esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Theoretical Model
#### 3.3.3 Frequency Response  
In this section, using Bode diagrams, a study is made of the behavior at the sensor frequency [36]. With this type of diagram, the behavior of magnitude vs. angular frequency and phase vs. angular frequency and from these graphs it is possible to determine the frequencies where the sensor operates effectively.  
First, we performed a change of variable \(s=\omega i\) in the transfer function (25) [36; 38] and obtained (after some algebraic manipulations):  
\[G(\omega i)=\frac{867.672\times 10^{3}}{\omega i+0.0209}e^{-i\omega 1129.34}. \tag{26}\]  
Figure 4: (**a**) Pole-zero map obtained for the chemical sensor based on the ZnAl\({}_{2}\)O\({}_{4}\) oxide; (**b**) Establishment time measured for our chemical sensor.  
Removing the complex term from the denominator  
\[G(\omega i)=\frac{867.672\times 10^{3}}{\omega i+0.0209}\left(\frac{-\omega i+0.0209 }{-\omega i+0.0209}\right)e^{-i\omega 1129.34}, \tag{27}\]  
We have:  
\[G(\omega i)=\left(\frac{18134.34}{436.81\times 10^{-6}+\omega^{2}}-\frac{867.672 \times 10^{3}\omega}{436.81\times 10^{-6}+\omega^{2}}i\right)e^{-i\omega 1129.34}. \tag{28}\]  
From expression (28), the magnitude was calculated as:  
\[|G(\omega i)|=\sqrt{\left(\frac{18,134.34}{436.81\times 10^{-6}+\omega^{2}} \right)^{2}+\left(\frac{\omega 867.672\times 10^{3}}{436.81\times 10^{-6}+ \omega^{2}}\right)^{2}}, \tag{29}\]  
and the phase with  
\[\theta(\omega i)=tg^{-1}\left(-\frac{867.672\times 10^{3}\omega}{18134.34}-11 29.34\omega\right)=tg^{-1}(-1177.1869\omega), \tag{30}\]  
where \(-1129.34\omega\) was the offset angle due to the shift time. The shift time only affected the phase and did not affect the magnitude, as shown in Equations (29) and (30). Figure 5 shows the Bode diagram of the transfer function.  
According to Figure 5, there was a cutoff frequency of approximately 1.91 kHz. Consequently, the propane gas sensor would operate adequately within the interval from 1 Hz to 1.91 KHz, while for higher frequencies its resistance would decay, as Figure 5 shows.  
Figure 5: Frequency behavior of the ZnAl\({}_{2}\)O\({}_{4}\) sensor.
## 3 Results and Discussions
### Dynamic Modulus of Elasticity  
The determination of the dynamic modulus of elasticity was based on the first resonant frequency of the cylindrical specimen, which was determined by means of the impact echo method. The free vibration response of all specimens (20 for each of the mix proportions presented in Table 1) was recorded, as shown in Figure 7a for the Ref mix, using the experimental setup presented in Figure 4. The Fast Fourier Transform (FFT) was applied to the recorded signal so that to obtain the response spectrum of the specimens, as shown in Figure 7b, from which the fundamental frequency of vibration was identified. For each cylindrical specimen, there were considered at least four measurements from which the fundamental frequency of vibration was calculated, as shown in Figure 8. The different scales used for the vertical axis of the graph were necessary since the response spectrum amplitude varied from one impact to another because the impact energy was different. The use of a single scale for the vertical axis would result in some of the response spectra being displayed with very small peaks. However, the fundamental longitudinal frequency of vibration did not vary by much, proving the consistency of the obtained data.  
Figure 8: Multiple measurements for the fundamental frequency of vibration.  
Figure 7: Assessment of the fundamental frequency of vibration. (**a**) Damped free vibrations; (**b**) Response spectrum (FFT).  
By applying Equations (1) and (2), the dynamic modulus of elasticity was obtained for all specimens belonging to the considered mix proportions. The data are summarized in Table 2, where the presented values are the averaged ones from all the measurements.  
