esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### ANOVA: Postures and Driving Factors
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Movement** & **Body Part** & \begin{tabular}{c} **Dependent** \\ **Variable** \\ \end{tabular} & **Range [\({}^{\circ}\)]** & \begin{tabular}{c} **Absolute Range** \\ [\({}^{\circ}\)] \\ \end{tabular} \\ \hline \begin{tabular}{c} Flexion \\ Extension \\ \end{tabular} & Trunk & Inclination & \(-13.56\) to \(16.99\) & \(30.55\) \\ \hline \begin{tabular}{c} External \\ Internal Rotation \\ \end{tabular} & Trunk & Inclination & \(-5.17\) to \(3.31\) & \(8.48\) \\ \hline \begin{tabular}{c} Flexion \\ Extension \\ \end{tabular} & Arm & Inclination & \(33.09\) to \(37.70\) & \(4.61\) \\ \hline \begin{tabular}{c} External \\ Internal Rotation \\ \end{tabular} & Arm & Inclination & \(-31.1\) to \(19.7\) & \(50.80\) \\ \hline \begin{tabular}{c} Abduction \\ Adduction \\ \end{tabular} & Head & Subjects & \(-10.46\) to \(12.11\) & \(22.57\) \\ \hline \begin{tabular}{c} External \\ Internal Rotation \\ \end{tabular} & Head & Subjects & \(-25.69\) to \(3.25\) & \(28.94\) \\ \hline \begin{tabular}{c} External \\ Internal Rotation \\ \end{tabular} & Head & Subjects & \(24.40\) to \(33.81\) & \(9.41\) \\ \hline \begin{tabular}{c} Flexion \\ Extension \\ \end{tabular} & Thigh & Subjects & \(-5.71\) to \(-1.78\) & \(3.93\) \\ \hline \
\begin{tabular}{c} External \\ Internal Rotation \\ \end{tabular} & Arm & Torque & \(-30.49\) to \(-6.77\) & \(23.72\) \\ \hline
\begin{tabular}{c} Flexion \\ Extension \\ \end{tabular} & Forearm & Frequency & \(3.76\) to \(16.51\) & \(12.75\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: ANOVA between postures and driving factors. The independent variables are flexion–extension, internal–external rotation, and abduction–adduction of the respective body parts. The dependent variables are steering wheel inclination (Inclination), inter-subject variability (Subjects), torque (Torque), and cabin vibration (Frequency). amplitude of 12.75\({}^{\circ}\)). This phenomenon could be explained by a compensatory muscular mechanism between engine oscillations and the biomechanical response of the forearm, where the vertical transmission of vibrations activates involuntary postural adjustments.
## 7 Conclusions  
To solve complex scientific and technical issues in heterogeneous CSNs with DCSs, effective mathematical models were developed to evaluate the operation and reliability of such networks. The success of this approach validates the proposed methodology.  
Based on these mathematical models, modern software tools were constructed to facilitate the selection of an effective client-server type CN structure, specifically for solving ANN modeling problems in complex systems across various applications. These tools enhance system efficacy and support complex distributed computing processes to tackle these challenges.  
The developed DSS was tested in the context of selecting an efficient DCS structure for LLC \"RITS\" in Krasnoyarsk city. Using the available enterprise data, the system determined the following results:  
1. The total hardware cost of the FFGA-optimized configuration is 250 kUSD.
2. The performance is 1220.745 TFLOPS;
3. The availability factor is 99.03%.  
In future work, the automated DSS will be integrated into both governmental and commercial sectors, boosting the efficiency of distributed problem-solving for structural and parametric synthesis of ANN models in complex systems, leveraging GRID technology. Future research should focus on refining the current models, broadening the system's capabilities, and applying it across various industrial and scientific domains. This will ultimately enhance the efficiency of solving complex problems within distributed computing networks.  
Formal analysis, I.M., A.G., V.V.T., and S.O.K.; funding acquisition, A.B., A.G. and V.N.; investigation, V.T.; methodology, V.N.; project administration, A.B., A.G. and V.N.; resources, I.M., A.B. and S.O.K.; software, V.T., A.G., V.V.T. and S.O.K.; supervision, V.N.; validation, V.T., A.B., V.V.T. and S.O.K. visualization, I.M.; writing--original draft, V.T. and V.V.T.; writing--review and editing, I.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
All data contains within the study.  
