esa-sceva/satcom-chunk-collection huggingface dataset:
# XClinic Sensors: Validating Accuracy in Measuring Range of Motion Across Trauma Conditions
## 4 Discussion
Further insights emerged from the associations between knee ROM deficits and domains of the SF-36 scale, including \"physical functioning\", \"social functioning\", and \"pain\". These correlations suggest that joint restrictions in the knee affect not only physical abilities but also social participation and pain experiences, highlighting the broader impact of these deficits on quality of life (QoL), which is consistent with the current literature [39]. Addressing these limitations is critical for improving the overall well-being of affected individuals. The relationship between hip deficits and Berg Balance Scale scores demonstrates the importance of hip mobility in maintaining balance and preventing falls. As the hips are crucial for weight shifting and postural control, restrictions in their movement compromise balance [40]. These findings highlight the importance of incorporating hip-targeted mobility interventions into rehabilitation programs that focus on balance restoration. Additionally, the correlation between left hip deficits and higher scores on the Tampa Scale of Kinesiophobia (TSK) suggests that limited hip mobility contributes to fear of movement, a psychological barrier that may delay recovery [41]. Addressing ROM deficits can alleviate these fears and enhance patient confidence in their physical abilities. A comprehensive rehabilitation assessment is critical for developing personalized therapeutic plans. In this regard, advanced movement analysis tools and functional assessment scales are indispensable for this purpose. These tools offer precise and objective evaluations of a patient's progress, enabling clinicians to identify compensatory patterns, inefficiencies, or abnormalities that may hinder recovery. Integrating movement analysis technologies into clinical practice allows for better customization of physiotherapy interventions, ensuring that treatment is optimally tailored to the individual needs. Additionally, functional assessment scales provide a reliable means of monitoring the recovery over time. By measuring aspects such as balance, strength, and joint mobility, these tools enable clinicians to pinpoint specific areas that require more focused attention. This continuous evaluation ensures that rehabilitation remains dynamic and responsive to the patient's evolving condition, ultimately resulting in a more effective recovery.
## 5 Conclusions  
We proposed a photoacoustic imaging system for detecting caries lesions inside teeth. First, we prepared hidden caries models using a pigment with an absorption spectrum similar to that of real caries lesions and irradiated the occlusal surface of the model teeth containing the pigment with 532-nm-wavelength laser pulses. We found that the frequency spectra of the photoacoustic waves from the simulated caries lesions included components in the range of 0.5-1.2 MHz that were not seen in the spectra of waves from healthy parts of the teeth. This indicated that hidden caries could be detected using a photoacoustic imaging technique. We then fabricated an imaging system composed of a bundle of hollow-optical fibers for laser radiation and an acoustic probe that is attached to the tooth surface for clinical applications. The results of ex vivo imaging experiments using model teeth and an extracted tooth with hidden caries lesions demonstrated that caries lesions inside teeth that cannot be seen in a visual inspection can be detected by focusing on the above frequency components of the photoacoustic waves. By using this method, one may obtain images of hidden caries lesions non-invasively without X-ray radiation. We believe that this is the first report on the feasibility of photoacoustic method for imaging hidden caries lesion.  
At this stage, our proposed method only detects the optical absorption caused by the color difference in the dentin and the detectable size of the lesion is limited to the one larger than around 1 mm. We are working on a photoacoustic method using different polarization directions to detect lesions without a brownish color. In addition, we are working on an image reconstruction technique such as circular back-propagation method to improve the imaging resolution for detection of much smaller caries lesions.  
**Author Contributions**: [PERSON] performed the experiments and analyzed the data; [PERSON] contributed the materials; and [PERSON] conceived and designed the experiments.  
**Conflicts of Interest**: The authors declare no conflict of interest.
# Investigation on Hydroplaning Behaviors of a Patterned Tire on a Steel Bridge Deck Pavement
## 5 Conclusions  
This study developed a tire-fluid-pavement interaction model for hydroplaning analysis on the SBDP. The results of this study can be summarized as follows:  
(1) The developed tire-fluid-pavement interaction model for SBDP based on the CEL algorithm was verified by the NASA hydroplaning equation and the Gallaway hydroplaning equation. The model can be used to evaluate the hydroplaning performance for tire on the SBDP.  
