esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
According to the statistical results in Table 6, the average coherence coefficient of Rongbuk Glacier is 0.2694, and the coherence coefficient of Yangbulake Glacier and its surrounding glaciers is 0.2622. The average coherence coefficients of both regions are around 0.26. The proportion of pixels with a coherence coefficient less than 0.4 in the two regions is 83.6998% and 84.2363%, respectively. This means that only about 15% of the pixels in the two regions have a coherence coefficient greater than 0.4. The coherence coefficient of satellite radar remote sensing data in the two glacier areas is relatively low, and the glacier movement speed results inverted by this method need to be studied. The maximum values of the two regions are 0.9859 and 0.9718, both approaching 1. From the map, it can be seen that the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study Area** & **Min** & **Max** & **Mean** & **StdDev** & **<0.4 (\%)** \\ \hline Rongbuk Glacier & 0.0006 & 0.9859 & 0.2694 & 0.1677 & 83.6998 \\ \hline Yangbulake Glacier, etc. & 0.0007 & 0.9718 & 0.2622 & 0.1541 & 84.2363 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The results of the coherence coefficient. region with the highest coherence coefficient of the Rongbuk Glacier is located at glacier termini. This is because the glacier at glacier termini moves slowly and maintains a certain coherence during a revisit period, making satellite inversion results more reliable. For the Yangbulake Glacier, the coherence coefficient of the sampling location at the glacier termini is about 0.2, indicating low coherence. This suggests that the inversion results at this location are distorted and have poor credibility. This is consistent with the Sentinel-1 radar data and GB radar interferometry measurement results analyzed earlier at the termini of the Rongbuk Glacier, while there is a significant difference at the termini of the Yangbulake Glacier. From this perspective, the large-scale inversion of Sentinel-1 radar satellite remote sensing and GB radar interferometry measurement provide consistent and reliable monitoring results for the slow-moving Rongbuk Glacier termini covered by debris. However, the termini of the Yangbulake Glacier is less affected by debris, and the glacier is exposed during the melting season. Glacier movement speed is faster, and Sentinel-1 radar satellite  
Figure 9: InSAR coherence coefficient maps and statistical results. (**a**) Coherence coefficient map and statistical results of Rongbuk Glacier; (**b**) Coherence coefficient map and statistical results of Yangbulake Glacier, etc. remote sensing results are distorted due to longer revisit periods and lower coherence, which is significantly different from GB radar interferometry measurement results. This indicates that GB radar systems can quickly extract glacier movement speed through interferometric measurements, with a short monitoring time, high accuracy, and more reliable results. This indicates the advantages and potential of GB radar system interferometry measurement in the study of glacier dynamics and real-time monitoring.
## 3 Results
### Targeted Drug Delivery
#### 3.2.2 Passive Targeting: The Enhanced Permeability and Retention Effect
Some tumors exhibit limited vascular permeability, reducing the effectiveness of passive targeting. Moreover, the dense extracellular matrix within tumors can impede the diffusion of nanoparticles, limiting their penetration into the deeper tumor tissues. Researchers are actively exploring strategies to enhance the EPR effect, such as using vasodilators to increase tumor blood vessel permeability or designing nanoparticles that respond to the tumor microenvironment by releasing their payload in response to specific stimuli like pH or enzymes. By using vascular normalization agents, such as anti-angiogenic drugs, it is possible to temporarily stabilize the abnormal blood vessels in HCC tumors. This enhances the penetration of nanoparticles into the tumor, thus improving the efficacy of EPR-based drug delivery, as shown by [PERSON] et al. in their study [63]. A study published by [PERSON] et al. explored the use of polymeric micelles loaded with sorafenib, an HCC therapeutic, to harness the EPR effect. The study revealed that these micelles accumulated within the tumor, improved therapeutic efficacy, and lowered toxicity in liver tissues. It also highlighted that modifying the micelles with pH-sensitive coatings enhanced drug release at the tumor site, further improving targeting in the HCC microenvironment [64]. The study by [PERSON] et al. showed that passive targeting can depend on pH, as well as surface changes produced on tumor cells. They successfully created galactose- and PEG-decorated disulfide-linked core-shell mesoporous silica nanoparticles (GPDC-MSNs), designed for selective tumor accumulation and controlled drug release within HCC cells [65]. Despite promising preclinical findings, translating the EPR effect into clinical efficacy in HCC patients has been challenging. Heterogeneity in vascularization, patient liver function, and tumor size all contribute to variability in the EPR effect's efficacy. This suggests a need for personalized approaches, where patients' vascular profiles might be used to determine the suitability of EPR-based therapies.
