esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Stochastic Ray Tracing to Simulate a Dense Time Series of Gross Primary Productivity
## 3 Results
Using the modifier functions \(T_{mod}\) and \(h_{mod}\) (_i.e._, case 2), the photosynthetic capacity was found optimal at humid conditions (\(h>75\%\)) and a temperature of 19.5 \({}^{\circ}\)C. With the exception of \(\chi\), the duration of the initial linear portion of the light response curve, the optimization was unconstrained and photosynthetic capacity and quantum yield were obtained within their reported ranges [55,59]. For example, the quantum yield \(\alpha_{opt}\) was found to be 0.097 (Table 2) which is among the lower range (_i.e._, 0.09 to 0.11 \(\upmu\)mol\(\cdot\)C \(\upmu\)mol\({}^{-1}\) photons) reported by [55] for the leaf level, while values for optimal photosynthetic capacity are commonly found between 11 and 27 \(\upmu\)mol C m\({}^{-2}\cdot\)s\({}^{-1}\) for the leaf level (e.g., [PERSON] 1992 in [59]). Figure 8 demonstrates improvements in using the modifier functions (cases 2-4) for the same \(GF_{\perp}\) used in Figures 4-6 (_i.e._, \(GF_{\perp}\) = 15%). The scatter plot (Figure 8a) shows a much improved correspondence with \(P_{gs,EC}\); however, the results indicate that biases remain that are associated with the diurnal response averaged over the study period (Figure 8g) and the seasonal course of \(P_{gs,EC}\) (Figure 8). Underestimates at noon may indicate that light inhibition of foliage respiration is underestimated in EC data. The decline in \(P_{gs,EC}\) shows a more linear course from 13:00 PST onward than \(P_{gs,sim}\). This may indicate that down-regulation of photosynthesis is more linear with changes in relative humidity than what is currently modelled using the bell-shaped modifier functions. For example, [PERSON] _et al._[11] have used a linear function and a threshold to model temperature effects on the light use efficiency, and used an exponential relationship with vapour pressure deficit. Seasonal trends in \(P_{gs}\) can be expected and result from different phenological stages of growth. To capture seasonal variation, the third case was investigated where \(T_{mod}\) parameters were fixed to estimates obtained in case two and parameters for \(h_{mod}\) and \(D_{mod}\) were unconstrained (Figure 8b,e,h,k). The correspondence with \(P_{gs,EC}\) further improved from \(r^{2}\) = 0.71 to \(r^{2}\) = 0.74 and the results showed no significant bias between months. Inclusion of shade acclimation of leaves in case four caused a minor improvement in the correlation with \(P_{gs,EC}\) (Figure 8c) and an improved precision at Noon and during the afternoon can be observed (Figure 8i), where presumably the distribution of radiation across the canopy matches the nitrogen allocation profile. Finally, Table 2 shows the sensitivity of photosynthesis parameters to changes in gap fractions, \(GF_{\perp}\).
