esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Modelling of Material Removal Rates and Research on Material Removal Methods
#### 2.4.2 Material Removal Rate Modelling  
The number of laser pulses given to the same location determines the laser's ablation threshold, as has been demonstrated [40]. The ratio of the spot diameter to the length d of the overlapping region between two adjacent pulses along a linear scan is known as the spot overlap ratio. The number of laser pulses per etched region at once increases as the spot overlap ratio of the laser increases; as a result, the overlap ratio of the pulses might affect the laser's ablation threshold. A schematic illustration of the spot overlap rate is shown in Figure 5. The time between two adjacent pulses can be roughly calculated as the  
Figure 4: Schematic diagram of femtosecond laser energy distribution.  
reciprocal of the repetition frequency since the duration of a femtosecond laser pulse is significantly shorter than the space between adjacent pulses.  
\[\mathrm{v}\cdot\frac{1}{f}=\omega_{\mathrm{d}}(1-\varphi) \tag{4}\]  
where \(\mathrm{v}\) is the scanning speed of the laser and \(\varphi\) is the spot overlap rate. From Equation (4), the spot overlap rate can be obtained:  
\[\varphi=\left(1-\frac{\mathrm{v}}{\omega_{\mathrm{d}}f}\right)\cdot 100\% \tag{5}\]  
Equation (5) demonstrates how the laser repetition frequency and scanning speed might affect the laser ablation threshold. It takes a significant amount of work to optimize the laser processing parameters to acquire the desired material removal effect. By adjusting the repetition frequency, energy\(-\)flux density, and scanning speed of the femtosecond laser, this experiment examines the impact of each femtosecond laser parameter on the rate of material removal from nano\(-\)crystalline diamond coatings.  
The amount of material removed per unit of time is known as the material removal rate. A mathematical model can be created to determine the material removal rate under various laser processing parameters in order to analyze the rate of material removal caused by a femtosecond laser, as shown in Figure 6 below:  
The removal rate for the material under various laser processing settings is calculated by mathematically modelling the morphology of the laser ablation kerr, and the fitted functional model is integrated over the width. The expression for the removal rate of the material is provided below:  
\[f=[ab-2\int_{0}^{\frac{3}{2}}f(x)dx]\mathrm{v} \tag{6}\]  
where \(f\) is the material removal rate; \(a\), \(b\) is the width of the ablation area and the ablation depth; \(f(x)\) is the fitting function for the cut-out profile; and \(\mathrm{v}\) is the laser scanning speed.  
Figure 5: Schematic of the spot overlapping rate.  
Figure 6: Model for the shape of ablation kerrs: (**a**) cross-sectional morphology (\(a\), \(b\) is the width of the ablation area and the ablation depth) (**b**) mathematical model \(f(x)\).
# Question Difficulty Estimation Based on Attention Model for Question Answering
## 4 Experiments
### Performance of Question Answering with Difficulty Level  
In this section, we solve the question answering with a predicted difficulty level to verify that the performance of question answering is improved with the difficulty level. We choose the multi-level context matching model [9] as a question answering model, since is currently the state-of-the-art model for the DramaQA QA task. The multi-level context matching model is designed to understand the multimodal story of a drama. This QA model consists of two streams for a vision and a textual modality. Each stream of modality is combined with embeddings from a questionary sentence and information components  
Figure 5: Performance change of the proposed model according to the number of information components. The X-axis denotes the information components used where QS stands for a questionary sentence. (**a**) RACE. (**b**) QuAC. (**c**) DramaQA.  
using a context matching module and then predicts a score for each answer. Since it does not adopt any difficulty level, we modify it to use the proposed difficulty level by regarding the difficulty level as an additional modality of the question answering (There will be several methods to utilize the difficulty level in the QA model. However, this experiment focuses on showing that the difficulty level helps the QA model to get better performance than the model without the level). That is, the modified QA model consists of three streams including the difficulty level information.  
Table 6 shows the question answering on the drama QA dataset is improved by adding the difficulty level. The '+ Difficulty level' indicates the inclusion of the difficulty level to the multi-level context matching model. The QA model with the difficulty level achieves better performance than the QA model without the level. With the difficulty level, the accuracy rises to 73.83% which is higher up to 2.69% than that of the QA model. These results imply that the question difficulty estimation helps the performance of question answering tasks improved. Especially, the improvement in difficult (level 3 and level 4) questions is larger than that in easy (level 1 and level 2) questions. This is because the proposed method has achieved better performances on difficult questions than on easy questions (refer to Section 4.3 and Figure 4). From these results, we verify the usefulness of the question difficulty estimation.
