esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Hypotheses and Model Development
### Compatibility  
In DOI theory, compatibility is the first variable that is expected to be able to foresee CC adoption. This is also called the extent to which an innovation matches the past practices, current values, and present needs of the potential adopter [104]. Moreover, compatibility examined how much innovation can conform to the existing systems. It has also been noted that the characteristics of a new technology innovation can impact potential innovation adopters. Further, DOI studies have accentuated how significant compatibility is when assessing the disposition of organizations for implementing new technology [104,126].  
Several studies have examined compatibility as an important addition to the variance concerning IT managers' inclination to adopt CC [30; 31; 35; 127; 128]. Hence, the following hypothesis is devised:  
**Hypothesis 1 (H1).**_Compatibility positively impacts CC adoption in HEIs._
## 2 Materials and Methods
### Temperature Measurement on Liquids  
Two different measuring devices were used to determine the cell culture medium temperature during NIPP treatment. The detection of the surface temperature of the liquid was performed by a FLIR One infrared camera (Teledyne Flir, Wilsonville, OR, USA). The temperature below the liquid surface was determined using a Lab Thermometer IP65 with an LT-101 temperature sensor (TFA Dostmann, Reicholzheim, Germany).
## 4 Discussion
The plasticity of the pipeline during calculations affects the allowable stress values that can occur in its wall without plastic deformation during repairs. Based on this, the yield strength of steel is taken as allowable stress value so that the pipeline deformations occur in the elastic zone. This value is taken into account for a specific pipeline material during the calculations made using the proposed mathematical model. Nevertheless, it should be noted that during the operation of the pipeline, the material of its wall ages. The aging process of the pipeline wall material consists in changing its strength properties [59]. This, in particular, affects the yield strength and tensile strength of the material and may be especially relevant for pipelines with a long period of operation. Of particular interest for this study is the change in the yield strength of the pipeline wall material during its operation. This parameter in the process of steel aging increases to a peak value, and then decreases to a value 10% lower than the initial one [60]. In order to take into account such a change in properties, it makes sense to reduce the allowable values of stresses in the pipeline wall used in calculations according to the proposed mathematical model by a safety factor equal to 10-15%. This will ensure the possibility of centering the ends of the pipeline in the elastic zone of its deformations, even for long-term operated pipelines. As for possible defects in the pipeline wall, they do not have a significant effect on the calculation results, since the process of centering the ends of the pipeline is carried out after cutting out its defective section. That is, in this case, there are no stress concentrators in the pipeline wall, which cause uneven distribution of stresses. It is also worth noting that the main types of stresses that occur in the pipeline wall are bending stresses. In the process of centering the ends of the pipeline, possible tensile stresses caused by longitudinal elongation or contraction of the pipeline are eliminated through the use of gripper rollers in the design of the proposed devices. Due to this, the grips of the devices do not create significant longitudinal deformations of the pipeline wall, but only its bending is carried out due to transverse movements of the pipeline ends.
## 4 Results  
### Effect of Uniform Corrosion  
The effect of uniform corrosion degradation on the SCFs is shown in Figure 10. The corrosion depth d ranges from 0 to 2.5 mm. Using a corrosion rate of 0.1 mm/year [30] and a service life of 25 years, the maximum uniform corrosion depth is 2.5 mm. Figure 10 shows the variation of the SCFs at the critical points as a function of the uniform corrosion depth. The SCFs are calculated using the FE method and the Efthymiou equations, respectively.  
The FE results show that the SCFs increase with d, and the effects of d on the SCFs are linear. The effects of uniform corrosion on the SCFs cannot be neglected. The effects of d on the SCFs depend on the loading condition and the location. For the SCF of the saddle, the effect of d on the SCF of the saddle is significant. Under axial loading, the SCF of the saddle increases by 16%, and under OPB, the SCF of the saddle increases by 14%. For the SCF of the crown, d has a small impact on the SCF of the crown, especially in the case of IPB.  