## 2 Structural Design of the Platform
### Dense Bead Shaft Joint  
#### 2.2.1 Design of Joint and Leg  
The parallel platform uses an RR-R-RR series structure for each leg's kinematic chain. Each subchain contains two joints and one leg, and thus, includes five degrees of freedom of movement. Parts with freedom of movement, such as the joints and the legs, are very important to the overall stiffness of the platform, and the accuracy of the platform's motion is mainly dependent on the gap sizes of the moving parts. Therefore, the joint and leg designs are critical to the accuracy and the stiffness of the machine. The joint uses two rows of high-precision large-diameter dense-bead steel ball arrays symmetrical on both ends of the joint shaft to replace the pair of bearings, which can increase the diameter of the joint shaft and effectively reduce the offset amount and overall size of the offset joint. Pre-tighten the steel balls quantitatively through nuts and end caps to reduce axial clearance. Design the installation benchmark at one end of each joint shaft. The above-mentioned design of the joint greatly improves the bearing capacity, the motion precision, and stiffness of the joint. The prototype and explosion diagram of the joint are shown in Figure 2.  
To allow it to adapt to the structural form of the joint and the mounting and positioning methods, the leg structure is designed as shown in Figure 3. In addition, to provide further improvements in the bearing capacity, the stiffness and the motion accuracy of the legs, high-precision steel balls are used, and the diameters of the steel balls used in the dense ball steel arrays arranged at both ends of the bearing are increased.  
To improve the stiffness of both the joints and the legs, the materials and the heat treatment method are selected, and the material hardness is controlled strictly. At the same time, the axial pre-tightening forces of the joints and the legs are controlled quantitatively to ensure that the pre-tightening states of the twelve joints and six legs are both consistent and interchangeable. In addition, to improve the movement accuracy of the entire machine, the joint installation process has been improved and an installation benchmark has been defined for the new joint. The gap control assembly method that was used for the original joint end face and the joint seat has been changed to a direct contact installation process. This process greatly improves the installation accuracy of the joints and the mobile platform,  
Figure 3: 3D model of the leg.  
Figure 2: Prototype and explosion diagram of joint.  
while the legs and the slider make the physical platform configuration parameters closer to the theoretical configuration parameters, thus improving the motion accuracy of the entire platform.
## 3 Development and Verification of the Inspection Device for Field Application
### Comparison of Simulation Results and Field Measurements in Supplemented Shielding Conditions  
After adjusting the shielding conditions in the simulation, radiation dose values leaking in the width direction were reduced as shown in Figure 10. The target dose of \(1\) uSv/h could now reach a maximum distance of 26 m.  
This was about 5 m less than the value initially conservatively estimated by the theoretical equation, indicating a close approximation considering the analysis error rate and theoretical calculation.  
Using an improved inspection device depicted in Figure 11, radiation leakage doses from a field-installed pipe were examined. The method involved dividing the area into 20\({}^{\circ}\) sections around the radiation source and using a portable survey meter for measurements.  
Results showed significantly reduced radiation leakage doses compared to initial levels, with the distance reduced from 55 to 26 m. Consistent radiation doses were measured across the section, aligning closely with theoretical predictions and simulations indicating similar or lower doses than the target.  
In this study, we developed a high-quality automatic radiography inspection device with stable and effective shielding, validated through comparison of actual leakage measurements with simulations based on conservative theoretical formulas [27; 28; 29; 30].  
Figure 10: Simulation results after supplemented shielding conditions in the DDA width direction. (**a**) Dose distribution derived from X–Y axis. (**b**) Dose distribution derived from the Y–Z axis (section at 55 m based on the X-axis).
