esa-sceva/satcom-chunk-collection huggingface dataset:
# Vocabulary Retention Under Multimodal Coupling Strength Index (MCSI): Insights from Eye Tracking
## 5 Discussion
### Theoretical and Practical Implications
#### 5.3.2 Practical Implications: Translating Findings into Action  
The demonstrated efficacy of synchronized audiovisual input translates into actionable recommendations across key stakeholder levels:  
* For Individuals (Learners): Actively utilize vocabulary learning resources that pair word pronunciations with semantically relevant images (e.g., flashcards with integrated audio, language apps with picture-sound links). Consciously link new words' sounds to their visual meanings during study.
* For Managers/Coordinators (Language Program Leaders, EdTech Developers, Curriculum Designers): Prioritize the design and selection of learning materials/apps featuring tightly synchronized audio (pronunciation) and semantically congruent images. Leverage insights from eye tracking (minimizing visual search) and MCSI (efficiency gains) by ensuring designs promote direct sound-image binding and avoid extraneous elements. Advocate for evidence-based resources incorporating these principles.
* For Institutions (Educational Policy Makers, Funding Bodies): Develop guidelines mandating effective multimodal integration (semantic alignment, synchronization) in procured learning resources. Invest in enabling infrastructure (e.g., reliable AV equipment, software) to support widespread implementation of these validated multimodal approaches.
# 25-34 GHz Single-Pole, Double-Throw CMOS Switches for a Ka-Band Phased-Array Transceiver
###### Abstract  
This paper presents two single-pole, double-throw (SPDT) mm-wave switches for Ka-band phased-array transceivers, fabricated with a 65-nm complementary metal oxide semiconductor (CMOS) process. One switch employs cross-biasing (CB) control with a single supply, while the other uses dual-supply biasing (DSB) control with positive and negative voltages. Negative voltages were generated internally, using a ring oscillator and a charge pump. Identical gate and body floated N-type metal oxide semiconductor field effect transistors (N-MOSFETs) in a triple well were used as the switch core transistors. Inductors were used to improve the isolation between the transmitter (TX) and receiver (RX), as well as insertion loss, by canceling the parasitic capacitance of the switch core transistors at resonance. The size of the proposed radio frequency (RF) switch is 260 \(\upmu\)m \(\times\) 230 \(\upmu\)m, excluding all pads. The minimum insertion losses of the CB and DSB switches were 2.1 dB at 28 GHz and 1.93 dB at 24 GHz, respectively. Between 25 GHz and 34 GHz, the insertion losses were less than 2.3 dB and 2.5 dB, the return losses were less than 16.7 dB and 17.3 dB, and the isolation was over 18.4 dB and 15.3 dB, respectively. The third order input intercept points (IIP3) of the CB and DSB switches were 38.4 dBm and 39 dBm at 28 GHz, respectively.  
single-pole-double-throw; CMOS; RF switch; Ka-band; phased-array transceiver; gate-body floating; high isolation +
Footnote †: journal: Applied Sciences
## 7 Limitations of _Rmr_ and \(Q\) Systems
### Limitations Related to \(Q\) System  
Empirical design methods have been accepted for a long time, owing to the lack of appropriate design methods and undeveloped theory, which show quite adverse results during construction [145]. An important benefit of the \(Q\) system is that this system is based on well-documented case records for its original development. However, compared to \(RMR\), this system is not covering new fields in its use. Therefore, the support chart of \(Q\) system gives only a suggestion of the support to be applied and this support system should be tempered by practical and sound engineering judgement [138]. In this system, \(RQD/\!J_{n}\) is an indication of relative block size, however, this is not understandable [146], which was supported by [PERSON] [60]. The effect of water in the \(Q\) system was discussed by [PERSON] through example, which shows that in exceptionally high-flow cases, the \(Q\) system is unable to recommend suitable support [138].  
The application of \(SRF\) is unclear for buckling, rock-burst, and/or squeezing conditions, or for weakness zones [56]. The \(SRF\) values were adjusted in 1993, in overstressed massive brittle rocks based on limited data [68]. This increase is from a value of 20 to 400 based on the strength-stress ratio but no criteria have been selected for the rating selection of \(SRF\) even though \(SRF\) was a fine tuning parameter [9]. In case of squeezing rock, [PERSON] selected the range of \(SRF\) from 5 to 20 [9]; however, this \(SRF\) range does not effectively characterize the squeezing influence due to the deficiency of enough case records and resulting in unsafe tunnel practice when using the \(Q\) system for large underground excavations in squeezing ground [128]. The true nature of the rock-mass is not explicitly reflected in the \(Q\) system, although it is crucial for the prediction of the support measures (e.g., popping ground, squeezing, or swelling) [20]. According to [PERSON] [147], the \(Q\) system is not applicable in weak rock and weak zones, which was also confirmed by comparison of \(Q\)-system support with that of \(NATM\) (New Austrian Tunneling Method) support in swelling rock [148]. [PERSON] and his co-researchers [127] have presented an empirical approach (Equation (71)) for critical overburden calculation when squeezing may happen.  
\[H>350Q^{1/3} \tag{71}\]  
In the calculation of H expressed above, the method of calculating \(Q\) is uncertain. This was also pointed out by [PERSON] [138].  
