esa-sceva/satcom-chunk-collection huggingface dataset:
# A C-Based Framework for Low-Cost Real-Time Embedded Systems
## 4 Core Framework Components
### Control Function  
The user-written control function, depicted as a gray block in Figure 2, is responsible for implementing the C phase of the cyclic executive, as well as handling all sporadic events generated by other parts of the firmware. As such, RTFw invokes it with four arguments:  
* A pointer to the RTFw _state_. Although simple time-triggered control functions may let RTFw manage it automatically, more complex control functions can query it to obtain detailed runtime information about all RTFw components. Moreover, during sporadic event handling, the control function must inform RTFw of any updates it made to PI variables, so that the control task can refresh them appropriately.
* The current _cycle number_, which gives the control function information about elapsed time with a granularity of the cyclic executive period. Since this data item has limited width (32 bits in the current implementation), RTFw provides functions for modulo-\(2^{32}\) time calculations. In order to support more sophisticated time-driven execution patterns, RTFw also provides multiple one-shot and periodic timers that the control function can set at will, with the same resolution. Higher-resolution timers for event-driven programming are made available by the underlying operating system and utility libraries.
* The _event type_ that triggered the current invocation. The first and most important information that the control function can derive from this argument is whether it should perform a time-triggered C phase or handle a sporadic event. In the second case, it conveys more information about the event itself.
* A pointer to a _user-defined data_ structure, which the user communicates to RTFw upon system initialization and is then made accessible to the control function upon every invocation. Besides propagating the pointer, RTFw does not use this structure in any other way.  
As it executes, the control function has access to the PI variables stored in memory area [A] through data flow 2 in Figure 2, as well as two other memory areas, called input and output shared memories [B] and [C], through data flows 3 and 6. Those are used to share part of the real-time control state with the UI and proxies, as described in Section 4.5.
## 3 Results
### Evolution of PM\({}_{10}\) Pollution during Traffic Restrictions and Normal Conditions
As an example, at Bucharest B-3 station, PM\({}_{10}\) mean mass concentration in April 2020 was 26.31 \(\mathrm{\SIUnitSymbolMicro m}^{-3}\) (95% CI: 22.09-29.72 \(\mathrm{\SIUnitSymbolMicro m}^{-3}\)), while in April 2022, the PM\({}_{10}\) mean value was 22.74 \(\mathrm{\SIUnitSymbolMicro m}^{-3}\) (95% CI: 20.12-25.35 \(\mathrm{\SIUnitSymbolMicro m}^{-3}\)). The same observation applies to Iasi, whose PM\({}_{10}\) mean in April 2020 was 30.76 \(\mathrm{\SIUnitSymbolMicro m}^{-3}\) (95% CI: 25.82-35.70 \(\mathrm{\SIUnitSymbolMicro m}^{-3}\)), while in April 2022, the PM\({}_{10}\) mean value was 28.05 \(\mathrm{\SIUnitSymbolMicro m}^{-3}\) (95% CI: 24.26-31.83 \(\mathrm{\SIUnitSymbolMicro m}^{-3}\)). Various factors, such as meteorological conditions (e.g., temperature and thermal inversion) and local and/or regional air pollution sources, can influence PM\({}_{10}\) concentrations. These confounding factors make it challenging to directly compare monthly averages to determine changes in concentrations over time. During the COVID-19 pandemic, various restrictions led to changes in human mobility patterns in the studied cities, which may have influenced pollution concentrations. As no traffic volume data were available for the investigated periods, data obtained from Google COVID-19 Community Mobility Reports [37] were used as proxies to observe human behavioral changes. Figure S1 in Supplementary Material shows the recorded human behavior in Bucharest, Brasov, and Iasi during the specified periods. More specifically, for Bucharest (Figure S1a), during the lockdown period imposed by the authorities, there was a decrease in mobility at the workplaces of over 60% compared with the baseline; at the same time, the residential activities increased to about 22% in some days. Figure S1b shows a decrease of more than 80% for activities related to visiting shops, retail establishments, grocery stores, and pharmacies. For Brasov (Figure S1c), a decrease in mobility to the workplaces of about 50% could be observed during the lockdown, while the residential activities increased by approximately 23%. In Figure S1d, a decrease of more than 70% comparedwith the reference value for activities related to visiting shops, retail establishments, grocery stores, and pharmacies could be observed. In the case of Iasi, Figure S1e shows that during the lockdown period, there was a decrease in mobility to the workplace of about 45% compared with the baseline, while residential activities increased by about 18%. In Figure S1f, a decrease of more than 70% compared with the reference value for activities related to visiting shops, retail establishments, grocery stores, and pharmacies can be observed on some days. These patterns in Figure S1 emphasize the reduced levels of activity during the period of isolation (lockdown) and a gradual return to regular activities as usual. These data might be considered as a proxy for understanding the decline in PM\({}_{10}\) concentrations.
