esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion of Results
### Deviation Frequency Estimates for Different GNSS Signal Components
\begin{table}
\begin{tabular}{c c c} \hline \hline
**SBAS Satellite Number** & **Mean Elevation, deg** & **Mean Azimuth, deg** \\ \hline S28 & 27.08 & 206.22 \\ S32 & 29.39 & 193.58 \\ S37 & 26.60 & 152.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Characteristics of SBAS satellite observations. Figure 6: The same as in Figure 3 but for the L1X and L5I components of SBAS signals. Deviation frequency distribution on 13 April 2021 (**a**,**c**) and 16 April 2021 (**b**,**d**) for SBAS signals. Figure 7 (upper panels) proves that the deviation frequency at 20 Hz is typical during the most part of the day on 16 April (under weak geomagnetic disturbance). At the same time, there are short periods when the frequency drastically decreases to ~17 Hz. The first and the third decreases occurred near the time of local noon and local midnight. This differs on 13 April (the quiet day) when the deviation frequency varied more significantly (up to 15 Hz) and more frequently. The most profound drop of deviation frequency values (15 Hz) corresponded to the local midnight as in the previous case. Figure 7 (lower panels) shows that the characteristic deviation frequency of 20 Hz was not always dominant during the 24 h. This means that the deviation frequency can take other values during most of the day. Unfolding of how the particular deviation frequency value is related to certain processes in the ionosphere is the issue for future research. In this case study no clear tendency in the character of \(N_{CFd}\) variations was revealed. Nevertheless, the observed \(N_{CFd}\) value at the particular moments was higher under quiet geomagnetic conditions (\(N_{CFd}\leq\) 0.9-1.0) than under disturbed conditions (\(N_{CFd}\leq\) 0.8).
## 4 Discussion
The advantage of hard prior reconstructions is that the total number of unknowns are reduced dramatically, thereby making the problem better posed and immensely reducing the computational complexity [31]. However, a drawback is that the spatial resolution is now limited by the size of the defined regions and its accuracy is highly dependent on the quality of the a priori information. The dependency on the uncertainty of the lesion location from the tissue surface can be addressed by modifying the objective function to also reconstruct spatial parameters such as the size and depth of the lesion or the gradient between the inclusion and background. Another limitation of a priori spatial constraints is that it assumes optical and structural contrast are perfectly colocalized, which is likely not true in complex biological tissues. Furthermore, we have modeled an ellipsoidal inclusion to represent a lesion containing tissue. However, the shape of the lesion does not have to be ellipsoidal and we can apply any arbitrary shape of the inhomogeneity to the hybrid DOI problem. In summary, this new hybrid DOI method is promising for improving broadband characterization of deep tissue inhomogeneities using a simple reflectance-based probe. Our future work includes studying the effect of optical attenuation in deeper tumors and attempting to improve reconstruction of deeper inhomogeneities by providing the hybrid method an advantage of viewing multiple transverse views of the tissue at each grid point. We also intend to study the influence of noise model and depth uncertainty of phantom inclusions in the mismatch of simulated and experimental result accuracy. We are also applying the hybrid DOI method to characterize human breast lesions and evaluate the clinical utility of the enhanced optical information. [PERSON] executed the simulations, performed the experiments, analyzed the data, and wrote the paper; [PERSON] provided ideas and directions, analyzed the data, and reviewed the manuscript; [PERSON] designed and fabricated the tissue-simulating phantoms; [PERSON] fabricated the tissue-simulating phantoms; [PERSON] conceptualized the idea, supervised the entire work, analyzed the data, and reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript. This research was funded by the American Cancer Society, Pilot Project Grant from Institutional Research Grant number IRG-14-195-01. The authors declare no conflicts of interest.
## 3 Results
### SVM-CSDPC Disease Prediction Model Application and Results Analysis  
When we built the SVM-CSDPC model, we normalized the data. The initial value of in \(c_{+}\) and \(c_{-}\) the SVM method is set to the inverse of the number of samples. We use the relationship between the positive penalty coefficient \(c_{+}\) and the negative penalty coefficient \(c_{-}\) used in the libsvm package proposed by [PERSON]. The inverse of the number of samples. The Gaussian kernel function has strong learning ability, and its function parameter \(\sigma\) can be flexibly and freely set. Therefore, in the process of establishing the model, the kernel function defaults to a Gaussian kernel function. The ROC curve of the Cox-CSDPC disease prediction model is shown in Figure 6.  
