esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
Descriptive statistics were calculated to characterize patient demographics, clinical variables, and score distributions for the E-DII and FACT-Cog. Those with incomplete DHQIII records or records containing DHQIII results with remarkably high or low energy estimates, defined as <600 or >3500 kcal [50], were excluded from analysis. Normality of dietary and cognitive data were assessed with Shapiro-Wilk tests and Q-Q plots. Due to the skewness of the primary outcomes, medians and interquartile ranges (IQR) were calculated for continuous variables, and frequencies and percentages for categorical variables. All analyses were based on intent-to-treat principles, regardless of intervention participation.  
To determine if an educational cooking class changed dietary factors, including intake of calories, total fiber, and fiber-rich foods (fruit, vegetables, whole grains, nuts, seeds, and legumes), and the inflammatory potential of the participants' diet (E-DII scores), baseline values were compared to those at one-month post-intervention via a Wilcoxon signed-rank test. A McNemar test was used to evaluate the difference in proportions of those who did and did not meet at least 75% of the AICR fiber intake goal (22.5/30 g fiber) at baseline and follow-up. A Wilcoxon signed-rank test was used to analyze the change in cognition via FACT-Cog categories between timepoints. Spearman correlations were performed to assess the relationship between dietary factors and cognitive outcomes.  
Univariate and multiple linear regression was used to identify if dietary factors (at baseline, one-month post-intervention, and the change scores) were a predictor of cognitive function. Change scores were calculated as the value at one month minus the value at baseline. Dietary intake models were adjusted for age, BMI, education, physical activity category (< or \(\geq\)500 MET-min), RD visit prior to cooking class, cancer type, and energy intake. E-DII models were adjusted for the same, except for energy, as this was already accounted for in the E-DII calculation. Race/ethnicity or sex were not included in the final model, due to homogeneity in the study population. The confounders were chosen in an a priori fashion, based on clinical knowledge. All tests were two-sided, and a \(p\)-value < 0.05 was considered statistically significant. Statistical analyses were completed using the Statistical Package for Social Sciences (SPSS(tm) software version 26.  
Power and sample size analysis: few studies exist examining the change in DII scores over time, or within the study population of cancer survivors. Therefore, this study serves to provide key information with an adequate sample size for future investigations. Based on previous research, a sample size of 20 was determined to achieve 80% power to detect a change in DII scores [23; 51]. Therefore, the goal for recruitment was set at 24 participants to account for a 20% attrition rate.
# Age- and Sex-Adjusted Reference Intervals in Tear Cytokine Levels in Healthy Subjects
## Appendix C Reference Intervals Classification
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Age**} & \multirow{2}{*}{**Sex**} & \multirow{2}{*}{**OSDI**} & \multirow{2}{*}{**T-BUT**} & \multirow{2}{*}{**Schimmer**} & \multicolumn{2}{c}{**Comeal**} & \multicolumn{2}{c}{**EGF**} & \multicolumn{2}{c}{**Fractalike**} & \multicolumn{2}{c}{**IL-IRA**} & \multicolumn{2}{c}{**IL-IRA**} & \multicolumn{2}{c}{**IP-10**} & \multicolumn{2}{c}{**MCP-1**} & \multicolumn{2}{c}{**VEGF**} \\ \cline{3-19}  & & & & & & & & & & & & & & & & & & & & \\ \hline
