esa-sceva/satcom-chunk-collection huggingface dataset:
# An Investigation of Optically Very Thin Ice Clouds from Ground-Based ARM Raman Lidars
## 2 Data
### Transparent and Collocated Profiles  
For a comparison between the RL and CALIPSO, the profiles from both measurements are limited to those in which the lidar passes through the atmosphere without fully attenuating, i.e., transparent profiles, unless otherwise stated. For the RLs, transparent profiles are defined as those where the signal-to-noise ratio (SNR) is greater than 1 at 16 and 18 km at SGP and TWP, respectively. For CALIPSO, transparent profiles are defined as those where all single laser shots (1/3 km resolution) detect the ground within the 5 km profile (final data product resolution). Collocated profiles are defined for the RL as \(\pm\)4 h of a CALIPSO flyover and for CALIPSO as within 5\({}^{\circ}\) latitude by 5\({}^{\circ}\) longitude box centered over the SGP and TWP sites when the RL is operational.  
To force more consistent transparent collocated profiles between the RL and CALIPSO, the percentage of transparent profiles are matched for each collocated flyover section. The percentage of transparent profiles refers to the number of transparent collocated profiles divided by the total number of collocated profiles. The collocated flyover section refers to all RL profiles \(\pm\)4 h of a CALIPSO flyover and all CALIPSO profiles within a 5\({}^{\circ}\) latitude by 5\({}^{\circ}\) longitude box centered over the RL for the same flyover time. To match the percentage of transparent profiles, transparent profiles for the platform with a higher percentage are randomly thrown away until the percentage matches the percentage of the other platform for the given collocated flyover section.  
[PERSON] et al. [8] compared the transparent collocated ice cloud column optical depth between the CALIPSO and RL. They found that CALIPSO mean ice cloud column optical depths are larger than the RL with the default resolutions (10 min/30 m) by 92% and 63% at SGP and TWP, respectively. They showed that these ice cloud column optical depth differences can largely be reconciled by using consistent resolution, lidar ratio, and transparent percentage, and accounting for the RL's higher cloud fraction. The higher cloud fraction from the RLs than CALIPSO highlights the RL's high sensitivity for detecting optically thin ice clouds. CALIPSO can detect ice clouds with optical depths of 0.01 or less with sufficient averaging [19]. This study will focus on ice clouds with ice cloud column optical depths below 0.01 from both the RL and CALIPSO and compare them.
## 3 Results and Discussion
### ESI-MS of Uranyl Complexes with Glycine, Aspartic Acid, Cysteine, and Histidine
The main cationic and anionic uranyl complexes identified here suggest equatorial coordination number 3 in [UO\({}_{2}\)(Cl)(AA)]\({}^{+}\), 4 in [UO\({}_{2}\)(AA-H)(AA)]\({}^{+}\) and [UO\({}_{2}\)(Cl)\({}_{2}\)(AA-H)]\({}^{-}\), 5 in [UO\({}_{2}\)(Cl)(AA)\({}_{2}\)]\({}^{+}\) and [UO\({}_{2}\)(Cl)(AA-H)\({}_{2}\)]\({}^{-}\), and 6 in [UO\({}_{2}\)(AA-H)\({}_{2}\)]\({}^{+}\) and [UO\({}_{2}\)(AA-H)\({}_{3}\)]\({}^{-}\). Coordination of the amino acids to \"oxophilic\" uranyl is implicitly assumed to be through both oxygen atoms of carboxyl for neutral AAs or carboxylate for deprotonated AAs. Scheme 1 shows possible structures for the mentioned complexes where zwitterionic forms of neutral AAs are presumed. Figure 2: ESI-MS of a uranyl chloride + histidine solution (1:4) in negative ion mode. Gas-phase anionic uranyl-carboxylate complexes [UO\({}_{2}\)(RCO\({}_{2}\))\({}_{3}\)]\({}^{-}\) (R = Me, Ph) were studied by [PERSON] et al. using MS coupled to IRMPD spectroscopy, complemented by DFT computations [39]. The IRMPD results indicated that the complexes comprised two bidentate carboxylates and one monodentate carboxylate, while DFT pointed to two energetically indistinguishable conformers, with either two bidentate and one monodentate or three bidentate carboxylates. In contrast to simple carboxylates, the amino group in amino acids, as well as the sidechain functionalities of cysteine (thiol), histidine (imidazolacle), and aspartic acid (additional carboxyl), can coordinate to uranyl. Insight into the actual uranyl coordination was searched for with MS\({}^{n}\) experiments described below. CID and Reactivity of Uranyl-Glycine, Uranyl-Aspartic Acid, Uranyl-Cysteine, and Uranyl-Histidine Complexes  
Collision-induced dissociation (CID) experiments using the helium buffer gas as a collision partner were performed for dominant cationic and anionic uranyl-amino acid complexes. Also reported here are results for some reactions of complexes with water or oxygen present in the ion trap as background gases.