The number of model training iterations is 1,308,633, the training duration is about 84.0 h, the minimum value for the quadratic programming solution is \(-\)106,779.077, the kernel function parameter is 0.413, the support vector number is 307,073, and the number of support vectors on the boundary is 77,959, ACC = 82.3%, AUC = 0.880 and the Yoden index maximum is 0.618. The sensitivity is 0.808 and the specificity is 0.810.  
Figure 5: S-Cox-CSDPC disease prediction model ROC curve.  
The predictive index obtained by the SVM-CSDPC model performs well and reaches some expected standards. However, the training time of the whole model is longer, the model is greatly influenced by the choice of kernel function and penalty factor, and the prediction accuracy needs to be improved.
# Cyanobacterial Potential for Restoration of Loess Surfaces through Artificially Induced Biocrusts
## 4 Discussion
### Production of Toxin, Biomass and Polysaccharide by the Cyanobacterial Strains
#### 4.1.2 Production of Biomass  
Statistical analyses indicated a positive correlation between biomass production and the presence of nitrate as a source of nitrogen (Table 4). Most of the analyzed strains (four out of five) showed better growth when cultivated in a medium supplied with NaNO\({}_{3}\) (Table 2). The only strain that showed a higher biomass production in BG11 N\(-\) medium was _Nostoc_ sp. L6. These results are consistent with previous studies [91, 92] where biomass production was increased with higher NaNO\({}_{3}\) content in the culture medium. Higher intensity and longer illumination times e.g., [92, 93, 94], as well as the addition of organic carbon to the medium [94], also increase biomass production.  
The absence of NaNO\({}_{3}\) in the culture medium resulted in some strains (_Chroococcidipsis_ sp. L1, _Nostoc_ sp. L5, and _Nostoc_ sp. L6) reaching the stationary phase earlier than when cultivated in the N-containing medium (Table 2). The decrease in biomass after seven weeks of cultivation was evident in all three strains, of which _Chroococcidipsis_ sp. L1 strain showed the most drastic decrease. Although some _Chroococcidipsis_ strains can fix N\({}_{2}\), special conditions are required, including low light density and reduced O\({}_{2}\) tension [95, 96], which were not met in this study.
## 3 Materials and Methods
### Characterization of Silica Particles
#### 3.5.2 Elemental Analysis  
Elemental analyses of bare and PEG-grafted silica particles were performed using a TruSpec-CHN Micro elemental analyzer (LECO, Saint Joseph, MI, USA) according to the corresponding ASTM procedures (D-5291, E-777, E-78). Briefly, 0.15 g of dried samples were analyzed to determine the total carbon (C), hydrogen (H), and nitrogen (N) content.
## 3 Results and Discussion
### Mobile ADCP  
Analysis of the pattern of temporal variation derived from MVBS of moored ADCP has demonstrated meaningful information for diel vertical migration of zooplankton. Such information provides a rough estimation and correlation between water environments and their effects on zooplankton migration behavior. However, in general, zooplankton does not present uniform horizontal or vertical distribution but instead tends to be patchy, with different vertical abundance differences at each bin/depth due to vertical migration. On the other hand, under the condition that moored ADCP cannot reach broad spatial data, the mobile ADCP might be a solution, although the frequency used in the mobile ADCP is lower compared with moored ADCP. As a result, the MVBS value might be lower than the mobile ADCP, so verification of the MVBS value from both ADCP is needed. Comparison of echograms obtained from moored ADCP and mobile ADCPs was analyzed by observing MVBS values based on the exact time and location using the echo intensity tracing method. The two frequency, MVBS\({}_{\text{307.5 kHz}}\), and MVBS\({}_{\text{750 kHz}}\) echogram were also used to identify DVM (Figure 6).  