59 & F & 90.0 & 2.3 & 0.0 & 4.0 & 1040 & Normal & 1220 & High & 96,500 & High & 1780 & High & 110,000 & Normal & NA & NA & 1490 & High \\ \hline
55 & M & 45.0 & 1.0 & 0.0 & 4.0 & 937 & Normal & NA & NA & 100,000 & High & 16,900 & High & 3300 & Low & NA & NA & 2100 & High \\
47 & F & 66.7 & 0.7 & 0.0 & 3.0 & 117 & Normal & 615 & Normal & 45,400 & High & 327 & Normal & 244 & Low & NA & NA & 219 & Normal \\ \hline
69 & M & 57.5 & 2.3 & 2.0 & 3.0 & 104 & Normal & 1050 & Normal & 48,300 & High & 1480 & High & 3230 & Low & NA & NA & 862 & High \\ \hline
55 & M & 75.0 & 1.0 & 2.0 & 3.0 & 175 & Normal & 1000 & Normal & 43,100 & High & 2390 & High & 2430 & Low & NA & NA & 1050 & High \\ \hline
54 & F & 75.0 & 1.0 & 1.0 & 3.0 & 292 & Normal & 2660 & High & 47,900 & High & 13,400 & High & 38,600 & Normal & NA & NA & 3050 & High \\ \hline
60 & F & 78.1 & 1.0 & 0.0 & 3.0 & 468 & Normal & 1750 & High & 65,300 & High & 1370 & High & 40,400 & Normal & NA & NA & 699 & High \\ \hline
58 & F & 45.0 & 2.0 & 2.0 & 3.0 & 27 & Low & 3890 & High & 88,800 & High & 3760 & High & 149,000 & Normal & NA & NA & 805 & High \\ \hline
72 & F & 85.0 & 2.0 & 2.0 & 4.0 & 303 & Normal & 860 & High & 7420 & High & 1640 & High & 3800 & Normal & NA & NA & 1020 & High \\ \hline
53 & M & 85.0 & 1.3 & 1.0 & 3.0 & 1040 & Normal & 1200 & High & 3900 & High & 19,700 & High & 12 & Low & NA & NA & 88 & Normal \\ \hline
75 & F & 34.4 & 1.0 & 0.0 & 3.0 & 360 & Normal & 1490 & High & 140,400 & High & 4830 & High & 29,300 & Normal & NA & NA & 254 & Normal \\ \hline
51 & F & 50.0 & 0.7 & 1.0 & 3.0 & 27 & Low & 9950 & High & 69,500 & High & 4470 & High & 32,500 & Normal & NA & NA & 2870 & High \\ \hline
49 & F & 61.4 & 1.0 & 1.0 & 4.0 & 37.3 & Low & NA & NA & 1160 & Normal & 562 & High & 9250 & Normal & 12.3 & Low & NA & NA \\ \hline \hline \end{tabular}  
* M = MaleF = Female; OSD = Ocular surface index score; T-BUT = Fluorescent star break-up time; NA = Not Available.  
\end{table} 
Table 22: Complete classification of utility sample by reference intervals (RIs) estimated with the fitted CAMSS model.
# Photonic Integrated Circuits for NGPON2 ONU Transceivers (Invited)
## 4 PIC for Next-Generation PON
### Band and Wavelength Selection Criteria
The broad requirements of the technology in terms of the client locations may require industrial temperature ranges (\(-40\) to \(85\)\({}^{\circ}\)C), which bring extra requisites to the laser wavelength choice or thermal control mechanisms. Directly modulated lasers have good optical output power and are easy to drive and modulate. However, the inherent high wavelength fluctuation when modulated or the inherent chirp can result in propagation limitations. Laser design/control efforts are being made to minimize these effects and currently, there are already some devices in the market able to cope with some of the most stringent standard requirements [81], e.g., the NGPON2. With an external cavity, EMLs result in a much lower chirp, however, having typically a more complex tunability mechanism when compared to the simple current or thermal tuning of the DML [7]. Figure 6: Representation of a generic statistical distribution of lasers over their initial wavelength (\(\lambda\)) in a set of three wafer runs. Different wafers may result in different central \(\lambda\)s. (**A**): distribution of initial \(\lambda\) of wafer #2; (**B**): example of an initial \(\lambda\)s distribution for three different wafers.