## 2 Methodology
### PSO-XGBoost  
#### 2.1.1 Particle Swarm Optimization  
The principle of the particle swarm optimization (PSO) method [35] involves configuring a swarm of m particles, each with a position represented as \(\mathbf{x}_{i}=\left(x_{i1},x_{i2},\ldots,x_{iD}\right)^{T}\) in a D-dimensional space and a corresponding velocity, denoted \(\mathbf{v}_{i}=\left(v_{i1},v_{i2},\ldots,v_{iD}\right)^{T}\). Position and velocity are two unique characteristics of each particle. A random function is used for determining a particle's initial position and velocity. Assuming that velocity and position are independent features of each particle, their positions and velocities are determined using random functions. During each iteration, the particle identifies its best cost solution, termed the local best \(\mathbf{Pbest}_{i}=\left(p_{i1},p_{i2},\ldots,p_{iD}\right)\) in comparison to its previous states. Concurrently, the particle swarm collectively identifies the lowest-cost solution among all these local bests within the search space, which is designated as the global best \(\mathbf{Gbest}_{i}=\left(g_{1},g_{2},\ldots,g_{D}\right)\). The speed and position of each particle are changed in accordance with the following equation:  
\[v_{id}^{h+1}=wv_{id}^{h}+c_{1}rand_{1}\left(p_{id}^{h}-x_{id}^{h}\right)+c_{2} rand_{2}\left(g_{d}^{h}-x_{id}^{h}\right) \tag{1}\]  
\[x_{id}^{h+1}=x_{id}^{h}+v_{id}^{h+1} \tag{2}\]  
where i = 1, 2, \(\ldots\), m, and i is the position of the particle, d = 1, 2, \(\ldots\), D, m is the particle size, \(p_{id}^{h}\) is the dth dimension component of the \(\mathbf{Pbest}_{i}\), which is the individual optimal location of the particle j in the hth iteration. \(g_{d}^{h}\) is the dth dimension compoent of the \(\mathbf{Gbest}_{i}\), which is the optimal position of all particles in the hth iteration; \(w\) is the inertia weight coefficient, \(c_{1}\) and \(c_{2}\) are learning factors, and \(rand_{1}\) and \(rand_{2}\) are random numbers in the range [0, 1].
## 3 Results and Discussion
### The Results for the SSVEP-Based BCI with Multi-Task Learning
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Duration**} \\ \cline{2-5}  & **4 s** & **3 s** & **2 s** & **1 s** \\ \hline C & 91.5 \(\pm\) 13.2 & 89.8 \(\pm\) 15.2 & 84.6 \(\pm\) 17.8 & 69.1 \(\pm\) 20.5 \\ FC & 91.8 \(\pm\) 13.6 & 89.6 \(\pm\) 15.1 & 83.1 \(\pm\) 17.8 & 65.9 \(\pm\) 20.5 \\ MTL & 93.4 \(\pm\) 14.2 & 90.4 \(\pm\) 15.0 & 86.9 \(\pm\) 16.9 & 84.0 \(\pm\) 16.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The accuracy (\(\mu\)\(\pm\)\(\sigma\%\)) of the proposed neural network using the time-domain feature. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Duration**} \\ \cline{2-5}  & **4 s** & **3 s** & **2 s** & **1 s** \\ \hline C & 86.5 \(\pm\) 14.6 & 79.1 \(\pm\) 18.9 & 69.8 \(\pm\) 21.1 & 30.1 \(\pm\) 12.6 \\ FC & 85.6 \(\pm\) 14.7 & 77.5 \(\pm\) 19.2 & 64.9 \(\pm\) 21.5 & 26.4 \(\pm\) 12.4 \\ MTL & 92.7 \(\pm\) 10.5 & 92.2 \(\pm\) 11.3 & 89.2 \(\pm\) 12.9 & 77.6 \(\pm\) 15.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The accuracy (\(\mu\)\(\pm\)\(\sigma\%\)) of the proposed neural network using the frequency-domain feature. The Wilcoxon signed-rank test was conducted to compare the performance of MTL against FC and C. The results show that MTL significantly outperforms FC (\(p\) = 0.0114) and C (\(p\) = 0.00222), both at \(p\) < 0.05. These findings suggest that the improvements achieved by MTL are unlikely due to random variation, demonstrating that the proposed method provides a more effective approach compared to the baseline methods.
# Current Trends in Orthognathic Surgery in Poland--A Retrospective Analysis of 124 Cases
## 1 Introduction  
Attempts to define the appearance of an ideal face have been made by scientists since ancient times. The canons of the beauty of the human face changed over time, but what is symmetrical, proportional and harmonious has always been considered attractive [1, 2].  
For many people, the pursuit of perfection in the physical appearance has now become a goal in itself.  
The growing awareness of the society about possibilities of the orthodontic and surgical treatment and the potential of orthognathic surgery means that the requirements imposed on doctors are more demanding and that the needs for such treatment have increased significantly over the last 20 years [3, 4]. The necessity to treat skeletal malocclusion provided a reason for distinguishing a specific medical discipline which is orthognathic surgery [5].  