# Secure Monitoring System for IoT Healthcare Data in the Cloud
## 5 Proposed System
### Involved Technologies
#### 5.1.4 Machine Learning (ML)  
Machine learning (ML) algorithms can help identify various virus kinds more accurately so that they can be dealt with and prevented as soon as feasible. Additionally, this will stop susceptible populations from spreading. Based on the properties of the air in the environment, ML can be helpful in the prognosis, diagnosis, and control of patients infected with certain types of flu throughout the period of preventing and controlling the pandemic [20]. The respiratory pattern differs from the typical cold and other infections,according to the most recent clinical studies [21]. However, with the improved efficiency of these algorithms and the capacity to utilize huge volumes of data with various properties, the advantages of adopting ML techniques in risk management will be highly beneficial [22]. Artificial Intelligence (AI) technology and machine learning can be treated as subversive technologies that change established procedures for monitoring effective virus recognition, and in particular, because of the current situation, it could contribute to the prevention and control of fatal viruses like COVID-19.
# Map Matching Based on Seq2 Seq with Topology Information
## 2 Related Work  
Our work comprises three main components: the creation of a dataset featuring a topological network structure, the development of a deep learning model based on the seq2 seq approach, and the training and experimental validation of this model through comparisons with other existing models.
# In Vivo Biosynthesis of Au and Ag NPs Using Two _Medicago sativa_ L. Genotypes
## 5 Conclusions  
Based on the data obtained in this research, two-week-old seedlings of two _Medicago sativa_ L. genotypes _'Kometa'_ and _'la Bella'_ could absorb both precursors (AgNO\({}_{3}\), HAuCl\({}_{4}\)) into their tissues. The potential in vivo biosynthesis of Ag and Au NPs negatively affected plant growth after precursor uptake. Spectrophotometry methods statistically significantly (\(p<0.05\)) confirmed the changes in light absorption compared to the control. Confocal microscopy also showed that particles from the Ag and Au precursors were visible in the roots of both genotypes. However, confocal microscopy was not the most accurate method for NP visualisation, as the magnification needed to be higher. Futurestudies are needed to provide further updates on NP biosynthesis studies directly with _Medicago sativa_ L. genotypes _'Kometa'_ and _'la Bella'_ and to make a comparison between them, as other studies have not directly compared these genotypes. Additionally, it is necessary to improve methods of NP visualisation and detection to confirm the synthesis of NPs based on more reliable results, which would provide high-quality microscopy images to determine the size, shape, and number of visible particles and clarify which of the _Medicago sativa_ L. genotypes more efficiently biosynthesises Au and Ag NPs. Moreover, it is important to detect NP biosynthesis in older alfalfa plants, which would probably be more similar to the natural conditions in the soil.  
L.J.: Experimental design, conceptualisation, methodology, conducting an experiment, writing--original draft. I.P.: Experimental design, conducting an experiment, review & editing. M.J.: Conducting an experiment, review & editing. R.G.: Providing seeds, review & editing. I.K.: review & editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Daugavpils University research project No. 14-95/2023/9.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy.  
The authors are thankful to DANKO Plant Breeding Sp. z o.o., Choryn 27, 64-000 Koscan, Poland for kindly provided seeds. The authors are thankful to the Department of Ecology and Department of Technology, Daugavpils University, especially [PERSON] for visualisation of samples with confocal microscopy and [PERSON] for sample analysis of two-beam spectrophotometry with the spectrophotometer SHIMADZU UV-2550 PC.  
The authors declare that there are no conflicts of interest.
## 3 Proposed Design and System-Level Development
### Schematic of Proposed Architecture  
Figure 2 shows the schematic diagram of the proposed architecture, which contains three PV modules. All PV modules are connected in series through string switches, which are controlled in an On/Off manner to control the flow of the common string current (\(I_{string}\)). Each PV module is equipped with a buck converter, where the switches of all these converters are controlled by pulse width modulation (PWM) pulses. Note that On/Off signals for all string switches and PWM pulses for all buck converters are controlled by a current equalizer manager. This manager block takes the current, voltage values of each module, and \(I_{string}\) value through sensors. Based on this information, the current equalizer manager applies its control mechanism, the details of which are explained in the upcoming section.