## 2 System Configuration of Open-Area EMC Testing  
### Review on EM Radiative Characteristics  
In electromagnetic compatibility testing, the signal and interference are transmitted to the measurement antennas in free space, whose radiative characteristics can be divided into three fields: the reactive near field, radiative near field, and far field (radiation zone). The reactive near field is near to 0.159 times the wavelength (referred to as the \(\lambda\)). Given the allocation of civil aviation bands between 118 MHz and 137 MHz and the operation of the VHF omnidirectional range (VOR) between 108 MHz and 117.95 MHz, we have focused our EMC testing specifically on the VHF band to address aviation equipment EMC issues. Assuming that EMC testing is operated at 100 MHz, the upper bound of the reactive near field is about 0.5 m, which is shorter than the actual distance between the radiant source and measurement antenna (which is always larger than 1 m).  
The radiative near field (or Fresnel region) covers the region of the reactive near field to the Fraunhofer distance [8], which is calculated as \(2D^{2}/\lambda\) (where D denotes the aperture of the radiant source). Assuming that \(D=0.2\) m and \(\lambda=3\) m (corresponding to 100 MHz), the Fraunhofer distance is about 0.0267 m, which indicates that there is no radiative near field for 100 MHz EMC testing (VHF aeronautical telecommunication band). When \(\lambda=0.03\) m (corresponding to 10 GHz), the Fraunhofer distance is about 2.67 m, which covers the one-meter EMC testing. In this case, the electric field strength is proportional to the inverse square of the distance, i.e., \(1/r^{2}\).  
The radiative far field (radiation zone) covers the region out of the radiative near field, which is the main working zone for VHF band EMC testing. In this case, the electric field strength is proportional to the inverse distance, i.e., \(1/r\). If there is a measurement antenna placed 3 m away from the internal radiant source (such as the device under test (DUT)) and another measurement antenna placed 6 m away from the internal radiant source, there is a 6 dB falloff between the near-site antenna and far-site antenna. In contrast, if there is a measurement antenna placed 103 m away from the environmental radiant source and another measurement antenna placed 106 m away from the environmental radiant source, there is only a 0.2494 dB falloff between the near-site antenna and far-site antenna (in this case, we obtain the plane wave approximation). Based on this, we can estimate the distance of radiant sources via the two-antenna method by calculating the near-far amplitude falloff of different signals on the frequency spectrum, which is the principle of this paper.
# Application of Logistic Regression for Production Machinery Efficiency Evaluation
## 2 Logistic Regression Model  
Logistic regression is a model that allows the influence of several variables \(X_{1}\), \(X_{2}\),..., \(X_{k}\) on the dichotomous variable \(Y\) in the mathematical form to be presented. The logistic regression model is based on a logistical function that takes the following form:  
\[f(x)=\frac{e^{x}}{1+e^{x}}=\frac{1}{1+e^{-x}}, \tag{2}\]  
where \(e\) is the Euler number, and \(x\) is the value of the explanatory variable \(X\).  
The use of logistic regression is supported by the fact that it is not required to meet many assumptions that are formulated in relation to linear regression and general linear models. These include, first of all, the linearity of the relationship between a dependent and an independent variable, as well as the normality and homoscedasticity of the distribution of independent variables. In addition, observations must be reported using metric measurement systems.  
The logistic regression model can be written in several ways. Assuming that \(Y\) stands for a dichotomous variable with values 1, for the occurrence of the event we are interested in (success), and 0, for the opposite case (failure), the logistic regression model is described by Equation (3):  
\[P(Y=1|x_{1},x_{2},\ldots,x_{k})=\frac{e^{\beta_{0}+\sum_{i=1}^{k}\beta_{i} \cdot x_{i}}}{1+e^{\beta_{0}+\sum_{i=1}^{k}\beta_{i}x_{i}}}, \tag{3}\]  
where \(\beta_{i}\)\(i=0,\ldots,k\) are logistic regression factors, while \(x_{1},x_{2},\ldots,x_{k}\) are independent variables, which can be measurable or qualitative.  
