esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Method
### Trajectory Replanning Using Gradient-Based Optimization
#### 2.2.3 Kinematic Constraint Cost Function  
The kinematic constraint cost function ensures that the replanned trajectory adheres to the nonholonomic motion constraints specific to fixed-wing UAVs.  
It is crucial to recognize that the movement of fixed-wing UAVs is intrinsically coupled in the xy-coordinate plane. Consequently, when planning the trajectory, it is crucial to verify that each waypoint satisfies the kinematic requirements of the UAV. Specifically, the subsequent waypoint should align with the UAV's current heading direction. Failure to do so could result in a trajectory that is incompatible with the UAV's motion capabilities.  
This consideration is a key innovation in addressing the unique challenges posed by fixed-wing UAVs.  
Therefore, we design the kinematic constraint cost function as follows:  
\[\mathbb{J}_{k}=\sum_{i=0}^{n}c_{i}^{2} \tag{26}\]  
with  
\[c_{i}=\left\{\begin{aligned} \tan\left(\xi_{i}[3]\right)-\frac{ \xi_{i+1}[2]-\xi_{i}[2]}{\xi_{i+1}[1]-\xi_{i}[1]}&\quad if\ \xi_{i+1}[1]\
eq\xi_{i}[1],\\ \tan\left(\xi_{i}[3]\right)-\text{sign}(\xi_{i+1}[2]-\xi_{i}[2] )\frac{\pi}{2}&\quad if\ \xi_{i+1}[1]=\xi_{i}[1].\end{aligned}\right. \tag{27}\]  
Here, \(\xi_{i}[1],\xi_{i}[2],\xi_{i}[3]\) represent the first, second, and third elements of \(\xi_{i}\), respectively. The term \(c_{i}\) quantifies the deviation between the UAV's heading direction and the direction of the trajectory segment connecting waypoints \(\xi_{i}\) and \(\xi_{i+1}\). Minimizing this cost function ensures that the trajectory maintains compatibility with the UAV's kinematic constraints, thereby enhancing overall navigation performance.  
By carefully designing and minimizing this kinematic constraint cost function, we address the core challenge of maintaining a feasible and efficient trajectory for fixed-wing UAVs in complex environments.
# Acute Effect of a Single Functional Neurology Intervention on Muscular Trigger Point
## 4 Discussion
### Study Limitations and Future Lines of Investigation  
The study's main limitations are its small sample size of 63 participants and short duration, affecting the generalizability and long-term assessment of functional neurology interventions. Future research should use larger, more-diverse populations and a longitudinal approach to evaluate sustained impacts. Investigating the underlying mechanisms of these interventions and conducting comparative studies with established treatments like dry needling, manual therapy, and pharmacological interventions can help validate findings and determine the most beneficial patient profiles. A notable gender imbalance was present in the experimental group (22 females vs. 41 males), which could introduce sex-related variability in neuromuscular responses. Although randomization was applied, future studies should employ stratified randomization to ensure equal distribution of key variables such as gender and age. Additionally, further research should investigate potential sex differences in response to functional neurology interventions.  
One of the main limitations of this study is the absence of a sham control group. While the control group did not receive any intervention, this does not allow for differentiation between the specific effects of the functional neurology intervention and potential placebo effects, such as expectation bias or psychological influences. Future studies should incorporate a sham intervention that mimics the treatment conditions without inducing neurological stimulation, allowing for a more rigorous evaluation of the intervention's true efficacy. This study is limited by its relatively small sample size and the specificity of its population, which may impact the generalizability of the findings. Additionally, the intervention was conducted as a single session, and there was no follow-up period to assess the persistence of its effects over time. Future studies should consider a longer observational period to determine whether multiple sessions of functional neurology interventions lead to sustained improvements. Furthermore, the study was conducted in a controlled laboratory setting, which may not fully replicate real-world clinical conditions.  