# Beam Scanning and Capture of Micro Laser Communication Terminal Based on MEMS Microm mirrors
## 4 MEMS Micromirror Scanning Capture System
### MEMS Micromirror Scan Time  
In polar and Cartesian coordinate systems, the equidistant and equilinear velocity spiral curve can be represented as follows:  
\[\left\{\begin{array}{ll}\rho=\frac{I_{\theta}}{2\pi}\theta\\ I_{\theta}=(\frac{\sqrt{2}}{2}-k_{0})\theta_{b}\end{array}\right. \tag{18}\]  
\[\left\{\begin{array}{ll}x=\frac{I_{\theta}}{2\pi}\theta\cos(\theta)\\ y=\frac{I_{\theta}}{2\pi}\theta\sin(\theta)\end{array}\right. \tag{19}\]  
where \(\theta_{b}\) represents the divergence angle of the laser beam; \(I_{\theta}\) represents the spacing between adjacent spiral curves, also known as the scanning step size, which is the distance between two scanning points; \(k_{0}\) is the additional overlap factor; \(\rho\) represents the polar radius of the scanning spiral curve; and \(\theta\) represents the polar angle of the scanning spiral curve. Let the time interval between adjacent scanning points be \(\Delta t\), which represents the dwell time of the beam at a specific point during scanning. In the absence of beacons, the dwell time is only related to the bandwidth \(F_{ac}\) of the actuator, denoted as \(\Delta t=\frac{1}{F_{ac}}\). Therefore, the spiral scanning speed can be represented as Equation (20):  
\[v_{0}=\frac{I_{\theta}}{\Delta t}. \tag{20}\]  
In conclusion, the expression of the length \(l\) of the spiral scanning line in the polar coordinate system can be obtained by the following equations.  
\[l=\frac{I_{\theta}}{4\pi}[\theta\sqrt{1+\theta^{2}}+\ln(\theta+\sqrt{1+\theta ^{2}})]. \tag{21}\]  
When the polar angle \(\theta\) of the scanning spiral curve is greater than \(\pi\), the length \(l\) of the spiral scanning line can be simplified to  
\[l\approx\frac{\pi\rho^{2}}{I_{\theta}}=\frac{I_{\theta}}{4\pi}\theta^{2}. \tag{22}\]  
Therefore, when the polar angle of the scanning spiral curve is \(\theta\), the scanning time \(t\) is  
\[t=\frac{\Delta t}{4\pi}\theta^{2}. \tag{23}\]  
Figure 4: The schematic diagram of equidistant and equilinear velocity spiral scanning.  
When the scanning polar radius \(\rho\) is the half angle \(\theta_{u}\) of the capture uncertainty zone, \(\rho=\theta_{u}\). After scanning the entire capture uncertain area, the spiral curve reaches its maximum polar angle \(\theta_{\text{max}}\) as follows:  
\[\theta_{\text{max}}=\frac{2\pi}{I_{\theta}}\theta_{u}. \tag{24}\]  
The scanning time \(t_{u}\) required to scan the entire capture uncertainty region is given as  
\[t_{u}=\frac{\pi\theta_{u}^{2}}{I_{\theta}^{2}}\Delta t. \tag{25}\]  
For a \(\pm 30^{\circ}\) uncertain area with a scanning accuracy of 0.1\({}^{\circ}\), a laser beam divergence angle of 1\({}^{\circ}\) and a MEMS micromirror scanning bandwidth of 50 Hz, the scanning time can be obtained through simulation, as shown in Figure 5.  
From the results, it can be seen that as the half-angle of the uncertain area gradually increases, so does the scanning time. When the area half-angle is 30\({}^{\circ}\), the time is 53.8 s.
# Optimal Sizing of Battery Energy Storage System for Implicit Flexibility in Multi-Energy Microgrids
## 3 Results
### Sensitivity Analysis of DAM Price Spread to Incentivize Pure BESS Arbitrage
Notably, for \(\Delta=10\), the NIE becomes non-zero: during the final hours of the day, the CHP unit is shut down to allow the BESS to charge from the grid and valorize this energy flow as NIE. However, the \(\Delta=10\) scenario is highly unrealistic and would never occur under current Italian electricity  
Figure 10: Impact of increasing the DAM price differential on the optimal electrical scheduling of the MEMG with the _optimal_ BESS configuration (2 MW/4 MWh) on 15 July 2024.  