## 5 Discussion
### Visualization  
#### 5.2.1 Visualization of Hidden Representations  
To assess the impact of IMAs and IUAs on model feature extraction, we utilized t-SNE to visualize the hidden representations of our models (refer to Figure 6).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name of Model** & **Acc. (IMAs)** & **F1 (IMAs)** & **Acc. (IUAs)** & **F1 (IUAs)** \\ \hline Uni2 Mul-S-Concatenate (w/o pre-train) & 66.52 & 65.47 & 68.71 & 64.60 \\ Uni2 Mul-S-Concatenate & 71.99 & 70.39 & 73.96 & 73.17 \\ Uni2 Mul-M-Concatenate (w/o pre-train) & 70.46 & 67.95 & 69.58 & 68.83 \\ Uni2 Mul-M-Concatenate & 72.87 & 70.35 & 75.05 & 74.36 \\ \hline Uni2 Mul-S-Attention (w/o pre-train) & 69.15 & 65.61 & 67.61 & 61.37 \\ Uni2 Mul-S-Attention & 72.65 & 71.53 & 73.96 & 73.26 \\ Uni2 Mul-M-Attention (w/o pre-train) & 70.24 & 64.35 & 70.90 & 69.56 \\ Uni2 Mul-M-Attention & 72.87 & 71.16 & 76.37 & 75.26 \\ \hline Uni2 Mul-S-Transformer (w/o pre-train) & 68.71 & 63.64 & 65.21 & 59.62 \\ Uni2 Mul-S-Transformer & 71.77 & 69.17 & 75.05 & 73.87 \\ Uni2 Mul-M-Transformer (w/o pre-train) & 69.58 & 63.77 & 71.77 & 67.96 \\ Uni2 Mul-M-Transformer & 73.09 & 70.06 & 76.59 & 74.68 \\ \hline Uni2 Mul-S-Conformer (w/o pre-train) & 69.37 & 64.57 & 72.21 & 68.56 \\ Uni2 Mul-S-Conformer & 72.21 & 70.62 & 76.15 & 75.00 \\ Uni2 Mul-M-Conformer (w/o pre-train) & 71.55 & 68.40 & 73.30 & 71.84 \\ Uni2 Mul-M-Conformer & 73.09 & 71.50 & 76.81 & 75.08 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results for MEC with different fusion strategies in stage two.  
Figure 6: Unimodal and multimodal representation. (**a**,**b**) are the results from unimodal models trained using IMAs and IUAs, respectively; (**c**,**d**) are the results from models trained using IMAs and IUAs with Conformer-based fusion strategy. In (**a**,**b**), the markers of different colors represent different modalities, and shapes filled with different colors represent semantic spaces of different modalities. In (**c**,**d**), the markers of different colors represent different emotional categories, and shapes filled with different colors represent classification distribution. _V_: Vision, _A_: Audio, _T_: Text.  
For unimodal representations, models trained using IMAs displayed irregular clustering in the semantic space with diffuse distributions (see Figure 6a). Conversely, models trained using IUAs formed more regular, spherical-like clusters (see Figure 6b) that adhered to a uniform Gaussian distribution.  
In terms of multimodal representations, the Conformer-based fusion model trained with IMAs were not well concentrated, with multiple overlapping regions, resulting in suboptimal classification performance and limited generalizability (Figure 6c). In contrast, the model trained with IUAs exhibited distinct classification boundaries and minimal overlap in the classification distribution, yielding comparatively superior classification performance and adequate generalizability (see Figure 6d).
## 6 Application Example
### Design of an Emergency Response Supporting System  
Figure 8 uses the nomenclature proposed in [38] to represent the design of a simple and hypothetical IoT-based system, which could be used in the described emergency in order to allow the information flow among the participants. This system can be implemented using technology that is already available in the organizations that participated in the response process.  
The system design was simplified to ease its readability; however, the system model is still complex enough as to show the usefulness of the proposed modeling approach. In this case we have considered that all resources are present during the same time slot; _i.e._, all of them are working simultaneously.  