(2) The complex tire pattern can provide more drainage channels for water and reduce the occurrence of hydroplaning.  
(3) The hydroplaning speed decreases with the increase in the tire slip ratio, and increases with the tire inflation pressure. The hydroplaning speed decreases by about 8% when the slip ratio increases from 20 to 100%, so emergency braking should not be performed when driving on rainy days, but drivers should slow down until the vehicle stops. At the same time, the tire should have adequate inflation pressure to improve the anti-hydroplaning performance in rainy days.  
(4) The hydroplaning speed decreases with the increase in water film thickness. The descent of hydroplaning speed is rapid when the water film thickness is small, but when the water film thickness exceeds 7 mm, the descent of hydroplaning speed is not significant.  
Figure 20: Hydroplaning results under different conditions (**a**) hydroplaning speed variations with inflation pressure under different slip ratios; (**b**) hydroplaning speed variations with water film thickness under different SBDPs.  
(5) The macrotextures on the SBDP surface can form drainage channels for water, and the aggregate gradation optimization for SBDP is vital to reduce the occurrence of hydroplaning.  
Conceptualization, Y.L. and Z.Q.; methodology, Y.L. and C.L.; investigation, Y.L. and Q.H.; data curation, C.L. and Q.H.; software, Y.L.; writing-original draft, Y.L.; writing--review and editing, Z.Q. and Q.H.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 52008102 and the Natural Science Foundation of Jiangsu Province, grant number BK20200384.  
Not applicable.  
Data Availability Statement: Data are available upon request from authors.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Human Health Risk Assessment (HHRA)  
The HHRA model developed by the United States Environmental Protection Agency (USEPA) is employed in this study to ascertain the potential health risks to the public. This is a practical means that benefits safe groundwater supply. This method combines contaminants with human health and gives a quantitative characterization of the impacts on human health from exposure to polluted groundwater. In general, there are various exposure pathways through which the polluted groundwater damages human health, with oral intake and dermal contact being the most common. In consequence, this study estimates the potential human health risks from groundwater pollutants via such two main exposure pathways. Additionally, this study considers physiological differences to divide the populations into four groups: infants, children, adult females, and adult males. The potential health risks of pollutants to humans in the HHRA model are determined as follows:  
\[HQ=\frac{CDI}{RfD} \tag{9}\]  
\[HI=\sum\limits_{i=1}^{n}HQ_{i} \tag{10}\]  
where \(CDI\) represents the chronic daily intake dose of a specific pollutant (mg/kg/day), \(RfD\) is the reference dose for a specific pollutant, \(HQ_{i}\) denotes the hazard quotient of the ith pollutant (dimensionless), and HI is the health risk index (dimensionless). For different exposure pathways, the calculation of _CDI_ would also be distinct, and the _CDI_ through oral and dermal exposure pathways can be obtained as following equations:  
\[CDI_{oral}=\frac{C_{i}\times IR\times EF\times ED}{BW\times AT} \tag{11}\]  
\[CDI_{dermal}=\frac{C_{i}\times K_{p}\times SA\times T\times EF\times ED\times EV \times CF}{BW\times AT} \tag{12}\]  
where \(C_{i}\) signifies the pollutant concentration (mg/L), _IR_ signifies the average drinking rate (L/d), _EF_ is the exposure frequency (d/a), _ED_ is the exposure duration (a), BW indicates the body weight (kg), _AT_ indicates the average time (d), _K\({}_{p}\)_ denotes skin permeability coefficient (cm/h), _SA_ denotes the skin contact surface area (cm\({}^{2}\)), \(T\) is the contact duration (h/d), _EV_ indicates the average frequency of dermal contact (time/day), and CF indicates unit conversion factor (L/cm\({}^{3}\)). The values of the aforementioned parameters used in this study are given in Table 1, reference from the existing studies [14].