market conditions. This confirms that for the system under analysis with a 2 MW/4 MWh BESS, exploiting NIE is not economically viable in practice. To assess the extent to which the BESS size selected for the MEMG might limit the system's ability to exploit the incentives related to NIE for pure arbitrage, we also analyzed the scenario with a larger BESS configuration of 4 MW power and 16 MWh energy capacity operating on 12 December under varying \(\Delta\) values. The results are presented in Appendix B (Figure A1 and Table A9). These show that with this larger storage capacity, when \(\Delta\) reaches 3, the system starts to find pure arbitrage economically viable. In this case, the system is no longer constrained by the EUR/kW capacity charge in the electricity bill and reaches peak power withdrawals exceeding 5000 kW. However, it should be noted that with \(\Delta=3\), the DAM price spread would reach around EUR 800/MWh, which is still an unrealistic market condition. This highlights the significant challenges of using the DAM price signal alone to incentivize energy shifting under the current Italian electricity billing structure.
## 5 Results and Discussion
### Assessment of Outer Model
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Constructs** & **Indictors** & **Loadings** & **AVE** & **CR** \\ \hline Risky behavior & X1 & 0.677 & 0.501 & 0.834 \\  & X4 & 0.764 & & \\  & X7 & 0.657 & & \\  & X8 & 0.745 & & \\  & X10 & 0.690 & & \\ Risk perception & Y1 & 0.748 & 0.609 & 0.956 \\  & Y2 & 0.766 & & \\  & Y3 & 0.858 & & \\  & Y4 & 0.840 & & \\  & Y5 & 0.832 & & \\  & Y6 & 0.799 & & \\  & Y7 & 0.806 & & \\  & Y8 & 0.823 & & \\  & Y10 & 0.734 & & \\  & Y11 & 0.805 & & \\  & Y12 & 0.793 & & \\  & Y13 & 0.768 & & \\  & Y14 & 0.788 & & \\  & Y15 & 0.510 & & \\ Positive affect & Z1 & 0.687 & 0.509 & 0.892 \\  & Z2 & 0.748 & & \\  & Z3 & 0.804 & & \\  & Z4 & 0.736 & & \\  & Z8 & 0.705 & & \\  & Z10 & 0.695 & & \\  & Z11 & 0.701 & & \\  & Z13 & 0.621 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Measurement model (outer model) assessment. The findings show that gender had the highest significance with regard to the formative construct of personal characteristics, with a \(t\)-value of 3.825. Unexpectedly, the second-highest significance was trip distance with a \(t\)-value of 3.316, while age recorded the lowest \(t\)-value of 1.999. Riding experience did not meet the criteria of a \(t\)-value above 1.96 and factor loading of 0.5. Therefore, riding experience was excluded from the PLS-SEM model. The remaining four formative indicators were significant, with \(t\)-values ranging from 1.999 to 3.825 and \(p\) values less than 0.05. These findings imply that the four indicators did represent the sample data collected. The details of the formative indicator outer weights are tabulated in Table 8.
## 4 Controller Design
### RBFNN Adaptive Terminal Sliding Mode Controller
\begin{array}{l}\dot{V}\leq-\mu\left[\frac{1}{2}s^{2}+\frac{1}{2\lambda_{1}} \widehat{a}^{2}+\frac{1}{2\lambda_{2}}\widehat{b}^{2}+\frac{1}{2\lambda_{3}} \widehat{c}^{2}+\frac{1}{2}\widetilde{\mathbf{W}}^{\text{T}}\Gamma^{-1}\widetilde{ \mathbf{W}}\right]+l\\ \leq-\mu V+l\end{array}  \tag{50}\]  
