esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Strengths and Limitations
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**T**** therapy Name** & **Application** & **T**** therapy** & **Utilized Tools** & **Session** & **R****results and Success** & **References** \\  & & & **Education about the** & & & & \\  & Chronic pain, patients & & **reusphology of pain,** & **Educational** & & & \\  & with total knee & **nociception, brain and** & **materials** & & & & \\ Pain Neuroscience & ** arthroplasty, chronic** & **nervous system role in** & **(booklets, books, presentations),** & & & & \\ Education (PNE) & **fatigue syndrome,** & **rain perception,** & **presentations),** & & & & \\ fibromyalgiaalgia,** & **Recommended** & **one-on-one sessions,** & & & & & \\ osteoarthritis & **combination with** & **combination with exercise** & & & & & \\  & & **I****t encompasses a wide set** & & & & & \\  & & **of interventions** & & & & & \\  & & **including cognitive** & & & & & \\ Behavioral therapy & **chronic pain, patients** & **reconditioning and** & & & & & \\ behavioral modifications & **-** & **45 min-1.5 h** & **Proven effectiveness in relieving anxiety** & & & & \\ (CBT) & **arthroplasty** & **of specific activities,** & & & & & \\  & & **leading to the reduction** & & & & & \\  & & **and/or modification** & & & & & \\  & & **of pain** & & & & & \\ \hline  & & **Its primary mechanism** & & & & & \\  & & **for lowering** & & & & & \\  & & **kinesiophobia focuses on** & & & & & \\  & & **reducing fear of** & **VR** & **device,** & & & \\  & **neck pain, back pain,** & **movement by diverting** & **VR** & **-based computer** & & & \\  & **musuloskeletal** & **the patient’s attention** & **game, VR-based** & 10–40 min & & & \\  & **disorders** & **away from painful** & **snienedo Wi** & & & 65\% of patients with & \\  & & **stimul, thereby** & **sports program** & & & & \\  & & **fromemented** & & & & & \\  & **combination with exercise** & & & & & \\ \hline Mobile Applications & **Musculsoleletal pain,** & **rehabilitation plans,** & **r** & & & & \\ (myMove, Kaaí Health) & **chronic back pain** & **Personalized** & **r** & & & & \\  & & **real-time progress** & & & & & \\  & & **monitoring, reducing fear** & & & & \\ \hline Robotic & **Chronic back pain,** & **Use of robotic technology** & & & & & \\ Rehabilitation & **post-surgical** & **for precise motor** & **reves, reducing** & & & & \\ (Lokemat) & **rehabilitation** & **avoidance** & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Therapics supporting the management of kinesiophobia. In conclusion, this systematic review provides valuable insights into the role of kinesinobia as a common barrier to PA in populations with chronic diseases. It clarifies the harmful impact of kinesiophobia on quality of life and identifies promising therapeutic strategies. However, further research is necessary to address methodological heterogeneity, refine disease-specific analyses, and develop standardized guidelines for effectively managing kinesiophobia. These advances will facilitate a clearer understanding of its multifaceted effects and support the implementation of targeted interventions to improve patient outcomes.
## 5 Conclusions and Recommendations  
Based on the mining technical conditions, the impact of mining and backfilling processes on surface structures was analyzed using numerical simulation. The results are as follows:  
1. To ensure that the main building clusters remain within safe limits during the mining and backfilling process, and to guarantee the safety and stability of key structures within the surface movement zone, the backfill ratio for the YZS mining area is carefully considered. Surface subsidence, terrain curvature deformation, ground inclined deformation, and lateral deformation within the simulation range were calculated for two different backfill ratios. By combining these results with stability criteria, an appropriate backfill ratio was selected, providing a reference for practical mine backfilling.
2. Considering the actual situation of the mine, a total of 52 surface monitoring points were set up. Under the condition of working condition 1, the settlement value of 7 monitoring points exceeded 30.00 mm, and a large area of subsidence occurred in the YZS mining area. Under the condition of working condition 2, the surface subsidence of 3 monitoring points exceeded 30.00 mm, and the subsidence range of the YZS mining area was also significantly reduced. In addition, after improving the filling rate of the YZS mining area, the terrain curvature deformation, ground inclined deformation, and lateral deformation also decreased to varying degrees, indicating that improving the filling rate of the YZS mining area has a significant effect on the safety and stability of the mine surface. Therefore, it is suggested that the filling rate in the mine using plan 2 (100% of YZS filling, 94% of YH filling, and 90% of BJ filling) should be taken as the actual filling rate.