In the \(Q\)-system support chart, the determination of support is based on \(Q\) value and \(D_{\epsilon}\). The \(D_{\epsilon}\) is the function tunnel span and ESR. The ESR value should be based on the safety of the working team according to the country's safety criteria because each country has its own criteria [138]. Application of the \(Q\) system is more prevalent in jointed rock without overstressing [138] and displays optimal performance for a tunnel with \(\mathrm{D_{e}}\) between 2.5 and 30 and \(Q\) values between 0.1 and 40 [149].
# An Investigation of Optically Very Thin Ice Clouds from Ground-Based ARM Raman Lidars
###### Abstract  
Optically very thin ice clouds from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and ground-based Raman lidars (RL) at the atmospheric radiation measurement (ARM) sites of the Southern Great Plains (SGP) and Tropical Western Pacific (TWP) are analyzed. The optically very thin ice clouds, with ice cloud column optical depths below 0.01, are about 23% of the transparent ice-cloudy profiles from the RL, compared to 4-7% from CALIPSO. The majority (66-76%) of optically very thin ice clouds from the RLs are found to be adjacent to ice clouds with ice cloud column optical depths greater than 0.01. The temporal structure of RL-observed optically very thin ice clouds indicates a clear sky-cloud continuum. Global cloudiness estimates from CALIPSO observations leveraged with high-sensitivity RL observations suggest that CALIPSO may underestimate the global cloud fraction when considering optically very thin ice clouds.  
remote sensing; lidars; ice cloud optical depth; ice cloud detection +
Footnote †: journal:  
0000-0002-4811-1900][PERSON]
# GIS-Based Flood Assessment Using Hydraulic Modeling and Open Source Data: An Example of Application
## 2 Materials and Methods
### Materials and Software Used  
For the research, the following datasets were utilized:  
* hydrological data, specifically flow rate and water levels, measured at hydrometric stations along the analyzed river segment, obtained from the Crisuri Water Basin Administration [54]. Daily, monthly, and annual average values for five consecutive years were considered;
* Digital Elevation Model (DEM) with a spatial resolution of 1 m (1 point/m\({}^{2}\)) generated from LiDAR data, a product obtained through the LAKI I and LAKII projects of the National Agency for Cadastre and Real Estate Publicity (ANCPI) [58];
* field data, specifically detailed topographic surveys, including the measurement of elevation points on the thalweg of the Cigher River and its minor riverbed;
* aerial photographs from the Cigher River basin, captured using a UAV DJI Phantom 4 device available in the GIS and Remote Sensing Laboratory of ULS Timisoara;
* Corine Land Cover (CLC) database, 2018 edition, vector data, used for analyzing the impact of floods on land use classes, available on the Copernicus Land Monitoring Service platform [59];
* auxiliary geospatial data (basin boundaries, hydrographic network, component sub-basins, administrative-territorial maps), available from ABA Crisuri [54] and specialized platforms [55], for locating and characterizing the area of interest.  
The data and scientific information used in the research were processed through various methods and software tools as follows: for processing topographic data collected in the field, AutoCAD Map 3D 2016 software (version 19.0) [60] was used; for geospatial data processing and cartographic representation, ArcGIS 10.4 for Desktop software [61] was employed; for hydraulic modeling, HEC-RAS software, version 6.5 [47], was utilized.