An equivalent form of the logistic regression equation can be written as the odds for the occurrence of the event (success) we are interested in:  
\[\frac{P(Y=1|X)}{1-P(Y=1|X)}=e^{\beta_{0}+\sum_{i=1}^{k}\beta_{i}\cdot x_{i}}. \tag{4}\]  
In a special case, for one independent variable the logistic regression equation takes the following form:  
\[P(Y=1|X)=\frac{e^{\beta_{0}+\beta_{1}\cdot x_{1}}}{1+e^{\beta_{0}+\beta_{1} \cdot x_{1}}}. \tag{5}\]  
If, in turn, both sides of the Equation (5) are logarithmized, the logit form of the logistic model will be obtained:  
\[\textit{logit}\;P(Y=1|X)=\textit{ln}\frac{P(Y=1|X)}{1-P(Y=1|X)}=\beta_{0}+ \beta_{1}\cdot x_{1}. \tag{6}\]  
The condition necessary for logistic regression is a sufficiently large sample, the number of which should be \(n>10(k+1)\), where \(k\) is the number of parameters.  
Important concepts related to logistic regression are the odds and the odds ratio. The odds are defined as the probability of an event occurring \(P(A)\) divided by the probability of an event not occurring, \(1-P(A)\):  
\[(\text{Odds})S(A)=\frac{P(A)}{P(non-A)}=\frac{P(A)}{1-P(A)}. \tag{7}\]  
The odds ratio, in turn, marked \(OR\), is defined as the odds of one event occurring \(S(A)\) divided by the odds of another event occurring \(S(B)\):  
\[OR_{AxB}=\frac{S(A)}{S(B)}=\frac{P(A)}{1-P(A)}:\frac{P(B)}{1-P(B)}. \tag{8}\]
###### Abstract  
We report the improvement of the electrical performance of field effect transistors (FETs) fabricated on monolayer chemical vapor deposited (CVD) MoS\({}_{2}\), by applying an interacting fluoropolymer capping layer (Teflon-AF). The electrical characterizations of more than 60 FETs, after applying Teflon-AF cap, show significant improvement of the device properties and reduced device to device variation. The improvement includes: 50% reduction of the average gate hysteresis, 30% reduction of the subthreshold swing and about an order of magnitude increase of the current on-off ratio. These favorable changes in device performance are attributed to the reduced exposure of MoS\({}_{2}\) channels to the adsorbates in the ambient which can be explained by the polar nature of Teflon-AF cap. A positive shift in the threshold voltage of all the measured FETs is observed, which translates to the more desirable enhancement mode transistor characteristics.  
Keywords:transition metal dichalcogenides; 2-Dimensional materials; molybdenum disulfide; chemical vapor deposition; field effect transistors; fluoropolymer; passivation +
Footnote †: journal: Applied Physics Letters
## 3 Experiment and Result
### Verification by Flight Crew Reports  
From 1 April to 6 June 2021, a total of 10 wind shear events were reported by crews, which were collected and provided by BDIA. PoD (Percentage of detection or Hit Rate) and PoTA (Percentage of Time on Alert) are adopted as evaluation indicators for the proposed approach [4; 29]. PoD and PoTA are defined as the ratio of the number of hits to total number of pilot reports and the ratio of alert duration to total length of time investigated, respectively.  
The relationships between the threshold for the number of windshear ramps and PoD/PoTA are listed in Table 2. A lower threshold ensures a higher PoD value, but corresponding larger PoTA value, resulting in a high false alarm rate. On the contrary, a higher threshold may lead to a missing report. Therefore, an effective and appropriate threshold needs to take into account both PoD and PoTA. When the threshold is set at 500, 100% POD is reached, and PoTA also reaches an acceptably low value of 0.81%, as bolded in Table 2. Since pilot reports are limited for the initial detection, more windshear events need to be accumulated to find an optimized threshold.  
Table 3 lists the windshear records from crew reports during the initial detection. These windshear events refer to the fact that pilots encountered significant airspeed loss or gain (usually exceeding 7.7 m/s) along the glide path. The windshear message reported by the crew include the runway number, the distance from the runway, and the height of the aircraft. The windshear alert from lidar includes the runway number, subareas, and windshear value. Most of the windshear events occurred in the landing process from the south. The microburst caused three consecutive windshear events. Windshear alerts from the lidar hit all the events reported by pilots accurately with a threshold of 500, based on the method proposed in this work.  
\begin{table}
\begin{tabular}{c c c} \hline
**Windshear Threshold** & **POD (\%)** & **PoTA (\%)** \\ \hline
50 & 100 & 12.69 \\
100 & 100 & 7.77 \\
200 & 100 & 3.51 \\
300 & 100 & 1.77 \\
400 & 100 & 1.31 \\
**500** & **100** & **0.81** \\
600 & 90 & 0.69 \\
700 & 80 & 0.55 \\
800 & 80 & 0.34 \\
900 & 70 & 0.22 \\
1000 & 70 & 0.16 \\ \hline \end{tabular}
\end{table}
Table 2: The relationship between threshold and PoD/ PoTA.