By taking a look at the data presented in Table 2, it can be observed that the higher the rubber content, the lower the fundamental frequency of vibration (Figure 9). The vertical axis represents the amplitude of the signal, whereas the horizontal axis represents the frequency. Since the amplitude of the peak does not provide valuable information, the unit of measure was not converted into units of measure for acceleration. The information that was relevant for the purpose of the research was the one given on the horizontal axis, the fundamental frequency of vibrations and the downward shift of this frequency with the increase in the content of rubber replacement. Additionally, the lower mass of the cylinders, owing to smaller densities of RuC (Figure 5), leads to lower values for the dynamic modulus of elasticity. The decrease was 37.6% for 40% replacement of sand and reached 65.7% for 80% replacement, similar decrease rates to the static modulus of elasticity.
## 3 Results and Discussion
### Physicochemical Stability of Vitamin-Loaded Nanoemulsions
#### 3.1.2 Impact of Storage Conditions on Vitamin-Loaded Nanoemulsions  
Storage time and temperature are those parameters that can be used to assess the stability of nanoemulsions. In order to observe the coalescence, Ostwald ripening and flocculation phenomena, all formulations were stored for 30 days at 25 and 40 \({}^{\circ}\)C, as well as to access the effect of storage time and temperature on mean droplet size with the aim of determining the stability of all emulsions. In order to explore the influence of temperature and time on MPS, nanoemulsions having codes (NE1-NE6) comprising BC alone or combined with VE were stored at 25 and 40 \({}^{\circ}\)C over 30 days. After storage of 30 days, all nanoemulsions that were kept at 25 \({}^{\circ}\)C showed more stability, and an extreme increment in MPS around 21 nm was observed in the case of NE6 nanoemulsion, as portrayed in Figure 2A. NE3 nanoemulsion presented the smallest increase in MPS, about 9 nm over 30 days' storage. At 40 \({}^{\circ}\)C, all nanoemulsions displayed a significant increase in MPS and MCT-loaded nanoemulsions encapsulating BC and VE represented as NE4 and NE6 showed an extreme rise in MPS (\(\sim\)29 nm), as expressed in Figure 2B. Many researchers have covered the increasing phenomenon of MPS caused by storage circumstances in their investigations [9; 44; 45]. As an example, [PERSON] and co-workers found superior stability of nanoemulsions enriched with FSO as compared to MCT because of greater interfacial tension among aqueous suspension of nanoemulsion and FSO [45].
## 3 Proposed Multiple Stationary Human Targets Detection Method
### Step C: TPOI Clustering  
If there is no TPOI in the output of step B, we decide that there is no human target in the detection area. If there are TPOIs in the output of step B, the clustering algorithm is performed to obtain the number and range information of human targets.  
The K-mean clustering [53] is used in our proposed method. Assume that the output result of step B contains \(N_{T}\) targets with \(N_{s}\) TPOIs \((s_{1},s_{2},\dots,s_{N_{t}})\), where \(s_{ir}\) is the range location index of the \(i\)th TPOI \(s_{i}\). First, randomly set \(N_{T}\) centroids \((T_{1},T_{2},\dots,T_{N_{T}})\), where \(T_{jr}\) is the range location index of the \(j\)th centroid \(T_{j}\). Then, the centroid which is nearest to each TPOI is searched using the Euclidean distance as:  
\[D_{ij}=\|s_{ir}-T_{jr}\|^{2}. \tag{9}\]  
The centroid is updated using its near TPOIs:  
\[T_{jr}=\frac{1}{m_{j}}\underset{i\in O_{j}}{\sum}s_{ir}, \tag{10}\]  
where \(O_{j}\) is the set of \(m_{j}\) TPOI indices which is near to the \(j\)th centroid. Finally, an iterative procedure is used to update the centroids of TPOIs. The sum of the square error between centroids and their corresponding TPOIs is:  
\[J=\underset{j=1}{\overset{N_{T}}{\sum}}\underset{i\in O_{j}}{\sum}(s_{ir}-T_{ jr})^{2}. \tag{11}\]  
The centroids are continuously updated using Equation (10), and the iteration is stopped when the value of \(J\) reaches a minimum. The range information of human targets can be obtained by the final range indices of the centroids.