## 1 Introduction
2021 [6] added serum lactate and comorbid liver disease to the original LRINEC score and re-defined the cut-off values for three variables to develop the MLRINEC score. Logistics regression was used to determine the association with NF after adjustment for confounders and the MLRINEC score was developed by them. Moreover, there were many researchers investigating for finding an effective diagnostic tool to develop the LRINEC score. However, the LRINEC score is a sensitive diagnostic tool, but it is less specific. In Pathophysiology, disseminated intravascular coagulation (DIC) can occur in severe NF, where there is a widespread activation of the coagulation cascade. This leads to the formation of clots throughout the small blood vessels, depleting fibrinogen and platelets and causing bleeding and organ dysfunction. Utilizing fibrinogen and platelets to diagnose NF by considering DIC is associated with NF by fibrinogen levels which may drop due to consumption, and platelet counts are also reduced. Thrombocytopenia (low platelet count) can be observed in NF patients and may be indicative of severe systemic inflammation or disseminated intravascular coagulation (DIC), which can occur in severe infections. Increased levels of fibrin degradation products like D-dimer are also indicative of DIC. Both fibrinogen and platelets contribute to the formation of microthrombi in the blood vessels within the affected tissue. This can lead to further tissue ischemia and damage, exacerbating the disease. Incorporating platelet counts into diagnostic models, like the modified LRINEC score, could enhance the accuracy of distinguishing NF from other soft tissue infections. [PERSON] et al. [3] introduced a classical LRINEC score that incorporates six parameters from a blood test, while [PERSON] et al. (2015) [4] suggested improving accuracy by replacing glucose and sodium levels with erythrocyte count and fibrinogen levels. Therefore, in this study, we further adapt the LRINEC score by substituting platelet counts for glucose levels used in the classical LRINEC score. We modified it by incorporating the six laboratory blood tests: creatinine (CT), hemoglobin (HB), platelet count (PT), sodium (NA), white blood cells (WBCs), and C-reactive protein (CRP). The findings of this study can be applied to diagnose and treat necrotizing fascitis (NF) more effectively, thereby reducing the severity of the disease in affected patients.
## 1 Introduction  
The trend towards reducing the power consumption and improving the scale-down properties of complementary metal-oxide semiconductor (CMOS) devices results in a decreased voltage supply. To achieve a high on/off current ratio at a low supply voltage, the subthreshold swing (SS) must be lowered. However, metal-oxide-semiconductor (MOS) field-effect transistors (MOSFETs) cannot lower the SS below 60 mV/dec due to the physical limitations of their operation [1; 2; 3]. To overcome this problem, new devices based on various principles have been proposed and studied, such as the negative capacitance field-effect transistor (NCFET) [4; 5; 6], resistive gate field-effect transistor (FET) (ReFET) [7], nano-electro mechanical FET (NEMFET) [8; 9], positive feedback FET [10; 11], impact ionization metal-oxide-semiconductor (I-MOS) [12; 13], conventional transistor with an oxide-based threshold switching device [14], and tunnel FET (TFET) [15; 16; 17; 18; 19; 20; 21; 22]. Among them, TFETs are considered as one of the most promising ultra-low power devices due to its high CMOS process compatibility and low-level leakage current [15; 16; 17; 18; 19; 20; 21; 22].  
However, TFETs suffer from low-level on-current (I\({}_{\text{on}}\)) as an alternative of MOSFETs. Therefore, there have been much research to improve the I\({}_{\text{on}}\) of TFETs by modifying the materials and structures of TFETs [23; 24; 25; 26; 27; 28; 29; 30; 31]. Among them, metal gate technology, which is also widely used in MOSFETs, can improve the gate controllability and increase I\({}_{\text{on}}\) by eliminating the polysilicon depletion effect [32]. However, metal gates have different work-functions for each grain due to different grain orientations during the deposition process. This results in a work-function variation (WFV) effect that causes variations in the threshold voltage (V\({}_{\text{th}}\)) and other electrical characteristics of TFETs [33]. In this study, the WFV effects of a planar TFET, a double gate (DG) TFET, and an electron-hole bilayer TFET (EHBTFET) are compared using technology computer-aided design (TCAD) simulations to analyze the effects of the structure and to find a way to improve the WFV of TFETs. In the case of the planar TFET and DG TFET, a tunneling current occurs along the channel (lateral tunneling) at the source junction. On the contrary, the tunneling current of the EHBTFET is generated across the channel (vertical tunneling) at the body between two gates, and the electrical characteristics of this structure have been actively studied through TCAD simulations and modeling [34; 35; 36; 37; 38].