# InoT Sensor Network for Wild-Animal Detection near Roads
## 4 Experiments
After going to the installation site, it turned out that the roadside-marker post on which this DSN was installed was slightly turned in relation to the road, which had the effect that one of the sensors covered a segment of the road rather than the sidewalk. This imposes certain requirements, although not as strict as in the case of the light-beam barrier variant, for the accuracy of the DSN mounting on the roadside-marker post. There are also possible problems with car detection in turn segments of the road. In this case, the modified DSNs should be installed (with different angles of sensor optical nodes), or sensors that are tilted towards the roadway should be disconnected. From the provided photos (Figure 13a), it can be seen that three objects are moving from the road (dark area on the left side of the photo) towards the fields. The DSN1 and DSN2 sensors recorded objects near the road. The photos in Figure 13b also show one captured animal moving from the right side (from the field) to the left (towards the road) and crossing the road. In this case, the DSN1 sensor detected animals. After extending the research in March and April 2022, as expected, a significant increase in animal activity was observed in the monitored section (especially in April). On 6 April at 5:00 a.m. 54 min, a series of thermal imaging images of a timed emergency were captured (Figure 14), where a group of several deer ran across the road from the right side, and the last animal was nearly hit by a passing car. DSN nodes in the experimental setup start working after dark till early morning. At different times of the year, different wildlife activities are seen. For example, on 14 November 2021, four activations per day were recorded. After analyzing the thermal images, a small animal (a cat or small dog) is seen moving in the field next to the road in the sensitivity zone of the system, resulting in three DSN triggers. One DSN triggering can be classified as false. This gives us 25 percent false DSN triggering. On the other hand, after one week, we had 24 DSN triggers. After analysis of the thermal photos, we identified a group of three (to the best of the authors' knowledge from the acquired data) deer walking back and forth across the field for several hours. In all cases of DSN triggering, it can be said that the wild animals were within the DSN coverage field. This gives us zero percent false positive DSN triggers. The required network coverage in the experimental setup of the research is within 1 km. ; the DSN nodes are in a direct line of sight which makes a LoRa link virtually uninterrupted, and the packet-reception ratio in the experiment was close to 100%. The redundant repeating of data packets three times was applied in the system-communication algorithm for lost packet control. An analysis of the data taken during one month shows that false triggering of the DSN nodes is 2-3%.
# An Information Theoretic Approach to Privacy-Preserving Interpretable and Transferable Learning
## 3 Variational Membership-Mapping Bayesian Models
### A Prior Model  
Given a dataset: \(\{(x^{i},t^{i})\mid i\in\{1,\cdots,N\}\}\), Algorithm 1 can be used to build a membership-mappings model characterized by a set of parameters, say \(\mathbb{M}^{x\to t}=\{a^{x\to t},a,\)\(M,\sigma,w\}\) (where \(x\to t\) indicates the mapping from \(x\) to \(t\) has been approximated by the membership mappings). It follows from (22) that the membership-mappings model predicted output corresponding to an input \(x\) is given as  
\[\hat{t}(x;\mathbb{M}^{x\to t})=(\alpha^{x\to t})^{T}(G(x))^{T} \tag{48}\]  
where \(G(\cdot)\in\mathbb{R}^{1\times M}\) is a vector-valued function defined as in (21). The \(k\)-th element of \(\hat{t}\) is given as  
\[\hat{t}_{k}(x;\mathbb{M}^{x\to t})=(G(x))\alpha_{k}^{x\to t} \tag{49}\]  
where \(\alpha_{k}^{x\to t}\) is the \(k\)-th column of matrix \(\alpha^{x\to t}\).  
Expression (49) allows estimating for any arbitrary \(x\) the corresponding \(t\) using a membership-mappings model. This motivates introducing the following prior model:  
\[t_{k} =(G(x))\theta_{k}+e_{k} \tag{50}\] \[\theta_{k} \sim\mathcal{N}(\alpha_{k}^{x\to t},\Lambda_{k}^{-1})\] (51) \[e_{k} \sim\mathcal{N}(0,\gamma^{-1})\] (52) \[\gamma \sim\text{Gamma}(a_{\gamma},b_{\gamma}) \tag{53}\]  
where \(k\in\{1,\cdots,q\}\); \(\mathcal{N}(\alpha_{k}^{x\to t},\Lambda_{k}^{-1})\) is the multivariate normal distribution with mean \(\alpha_{k}^{x\to t}\) and covariance \(\Lambda_{k}^{-1}\); and \(\text{Gamma}(a_{\gamma},b_{\gamma})\) is the Gamma distribution with shape parameter \(a_{\gamma}\) and rate parameter \(b_{\gamma}\). The estimation provided by membership-mappings model \(\mathbb{M}^{x\to t}\) (i.e., (49)) is incorporated by the prior model (50)-(53), since  
\[\mathbb{E}[t_{k}]=\hat{t}_{k}(x;\mathbb{M}^{x\to t}). \tag{54}\]
## 2 Materials and Methods
For both analyzed cases, the meshing was carried out using linear tetrahedrons elements, with the average size of 0.1 mm, grading factor 1.5 and maximum turn angle 60 deg. For the model without implant, the meshing contains 38,243 nodes and 22,179 elements. For the model with implant, the meshing contains 59,780 nodes and 34,526 elements.
