slots at the channel, a novel randomization algorithm is introduced for energy efficiency and QoS provisioning. For example, sensor node \"A\" wants to access a slot number 5, and if slot number 5 is already occupied by node \"B\", then this algorithm helps to redirect node \"A\" to another available empty slot to avoid the potential collision on the network. Extending the network lifetime, a head node is dynamically selected in each region. Furthermore, the PT model introduces significant features e.g., the pheromone sensitivity, which helps in predicting the amount of the pheromone and packet generation rate and in determining the traffic-forwarding link capacity to send the packets according to the capacity of the link. As a result, the PT model provides the shortest path from the source to the destination without overlap. In addition, it is also capable of load balancing and fault tolerance in the case of route failure and possible congestion. Therefore, O-MAC with PT maintains the state of the art in terms of energy efficiency and also develops the tradeoff between energy efficiency and time management. In the simulation platform, the state-of-the-art hybrid and individual layered configurations have been implemented to obtain a complete performance evaluation. The simulation experiments and analytical performance and evaluation demonstrate that O-MAC with PT substantially improved the communication performance and outperformed other competing hybrid protocols in terms of energy efficiency and QoS parameters. These results highlight the benefits of the novel modular energy-efficient and robust paradigm (O-MAC with PT) for deploying general scenarios as well as particular scenarios in WSNs. The remainder of this paper is organized as follows: in Section 2, we present the system model for the proposed work. In Section 3, the O-MAC protocol design goals and architecture are described. In Section 4, the pheromone termite model is presented. In Section 5, the simulation setup and evaluation of the results is discussed. Finally, Section 6 concludes the paper.
# Contactless Real-Time Eye Gaze-Mapping System Based on Simple Siamese Networks
## 3 Eye Gaze-Mapping System
### Gaze-Mapping System Parallelization and Optimization  
The gaze-mapping system consisted of modules for facial detection, alignment, and recognition, and for gaze estimation. The system was implemented in parallel with queued and multiple processes to maximize resource utilization. We allocated the CPU resources according to the task load of each module and adjusted the overall inference speed.  
Moreover, to share user IDs among different devices (see Figure 6), the ID information was synchronized in real-time using Python remote object (PYRO). All modules included in the gaze-mapping system were optimized using TensorRT, a platform for optimizing inferences in deep-learning models. In a CNN, the convolution, bias, and ReLU layers could be combined into one CBR layer to increase memory efficiency and computational speed. The platform was 45 times faster than the CPU-only implementation of INT8 and FP16 precision.  
Figure 4: Description of gaze-matching module. Gaze and position information for both eyes were used as input to each Siamese network. The final matching point was calculated through the fully connected layer. In the figure, 0 is the start point of gazing and 1 is the last point.
# Measurement of Head-Related Transfer Functions: A Review
## 6 Post-Processing of Measured HRTFs
### Windowing  
To eliminate reflections, the measured HRTFs should be carefully truncated. In general, the truncation is done in the time domain by applying a window function, e.g., half Hann-window. Windowing may lead to a loss of information at low frequencies, and the cut-off frequency depends on the window length. The low-frequency components can be reconstructed by using the methods described in Section 6.3. Several studies proposed to truncate the impulse responses by applying a frequency-dependent window [153; 175; 176]. If the reflections are caused by the measurement equipment or some small-size objects, the energy of these reflections is mainly at mid and high frequencies. In this case, the impulse response at mid and high frequencies needs to be truncated while the low-frequency component can be retained [153]. The onset delays in HRIRs caused by the system latency or the distance between the sound source and the subject can be appropriately truncated, and a fade-in window is commonly applied before the peaks of HRIRs to avoid discontinuities [126; 174]. The final length of HRIRs is different in each study and varies from 2.5 to 20 ms.
# Design Strategies for Mobile Click-and-Load Waiting Scenarios
## 5 Discussion
### Layout Strategies in Page Hierarchy Transitions  
Compared to icon and interface strategies, layout strategies have a relatively weaker overall impact on the user's waiting experience, with significant differences only observed in time perception. The results show that the step-by-step loading strategy (text first,followed by images) yields higher mean values across multiple dimensions, including time reproduction, perceived loading speed, satisfaction, and emotional valence when compared to the full-load strategy. This confirms its advantage in reducing subjective waiting times and enhancing overall user experience.  
In content page scenarios, the step-by-step loading strategy is particularly effective. Employing a \"progressive content presentation\" approach helps guide users to form reasonable expectations about loading, ultimately improving both satisfaction and emotional experience. However, unlike in tab and half-screen page scenarios, the full-load strategy scores higher on arousal, possibly due to the strong visual stimulation of loading a large amount of content all at once, which increases user attention [45]. Therefore, in scenarios where it is necessary to capture user interest or attention (such as when entering a new content level), designers may consider using the full-load strategy. However, it is crucial to balance the potential waiting anxiety it may cause.  
Notably, in the half-screen page scenario, the step-by-step loading strategy achieved the highest satisfaction score and the lowest time reproduction score (F\({}_{\text{tab}}\) = 4.259, F\({}_{\text{content}}\) = 4.715, F\({}_{\text{half-screen}}\) = 4.904). In this context, text carries more informational value than images (e.g., in comment sections), so the step-by-step approach that prioritizes text aligns better with user cognitive preferences and helps alleviate waiting anxiety. Designers should consider specific usage scenarios when designing page layouts, identify whether users are more focused on text or images, and arrange the loading order accordingly.  
Although most dimensions in this experiment did not show significant differences, and only the \"text first, then images\" step-by-step format was tested, future research could explore a broader range of layout strategies and expand the sample size to provide more targeted design recommendations.
## 4 Discussion
Therefore, no single-trial RE measures were used in this study for calculating mean CPRS effects (averaged across speeds and subjects) or percentage of subjects receiving certain amounts of benefit (averaged across velocities). Furthermore, as RE was assessed at speeds up to the second ventilatory threshold, a non-negligible amount of energy may have been produced through anaerobic Lict metabolism by anaerobic glycolysis, even though this was partly controlled by excluding subjects that exceeded an RE of >1.0. A small amount of the observed variation between subjects may thus have been due to lactic anaerobic contributions. By spot checking via individual lactate measurements with the maximum additional lactate accumulation observed reaching 2.0 mmol L\({}^{-1}\), an upper limit of 10% to 13% for this lactic contribution to total energy consumption could be estimated (with an additional lactate accumulation of 1 mmol L\({}^{-1}\) lactate corresponding to an additional 3 mL min\({}^{-1}\) kg\({}^{-1}\) oxygen consumption) [4, 47]. Despite the fact that using only spirometric gas exchange rates to calculate energy expenditure represents the standard practice in the current footwear literature, future research should consider lactate accumulation at running speeds close to the second ventilatory threshold to increase the accuracy of CPRS effect measurements. Moreover, this study is limited by examining only four different CPRS models. It is likely that CPRSs from other manufactures may yield different results.