# Synthesis of Hydrogel-Based Microgels and Nanogels Toward Therapeutic and Biomedical Applications
## 3 Fabrication of Hydrogels
### Physical Crosslinking
#### 3.2.2 Hydrophobic Interaction  
Hydrophobic interactions occur when hydrophobic segments of amphiphilic polymers aggregate in aqueous environments to minimize exposure to water. These interactions are driven primarily by entropy, as the system reduces the ordered structure of water molecules around hydrophobic regions, increasing overall entropy. This process is significantly prominent for amphiphilic polymers such as Pluronic(r) and results in the formation of micelles, where the hydrophobic cores act as crosslinking points within the hydrogel network [69; 138; 143]. Hydrophobic interactions can be finely tuned by altering the number and chemical structure of hydrophobic groups, allowing precise control over the swelling dynamics and hysteresis, water retention, and drug loading efficiency [84; 153]. The reversible nature of hydrophobic interactions also enhances the injectability of gels. Furthermore, dense hydrophobic structures facilitate the rapid reformation of crosslinking points upon mechanical disruption, making these hydrogels ideal for applications requiring self-healing and reusability [19; 154; 155].
# A New Approach for Environmental Risk Assessments of Living Modified Organisms in South Korea
###### Abstract  
As the development and use of living modified organisms (LMOs) steadily increase, new risk assessment methods that reflect domestic natural ecosystems are being developed. Although LM plants are fundamentally necessary for environmental risk assessment, the introduced gene products and LMO proteins can replace transgenic plants. However, their use is problematic because of instability and indirect assessment data issues. This study proposes a risk assessment tool and scheme for introducing LMO proteins into genetically modified crops. The agroinfiltration method for transient LMO gene expression in plants is a practical tool which can be used to rapidly verify the putative risks of LMO proteins against insects using an LM crop mimic plant with a stably expressed LMO protein. This study used _Nicotiana tabacum_ leaves, which transiently but stably expressed the insecticidal LMO protein Vip3 Aa, for LMO risk assessments against _Spodoptera litura_. The Vip3 Aa protein was stably expressed for 5 d in the agroinfiltrated plants, and the protein was active against target insects for environmental LMO risk assessments. In the toxicity evaluation of Vip3 Aa-expressing plants against 5. _litura_, the number of deaths was higher in the Vip3 Aa-infiltrated _N. tabacum_-fed group than that in the recombinant Vip3 Aa-fed group. In addition, the cumulative number of deaths in the infiltration leaf-fed group was approximately 12-fold higher than that in the protein-fed group under low dosage conditions. This study aimed to develop a transient expression model which can be used to evaluate whether the overall risk of LMO protein is acceptable for use. These results support the usefulness of the transient expression model using an agroinfiltration method as a rapid risk validation tool for LMO proteins against herbivorous insects before producing transgenic plants.  