## 3 Challenges in China's Weather Routing Services
### Inadequate Capabilities in Ship Navigation Risk Identification and Precision Forecasting  
Hydrometeorological conditions significantly impact maritime safety, with severe weather events (e.g., typhoons, extratropical cyclones, dense fog) posing risks of speed loss, maneuvering difficulties, and collisions. Accurate risk identification is a universal challenge, but its implementation in China depends on integrating domestic forecast data and potentially specific regulatory requirements. Current navigation risk forecasting methods, predominantly based on the IMO MSC.1/Circ.1228 Revised Guidelines for the Avoidance of Dangerous Situations in Adverse Weather and Sea Conditions [48], evaluate risks such as parametric rolling, synchronous rolling, successive high wave attack, surriding and broaching-to using static vessel parameters combined with wave height, period, and direction. However, this framework lacks the comprehensive integration of dynamic vessel parameters, limiting its predictive accuracy and operational utility.  
Under wind-wave-current interactions, vessels exhibit coupled dynamic responses--including rolling, pitching, yawing, surging, swaying, and heaving--that induce instability. These responses are further complicated by navigational factors such as channel width and traffic density, exacerbating the challenges in risk identification. To address these limitations, a holistic risk assessment framework must be developed, integrating vessel-specific characteristics (static and dynamic parameters, as outlined in Table 1), motion response dynamics, and hydrometeorological conditions. Recent research has made significant strides in this area, particularly in modeling synchronous and parametric roll and developing more comprehensive risk assessment frameworks [29; 30]. Such a model would provide robust scientific support for adaptive route/speed adjustments and extreme weather avoidance.
## 2 Materials and Methods
### Recruitment, Screening, and Enrollment  
This study was conducted in partnership with a midwestern cancer center, where advertisement for the cooking class was facilitated through the cancer survivorship program via printed and e-mailed flyers, and social media accounts. At the time of class registration (offered October 2022 or January 2023), potential participants were asked if they were interested in participating in the research study and provided permission to be contacted for eligibility criteria screening. Cooking class registrants were screened via an online survey or telephone call for the following eligibility criteria: (1) adults (\(>\)/= 19 years) with a first-time, primary diagnosis of cancer; (2) post-surgery and completed primary adjuvant treatments (i.e., radiation and/or chemotherapy) at least six months prior to study enrollment (current hormonal therapy eligible); (3) have a computer, tablet, or smartphone with internet access; (4) ability to provide informed consent. Potential participants were excluded if they (1) were scheduled to receive surgery/treatment during the study; (2) had a second cancer diagnosis (excluding non-invasive skin cancers); or (3) were currently pregnant. Those who were at least six months post-primary treatments were sought to allow recovery from any treatment effects (e.g., nausea/vomiting, poor appetite, diarrhea) as these would impact full participation in a diet-based intervention. Participants that were non-English speaking were not enrolled due to our inability to provide a translated consent process or intervention in other languages.
## 3 Circuit Implementation  
### Class-C VCO with Start-Up Circuit  
Due to high power efficiency, a complementary cross-coupled Class-C VCO based on the design in [29] is chosen to be implemented. As shown in Figure 4, the NMOS cross-coupling pair \(M_{1}/M_{2}\) operates in the Class-C scheme. The current mirror including \(M_{1}/M_{2}\) and \(M_{B1}/M_{B2}\) work as a start-up circuit. When circuits are powered up, the bias voltage \(V_{B}\) is set high enough to ensure the pair \(M_{1}/M_{2}\) (operating in Class-B) has a enough negative resistance to start to oscillate. Then, currents flowing through \(M_{1}/M_{2}\) increase, as the oscillating amplitude increases. Thus, currents in \(M_{B1}/M_{B2}\) increase due to the current mirror scheme. The excess current, flowing in the tail resistor \(R_{tail}\), results in lower voltage at \(V_{B}\), then forcing the pair \(M_{1}/M_{2}\) to work in the Class-C domain.  
