esa-sceva/satcom-chunk-collection huggingface dataset:
# Load Models Representative of Brazilian Actual Traffic in Girder-Type Short-Span Highway Bridges
## 4 Structural Models
Comparative results show that the grid model was successfully calibrated to the shell model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Bridge**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Deflections (m) Due to a 3C**} & \multicolumn{2}{c}{**Natural**} \\  & & **Truck at Transverse Positions** & \multicolumn{2}{c}{**Frequencies**} \\  & & **Center** & **Above** & **(Hz)** & **1 st T** \\  & & **Line** & **Gird G1** & **1 st B** & **1 st T** \\ \hline \multirow{2}{*}{S-10} & Shell & \(6.10\times 10^{-4}\) & \(2.95\times 10^{-4}\) & \(11.63\) & \(14.21\) \\  & Grid & \(6.58\times 10^{-4}\) & \(2.98\times 10^{-4}\) & \(14.37\) & \(15.13\) \\ \hline \multirow{2}{*}{S-20} & Shell & \(4.14\times 10^{-4}\) & \(8.08\times 10^{-4}\) & \(7.76\) & \(7.25\) \\  & Grid & \(4.13\times 10^{-4}\) & \(9.10\times 10^{-4}\) & \(8.67\) & \(9.55\) \\ \hline \multirow{2}{*}{S-30} & Shell & \(4.77\times 10^{-4}\) & \(9.11\times 10^{-4}\) & \(6.40\) & \(7.22\) \\  & Grid & \(4.74\times 10^{-4}\) & \(9.01\times 10^{-4}\) & \(6.71\) & \(7.87\) \\ \hline \multirow{2}{*}{S-40} & Shell & \(6.72\times 10^{-4}\) & \(12.5\times 10^{-4}\) & \(4.56\) & \(6.21\) \\  & Grid & \(6.77\times 10^{-4}\) & \(12.3\times 10^{-4}\) & \(4.67\) & \(5.65\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Maximum deflections and natural frequencies from the grid model and shell model for simply supported bridges (B = bending; T = torsion). Values of deflections and natural frequencies for each bridge span depend on the adopted dimensions for the structural elements (see Table 2). It can be seen in Table 3 that vertical deflections increase with span length for loads above girder G1. As expected, natural frequencies associated to vertical bending and torsional modes decrease with the increase of the span length.
# Improvement of Global Ionospheric TEC Derivation with Multi-Source Data in Modip Latitude
## 3 Simulation  
Simulation was performed to understand the impacts of using multi-source data and a modip coordinate system. We used IRI-2016 to obtain the 'true' global TEC, evenly-distributed (ED) with a resolution of \(2.5^{\circ}\times 5^{\circ}\times 0.1\) h in latitude, longitude and time, respectively. The IRI model source code (in FORTRAN) can be accessed on the homepage at [[http://irimodel.org](http://irimodel.org)]([http://irimodel.org](http://irimodel.org)) (accessed on 28 March 2021). The flag of the 'URSI foF2 model' was triggered on and the scenarios from the database were used. The global TEC were fitted with SH functions in both geomagnetic and modip latitudes, and the results were analyzed. We compared the RMSE of different degrees and orders in the SH functions; the comparisons are not shown here but the main conclusions remain unchanged. Considering the time consumption and computer capacity, as well as references [PERSON] (2007), [PERSON] et al. (2014) and [PERSON] et al. (2015), an SH function with a degree and order of 15 is used in this paper.
# An Estimation of Speech Privacy Class Based on ISO Parameter
## 3 Methodology
### Articulation Index AI  
Another way to describe the speech privacy is defined by the ASTM E1130-16 standard [36]. This standard is applied in the analysis of open-plan offices, but examples of its use in other types of spaces can be found in the literature [52]. In contrast to the _SPC_ parameter, which does not consider the spectrum of the speech signal as a factor influencing privacy, the E1130-16 standard introduces the Articulation Index (_AI_) parameter, which describes the speech signal source itself. The parameter is defined by the following equation:  
\[AI=\sum_{i=1}^{15}w_{i}\times SNR_{i}=\sum_{i=1}^{15}w_{i}\times\left(L_{\text{ Speech}}-D-L_{bA}\right), \tag{10}\]  
where \(w_{i}\) is the frequency weighting factor, defined in tabular form in the standard, and _SNR_ is the speech signal-to-noise ratio. _SNR_ is determined from the selected source reference sound pressure level (\(L_{\text{{Speech}}}\)), defined in tabular form in the standard. The value of \(D\) is obtained as the difference between the measured sound level 1 m away from the source and the sound level measured at the potential listener's location. The background noise level (\(L_{bA}\)), weighted by the A curve, is obtained as the ambient noise recorded at the location where speech is heard. The useful signal level represents the difference between the normalized speech spectrum (defined by standard) and the measured levels difference \(D\). In this way, the speech privacy analysis introduces the speech sound level in 1/3-octave bands and weighting factors, from 200 Hz to 5000 Hz. The value of the Articulation Index _AI_ is obtained in the range from 0 to 1, wherein a value of 0 means that the speech is completely unintelligible at the receiving location.