Although the results of the Efthymiou equations deviate from those of the FE method, similar trends with the corrosion depth are observed. For the case of axial loading, the SCF of the crown slightly decreases with the increase in d, which is not consistent with the FE results. The other SCFs are all increased with d. Compared with the FE results, the Efthymiou equations slightly underestimate the increase in the SCFs for the saddle under  
Figure 10: SCFs as a function of uniform corrosion depth.  
Figure 9: Ra as a function of corrosion time T, [PERSON] et al. [33] and [PERSON] et al. [34].  
axial loading when d becomes large. The difference in the results of FE and Efthymiou equations may be partly attributed to the different thinning strategies of the two methods. In the FE method, it is assumed that the corrosion degradation does not happen at the interior region, and the material inside the brace including the part material located at the outer surface of the chord remains unchanged. It is assumed that the thicknesses of the chord and brace are both reduced uniformly when using the Efthymiou equations.
## 4 Simulation of the Proposed Control
### Study Cases  
Three different control-strategy cases were compared to verify the appropriacy of the proposed control strategy in this paper. For each case, a symmetric three-phase to ground fault on the on-shore PCC side was simulated. To satisfy the LVRT requirements of various countries, Germany and China LVRT requirements were applied in this paper [32]. The three different strategies are expressed in Table 3. Firstly, a case for a DC chopper and surge arrestor implemented system to ride through a fault was simulated. Secondly, a fast DFIG turbine deloading control strategy with a fast communication case was simulated. Finally, the proposed coordinated droop control strategy was simulated. The simulation scenarios are outlined in Table 4. For Scenario A, an on-shore-side 150-ms fault with 0.001\(\Omega\) fault resistance was simulated. For Scenario B, a fault with a 2.95\(\Omega\) fault resistance with the same location and fault duration was simulated. In Scenario C, the same fault location and resistance as Scenario B for a 625-ms fault duration was simulated. Finally, a 0.001\(\Omega\) fault resistance case for 150 ms with a 400-MW wind turbine generation case was simulated. In Figures
## 8 Conclusions
### Major Findings  
We summarized the major findings in the following aspects.  
First, analysis of the annual trend of publications demonstrated that there was a dramatic growth in the research interests in the NLP-enhanced clinical trial research. In addition, research interests had become diverse among scholars.  
Second, the USA, as well as several institutions such as _Stanford University_, _Columbia University_, and _the National Institutes of Health_, had contributed the most to the NLP-enhanced clinical trial research.  
Third, the collaborations among European countries/regions in conducting NLP-enhanced clinical trial research were very close. The collaborations among institutions and authors from the same countries/regions were also close, particularly those from the USA.  
Fourth, science mapping revealed several major research themes, including _Human-focused issues_, _Systems and applications_, _Clinical trials processing_, as well as _Diabetes-related issues_.  
Fifth, STM identified 16 major topics, within which the most discussed included _Interoperability_, _Clinical decision support_, _Information extraction_, and _Eligibility criteria_.  
Besides, several topics with an increase in research interest among authors had been identified, for example, _Life quality_, _Drug, Social & emotional_, _Information extraction_, _Eligibility criteria_, and _Drug side effect_.  
In addition, several potential inter-topic research directions had also been uncovered, for example, _Human-computer interaction and Protocol_, _Eligibility criteria and Clinical decision support_, _Text mining and Information extraction_, _Protein & Gene_ and _Drug side effect_, as well as _Intervention and Social & emotional_.