3. It is recommended that the mine establish a comprehensive surface displacement monitoring system to verify the accuracy of the numerical simulations.  
Conceptualization: Z.Y.; methodology: Y.R.; formal analysis and investigation: Z.Y. and X.Z.; writing--original draft preparation: Y.G. and Z.X.; writing--review and editing: Z.Y. and Q.Z.; funding acquisition: Y.R.; resources: Y.R. and X.Z.; and supervision: Z.X. and Q.Z. All authors have read and agreed to the published version of the manuscript.  
Research grants from funding agencies: 2023 Jiangxi Province 'Science and Technology + Emergency' Joint Plan Project (No.2023 KYG01002); Jiangxi Provincial Key Laboratory of Environmental Pollution Prevention and Control in Mining and Metallurgy (No.2023 SSY01071).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
All the authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Spatial Resolutions of On-Axis and Off-Axis Transmission Kikuchi Diffraction Methods
## 2 Materials and Methods
### Tkd  
Both on-axis and off-axis TKD spatial resolutions were measured by using a TECCAN MIRA3 SEM (TESCAN, Brno, Czech Republic) equipped with a Bruker e\({}^{-}\)Flash(FS) detector. For the off-axis TKD, the sample stage was tilted by \(-20^{\circ}\) and the scintillator was set in the vertical position. For the on-axis TKD, a Bruker OPTIMUS(tm) detector head (Bruker Nano GmbH, Berlin, Germany) was set horizontally,and the tilt angle was set to 0\({}^{\circ}\). During the measurements in both configurations, the spot size was set to 4.8 nm. The operation and detector distances were 5 and 14.2 mm, respectively. The image resolution of all Kikuchi patterns was \(640\times 480\) pixels, while the contrast was 3%. To ensure a good pattern quality, the beam current and exposure time in the on-axis and off-axis TKD measurements were set to 1 nA and 70 ms, and 2 nA and 150 ms, respectively. All samples were oriented so that their grain boundaries were parallel to the incident electron beam and perpendicular to the scanning direction. The Kikuchi pattern at each step was recorded while the beam was scanned across the grain boundary at accelerating voltages of 20-30 kV with increments of 5 kV and scanning step of 2 nm.
## 2 Literature Review
### Studies Assessing FD Using Sensors  
[PERSON] et al. (2018) utilized longitudinal smart home data collected from 29 OAs for an average of more than two years to predict standardized functional health scores. The sensors in this study were passive infra-red (PIR) presence sensors that tracked participants' activities by capturing the movement whenever an event was detected by a sensor. These sensors were placed in different locations of the houses, and the number of sensors installed in each apartment differed based on the size and shape of the house. Their functional health was assessed every six months using Instrumental Activities of Daily Living-Compensation score (2018). The Spearman correlation was used in order to evaluate the relationship between functional health and the sensor data. The results were inconclusive in detecting functional health but emphasized the need for more in-depth feature selection analyses for future works. [PERSON] et al. (2018) focused on evaluating a sensor system being facilitated to monitor functional ability in OAs based on data displays. The data collection took place in TigerPlace, an assisted living facility at the University of Missouri, where data from 14 residents were collected. The sensor network comprised a bed sensor or a wall-mounted room sensor detecting motion, a stove temperature sensor, a sensor detecting restlessness in bed, and physiological sensors measuring pulse and breathing rates. Due to a lack of a patient-focused design, the study was unable to verify the relationship between the sensor data and functional evaluation of the residents.  
Both studies underscore the potential of using sensor-based systems in assessing functional health, and they also reveal the critical need for refined methodologies and patient-centric designs to improve accuracy and applicability in real-world settings.
