esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Processing of Raw Data
However, it is worth noting that this increase is not monotonic. The energy value at 13 t, as measured in the third measurement, can clearly be identified as an outlier. This may be due to measurement distortion, so we have excluded it from further analysis. Figure 9b shows the energy distribution of the frequency subbands present in the fifth layer after wavelet packet decomposition. It is evident that the energy of the signal between 0-600 kHz constitutes 98.5% of the total energy, with the majority concentrated around 100 kHz and 350 kHz. The energy of the signal with a frequency above 600 kHz accounts for only 1.5% of the total energy. This indicates that while the energy of the signal is widely distributed in the frequency domain, the high-frequency component of the signal is easily attenuated, and most of the signal's energy is concentrated in the low-frequency part. We processed the raw data in Figure 9a by using the signal energy at a tension of 0 tons as the reference point, in the form of an energy ratio. The relative energy ratio is illustrated in Figure 9c. It can be observed that the trends of the signal energy ratios are essentially the same. The values at the black circle markers in the figure may indicate interference from the equipment and environment during the acquisition process and are labeled as abnormal values. Therefore, in the subsequent analysis, we use the average energy ratio of other measurement curves to replace it.
# In Situ Experimental Study of the Friction of Sea Ice and Steel on Sea Ice
## 3 Results
### Effect of Sea Ice Column Grain Orientation  
Figure 8 shows the variations of \(\mu_{\rm k}\) with velocity for SS, CS, and sea ice sliders sliding on ice cover in different directions in relation to the ice columnar grain orientation. The air temperature was the same in both cases shown in each subgraph, and the surface roughness of the lateral ice surface was assumed equal in the directions vertical and parallel to the ice columnar grain orientation. Figure 8 presents the mean values determined from coefficients at all normal stresses. The standard deviations in the two directions were small, indicating no obvious differences between them. To provide further justification for the diversity of \(\mu_{\rm k}\) for different grain orientations, rank sum tests were conducted as shown in each subgraph. The results gave statistical significances higher than 0.05, which confirmed that friction is independent of ice grain orientation. As for the effect of ice grain structure on friction, several previous investigations have been performed. [PERSON] and [PERSON] (2013) also pulled ice blocks in lateral surface along an ice-top track in directions parallel and vertical to ice columnar grain orientation and found no differences in the kinetic friction coefficients between these two configurations. [PERSON] et al. (2013) examined the effect of grain size (2.5-6.0 mm) on the friction of granular freshwater ice sliding against itself, and no difference was observed. [PERSON] and [PERSON] (2015) investigated the effect of grain type by measuring the \(\mu_{\rm k}\) of granular ice and S2 columnar ice on themselves and, no significant effect was detected.  
Figure 8: Variations of \(\mu_{\rm k}\) with velocity \(V\) of friction for (**a**) SS, (**b**) CS, and (**c**) sea ice sliders in directions parallel and vertical to ice columnar grain orientation. The data were averaged over the normal stresses, and the error bars represent the standard deviation. Also shown are the test air temperature, the standard deviations of both datasets, and the significance coefficients of the rank sum tests.
## 5 Conclusions  
An XGBoost model was developed for classification of APS and NS states, achieving an overall accuracy of 99.93%. The SHAP technique was employed to compute the global importance of the physiological signals used for detection of APS by the XGBoost model. The order of importance for the variables from most to least was determined to be GSR, HR, ST, and motion sensor ACC readings. Increases in GSR and HR were positively correlated with the occurrence of APS, as indicated by high positive SHAP values. The SHAP method highlighted the importance of local biosignals for particular instances of correctly and misclassified samples, further validating our approach. These findings underscore the significant contributions of GSR and HR signals in detecting APS, supporting our conclusions about the model's effectiveness in APS detection.  
Conceptualization, M.M.R., M.R.A. and A.C.; methodology, M.M.A.-L. and M.M.R.; software, M.M.A.-L. and M.M.R.A.; validation, M.M.A.-L. and M.M.R.; formal analysis, M.M.A.-L., M.M.R. and A.C.; investigation, M.M.A.-L., M.M.R. and A.C.; resources, M.P., L.Q. and A.C.; data curation, M.P., M.M.A.-L. and M.R.A.; writing--original draft preparation, M.M.A.-L.,M.M.R. and A.C.; writing--review and editing, M.M.A.-L., M.M.R., A.C., L.S., L.Q. and M.A.; visualization, M.M.A.-L. and M.R.A.; supervision, A.C., L.Q. and M.M.R.; project administration, M.P., M.M.A.-L. and M.M.R.; funding acquisition, A.C, L.Q. and L.S. All authors have read and agreed to the published version of the manuscript.  