By selecting the appropriate design parameters \(k\), \(p\), \(q\), \(\lambda_{i}\), \(\sigma_{i}\) and \(\Gamma\), satisfy \(\mu>\frac{l}{\zeta}\). If \(V\geq\zeta\), then \(\dot{V}<0\), that is, \(V\leq\zeta\) is an invariant set. If \(V(0)\leq\zeta\) holds, then for \(\forall t\geq 0\), that is \(V(t)\leq\zeta\). According to the definition of \(V\) and Equation (50), we can know \(V\) is bounded. Thus, closed-loop error signals \(\epsilon_{1}\), \(\epsilon_{2}\), \(\widetilde{\mathbf{W}}\), \(\widetilde{a}\), \(\widetilde{b}\), and \(\widetilde{c}\) are bounded, so, \(\widetilde{\mathbf{W}}\), \(\tilde{a}\), \(\tilde{b}\), and \(\tilde{c}\) are bounded. According to Equation (50), it can be concluded that  
\[0\leq V(t)\leq\frac{l}{\mu}+[V(0)-\frac{l}{\mu}]\exp^{-\mu t} \tag{51}\]  
The above inequality (51) means that there is a moment \(T\), and for \(\forall t>T\), \(0\leq V(t)\leq\frac{l}{\mu}\) is always established. According to the definition of \(V\), it is easy to know that \(e_{1}\), \(e_{2}\), \(\widetilde{\mathbf{W}}\), \(\widetilde{a}\), \(\widetilde{b}\), and \(\widetilde{c}\) converge to the following compact sets respectively:  
\[\Omega_{\epsilon_{1}}=\left\{e_{1}\middle|\middle|e_{1}\middle|\leq\frac{1}{ \beta}\sqrt{\frac{2l}{\mu}}\right\} \tag{52}\]  
\[\Omega_{\epsilon_{2}}=\left\{e_{2}\middle|\middle|e_{2}\middle|\leq\left(\frac{ 2l}{\mu}\right)^{q/2p}\right\} \tag{53}\]  
\[\Omega_{\widetilde{a},\widetilde{b},\widetilde{c}}=\left\{\widetilde{a}, \widetilde{b},\widetilde{c}\middle|\middle|\widetilde{a},\widetilde{b}, \widetilde{c}\middle|\leq\sqrt{\frac{2\lambda_{i}l}{\mu}}\right\} \tag{54}\]  
\[\Omega_{\widetilde{\mathbf{W}}}=\left\{\widetilde{\mathbf{W}}\middle|\middle| \widetilde{\mathbf{W}}\middle|\leq\sqrt{\frac{2\|\Gamma\|l}{\mu}}\right\} \tag{55}\]  
From the above conclusion, it can be seen that the tracking error of the system can be adjusted to any small value by selecting the appropriate design parameters. At the same time, the influence of initial estimation error \(\widetilde{W}(0)\), \(\widetilde{a}(0)\), \(\widetilde{b}(0)\), and \(\widetilde{c}(0)\) on system performance can be adjusted to any small value by selecting appropriate design parameters. Therefore, the introduction of RBFNN to estimate the additional resistance interference in the system will weaken the gain of the switching function to a certain extent to avoid chattering and improve the anti-interference ability of the control system.
## 4 Discussion
### Uncertainties in the GPP Estimates Made with SIF and PRI
Therefore, it is important to eliminate the effects of varying concentrations of xanthophylls, chlorophylls, anthocyanins, and carotenes on photosynthesis using the PRI, especially for different canopy structures and crop growth stages [55]. Although some studies have reported that the seasonally corrected PRI can be a good indicator of photosynthetic status under various environmental conditions, the performance of GPP estimation using \(\text{SIF}_{\text{TOT\_FULL\_PSII}}\) and \(\Delta\text{PRI}\) has not been properly explored [28; 29; 34; 35]. In this study, we assessed the performance of GPP estimation using \(\text{SIF}_{\text{TOT\_FULL\_PSII}}\) and \(\Delta\text{PRI}\). Compared to the \(\text{GPP}_{\text{TOT\_PRI}}\) estimated using \(\text{SIF}_{\text{TOT\_760}}\) and PRI, \(\text{GPP}_{\text{TOT\_APRI}}\) estimated using \(\text{SIF}_{\text{TOT\_FULL\_PSII}}\) and \(\Delta\text{PRI}\) correlated better with \(\text{GPP}_{\text{EC}}\) for both the half-hourly and daily data (Figure 4). This indicates that GPP was monitored better by using broadband photosystem-level PSII SIF (\(\text{SIF}_{\text{TOT\_FULL\_PSII}}\)) and the corrected PRI (\(\Delta\text{PRI}\)). Nevertheless, the potential underlying mechanisms for linking \(\Delta\text{PRI}\) to NPQ at the canopy scale must be explored.