## 2 Experimental Section
### Solid-Phase Microextraction  
The milk and QF samples were held at \(-\)80 \(\,\)C until analysis. To avoid contamination by volatiles in the laboratory, the samples were defrosted at 24 \(\,\)C in a refrigerator used only for that purpose. The WPC was stored at 20 \(\,\)C. Samples, consisting of 10 mL milk or 0.2 g WPC dissolved in 10 mL water or 5 g finely diced QF, were placed in a 20-mL amber glass vial with a poly(tetrafluoroethylene)/silicone septum (Supelco, Bellefonte, PA, USA) along with 10 \(\,\)mL of 100 ppm 2-methyl-3-heptanone, which served as the internal standard (Sigma Aldrich, St. Louis, MO, USA). Four grams NaCl (Sigma Aldrich) was added to the liquid samples followed by 10 s of agitation at 500 rpm. The sample was equilibrated at 21 or 40 \(\,\)C for various lengths of time. A 2-cm, 50/30-\(\upmu\)m film thickness DVB/Carboux/PDMS Stableflex SPME fiber (Supelco) was exposed to the sample with continuous stirring at 40 \(\,\)C for 30-60 min. The analytes were desorbed for 5 min from the fiber onto the column through a splitless injector at 250 \(\,\)C. A Varian gas chromatograph (model CP 3380, Varian, Walnut Creek, CA, USA) equipped with a 30-m, 0.25-mm i.d., 0.25-\(\upmu\)m film thickness DB-5 column (Restek US, Bellefonte, PA, USA) and a Varian Saturn 2000 mass spectrometer were used to separate and identify volatile compounds. The flow rates of the helium carrier gas and the oven temperature programming are shown in Table 1. The peak areas of volatile compounds were taken to be their relative abundances. The internal standard exhibited a peak area around 1000, which was equivalent to a concentration of 0.10 ppm.
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
## 3 The Composition of the INS
### INS Components
#### 3.1.11 Gyro-Compass  
A gyro-compass is used to detect the direction of the vessel's head in relation to the true (geographic) north by using physical laws, influences of gravity and the Earth'srotation [68; 69]. Magnetic compasses are deflected from external magnetic fields. However, magnetic fields do not affect gyro-compasses. This is why gyro-compasses are widely used on vessels as a primary device for the detection of the true north.
## 5 Development of New Equations for the Live Load Distribution Factors  
The sensitivity analysis revealed that the AASHTO LRFD [13] equations produce highly conservative values for the _LDF_s for bending moment and shear force for skewed bridge with HPS girders. The skewness at the abutments and piers of bridges also changes the load path, thus increasing the complexity of the bridge behavior. Live loads naturally seek the shortest path to the obtuse corner of the bridge's superstructure, producing higher torsional moments and hence a greater reaction at obtuse corners of the bridge. This means that the _LDF_s for non-skewed bridges do not apply to skewed bridges and it is therefore necessary to develop new simplified equations for skewed bridges to ensure the safety and economy of these structures. To achieve this, statistical analyses based on least square regression [35] were performed to develop a new set of equations for skewed composite slab-on-girder bridges with HPS girders.  
Based on the trend in the data revealed by the analyses presented above, the _LDF_s for the bending moment and shear force are assumed to take the following form;  
\[LDF_{s}=DF_{T}\times CF \tag{1}\]  
Figure 8: Effect of key parameters on bridge _LDF_s: (**a**) lane loads; (**b**) skew angle.  
where \(CF\) is a correction factor whose expression must be determined via statistical analyses and \(DF_{T}\) is the \(LDF\) equation deduced by [PERSON] and [PERSON] [15] for a non-skewed I-girder bridge and is as follows;  
\[DF_{T}=0.00013L^{2}-0.021L+1.25\sqrt{S}-(\frac{S+7}{10}) \tag{2}\]  
Thus, the following equations were developed to describe the distribution factors for the bending moment (\(MDF\)) and shear force (\(SDF\)) for both external and internal girders.  
* Live load distribution factors for bending moment \[MDF_{ex}=5.09 DF_{T}L^{-0.64}S^{0.47}N_{L}^{0.21}e^{-0.08\text{Tan}(\theta)}\] (3) \[MDF_{in}=42.40 DF_{T}L^{-0.74}S^{0.074}N_{L}^{0.057}e^{-0.06\text{Tan}(\theta)}\] (4)
* Live load distribution factors for shear force \[SDF_{ex}=12.61 DF_{T}L^{-0.69}S^{0.28}N_{L}^{0.25}e^{-0.125\text{Tan}(\theta)}\] (5) \[SDF_{in}=57.76 DF_{T}L^{-0.80}S^{0.16}N_{L}^{0.2028}e^{-0.06\text{Tan}(\theta)}\] (6)  
where the subscripts, \(ex\) and in, denote the internal and external girders of the bridge, respectively.
