esa-sceva/satcom-chunk-collection huggingface dataset:
# Imaging of Cell Structures Using Optimized Soft X-ray Contact Microscopy
## 3 Results and Discussion
### Optimization of SXCM Technique for Cell Imaging
#### 3.1.2 Soft X-ray (SXR) Pulses Number Optimization  
In addition to using an appropriate developer, PMMA degradation depends on the SXR absorbed energy. Therefore, we examined the correlation between the applied number of SXR pulses and PMMA degradation (Figure 5).  
Figure 4: The developer concentration influences SXCM imaging. MIBK:IPA ratio: (**a**) 1:1; (**b**) 1:2 and (**c**) 1:3 (the development time was 4 min). AFM deflection images of fixed HCC38 cells recorded using Xe120 AFM (Park Systems) with OTR4 cantilevers (0.02 N/m, Bruker, Camarillo, CA, USA). Scan size of \(80\times 80\)\(\upmu\)m\({}^{2}\); 600 SXR pulses.  
The PMMA degradation increased in direct proportion to the number of SXR pulses in the range from 200 to 1000 (linear exposure region) with a regression correlation coefficient R\({}^{2}\) equal to 0.9938. The PMMA exposure to pulses above 1000 did not cause higher PMMA degradation (the saturation region). To ensure the full linear region between the dissolved PMMA thickness and SXR absorbed energy, 400-800 SXR pulses were chosen for further experiments. To verify which number of pulses gives better resolution of cellular imprints, the SXCM images of HCC38 cells were prepared for exposures of 400, 600, and 800 pulses (Figure 6).  
The imprints obtained were visualized using AFM working in a contact mode (Figure 6). After the exposure to 400 SXR pulses, the cell outlines can be observed; however, such large organelles as nuclei are barely visible (Figure 6a). After the exposure to 600 SXR pulses, the cell nucleus is clearly visible (Figure 6b). Inside the cell nucleus, two nucleoli are located (indicated by the black arrows), while the outside region is rich in the rounded structures, which were presumed to comprise endoplasmic reticulum, vacuoles, and mitochondria (outlined with a frame). Within the cell's peripheral region, filamentous structures could be identified (indicated by the green arrow) as the bundles of cytoskeletal filaments. In some cells, the protrusions were also observed (images not shown). Increasing the absorbed energy up to 800 SXR pulses enhanced the image of the cell nucleus in HCC38 cells (Figure 6c). These cells usually contain more than one nucleolus in their nuclei (indicated by the black arrows). They are poorly visible when lower numbers of SXR pulses were applied. These results showed that depending on the cellular structure studied, the adjustment of the number of pulses should always be conducted.  
Figure 5: Dissolved PMMA thickness versus the number of SXR pulses (MIBK:IPA 1:2 ratio (_v/v_), development time: 4 min). PMMA thickness was obtained from AFM images (AFM, di CPII, Veeco) as described in Material and Methods. Data are expressed as a mean \(\pm\) standard deviation from 10 images.
# Effects of Low Intensity Continuous Ultrasound (LICU) on Mouse Pancreatic Tumor Explants
## 2 Materials and Methods
### Attenuation of the Ultrasounds on the Samples  
The attenuation inside the chamber is negligible due to its size. In fact, minimal variations of the pressure amplitude measurements were detected by the needle hydrophone within a maximum pressure location.  
Figure 1: **(A)** Photograph of the ultrasound exposure system. A cylindrical open chamber was formed using a crystal ring (with an inner diameter of 10 mm, wall thickness 1 mm, height 4 mm) glued onto a microscope slide. The cavity was activated by an ultrasonic actuator; a small piezoelectric ceramic Ferroperm pz26 of rectangular area (30 mm \(\times\) 15 mm \(\times\) 1.5 mm), resonant at a frequency close to 1 MHz, was placed also on the microscope slide and close to the chamber of treatment. **(B)** 3D acoustic pressure pattern formed inside the acoustic cavity when a continuous wave at \(f\) = 1009 kHz (resonance frequency) and a fixed voltage of \(V_{P\text{-}}\)p = 10 V is applied.  