## 3 Methods and Materials Processing
### Sbas-InSAR  
SBAS-InSAR is a classic time series InSAR technique proposed by [PERSON] et al. [33] in 2002, and it has since been further developed and refined by researchers, including [PERSON] [34]. This method has been widely applied in the assessment of geological hazards such as landslides [35], earthquakes [36], and ground subsidence [37], owing to its ability to overcome the measurement errors caused by atmospheric effects, temporal and spatial decorrelation in the D-InSAR technique, and the low resolution of external DEM. The basic principle of the SBAS-InSAR method is to generate an interferogram with higher coherence by setting short spatial and temporal baseline thresholds and then performing spatial filtering (multi-looking) on the differential interferometric phases to achieve phase noise reduction. The region's slowly-decorrelating filtered phase pixels (SDFPs), i.e., highly coherent ground target points, are identified based on the average coherence [38]. Then the observation equation is established on the identified SDFPs, and the least-squares solution of the surface deformation sequence is obtained in the sense of the minimum norm using the singular value decomposition (SVD) algorithm [33; 34; 39]. Finally, the atmospheric and noise phases are separated by spatial adaptive and temporal filters to obtain information such as numerical elevation model errors and temporal deformation sequences. The permanent scatterers used as part of the PS-InSAR technology are primarily located in urban areas, while SDFPs are widely distributed in natural environments, such as on exposed rocks, grasslands, bushes, etc. Therefore, SBAS is more suitable for non-urban  
Figure 2: Flowchart of data processing, including time series InSAR and wavelet analysis.  
areas such as mountainous regions and valleys [40]. Discussion on the principles of SBAS-InSAR technology has been established in numerous articles and will not be elaborated further in this paper. For further information, please refer to the citations [33], [36], and [40] in the reference list.
## 3 Results
### Scenario I  
In terms of IoU values, we can observe four main findings. Firstly, models trained with the Manual labels outperform models trained with the OBIA labels in both camps during both seasons under all four training strategies. Secondly, the accuracy differences in Kule cases are higher than those in the Nguyenygiel cases. For example, in the Kule Dry case, the OBIA-OCOS model can produce an IoU value of 0.5113, while it is 0.5857 for the Manual-OCOS model. However, for the Nguyenygiel Dry case, the IoU values of the OBIA-OCOS model and the Manual-OCOS model are 0.6051 and 0.6377, respectively. Thirdly, the IoU values from the wet season are higher than those from the dry season. For instance, the IoU values of the OBIA-OCOS model and the Manual-OCOS model in the Kule Wet case are 0.5539 and 0.6400, respectively, which are higher than the values (0.5113, 0.5857) from the dry season. Last but not least, we can observe that the changes in IoU \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Refuge** & **Season** & **Training** & **Baseline** & **Scenario I** & \multicolumn{4}{c}{**Scenario II**} \\ \cline{3-10}
**Camp** & **Sears** & **Manual** & **OBIA** & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** \\ \hline \multirow{4}{*}{Kule} & \multirow{4}{*}{Dry} & OCOS & 0.7909 & 0.8029 & 0.7360 & 0.7752 & 0.7704 & 0.7661 & 0.7625 \\  & & OCTS & 0.7882 & 0.8367 & 0.8047 & 0.7862 & 0.7871 & 0.7777 & 0.7760 \\  & & TCOS & 0.7950 & 0.8374 & 0.7923 & 0.7901 & 0.7849 & 0.7791 & 0.7726 \\  & & TCTS & 0.7921 & 0.8232 & 0.8132 & 0.7914 & 0.7882 & 0.7827 & 0.7822 \\ \hline \multirow{4}{*}{Kule} & \multirow{4}{*}{Wet} & OCOS & 0.8642 & 0.8455 & 0.8402 & 0.8346 & 0.8374 & 0.8374 & 0.8364 \\  & & OCTS & 0.8562 & 0.8511 & 0.8787 & 0.8616 & 0.8568 & 0.8352 & 0.8410 \\  & & TCOS & 0.8573 & 0.8629 & 0.8535 & 0.8583 & 0.8512 & 0.8385 & 0.8372 \\  & & TCTS & 0.8518 & 0.8402 & 0.8674 & 0.8603 & 0.8471 & 0.8418 & 0.8389 \\ \hline \multirow{4}{*}{Nguenyygiel} & \multirow{4}{*}{Dry} & OCOS & 0.7815 & 0.8048 & 0.8028 & 0.7979 & 0.7987 & 0.7930 & 0.7863 \\  & & OCTS & 0.7940 & 0.8066 & 0.7710 & 0.7921 & 0.7891 & 0.7934 & 0.7912 \\ \cline{1-1}  & & TCOS & 0.7883 & 0.8111 & 0.7650 & 0.7911 & 0.7918 & 0.7902 & 0.7899 \\ \cline{1-1}  & & TCTS & 0.7886 & 0.8026 & 0.8063 & 0.7971 & 0.7935 & 0.7905 & 0.7886 \\ \hline \multirow{4}{*}{Nguenyygiel} & \multirow{4}{*}{Wet} & OCOS & 0.8189 & 0.8376 & 0.8316 & 0.8245 & 0.8106 & 0.8135 & 0.8198 \\ \cline{1-1}  & & OCTS & 0.8114 & 0.8320 & 0.8291 & 0.8215 & 0.8183 & 0.8154 & 0.8159 \\ \cline{1-1}  & & TCOS & 0.8147 & 0.8545 & 0.8318 & 0.8263 & 0.8247 & 0.8210 & 0.8249 \\ \cline{1-1}  & & TCTS & 0.8260 & 0.8432 & 0.8337 & 0.7610 & 0.8196 & 0.7876 & 0.8224 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The Precision values of built-up structures of all implemented experiments.