## 2 Materials and Methods
### Antibacterial Screening  
Various concentrations of the synthesized AgNPs using latex extracts (prepared with distilled water) were screened for antibacterial activity against three Gram-positive bacterial strains _Bacillus subtilis_ (ATCC 6653), Methicillin-resistant _Staphylococcus aureus_ (MRSA) (ATCC 43300), _Staphylococcus aureus_ (ATCC 29213), and two Gram-negative bacterial strains _Escherichia coli_ (ATCC 25922) and _Pseudomonas aeruginosa_ (ATCC 27853).  
The agar disc diffusion technique, recommended by the Clinical and Laboratory Standards Institute [24], was employed to determine the in vitro antibacterial screening of latex and AgNP samples. After preparing sterile Whatman filter paper No. 1, approximately 20 \(\mu\)L of the respective AgNP concentrations (3.125, 6.25, 12.5, 25, 50, and 100 mg/mL) were applied and allowed to dry at room temperature [25]. The bacterial strains were cultured aseptically (37 \({}^{\circ}\)C) for 24 h using Mueller-Hinton (MH) agar media (Biolab, Merck, Darmstadt, Germany). Subsequently, the bacteria were resuspended in sterile distilled water and vortexed (Model: VM-1000, Taiwan). The optical density (OD) of the bacterial strains (OD 0.08-0.1 at \(\lambda\) 625 nm) was determined using a UV-vis spectrophotometer (Agilent Technologies Cary 60 spectrophotometer, Santa Clara, CA, USA).  
The inoculum was streaked across the four quadrants of the agar medium, and subsequently, the prepared discs containing samples of latex AgNPs were meticulously positioned onto the agar surface using aseptic forceps. The Petri dishes were then incubated at a temperature of 37 \({}^{\circ}\)C for a duration of 18-24 h, following which the zones of growth inhibition were carefully examined to ascertain the antibacterial efficacy of the AgNPs through the utilization of latex extracts. The experiment was replicated three times, with streptomycin (for Gram-positive bacteria) and gentamicin (for Gram-negative bacteria) being employed as the standard positive controls for antibacterial activity, while sterile distilled water was employed as the negative control. Additionally, the AgNO\({}_{3}\) solution (1 mM) was also subjected to testing. The diameter of the zone of inhibition was quantitatively measured in millimeters, the resultant data were recorded and averaged, and visual representations of the Petri dishes were captured.
# CF-mMIMO-Based Computational Offloading for UAV Swarms: System Design and Experimental Results
## 4 Experimental Validation
### Experimental Setup  
Each UAV platform has a 1400 mm wheelbase, 15 kg payload capacity, and a full-load flight endurance of 35 min. The onboard Jetson Orin NX (NVIDIA, Santa Clara, CA, USA) module contains 1024 CUDA cores, 64 Tensor cores, and a 6-core Carmel ARM CPU, supporting 100 Tops AI inference.  
The cloud server is a Y9000P laptop equipped with an Intel Core i9-13900 HX processor (Intel, Santa Clara, CA, USA), NVIDIA RTX 4080 Ti GPU (NVIDIA, Santa Clara, CA, USA),  
Figure 3: UAV platform.  
Figure 2: Cloud and edge communication platform.  
32 GB DDR5 memory, and a 1 TB SSD. The CF-mMIMO base station operates in the 3.7-3.8 GHz band and is deployed at a 15 m elevation. UAV field trials are conducted 28.44 km from the base station, with UAVs hovering at 5 m altitude and 8 m horizontal spacing.  
The CPU/GPU usage on UAVs is monitored via jtop. The GPU usage of cloud and edge servers is tracked using nvidia-smi, and CPU load is measured by aggregating inference thread utilization using top.
## 4 Conclusions  
The close-range terrestrial photogrammetry technique was applied to measure the displacements of paving blocks, indicating the ground movement caused by deep excavation works. Sequential sets of PCD were generated using Pix4D software version 4.8.1 with SfM photogrammetry. The workflow for measuring temporal changes in the displacements of the paving blocks by computing the PCD sets is provided. The requirements of the photogrammetric technique, such as ground sampling distance, image network geometry, and camera parameters, were presented to obtain reliable point-cloud data.  