# Short-Term and Long-Term Travel Time Prediction Using Transformer-Based Techniques
## 3 Proposed Methodology
### Short-Term and Long-Term Integrated Encoder-Decoder
The result is structured into \(\hat{Y}\), a practical forecast format used for hourly traffic management predictions. In our design, we strategically utilize periodic segments together with the architecturally distinct dual aspects of the Transformer encoder blocks, specifically LDEB and SDEB in SLI-E, to separately learn short-term and long-term dependencies. These components are effectively integrated with SLI-D, which utilizes attention mechanisms to focus on various temporal horizons. This strategic integration not only sets our model apart from conventional Transformer applications but also is specifically designed to tackle the complex multi-horizon forecasting challenges associated with traffic data patterns.
## 3 Results and Discussion  
### Morphology and Nanoparticle Size Distribution  
Morphology, size, and size distribution of the nanocrystals were studied for Ag NPs fabricated in all three different aqueous solutions, finding noticeable differences between them. Representative TEM micrographs are shown in Figure 2, clearly illustrating the effect of CTAB (Figure 2b) and PVP (Figure 2c) on the size distribution and morphology of the NPs. Ag NPs dispersed in pure water (Figure 2a) were heavily agglomerated, making it impossible to perform any meaningful statistical analysis on the images. However, it can be seen that the solution contained some large particles (>100 nm) with an irregular shape. The addition of CTAB had two visible effects: (i) it produced an improvement of the size distribution of the nanocrystals, and (ii) it reduced the agglomeration. Nearly-spherical NPs were dominant in the solution containing CTAB, but some disk-like planar structures were also present. It is unclear to us why the CTAB favored the formation of the latter type of nanocrystals, but it should be noted that we have observed them only in the CTAB samples. Finally, the use of PVP yielded even better results, producing mostly spherical particles with virtually no agglomeration, smaller size, and narrower size distribution.  
Figure 2: Transmission electron microscopy (TEM) micrographs illustrating the size, morphology, and agglomeration level of Ag nanoparticles (NPs) obtained from femtosecond laser ablation of silver in (**a**) water, (**b**) hexadecyltrimethylammonium bromide (CTAB), and (**c**) polyvinylpyrrolidone (PVP) for 50 fs laser pulses centered at 800 nm and a fluence of 10.2 J/cm\({}^{2}\). Histograms correspond to the statistical analysis of (**d**) CTAB and (**e**) PVP. The blue lines in the histograms represent the best fit to a log-normal distribution. SD: Standard deviation.  
We have determined the nanocrystal size and size distributions from a statistical analysis with the TEM images obtained from the samples with CTAB and PVP. Frequency histograms are depicted in Figure 2d,e for CTAB and PVP, respectively. In both cases, the size distribution closely follows a log-normal distribution with mean diameter of about 34 and 21 nm, and standard deviations of 16.3 and 15.9 nm for CTAB and PVP, respectively. This result quantitatively confirms that the use of PVP produced smaller particles with a better size distribution, whereas agglomeration made the nanoparticles dispersed in pure water nearly useless, unless the concentration is low.
## 5 Conclusions  
Experiments were designed to measure the effectiveness of _liveGT_ for long-term protein secretion and functionality. In vitro GET of pDNA encoding insulin and glucokinase resulted in significantly increased protein expression and glucose consumption. Glucokinase demonstrated its ability to act as a sensor, preventing hypoglycemia. Delivery of reporter genes using localized in vivo electro gene therapy (_liveGT_) with biphasic pulses may reduce muscle twitching and increase delivery efficiency, though further optimization is needed to achieve physiologically relevant expression levels. Moreover, _liveGT_ of insulin and glucokinase with monophasic pulses led to significant decreases in serum glucose levels for up to 21 days and increased serum human insulin levels. Therefore, _liveGT_ has the potential for prolonged protein expression, offering an alternative gene therapy platform for protein replacement therapies.  
