esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Feature Fusion Based Dual Branch Network for X-ray Security Inspection Image Classification
## 2 Related Works
### Class Imbalance and Its Treatment  
Class imbalance problem refers to the fact that the gap between training samples of various classes is comparatively large. On this basis, imbalanced training samples will make the model more likely to predict the sample as the majority class. This is as even if the model predicts all the samples as the majority class, it can still achieve a high accuracy rate. Nonetheless, this will bring about the incapability of the model to predict minority classes, and furthermore the loss of generalization. The class imbalance problem regularly occurs, not only in classification, but also in other computer vision tasks such as object detection and semantic segmentation.  
The class imbalance problem is mainly treated by two aspects, namely data and algorithm. Data level treatment balances samples of various classes by sampling the whole dataset, including under-sampling majority class samples and over-sampling minority class samples. In addition, some operations apply data augmentation of minority class samples [32], etc. Algorithm level treatment is mainly based on improving loss function. By increasing the cost that samples with minority class yield, or ignoring the contribution that some samples with majority class make, the attention to the minority class can increase. Research in [33] proposed an online hard example mining algorithm (OHEM), which solves the class imbalance problem by retraining some samples that yield much loss in model training process.  
As for binary classification, \(p\) denotes probability predicted by the model, \(y\) denotes ground-truth label, and \(y\in\{0,1\}\). Define \(p_{t}\) as Formula (1).  
\[p_{t}=\left\{\begin{array}{ll}p&\text{if }y=1\\ 1-p\,&\text{otherwise}\end{array}\right. \tag{1}\]  
Then, cross entropy (CE) is defined as \(\text{CE}(p_{t})=-\log(p_{t})\).  
Proposed in [6], the design of focal loss is CE based. Focal loss introduces weight \(\alpha\) for the minority class and \(1-\alpha\) for the majority class, to balance the importance of majority and minority class samples. It also adds a formulating factor \((1-p_{t})^{\gamma}\) with the focusing parameter \(\gamma\geq 0\), to distinguish difficult samples (\(p_{t}\leq 0.5\)) from easy samples (\(p_{t}>0.5\)). By defining \(\alpha_{t}\) analogously to \(p_{t}\), focal loss (FL) defines as Formula (2).  
\[\text{FL}(p_{t})=\alpha_{t}(1-p_{t})^{\gamma}\text{CE}(p_{t}) \tag{2}\]  
Extend to multi-label classification, for \(\mathbf{p}\in\mathbb{R}^{C}\) and \(\mathbf{y}\in\mathbb{R}^{C}\) where \(C\) denotes the number of classes appearing in the dataset. Then, focal loss transforms into Formula (3).  
\[\text{FL}(\mathbf{p}_{t})=1^{\gamma}[\boldsymbol{\alpha}_{t}\odot(1-\mathbf{ p}_{t})^{\gamma}\odot\text{CE}(\mathbf{p}_{t})] \tag{3}\]  
where \(\odot\) denotes element-wise multiplication among vectors.
# Investigation of Electro-Elastic Properties for LN Single Crystals at Low Temperature
###### Abstract  
Lithium niobate crystals (LiNbO\({}_{3}\), LN) are multifunctional crystal materials with many outstanding properties. In this work, the electro-elastic properties of LN single crystals were explored at temperatures from \(-150\)\({}^{\circ}\)C to \(150\)\({}^{\circ}\)C. The temperature dependences of dielectric permittivities, elastic compliances, electromechanical coupling factors and piezoelectric coefficients were determined using the impedance method. The LN crystals possessed large dielectric permittivities, the \(\varepsilon_{11}^{T}/\varepsilon_{0}\) and \(\varepsilon_{33}^{T}/\varepsilon_{0}\) were 83.2 and 29.4 at room temperature, respectively. The elastic compliances \(s_{11}\), \(s_{13}\), \(s_{33}\) and \(s_{44}\) presented a positive increase as the temperature increased, and the variations were 5.0%, 8.2%, 4.6% and 5.4%, respectively, showing a good temperature stability. Moreover, the temperature dependence of the electromechanical coupling factors and piezoelectric coefficients for different vibration modes were studied with a temperature range from \(-150\)\({}^{\circ}\)C to \(150\)\({}^{\circ}\)C, where the thickness shear vibration mode d\({}_{15}\) presented a large piezoelectric response and minimal temperature variation.  
