esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Approach to Image-Based Estimation of Food Volume
## 4 Discussion  
In this paper, we have introduced a system to calculate food intake quantity using image-based modeling. The approach includes a semi-automatic segmentation method, which was designed to  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Processing Step** & **Time** & **Ratio** \\ \hline Segmentation & 4.52 & 19.2 \% \\ Size Reference & 2.90 & 12.3 \% \\ Model Reconstruction & 16.1 & 68.5 \% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Processing time of the whole process.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Row** & **Food** & **Error (\%)** \\ \hline
1 & Rice & & 5.1\% \\
2 & Pennette & & 6.8\% \\
3 & Fusilli & & 7.1\% \\
4 & Chifferi & & 8\% \\ \hline  & & average & 6.7\% \\ \hline  & & average processing time & 24.01 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Error rate in the reconstructed models’ volume estimations for non-solid shapes.  
Figure 7: Calculated volume for (**a**) 75 g and (**b**) 150 g of different types of pasta. For each type, the calculation was repeated four times on two different dishes.  
run on smartphones and to be interactive and user-friendly. Instead of taking a few images as usually happens, the user is asked to perform the easier task of taking a short video of the food from which the system automatically extracts the set of six frames that guarantee the best visual coverage of the food. The image-based modeling approach is customized to limit the processing cost. Measurement accuracy is granted by using a small checkerboard as size and ground-plane reference.  
In our tests, we achieved an average 92% accuracy, with a processing time of about 23 s on a regular PC for the whole process. Although the accuracy of the method drops with low-context or small food items, the results indicate that the approach could be a good choice for food intake monitoring.  
As a future development, parallelizing the code, possibly on a GPU, could potentially reduce processing time significantly, especially during the modeling process that accounts for most of it. In addition, since the method currently assumes that there is only one food item in the image, handling more than one food item and estimating the volumes separately could be worth considering. Moreover, different ways of serving the same dish, which, for example, could lead to different amounts of sauce on pasta, should be considered when the estimated volume is going to be used for calculating nutrition facts. Last but not least, a volume estimation algorithm for non-dense point-clouds, which could work also with non-convex models, needs to be developed.  
This work was partially supported by Active and Assisted Living (AAL) programme of European Union through the HELICOPTER project.  
All the authors conceived and designed the experiments; [PERSON] developed the software and performed the experiments; all the authors analyzed the data; all the authors wrote the paper.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Data Processing  
The marker trajectory data were filtered with a Butterworth fourth order filter at a cut-off frequency of 12 Hz [47]. The data were then exported to Visual 3D program (C-motion, Germantown, MD, USA) to define segments from body markers and joint kinematics for subsequent analyses. Spatiotemporal variables, such as contact time, stride length, and stride frequency were computed. Contact time was the duration between initial contact and toe-off for each step. Stride length was calculated as the product of the treadmill speed and time between successive initial contacts of the right foot [29]. Stride frequency was calculated by reciprocal of averaged stride time of the ten strides. Vertical instantaneous loading rate was calculated as the maximum slope of the vertical impact peak [28]. Joint angle and moment in sagittal and coronal planes including initial, peak, and total range of motion (RoM) of the rearfoot, forefoot and knee joint were selected for this study. An inverse-dynamic model which comprised of the thigh, shank, rearfoot, and forefoot segments were built in Visual 3D program (C-Motion Inc., Germantown, MD, USA) to determine peak sagittal and coronal rearfoot and knee joint moments. In brief, the metatrasophalangeal joint was modeled as a single hinge joint rotating about an axis perpendicular to the sagittal plane (i.e., heel-to-teo direction). The segmental masses of segments were taken from a universal model from Visual3D program. The segmental mass of forefoot and rearfoot was then partitioned in the same ratios as their respective volumes, modeling each as simple geometric solids with uniform densities [48]. The positive values denoted as rearfoot dorsiflexion/inversion, forefoot dorsiflexion/inversion, and knee extension/adduction. All GRF and joint moments were normalized to body weight (BW).