The attenuation inside the tissue depends linearly on the ultrasound frequency and increases with the sample volume. In our experiments it can be considered negligible due to the small size of the tissue samples (thickness of 1 mm), which are much smaller than any organ, such as liver or kidney, for which authors like [PERSON] or [PERSON] (2017, 2018) reported acoustic attenuation of \(\alpha\) \(\sim\) 0.3-1 dB/MHz\({}^{-1}\) cm\({}^{-1}\). An injury threshold for organs exposed to ultrasounds has been reported in the literature for average intensities: up to 30 kW/cm\({}^{2}\) for a total duration of 10 min is safe in organs such as kidneys. A spatial peak intensity threshold of 16,620 W/cm\({}^{2}\) was needed before a statistically significant portion of the samples showed injury. This is nearly seven times the 2400 W/cm\({}^{2}\) maximum output of the clinical prototype used to move kidney stones effectively in pigs and more than 30 times the intensity generated in our acoustic resonator. Our experiments were performed at intensity levels significantly lower than this tissue injury threshold.
## 4 Results and Discussion  
### \(k\)-Space Continuation and Modal Character  
Identifying the critical surface in \(\{M,K,Pr,G\}\) parameter space for the co-existence of the pair of intermediate wavenumber propagating modes and the long wave stationary modes (long wave thermal mode and long wave neutral temperature mode) is essential in mapping the parameter space. Continuation methods based on [PERSON] iteration must search for either purely real \(\omega\) (long wave stationary modes) or genuinely complex \(\omega\) (intermediate wavenumber propagating modes). Figure 2 shows this critical curve \(k-M\) and \(\omega-M\) for four mode co-existence at fixed parametric values \(\{K=0.0001,Pr=1.001,G=1\}\) found by numerically solving for \(\Delta=0\) and \(\Delta_{\omega}=0\) simultaneously by [PERSON]'s method. For \(M\in\{500,1500\}\), it can be seen that the wavenumber of the crossover point is a decreasing function, whilst the growth rate is an increasing function. The four modes that co-exist along this critical curve are the two long wave stationary modes and two intermediate modes propagating from opposing directions. Figure 3 shows an analogous \(k-\Pr\) curve for the crossover point from stationary long waves to propagating steady intermediate waves. A Prandtl number of 1 is common for gases and a Prandtl number of 10 is common for liquids. The plots in Figure 3 show how the behavior of the fluids changes from a near gaseous state to a near liquid state.  
Once the critical surface dividing the wavenumber space into the two different regions of wave character has been identified, it is a simple matter to use parameter space continuation methods in \(k\) to find the remainder of the dispersion relation. Figure 4 summarizes the dispersion relation by plotting maximum growth rate and the wavenumber at which it is achieved for a given Marangoni number \(M\) at the values \(\{K=0.0001,Pr=1.001,G=1\}\). There is jaggedness in the graph in Figure 4a as the wavenumber of maximum growth can switch modes. The normalized group velocity is also plotted as a function of the Marangoni number. It is clear that neutral stability is found by ramping up the Marangoni number to \(M\sim 1100\) for the other parameters fixed at these values.  
Figure 4: _Cont._  
Figure 3: Magnitude of velocity field on \(z=0\) mm plane: (**a**) crossover wavenumber \(k\); (**b**) growth rate \(\omega\); for which \(\Delta=0\) and \(\Delta_{\omega}=0\) simultaneously at the parametric values \(\{K=0.0001,M=500,G=1\}\) and \(\Pr\in\{0.1,10\}\). Along this critical curve, four modes co-exist—the two long wave stationary modes and two opposite-directed intermediate propagating modes.  
Figure 2: Magnitude of velocity field on \(z=0\) mm plane: (**a**) crossover wavenumber \(k\); (**b**) growth rate \(\omega\); for which \(\Delta=0\) and \(\Delta_{\omega}=0\) simultaneously at the parametric values \(\{K=0.0001,\,Pr=1.001,\,G=1\}\) and \(M\in\{500,1500\}\). Along this critical curve, four modes co-exist—the two long wave stationary modes and two opposite-directed intermediate propagating modes.
# NaviSoC: High-Accuracy Low-Power GNSS SoC with an Integrated Application Processor
## 3 Circuit Implementation
### Intermediate Frequency Block and ADC  
The intermediate frequency signal processing chain was composed of four gain stages and a 3-bit SAR (successive approximation) ADC. Figure 15 shows the block diagram of the implemented analog IF with ADC. For clarity, only in-phase path and single-ended versions of the I/Q chain are presented.  