###### Abstract  
Visible light communication (VLC) is considered to be a promising technology for realizing intelligent transportation systems (ITSs) and solving traffic safety problems. Due to the complex and changing environment and the influence of weather and other aspects, there are many problems in channel modeling and performance analysis of vehicular VLC. Unlike existing studies, this study proposes a practical vehicle-to-infrastructure (V2I) VLC propagation model for a typical mountain road. The model consists of both line-of-sight (LOS) and non-line-of-sight (NLOS) links. In the proposed model, the effects of vehicle mobility and weather conditions are considered. To analyze the impact of the considered propagation characteristics on the system, closed-form expressions for several performance metrics were derived, including average path loss, received power, channel capacity, and outage probability. Furthermore, to verify the accuracy of the derived theoretical expressions, simulation results were presented and analyzed in detail. The results indicate that, considering the LOS link and when the vehicle is 50 m away from the infrastructure, the difference in channel gain between moderate fog and dense fog versus clear weather conditions is 1.8 dB and 3 dB, respectively. In addition, the maximum difference in total received optical power between dense fog conditions and clear weather conditions can reach 76.2%. Moreover, under clear weather conditions, the channel capacity when vehicles are 40 m away from infrastructure is about 98.9% lower than when they are 10 m away. Additionally, the outage probability shows a high correlation with the threshold data transmission rate. Therefore, the considered propagation characteristics have a significant impact on the performance of V2I-VLC.  
a _et al._ Article  
16 July 2024  
Revised: 24 August 2024  
Accepted: 25 August 2024  
Published: 27 August 2024
## 2 Methodology
### Array Factor and Parameter Calculation  
The far-field radiation performance of an antenna array is primarily determined by its array factor (\(AF\)), which depends on the geometric configuration of the antenna structure, the relative amplitudes, and the phase shifts applied to each individual element [24; 25]. For a two-dimensional antenna array composed of \(N\) antenna elements positioned in the complex plane, each element's location \(Z_{N}\) can be represented by a complex number \(x_{n}+iy_{n}\). Thus, the array factor \(AF(\theta)\) for this configuration is mathematically expressed as follows:  
\[AF(\theta,\phi_{0})=\sum_{n=1}^{N}I_{n}e^{ik(x_{n}\cos(\theta)\cos(\phi_{0})+y _{n}\cos(\theta)\sin(\phi_{0}))} \tag{3}\]  
where \(k=\frac{2\pi}{\lambda}\) describes the wavenumber and the wavelength \(\lambda\) of the frequency of evaluation for the array factor. The amplitude excitation \(I_{n}\) of the \(n\)th element and its position are represented in Cartesian coordinates. The cos and sin operators describe the phase shifts along their respective axes, considering no steering angle in elevation (\(\theta_{0}\)) and azimuth cut angle \(\phi_{0}=90^{\circ}\).  
Throughout the optimization process, \(AF(\theta)\) is evaluated from 0 to \(\pi\), assuming symmetrical responses in the lower hemisphere [25]. The operational bandwidth is defined by the ratio of the upper frequency limit to the lower frequency limit within which the AF maintains an acceptable SLL performance.
# Reliability of NAND Flash Memories: Planar Cells and Emerging Issues in 3D Devices
## 3 NAND Flash Memory Reliability
### Charge Detrapping  
Early investigators of SILC in stressed oxides observed a transient component in the leakage current, that was attributed to charging and discharging of oxide traps [210; 211; 212; 213; 214] and was exploited to extract information on the oxide quality [215; 216]. The detrapping current followed a \(1/t\) dependence down to very short times [217], in agreement with tunneling models [218]. The impact of this phenomenon on Flash reliability was first discussed in [219; 220], pointing out that the log-time dependence of the \(V_{T}\) shift could lead to retention errors even in the absence of SILC and could dominate the charge loss with respect to SILC itself [63]. Moreover, as charge is trapped into the oxide during P/E cycling and detrapped in-between, a dependence on the cycling pattern arises, which complicates the development of comprehensive models. Such task began in [221; 222] and developed into a full model accounting for the major experimental evidence in [223; 224; 225].  
Figure 17: Pictorial view of the SILC distribution in Flash cells. The center picture (**b**) shows a trap distribution in the tunnel oxide region of the cell. One- and two-trap-assisted tunneling mechanisms (respectively TAT and 2 TAT) are depicted as (**a**,**c**), respectively.  
Figure 18: **(Left)** average gate current density vs. floating-gate bias for two cells affected by anomalous SILC, together with model predictions; (Right) cdf of the array leakage currents at fixed bias for different gate oxide thicknesses and model results. In all cases, symbols = experimental data, lines = model results.