The high scatterers observed from the mobile ADCP showed that zooplankton spread along the track at one layer during certain times. We used the echo intensity tracing analysis for identifying the signal from both ADCP. Different MVBS variance of zooplankton at daylight, twilight, and night time was considered separately in the following analysis. The results show that strong MVBS were spread along the three different times. During the day, strong MVBS were around the bottom area, between 14 to 16 m of depth with MVBS value between \(-\)85 to \(-\)70 dB. At night, a large distribution of strong MVBS was formed between 13 to 14 m with MVBS value between \(-\)85 to \(-\)75 dB. The vertical distribution of MVBS in the night time was found varying from 10 to 13 m with MVBS value between \(-\)80 to \(-\)70 dB. The nighttime observation was still at the initial rise stage where zooplankton had just started moving to the upper layer. As shown from temporal variation observed from moored ADCP in Figure 5, zooplankton completely moved to the shallow in the midnight, indicated from the static red color at a depth between 11.5 to 13 m. Unfortunately, the lack of mobile ADCP data made it difficult to analyze during that period.  
Figure 5: Diel vertical migration of zooplankton at the water column from an upward looking moored ADCP in daily periods. During the night, zooplankton moved to shallower depths compared to daylight.
## 2 Study Area and Datasets
### Remotely Sensed Data  
We used a series of AVHRR NDVI product suites with an 8-day temporal resolution and a 5 km spatial resolution produced by [PERSON] et al. [47]. The NDVI product representing the period of 1982-2018 was generated from AVHRR data by a refined VIRR method [48].  
We also used the MODIS NDVI product (MOD13A2) with a 16-day temporal resolution and a 1 km spatial resolution [56] for 2009-2013 as a gridded level-3 product in the sinusoidal projection obtained from the Land Processes Distributed Active Archive Centre ([[https://lpdaac.usgs.gov](https://lpdaac.usgs.gov)]([https://lpdaac.usgs.gov](https://lpdaac.usgs.gov)), accessed on 15 September 2020). We extracted the NDVI values corresponding to the ground-based sampling sites, which can be used as an independent variable for training the models.
###### Abstract  
Optical modulators for optical interconnects require a small size, small voltage, high speed and wide working spectrum. For this purpose, we developed Si slow-light Mach-Zehnder modulators via a 180 nm complementary metal-oxide-semiconductor process. We employed 200 \(\upmu\)m lattice-shifted photonic crystal waveguides with interleaved p-n junctions as phase shifters. The group index spectrum of slow light was almost flat at \(n_{\mathrm{g}}\approx 20\) but exhibited \(\pm 10\%\) fluctuation over a wavelength bandwidth of 20 nm. The cutoff frequency measured in this bandwidth ranged from 15 to 20 GHz; thus, clear open eyes were observed in the 25 Gbps modulation. However, the fluctuation in \(n_{\mathrm{g}}\) was reflected in the extinction ratio and bit-error rate. For a stable error-free operation, a 1 dB margin is necessary in the extinction ratio. In addition, we constructed a device with varied values of \(n_{\mathrm{g}}\) and confirmed that the extinction ratio at this speed was enhanced by larger \(n_{\mathrm{g}}\) up to 60. However, this larger \(n_{\mathrm{g}}\) reduced the cutoff frequency because of increased phase mismatch between slow light and radio frequency signals. Therefore, \(n_{\mathrm{g}}\) available for 25 Gbps modulation is limited to up to 40 for the current device design.  
silicon photonics; optical interconnect; photonic crystal; optical modulator; silicon modulator; Mach-Zehnder modulator +
Footnote †: journal: _photonics_
# Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing
## 2 Literature Review
This approach aligns with recent developments in computerized adaptive testing, where response time models are used to adjust test difficulty in real time, reducing test anxiety and cognitive overload [PERSON] and [PERSON] (2010). Despite these advancements, gaps remain in the empirical validation of response time models across diverse testing conditions. Future research should explore hybrid models that integrate IRT with machine learning to refine adaptive testing strategies [PERSON] and [PERSON] (2010). Among the few response-time-enhanced IRT models, some approaches modify the probability function by incorporating response time as a multiplicative factor, while others subtract a penalty based on response time. However, there is no consensus on which approach best optimizes adaptive testing performance, as no comprehensive evaluation has been conducted across multiple models under controlled conditions [PERSON] and [PERSON] (2010). Despite growing interest in response time modeling, a systematic comparison of response time-integrated IRT models in adaptive testing remains largely unexplored. While previous studies have proposed individual modifications to IRT models, no study has empirically evaluated multiple models on real student data using standardized model comparison metrics such as AIC, BIC, AUC, and item-test correlation. This study addresses this gap by systematically evaluating six alternative response-time-enhanced IRT models alongside the standard 1 PL Rasch model. These models vary in their approach to integrating response time, either multiplicatively or subtractively, offering unique perspectives on how response time influences ability estimation. Through empirical testing on real-world data from 150 students answering 30 test items, this study aims to identify which response-time-aware model best enhances adaptive test efficiency. By conducting this comparative analysis, this research contributes to the development of next-generation adaptive testing systems, where response time can be leveraged not only to refine ability estimation but also to optimize real-time question selection strategies.