living modified organism; risk assessment; agroinfiltration; Vip3 Aa; _Spodoptera litura_; insecticidal toxin
# Ground-Borne Vibration Model in the near Field of Tunnel Blasting
## 2 Dimensional Analysis
### Method  
In order to more accurately and effectively predict the blasting vibration velocity and guide the actual engineering construction, it is necessary to comprehensively consider the factors affecting the blasting vibration, separate these factors from \(K\) and \(\partial\), and specify the specific form of each factor in the formula, so as to determine the specific contribution value of these factors to the blasting vibration velocity. In theory, the most accurate prediction formula takes all the influencing factors into account, and the blasting vibration velocity can be obtained only by bringing the independent variables into the calculation. The main factors affecting blasting vibration shall be comprehensively considered: maximum charge per delay \(Q\), delayed initiation time of each stage \(T\), initial density \(\rho_{r}\), longitudinal wave velocity \(c_{r}\), decoupling coefficient \(b/a\), distance from the detonation center \(R\), length of blast hole \(l\), radius of blast hole \(b\), number of free surfaces \(n\) (value of 1 or 2: number of free surfaces corresponding to cut hole blasting is 1; number of free surfaces corresponding to other hole blasting is 2), free surface area \(A\), etc. The symbols, units, and dimensions of influencing factors are shown in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline
**Variable Type** & **Parameter** & **Symbol** & **Unit** & **Dimension** \\ \hline dependent variable & peak particle velocity & \(V\) & m/s & [L\(\cdot\)T\({}^{-1}\)] \\ \hline  & maximum charge per delay & \(Q\) & kg & [M] \\  & initiation time per delay & \(T\) & s & [T] \\  & initial density & \(\rho_{r}\) & kg/m\({}^{3}\) & [M\(\cdot\)L\({}^{-3}\)] \\  & longitudinal wave velocity & \(c_{r}\) & m/s & [L\(\cdot\)T\({}^{-1}\)] \\ independent variable & uncoupling coefficient & \(b/a\) & 1 & [1] \\  & distance from the detonation center & \(R\) & m & [L] \\  & length of blast hole & \(l\) & m & [L] \\  & radius of blast hole & \(b\) & m & [L] \\  & number of free surfaces & \(n\) & 1 & [1] \\  & area of free surfaces & \(A\) & m\({}^{2}\) & [L\({}^{2}\)] \\ \hline \end{tabular}
\end{table} 
Table 1: Influencing factors of blasting vibration velocity. The variables \(Q\), \(R\), \(T\), \(V\), \(\rho_{r}\), \(C\), \(l\), \(b\), \(b/a\), \(A\), and \(n\) can be written into a matrix in the form of mass (M), length (L), and time (T):  
\[A=\left[ \begin{array}{cccccccccccc}1&0&0&0&1&0&0&0&0&0\\ 0&1&0&1&-3&1&1&1&0&2&0\\ 0&0&1&-1&0&-1&0&0&0&0&0\end{array} \right] \begin{array}{c}\text{M}\\ \text{L}\\ \text{T}\end{array}  \tag{4}\]  
By solving the homogeneous equations \(Ay=0\), eight independent zero-dimensional quantities can be obtained:  
\[ \begin{cases}\pi_{1}=&R^{-1}TV\\ \pi_{2}=&Q^{-1}R^{3}\rho_{r}\\ \pi_{3}=&R^{-1}TC\\ \pi_{4}=&R^{-1}b\\ \pi_{5}=&R^{-1}l\\ \pi_{6}=&n\\ \pi_{7}=&R^{-2}A\\ \pi_{8}=&\dfrac{b}{a}\end{cases}
## 3 Results and Discussion  
### Microstructure Images  
The microstructures of pure copper before and after four ECAP passes are shown in Figure 4. Figure 4a shows the optical micrograph (before ECAP) and Figure 4b shows the SEM image of ultrafine-grained copper (after ECAP). Approximately, a 100% reduction in grain size was observed after the ECAP process. The average grain size before and after ECAP was 1000 nm and 600 nm, respectively [9].  
Figure 4: Microstructure of coarse grained copper (before ECAP) and ultrafine grain copper (after four passes) [9]. **(a)** Optical microscope image; and **(b)** Scanning electron microscope (SEM) image.
## 1 Introduction
Sensors that detect the reflectance usually show their results as vegetation indices. Common sensors on the market, such as the GreenSeeker (Trimble Inc., Sunnyvale, CA, USA) mostly show their measuring results using the normalized differentiated vegetation index (NDVI). This vegetation index is one of the oldest [38] and has serious problems in detecting the biomass and N uptake of plants at higher growth stages because of a significant saturation effect for various crop species [39; 40; 41; 42]. As a result of this saturation effect of the NDVI, another vegetation index, the red-edge inflection point (REIP) [43], was recommended to detect the N status of plants [44; 45]. In this study, we use the vegetation index REIP, which has shown a slight saturation effect only in the later growth stages of a crop [46; 47] and has consistently shown a good correlation of the REIP vegetation index and above-ground N uptake of corn plants [45]. As described, optical sensors have already proven their ability to detect the biomass and nutrient status in conventional grown cereal plants. As corn (_Zea mays_ L.) was in 2021 the most cultivated grain worldwide [48], and although conventional corn cultivation predominates in Germany, a clear expansion of organically grown corn can be observed [49]. Most of the studies using optical sensors to detect the nutrient status of plants in agriculture are made for conventional agricultural systems to guide fertilization. The aim of this study was to research the ability of optical sensors using the REIP vegetation index to detect differences in development of corn plants, where different composs fertilizers were used in an organic field trial.