The authors declare no conflicts of interest.
## 1 Introduction
### Research Contributions  
The following are our summarized contributions.  
1. _Presenting an enhanced secure Block-Comb multiplication method on 8-bit AVR MCUs_ We present an enhanced secure Block-Comb multiplication method on 8-bit AVR MCUs. Through experiments with SPA traces analysis and security analysis using clustering algorithms, we show that the number of used garbage registers does not affect SPA security. With this fact, we configure that our method makes use of a single garbage register, which saves seven registers. In order to further extend the block size of our Block-Comb method, we apply a multiplier encoding technique for optimizing the usage of the registers on 8-bit AVR MCUs. As a result, we extend the block size of the secure Block-Comb multiplication method from four (32-bit wise) to eight (64-bit wise), identical to the maximum block size on AVR MCUs, which significantly decreases the number of partial multiplications from nine to three when calculating a \(BF\) multiplication over \(GF(2^{128})\). The proposed method can be a building block for binary field multiplication in the GHASH function of GCM and NIST-compliant binary ECC.
2. _Implementing the proposed secure Block-Comb multiplication method on an ATmega128 MCI_ By implementing on 8-bit ATmega128, we show that our proposed multiplication technique consumes much less running time. In our method, the basic multiplication unit is 64-bit wise and we apply an enhanced Karatsuba technique when calculating a 128-bit wise \(BF\) multiplication. The proposed method takes 3816 \(cc\), including a multiplier encoding process, when computing a 128-bit \(BF\) multiplication, almost 32.8% faster than that of [PERSON] et al.'s (Consumes 5675 \(cc\)), while providing TA/SPA security. Without the multiplier encoding process, the proposed takes 3490 \(cc\), which is almost a 38.5% improvement of [PERSON] et al.'s method.
3. _Application to GHASH function of GCM mode_ We show how to apply the proposed \(BF\) method to the GHASH function of GCM. Even though our method requires a multiplier encoding process before executing \(BF\) multiplication, this process can be omitted in the context of the GHASH function. In other words, in the 128-bit binary field multiplication of the GHASH function, one of the two inputs is fixed as a Hash key. Thus, we can apply multiplier encoding to the hash key and store the encoded hash key in memory before starting the GHASH function, and reuse it during the GHASH function process. In the GHASH function, the inputs and output of \(BF\) multiplication need to be bit-reflected, which requires three bit-reflection operations per \(BF\) multiplication. We propose a technique that can decrease the count of bit-reflection from three to one. We present an enhanced GHASH function implementation on ATmega128, and it provides an improved performance by over 42% compared to the previous best result.
## 4 Materials and Methods
### Data Preprocessing  
A workflow based on open sources was implemented for data preprocessing [64]. Firstly, it was necessary to transform raw data into an easy-to-use format, such as mzXML, by means of conversion from Proteowizard [65; 66]. This workflow integrates the functions of three R packages (_xcms_, _cliqueMS_, and _eRah_) [67; 68; 69] and brings together the two most important strategies for GC/MS data preprocessing (ion-fragment/feature determination and compound determination). Through this workflow, a matrix with intensities of features or ion peaks detected in exhaled-air samples was generated and compounds to which they corresponded were determined and identified. The compound identification was carried out by matching with the NIST (National Institute of Standard and Technology) spectral library based on mass spectra and retention indexes. For the present study, nine compounds were selected and theses levels were assessed using their most characteristic features: acetone (\(m/z=58\)), isoprene (\(m/z=67\)), toluene (\(m/z=91\)), p/m-xylene (\(m/z=91\)), o-xylene (\(m/z=91\)), styrene (\(m/z=104\)), benzaldehyde (\(m/z=106\)), naphthalene (\(m/z=128\)), and 2-ethyl-1-hexanol (\(m/z=57\)).