Although the results indicate a significant improvement in PPT following the functional neurology intervention, this study did not assess whether this change translated into functional benefits, such as improved shoulder range of motion or quality-of-life measures (e.g., SF-36, DASH questionnaire). Future research should incorporate objective functional assessments to determine whether the observed pain relief leads to meaningful clinical improvements.
## 1 Introduction

Table 1: Different methods of DED. Compared to other additive manufacturing methods like laser powder bed fusion (LPBF) and binder jetting (BJ), wire-based directed energy deposition (WDED) offers unique advantages in terms of material efficiency, build size, and deposition rate. WDED has a much faster deposition rate, making it advantageous for producing large-scale samples such as those being used in aerospace and heavy industries, when speed and scale are critical. Its use of wire feedstock also ensures higher material usage with less waste, in contrast to powder-based methods that often experience material loss during recoating. Moreover, WDED has fewer size restrictions, enabling the production of large parts without the limitations of a fixed-build chamber, as seen in PBF and BJ. Eventually, WDED excels in processing high-performance alloys like titanium and nickel-based superalloys, offering the ability to achieve superior mechanical properties and tailored microstructures, making it ideal for industrial applications.
# Edge and Cloud Computing in Smart Cities
## 4 Application Domains
### Smart Cities and IoT-Based Urban Management  
The increasing deployment of IoT devices in urban environments has transformed smart-city management, allowing real-time monitoring of environmental parameters, traffic regulation, and automated public services. Edge computing enables localized processing of urban data streams, ensuring faster decision-making in applications such as smart grids, intelligent waste management, and real-time infrastructure monitoring [129; 130].  
Cloud computing enhances large-scale urban planning by aggregating historical and real-time data, providing predictive analytics for energy demand, traffic optimization, and air quality management. The hybrid edge-cloud framework ensures that time-sensitive decisions, such as adjusting traffic lights during peak hours or detecting environmental hazards, are handled at the edge while comprehensive analysis and governance remain cloud-centric [131; 132].  
Energy grid optimization benefits from edge intelligence, where real-time monitoring of consumption patterns enables adaptive load balancing. AI-driven demand-response mechanisms adjust power distribution based on usage trends, improving grid stability and sustainability. Environmental monitoring leverages IoT sensors deployed across urban regions, providing real-time data on air pollution, noise levels, and climate conditions. These insights drive proactive urban policymaking, ensuring sustainable and resilient smart-city ecosystems [133; 134; 135].
## 4 Discussion  
### Process Condition  
Although the technological aspects of the SLM and EBM fabrication processes were similar, the properties of the SLM- and EBM-fabricated Ti-6 Al-4V alloys were different. Because of the layer-by-layer production process, the growth of epitaxial grains was revealed. Thanks to that, the microstructural differences between the parallel _XZ_ plane and the perpendicular _XY_ plane cross-sections of the samples were observed for both methods. The dark bands observed on SLM-fabricated samples during the light microscopy and the aluminum segregation revealed by the EDS results suggest that melt pool boundaries are areas of aluminum segregation and should not be connected to the powder size and the thickness of the powder layer. The aluminum aberrance within the layers in both SLM- and EBM-fabricated materials is probably the effect of evaporation, which was also suggested for SLS by other authors [79]. The difference in the morphology of melt pool boundaries, thickness and the distance between them was the result of the different power of the laser and electron beams and therefore the variation in heat transfer characteristics during the fabrication methods. The rate of solidification in the EBM fabricated material was much slower than in SLM material, because the diffusion process caused equilibrium \(\alpha\)-phase precipitation on the epitaxially-grown grain boundaries. The precipitation of \(\alpha\)-phase on the original \(\beta\)-phase boundaries  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Process** & **Cutting Direction** & **R\({}_{02}\) (MPa)** & **UTS (MPa)** & **Elongation (\%)** \\ \hline ASTM F136 & & \(>\)795 & \(>\)860 & \(>\)10 \\ \hline \multirow{2}{*}{Wrought} & _XY_ & \(832\pm 10\) & \(933\pm 7\) & 13.0 \(\pm\) 1.5 \\  & _XZ_ & \(836\pm 9\) & \(942\pm 8\) & 12.5 \(\pm\) 1.2 \\ \hline \multirow{2}{*}{EBM} & _XY_ & \(846\pm 7\) & \(976\pm 11\) & 15.0 \(\pm\) 2.0 \\  & _XZ_ & \(845\pm 9\) & \(972\pm 14\) & 14.2 \(\pm\) 1.5 \\ \hline \multirow{2}{*}{SLM} & _XY_ & \(1273\pm 53\) & \(1421\pm 120\) & 3.2 \(\pm\) 0.5 \\  & _XZ_ & \(1150\pm 67\) & \(1246\pm 134\) & 1.4 \(\pm\) 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Tensile properties of fabricated samples.  