Conceptualization, A.B. and M.F.; methodology, A.B., J.H. and M.F.; software, A.O.; validation, J.H. and A.B.; formal analysis, J.H. and A.B.; investigation, J.H., N.K., T.G. and A.B.; resources, A.B.; data curation, J.H., N.K., A.O., T.G. and A.B.; writing--original draft preparation, J.H., N.K. and A.B.; writing--review and editing, J.H., N.K., M.F. and A.B.; visualization, J.H. and A.B.; supervision, A.B.; project administration, A.B.; funding acquisition, A.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded, in part, by the Commonwealth Health Research Board (grant number 236-13-22) and start-up funds from the University of South Florida.  
The animal study protocol was approved by the Institutional Animal Care and Use Committee at the University of South Florida (IS0011369; approved 13 January 2023).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors would like to thank [PERSON] allowing the utilization of his lab space to conduct in vitro experiments.  
Author [PERSON] was employed by the company Asante Bio and Pele Therapeutics. Authors [PERSON] and [PERSON] were employed by the company Pele Therapeutics. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Proposed Method
### Data Preparation  
The methodology currently used provides for the establishment of a relationship between the filtration properties of host rocks and the apparent resistivity measured at the exploration stage. To do this, resistivity logging data are compared with the results of hydrogeological studies of wells and results of granulometric studies [33] (Figure 3).  
As a result, a correspondence table is compiled, intermediate values are obtained by interpolation. The disadvantages of the adopted methodology include the fact that it uses data from only one logging method and cannot be used if the recording quality is insufficient, as well as the fact that it uses the average resistivity value within the selected interval, which cannot be accurately determined for intervals with thickness less than 1.5-2 m, since the distance between the measuring electrodes during logging is 1 m. Given the shortcomings of the existing \(K_{f}\) estimation methodology, we proposed a machine learning model that received basic logging data as the input and generated filtration coefficients as the output. Such a model could be trained on data from exploratory wells  
Figure 2: Methodological scheme of the study.  
that have actual \(K_{fpo}\) (pumped out). The trained model could then be used to calculate \(K_{f}\) of the technological wells.  
To form a dataset for training regression models, we used the information collected from the results of research at the exploration stage, shown in Figure 3. Figure 3 shows the boundaries of the well intervals (Start dept, End dept), within which the values of the filtration properties (\(K_{f}\) column) are known; for the same intervals, the granulometric study data, the proportion of clay fraction, and the geochemical (color) and lithological (Lito code) codes are given. In total, there were 3558 intervals in the collected dataset, with a thickness of 0.5 to 2.2 m. However, to build regression models that will be used on production wells, one can use only logging data, as well as the lithological code of rocks, because the rest of the parameters are obtained in the course of laboratory studies and are not available for production wells. For exploratory wells, the standard set of logs included AR (apparent resistivity), SP (spontaneous polarization potential), GL (gamma logging), and inclinometry (determines the angle of inclination of the well). AR and SP were the main methods for lithological classification, with impermeable rocks (clays) corresponding to low AR and high SP values, while highly permeable rocks, vice versa, had high AR and low SP values. The GL and inclinometry were in no way related to the filtration properties; therefore, to build regression models, we chose sections of the AR and SP curves with a length of 0.5 m, as well as the lithological code of the rock, as input parameters.
## 6 Limitation and Future Work  
As the study aimed to detect degradation of vegetation, rangelands (i.e., shrubland and grasslands), the authors acknowledged some limitations that need to be considered when using these findings. The TSS-RESTREND was able to identify the breakpoints, their location, time, and type and direction of change in productivity at the state level. Due to the limited availability of ground biomass data, pixels that exhibited significant changes were validated using derived productivity from the RPMS [45] that showed only 55% agreement. Ground-based data can be more accurate means in assessing the identified breakpoints. Another important factor that can contribute to degradation is the temperature which can significantly affect NM's dryland ecosystems.  