The first stage--preamplifier was optimized for low noise. The gain of the PGAs and low-pass filter (LPF) is programmable, allowing a 48 dB dynamic range to be obtained. The bandwidth of LPF is configurable to 4, 8, 16, 26 MHz and sets the overall cutoff frequency of the I/Q baseband. In order to avoid saturation of the blocks, two DC (direct current) offset cancellation (DCOC) loops were employed. The DC offset was filtered with a pseudo-resistor low-pass filter and subtracted in the preamplifier and the PGA2, which were designed as double differential amplifiers. The DCOC loops set the overall high-pass cutoff frequency to 50 kHz to avoid slow settling without compromising signal integrity. A digital automatic gain control (AGC) circuit was included to ensure a constant signal magnitude at the ADC input. The whole dual-frequency I/Q IF chain with ADC included draws 6.2 mA from a 1.5 V supply. The input referred RMS noise of IF integrated from 100 kHz to 32 MHz is 46 \(\mu\)V. Both supply and ground were separated between the analog and mixed-signal parts of the baseband to avoid interferences. Figures 16 and 17 show the IF chain gain characteristics for gain and band sweep, respectively.  
Figure 14: Conversion gain and noise figure of the L5/E5 radio-frequency front-end.  
Figure 15: Block diagram of dual-frequency analog IF and ADC.  
The input referred voltage noise is presented in Figure 18. It can be seen that the voltage noise density is kept below 20 nV/VHz and above 1 MHz. To verify the impact of the IF chain noise on the overall system performance, the noise resulting from previous stages was calculated as  
\[\frac{V_{nRF}}{\sqrt{df}}=\sqrt{kTR_{ANT}F_{RF}}G_{RF}\approx 120\ \frac{nV}{\sqrt{Hz}}, \tag{1}\]  
where \(k\) is the Boltzmann constant, \(T\) is the temperature in Kelvins, \(R_{ANT}\) is antenna impedance, \(F_{RF}\) is RF the front-end noise figure in a linear scale and \(G_{RF}\) is the total voltage gain of the RF front-end. Antenna resistance is equal to 50 \(\Omega\), temperature is equal to 300 K and L1/E1 RF front-end the noise is equal to 120 nV/VHz. As this value is much higher than the noise of the IF chain, it can be concluded that the IF chain noise does not have an important impact on the overall system performance.  
Figure 16: Gain characteristics of the intermediate frequency (IF) chain for different gain settings.  
Figure 17: Gain characteristics of the intermediate frequency chain for different band settings.
# Cloud-Edge Collaboration-Based Knowledge Sharing Mechanism for Manufacturing Resources
## 3 System Realization  
The use of ontologies enables abstraction, standardization, and formalization of equipment attributes and their relationships within the intelligent manufacturing field. Therefore, reusable knowledge extraction and storage can be achieved through the equipment's ontological modeling and processing. Due to its ease of use and maintainability, this research utilizes the open-source software Protege with a graphical user interface to construct the devices' ontology. Compared with the existing research on ontology modeling in the manufacturing environment, the ontology model construction method proposed in this paper is developed around the manufacturing task, not only modeling the physical resources involved in the manufacturing process, but also the surrounding environment elements related to the resource, the evaluation of the execution effect, and the matching degree of the action and the task. In the description of the manufacturing process, this article also proposes a task decomposition scheme that allows it to adopt a more fine-grained representation method, and also provides more knowledge materials for the reuse of knowledge.  
The data flow of system operation is shown in Figure 2, including two parts: data uplink and data downlink. When data collection is performed, the edge server will first check the data collection node. If the local node does not have a mapping relationship with the mid-node of the ontology model, the mapping relationship will be established based on the characteristics of the data and the node type, and then enter the task decomposition steps, and collect knowledge based on the decomposed action primitives. After completing the storage of the local knowledge base, upload and update the cloud knowledge base. When reusing knowledge, the local server will first decompose the target task, and then perform similar task matching based on the action meta-model. If there is not enough knowledge available locally, it will perform a cloud knowledge query. After finding enough action primitives, the edge server will combine tasks and complete the configuration of local resources. As for the establishment of the ontology model and the task decomposition scheme, it will be described in the latter part of this section.
# A QoS Classifier Based on Machine Learning for Next-Generation Optical Communication
## 3 ML Procedures
### Decision Tree  
A typical type of supervised learning algorithm is the decision tree (DT). It formulates the classification model by creating a DT. Each node in the tree denotes a test on an attribute, and each branch descending from that node, represents one of the attribute potential values [15]. In a DT, there are two nodes, which are the decision node and leaf node. Each leaf node represents the result, while the internal nodes indicate the dataset's features and branches its decision-making processes, as shown in Figure 3.  