# Integrated Assessment of Surface Water Quality in Danube River Chilia Branch
## 2 Materials and Methods
### Mapping Method for the Water Quality Parameters  
Nowadays, it is easy and accessible to record the coordinates, tracks, waypoints, and other related positioning information that accompany the measured datasets when collecting field data, no matter the domain. It has become a standard procedure in the data collection process to record also the spatial information for every type of field data. If the measured datasets can be processed, analyzed, and displayed in several ways, adding GPS information to those datasets enables further the development of spatial processing, analysis and representation of the same data through maps and a variety of GIS products that not only aid and accompany the tabular and graphical representation of the data but sometimes offers more information by integrating other spatial variables that enables the author to better disseminate the results. For this study, the Canadian Water Quality Index with respect to the nutrient concentration was calculated over 7 years of monitoring in 5 sampling stations along the Chilia branch of the Danube River in the Danube Delta. The database contained the point coordinates for each sampling station, which determined a GIS approach towards the map representation of the nutrient data. Taking into account that the distance between the upstream and the downstream sampling stations is roughly 100 km determined a certain data representation procedure (Figure 2).  
In order to have a readable and comparable representation of the data, certain aspects in the map layout had to be exaggerated due to the low scale of the monitored area. Usually, this type of data is interpolated along the river surface and length but that would imply that the data could not be easily read. This considerable aspect determined an exaggerated feature approach which consisted in representing the data through a color-shaded grid of the interpolated data. Thus, for the whole stretch of the river segment, a polygon grid was created and altered according to the river flow. This grid exceeds the area of interest, but by doing this its cell size is exaggerated enough to represent the data accurately (Figure 3, up and middle).  
In order to give the grid cells interpolated values along the river course, the yearly values of the parameter were interpolated. The raster result was sampled according to the grid's cell centroids and then the attributes of the samples were transferred to the cells. The resulting grid cells were color-displayed based on 10 graduated quantile classes (Figure 3, down). The same methodology was applied for all the yearly values of the parameter and by keeping the scale of the graduated cell values enabled in having a consistent data representation which can be compared throughout the years. This is a basic GIS procedure in order to represent interpolated data for large areas and it was done by using QuantumGIS (QGIS) application, version 3.10.  
Figure 2: The area included in the grid processing.
## 2 Materials and Methods
### Sample Characterization  
#### 2.2.1 Surface Inspection  
After cleaning, the samples were inspected in transmission and reflection by means of a spot lamp. A modified inspection microscope Leica type INM200 was used for a more precise and sensitive inspection of the cleaned surfaces. The microscope is equipped with an automated objective revolver (magnifications: 2.5\(\times\), 5\(\times\), 10\(\times\), 20\(\times\), 50\(\times\), 100\(\times\)) and an UI-3060 CP-C-HQ camera with a 2.3 MP Sony sensor (1936 \(\times\) 1216 px, 166 fps). The modified system can detect particles greater than 83 nm by using the objective with the highest magnification of about 100\(\times\) and using bright field illumination. To avoid particle contamination during handling and measurement, the whole system is installed in an ISO 1 housing. The whole surface except 2 mm edge exclusion was scanned automatically. The detected particles were assigned in a histogram with defined particle size classes (83-100 nm; 100-200 nm; 200-300 nm; 300-500 nm; 500-1000 nm; 1000-5000 nm and greater than 5000 nm).  
In addition, the root-mean-square roughness was measured by means of a white light interferometer Zygo NewView 5300 to detect changes in the surface roughness.
## 6 Discussion
### The Importance of the Findings  
When making a general commentary on the results obtained from this study, a trend of increasing greenness in Turkiye was identified, consistent with many global regions [3]. The observed low GPP values in the semi-arid regions of Turkiye (Konya, Sanliurfa, and Erzincan) are attributed to the high sensitivity of semi-arid ecosystems to water availability. Adverse trends in rainfall can lead to abrupt declines in vegetation productivity or the loss of ecosystem resilience [110]. Additionally, vegetation in arid areas is more sensitive to changes in environmental factors, which can significantly underestimate maximum LUE values in arid ecosystem models [111]. Consequently, the lack of appropriate LUE parameters in arid areas and the inability to accurately simulate the complex relationship between water and the LUE contribute to most LUE models yielding low GPP estimates [112].  