Figure 10: Cytotoxicity of Ti-6 Al4V alloy processed by (**a**) EBM and (**b**) SLM after 24, 48 and 72 h.  
is typical for Ti-6 Al-4V alloys cooled from the \(\beta\)-phase region. Although the SLM- and EBM-fabricated samples had significantly different microstructures and allergenic aluminum content deviations, no toxicity effect was detected in either sample.
## 2 The Total Potential Energy of Plane Stress Members  
As explained in the above paragraphs, the plates considered are divided into triangular elements such that the state variables are the displacements \(\mathrm{u_{i}}\) and \(\mathrm{v_{i}}\) at the nodes in x and y directions, respectively. Considering a linear variation in the displacements over the triangle, the displacement fields, such as u(x,y) and v(x,y), can be written as in Equations (1) and (2), respectively. \[\mathrm{u(x,y)}=\mathrm{u_{i}}+\mathrm{C_{1}x+C_{2}y}, \tag{1}\]  
\[\mathrm{v(x,y)}=\mathrm{v_{i}}+\mathrm{C_{3}x+C_{4}y}, \tag{2}\]  
When the particular derivatives of displacement fields are taken with respect to x and y, the strains are expressed in terms of the constants \(\mathrm{C_{1}}\), \(\mathrm{C_{2}}\), \(\mathrm{C_{3}}\), and \(\mathrm{C_{4}}\). \[\varepsilon_{\mathrm{x}}=\frac{\partial\mathrm{u}}{\partial\mathrm{x}}= \mathrm{C_{1}}, \tag{3}\]  
\[\varepsilon_{\mathrm{y}}=\frac{\partial\mathrm{v}}{\partial\mathrm{y}}= \mathrm{C_{4}}, \tag{4}\]  
\[\gamma=\frac{\partial\mathrm{u}}{\partial\mathrm{y}}+\frac{\partial\mathrm{v }}{\partial\mathrm{x}}=\mathrm{C_{2}}+\mathrm{C_{3}}, \tag{5}\]  
The strains \(\varepsilon_{\mathrm{x}}\), \(\varepsilon_{\mathrm{y}}\), and \(\gamma\) define the normal strain in x and y directions and the shear strain, respectively. If the coordinates of node i are the origin point, the nodal displacements of nodes i, j, and k can be expressed as follows, according to Figure 1:  
\[\mathrm{u(0,0)}=\mathrm{u_{i}},\ \mathrm{v(0,0)}=\mathrm{v_{i}}, \tag{6}\]  
\[\mathrm{u(a_{j},b_{j})}=\mathrm{u_{j}},\ \mathrm{v(a_{j},b_{j})}=\mathrm{v_{j}}, \tag{7}\]  
\[\mathrm{u(a_{k},b_{k})}=\mathrm{u_{k}},\ \mathrm{v(a_{k},b_{k})}=\mathrm{v_{k}}, \tag{8}\]  
Figure 1: The triangular element. By using Equations (1) and (2), the relations between the nodal displacements is written as Equation (9):  
\[\left[ \begin{array}{c}\mathrm{u_{j}}\\ \mathrm{v_{j}}\\ \mathrm{u_{k}}\\ \mathrm{v_{k}}\end{array} \right]=\left[ \begin{array}{cccc}\mathrm{a_{j}}& \mathrm{b_{j}}&0&0\\ 0&0&\mathrm{a_{j}}&\mathrm{b_{j}}\\ \mathrm{a_{k}}&\mathrm{b_{k}}&0&0\\ 0&0&\mathrm{a_{k}}&\mathrm{b_{k}}\end{array} \right]\left[ \begin{array}{c} \mathrm{C_{1}}\\ \mathrm{C_{2}}\\ \mathrm{C_{3}}\\ \mathrm{C_{4}}\end{array} \right]+\left[ \begin{array}{c}\mathrm{u_{i}}\\ \mathrm{v_{i}}\\ \mathrm{u_{i}}\\ \mathrm{v_{i}}\end{array}