Figure 3: The decision tree classifier logic.  
A major advantage of the DT is solving the nonlinear classification problems. It is capable of formulating nonlinear decision boundaries, unlike the linear decision boundaries, so the DT solves more complex problems [16].
# PpyNT/NR/NBR Composites with Excellent Microwave Absorbing Performance in X-Band
## 3 Results and Discussion
### MA Performance of PPyNT/NR/NBR Composites
#### 3.2.1 Optimization of PPyNT Content
For a better understanding of the reason for the variation of absorbing performance along with PPyNT content, the dielectric loss tangent (tan\(\delta\epsilon=\epsilon^{\prime\prime}/\epsilon^{\prime}\)) and the attenuation constant \(\alpha\) (described by Equation (S3), see Supplementary Materials) of different composites are compared. Tan\(\delta\epsilon\) refers to the ability of materials to dissipate electromagnetic energy via dielectric loss, while the attenuation constant reflects the comprehensive attenuating capability of materials [17]. As seen clearly in Figure 3a,b, both tan\(\delta\epsilon\) and \(\alpha\) rise gradually with the increase in PPyNT content, suggesting the enhanced attenuating capability for the composite with higher PPyNT content. It is contrary to the observation in their MA performance, which is depressed while PPyNT content is over 6 phr. This result is attributed to the poor impedance matching (\(|Z_{\rm in}/Z_{0}|\)) resulting from excessive fillers. Impedance matching reflects the extent of electromagnetic waves entering the interior of the material and is calculated according to Equation (S4) (see Supplementary Materials) [29]. The value of \(|Z_{\rm in}/Z_{0}|\) closer to 1, the better impedance matching [17]. Figure 3c-f shows contour maps of \(|Z_{\rm in}/Z_{0}|\) values for PPyNT/NR/NBR (50/50) with different PPyNT contents. It is clear that the composite with 6 phr PPyNT has the largest area with \(|Z_{\rm in}/Z_{0}|\) values approaching 1 (Figure 3d) among all samples. Further increase in PPyNT content results in a greater deviation from 1 in \(|Z_{\rm in}/Z_{0}|\). Overall, owning to the good balance achieved between attenuation capability and impedance matching, the PPyNT/NR/NBR (50/50) composite with 6 phr PPyNT is near optimal for achieving an excellent MA performance. Figure 3: (**a**) Dielectric loss tangent factor, (**b**) attenuation constant and (**c**–**f**) 2D contour maps of \(|Z_{\text{in}}/Z_{0}|\) values for PPyNT/NBR/NBR (50/50) with different PPyNT contents. Figure 2: RL plots of PPyNT/NR/NBR (50/50) with (**a**) 3 phr PPyNT, (**b**) 6 phr PPyNT, (**c**) 9 phr PPyNT, (**d**) 12 phr PPyNT and EAB of PPyNT/NR/NBR (50/50) with (**b’**) 6 phr PPyNT and (**c**) 9 phr PPyNT.
## 2 Backgrounds
### Using Chatbots in Qualitative Research
An explorative test of the suitability of ChatGPT for qualitative analysis [41] showed that ChatGPT is a valuable tool in qualitative analysis, provided that the researcher can ask appropriate questions (prompts). [PERSON] investigated if ChatGPT can be used to perform thematic analysis of semi-structured interviews with a focus on using the analysis results to write user personas. The study showed that the model could build basic user personas with acceptable quality, deriving them from themes and that the model can generate ideas for user personas [42]. Further investigations verified that conducting an inductive thematic analysis with large language models is feasible and provides a high level of validity [43]. The quality and timeliness of turning audio files into readable text are among the main inefficiencies in qualitative research. However, an existing study demonstrated that ChatGPT can be an efficient tool for qualitative researchers who must clean up interview transcriptions [44]. Although ChatGPT seems to be the most highlighted representative of AI-based chatbots, other emerging chatbots, such as Gemini and Copilot, are increasingly gaining traction as supporters in qualitative analysis. A study conducting health-related research interviews with human responders, ChatGPT and Gemini (Bard) concluded that Gemini exhibited less interpersonal variation than ChatGPT and human respondents [45]. [PERSON] and [PERSON] studied the use of generative AI, focusing on Gemini and ChatGPT, to enhance qualitative research within higher education assessment. The results showed that Gemini has been less accommodating to text file conversions, though it could provide references in its responses (compared to ChatGPT, which gave general suggestions on how to find literature), and by default provided fewer recommendations in prompts [46]. Researchers have also raised several issues or difficulties with employing AI-based chatbots for qualitative research in the currently available literature. These challenges include the potential for missing subtle emotional cues or implicit themes (especially in human-centric research) [24], the fear of losing theoretical development [23], the reluctance of qualitative researchers to use AI because it conflicts with critical research approaches [34], the conflict between human judgment and AI in qualitative research (e.g., reflexivity and interpretation) [34], AI as a complementary tool (AI should assist but not replace human analysis) [24], non-reproducible results in qualitative analysis (AI variability) [34], AI lacks the ability to experience the world firsthand and interpret it through human interaction [24], concerns over the use of AI for coding sensitive data (restrictions on use with confidential transcripts) [24], and the lack of the researcher's creativity and reflexivity as essential elements in qualitative data analysis [34].