## 3 Simulation Results  
This simulation was compared with that of the conventional CSS method to demonstrate the performance of the proposed method, and the simulation considered two channel characteristics: multipath propagation and AWGN channels. To match the data rate, the CSS method is divided into 2 bands that also match the time bandwidth product with the proposed GSFM method. The simulation parameters are presented in Table 1.  
Figure 4: The orthogonality between waveforms, (**a**) Autocorrelation of the FT, (**b**) cross-correlation of the FT/FF and FT, (**c**) cross-correlation of the RT and FT and (**d**) cross-correlation of the RT/FF and FT.  
In Figure 6, the horizontal axis represents the scale of the SNR, and the vertical axis represents the uncoded bit error rate (BER). It is analyzed according to the SNR level in the multipath channel. The red line represents the GSFM, which is the proposed method, and the blue line represents the conventional CSS method. We could confirm that the proposed methods have a better BER performance than the conventional CSS method. In the figure, it can be seen that the proposed method has an average gain of approximately 2-3 dB compared with the conventional method. In particular, the performance of the proposed method is relatively good in an environment with a low SNR.
# Compression Challenges in Large Scale Partial Differential Equation Solvers
## 2 Compression Aspects of PDE Solvers
### High Entropy Data necessitates Lossy Compression  
Usually, double precision is used for coefficient vectors in order to avoid excessive accumulation of rounding errors during computation, even if the accuracy offered by 53 mantissa bits is not required for representing the final result. Due to rounding errors, the less significant mantissa bits are essentially random, and incur a large entropy. Lossless compression methods are, therefore, not able to achieve substantial compression factors, i.e., ratios of original and compressed data sizes. Examples of lossless compression schemes tailored towards scientific floating point data are fpzip [9], FPC [10], SPDP ([[https://userweb.cs.txstate.edu/~burtscher/research/SPDP/](https://userweb.cs.txstate.edu/~burtscher/research/SPDP/)]([https://userweb.cs.txstate.edu/~burtscher/research/SPDP/](https://userweb.cs.txstate.edu/~burtscher/research/SPDP/))) [11], Blosc ([[http://blosc.org/](http://blosc.org/)]([http://blosc.org/](http://blosc.org/))) [7], and Adaptive-CoMPI [12].  
In contrast, lossy compression allows much higher compression factors, but requires a careful selection of compression error in order not to compromise the final result of the computation. In general, there is no need for the compression error to be much smaller than the discretization or truncation errors of the computation. Lossy compression schemes that have been proposed for scientific floating point data in different contexts include ISABELA (In-situ Sort-And-B-spline Error-bounded Lossy Abatement, [[http://freescience.org/cs/ISABELA/ISABELA.html](http://freescience.org/cs/ISABELA/ISABELA.html)]([http://freescience.org/cs/ISABELA/ISABELA.html](http://freescience.org/cs/ISABELA/ISABELA.html))) [13], SQE [14], zfp ([[https://computation.llnl.gov/projects/floating-point-compression](https://computation.llnl.gov/projects/floating-point-compression)]([https://computation.llnl.gov/projects/floating-point-compression](https://computation.llnl.gov/projects/floating-point-compression))) [15; 16; 17], SZ ([[https://collab.cels.anl.gov/display/ESR/SZ](https://collab.cels.anl.gov/display/ESR/SZ)]([https://collab.cels.anl.gov/display/ESR/SZ](https://collab.cels.anl.gov/display/ESR/SZ))) 1.1 [18] and 1.4 [19; 20], multilevel transform coding on unstructured grids (TCUG) [21; 22], adaptive thinning (AT) [23; 24] and adaptive coarsening (AC) [25; 26], TuckerMPI [27; 28], TTHRESH [29], MGARD [30], HexaShrink [31], and hybrids of different methods [32].