## 5 Experimental Evaluation
### Statistical Validation and Robustness Analysis  
Bootstrap resampling analysis with 1000 iterations ensures statistical rigor and yields stable confidence intervals. HATC performance achieves 31.01% \(\pm\) 0.39% with 95% confidence interval [30.2%, 31.8%], while detection rate reaches 88.3% \(\pm\) 1.2% with confidence interval [87.1%, 89.5%]. False positive rate remains low at 2.3% \(\pm\) 0.4% with interval [1.9%, 2.7%], and statistical power exceeding 0.95 for detecting 5% performance differences.  
Parameter sensitivity analysis through Monte Carlo simulation with \(\pm\)20% parameter perturbations demonstrates system robustness across multiple dimensions. Algorithm weight variations produce less than 3% performance change, detection threshold sensitivity remains below 4% for \(\pm\)10% changes, environmental condition effects cause less than 5% degradation under extreme conditions, and simulated component aging over 1000 h results in less than 0.3% performance loss.  
Figure 8: ROC curves for all six attack detection scenarios. The ML-enhanced system demonstrates excellent discrimination capability with AUC values ranging from 0.81 to 0.91, significantly outperforming the baseline CRC-only detection (diagonal line).
# Toward More Sustainable River Transportation in Remote Regions of the Amazon, Brazil
## 3 Challenges in the Amazon Region: The Case of School Transport
### Possible Economic and Environmental Impacts
#### 3.1.2 Environmental Impact  
By optimizing the hulls of these boats, the height of the waves they produce may be lessened. As these boats often travel close to the riverbanks, waves can cause erosion through applied shear effects [60]. Lower waves will produce less applied shear, and the riverbanks will be preserved, which, in turn, avoids riverbank instability that can affect trees, shrubs, and plants growing nearby, as well as aquatic species [61].  
The reduction in gas emissions due to the use of less fuel is an obvious positive effect of optimizing the riverboats. Some of the greenhouse gases produced by the combustion of fossil fuels in inland navigation [62] are shown in Table 1[63]. The Intergovernmental Panel on Climate Change [63] proposes emission factors for various types of engines (diesel, gasoline) for marine and inland vessels. A full overview of these for lake, river, coastal, and ocean vessels is found in [62]. Table 1 shows the approximate gas emission factors for European ships and boats on inland waters [63], estimated for four-stroke gasoline engines. In this table, the possible reduction in gas emissions by the optimized school boat is shown per day and per year, assuming the 5% reduction in fuel consumption described in the previous subsection. The reduction in gas emission per day (in grams) was obtained by multiplying the mass of the saved 38 L of fuel (Section 3.1.1), assuming that a liter of fuel weighs 0.750 kg, by the corresponding emission factor (in g/kg fuel). The reduction per day was then multiplied by 365 to obtain the reduction of gas emissions per year (Table 1). The reduction of greenhouse gases by a single optimized boat is seen to be significant, particularly for carbon dioxide (CO\({}_{2}\)), which could be reduced by \(\sim\)33 tons per year. The reduction in gas emissions may also have long-term benefits to the environment. However, it is important to consider other possible external factors related to the adequate functioning of the propulsion system and the operational conditions of the boats, which can sometimes be subject to stochastic environmental interactions.
# Interpolation-Filtering Method for Image Improvement in Digital Holography
## 2 Image Quality Enhancements in Digital Holography  
### Recording and Reconstruction of Digital Holograms  
The principle of holographic registration implies the coherent addition of two beams obtained by the separation of the original radiation. The object beam is a wave containing information about the object obtained by reflection from or transmission through the object. The reference beam is a wave that has not diffracted from the object. A hologram is registered as follows:  
\[\begin{array}{c}H(\xi,\eta)=|A(\xi,\eta)+B(\xi,\eta)|^{2}\\ =|A(\xi,\eta)|^{2}+|B(\xi,\eta)|^{2}+A(\xi,\eta)B(\xi,\eta)^{*}+A(\xi,\eta)^{* }B(\xi,\eta)\end{array} \tag{1}\]  
where \(A(\xi,\eta)\), \(B(\xi,\eta)\)--object and reference waves; \(H(\xi,\eta)\)--intensity of the total wave; \((\xi,\eta)\)--coordinates in the hologram (camera) plane. The principal scheme of recording digital holograms is presented in Figure 1.  