# Supply-Scalable High-Speed I/O Interfaces
## 2 Basic Concept of Supply-Scalable I/O
The free-running frequency of a CMOS ring oscillator is expressed as  
\[f = \frac{1}{2N}\frac{\beta(V_{DD}-V_{TH})^{\alpha}}{CV_{DD}}, \tag{2}\]  
where \(N\), \(\beta\), \(V_{TH}\), and \(C\) represent the number of stages, transistor coefficient, threshold voltage of CMOS devices, and nodal capacitance of the ring, respectively [19]. \(\alpha\) is another coefficient that characterizes a transistor performance, which equals two in the conventional long-channel devices but becomes closer to one for recent short-channel devices. (2) is illustrated in Figure 5 for \(\alpha\) = 2 and 1.5. For both cases, we can see a linear-like relation as long as \(V_{DD}\) is higher than \(V_{TH}\), so we can assume that the supply voltage can be linearly scaled with the data rate [24]. The resulting power consumption and efficiency when we apply the linear scaling of supply voltage are depicted in the right-side plot and inset in Figure 4. The power consumption by the static current scales linearly with the frequency (P = VI), while the CMOS dynamic power scales cubically (P = CV\({}^{2}\)f) [44]. On the other hand, it is assumed that the signaling power cannot scale with the supply voltage due to the SNR constraint. The resulting efficiency curve is much flatter than that without supply scaling, implying that the power consumption is almost linearly proportional to the data rate, which is the ideal case assumed in [40]. It also shows that the optimum efficiency may locate at an intermediate speed because of the signaling power, which is practically observed in many state-of-the-art engineering samples [15; 27; 32]. In addition, the signaling power overhead at a higher data rate can be confirmed from the survey in Figure 6, where the energy efficiencies at the highest rate of various scalable I/O designs are scattered with respect to the highest data rate. The efficiency becomes worse at a higher data rate above a certain data rate (approximately 8 Gb/s), implying the energy overhead to drive such high-speed signaling, which is originated from heavy equalization circuits and a larger signal swing [44]. Figure 4: Impact of supply scaling on the power consumption and efficiency of I/O across the operating frequency.
# HERAS: A Modular Matlab Tool Using Physical Optics for the Analysis of Reflector Antennas
## 2 Numerical Techniques for the Analysis of Reflector Antennas
It differs from the aperture distribution method in that the far-field is evaluated directly from the integration of these currents:  
\[\vec{E}=-j\omega\mu\frac{e^{-jk\tau}}{4\pi r}\iint_{S}\left[\vec{f}_{s}-\left( \vec{f}_{s}\cdot\hat{a}_{r}\right)\hat{a}_{r}\right]e^{|\vec{f}_{s}\cdot\hat{a} _{r}}\mathrm{d}S \tag{3}\]  
where \(\vec{E}\) is the far-field vector, \(\omega\) is the angular frequency, \(\mu\) is the permeability, \(\hat{a}_{r}\) is the unit vector for the far-field direction and \(\vec{r}_{s}\) is the integration vector over the reflector surface \(S\). These parameters are depicted in Figure 1. While this method gives more accurate results, the numerical implementation of the integral along the reflector surface, which is typically curved, leads to a higher computational cost. Other techniques to improve the accuracy of the results and the computational costs can be found in the literature [33; 34; 35]. Diffraction methods can be added to GO and PO in order to more accurately estimate the scattering properties of the surface discontinuities. These are defined in the geometrical theory of diffraction (GTD) [36] and the physical theory of diffraction (PTD) [37]. A modified theory of physical optics (MTPO) was presented in [38] to overcome the shortcomings of PO and approach a solution closer to PTD under some assumptions, such as a perfectly conducting body. However, this methodology has some inaccuracies for short distances as stated in [39]. Multilevel physical optics (MLPO) implements an algorithm based on the hierarchical decomposition of the radiating apertures and the aggregation of their smaller contributions to the PO integral to compute the far-field pattern with a reduction of the computational complexity to a level comparable to the fast Fourier transform [40; 41; 42]. The iterative physical optics (IPO) technique has been studied for the analysis of open-ended cavities based on the recursive application of the PO integral in order to take into account the multiple reflections inside the cavity [43; 44; 45]. Several additional numerical techniques and tools with the purpose of speeding up the analysis and synthesis of reflector antennas can be found in the literature [46; 47; 48; 49]. The tool described in this paper uses PO for the analysis of reflector antennas' radiation characteristics. This technique is generally well adapted to evaluate the performance of electrically large reflectors as generally used in communication satellites. Numerical techniques for the analysis of feeds and their illumination properties in addition to the use of coordinate system transformations are used in the tool and further detailed in [50]. Figure 1: Definition of parameters for PO integral and surface current calculation in a front-fed reflector configuration illuminated by a linearly polarised feed. (**a**) Surface vectors. (**b**) PO integral parameters.