# Time-Varying Preference Bandits for Robot Behavior Personalization
## 3 Problem Statement
### Problem Formulation  
In general, preference-based learning (PBL) assumes that there exists an optimal parameter \(\theta^{*}\) that best represents the user preference. Unlike the general assumption, since our goal is to model a time-varying preference, we assume that the optimal parameter \(\theta_{t}^{*}\) can be changed through time \(t\) and the deviation of parameter change is not arbitrary but bounded. The more formal definition of time-varying property is explained in Section 5. Our goal is to learn the time-varying parameter \(\theta_{t}^{*}\) using preference feedback collected by pairwise comparisons in an online manner.  
We consider a general PBL protocol: For each iteration, the robot demonstrates a query consisting of two trajectories, \(\xi_{t}^{A}\) and \(\xi_{t}^{B}\), and the user provides binary feedback \(R_{t}\in\{0,1\}\). \(R_{t}=1\) indicates that the user prefers \(\xi_{t}^{A}\) over \(\xi_{t}^{B}\); otherwise, \(R_{t}=0\) is given. From the assumption on user preference and reward model, the preference of \(\xi_{t}^{A}\) over \(\xi_{t}^{B}\) is equivalent to \(r_{t}(\xi_{t}^{A})>r_{t}(\xi_{t}^{B})\). While the inequality between \(r_{t}(\xi_{t}^{A})\) and \(r_{t}(\xi_{t}^{B})\) is deterministic given a reward function, the user response can be noisy and uncertain [4; 5]. Hence, the user's noisy response is usually modeled as a probabilistic distribution [19].  
\[P\Big{(}R_{t}\mid\xi_{t}^{A},\xi_{t}^{B};\theta_{t}^{*}\Big{)}=\frac{e^{\big{(} R_{t}r\big{(}\xi_{t}^{A},\theta_{t}^{*}\big{)}+(1-R_{t})r\big{(}\xi_{t}^{B}, \theta_{t}^{*}\big{)}\big{)}}}{e^{r\big{(}\xi_{t}^{A},\theta_{t}^{*}\big{)}}+ e^{r\big{(}\xi_{t}^{B},\theta_{t}^{*}\big{)}}}, \tag{2}\]
## 3 Results
### The Relationships between Meteorological Factors and SIF  
Some researchers agree that SIF is affected by meteorological factors such as precipitation, temperature, and PAR [28; 45; 46; 47]. We examined the relationships between SIF and meteorological factors on daily and 16-day time scale.  
Figure 7 shows the relationships between SIF and meteorological factors at 16 days and at a regional scale. Significant correlations between SIF and temperature and SIF and radiation (R\({}^{2}\) = 0.68, P \(<\) 0.001; R\({}^{2}\) = 0.49, P = 0.005, respectively) were found. Precipitation had no significant correlation with SIF at the regional scale which indicated that precipitation was not the factor restraining plant growth in Greater Khingan Mountains.  