LN crystals; electro-elastic properties; temperature stability +
Footnote †: journal: Applied Sciences
## 6 Types and Challenges of NCS
### Challenges of NCS
#### 6.2.1 Sampling  
Before being transmitted over a communication network, the continuous signals should be sampled for simplicity. For sampling the above continuous-time signals, there are basically two ways: one is time-triggered, and another is event-triggered. In time-triggered sampling also known as Riemann sampling, the sampling moment happens at fixed time intervals. Periodic sampling was usually employed in the early digital management systems, as the analysis and style of these sampled systems is simple. At the instance the value of the sampled signal approaches zero, the output obtained is equivalent to the continuous-time system. Another sampling is event-triggered, which is generally denoted as Lebesgue sampling. In this the sampling occurs when an event is triggered. This sampling significantly reduces the network traffic volumes. Over the last decade, there  
Figure 11: Challenges of NCS.  
has been an increasing interest in the control of NCSs through event-triggering because of its advantage over time-triggering; see, e.g., References [59; 73; 78].  
In NCSs, the limited bandwidth restricts the system performance [149]. This occurs when the sampling period is small, thereby generating a lot of data that overloads the network and causes congestion. It may result in increased packet losses and longer delays, which degrades the performance. The relationship between the sampling period, the network load, and the system performance in an NCS is illustrated in Figure 12[150]. For instance, decreasing the sampling period through the values corresponding to points \"a\" and \"b\", the device performance improves. In addition, the device performance deteriorates as a result of network congestion when the sampling period is reduced, as indicated by the values corresponding to points \"b\" to \"c\". An optimal sampling period exists at which the system performance is the best (point \"b\" in Figure 12).  
Table 6 shows the quantitative data for the sampling rate considered in the different research works. There are mainly periodic and aperiodic sampling approaches. In periodic case, the lowest sampling rate considered was 22.1 ms [40], whereas the highest sampling rate considered was 1730 ms [149]. For the case of aperiodic sampling, the lowest range is between 4 ms to 4.7 ms in Reference [119], whereas the highest range was between 30 ms to 1380 ms in Reference [96].  
Figure 12: Relationship between system performance, network congestion and sampling period.
## 5 Thermochemical Conversion of Carbonaceous Feedstocks
### Hydrothermal Gasification  
Unlike reforming and dry gasification, hydrothermal gasification is a biomass conversion process where relatively lower temperatures (around 600 \({}^{\circ}\)C versus 800-1200 \({}^{\circ}\)C) and significantly higher pressures (above 25 MPa) are used [121; 122; 123]. Water is used as the reaction medium, and this technology is suitable for wet biomasses and organic wastes, avoiding the costly drying step of feedstocks.  
Hydrothermal gasification processes can be classified, according to the reaction temperatures, into the following: (1) The first is aqueous phase reforming, with temperatures ranging from 215 \({}^{\circ}\)C to 265 \({}^{\circ}\)C. If the feedstock concentration is very low (about 1%), it is thermodynamically possible to obtain hydrogen. The main output products are H\({}_{2}\) and CO\({}_{2}\). Subcritical conditions are not suitable for gasifying high-molecular-weight constituents, like lignin or cellulose [124; 125; 126]. (2) The second is near critical gasification, with temperatures ranging from 350 \({}^{\circ}\)C to 400 \({}^{\circ}\)C. Biomass is gasified at near critical gasification conditions to obtain high degrees of carbon conversion to methane [127]. (3) The third is supercritical water gasification (SCWG), with a temperature higher than 374 \({}^{\circ}\)C (usually higher than 500 \({}^{\circ}\)C) to obtain H\({}_{2}\) and CO\({}_{2}\) as the main products. This process shows high conversion rates; however, it is highly dependable on factors like operation conditions, feedstocks, the nature of the catalyst (if used), or the reactor design. In this type of gasification, supercritical water has a twofold role as a reactant and medium of biomass. Under supercritical conditions, an optimal environment is created for hydrolysis and pyrolysis reactions due to the generation of H+ and OH\(-\) ions [126]. In addition, free radicals are generated, enhancing gas formation, leading to a high gas yield [127]. Because of the higher solubility and reactivity of organic compounds in supercritical water, SCWG produces less tar and char as a by-product, in comparison with conventional dry gasification. This has dual benefits: tar and char are difficult to gasify; on the other hand, they cause a reduction in the energy efficiency of the process by means of reactor plugging, heat exchanger fouling, and catalyst deactivation [128].