Financial support from the NIH under grants 1 DP3 DK101075, 1R01 DK130049, and R01 DK135116 and the JDRF under grants 2-SRA-2017-506-M-B and 3-APF-2022-1134-A-N was made possible through collaboration between the JDRF and the Leona M. and Harry B. Helmmsley Charitable Trust, and is gratefully acknowledged.  
This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the Illinois Institute of Technology (protocol code IRB 2019-018, date 16 October 2019).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author.  
The authors declare that they have no known competing financial interests or personal relationships that could have influenced or appeared to influence the work reported in this paper.  
The following abbreviations are used in this manuscript:  
APS Acute Psychological Stress SHAP Shapley Additive Explanations GSR Galvanic Skin Response HR Heart Rate T1D Type 1 Diabetes AID Automated Insulin Delivery ML Machine Learning XGBoost Extreme Gradient Boosting LIME Local Interpretable Model-Agnostic Explanations NS Non-Stress MS Mental Stress EAS Exciting Anxiety Stress PPG Photoplethysmogram BVP Blood Volume Pulse ST Skin Temperature ACC Accelerometer EDA Electrodermal Activity SCL Skin Conductance Level SCR Skin Conductance Response
## 2 Materials and Methods
### Error-Based Evaluation Metrics
#### 2.2.5 Factor Ranking and Selection by the Information Gain Ratio Technique  
Several techniques for factor ranking and selection have been proposed, but the relative advantages and weaknesses of these techniques are unknown [118]. Factor ranking techniques evaluate the relevance of each factor independently and eliminate factors determined to be irrelevant or redundant. They also search for the subset of factors that offers the largest reduction in dimensionality [118].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted Target**} \\ \cline{3-4}  & & **Gully Erosion (+)** & **Non-Gully Erosion (-)** \\ \hline \multirow{2}{*}{Actual target} & Gully erosion (+) & TP & FP \\ \cline{2-4}  & Non-gully erosion (-) & FN & TN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrix of machine learning models in this study.  
In this study, we used the information gain ratio (IGR) method to select and rank the most important factors for gully erosion modeling and susceptibility mapping. The IGR method is applied as follows [119]:  
Let \(T\) be the total number of tuples in the training dataset; \(Tj\) as the total number of positive or negative tuples in the training dataset; \(v\) is the total number of classes in the dataset; and S is slope angle, which is one of the gully conditioning factors.  
\[GainRatio(Slope)=\frac{Gain(Slope)}{SplitInfo(Slope)} \tag{18}\]  
\[\text{where; }SplitInfo(T)=-\sum_{j=1}^{v}\frac{\left|T_{j}\right|}{\left|T \right|}\log_{2}(\frac{\left|T_{j}\right|}{\left|T\right|}) \tag{19}\]  
\[Gain(Slope)=I(p,n)-E(Slope) \tag{20}\]  
\[E(Slope)=-\sum_{i=1}^{m}\frac{p_{i}+n_{i}}{p+n}I(p_{i},n) \tag{21}\]  
\[I(p_{i},n)=-\frac{p}{p+n}\log_{2}\frac{p}{p+n}-\frac{n}{p+n}\log_{2}\frac{n}{ p+n}. \tag{22}\]  
\(E(Slope)\) represents the entropy of the slope angle factor in the training dataset, \(I(p,n)\) denotes the information needed to satisfy a given training dataset, \(p\) is the total number of positive tuples in the training dataset, \(n\) is the total number of negative tuples in the training dataset, and \(m\) is the number of values for the slope angle factor.