Daily mean meteorological parameters and SIF at the regional scale were also calculated, and scatter plots were drawn (Figure 8). Only temperature had a significant correlation with SIF (R\({}^{2}\) = 0.40, P \(<\) 0.001). Based on the significant correlations between SIF and temperature at both 16-day and daily time scales, we concluded that, at the regional scale, temperature may affect SIF via photosynthesis.  
Figure 6: Coefficient of determination (R\({}^{2}\)) obtained from a linear regression model between SIF and EVI computed every 16 days and considering different spatial scales.  
Figure 7: Scatter plot between SIF and precipitation (left), temperature (middle), and PAR (right) over the course of 16 days.  
To understand the relationship between temperature and SIF at a 16-day time scale, we downloaded the daily mean temperatures of 27 ground stations within and around the study region in 2015 and 2016. We used kriging interpolation to obtain spatial continuous maps of air temperature (Figure 9). The results are shown in Figure 10. There was a significant correlation between SIF and temperature in the growing season, except on Julian day 257 when vegetation became yellow, and when the chlorophyll content decreased in the autumn. In the growing season, canopy capacity and SIF increased with increasing air temperatures.  
Figure 8: Scatter plot between SIF and precipitation (left), temperature (middle), and PAR (right) at a daily scale.  
Figure 9: _Cont._  
Figure 10: _Cont._  
Figure 9: Kriging interpolation of 16-day mean temperature in 2015 and 2016.
# A Hybrid Degradation Modeling and Prognostic Method for the Multi-Modal System
## 3 Experiment and Discussion  
In this section, we investigate the performance of proposed method using a synthetic dataset and an aero-engine turbofan dataset from NASA. The synthetic dataset is used to theoretical analysis. The turbofan aero-engine degradation dataset was published publicly in NASA's Prognostics Repository [37]; it simulates the degradation of engines in four working environments, so should verify the generality of the proposed model.
## 3 Control Layer Architecture
### Control Metadata  
The control metadata used in the control layer are context measurements and control directives. When two nodes come into range, they disseminate control metadata before delivering or relaying buffered messages. The following two sections describe the aforementioned concepts.
## 2 Results and Discussion
### Method Validation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Compounds**} & \multirow{2}{*}{**Linear Equation**} & **Linearity** & \multirow{2}{*}{\(R^{2}\)} & **LODs 1** & **LOQs 2** \\  & & **Range (\(\upmu\)g/L)** & & **(\(\upmu\)g/kg)** & **(\(\upmu\)g/kg)** \\ \hline \multirow{3}{*}{**Carbendazim**} & Y = 1.01 C + 0.091 & 0.1–100.0 & 0.9996 & 0.03 & 0.09 \\  & Y = 1.12C + 0.062 & 0.1–100.0 & 0.9994 & 0.06 & 0.18 \\  & Y = 0.94C + 0.045 & 0.1–100.0 & 0.9998 & 0.01 & 0.09 \\  & Y = 0.98C + 0013 & 0.1–100.0 & 0.9999 & 0.06 & 0.18 \\  & Y = 0.85C + 0.126 & 0.5–100.0 & 0.9996 & 0.10 & 0.30 \\  & Y = 1.05C + 0.315 & 1.0–500.0 & 0.9995 & 0.30 & 0.90 \\  & Y = 0.81C \(-\) 0.094 & 0.1–100.0 & 0.9993 & 0.03 & 0.09 \\  & Triadimefon & Y = 0.99C \(-\) 0.003 & 0.1–100.0 & 0.9995 & 0.01 & 0.03 \\  & Difenoconazole & Y = 1.12C + 0.211 & 0.5–100.0 & 0.9996 & 0.10 & 0.30 \\ \hline \hline \end{tabular}  
* \({}^{1}\) LODs: limits of detection, 2 LOQs: limits of quantitation.  