Different from [29], the current source is replaced by a tail resistor in the proposed VCO to eliminate the flicker noise generating from the tail transistor. Moreover, the voltage biasing scheme of the PMOS pairs \(M_{3}/M_{4}\) is removed, since the DC gain of a operational amplifier (op-amp) could be limited seriously with low supply voltage. The removal of  
Figure 4: Structure of the proposed complementary cross-coupled Class-C VCO.  
this function would not have much influence on the Class-C operation, since the currents are reused in the pair \(M_{1}/M_{2}\) and \(M_{3}/M_{4}\), and Class-C shaping of the current can flow in both \(M_{1}/M_{2}\) and \(M_{3}/M_{4}\) as well. To verify that, the current shapings are shown in Figure 5 with transient simulation.  
Moreover, the sub-sampling loop of the PLL can lock at an arbitrary integer-N ratio of the reference frequency \(f_{REF}\) without an FLL [22], thus, the smallest tuning step of capacitor banks should smaller than \(f_{REF}\) to allow ADELL to select proper digital-control bits for the VCO. In order to ensure the desired large frequency tuning range (about 10%) as well, a 16-bit fine cap bank and an 8-bit coarse cap bank are proposed (Figure 4). Figure 6 captures the both tuning curves of the coarse bank and the fine bank.
# A New Low-Cost Compact Antenna for the 2.45 and 5.8 GHz ISM Bands
## 2 Dual-Band Antenna Design  
### Technical Specifications and Choice of Antenna Type  
The objective of the design is to provide a compact, low-cost, dual-band antenna operating at 2.45 GHz and 5.8 GHz with high performance; more specifically, (i) a reflection coefficient of less than \(-10\) dB, (ii) a bandwidth of at least 100 MHz, (iii) a gain of at least 2 dBi, and (iv) an efficiency of at least 80% in both bands.  
In particular, patch antennas (or printed-on-substrate antennas) are designed chosen because they occupy little space in RF energy collection systems and can offer robust and inexpensive solutions. Consequently, in order to obtain a low-cost, relatively compact, and high-performance antenna, a microstrip patch antenna integrating slots based on a FR-4 substrate is chosen for this work.
### Background
#### 1.1.2 Diagnostic Conundrum  
NPH symptoms can vary from person to person but typically include the triad of gait disturbance, urinary incontinence, and cognitive impairment. Other symptoms, such as headache and sleep and mood disturbances, particularly apathy, have been widely described for NPH [12]. However, these clinical findings are also commonly noted with other neurodegenerative disorders, such as Parkinson's disease (PD) and Alzheimer's disease (AD). Furthermore, the gait and cognitive profile in NPH share similarities with other degenerative parkinsonian disorders such as PD and progressive supranuclear palsy (PSP). To further complicate the matter, questions about co-pathology with other neurodegenerative disorders have been circulated. As such, clinical overlaps can lead to missed or delayed diagnoses. This overlap also extends to radiological markers. MRI brain and CT head are commonly utilized imaging modalities, with variable uses of MRI sequences, such as CISS or CSF flow to aid in NPH diagnosis. Few radiological markers for NPH are commonly utilized in clinical practice; these include increased Evans index, disproportionately enlarged subarachnoid hydrocephalus (DESH), narrower callosal angles, and increased vertical plan Evans index, to name a few. Despite these widely known markers, their identification remains limited to the clinician's experience.  
Several studies have assessed the diagnostic accuracy of MRI and CT scans for NPH. For instance, a study published in the journal Neurology in 2016 reported that the diagnostic accuracy of MRI for NPH was approximately 75% [13], while CT scans had a diagnostic accuracy of 70% [14]. Furthermore, a 2019 study published in the journal _Neurosurgery_ reported that the sensitivity and specificity of MRI for NPH were 86% and 80%, respectively, while the sensitivity and specificity of CT scans were 70% and 78%, respectively [10; 15; 16].  
Similarly, lumbar punctures or lumbar drain tests have limitations, with a reported 85% diagnostic accuracy [14; 17]. NPH is also unique in that the definite category of diagnosis depends on the response to treatment itself, creating an additional diagnostic conundrum. And while surgery can largely benefit people with NPH, it is also associated with higher adverse events throughout a lifetime. Additionally, the response could be minimal to negative in some NPH cases. As such, there is a continued need for highly sensitive and specific non-invasive diagnostic tests to identify NPH and predict surgical response.