The term \"orthognathic surgery\" denotes operations in the field of maxillofacial surgery aimed at correcting deformities in the structure of the facial skeleton [6; 7]. Treatment of skeletal deformities leads to an improvement in the function of the stomatognathic system and the appearance of the patient's face as a result of actions of orthognathic surgery combined with elements of plastic surgery. The word \"orthognathic\" comes from Greek, and literally means \"straight bones\" (orthos--to straighten, gnathos--bone) [8]. The basis for orthognathic treatment is activation of the craniofacial regions that require correction by osteotomy, and their stabilization in a new position with a selected anastomosis [9; 10].
## 3 Results  
As it has already been mentioned, it has been experimentally proven that a train of circularly polarized laser pulses acting on a PMA ferromagnetic sample can lead to a magnetization switching [10] depending on the laser helicity (\(\sigma^{\pm}\)) and the initial magnetic state (up \(\overrightarrow{m}\uparrow\) or down \(\overrightarrow{m}\downarrow\)). Before analyzing the role of IFE and MCD in these HD-AOS processes, we first describe the heating caused by the laser, which is common for both IFE and MCD scenarios.  
The heating induced by the laser pulses is described by the 2 TM, Equation (2). Figure 1b shows a typical temporal evolution of the temperature of the electrons (\(T_{e}\)) and lattice (\(T_{I}\)) in the center of the sample under two consecutive laser pulses, with \(\Delta t_{L}\ =\ 1\) ns being the time between them. Due to the low electron thermal capacity, \(T_{e}\) increases faster than \(T_{I}\) (see Figure 1c). Electrons and phonons equilibrate their temperatures sometime after the laser pulse is switched off (see Figure 1b), and their temperatures relax towards the substrate temperature (\(T_{sub}\ =\ 300\) K) for longer times. Therefore, the first effect of the laser is the sudden temperature increase of the electron temperature, which with high enough fluence can reach or even overcome \(T_{C}\). In such a situation, the sample demagnetizes locally in the area exposed to the laser beam. An example of this demagnetization process is shown in Figure 1d, where the out-of-plane magnetization component averaged over the sample volume is plotted as function of time during the application of two laser pulses. In order to promote the switching of the local magnetization under the laser spot, additional ingredients that induce some asymmetry depending on the helicity of the laser are needed. In what follows, we will evaluate the effect of the IFE and the MCD in two different helicity-dependent all-optical processes: the local magnetization switching (Section 3.1) and the domain wall motion (Section 3.2).
# The Effect of Energy Loss Straggling on SEUs Induced by Low-Energy Protons in 28 nm FDSOI SRAMs
###### Abstract  
Sensitive volume thickness for silicon on insulator (SOI) devices has scaled to the point that energy loss straggling cannot be ignored within the development of the manufacturing process. In this study, irradiation experiments and Geant4 simulation were carried out to explore the influence of energy loss straggling on single event upsets (SEUs) caused by sub-8 MeV proton direct ionization. We took a 28 nm fully-depleted SOI static random-access memory (SRAM) as the research target. According to our results, the depositing energy spectrum formed by monoenergetic low-energy protons that penetrated through the sensitive volume of the target SRAM was extremely broadened. We concluded that the SEUs we observed in this article were attributed to energy loss straggling. Therefore, it is sensible to take the new mechanism into consideration when predicting proton-induced SEUs for modern nanometer SOI circuits, instead of the traditional linear energy transfer (LET) method.  
energy loss straggling; low-energy proton; single event upset; sensitive volume; fully-depleted silicon on insulator; linear energy transfer +
Footnote †: journal: Applied Sciences
###### Abstract  
In optical communication and sensing, silicon nitride (SiN) photonics plays a crucial role. By adeptly guiding and manipulating light on a silicon-based platform, it facilitates the creation of compact and highly efficient photonic devices. This, in turn, propels advancements in high-speed communication systems and enhances the sensitivity of optical sensors. This study presents a comprehensive exploration wherein we both numerically and experimentally display the efficacy of a SiN-based ring resonator designed for refractive index sensing applications. The device's sensitivity, numerically estimated at approximately 110 nm/RIU, closely aligns with the experimental value of around 112.5 nm/RIU. The RR sensor's Q factor and limit of detection (LOD) are 1.7154 \(\times\) 10\({}^{4}\) and 7.99 \(\times\) 10\({}^{-4}\) RIU, respectively. These congruent results underscore the reliability of the two-dimensional finite element method (2D-FEM) as a valuable tool for accurately predicting and assessing the device's performance before fabrication.  