## 3 Results
The others with the high level were epicatechin and sinapinic acid (the exceptions were cultivars Borowska and Bamberka, collected in the second and third time of harvest). \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**Cultivar**} & \multicolumn{3}{c}{**Number of Days after Sowing**} & \multirow{2}{*}{**Means for Cultivar**} \\  & & **31** & **38** & **45** & \\ \hline \multirow{6}{*}{ABTS} & Warta & 217 \(\pm\) 4 e & 1755 \(\pm\) 3 b & 163 \(\pm\) 1 a & 1855 \(\pm\) 25 A \\  & Bamberka & 225 \(\pm\) 3 f & 255 \(\pm\) 1 g & 193 \(\pm\) 3 c & 225 \(\pm\) 27 C \\  & Borowska & 173 \(\pm\) 7 b & 273 \(\pm\) 2 h & 200 \(\pm\) 1 d & 216 \(\pm\) 45 B \\ \cline{2-6}  & Means for & & & & \\  & harvest time & 205 \(\pm\) 24 B & 234 \(\pm\) 45 C & 185 \(\pm\) 17 A & \\ \hline \multirow{6}{*}{FRAP} & Warta & 1511 \(\pm\) 98 c & 1549.39 \(\pm\) 5 c & 1399.37 \(\pm\) 6 b & 1486.54 \(\pm\) 84 B \\  & Bamberka & 1368 \(\pm\) 12 b & 1890.06 \(\pm\) 8 d & 922.57 \(\pm\) 7 a & 1393.63 \(\pm\) 419 A \\  & Borowska & 1539 \(\pm\) 10 c & 1963.47 \(\pm\) 15 e & 1523.29 \(\pm\) 10 c & 1675.25 \(\pm\) 217 C \\ \cline{2-6}  & Means for & & & & \\  & harvest time & 1477 \(\pm\) 94 B & 180 \(\pm\) 192 C & 1282 \(\pm\) 275 A & \\ \hline \multirow{6}{*}{Total polyphenols} & Warta & 827 \(\pm\) 24 f & 7582 \(\pm\) 14 c & 6048 \(\pm\) 14 a & 7301 \(\pm\) 986 A \\  & Bamberka & 796 \(\pm\) 14 d & 8194 \(\pm\) 21 e & 8249 \(\pm\) 21 f & 8135 \(\pm\) 133 B \\ \cline{1-1}  & Borowska & 689 \(\pm\) 15 b & 8964 \(\pm\) 29 g & 6036 \(\pm\) 8 a & 7297 \(\pm\) 1304 A \\ \cline{1-1} \cline{2-6}  & Means for & & & & \\ \cline{1-1}  & harvest time & 7709 \(\pm\) 628 B & 824 \(\pm\) 600 C & 6778 \(\pm\) 1104 A & \\ \hline \end{tabular} Results are expressed as mean \(\pm\) SD; n = 3; mean values with different letters (a–h) within the individual rows and columns (without last column) are statistically different \(p\leq\) 0.05; mean values with capital letter (A–C) within last column (for cultivar) or rows (for harvest time) are statistically different at \(p\leq\) 0.05.  
\end{table} 
Table 2: Antioxidant activity [μmolTrolox/1 g DM] and total polyphenol [mg/100 g] content in the tested cultivars of white mustard in particular harvest dates.
# Assessment of Spatial Variability in Ground Models Using Mini-Cone Penetration Testing
## 3 Experimental Program  
### Soil Index Properties  
The soils used for the ground model were silica sand samples and weathered soil samples. To understand the physical properties of the samples, tests such as specific gravity, particle size analysis, compaction, Atterberg limits, and relative density were conducted, and the results are presented in Table 1. The soil index properties of the soil samples used in this study are as follows: For the silica sand, the specific gravity (\(G_{s}\)) is 2.65, the maximum void ratio (\(e_{max}\)) is 1.06, the minimum void ratio (\(e_{min}\)) is 0.64, the uniformity coefficient (\(C_{u}\)) is 1.03, and the coefficient of curvature (\(C_{g}\)) is 1.76; the sand is classified as SP (poorly graded sand) according to the unified soil classification system (USCS). The weathered soil has a \(G_{s}\) of 2.67, \(e_{max}\) of 1.12, \(e_{min}\) of 0.44, \(C_{u}\) of 3.57, and \(C_{g}\) of 9.28; the soil is classified as SM (silty sand) according to the USCS.
###### Abstract  
With the advent of communication networks, protecting data from security threats has become increasingly important. To address this issue, we present a new text encryption scheme that uses a combination of elliptic curve cryptography and max-plus algebra-based wavelet transform to provide enhanced security and efficiency. The proposed encryption process consists of three main phases. In the first phase, the plaintext is encoded using ASCII characters, followed by the introduction of diffusion in its representation. In the second phase, points are computed on an elliptic curve, and a mapping method is applied to introduce randomness into the data. Finally, in the third phase, the output is decomposed using a max-plus algebra-based wavelet transform to generate the ciphertext. We conduct a comprehensive security analysis of our scheme that includes NIST analysis, entropy analysis, correlation analysis, key space, key sensitivity, plaintext sensitivity, encryption quality, ciphertext-only attack, known-plaintext attack, chosen-plaintext attack, and chosen-ciphertext attack. The findings indicate that the proposed scheme exhibits excellent encryption quality, surpassing a value of 7%, which is closer to the ideal value. Moreover, the sensitivity of the plaintext is greater than 91%, indicating its high sensitivity. The correlation between the plaintext and ciphertext is very close to the ideal value of zero. The encrypted texts exhibit a high level of randomness and meet the necessary criteria for a strong key space. These characteristics contribute to its superior security, providing protection against various cryptographic attacks. Additionally, the encryption process for a 5995-character plaintext only takes 0.047 s, while decryption requires 0.038 s. Our results indicate that the proposed scheme offers high levels of security while maintaining reasonable computational efficiency. Thus, it is suitable for secure text communication in various applications. Moreover, when compared with other state-of-the-art text encryption methods, our proposed scheme exhibits better resistance to modern cryptanalysis.  