\end{table} 
Table 3: Validation parameters obtained for the nine target fungicides in fruits. The limits of detection (LODs) and limits of quantitation (LOQs) of the developed method for the nine target fungicides are shown in Table 3. The calculated LODs (S/N = 3) and LOQs (S/N = 10) were in the ranges of 0.01 to 0.30 \(\upmu\)g\(\cdot\)kg\({}^{-1}\) and 0.03t o 0.90 \(\upmu\)g\(\cdot\)kg\({}^{-1}\), respectively. The tests of the precision and accuracy of the above method were carried out applying three blank typical fruit samples (apple, orange, and grape) spiked with nine fungicides at spiking concentrations of 1.0, 40.0, 80.0 \(\upmu\)g/kg, respectively. The results are listed in Table 4. It was revealed that the mean recoveries of the nine fungicides ranged from 84.9% to 105.2%, with relative standard deviations (RSDs) in the range of 0.8% to 8.2%.
## 2 Methods
### Performance Evaluation  
Where ground truth is available, we use the root-mean-squared error (RMSE), relative dimensionless global error (ERGAS), universal image quality index (UIQI), spectral angle mapper (SAM) in degrees, signal-to-reconstruction error (SRE) in dB, and structural similarity index measure (SSIM) metrics as illustrated in [24; 47; 48]. They are used for quantitatively comparing the methods described in Sections 2.1 and 2.2. In order to give a fair comparison, a set number of edge pixels are left out of metric calculations. The number of pixels to ignore was chosen as six, as suggested for the SSSS method [7].  
RMSE measures the size of the error between the estimated image and the ground truth. Lower values are better. It is calculated for the 20 m and 60 m bands, separately, as well as for the whole scene when ground truth data is available. RMSE is not useful when comparing across different datasets, while the ERGAS and UIQI metrics were developed for that purpose. ERGAS was specifically designed to compare how effectively an image has been super-resolved. It not only normalizes the values with relation to the dynamic range but also with regard to the scale difference between the low initial resolution and high final resolution. ERGAS is therefore calculated separately for the 20 m and 60 m bands. Lower values are better for ERGAS as well, and an ERGAS below 3 is considered good [24]. SAM maps the angular differences in the spectral signatures of each pixel and returns a value in degrees. A value of zero implies that the spectra are identical, apart from possibly a scaling factor applied identically to all bands. The mean SAM value is calculated over all the pixels in the scene. When ground truth is available, it is calculated for all bands as well as for only the 20 m bands.  
UIQI takes a value between \(-1\) and 1, with 1 signifying identical images. It measures correlation, luminance distortion, and contrast distortion. SSIM compares local patterns of pixel intensities. It includes normalization factors for the dynamic range of the image. Its values are also in the range \([-1,1]\) with 1 signifying very similar images. SRE measures the ratio between the ground truth pixel values and the estimation error and is reported in dB. Therefore, somewhat counter-intuitively, for a metric with error in its name, a higher value is better for SRE. UIQI, SSIM, and SRE are calculated independently for each band, and averages are reported for 20 m, 60 m, and all bands as applicable.  
In short, for RMSE, ERGAS, and SAM values closer to 0 are better, while for UIQI and SSIM values closer to 1 are better.  
All experiments were run on an AMD Ryzen 9 3900X 12-Core Processor with 16 GB of 2133 MHz DDR4 system memory and GeForce RTX 2070 SUPER graphics processing unit with 8 GB of VRAM. Running times are the average of 10 runs of the same data on this computer.
## 2 Experimental Program
### Experiment Content and Methodology
#### 2.3.2 Strain Monitoring  
Strain measuring points were arranged at the mid-span section, 1/4-span section, and support section of both track lines. The strain of the slab-track concrete and the CA mortar were measured by the distributed Fiber Bragg Gratings (FBG) strain sensor. The FBG strain sensor was embedded before the concrete pouring or the CA mortar infusion. The moi-sm130 FBG demodulator was used for the data acquisition. The layout of the strain measuring points is shown in Figure 4.
# Cooperative Caching and Resource Allocation in Integrated Satellite-Terrestrial Networks
## 2 Related Work  
This section reviews the related work of cooperative caching in terrestrial networks, satellite networks, and satellite-terrestrial networks, respectively.