Keywords:silicon nitride; ring resonator; refractive index sensor; integrated photonics; finite element method; reactive ion etching; electron beam lithography +
Footnote †: journal: Applied Sciences
# Federated Learning for Clients' Data Privacy Assurance in Food Service Industry
## 5 Results and Discussion
### Privacy and Performance Trade-Off
We selected FL because we felt it had the level of security that clients would find acceptable and the improvement in model utility that justifies participation. Many edge computing devices employ chip-based processing units, such as the Google Coral tensor processing unit (TPU), for high-performance neural net inferencing. Currently, these require quantized model parameters, and these can limit model classification or segmentation performance. This creates a trade-off between performance, speed, and privacy. As edge device processing unit technology improves, it may be possible to reduce or eliminate quantization, further improving the usefulness of FL. In this study, we show that using the principle of FL, with a negligible percentage of performance, clients can ensure that their private local data is safe while benefiting from an accurate and reliable contamination detection model. The impact of the number of clients selected in each round of FL training is also a bit of a trade-off. Using fewer clients for training on each round usually means many more rounds of communication to reach the maximal model performance. With more clients, we might reach maximal model performance sooner, but the generalization may be worse.
## 3 Results and Discussion
### Experimental Verification  
Figure 11 illustrates the optical microstructure of borided 316L SS at 900 \({}^{\circ}\)C and 950 \({}^{\circ}\)C for 2 h and 4 h and the measured and predicted values of boride thickness on the surface of steel. The optical images labelled \"a\" and \"b\" letter belong to steel borided at 900 \({}^{\circ}\)C for 2 h and 4 h, whereas the optical images labelled \"c\" and \"d\" letter correspond to 950 \({}^{\circ}\)C for 2 h and 4 h, respectively. In Figure 11, the blue line on the optical microstructure indicates the total boride region on the 316L SS. As seen in Figure 11, when temperature and time increased, the boriding thickness ascended. The boride thickness values of 316L SS were compared with the predicted values presented in Figure 11e. The input parameters of the experimental studies such as temperature, duration time, boriding method and alloy composition for 316L SS were processed by the DNN model to produce the corresponding output parameter. The DNN model was used to predict the boride thickness of steels in this study. The actual values were estimated using the trained DNN model that exhibited the highest result in Section 3.3. As illustrated in Figure 11e, the predicted values closely matched the experimentally measured values, confirming that the DNN model provides reliable and accurate predictions. These findings underscore the model's robustness, highlighting its applicability across varying conditions and reinforcing its potential for generalizability in diverse boriding scenarios. The consistency between predictions and experimental data demonstrates the DNN model's effectiveness as a practical tool for accurately forecasting boride layer thickness.  
Figure 10: Comparison between the measured and predicted boride thickness for best models: (a,c,e) 70% training/30% test splitting, (b,d,f) 80% training/20% test splitting.