# Precipitation-Use Efficiency and Its Conversion with Climate Types in Mainland China
###### Abstract  
The impacts of climate change on ecosystem productivity and water resources over a long term in China are not well quantified. Precipitation-use efficiency (PUE) is a key parameter that describes carbon and water exchange in terrestrial ecosystems. Research on the response of regional PUE to climate change and its driving forces is of great significance to climate-change mitigation and the sustainable development of regional ecology. Based on an improved actual evapotranspiration (ET\({}_{\text{a}}\)) model, the responses of ET\({}_{\text{a}}\), net primary productivity (NPP), and PUE to climate change in different climatic regions of China were analyzed; the contributions of various environmental factors to PUE changes were quantified; and the conversion characteristics and regulatory mechanisms of the PUE regime in different climatic regions were identified. The results indicate that the improved ET\({}_{\text{a}}\) model, after considering the limiting effect of energy on ET\({}_{\text{a}}\) in humid regions, can simulate the ET\({}_{\text{a}}\) distribution in China well. Over the past 58 years (1960-2017), ET\({}_{\text{a}}\) and NPP have increased in the western regions and decreased in the eastern regions, with the boundary at 103\({}^{\circ}\) E. PUE presents a \"low-high-low\" spatial distribution from northwest to southeast in China. It is noteworthy that there was a zonal distribution for a high value area of PUE, which coincided with the summer monsoon transition zone. The soil moisture (SM) increase in arid regions is the main driving force of the PUE increase, whereas the annual net radiation (Rn) change in humid regions is the main driving force of the PUE change. The transition zone is the conversion zone, where the prevailing factor limiting vegetation growth transitions from water to energy.  
typical climatic zones; precipitation-use efficiency; net primary productivity; contribution of environmental factors; transformation characteristics 2022142467. [[https://doi.org/10.3390/rs14102467](https://doi.org/10.3390/rs14102467)]([https://doi.org/10.3390/rs14102467](https://doi.org/10.3390/rs14102467))
## 4 Process of Remediation for Oil-Contaminated Soil
### Techniques for Chemistry Remediation
#### 4.3.2 Photochemistry  
Photochemical oxidation changes a chemical under sunshine. In this approach, a contaminant-catalyst combination is bombarded with light (U.V. or sunlight), causing organic contaminants to oxidise and create CO\({}_{2}\) and water. A stable equivalence range and an unoccupied high-energy conduction band characterise semiconductor stimulators. Irradiating these semiconductors starts photocatalytic processes that promote an electron into the delivery range and produce a puncture in the equivalence band [42]. Photocatalytic reactions create hydroxyl (O.H.\(-\)) and superoxide (O\({}_{2-}\)) anion radicals, which oxidise organic pollutants at the semiconductor surface. High temperatures, exposure periods, or oxygen concentrations accelerate photochemical oxidation in nature.  
The photochemical oxidation process degrades oil in soils. Additionally, researchers have employed semiconductor photocatalysts to remediate organic contaminants in soil. The photocatalytic pre-treatment improved biodegradability and oil solubility [43]. Photocatalytic treatment of oil-polluted soil with titanium dioxide (TiO\({}_{2}\)) and bioremediation may eliminate total petroleum hydrocarbons (T.H.P.s) only if enough light is infiltrated [44]. Previous results show that oxidative photodegradation removes all types of total petroleum hydrocarbons, indicating nonselective elimination. In contrast, removal increases with humidity content, keeping with the concept that water is necessary for hydroxyl radical formation. Photochemical oxidation of oil using a TiO\({}_{2}\)/zeolite composite is an environmentally friendly technique to eliminate oil deposits.
## 2 Materials and Methods
### Structural Design of the Removal System  
#### 2.2.1 Integral Structural Design  
The elimination system was comprehensively designed in accordance with the scheme requirements, incorporating four critical modules: a rolling extrusion separation module, a sorting module, a visual inspection module, and a fine rejection module. These key modules were integrated into a unified system using aluminum profiles as the structural framework and supported by corresponding connectors, as illustrated in Figure 9. The operational workflow proceeds as follows: First, quick-frozen materials are introduced through the inlet of the separation module, where rolling extrusion mechanisms effectively disentangle adhered foreign objects. Subsequently, the separated materials enter the sorting module via its designated inlet, enabling differential screening of raisins and contaminants through vibratory or sieving actions. Finally, residual raisins still adhering to foreign objects or incompletely separated impurities undergo precision rejection via the integrated visual inspection and fine rejection module, thereby completing the full-cycle elimination task.  