## A Parallel Search Strategy Based on Sparse Representation for Infrared Target Tracking  
**[PERSON], [PERSON] *, [PERSON] and [PERSON]**  
Department of Automatic Test and Control, Harbin Institute of Technology, Harbin 150080, China;  
E-Mails: [EMAIL_ADDRESS] (Z.S.); [EMAIL_ADDRESS] (P.F.); [EMAIL_ADDRESS] (S.J.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-451-8641-3532.  
Academic Editor: [PERSON]  
_Received: 24 April 2015 / Accepted: 1 July 2015 / Published: 27 July 2015_  
**Abstract:** A parallel search strategy based on sparse representation (PS-L1 tracker) is proposed in the particle filter framework. To obtain the weights of state particles, target templates are represented linearly with the dictionary of target candidates. Sparse constraints on the coefficient guarantee that only true target candidates can be selected, and the nonnegative entries denote the associate weights of efficient target states. Then the optimal target state can be estimated by the linear combination of above weighted states. In this way, efficient target states are selected simultaneously from all the particles, which we call a parallel search strategy. Experimental results demonstrate excellent performance of the proposed method on challenging infrared images.  
**Keywords:** infrared target tracking; sparse representation; parallel search
# Scalable Resource Provisioning Framework for Fog Computing Using LLM-Guided Q-Learning Approach
###### Abstract  
Fog computing is one of the growing distributed computing platforms incorporated by Industries today as it performs real-time data analysis closer to the edge of the IoT network. It offers cloud capabilities at the edge of the fog networks through improved efficiency and flexibility. As the demands of Internet of Things (IoT) devices keep varying, it is important to rapidly modify the resource allocation policies to satisfy them. Constant fluctuation of the demands leads to over or under provisioning of resources. The computing capability of the fog nodes is small, and hence there is a necessity to develop resource provisioning policies that reduce the delay and bandwidth consumption. In this paper, a novel large language model (LLM)-guided Q-learning framework is designed and developed. The uncertainty in the fog environment in terms of delay incurred, bandwidth usage, and heterogeneity of fog nodes is represented using the LLM model. The reward shaping of a Q-learning agent is enriched by considering the heuristic value of the LLM model. The experimental results ensure that the proposed framework is good with respect to processing delay, energy consumption, load balancing, and service level agreement violation under a finite and infinite fog computing environment. The results are further validated through the expected value analysis statistical methodology.  
Keywords: fog computing; large language model; resource provisioning; Q learning; scalability +
Footnote †: journal: _Algorithms_
## 3 Materials and Methods
### Static Detection
#### 3.3.1 Selection of Prior Boxes  
The object detection algorithm predicts the position information and size information of the target object by setting multiple prior boxes (anchor points) with different scales at the same location, and then continuously optimizing and adjusting the size of the prediction box during the training process until it exactly matches the size of the target object. Therefore, the size of each prior box greatly affects the detection performance of the final model. Providing the network model with several sets of common prediction box sizes at the beginning of training greatly accelerates the model fitting, shortens the computation time, and improves the detection accuracy. The prior box size data of the original YOLOv4 model were obtained by debugging the COCO data set, which consisted of nine rectangular boxes of different sizes composed of large, medium, and small groups, but were not suitable for the moso bamboo shoot data set. Therefore, for the special morphological characteristics of moso bamboo shoots, the prior box size was re-selected based on the results of the target frame clustering analysis of the data set to improve detection accuracy. According to the principle of the k-means++ algorithm [38], after the random selection of a sample from a cluster center, the shortest distance between each sample and the randomly selected cluster center was calculated. Then, according to Equation (1), each sample was selected as the next probability of the cluster center, and nine cluster centers were selected according to a nine-slot roulette wheel. The distance between each sample and the cluster center was calculated and allocated to the class corresponding to the minimum distance. The cluster center of each class was recalculated until the cluster center remained unchanged.  
\[\mathrm{P_{i}=D(x)^{2}/{\sum_{Y\in\times D(x)^{2}}}} \tag{1}\]  
In Equation (1), \(\mathrm{P_{i}}\) is the probability of each sample being selected as the next cluster center, x represents each sample, X represents the set of all samples, and D(x) is the shortest distance between each sample and the existing cluster center.