## 4 Experimental Process and Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Client(s)** & **SF\({}_{\text{BT}}\)** & **SF\({}_{\text{MC}}\)** & **SR\({}_{\text{BT-MC}}\)** & **Time-Saving Ratio (\%)** & **LOOCV (\%)** \\ \hline
1 & 0.702 & 1.215 & 0.578 & \(-\)73.077 & 4.995 \\
2 & 0.745 & 2.000 & 0.372 & \(-\)168.565 & 8.075 \\
3 & 1.188 & 2.469 & 0.481 & \(-\)107.813 & 6.115 \\
4 & 1.424 & 2.972 & 0.479 & \(-\)108.621 & 6.141 \\
5 & 1.753 & 3.143 & 0.558 & \(-\)79.310 & 5.196 \\
6 & 1.996 & 3.261 & 0.612 & \(-\)63.417 & 4.683 \\
7 & 2.082 & 3.410 & 0.610 & \(-\)63.823 & 4.696 \\
8 & 2.686 & 3.869 & 0.694 & \(-\)44.082 & 4.059 \\
9 & 3.167 & 3.829 & 0.827 & \(-\)20.916 & 3.312 \\
10 & 3.672 & 3.943 & 0.931 & \(-\)7.404 & 2.876 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Speedups, speedup ratios, time-saving, and leave-one-out cross-validation for BT to multicast solution. LOOCV: leave-one-out cross-validation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Solution** & **Mean** & **Stdr. Deviation** & \(t\) **Stat** & \(p\) **value (1-tailed)** & **F** & \(p\) **value** \\ \hline BT & \(1.13\times 10^{3}\) & \(1.74\times 10^{2}\) & \multirow{2}{*}{2.860} & \multirow{2}{*}{\(3.75\times 10^{-3}\)} & \multirow{2}{*}{\(1.02\times 10^{1}\)} & \multirow{2}{*}{\(2.17\times 10^{-3}\)} \\ Multicast & \(1.45\times 10^{3}\) & \(5.31\times 10^{2}\) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of Student’s \(t\) test and [PERSON]’s test carried out to evaluate the difference of means and variances between the data obtained from BT and multicast solutions.
## 4 Computational Experiments
### Experimental Results
#### 4.2.1 Mean Value Problem  
Should decision makers neglect the uncertain amount of construction waste and the uncertain demand for recycled materials, they may opt for the mean values of parameters in all possible scenarios as deterministic parameters. The plan for the size and location of recycling centers can be obtained by solving the following model [M2]:  
**Newly defined parameters:**  
\(V_{s}^{avg}\): the average weight of construction waste generated by the construction site \(s=1,\ldots,S\) or the average weight of recycled building materials required by the construction site \(s=S+1,\ldots,S+S^{\prime}\) of all scenarios  
**Newly defined variables:**  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Recycling Center ID** & **The Optimal Area (m2)** & **The Maximum Area (m2)** \\ \hline HDZ5 & 4392.0 & 4392.0 \\ HDZ6 & 6665.5 & 6665.0 \\ HDZ7 & 3619.6 & 3619.6 \\ BZ6 & 21,041.0 & 21,041.0 \\ ZZ22 & 0.0 & 27,573.6 \\ ZZ23 & 10,242.0 & 10,242.0 \\ ZZ24 & 29,255.7 & 30,221.6 \\ ZZ25 & 2228.7 & 12,096.9 \\ ZZ26 & 0.0 & 132,471.5 \\ ZZ27 & 7923.0 & 7923.0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The planned construction area of recycled centers at 10 locations.  