GPP values vary across different geographical regions, and it is strongly recommended that these values be examined on a region-specific basis. Identifying the parameters that most significantly affect GPP and, if necessary, using local adjustment coefficients to enhance the representational accuracy of GPP datasets are crucial. Given Turkiye's geographical position, surrounded by seas on three sides and hosting a variety of microclimates, it is essential to increase the number of ground observation stations to enable more accurate carbon cycle analyses. Doing so across Turkiye's geographical regions would be beneficial given the differences in GPP magnitudes and trends. These measurements should then be used to calibrate remote sensing datasets, ensuring that they accurately reflect the distinct environmental and ecological characteristics of each region in Turkiye. This approach will not only improve the accuracy of current GPP estimations but also enhance our understanding of regional ecological dynamics, contributing to better environmental management and policymaking.
# Modified Joint Source-Channel Trellises for Transmitting JPEG Images over Wireless Networks
## 5 Conclusions  
In this study, we reported the construction of a two-tiered model for transmitting JPEG images over a wireless network. The two tiers comprised a serial VLC trellis and a linear block code trellis. The model exhibits improved robustness when transmitting a JPEG image over AWGN and slow-fading channels compared with conventional source-channel tandem models. Despite its trellis code structure, the model enables decoding through several algorithms. Our JSCC model employs concatenated encoding and expurgation to control for errors in its innovative two-tiered arrangement of bit streams according to data relevance. For the proposed scheme, this study used an expurgating trellis instead of the original trellis structure when constructing the JSCC. In transmitting the 512 \(\times\) 512 JPEG Lena image at a transmission rate of 1.7 bits/pixel over channel frequencies with bit error probabilities \(10^{-3}\), the model outperformed the conventional source-channel tandem model by 18 dB. General decoding algorithms optimized for the conventional tandem model experience noticeable declines in performance when the sequences to be transmitted are long. By combining encoding and decoding algorithms in a modified trellis construction, our JSCC model achieved acceptable performance even when transmitting long sequences. Additionally, whereas the tandem model preserves image quality according to the relative importance of image components by expurgating random code sequences alone, our JSCC model achieved acceptable performance even when transmitting long sequences.  
model also applies the BCJR algorithm for decoding, improving accuracy when decoding long image sequences. However, our experiments also revealed that performance declined as the level of expurgation increased.  
Y.-C.L.: conceptualization, data curation, methodology, and writing--original draft. J.-J.W.: study design, reviewing the manuscript, and writing--original draft. S.-C.Y.: resources, validation, visualization, and review and editing. C.-C.C.: formal analysis, supervision, review and editing, and project administration. All authors have read and agreed to the published version of the manuscript.  
This paper received funding support from the National Science and Technology Council, Taiwan, under grant number NSTC 109-2221-E-167-002-MY3.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare that they have no competing interests.
# Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data
## 2 Materials
### Sentinel-2 Optical Time Series  
The sugarcane harvest dates were determined based on the search for the characteristic changes in the Sentinel-2 _NDVI_ time series [13]. The Sentinel-2 data are publicly available by the EOS LandViewer service [38]. The research involves 76 scenes of Level-1C Sentinel-2 _NDVI_ in descending mode (granule L1C_T22 KEB_A018382, afternoon at 01h32 UTC--morning at 10h32 BRT).  
The _NDVI_ time series were constructed using EOS Statistics API [39]. The harvest dates were identified by visual interpretation of the Sentinel-2 MSI images obtained from the EOS LandViewer service [38].  
Preliminary studies show that a lot of sugarcane fields are harvested by parts. Dimensions of those parts are often from 5 to 10 ha. Rapid decrease or sharp drop of _NDVI_ down to the value of 0.20-0.30 corresponds to the harvest period. The _NDVI_ drop within a small field fragment is hard to trace from time series of average _NDVI_. To do that, large fields should be divided into parts, in which the index decrease will be more detectable.  
The field polygons were divided by a grid into square parcels (cells) of 10 ha area. Cell fragments with an area less than 1 ha generated by intersecting the grid with the polygon boundaries, were not considered. The number of cells is 574.  
The time series was extended by 2 months after the end of the monitoring period. Additional time interval is required to monitor the _NDVI_ change after possible drop at the end of the calendar year to eliminate _NDVI_ false minimums due to clouds and the ones not related to harvest. Thus, the _NDVI_ time series was constructed on the interval of 1 January 2018-28 February 2019.