Vertical displacements of the paving blocks exceeding 5 mm could be detected through the workflow using the C2C algorithm. However, the detection of the horizontal displacements was less successful than that of the vertical displacements, possibly because of the theoretical limit of the adopted C2C algorithm. The measurement accuracy of the proposed workflow can be improved by introducing a vector-based algorithm to compare PCD sets.  
Conceptualization, T.K. and Y.-H.J.; methodology, T.K.; software, J.J.; validation, H.C., J.J. and H.M.; formal analysis, H.C. and H.M.; investigation, H.C.; resources, H.M.; data curation, J.J.; writing--original draft preparation, T.K. and H.C.; writing--review and editing, Y.-H.J. and J.J.; visualization, T.K. and H.C.; supervision, Y.-H.J. and H.M.; project administration, Y.-H.J. and H.M.; funding acquisition, Y.-H.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a grant (No. 22 SCIP-C151582-04) from the Construction Technologies Program funded by the Ministry of Land, Infrastructure and Transport of the Korea government and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2C1089155). This work was also supported by Inha University Research Grant.  
Not applicable.  
Data Availability Statement: Data available on request from the authors. The data that support the findings of this study are available from the corresponding author, Y.-H.J., upon reasonable request.  
The authors declare no conflict of interest.  
Figure 16: Enlarged views of the area with 10 mm horizontal displacement: (**a**) before displacement occurred; (**b**) after 10 mm displacement; (**c**) differences between PCD.
## 4 Incorporation of Synthetic Data
### Data Fitting with One Parameter
#### 4.1.1 \(N=1\)  
Figure 6 illustrates an instance of fitting parameter \(b\) to synthetic data for a particular group through the aforementioned methodologies. The process of Stochastic Gradient Descent (SGD) is depicted in Figure 6a, showcasing the progression of negative log-likelihood values and the corresponding estimated parameter values throughout each iteration. The equilibrium solution employed in generating the data are represented in Figure 6b, while the equilibrium solution derived from the parameters estimated via SGD is displayed in Figure 6c. The inferred equilibrium solution closely aligns with the genuine equilibrium solution's territory, although the latter exhibits greater symmetry and a slightly heightened peak value.  
To assess convergence frequency and the impact of dataset size, we conducted 60 maximum likelihood estimation trials to fit \(b\) using data generated from \(b=0.25\). We explored three dataset sizes: \(M=100\), \(M=200\), and \(M=300\), presenting the outcomes in Figure 7. A significant majority of trials clustered around the true parameter value of 0.25, with fewer deviating. This trend intensified as dataset size increased, supported by decreasing variances: 0.0085, 0.0072, and 0.0060, respectively.  
In a similar vein, Figure 8 fits the parameter \(\eta\) using data generated from \(\eta=5\). The iterations of SGD are depicted in Figure 7(a), the equilibrium solution employed in generating the data are represented in Figure 7(b), while the equilibrium solution derived from the parameters estimated via SGD is displayed in Figure 7(c). As we saw previously, the estimated equilibrium solution closely aligns with the true equilibrium solution's territory with less symmetry.  
Figure 6: Iterations of SGD minimizing the negative log-likelihood function to estimate \(b\) using data generated with \(M=300\), \(N=1\), \(b=0.25\), \(a=1\), and \(\eta=5\), and the equilibrium solutions to System (2) using the estimated parameter and the parameter used to generate the data. (**a**) Iterations of SGD and corresponding estimated \(b\) (**top**) and negative log-likelihood value (**bottom**). (**b**) PDF used to generate data. (**c**) PDF from estimated parameters.  
Figure 7: The histogram of estimated \(b\) values from 60 trials of SGD for various values of \(M\), with data generated from \(b=0.25\), \(a=1\), \(\eta=5\). The average values from the simulations are 0.2537, 0.2342, 0.2464, respectively. The variances from the simulations are 0.0085, 0.0072, 0.0060, respectively.  
Figure 9 analogously assesses convergence frequency and the effect of data set size for \(\eta\). Via 60 maximum likelihood estimation trials, we fit \(\eta\) to data generated from \(\eta=5\) for three different data set sizes. Average values closely aligned with the true parameter although, interestingly, trial averages consistently underestimated the actual parameter value. Furthermore, as dataset size increased, variance diminished: 0.1171, 0.0772, and 0.0462, respectively.