The procedure for extracting information from a hologram is reconstruction. Reconstruction from analog holograms is performed optically by illuminating them. For digital holograms, both optical and numerical reconstruction is possible. In numerical reconstruction, the radiation propagation from the hologram to the cross-section of the 3D-scene is calculated. In this work for reconstruction, we used direct calculation of Fresnel diffraction (single-FFT method) [73]  
\[R(x,y,z)=\frac{exp(ikz)}{i\lambda z}exp\left(\frac{ik\left(x^{2}+y^{2}\right)} {2z}\right)\cdot\mathrm{FFT}\Bigg{\{}H(\xi,\eta)exp\left(\frac{ik\left(z^{2}+ \eta^{2}\right)}{2z}\right)\Bigg{\}} \tag{2}\]where FFT--fast Fourier transform, \(k\)--wave number, \(z\)--distance from the object to the hologram plane, (\(x\),\(y\),\(z\))--coordinates in the reconstruction plane at distance z from the hologram, (\(x\),\(y\))--coordinates in the hologram plane. The sum of the intensities of the reference and object intensities is represented by the zero-order. The two right-hand terms of Equation (1) correspond to twin and real images, respectively [52]. When calculating the light propagation from the hologram plane to the object plane, the reconstructed wave will contain these three diffraction orders.  
For optical reconstruction of the image from a hologram, spatial and temporal light modulators are used, which perform the function of a material analog hologram. By displaying the hologram on the spatial light modulator and illuminating it with a laser beam, the image is optically reconstructed [74].
## 5 Discussion and Findings
### Predicting Injuries
#### 5.2.3 Age  
Figure 17 provides useful information on injury rates among different age groups of players. Notably, the 'Veteran' category, which includes athletes aged 23 to 31, has an incredibly high injury rate of nearly 60%. This means that six out of every ten players in this group were injured during the season, underscoring the importance of age as a reliable predictor of injuries. Despite its low feature importance score of 0.14, this research highlights the importance of age in injury prediction. It emphasizes the importance of managing veter players carefully, balancing their game time with strategies to minimize overexertion and injury.  
In contrast, the 'Prime' and 'Youngster' groups have injury rates that are roughly 30% and 25%, respectively. This implies that age may not be the most critical factor impacting injuries in these age groups. Instead, other variables or events may contribute to the occurrence of injuries.  
In conclusion, while age is not a strong predictor of injuries for all players, it is a critical component for the 'Veteran' category, where approximately 60% of players have been injured. This finding supports the study's conclusions about the role of age in injury prediction and emphasizes the importance of cautious management of senior players to reduce injury risks.  
Figure 16: Total Injuries by Average Minute per Match Category.  
This research identifies three key indicators of injuries in football teams: squad composition, average minutes per match, and age category. These are important predictors of injuries in football clubs. These findings highlight their significance and are consistent with previous research, underlining their vital roles in injury prediction and management. Effective squad management, playing time allocation, and age category consideration are all critical techniques for injury prevention and player well-being.
# Adaptive TCP Transmission Adjustment for UAV Network Infrastructure
## 5 Performance Evaluation
### Empirical Study
#### 5.1.2 In Case of Transient Link Instability Only  
In this experiment, by transmitting low throughput of TCP flows with only two Operating_UAVs, it prevents buffer overflow caused by heavy traffic that make segment loss events occur by transient link instability only. For transmission, Iperf application was used to create TCP flows and data log for throughput measurement. Two Operating_UAVs simultaneously sent data to GCS via Routing_UAV 1. We evaluated TCP-CUBIC and \(ASRAN\) at the same time to obtain throughput under fair environment. For performance comparison, the throughput was measured every second and the average throughput of all experimental data for 600 s was obtained. Table 1 compares the average throughput of TCP-CUBIC, \(ASRAN\) and theoretical model (shown in Section 4). As shown at the table, the result of \(ASRAN\) shows higher throughput compared to TCP-CUBIC. In addition, \(ASRAN\) has similar results as expected throughput (1.028 Mbits/s) calculated through theoretical model. The high standard deviation shows that the variation of throughput of the experiment was severe. This result shows that the throughput of the UAV network can be very unstable. In this experimental result, \(ASRAN\) shows a higher standard deviation than the TCP-CUBIC. This is because \(ASRAN\) aggressively increases the throughput and is likely to reach the bandwidth limit. Since the \(ASRAN\) is faster than the existing TCP, we analyzed how \(ASRAN\) violated fairness through [PERSON]'s fairness index [26]. The [PERSON]'s fairness index was 0.925, which indicates that ASRAN does not significantly impair fairness. Figure 5 shows the typical throughput fluctuation of experiments. It is possible to observe the point where the transmission becomes impossible (zero throughput) due to the transient link instability of wireless multi-hop network. In most cases, the throughput of experiments were unstable. In this environment, most of experiment time, \(ASRAN\) shows the higher throughput compared to original TCP-CUBIC. This is because \(ASRAN\) recovers CWND more quickly most of time by using the Slow-Start algorithm when segment loss occurs.  