# Comparative Analysis of Current Control Techniques to Support Virtual Inertia Applications +
## 5 Conclusions  
The design and comparative performance analysis of the PI, PR, hysteresis, MPC, and the supplementary ADP controller were presented in this paper. Simulation studies showed that the PI Type-2 controller had a slow response and high overshoot and was not suitable for virtual inertia systems. The oversizing of the inverters required to handle these overshoots can become an economic barrier. The PR controller on the other hand showed a lower overshoot for a similar response time. However, the performance may deteriorate in weak grids (or microgrids), where the system frequency can deviate by a substantial amount. The hysteresis control and the MPC present robust control options with minimal overshoot and fast response times. However, they suffer from the disadvantages of a variable switching frequency unless appropriate improvements are made in the controller to address these issues. The integration of a supplementary ADP controller was able to reduce the overshoots while achieving a similar response time as the PI and PR controllers. Moreover, the switching frequency remained constant, unlike the hysteresis and MPC controllers. Hence, from the point of view of implementation simplicity, the PI, PR, and hysteresis control appear to be good choices. In weak grids, where the frequency of the system can drop in a very short time, the hysteresis or even the MPC technique with fast response may be more desirable. If the hysteresis or the MPC control approaches are used, proper care needs to be taken in the filter design. Moreover, the MPC will demand higher computational power and improved semiconductor switches. Integrating the traditional PI controller with the ADP can combine the advantage of the PI controller with a lower overshoot and may be more robust in weak grids than PR controllers.  
Conceptualization, I.T. and R.T.; formal analysis, U.T. and I.T.; funding acquisition, T.M.H. and R.T.; investigation, U.T., D.S. and N.M.; methodology, U.T., D.S., N.M., I.T. and R.T.; project administration, R.T.; validation, U.T. and Z.N.; visualization, Z.N. and T.M.H.; writing, original draft, U.T., D.S. and N.M.; writing, review and editing, Z.N., T.M.H., I.T. and R.T.  
This research was funded by South Dakota State University's (SDSU) Department of Electrical Engineering and Computer Science PhD Funding, South Dakota Board of Regents (SDBoR) and the National Science Foundation (NSF) under Grant Number ECCS-1608722 and the NSF Major Research Instrumentation (MRI) under the Grant Number MRI-1726964 and Microsoft.  
This paper is an expanded version of our work in [10]. The authors would like to thank Department of Electrical Engineering and Computer Science at South Dakota State University (SDSU) for providing the laboratory resources to perform the studies.  
The authors declare no conflict of interest.
###### Abstract  
Human-machine interaction covers a range of applications in which machines should understand humans' commands and predict their behavior. Humans commonly change their mood over time, which affects the way we interact, particularly by changing speech style and facial expressions. As interaction requires quick decisions, low latency is critical for real-time processing. Edge devices, strategically placed near the data source, minimize processing time, enabling real-time decision-making. Edge computing allows us to process data locally, thus reducing the need to send sensitive information further through the network. Despite the wide adoption of audio-only, video-only, and multimodal emotion recognition systems, there is a research gap in terms of analyzing lightweight models and solving privacy challenges to improve model performance. This motivated us to develop a privacy-preserving, lightweight, CNN-based (CNNs are frequently used for processing audio and video modalities) audiovisual emotion recognition model, deployable on constrained edge devices. The model is further paired with a federated learning protocol to preserve the privacy of local clients on edge devices and improve detection accuracy. The results show that the adoption of federated learning improved classification accuracy by ~2%, as well as that the proposed federated learning-based model provides competitive performance compared to other baseline audiovisual emotion recognition models.  