For future, the authors would address some of these limitations using ground observations (when available) for example from the long-term rangeland monitoring observatories at the Jornada Experimental Range. The obtained breakpoints seemed consistent with some of the disturbances (drought and fire). However, the authors would consider the combined use of temperature and precipitation to improve the detection of breakpoints such as the pixels that have been classified as indeterminant. Additional quantitative analysis would be conducted to determine how drought may affect the timing of breakpoints, rangeland condition, and productivity, which can be useful in developing rangeland management practices to adapt and mitigate drought impacts.
# Latitude-Adaptive Integer Bit Allocation for Quantization of Omnidirectional Images
## 6 Experiments
### Experimental Setup  
Several experiments are conducted to assess the compression performance of the proposed method, categorized into three parts. In the initial phase, we present the distribution of DCT coefficients produced by images from the Pano3D dataset [34] and explain our rationale for using empirical distributions. In the second phase, we apply our methodologies to the JPEG quantization table. Nearly all RDO methods are predominantly applied in the context of video processing and typically utilize the HEVC reference software ([[https://vcgit.hhi.fraunhofer.de/jvet/HM](https://vcgit.hhi.fraunhofer.de/jvet/HM)]([https://vcgit.hhi.fraunhofer.de/jvet/HM](https://vcgit.hhi.fraunhofer.de/jvet/HM))). Owing to their incompatibility with direct application to omnidirectional images, we have excluded these methods from the scope of our comparative analysis. Instead, we perform a comparative analysis of our methods against two previously JPEG-based approaches, utilizing the LIC360 dataset [5]. In the last phase, our methods are juxtaposed with two novel integer bit allocation approaches, utilizing both the Pano3D dataset [34] and LIC360 dataset [5].  
Compression methods are evaluated in two terms, i.e., rate and distortion. In the second part, we remove the entropy coding block from the JPEG coder and estimate the final rate using the first-order entropy (FOE) to assess the effectiveness of the technique.  
Besides, we compare our methods with others in the Bjontegaard gain (\(\Delta_{BD}\)) [35] in terms of the average percentage of rate difference for the same objective quality. In the third part, we evaluate the rate with the bit budget allocated to compress the image, the and bits per pixel (bpp) are calculated as the total bit budget divided by the number of pixels. The distortion is calculated using the WMSE, and our metric is WS-PSNR, where a higher value indicates superior performance. All experiments were conducted using MATLAB 2020b.
## 4 Case Study and Discussion
### Case Study
#### 4.2.2 Case Results
The total monthly net revenue is presented in Table 7. Although the total net revenue results did not show a direct effect on mitigating the variation in wind power generation, the results suggest the effects of the additional incentive on the total net revenue. In Section 3.2, the results of Cases 1 and 2 are the same since the REC multiplier of 1 in off-peak periods could not induce discharging power. The revenue in case 2-1 increased slightly (0.01-1.02%). The REC multiplier applied in case 2-2 increased the revenue for cases 1 and 2 by 1.6-5.8%, and by 1.6-5.1% for case 2-1. Although the REC multiplier in case 2-2 was double that in case 2-1, a drastic revenue increase was not observed as the amount of the discharging power was assumed to be limited by the discharging rate. In case 2-3, the revenue was higher by 2.4-9.4% for cases 1, 2, and 2-1, and by 0.8-4.5% for case 2-2.
# Simulation and Catastrophe Detection of Spontaneous Combustion Processes in Suffide Ores
## 3 Results  
### Experimental Results  
Figure 5 illustrates the temperature changes at various measurement points within the ore heap. As observed, the temperatures at measurement points D and H, located at the bottom edge of the ore heap, are higher than those at other points. At 224 min, during a sudden temperature rise, the temperature at point D reached 135.302 \({}^{\circ}\)C; at 284.5 min, it reached a maximum of 201.491 \({}^{\circ}\)C. In contrast, the temperature at point F, located in the middle layer of the ore heap, was slightly lower. This temperature distribution can be attributed to the fact that the ore near the surface reacts more readily with the surrounding air, causing heat to conduct primarily to the surface layer.  
Figure 5: Temperature variation at each measurement point within the ore heap.  
Furthermore, this phenomenon indicates that the response of the ore heap to changes in environmental temperature varies spatially. The areas closer to the surface of the ore heap demonstrated a more pronounced and rapid response, while the areas in the middle layer of the ore heap exhibited a longer response duration. For example, measurement point F showed a noticeable lag in response time compared to measurement point D, with the temperature stabilizing around 100 \({}^{\circ}\)C.  