## 3 Wireless Power Transfer System in Seawater
### Equivalent Coupler Loss Impedance (ECLI)  
From the above analysis, we know that the total loss of the WPT coupler in the seawater environment is composed of three parts: the copper loss in the coils, the eddy current loss in the seawater and the core loss in the ferrite cores; it can be expressed as  
\[P_{coupler}=P_{copper}+P_{eddy}+P_{core} \tag{11}\]  
where \(P_{copper}\) is the copper loss of two coils, \(P_{eddy}\) is the eddy current loss in the seawater, \(P_{core}\) is the core loss of ferrite cores.  
When the current modulus ratio of the transmitter and receiver is fixed, and the current is not large (in this case, the ferrite core works in the linear region), the total loss produced by the coupler is proportional to the square of the current. In order to express the loss level of the coupler more intuitively, we introduce the concept of the equivalent coupler loss impedance (ECLI):  
\[R_{coupler}=\frac{P_{coupler}}{I^{2}} \tag{12}\]  
where \(I\) is the equivalent value of \(I_{p}\) and \(I_{s}\), especially when \(I_{p}\)/\(I_{s}=1\), \(I=I_{p}=I_{s}\). It can be seen that \(P_{coupler}\) is proportional to \(I^{2}\), so we can use ECLI to study the loss level of the coupler.
## 6 Conclusions
### Limitations  
The study, while comprehensive, faces several limitations that must be acknowledged. The primary constraint is the dataset's limited scope, encompassing only player characteristics, performance indicators, and injury data from a single season. This limitation arises from the dynamic nature of football, where player transfers, team promotions, and relegations significantly alter team dynamics and player performance across seasons. The 2020-2021 EPL season was impacted by the COVID-19 pandemic, affecting various aspects including a delayed start and restricted fan attendance during the period for which the data was collected. Consequently, expanding the dataset to include multiple seasons or  
Figure 18: Significant ramifications for football teams, trainers, and players.  
data from different football leagues was not feasible due to the variability in league characteristics, such as match frequency, playing styles, and other contextual factors influencing performance and injury rates.  
Another significant limitation is the inherent unpredictability of injuries in football. Despite the study's ML models forecasting injuries with notable accuracy, the spontaneous nature of injuries in football, often resulting from unforeseen on-field events, presents a challenge. This unpredictability means that, regardless of the precision of predictive models, there will always be inherent limitations in forecasting injuries in a sport as unpredictable and complex as football.  
Additionally, the analysis was constrained by the availability of certain variables. While diverse in player-related characteristics and performance indicators, the dataset lacked more intricate elements related to player fitness and injuries. These variables, often held confidential by league officials and team management, such as detailed medical records and specific injury-related data, were not accessible for this study, thereby limiting the breadth of the analysis.  
Furthermore, it is essential to recognize potential biases introduced by the study's design and assumptions inherent in the modeling process. The representativeness of the sample is primarily constrained to a single, pandemic-affected season, which may not adequately reflect normal competitive conditions. This could influence the generalizability of the findings across different seasons with varying conditions and intensity. Additionally, assumptions made during the modeling, such as the linear relationship presumed between certain player attributes and performance outcomes, may not capture more complex interactions or nonlinear dynamics present in the actual data. Acknowledging these biases and assumptions is crucial for interpreting the study's findings accurately and understanding the scope of their applicability.