## 2 Materials and Methods
### Research Systematics  
The measurements evaluated in the orthopantomographies were the dental panoramic indices and the fractal dimension. The dental panoramic indices used were mandibular cortical index (MCI), visual estimation of cortical width (SVE), and mandibular cortical width (MCW). For the fractal dimension (FD) analysis, some areas of interest (ROIs), two of the trabecular bone and one of the cortical bone to be evaluated, were selected using ImageJ 1.54d software (Java 1.8.0_345(64-bit)) (Figure 1):  
* ROI 1: 25 \(\times\) 25 pixel square in the trabecular bone at the geometric center of the mandibular ramus.
* ROI 2: 25 \(\times\) 25 pixel square in the trabecular bone at the geometric center of the angle of the mandible.
* ROI 3: Rectangle in the basal cortical bone, starting distal to the mentonian foramen and amplifying distal to the mesial root of the first permanent molar, encompassing only the cortex of the mandible. This measurement is performed by applying the polygon tool of the ImageJ program.
* The measurements of ROIs were performed in the right mandibular region.
* The dental panoramic indices analyzed were MCW, MCI, and SVE (Figure 2). The MCW measures mandibular cortical width by drawing a line parallel to the long axis of the mandible and tangential to the inferior border of the mandible. The MCI describes the appearance of the mandibular lower cortex by classifying it among three types: C1, normal cortex with smooth and continuous endosteal margin; C2, endosteal margin with semilunar defects or endosteal cortical debris; C3, endosteal cortical debris and porous and permeable layer. Finally, the SVE refers to the simple visual estimation of the width of the mandibular inferior cortex, classified as thin if less than 3 mm and not thin if greater than 3 mm.  
As independent variables, age, gender, type of OI according to the classification of [PERSON] et al. in 1979, and treatment with antiresorptive drugs were considered. One of the variables evaluated was the influence on the cycles of bisphosphonates administered, for which two groups were established according to the number of cycles administered.  
Figure 1: Regions of interest (ROIs) of fractal dimension analysis selected in a panoramic radiograph: ROI 1, ROI 2 and ROI 3.  
Figure 2: Dental panoramic indexes analyzed [1; 26; 27].  
in each of the orthopantomographies of the study, the measurements were plotted and calculated. The pixel was the unit of measurement used as a reference. For better understanding of the study, pixels were converted to millimeters using the ruler shown on the panoramic radiograph. Each cohort of measurements was stored on an Excel spreadsheet and distributed according to the study and control sample. Once the measurements of all radiographic records were completed, to assess intraexaminer reliability, the procedures were repeated on 20% of the sample.
# The Impact of Freeze Drying on Bioactivity and Physical Properties of Food Products
## 3 Parameters of Freeze Drying
### Samples  
FD affects the product quality of the samples with its characteristic properties [69; 70]. Various results were obtained via applying FD regarding the physical properties, metabolic processes, bioactive properties, and components of different samples [71; 72]. The studies on the total phenolic content preservation of basil leaves (_Ocimum basilicum_) and purple carrots determined the effects of the sample species on the product quality of FD. The study concluded that an increase in the total phenolic compounds of basil leaves and a decrease in the total phenolic content of purple carrots were observed [73; 74]. As a result, the FD process of the various samples showed different preservation levels of the bioactive contents and physical properties of the samples. For example, according to [PERSON] et al., the internal structure and the number of main components of Gastrodia elata did not show any variation during FD [75]. However, in the research conducted by [PERSON] et al., FD changed the appearance, especially color and texture, and significantly preserved the amount of bioactive content in button mushrooms [51].  