Figure 6 illustrates the variation of SO2 gas concentration during the whole unsteady process of spontaneous combustion of the sulfide ore heap. The figure indicates that there is SO2 gas generation around 230-240 min into the experiment, though the gas concentration is not high. This suggests that some ores have entered a noticeable heat generation stage. This portion may be due to a few scattered ores around the sulfide ore heap during the experiment, which is not the primary focus of this study. At approximately 255 min, the SO2 concentration suddenly increases, indicating that most of the sulfide ore heap had entered a high-speed oxidation stage, approaching the spontaneous combustion stage. When the SO2 concentration reaches 20 ppm, the portable sulfur dioxide detector's alarm goes off, and the experiment must be stopped.  
The experimental results indicate that in the whole unsteady process of spontaneous combustion of sulfide ore heap, there is inevitably at least one catastrophe point corresponding to a catastrophe temperature. Once this catastrophe temperature is exceeded, it can be considered that the state of the ore heap has transitioned from safe to unsafe, and further reactions will lead to the combustion stage, resulting in a spontaneous combustion fire of the sulfide ore heap.
# The Properties and Durability of Self-Leveling and Thixotropic Mortars with Recycled Sand
## 2 Materials and Methods
### Materials
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Chemical Composition [\%wt]** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **K\({}_{2}\)O** & **Na\({}_{2}\)O** & **CaO** & **FeO** & **MgO** & **SO\({}_{3}\)** \\ \hline Natural sand & 57.8 & 11.7 & 1.9 & 2.7 & 17.5 & 5.2 & 3.1 & 0.2 \\ Recycled sand & 27.6 & 9.3 & 2.0 & 0.4 & 52.5 & 6.1 & 2.0 & 0.4 \\ \hline \end{tabular}  
* Obtained by energy-dispersive X-ray analysis (EDX).  
\end{table} 
Table 1: The average composition of used sands in terms of oxides *. Figure 2: SEM-(**left**) and energy-dispersive X-ray mapping analysis (**right**) of recycled sand. The main crystalline phases of the recycled sand are silicates, such as quartz (PDF 9000776), microcline (PDF 00-900-5308), calcite (PDF 7022027), portlandite (PDF 1000045), muscovite (PDF 00-900-6329), and laumontite (PDF R0601141). Significant peaks indicating the presence of calcium hydroxide and, its carbonated form, calcite are observed. The peaks of the quartz and feldspars are attributed to the natural sand and aggregates used in the manufacturing of the original concrete and mortar. Muscovite can be associated to the presence of bricks and ceramics. It is difficult to clearly distinguish the crystalline hydrated calcium silicate phases as their peaks overlap with the peaks of calcite and feldspars; however, the detection of calcium hydroxide is related to their occurrence. The results are in agreement with the chemical composition reported in Table 1; they are congruent with the mineralogical composition of the parent materials (concrete, mortar, and bricks) used for their production and they are consistent with other published studies [59; 60; 61; 62; 63]. Natural and recycled sands in a saturated surface dry (s.s.d.) condition were used to produce mortars. This prevents them from absorbing the free water that lends workability  
Figure 4: X-Ray diffraction pattern of recycled sand. Figure 3: Particle images and CE diameters [\(\upmu\)m] of RS as obtained by automated morphological imaging. to the mix [64; 65]. The same particle size distribution curve, reported in Figure 5, was used for both the sands. By observing the granulometric curve of the aggregate, it can be highlighted that close attention was used to prevent fines used in the production of mortars. It is well known that fines from recycled aggregate have a considerable negative impact on mortars and concrete rheology. This is caused by their tendency to agglomerate when water is added to the mix, as different small forces (such as capillary and electrostatic charges, or van der Waals forces) arise [66; 67; 68]. Furthermore, they show some hydraulic activity and a rough surface that determine a high water absorption capacity [67].
## 4 Experimental Results and Security Analysis
### Security Analysis  
The security of the cryptographic cipher depends on the key. For the proposed chaotic stream cipher, the keys are parameterized in both initial values and system parameters. The aim of the attackers is to find out these values. In this section, we analyze the resistance of the produced sequences by brute-force attack, chosen ciphertext attack, and guess-and-determine attack.