# Reconstruction of Epidemiological Data in Hungary Using Stochastic Model Predictive Control
###### Abstract  
In this paper, we propose a model-based method for the reconstruction of not directly measured epidemiological data. To solve this task, we developed a generic optimization-based approach to compute unknown time-dependent quantities (such as states, inputs, and parameters) of discrete-time stochastic nonlinear models using a sequence of output measurements. The problem was reformulated as a stochastic nonlinear model predictive control computation, where the unknown inputs and parameters were searched as functions of the uncertain states, such that the model output followed the observations. The unknown data were approximated by Gaussian distributions. The predictive control problem was solved over a relatively long time window in three steps. First, we approximated the expected trajectories of the unknown quantities through a nonlinear deterministic problem. In the next step, we fixed the expected trajectories and computed the corresponding variances using closed-form expressions. Finally, the obtained mean and variance values were used as an initial guess to solve the stochastic problem. To reduce the estimated uncertainty of the computed states, a closed-loop input policy was considered during the optimization, where the state-dependent gain values were determined heuristically. The applicability of the approach is illustrated through the estimation of the epidemiological data of the COVID-19 pandemic in Hungary. To describe the epidemic spread, we used a slightly modified version of a previously published and validated compartmental model, in which the vaccination process was taken into account. The mean and the variance of the unknown data (e.g., the number of susceptible, infected, or recovered people) were estimated using only the daily number of hospitalized patients. The problem was reformulated as a finite-horizon predictive control problem, where the unknown time-dependent parameter, the daily transmission rate of the disease, was computed such that the expected value of the computed number of hospitalized patients fit the truly observed data as much as possible.  
Keywords:dynamical systems; state estimation; model predictive controller; epidemiological models +
Footnote †: journal:
# Comparative Analysis of MoCA and DigiMoCA Test Results: A Pilot Study
## 2 Materials and Methods
### Participants and Procedures
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & & **D+MCI+** & **D+MCI-** & **D-MCI+** & **Healthy** & **n = 73** \\  & & **n = 21** & **n = 10** & **n = 13** & **n = 29** & **n = 73** \\ \hline Educational level & \textless{}12 & 95.2\% (20) & 80\% (8) & 69.2\% (9) & 44.8\% (13) & 50 \\  & \textgreater{}12 & 4.8\% (1) & 20\% (2) & 30.8\% (4) & 55.2\% (16) & 23 \\ \hline Household & LA & 33.3\% (7) & 20\% (2) & 15.4\% (2) & 31\% (9) & 20 \\  & LWO & 66.7\% (14) & 80\% (8) & 84.6\% (11) & 69\% (20) & 53 \\ \hline Marital status & Married & 23.8\% (5) & 50\% (5) & 46.2\% (6) & 44.8\% (13) & 29 \\  & Single & 14.3\% (3) & – & – & 6.9\% (2) & 5 \\  & Widowed & 61.9\% (13) & 50\% (5) & 46.2\% (6) & 27.6\% (8) & 32 \\  & Divorced & – & – & 7.7\% (1) & 20.7\% (6) & 7 \\ \hline Sex & Male & 28.6\% (6) & 30\% (3) & 46.2\% (6) & 24\% (7) & 22 \\  & Female & 71.4\% (15) & 70\% (7) & 53\% (7) & 75.9\% (22) & 51 \\ \hline Age (Mean) & \(75.78\pm 9.2\) & \(81.90\pm 9.1\) & \(74.00\pm 8.1\) & \(76.46\pm 9.2\) & \(71.66\pm 7.3\) & \(75.78\pm 9.2\) \\ \hline \hline \end{tabular}  
* LA: living alone. LWO: living with others.  
\end{table} 
Table 1: Participants’ sociodemographic characteristics. A sample size of 73 participants may not provide sufficient statistical power to detect subtle effects, especially when divided into four groups. However, as discussed in the rest of this paper, the results obtained demonstrate that this sample is sufficient for shedding some light about the applicability of digital instruments based on smart assistants for cognitive assessment in elder adults. On the other side, the geographical area where participants were enrolled do not have significant demographic, cultural, or socio-economic characteristics that make it singular when compared with other urban-rural areas in Western Europe, although its applicability in other areas of the world is not clear. In any case, the sample reasonably reflects the distribution of male and female individuals in the general population of that age in Western Europe (i.e., between 60 and 99 years).