artificial intelligence; emotion recognition; federated learning; machine learning; multimodal emotion recognition; 2024 14 1325 1
# Satellite and Ground Based Thermal Observation of the 2014 Effusive Eruption at Stromboli Volcano
## 5 Discussion
### Added Value of TET-1 Data
The problem is that TET-1 does not perform at the same retrieval rate as MODIS, because of its limited on-board data storage and its narrow swath (\(\sim\)10 times narrower than MODIS). Thus, MODIS-like instruments are still indispensable for operational use. An elegant way to estimate \(TADR\) from MODIS data is to apply the radiant density approach [61] to the MODVOLC database. Equation (18) provides the estimate of radiant density, where \(X_{SiO2}\) is the silica content (wt%) of the erupted lava [61]. \[c_{rad}=6.45\cdot 10^{25}\cdot\left(X_{SiO_{2}}\right)^{-10.4} \tag{18}\]  
By applying the radiant density approach for Stromboli, whose minimal silica content is 50 wt% [61], the radiant density is estimated to 1.4 \(\times\) 108 J/m\({}^{3}\). With upper estimated silica content for Stromboli of 55 wt%, the radiant density is even lower. Using these two estimates, \(TADR\) in the beginning of the eruption would be \(\sim\)3 and \(\sim\)10 times larger than our \(TADR\) at the beginning of the eruption (10.4 m\({}^{3}\)/s). Such high \(TADR\) was not modeled nor observed during 2007 eruption at Stromboli: maximum observed \(TADR\) during the 2007 eruption was 23 m\({}^{3}\)/s [8]. In addition, the here estimated radiant density is more comparable to the corresponding values of max and min \(TADR\) model for June 2003 eruption [5] than to the theoretical estimate based on Equation (18). Thus, we conclude that it is necessary to calibrate the approach suggested by [PERSON] _et al._[61] with high resolution \begin{table}
\begin{tabular}{c c c c c} \hline
**Date/Time** & **T (K)** & **A (Ha)** & \(VRP_{DB}\) (MV)** & \(VRP_{W}\) (MW) \\ \hline
2014/08/25 10:49 & 572 & 3.2 & 182 & 187 \\
2014/09/09 23:30 & 672 & 0.2 & 23 & 36 \\
2014/09/16 23:32 & 587 & 1.7 & 105 & 155 \\
2014/09/19 10:46 & 504 & 7.4 & 238 & 167 \\
2014/09/23 10:33 & 583 & 1.3 & 78 & 90 \\
2014/09/23 23:34 & 601 & 0.7 & 52 & 102 \\
2014/09/27 23:21 & 966 & 0.1 & 62 & 94 \\
2014/09/30 23:34 & 541 & 2.6 & 115 & 150 \\
2014/10/07 10:33 & 724 & 0.3 & 49 & 95 \\
2014/10/07 23:33 & 698 & 0.2 & 32 & 68 \\
2014/10/10 10:45 & 539 & 2.7 & 117 & 124 \\ \hline \end{tabular}
\end{table} 
Table 7: Temperature (\(T\)), area (\(A\)), and volcanic radiative power estimated using dual band (\(VRP_{BD}\)) and [PERSON]’s method (\(VRP_{W}\)) for TET-1 cloud free data. unsaturated data provided by TET-1. In the future, it should be possible to refine Equation (18) [61], but it would be necessary to monitor several eruptions, also during their waxing flow.
# Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
## 3 Methods
### Model Candidates  
We utilized ML algorithms or models capable of classifying values in an instance without considering trends or values from the previous measurement. The implemented code for all ML models is publicly available from sklearn and PyOD library [18; 22]. In this work, the models originated from two categories: proximity-based algorithms and linear models. Typically, proximity-based algorithms classify data by computing the distances between samples to compare each sample's relative distance to its surroundings (e.g., local outlier factor, cluster-based local outlier factor, and k-nearest neighbor). On the other hand, the linear models learn to formulate a mathematical function to create class-based boundaries within the dataset (e.g., Linear Discriminant Analysis and One-Class Support Vector Machine). The section focuses on brief descriptions of the investigated models without going in-depth into the models' technical details. Moreover, we intended to discover the best of the two ML categories for future CubeSat system implementation.