In Figure 8 we can see four islands: the affected area, the city command center, and two hospitals. We can also see ambulances transporting injured people to the hospitals, and several other node types working within the islands. Provided that the communication support collapses immediately after these events, during the preparedness phase the emergency response organizations must design a robust channel to exchange information among the main players (_i.e._, islands). The system implements such a channel using witness units (_i.e._, Local Information Repository--_LIR_) communicated through stable communication links. The information exchange is managed by a processing unit (_sharer_ in Figure 8),which plays an ad hoc process. Such a process takes advantage of the idle bandwidth of the channel in order to replicate the key information among the several witness units supporting the process (one unit per island). The nodes working within an island consume the information from the LIRs. These repositories can be implemented using several computing devices (typically, from a laptop to large servers), depending on the amount of information that they need to manage.  
Using the definitions presented in Section 4 we can determine, for instance, the probability that one HBS can feed a LIR with information when required, and also the probability that such information can be disseminated to other LIRs. In fact we can compute the HWSN represented in such a design, according to the behavior predefined for each node and link in order to determine fast information routes, slow routes and also broken routes. For instance, we can expect to have no communication between the resources in the islands and the ambulances when the later are travelling between the affected area and the hospitals and vice versa. Moreover, we can expect an intermittent communication with the ambulances when they arrive to an island.  
Computing this system model we can identify its strengths and weaknesses, and improve it iteratively at the design time. Thus, we can implement the system only when its design is robust enough. Moreover, the model can also be used to determine the best procedures to follow during the emergency diagnosis, response and recovery, by making these procedures more effective and efficient.
## 3 Derivation of a New Finite Element
### Computation of Nodal Shear Forces and Bending Moments  
The corresponding element equilibrium equation in FEM formulation is:  
\[[k]\{u\}=\left\{F_{q(x)}\right\}+\{Q\} \tag{30}\]  
where \([Q]\) is the vector of secondary variables. Assembling the stiffness matrix and the load vector of the structure allows us to first obtain the nodal displacements and rotations for all the finite elements. Shear forces and bending moments at both nodes of the finite element are further calculated from the vector of secondary variables \(\{Q\}\), as obtained from Equation (30):  
\[\{Q\}=[k]\{u\}-\left\{F_{q(x)}\right\}=\left\{V_{y1,}-M_{z1},-V_{y2},M_{z2}\right\} ^{\mathrm{T}} \tag{31}\]  
consisting of the discrete values of shear forces (\(V_{y1}\) and \(V_{y2}\)) and bending moments (\(M_{z1}\) and \(M_{z2}\)).
# On the Use of the Effective Width for Simply Supported Generally Loaded Box-Girders
## 3 State of Stress
### Load and Solicitation  
The load \(p(z)\) (positive upwards) is expanded in Fourier sine series as  
\[p(z)=\sum_{n}\hat{p}_{n}\sin\Bigl{(}\frac{n\pi}{\ell}z\Bigr{)} \tag{5}\]  
where the Fourier coefficient \(\hat{p}_{n}\), representing the amplitude of the \(n\)-th harmonic, is given by  
\[\hat{p}_{n}=\frac{2}{\ell}\int\limits_{0}^{\ell}p(z)\sin\Bigl{(}\frac{n\pi}{ \ell}z\Bigr{)}\,dz \tag{6}\]  
To evaluate the shear force \(T(z)\) and the bending moment \(M(z)\), the equilibrium equations \(T^{\prime}(z)+p(z)=0\), \(M^{\prime}(z)+T(z)=0\) (shear force positive counterclockwise, bending moment positive when inducing upward concavity) must be integrated, with the boundary conditions \(M(0)=M(\ell)=0\). Here, a dash denotes differentiation with respect to \(z\). Since the beam is isostatically constrained, this problem admits a unique solution, namely \[\begin{split} T(z)&=\sum_{n}\hat{T}_{n}\cos\Bigl{(} \frac{n\pi}{\ell}z\Bigr{)}\\ M(z)&=\sum_{n}\hat{M}_{n}\sin\Bigl{(}\frac{n\pi}{ \ell}z\Bigr{)}\end{split} \tag{7}\]  
where the solicitation-amplitudes \(\hat{T}_{n},\hat{M}_{n}\) are related to the load-amplitudes by  
\[\hat{T}_{n} =\frac{\ell}{n\pi}\hat{p}_{n} \tag{8a}\] \[\hat{M}_{n} =-\Bigl{(}\frac{\ell}{n\pi}\Bigr{)}^{2}\hat{p}_{n} \tag{8b}\]  
As an example, three cases are dealt with ahead (loads negative, since downward), namely, in Figure 3, (i) the uniformly distributed load \(p(z)=-p_{0d}=\text{const}\); (ii) the segment of uniform load \(p_{0s}\) distributed on an interval \(\Bigl{(}\xi-\frac{A}{2},\xi+\frac{A}{2}\Bigr{)}\), i.e, \(p(z)=-p_{0s}W\Bigl{(}\xi-\frac{A}{2},\xi+\frac{A}{2}\Bigr{)}\), where \(W(\cdot)\) is the window function; and (iii) the concentrated load \(P\) applied at the abscissa \(\xi\), i.e., \(p(z)=-P\,\delta(z-\xi)\), where \(\delta(\cdot)\) is the Dirac function. In the three cases, the Fourier coefficients read, respectively,  
\[\hat{p}_{n} =\frac{2p_{0d}\bigl{(}1-(-1)^{n}\bigr{)}}{n\pi} \tag{9a}\] \[\hat{p}_{n} =\frac{4p_{0s}}{n\pi}\sin\Bigl{(}\frac{\Delta n\pi}{2\ell}\Bigr{)} \sin\Bigl{(}\frac{\xi n\pi}{\ell}\Bigr{)}\] (9b) \[\hat{p}_{n} =\frac{2}{\ell}P\sin\Bigl{(}\frac{\xi n\pi}{\ell}\Bigr{)} \tag{9c}\]  
By using Equation (8), the relevant solicitation amplitudes are evaluated.  