# Fragility Assessment and Reinforcement Strategies for Transmission Towers Under Extreme Wind Loads
## 5 Concurrent Multi-Scale Reinforcement Modeling
### Model Validation  
To assess the accuracy and reliability of the concurrent multi-scale model, a modal analysis was conducted for both the rigid frame model and the multi-scale model of the transmission tower. Consistency was maintained by using identical material properties and structural configurations in both models. The first ten natural frequencies extracted from each model are summarized in Table 4. The comparison reveals close agreement, with a maximum deviation of only 1.12%, demonstrating that the multi-scale model effectively captures the global dynamic characteristics of the transmission tower. In addition, the tower's top displacement under varying wind speeds at a 0\({}^{\circ}\) wind direction was compared between the two models. Due to space constraints, this comparative analysis focuses on displacement results only. As shown in Figure 19, the displacement values obtained from the multi-scale model closely match those of the rigid frame model, with deviations remaining within 5%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mode Shape** & **Rigid Frame Model/Hz** & **Multi-Scale Model/Hz** & **Error/\%** \\ \hline
1 & 1.699 & 1.718 & 1.12\% \\
2 & 2.208 & 2.231 & 1.04\% \\
3 & 3.554 & 3.56 & 0.17\% \\
4 & 5.717 & 5.751 &  
Furthermore, a comparison of stress distributions in the No. 1 to No. 4 main structural rods at a wind speed of 33 m/s is presented in Figure 20. The beam element model yields a relatively coarse stress distribution due to its simplified representation of geometry and stress fields. In contrast, the solid elements within the multi-scale model provide a more refined and detailed depiction of localized stress concentrations. This enhanced resolution underscores the enhanced capability of the multi-scale model in accurately identifying critical stress regions that are essential for failure analysis and reinforcement design. Expectedly, the minor discrepancies in displacement and stress predictions between the two models can be attributed to the complex mechanical coupling between beam and solid elements at the interface. These interface points may introduce localized inaccuracies in force transfer, resulting in slight computational deviations. Nevertheless, the observed differences remain within an acceptable tolerance range and do not compromise the overall validity of the multi-scale modeling approach.
## 2 Materials and Methods
### Identifying Problem Side and Directional Preference  
DMA Clinical Pilates assessment was performed by the same assessor (B.C.K.) who is a physiotherapist with more than 15 years of clinical experience and a certified DMA Clinical Pilates instructor. The DMA Clinical Pilates exercise testing method was used to identify the problem side and directional (movement) preference of each participant through a series of movement-based tests and an algorithm (Figure 1) [10,16]. Details of the test movement and algorithm are described in the Supplementary File of a previously published study [10]. This assessment approach determines which directions of movement (e.g., flexion or extension) are the easiest and most comfortable for a person. The problem side might not correspond with the pain side for those with unilateral pain. On the other hand, people with pain on both sides might not be assessed to have problems on both sides. Flexion or extension preference was studied a decade ago [16], while lateral flexion preference to either side was studied recently [22].  
As the movement charting of rotation concerning exercise may be confusing, the study provided three scenarios with a diagnostic bullseye in Figure 1 to guide interpretation. Figure 1a is a common rehabilitation exercise, with knees rolling to the right, which results in left trunk rotation, and is a mid-range flexion rotation DMA Clinical Pilates exercise. Figure 1b is the attitude rotation DMA Clinical Pilates exercise used for left trunk rotation and extension (full-range) preference by moving the right leg to the left. Figure 1c is a mid-range extension rotation DMA Clinical Pilates exercise, similarly to the left.  
Each patient can present with a different combination of directional preferences: (1) mid-range or end-range; (2) flexion, extension or none; (3) lateral flexion to left, right or none; (4) rotation to left, right or none [10]. The problem side (opposite to directional preference) is first deduced based on the side with flexion or extension preference. If this method is unable to confirm the problem side, the side with lateral flexion preference is taken as the problem side. In cases whereby lateral flexion preference is absent, the side of rotation preference is used to indicate the problem side. Upon completion of the DMA  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Demographics** & **Mean (SD)** & **Range** \\ \hline Age, years & 32.1 (7.3) & 21 to 40 \\ Body mass index, kg/m\({}^{2}\) & 23.7 (3.5) & 16.8 to 33.3 \\ Pain in past 7 days VNS, out of 10 & 4.6 (0.8) & 4 to 7 \\ Patient-specific functional scale, out of 10 & 5.9 (1.5) & 2.5 to 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of participants with non-specific chronic LBP (\(n=40\)).  
Clinical Pilates assessment, the results are comprehensively mapped on the diagnostic bullseye chart, which provides a quick summary for clinical documentation.