\(\beta_{s,r}^{avg}\): continuous, the weight of construction waste delivered from the construction site \(s=1,\ldots,S\) to recycling center \(r\) or the weight of recycled building materials from recycling center \(r\) to construction site \(s=S+1,\ldots,S+S^{\prime}\)  
\(\gamma_{s}^{avg}\): continuous, the weight of construction waste delivered from the construction site \(s=1,\ldots,S\) to the landfill site  
**Mathematical model:**  
\[\begin{array}{ll}\textbf{[M2]}&\text{Maximize}^{\sum_{r=1}^{R}\sum_{s=S+1}^ {S+S^{\prime}}\beta_{s,r}^{avg}}\\ \text{s.t.}&\text{Constraints}\ \eqref{eq:M2}&\text{Constraints}\ \eqref{eq:M3}\\ &\gamma_{s}^{avg}+\sum_{r=1}^{R}\beta_{s,r}^{avg}=V_{s}^{avg}&\forall s=1, \ldots,S\\ &\sum_{s=1}^{S}\beta_{s,r}^{avg}\leq\varepsilon_{1}\alpha_{r}&\forall r=1, \ldots,R\\ &e^{2\sum_{s=1}^{S}\beta_{s,r}^{avg}\geq\sum_{s=S+1}^{S+S^{\prime}}\beta_{s,r} ^{avg}}&\forall r=1,\ldots,R\\ &\sum_{r=1}^{R}\beta_{s,r}^{avg}\leq V_{s}^{avg}&\forall s=S+1,\ldots,S+S^{ \prime}\\ &\sum_{r=1}^{R}\left(C_{1}^{2}\alpha_{r}+\sum_{s=1}^{S+S^{\prime}}C_{s,r}^{2} \beta_{s,r}^{avg}\right)+\sum_{s=1}^{S}C_{s}^{3}\gamma_{s}^{avg}\leq C^{max}& \end{array} \tag{10}\] \[\begin{array}{ll}\gamma_{s}^{avg}\geq 0&\forall s=1,\ldots,S+S^{\prime},r=1, \ldots,R\\ &\gamma_{s}=1,\ldots,S\\ &\forall r=1,\ldots,R\\ &\gamma_{r}=1,\ldots,R\\ &\gamma_{s}=S+1,\ldots,S+S^{\prime}\\ &\end{array} \tag{11}\]  
By solving [M2], we can also obtain the optimal value of \(\alpha_{r}\), denoted as \(\alpha_{r}^{**}\).
# Interoperability Reference Models for Applications of Artificial Intelligence in Medical Imaging
## 2 Materials and Methods  
### Materials  
#### 2.1.1 Interoperability  
The general definition of interoperability means the characteristic of different systems being compatible with one another without any limitations. In the healthcare information field, interoperability is defined as a function by which different medical information systems, medical equipment, and applications can optimize the health of an individual or humanity through compatibility [8].  
HIMSS includes three stages for interoperability in healthcare and medical technologies [9] as \"foundational\", \"structural\", and \"semantic\". Each stage can be summarized as follows:  
* Foundational: A basic characteristic of interoperability is establishing an interconnectivity protocol between two different medical systems and enabling them to share medical data.
* Structural: An exchange format and structure must be defined in order to maintain the integrity of medical data.
* Semantic: No issues should arise in the process of safely exchanging and interpreting medical data between two or more medical systems.  
According to HIMSS, interoperability is the ability to safely exchange or interpret medical data created by multiple systems or institutions without any excessive effort by the user. The establishment of interoperability is an element that is essential to the use of medical imaging data in the future. This study composed a reference model based on interoperability as defined by HIMSS.
# Mapping of the Upper Limb Work-Space: Benchmarking Four Wrist Smoothness Metrics
## 2 Materials and Methods
### Laboratory Acquisition Set Up  
Kinematic data recordings were made with the Vero Vicon system, consisting of 10 infrared cameras. During the acquisitions, 5 retroreflective markers were placed on specific anatomical landmarks of each subject in order to reconstruct the right upper limb. Markers were placed on D5 and C7 vertebras, acromion (shoulder; S), right elbow epicondyle (elbow; E), and on the styloid process of the ulna (wrist; W). Subjects held a 20-cm-long pointer, which was identified with two markers. Marker positioning is shown in Figure 2.  
The subject stood in the middle of the area tracked with the motion capture system and executed a set of point-to-point movements (P2P) and multi-directional exploration tasks (EXP). The P2P movement consisted of reaching 9 targets (markers) placed on a target board at 8 cardinal points (NE, E, SE, S, SW, W, NW, N) and in the center of a circle (labelled as O), starting from a reference position R; the EXP task consisted of moving the pointer from the central point O to the cardinal directions, as in previous similar protocols [28,29]. The subject was asked to perform natural movements at a relatively fast speed [29] and to wait in isometric configuration for one second after reaching each target before moving to the following one. The target board was positioned with 5 orientations, in order to cover a comprehensive volume of the workspace of the upper limb. The board was positioned frontally, with vertical orientation (Frontal), laterally on the left (Left) and on the right (Right), upwards (Up) and frontally in horizontal position (Horizontal). The movements were performed only with the right upper limb. Movements were repeated ten times for every direction.