## 3 Proposed SPMIPv6 Protocol
### Application Scenarios of IP-WSN based on SPMIPv6
#### 3.3.6 Agricultural Monitoring  
Accurate temporal and spatial monitoring can significantly increase agricultural productivity. Due to natural limitations, such as a farmers' inability to check the crop at all times of day or inadequate measurement tools, luck often plays too large a role in the success of harvests. Using a SPMIPv6 based IP-WSN, indicators such as temperature, humidity, soil condition, can be automatically monitored without labor intensive field measurements. For example, SPMIPv6 based IP-WSN could provide precise information about crops in real time, enabling businesses to reduce water, energy, and pesticide usage and enhancing environment protection. The sensing data can be used to find optimalenvironments for the plants. In addition, the data on the planting condition can be saved by sensor tags, which can be used in supply chain management.
# Monolithically Integrated THz Detectors Based on High-Electron-Mobility Transistors
## 6 Results
### THz Focal Plane Array  
Figure 10 presents the initial test measurements with the THz focal plane array. We recorded the focal point at discrete frequencies within the range of 100 GHz-1.1 THz. A color bar next to the image represented the intensity recorded at each pixel and the resulting detection current. The images illustrate the result obtained after averaging several frames, as discussed in Section 5.2.  
Figure 9: Measurement results: (**a**) raw data; (**b**) optical sensitivity; (**c**) optical NEP.  
The upper row of the figure displays the measurement at 100 GHz. The right-hand figure illustrates the measurement with a defective line in the bottom row of the four quadrants. One detector malfunctioned, preventing the entire line from being accurately evaluated. This line was no longer considered in the subsequent measurements.  
The second row presents the measurements at 200 GHz and 500 GHz, while the bottom row displays 800 GHz and 1.1 THz. As shown in the Figure 9a, the power of the THz source decreases exponentially with increasing frequency. This is directly reflected in the measurements despite calculating an average value over increasing frames. At 1.1 THz, which includes 100 frames, the SNR decreases noticeably, remaining just above 1.
## 3 Results and Discussion
### Scanning Electron Microscopy  
The initial analysis of the fractured cross-section of the samples, after the tensile tests, revealed smooth displacements in the layers related to the printing conditions and material deposition. In each of the selected samples from the five tests conducted, with infill levels of 15%, 25%, 50%, 75%, and 100%, small displacements were observed along the lines corresponding to the internal infill and outer layers after rupture, as illustrated in Figures 20 and 21 for the 15% and 100% infill samples.  
Additionally, a distinctive feature was observed in the final rupture region, where samples with higher infill percentages exhibited slight displacements relative to the central area of the cross-section. This variation in the rupture zone can be attributed not only to the application of a uniaxial load during the tensile test but also to the differences in internal infill density. The structural behavior of the samples was further analyzed using scanning electron microscopy (SEM), which provided detailed insights into the fracture surfaces.  
The SEM images revealed notable differences in the mechanical performance between samples with 15% and 100% internal infill. In the samples with 15% infill, the fracture surface exhibited significant displacement between internal layers, which was accompanied by a localized concentration of stress at specific points. This led to cleavage and the initiation of cracks in certain regions, reflecting the brittle nature of the structure. These features are indicative of a less densely packed internal structure that is more susceptible to crack propagation under the applied uniaxial load. Consequently, the reduced infill percentage significantly compromises the material's overall tensile strength.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Group**} & **Strain Maximum** & **Yield Stress** & **Modulus of Elasticity** \\  & **(MPa)** & **(MPa)** & **(MPa)** \\ \hline
1 (15\%) & 24.52 \(\pm\) 0.28 & 12.90 \(\pm\) 0.89 & 322.53 \(\pm\) 17.17 \\
2 (25\%) & 24.29 \(\pm\) 0.22 & 13.79 \(\pm\) 0.88 & 332.54 \(\pm\) 7.76 \\
3 (50\%) & 30.10 \(\pm\) 0.55 & 16.33 \(\pm\) 0.71 & 370.73 \(\pm\) 9.78 \\
4 (75\%) & 31.50 \(\pm\) 0.22 & 18.86 \(\pm\) 0.51 & 388.14 \(\pm\) 18.72 \\
5 (100\%) & 39.42 \(\pm\) 0.19 & 20.08 \(\pm\) 0.86 & 426.70 \(\pm\) 16.62 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Printing parameters.  