## 3 Modal Analysis
### Steady-State Static Analysis Module  
Static analysis forms the foundation of structural investigation, focusing on the structural response to static loads and assessing stiffness and load-bearing capacity [17]. The static load generated by the self-weight of the box-type substation initiates the initial stresses, which are crucial in the analysis. To evaluate the enclosure's vibration characteristics, a free modal analysis is initially conducted. In the steady-state static analysis, considering the weight of electrical equipment inside the enclosure, the equipment weight is equivalently applied to the surface of the enclosure's base frame. Additionally, fixed constraints are applied to the base frame of the substation enclosure, along with a vertical downward gravitational acceleration. Upon completion of the calculation, the deformation and stress cloud map of the substation enclosure's frame structure are obtained, as depicted in Figure 6. The results indicate that the maximum total deformation is 0.38246 mm, located at the center of the top cover, with other significant deformations concentrated at the bottom of the high- and low-voltage chambers. The maximum stress is 8.3121 MPa, primarily distributed over the main body of the box-type substation, meeting structural design requirements.  
Figure 5: Box grid model diagram of box-type substation.  
Figure 6: Static structural stress deformation diagram of box frame: (**a**) static structural stress; (**b**) static structural deformation.
# Improved Defect Detection Using Adaptive Leaky NLMS Filter in Guided-Wave Testing of Pipelines
## 5 Results  
In order to better understand this process, an example test case is initially shown where each stage of the algorithm is explained thoroughly. Then the experimental results are demonstrated in the second part of this section where the SNRs are calculated with regards to defects with different CSA sizes and excitation frequencies. In both cases the SNRs are calculated based on the following formula:  
\[\text{SNR}\ =\ 20\ \times\ \log 10\left(\frac{\sqrt{\frac{1}{\text{N}}\sum_{1} ^{\text{N}}\text{Signal}_{n}}}{\sqrt{\frac{1}{\text{M}}\sum_{1}^{\text{M}} \text{Noise}_{\text{m}}}}\right) \tag{6}\]  
where Signal is considered the window where the energy of the signal is received (between 3.0 m and 3.5 m depending on the frequency) and everything else is considered noise. In both tests, excitation sequences are 10-cycle Hann-windowed sine waves.
# Estimation of Thermal Diffusivity for Greenhouse Soil Temperature Simulation
## 3 Results and Discussion
### Calculation of Soil Thermal Diffusivity  
The thermal diffusivity of the greenhouse soil was calculated using methods discussed in Section 2.2 and field data from 1 to 7 September 2016. As shown in Table 1, the mean thermal diffusivity k of the greenhouse soil was calculated using the amplitude, phase-shift, arctangent, logarithmic, and min-max methods using the data collected at depths of 0.1 m and 0.3 m. The value of k for the greenhouse soil under different weather conditions and different greenhouse control methods was approximately 10 \(\times\) 10\({}^{-7}\) m\({}^{2}\) s\({}^{-1}\).
## 3 Results and Discussion  
### SPME-GC/MS: Chemical Volatile Composition of Spirulina  
By the SPME-GC/MS technique, in the volatile fraction it was possible to identify twelve components listed in Table 1. The most abundant fraction was the hydrocarbon one with heptadecane as the main component (85.0%) followed by pentadecane (6.2%) and hexadecane (5.0%) but with a markedly lower relative percentage. _Cis_-geranylacetone (<0.1%) and _trans_-\(\beta\)-ionone (1.9%) were the only two terpene compounds found. Among the compounds detected in trace amounts, 2-propylfuran, a furan derivative, was also identified.  
Hydrocarbons represent about half of the unsaponifiable fraction of spirulina. The presence of heptadecane (biogenic hydrocarbon) is particularly important as it is assumed to be an indicator of the cell density of cyanobacteria and green algae in the aquatic environment; in fact, it has been successfully used to reflect algal biomass in sedimentary studies [25]. An in vivo study also demonstrated the anti-inflammatory capacity of this volatile hydrocarbon thanks to its ability to modulate the activation of NF-kB [26]. A previous study also reported the presence of heptadecane as one of the main components in four different spirulina extracts from Egypt, obtained with solvents such as methanol, acetone, chloroform and petroleum ether by soaking treatment [27].  
SPME-GC/MS technique has already been used successfully for the characterization of the volatile profile of dried hop leaves [28], highlighting how this analytical sampling and analysis technique is absolutely suitable for identifying a large pool of volatile metabolites emitted by natural products.