## 2 Materials and Methods
### Data Processing and Statistical Analysis  
For biomass growth monitoring, discs were prepared in 3 biological replicates for each condition A and W and each was weighed 3 times (\(n=9\)). For EA-IRMS analyses, 3 biological samples were taken from each disc to account for any biomass heterogeneity (\(n=3\)). For contaminant measurements, chemical analyses were performed in triplicate on each liquid sample, and each vial containing the reacted sample was spectrophotometrically observed twice to account for any homogeneity in sample mixing (\(n=6\)).  
Based on our experimental growth data, although this study was not primarily focused on identifying the most accurate mathematical model to describe the metabolic processes of _G. phlegrea_, we nonetheless explored a possible approximation of biofilm growth dynamics over extended time periods. To this end, we applied simple regression models that do not incorporate specific environmental growth variables. We tested and compared three approaches: quadratic polynomial regression, three-parameter logistic regression, and two-parameter exponential regression.  
In these models, the response variable \(y\) represents surface biomass density, while the predictor variable \(x\) corresponds to time. The reference equations for the three regression models are as follows:  
* Second-degree polynomial, \(y=ax^{2}+bx+c\). In this equation, \(a\) and \(b\) are the growth rate coefficients of the second and first-degree functions of \(x\), respectively, while \(c\) represents the value of the surface density at time 0;
* Three-parameter logistic, \(y=c/\left\{1+exp[-a(x-b)]\right\}\). In this equation, \(a\) represents the growth rate, \(b\) is the value of \(x\) for which \(y=c/2\) and \(c\) represents the asymptotic algal density value reached at saturation according to this model;
* Two-parameter exponential, \(y=a\ exp(bx)\). In this equation, \(a\) represents the surface algal density at time 0 and \(b\) represents the growth rate.  
The statistical analyses and the calculation of the parameters a-b-c in the 3 models were performed through the use of the software JMP (version 17 Pro, SAS Analytics).  
The following statistical parameters were considered to determine the fit and significance of the evaluated models: Akaike Information Criterion (AICc), Bayesian Information Criterion (BIC), Sum of Squared Errors (SSE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and the R-squared coefficient (R\({}^{2}\)). For these parameters,unlike the R\({}^{2}\) coefficient, the mathematical model that best describes a given time series is the one that gives the lowest values within a group of models; the greater the numerical difference between one model and another, the better it describes the phenomenon.  
The significance level used in all statistical analyses is \(\alpha\) = 0.05. Data curation, evaluation of statistical parameters and graphs were performed using JMP17 Pro (SAS Analytics) and Microsoft Office Excel (version 2403).
# Streetlight Control System Based on Wireless Communication over DALI Protocol
## 4 Implementation of the Wireless Node  
As was already discussed in Section 3.3, we designed our own wireless node for several reasons. We will describe in detail both the hardware and the software of the UCODALI wireless node in detail below.
## 4 Conclusions  
Within the Cartosat-1 Scientific Assessment Programme the accuracy of multi-resolution DSMs generated for the French test sites of Mausanne les Alpilles and Salon de Provence was investigated. The investigation revealed that when the data processing is performed using standard commercial off-the-shelf software and when few GCPs are available with a vertical accuracy better than 2.5 m and geolocation error less than 1.5 m, for hilly territories (with an elevation range of about 600 m) it is possible to produce high-resolution DSMs (up to 5 m grid spacing) with a RMSE less than 7.1. These values confirm those obtained by other investigating teams within the C-SAP and are also comparable with those obtained by other authors using different high-resolution satellite data. The accuracy of the generated DSMs proved to fulfill the requirements of the French Reference 3D\({}^{\text{\textregistered}}\), so Cartosat-1 data may be used to produce such kind of product.  
For applications requiring low-resolution DSMs, Cartosat-1 DSMs performed better than the SRTM90 for Salon de Provence and similarly to SRTM90 for Mausanne les Alpilles. If data are available, Cartosat-1 DSMs can be successfully used to fill refine and fill gaps in SRTM DSMs. A more interesting application would be the refinement of SRTM 1 arc second DSMs (\(\sim\) 30 m resolution), which is only available for the USA.  