Conversely, the samples with 100% infill demonstrated greater structural homogeneity, with minimal displacement between layers. The SEM analysis revealed that the stress distribution was much more uniform, contributing to a more robust material with a higher capacity to absorb deformation energy. The presence of a continuous internal structure also allowed for better stress transfer across the material, reducing the likelihood of localized failures and resulting in a more ductile and resilient behavior under loading conditions. These findings highlight the importance of infill density in optimizing the mechanical properties of 3D-printed materials and ensuring the structural integrity of the prosthetic components.
## 3 Numerical Examples
### Experimental Data Processing
\begin{table}
\begin{tabular}{c c c c c} \hline \hline Fitting parameters, dynamic modulus master curve & \(\alpha\) & \(\beta\) & \(\delta\) & \(\gamma\) \\ (\(T_{0}\) = 21.1 \({}^{\circ}\)C) & 1.3530 & 2.8961 & 0.7631 & 0.6983 \\ \hline Fitting parameters, phase angle master curve & \(\xi_{1}\) & \(\xi_{2}\) & \(\xi_{3}\) & \\ (\(T_{0}\) = 21.1 \({}^{\circ}\)C) & \(-\)0.6555 & \(-\)0.0008405 & \(-\)0.59608 & \\ \hline Time-temperature shifting factor (\(\log(\alpha_{T})\)) & \(\mathbf{4.4}\)\({}^{\circ}\)C & \(\mathbf{21.1}\)\({}^{\circ}\)C & \(\mathbf{37.8}\)\({}^{\circ}\)C & \(\mathbf{54.4}\)\({}^{\circ}\)C \\  & 1.8454 & 0 & \(-\)1.3743 & \(-\)2.9746 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of fitting parameters for the Hot Mix Asphalt AC-13. Figure 3: Master curve fits to experimental data at reference temperature of 21.1 \({}^{\circ}\)C for creep compliance. Figure 2: Master curve fits to experimental data at reference temperature of 21.1 \({}^{\circ}\)C for (**a**) Dynamic modulus; (**b**) Phase angle.
# Combining Contrastive Learning with Auto-Encoder for Out-of-Distribution Detection
## 5 Results and Discussion
### Discrepancies and Strategies for Improvement  
The combination of contrastive learning and an auto-encoder in our OOD detection method holds promise, but the observed performance discrepancy between training on CIFAR-10 and testing on CIFAR-100, or vice versa, may be attributed to several factors, including similarity in object classes, shared dataset characteristics, and difficulty in obtaining feature distribution for an auto-encoder. To address these concerns, potential solutions involve diverse training data, fine-tuning the target dataset, class-agnostic features, cross-dataset validation, data augmentation techniques, and model complexity and regularization. By incorporating these strategies, it is possible to enhance the model's adaptability and improve its performance when confronted with similar yet distinct datasets like CIFAR-10 and CIFAR-100. The key is to promote feature generalization, reduce dataset-specific biases, and enable the model to effectively navigate the subtle differences between the training and testing datasets.
## Appendix A The Flamant-Boussinesq Approximation
### Point Load Model for Different \(z\)  
If we perform a sensitivity analysis where we draw the waveform of the Point Load Model along a fiber length (\(x\)), for different possible perpendicular separation (\(y\)), while the depth (\(z\)) varies, as indicated in Figure A3, we can observe how the waveform changes from a down-up configuration for shallow depths (\(z=0.5\)) to the highly attenuated up-down configuration for high values of \(z\). For intermediate \(z\) values, the singularities make the waveform irregular.