Figure 4: Network topology for experiment.
## 3 Performance Implication of NVM Based Storage
### Effect of the Memory Size  
In this section, we measure the performance of NVM when the memory size is varied. Figure 1 shows the benchmarking performance of HDD, RAMDisk, and PCMdisk as a function of the memory size when executing IOzone. As shown in Figure 1,b, not only RAMDisk but also HDD exhibit surprisingly high performances when the memory size is 1 GB or more. This is because all requested data already reside in the buffer cache when the memory size is large enough. However, as the memory size is small, there is a wide performance gap between HDD and NVM in read operations. Specifically, the throughput of the RAMDisk is over 5 GB/s for all cases but that of HDD degrades significantly as the memory size becomes 512 MB or less. The performance of the PCMdisk sits in b etween those of HDD and RAMDisk.  
This provides significant implications of utilizing NVM in future memory and storage systems. Specifically, the memory size is difficult to be extended any longer when considering the ever-increasing footprint of big data applications as well as the scaling limitation of DRAM. To relieve this problem, NVM may provide an alternative solution. That is, a future computer system can be composed of limited memory capacities but has NVM storage devices, not to degrade performances. Such \"small memory and fast storage\" hierarchies can be efficient for memory-intensive applications such as big data and/or scientific applications.  
Now, let us see the results for the write operations. As shown in Figure 1,d, the average performance improvements of the PCMdisk and RAMDisk are 1204% and 697%, respectively, compared  
Figure 1: Performance comparison of a hard disk drive (HDD), RAMDisk, and phase-change memory (PCM) disk varying the main memory size.  
to the HDD performances. The reason is that a write system call triggers flush operations to the storage even though the memory space is sufficient. Specifically, file systems perform flush or journaling operations, which periodically write the modified data to storage not to lose the modifications against power failure situations. When flush is triggered, the modifications are directly written to the original position in the storage, whereas journaling writes the modifications to the storage's journal area first and then reflects them to the original position later. The default period of journaling/flush is set to 5 s in the Ext4 file system, and thus write operations cannot be buffered although sufficient memory space is provided.
# Analysis on Influences of Squeeze Film Damper on Vibrations of Rotor System in Aeroengine
## 4 The Influence of SFD on the Vibration of the Rotor System  
### Calculation Method of Rotor Vibration Response  
Damping is not considered in the determination of dynamic differential equation of the high-pressure rotor system. The damping and excitation force of the rotor system need to be introduced into the calculation of unbalanced vibration response. The governing equation of the rotor system can be written as:  
\[\mathbf{M\ddot{q}}+(\mathbf{G}+\mathbf{C})\mathbf{\dot{q}}+\mathbf{K_{cg}q}= \mathbf{Q} \tag{8}\]  
where \(\mathbf{M}\), \(\mathbf{G}\), \(\mathbf{C}\), \(\mathbf{K_{cg}}\), and \(\mathbf{Q}\) are mass matrix, gyro moment matrix, damping matrix, stiffness matrix, and unbalanced excitation vector, respectively.  