## 4 Methodology
### Power System Model
The real time power dispatch in an electric power system can be described by a steady state AC-optimal power flow model (AC-OPF) [50], which is expressed by the following non-linear inequality constrained optimization model:  
\[\underset{\mathbf{X}}{\text{min}}\quad f(\mathbf{X}) = \sum_{i=1}^{N_{g}}c_{0,i}+c_{1,i}\,P_{G,i}+c_{2,i}\,P_{G,i}^{2}\] (5) \[s.\ t.\quad G_{P,i}(\mathbf{X}) = P_{i}(\mathbf{V})-P_{G,i}+P_{D,i}=0,  
\[Q_{G,i}^{min}\leq Q_{G,i}\leq Q_{G,i}^{max}\,,\quad i=1\dots N_{g} \tag{15}\]  
where the decision variables expressed by vector \(\mathbf{X}\)  
\[\mathbf{X}= \begin{bmatrix}\Delta&\mathbf{V_{m}}&\mathbf{P_{G}}&\mathbf{Q_{G}} \end{bmatrix} ^{T} \tag{16}\]  
are the set of bus voltage angles \(\Delta\), bus voltage magnitudes \(\mathbf{V_{m}}\) and active and reactive power generation \(\mathbf{P_{G}}\) and \(\mathbf{Q_{G}}\), respectively. Equation (5) is a scalar quadratic objective function, which describes the total operating costs for each committed generation unit in terms of its active power generation, while the non-linear equality constraints expressed by Equations (6)-(9) describe the set of active and reactive power balance equations derived from matrix Equation (4). Equations (10) and (11) are non-linear inequality constraints, which describe the transmission capacity limits \(S_{k}^{max}\) for each line, while the upper and lower limits of the decision variables are described by Equations (13)-(15). For each isolated sub network one bus is chosen as the voltage angle reference (see Equation (12)), i.e., the voltage angle of the reference bus is set to zero. The described AC-OPF model is implemented into the open source power flow library MATPOWER [51], which we utilize as the power system simulator in the context of the proposed simulation framework.
_Appl. Sci._**2015**, \(5\), 48-61; doi:10.3390/app5020048  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
Sum-Frequency-Generation-Based Laser Sidebands for Tunable Femtosecond Raman Spectroscopy in the Ultraviolet  
**[PERSON] 1,2, [PERSON] 1, [PERSON] 1 and [PERSON] 1,2,***  
1 Department of Chemistry and the Center for Sustainable Materials Chemistry,  
Oregon State University, 153 Gilbert Hall, Corvallis, OR 97331, USA;  
E-Mails: [EMAIL_ADDRESS] (L.Z.); [EMAIL_ADDRESS] (W.L.);  
[EMAIL_ADDRESS] (Y.W.)  
2 Department of Physics, Oregon State University, 301 Weniger Hall, Corvallis, OR 97331, USA  
* Author to whom correspondence should be addressed; E-Mail: [PERSON]RESS]; Tel.: +1-541-737-6704; Fax: +1-541-737-2062.  
Academic Editor: [PERSON]  
_Received: 11 March 2015 / Accepted: 13 April 2015 / Published: 16 April 2015_  
**Abstract:** Femtosecond stimulated Raman spectroscopy (FSRS) is an emerging molecular structural dynamics technique for functional materials characterization typically in the visible to near-IR range. To expand its applications we have developed a versatile FSRS setup in the ultraviolet region. We use the combination of a narrowband, \(\sim\)400 nm Raman pump from a home-built second harmonic bandwidth compressor and a tunable broadband probe pulse from sum-frequency-generation-based cascaded four-wave mixing (SFG-CFWM) laser sidebands in a thin BBO crystal. The ground state Raman spectrum of a laser dye Quinolon 390 in methanol that strongly absorbs at \(\sim\)355 nm is systematically studied as a standard sample to provide previously unavailable spectroscopic characterization in the vibrational domain. Both the Stokes and anti-Stokes Raman spectra can be collected by selecting different orders of SFG-CFWM sidebands as the probe pulse. The stimulated Raman gain with the 402 nm Raman pump is \(>\)21 times larger than that with the 550 nm Raman pump when measured at the 1317 cm\({}^{-1}\) peak for the aromatic ring deformation and ring-H rocking mode of the dye molecule, demonstrating that pre-resonance enhancement is effectively achieved in the unique UV-FSRS setup. This added tunability in the versatile and compact optical setup enables FSRS to better capture transient conformational snapshots of photosensitive molecules that absorb in the UV range.  
**Keywords:** femtosecond spectroscopy; four-wave mixing; tunable laser sidebands; stimulated Raman scattering; resonance enhancement; molecular vibrations