# Evaluation of a Vein Biometric Recognition System on an Ordinary Smartphone
###### Abstract  
Nowadays, biometrics based on vein patterns as a trait is a promising technique. Vein patterns satisfy universality, distinctiveness, permanence, performance, and protection against circumvention. However, collectability and acceptability are not completely satisfied. These two properties are directly related to acquisition methods. The acquisition of vein images is usually based on the absorption of near-infrared (NIR) light by the hemoglobin inside the veins, which is higher than in the surrounding tissues. Typically, specific devices are designed to improve the quality of the vein images. However, such devices increase collectability costs and reduce acceptability. This paper focuses on using commercial smartphones with ordinary cameras as potential devices to improve collectability and acceptability. In particular, we use smartphone applications (apps), mainly employed for medical purposes, to acquire images with the smartphone camera and improve the contrast of superficial veins, as if using infrared LEDs. A recognition system has been developed that employs the free IRveinViewer App to acquire images from wrists and dorsal hands and a feature extraction algorithm based on SIFT (scale-invariant feature transform) with adequate pre- and post-processing stages. The recognition performance has been evaluated with a database composed of 1000 vein images associated to five samples from 20 wrists and 20 dorsal hands, acquired at different times of day, from people of different ages and genders, under five different environmental conditions: day outdoor, indoor with natural light, indoor with natural light and dark homogeneous background, indoor with artificial light, and darkness. The variability of the images acquired in different sessions and under different ambient conditions has a large influence on the recognition rates, such that our results are similar to other systems from the literature that employ specific smartphones and additional light sources. Since reported quality assessment algorithms do not help to reject poorly acquired images, we have evaluated a solution at enrollment and matching that acquires several images subsequently, computes their similarity, and accepts only the samples whose similarity is greater than a threshold. This improves the recognition, and it is practical since our implemented system in Android works in real-time and the usability of the acquisition app is high.  
biometric recognition systems; vascular biometrics based on veins; smartphone ordinary cameras; apps for acquisition of veins +
Footnote †: journal: Applied Sciences
## 4 Results
### Comparison between Airborne and Sentinel-2 LAI Estimates  
As shown by Figure 4, the ALS- and DAP-based LAI estimates were compared with Sentinel-2-retrieved LAI, respectively. The ALS results have better agreement with Sentinel-2 results with an R\({}^{2}\) of 0.54 while DAP results show quite low correlation with Sentinel-2 retrievals. It should be noted that the Sentinel-2-based LAI estimates were obtained using a turbid medium radiative transfer model, implying no clumping effect was considered. This may explain the relatively high correlation between airborne and satellite estimates. In addition, we used a series of soil spectra obtained from a public library rather than the field measurements due to the high heterogeneity of the forest floor. The differences in LAI between ALS and Sentinel-2 are generally less than 1.5 despite the very few points falling outside this range. In contrast, the maximum difference in LAI between DAP and Sentinel-2 may be up to about 3.0.