In addition, samples that have different physical properties, including color, can cause unlike outcomes concerning physical and bioactive properties after the FD process. For example, a study concluded that different degrees of whiteness as a result of FD when white and purple garlic samples were used [76]. The study also found that purple garlic slices showed better preservation ability on the bioactive properties including total phenolic content, antioxidant capacity, and allicin content, which are important for the valuable usage of garlic, compared with white garlic slices. Correlatively, in a study conducted by [PERSON] et al., FD of various sweet potato flours, including yellow and purple, yielded different implications, showing that yellow sweet potato exhibited better retention of the antioxidant capacity and polyphenol content of the sample than purple sweet potato [77]. Different degrees of retention may come from the divergent amounts of the bioactive ingredients, including raw fiber content and protein content. Also, oven drying was used for drying and showed low retention ability for bioactive compounds, especially the main polyphenols (coumaric acid) and caffeine, which were only detected in the yellow sweet potato after the drying.
## 2 Airfoil-Based Flutter Energy Harvester System Design and Control Equations  
### Conceptual Designing  
In airfoil-based flutter piezoelectric energy harvester systems, when the airflow velocity exceeds a critical threshold, aeroelastic coupling induces flutter motion characterized by coupled plunge-pitch oscillations. Based on this fundamental principle and the structural designs reported in [28; 29], we designed a novel energy harvesting system, as illustratedin Figure 1. The proposed system consists of several key components: an airfoil-based structure for aerodynamic force capture, a rotating pitch mechanism enabling controlled pitch motion, two fixed brackets for structural support, and dual elastic beams that facilitate plunge motion through their bending deformation. The system's restoration mechanism is achieved through two spring rods connecting the rotating pitch component to the brackets, while energy conversion is accomplished by two PZTs mounted on the elastic beams. The material properties and structural dimensions of all components are listed in Table 1.  
The working principle can be described as follows: As the ambient fluid velocity increases, the system experiences growing aerodynamic negative damping effects. When this negative damping overcomes the system's inherent structural damping, the harvester enters a LCO state. During this state, the airfoil-based harvester undergoes sustained plunge-pitch oscillations: the plunge motion is facilitated by the elastic beam deformation, while the pitch motion is regulated by the spring-loaded rotating mechanism. These coupled oscillations induce cyclic strain in the PZTs, effectively converting the mechanical energy of flutter vibrations into electrical energy. This self-sustained oscillation mechanism enables continuous energy harvesting without external mechanical excitation, making it particularly suitable for sustained power generation in fluid flow environments.
## 3 Results and Discussion
### Propane Analysis
The decrease in crystal size in semiconductors increases the specific surface area [10; 20; 24]. Consequently, there is an increase in the number of potentially active catalytic sites that favor the high adsorption of gases on the semiconductor's surface. That is because the space charge zone, or depletion layer, which is a function of charge carriers, has a thickness \(L_{S}\) of less than 100 nm [61; 62]. According to some references, the charged outer layer depends on the partial pressure and the concentration of the test gas [34; 55]. However, the conductance (or electrical resistance) is closely related to the crystal size \(D\)[34; 68], which means that when \(D\) is less than 2\(L\) (in our case 44.02 \(\pm\) 0.50 nm), the response of semiconductor oxides is significantly increased [62]. Then, if the crystal (or particle) size is \(D\) < 2\(L\), the crystals will participate in the electronic transport during sensing [34; 68; 69; 70; 54], provoking variations in the electrical resistance and, therefore, increases in the sensor's response, high electrical sensitivity, thermal stability, and high efficiency in the gas detection [68; 70]. Thus, it is evident that obtaining nanometric particle sizes during the synthesis process will positively impact the detection properties, as reported in this work (see Figures 8-10).
# SOBER-MCS: Sociability-Oriented and Battery Efficient Recruitment for Mobile Crowd-Sensing
## 3 Background
### User Profiling  
User profiling involves the assessment of a user's interest domain through continuous monitoring and analytics over feature sets extracted from the acquired data [42]. User profiling has a crucial role in the proposed system architecture. For this study, we use the TrackMaison framework, which is presented in [41] to model user social activity in the MCS environment. As presented in Figure 4, it consists of two core components: the back-end cloud server undertakes the storage facility for raw data, analysis functionality for the aggregated data and machine learning models for user profiling; the front-end Java application is in charge of monitoring the interaction patterns between the user and the smartphone, as well as the transmission of session data to the cloud server for analysis.  