Figure 3: Loading conditions: (**a**) uniformly distributed load, (**b**) segment of load, (**c**) concentrated load; load equally divided between the two webs in the next numerical simulations.  
Both normal and tangential stress fields are obtained. These stresses are obtained here by using the De Saint Venant theory, in particular, the Navier theory is adopted for the normal stresses and the Jourawsky theory for the tangential ones.
## 2 Materials and Methods
### Calculation of the Heat Transfer Coefficient  
The heat transfer coefficient \(\alpha\) [Wm\({}^{-2}\)K\({}^{-1}\)] represents the amount of heat that transfers from the surface of the body into the fluid. Thus, it is used for the description of phenomena at the interface of a solid with a fixed arrangement of particles and the fluid in which a constant relative movement of the particles is present. In this experiment, the solid object was the wooden sample and the fluid was the ambient air. The value of the heat transfer coefficient depends on the dimensions and shape of the wall, the properties of the fluid and the hydrodynamic conditions in the fluid. In general, the heat transfer coefficient \(\alpha\) can be described as a function of parameters [32]:  
\[\alpha=f\cdot(w,\rho,\mu,c_{p},\lambda,\beta,g,\tau,t_{w},l_{1},l_{2},\dots) \tag{2}\]  
Figure 3: Configuration of a holographic variant of a single-wave Mach–Zehnder interferometer [28]. OL—object line, RL—reference line, D—divider, HB—holographic board, HHB—holder of holographic board, CM—mirror in cardan suspension, O\({}_{1}\), O\({}_{2}\)—object lenses, M\({}_{1}\), M\({}_{2}\), M\({}_{3}\)—mirrors, B\({}_{1}\), B\({}_{2}\)—perforated blinds, MO\({}_{1}\), MO\({}_{2}\)—microscopic object lenses, TO—testing object, HTO—holder of the testing object, C—Camera, G—ground glass, OS—system of object lenses.  
where \(w\) is the velocity, \(\rho\) is the density, \(\mu\) is the dynamic viscosity, \(c_{p}\) is the specific heat, \(\lambda\) is the thermal conductivity, \(\beta\) is the coefficient of thermal expansion, \(t_{f}\) is the fluid temperature, \(t_{w}\) is the wall temperature, \(\tau\) is the time and \(l_{1}\), \(l_{2}\) are the characteristic geometric dimensions.  
The local value of the heat transfer coefficient can be calculated with use of the equation [29]:  
\[\alpha_{x}=-\lambda_{v}\cdot(\Delta T/\Delta y)_{x}\cdot 1/(T_{x}-T_{\infty}) \tag{3}\]  
where \(\lambda\) is the coefficient of the thermal conductivity of the ambient air (for dry air at 20 \({}^{\circ}\)C, \(\lambda_{v}=2524\) W\(\cdot\)m\({}^{-1}\)K\({}^{-2}\)), \((\Delta T/\Delta y)_{x}\) is the derivation of the temperature at position \(x\), \(T_{x}\) is the body surface temperature at point \(x\) and \(T_{\infty}\) is the temperature of the ambient air.  
As the analysis of interferograms enables the determination of the temperature distribution in the fluid in detail, the method for the calculation of the heat transfer coefficient from the temperature derivatives is a useful tool in interferometric research of the heat transfer.