When considering the unbalance excitation of the disk, the unbalance force vector in the \(y\) and \(z\) directions is:  
\[\left\{\begin{array}{l}F_{y}=m_{e}\Omega_{3}^{2}\cos(\Omega_{3}t+\phi_{3}) \\ F_{y}=m_{e}\Omega_{3}^{2}\sin(\Omega_{3}t+\phi_{3})\end{array}\right. \tag{9}\]  
The initial phase of the center of mass of the disk is \(\phi_{3}\) (rad), the unbalanced mass is \(m_{e}\) (kg\(\cdot\)m), the angular velocity of the rotor is \(\Omega_{3}\) (_rad/s_), and the running time is \(t\) (s).  
When calculating the dynamic response, Rayleigh damping is simple and convenient, and is widely used in structural dynamic analysis. The damping considers that the damping matrix of the structure is a linear combination of the mass matrix and the stiffness matrix, and has the following form:  
\[\mathbf{C}=\alpha\mathbf{M}+\beta\mathbf{K} \tag{10}\]  
where \(\alpha\) and \(\beta\) are damping coefficients, which are related to the modal damping ratio \(\xi\) of the structure.  
\[\begin{array}{l}\alpha=2\omega_{1}\omega_{2}(\xi_{1}\omega_{2}-\xi_{2}\omega _{1})/({\omega_{2}}^{2}-{\omega_{1}}^{2})\\ \beta=2(\xi_{2}\omega_{2}-\xi_{1}\omega_{1})/({\omega_{2}}^{2}-{\omega_{1}}^{2} )\end{array} \tag{11}\]  
The numerical integration method can be used to solve the above equations. Newmark-\(\beta\) integration method is adopted in this paper, which belongs to one of the step-by-step integration methods [24; 25]. According to the size of the time step, when the dynamic response in the next time step is analyzed, the results have good convergence.
## 4 Pilot Plant Analyses  
### Experimental Setup  
The facility used to experimentally test the PCM at a real scale was designed and built at the University of Lleida, and it is integrated by three parts: (1) the heating system, which consists of a 24 kW\({}_{\mathrm{e}}\) electrical boiler that heats up the HTF acting as the heating energy source during a charging process in a real installation; (2) the cooling system, which is based on a 20 kWh air-HTF heat exchanger that simulates the energy consumption by the user during a discharging process in a real facility; and (3) the storage system, which consists of a stainless steel storage tank based on the shell-and-tube heat exchanger concept. The tank stores the thermal energy during the charging process and releases it during the discharging process. Figure 1 shows an overview of the pilot plant facility. All three systems are linked through a piping system that distributes the HTF: Syltherm 800 (Dow Heat Transfer Fluids, Midland, TX, USA). Moreover, rock wool and Foamglass (Pittsburgh Corning, Pittsburgh, PA, USA) are used as insulation to minimize the heat losses to the surroundings.  
To carry out an accurate analysis of the PCM thermal behavior and to get a precise map of temperatures during the charging and discharging processes, thirty-one temperature sensors PT-100  
Figure 1: Overview of the pilot plant facility available at the University of Lleida and used in the experimentation.  
with an accuracy of \(\pm\)0.1 \({}^{\circ}\)C were installed inside the storage tank at different positions (Figure 2a). Nineteen of these probes were located in the main part (from T\({}_{\text{PCM.1}}\) to T\({}_{\text{PCM.15}}\)), six probes were located in the corner part close to the U bend (from T\({}_{\text{c.1}}\) to T\({}_{\text{c.6}}\)) and six probes were located at the central part (from T\({}_{\text{in.1}}\) to T\({}_{\text{in.6}}\)). Each temperature sensor was associated to a control volume, which is defined as the theoretical volume of PCM in which the value of the temperature sensor could be stated as representative. Finally, two temperature sensors PT-100 were installed at the inlet and outlet of the HTF tubes bundle to measure the inlet and outlet HTF temperature. All the temperature sensors and HTF flow meters are connected to a data acquisition system, which controls, measures and records the information at a time interval of 30 s. Concerning to the distribution of the PCM inside the storage tank (Figure 2b), the biggest amount of material (78%) is located within the tubes bundle along the main part of the storage tank while the remaining 22% is located in the corners and in the central part of the tank.