The limits of the study are mainly related to the non homogeneous nature of the C-SAP dataset: only for the Mausanne les Alpilles test field were available high accuracy GCPs and reference DSM, while for Salon del Provence were delivered low accuracy GCPs and reference DTM. As a matter of fact, for Salon de Provence the absolute geolocation was performed with a lower accuracy and this may have affected the overall DSMs generation process. Moreover, no land cover information for the test sites was available to the investigation teams, so it was not possible to carry on an accuracy evaluation based on different land cover types.  
Comparing results to those described in literature, different authors may use different statistics for the evaluation process (e.g., mean of residuals, standard deviation, RMSE, LE90 or other statistics) computed on ground control points, independent check points, reference DTMs/DSMs, cartography, reduced study areas or something else. Consequently, results obtained by different authors, even on the same test site and using the same input data, may not always be easy to compare. Nevertheless, also considering all these limits, this study confirmed the outcomes of the other independent investigating teams and also confirmed the good quality of Cartosat-1 DSMs with relation to those generated using other high-resolution satellite data.
# SC-FDE Layer for Sensor Networks in Remote Areas Using NVIS Communications
## 3 Use Case  
One of the great advantages of the NVIS technology is that it enables the rapid deployment of medium-distance (less than 250 km) communications with no line of sight at a very low cost. This results in a very convenient alternative when designing IoT environments for remote scenarios where sensing (and/or actuating) nodes are deployed to span large-scale areas and no other communication technologies are available.  
In this case, as shown in Figure 3, the whole IoT scenario can best be viewed as a Ubiquitous Sensor Network [43] where sensors are grouped--by means of a concentrator/hub--around the NVIS node which, in turn, acts as a communication gateway to link with the nodes from neighboring IoT domains. In this way, the sensor network can use more standard communication technologies (e.g., Bluetooth, Wize) that are typically available in sensor devices. Additionally, following the principles of edge computing [44] and considering the limited bandwidth available at the NVIS link, this communication gateway can  
Figure 2: Clipping technique. The red line shows the original signal, and the blue line shows the clipped signal due to the threshold drawn in green [42].  
also behave as an edge node where data are aggregated and summarized. If an Internet connection were available at any IoT domain, the NVIS node could also behave as a sink node to forward all the collected data elsewhere.
## 2 Precision Seeding Technology
### Consistent Sowing-Depth Regulation Technology
Meanwhile, an electric-drive seed metering device will replace the mechanical-drive precision seeding in the development of precision seeding technology, due to its excellent characteristics of being suitable for a high-speed and highly precise seeding operation [6]. The second is the smooth seed-dropping; the seed-guide tube has a direct impact on the trajectory and speed of the seed falling into the seed bed, although the transformation of the shape of the seed-guide tube cannot overcome the sizeable relative velocity between the seed and the seed bed caused by bouncing or rolling problems. At present, the research into adding a stable migration device to a seed-guide tube can effectively achieve zero-velocity seeding but also increases the structure complexity, so the simplified optimization design of the seed-guide tube is a new research direction. The third is the seed falling into the seed bed with precision; the fall into the seed bed with precision technology research started late, and there is relatively little research; the current setup of the pressure plate or pressure seed wheel can effectively achieve the restraint of the seed, and there is currently more information regarding pneumatic seed metering for starting research. The fourth is consistent sowing depth consistent. The formation of sowing depth is mainly the result of the joint operation of several links, including furrow opening and soil return during covering and compacting. To ensure consistent sowing depth, it is necessary to ensure the terrain-adaptive furrowing, stable soil covering, and suitable real-time compacting. With the application of GPS, GIS, automatic control, sensor detection, and other technologies in the field of precision seeding, the existing precision seeding technology needs to be continuously developed to meet the needs of modern agricultural industrialization. There is a need to establish a joint tillage-operation and precision seeder, such as precision seeding and tillage, a precision fertilization joint in the operation of machinery for the developmental direction, and sizeable high-speed operation as a development trend to replace the original small and medium-sized precision seeder. At the same time, the application of precision seeding under no-till sowing conditions should be expanded to increase the scope of the application.