# Multi-Scale Twin Networks for Coastal Zone Change Detection in Remote Sensing Imagery
## 1 Introduction
These modules work collaboratively to process complex coastal zone changes, ultimately generating a high-quality change detection map. This approach efficiently combines the strengths of each attention mechanism, ensuring improved performance and accurate results despite the challenges posed by the coastal environment. The remainder of this paper is organized as follows. Section 2 reviews the related work on coastal zone change detection, highlighting the advances in deep learning methods and attention mechanisms. Section 3 introduces the proposed methodology, including the design and functioning of the AMMNet model and its core modules. Section 4 presents the datasets, experimental setup, and evaluation metrics, followed by a detailed discussion of the experimental results and comparisons with state-of-the-art methods. Section 5 concludes the study, summarizing the key contributions. Finally, Section 6 discusses the limitations of the proposed method and provides directions for future research.
## 3 Results and Discussion
### Reproducibility
#### 3.5.1 Data Availability  
From the review articles, three main sources of public bee audio data were identified. The first source is the so-called Nu-hive project [39] data, which consisted of audio for queen state identification, smoke reaction, and bee detection. The public release of this dataset is related to queen state identification from two different beehives. Authors in [52; 53; 54; 58] used the Nu-hive data for queen absence detection, whereas [48] used part of this data for bee detection. The second source is the OSBH project [40] audio data, acquired through a citizen science initiative where members of the general public were asked to record sounds from their personal beehives along with some metadata. Given its nature, recordings from the OSBH project present great diversity, including different recording devices, different environments where the hives were placed, and even different positions for the microphones inside the hive. This diversity, while making the dataset valuable for evaluation in real-life conditions, also makes it extremely challenging for machine learning applications. Authors in [21; 63; 64] used OSBH data for swarming detection, whereas those in [47; 48] used it for bee detection.  
Lastly, the third public source is a dataset called BUZZ, a bee detection dataset including audio of bee buzzes, ambient noise, and cricket sounds [41]. The data are available from six different hives, which have been each separated into different disjoint train, test, and validation subsets. In the first partition (termed BUZZI), the train and test samples are separated from the validation set by beehive and location. The second partition (BUZZZ2), in turn, separates the train and test samples by beehive and location, while the validation sets are by beehive, location, time (2017 vs. 2018), and bee race (_A. Mellifern ligustica_ vs. _A. mellifera carnica_). Authors in [41] created and used this datasets for bee detection using CNNs.
# Predicting Traffic Sign Retro-Reflectivity Degradation Using Deep Neural Networks
## 5 Results and Discussions
### Sensitivity Analysis For Predictor Variables
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Test Set**} & \multicolumn{2}{c}{**Train Set**} & \multicolumn{2}{c}{**Validation Set**} \\ \cline{2-7}  & **R2** & **MAE** & **R2** & **MAE** & **R2** & **MAE** \\ \hline Linear & 0.458 \(\pm\) 0.002 & 158.070 \(\pm\) 0.483 & 0.347 \(\pm\) 0.001 & 187.760 \(\pm\) 0.526 & 0.473 \(\pm\) 0.003 & 206.605 \(\pm\) 0.453 \\ Polynomial & 0.662 \(\pm\) 0.00 & 104.527 \(\pm\) 2.81 & 0.484 \(\pm\) 0.005 & 148.619 \(\pm\) 1.816 & 0.641 \(\pm\) 0.008 & 164.317 \(\pm\) 2.408 \\ NN one-hot encode & 0.696 \(\pm\) 0.189 & 89.238 \(\pm\) 20.076 & 0.493 \(\pm\) 0.121 & 131.022 \(\pm\) 6.356 & 0.664 \(\pm\) 0.155 & 145.569 \(\pm\) 17.192 \\ NN entity embedding & 0.674 \(\pm\) 0.240 & 91.204 \(\pm\) 23.455 & 0.475 \(\pm\) 0.173 & 132.115 \(\pm\) 10.051 & 0.640 \(\pm\) 0.222 & 148.903 \(\pm\) 24.300 \\ NN shared-entity & **0.754 \(\pm\) 0.013** & **83.534 \(\pm\) 1.776** & **0.533 \(\pm\) 0.001** & **128.681 \(\pm\) 0.602** & **0.716 \(\pm\) 0.002** & **139.721 \(\pm\) 1.319** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Model performance without using the orientation feature. The best performing models are in bold.