Figure 8: Residue rate of materials subjected to different quick-freezing temperatures employing various separation methods.  
Figure 7: Damage rate of materials subjected to different quick-freezing temperatures employing various separation methods.  
Figure 9: Overall architecture of the elimination system for foreign objects in raisins.
## 2 Methodologies for Parallel Numerical Computation  
### Arbitrary Lagrangian-Eulerian Based Fluid-Structure Interaction  
The water conveyance tunnel is different from the normal shield tunnels because of the water inside the tunnel. The interaction between water and tunnel affects the seismic responses of the tunnel [23]. The ALE method has been demonstrated as an effective way to tackle the FSI problems [17; 18; 24; 25]. Therefore, in this study the ALE method is adopted to perform the FSI during seismic analysis of the water conveyance tunnel.  
The mass and momentum governing equations [17] of the ALE method are expressed as Equations (1) and (2).  
\[\frac{\partial\rho}{\partial t}\mid_{\xi_{i}}+\rho\frac{\partial v_{i}}{ \partial x_{i}}+c_{i}\frac{\partial\rho}{\partial x_{i}}=0 \tag{1}\]  
\[\rho\frac{\partial v_{i}}{\partial t}\mid_{\xi_{i}}+\rho c_{i}\frac{\partial v _{i}}{\partial x_{j}}=\frac{\partial\sigma_{ij}}{\partial x_{j}}+\rho l_{i} \tag{2}\]  
where \(\xi_{i}\), \(\rho\), \(v_{i}\), \(l_{i}\), \(c_{i}\) and \(\sigma_{ij}\) signify the ALE coordinates, density, velocity of material (fluid), body force, velocity of convection and stress tensor, respectively.  
With regard to the tunnel structure, its deformation can be given with a constitutive equation as:  
\[\rho_{L}\frac{\partial^{2}s}{\partial t^{2}}\mid_{\xi_{i}}=\frac{\partial \sigma_{ij}}{\partial x_{i}}+b_{i} \tag{3}\]  
where \(\varsigma_{i}\), \(s\), \(\rho_{L}\) and \(b_{i}\) signify the Lagrangian coordinate, the displacement, Lagrangian structure density and the body force, respectively.  
On the FSI interface, the geometric compatibility and mechanical equilibrium conditions (Equations (4) and (5)) [17] should be satisfied, which cause the coupling of Equations (1)-(3).  
\[\upsilon\mid_{\xi_{i}}=\frac{\partial\varsigma_{i}}{\partial t}\mid_{\xi_{i}} \tag{4}\]  
\[F_{f}=-F_{s} \tag{5}\]  
where \(F_{f}\), \(F_{s}\) are the interactive force of fluid and structure arising at the FSI interface, respectively.  
The interaction force is calculated by using the penalty function method. The penalty function based FSI system is shown in Figure 1, where P, R and D represents the penetration, the penalty rigidity and the damping coefficient, respectively. The basic idea of the penalty function method is to assure that there is no penetration at the coupling interface by introducing the coupling force between fluid and structure. During the simulation, if the penetration appears, a coupling force will be introduced, and the high frequency is damped out with the purpose of restraining numerical oscillation.
## 3 Results
### Determination of the Fraction of Circulating Free DNA Methylation
methylation sites showed equivalent methylation proportions across the genes (Figure S2A). The median differences in the fraction of methylation on symmetrical sites was found to be 0.0 for each gene (range \(-\)0.03 to 0.04) (Figure S2B). Considerable variation in the _OLIG3_ symmetrical methylation fractions were apparent though (Figure S2A,B). Moreover, variation within individual patients was observed (Figure S2C), for instance Melanoma 1 showed substantial differences in the symmetrical methylation fractions for _GJB2_ (Figure S2A). To determine whether individual gene promoter methylation could discriminate between healthy and melanoma patients, we performed ROC analysis. The analysis was based on the median circulating free DNA methylation calculated from the fraction methylation of CpG sites in upper and lower DNA amplicon strands for each gene in each healthy and melanoma sample (Table S4). The two gene targets with the most significant hypermethylation in metastatic melanoma patient compared with normal control were _OLIG3_ and _PON3_, with AUC values of 1.00 using ROC analysis (Figure 2). Hypermethylation of _GJB2_, _HOXA9_, _MEOX2_ and _RASSF1_ also had significant AUC values of 0.80, 0.96, 0.92 and 0.84 respectively in discriminating between metastatic melanoma patients and healthy controls (Figure 2). Only 1 gene, _TFAP2B_, with an AUC value of 0.56 was not significantly hypermethylated in melanoma patients (Figure 2).