In the TrackMaison framework, the frond-end Java application continuously monitors user activity in five popular social network applications (i.e., Facebook, Twitter, LinkedIn, WhatsApp and Skype) along with built-in mobile sensors such as GPS. It is worth noting that the list of social network applications can be extended and/or modified; however, investigating the impact of different applications on user profiling is out of scope of this article. TrackMaison defines the following social behavior metrics: the social activity rate, which is an indicator of the data usage of the smartphone on a particular application, and the sociability factor, which indicates the time spent by a user on a particular application. Figures 5-7 depict the probability distribution of the activeness of selected users over the sensing time window for the three types of users. These figures were not presented in previous studies.  
Figure 4: Machine intelligence module that uses the TrackMaison framework in [20].  
By using different machine learning algorithm, SVM and DBSCAN in this study, three representative user profiles were identified from the real data traces. The highly-active user actively interacts with the device \(\approx\)70% of the monitored time window. In other words, when a highly-active user is assigned a sensing task, only 30% of the time does he/she need to prepare his/her smartphones to start sensing the task, which translates into energy overhead. The second type of user profile is the moderately-active user who actively interacts with the device \(\approx\)50% of the monitored time  
Figure 5: Probability of the selected high active user during sensing time window.  
Figure 6: Probability of the selected moderate active user during sensing time window.  
Figure 7: Probability of the selected least active user during sensing time window.  
window. The last type of sociability profile is the least active user type who actively interacts with the device \(\approx\)15% of the monitored time window, which translates into 85% energy overhead for the user to start sensing the tasks.
## 3 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{\(\gamma_{\text{H2O}}\)**in cm\({}^{-1}\)/atm**} & \multirow{2}{*}{\(\gamma_{\text{H2O}}\)} & \multirow{2}{*}{\(\gamma_{\text{air}}\)**in cm\({}^{-1}\)/atm**} & \multirow{2}{*}{\(\pi_{\text{air}}\)} \\ \cline{2-2} \cline{5-8}  & & & & & & \(\pi_{\text{air}}\) \\ \hline HITRAN2020 & 0.460 & \(\pm\)20\% & - & & 0.1032 & \(\pm\)4.0\% & 0.730 & \(\pm\)19.2\% * \\ This work & 0.430 & \(\pm\)5.6\% & 0.692 & \(\pm\)3.2\% & 0.1034 & \(\pm\)3.2\% & 0.761 & \(\pm\) 4.4\% \\ Improvement & & \(-\)4\(\times\) & - & -1.3\% & & \(-\)4.4\% \\ Rel. dev. to & & & & & & & \\ HITRAN2020 & \(-\)6.5\% & & - & 0.2\% & & 4.2\% \\ Red. dev. to & & & & & & & \\ GEISA [27]\({}^{*}\) & & \(-\)6.5\% & - & 0.2\% & & 4.0 \\ \hline \hline \end{tabular} Note: The expanded uncertainty for the HITRAN2020 values are taken as the upper limit multiplied by a coverage. Factor of 2 assuming a normal distribution. RD: relative deviation of this work from HITRAN2020. \({}^{*}\) The average uncertainty of 0.07 taken according to HITRAN [26] and [45]. \({}^{*}\) Thus far, we could not find the associated uncertainties for the GEISA line data here.  
\end{table} 
Table 1: Self- and air broadening coefficients and respective temperature dependence coefficients for the H\({}_{2}\)O absorption line at 7299.43 cm\({}^{-1}\). For this comparison table, all uncertainties (\(u_{\text{rel}}\)) are expanded (\(k=2\)), with \(k=2\) producing an interval with a level of confidence of 95% that the true value is included [28; 39]. For data quality reasons, the results presented in this work do address the traceability to the international system of units (SI). The traceability of the results to the SI are addressed via the traceability of input parameters such as the measured gas temperature and pressure which are traceable to respective PTB standards. The work presents specific methods and analysis, especially for the self-broadening measurements, that can be taken up by the spectroscopy measurement communities in future works to measure H\({}_{2}\)O line data in an extended wavelength range.