text encryption; elliptic curve; max-plus algebra; security analysis +
Footnote †: journal: Applied sciences
# A Novel Approach to Incremental Diffusion for Continuous Dataset Updates in Image Retrieval
## 7 Experiments
### Experimental Setup  
#### 7.1.1 Datasets  
We test on the revisited Oxford Buildings [24] and Paris [25] datasets. The datasets are referred to as \(\mathcal{R}\)Oxford and \(\mathcal{R}\)Paris, containing 4993 and 6322 images, respectively. For each dataset, 70 additional images are used as queries. According to the difficulty in retrieving their relevant images, these queries are categorized into three classes, easy, medium, and hard [26]. As is common practice, we adopt the mean average precision (mAP) as the performance evaluation protocol.
## 4 Proposed 3D-UWSN Design
### Emergency Event Transmission  
The 3D-UWSN is an environment in which the sensors give current, collected data that can either be critical or non-critical. In the case of a critical, sensed message, the occurrence of the event is predicted, and, hence, it is forwarded by selecting a forwarder using the weighted method. The weighted value for neighboring CHs is computed and then a higher-weighted CH is selected to forward the emergency event message. The constraints that are taken into account are distance, load and residual energy. The Euclidean distance is presented to determine the distance between the two CHs, and it is expressed for \(CH_{i}\) and \(CH_{j}\):  
\[D_{(CH_{i}CH_{j})}=\sqrt{(x_{2}-x_{1})^{2}+(y_{2}-y_{1})^{2}+(z_{2}-z_{1})^{2}} \tag{25}\]  
The sensor nodes are deployed in 3D space, and so the coordinates of the CHs \(CH_{i}\) and \(CH_{j}\) are indicated as \((x_{1},y_{1},z_{1})\) and \((x_{2},y_{2},z_{2})\). Along with this distance, the remaining energy \(RE\) and the load at the CH \(L_{CH_{i}}\) is determined as a weight value \(W_{CH_{i}}\).  
\[W_{CH_{i}}=\left(w_{1}*D_{(CH_{i},CH_{j})}\right)+\left(w_{2}*RE\right)+\left( w_{3}*L_{CH_{i}}\right) \tag{26}\]  
From the estimated weight, the forwarder is selected, and the data are transmitted. The load depends on the available packets that are yet to be transmitted from the CH. Here, the weights \(w_{1}+w_{2}+w_{3}=1\) are constant. Once the CH with high weight is selected as forwarder, then the emergency event is forwarded and reaches the AUV.
# A Novel Evolutionary Algorithm for Designing Robust Analog Filters
## 4 Experiments and Results
### Statistical Results of the Three Methods for Evolving Robust Filters
#### 4.4.4 Comparison with Other Approaches for Robust Filter Design  
There are several previous works focusing on evolving (robust) passive analog filters. [PERSON] and [PERSON] [50] suggested a simple linear genome method to evolve lowpass and bandpass filters with a small number (<50) of components. Particularly related to this work, they used 5%, 10%, and 20% disturbances to the component values to evaluate the robustness of the evolved circuits. Although they did not evolve robust circuits in their work, they suggested that a consideration of manufacturing errors (tolerances) of component values is needed and that in evolvable hardware, the component errors intrinsic in physical components can be absorbed during the evolutionary process. [PERSON] and [PERSON] [53] proposed using a symbiotic organisms search (SOS) algorithm to determine the values of the passive components (resistors and capacitors) used in active filters. They compared SOS performance with those obtained using other optimization methods, such as particle swarm optimization, seeker optimization algorithms, and differential evolution, and found that SOS works best. However, they used a fixed topology of the active filters and did not consider the robustness of the evolved filters.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Algorithm (Fitness Evaluations)** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **Mean** & **Stdev** \\ \hline GP (100,000) & 34 & 26 & 35 & 24 & 14 & 20 & 24 & 25 & 26 & 46 & 27 & 8.9100 \\ GP (500,000) & 46 & 47 & 37 & 47 & 57 & 40 & 40 & 45 & 48 & 79 & 51.7500 & 15.3200 \\ GPRMS (100,000) * & 28 & 15 & 36 & 21 & 28 & 14 & 43 & 26 & 31 & 20 & 26.2000 & 9.1100 \\ GPRPE (1,000,000) & 67 & 36 & 46 & 36 & 37 & 57 & 29 & 63 & 27 & 36 & 43.4000 & 14 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The sizes of filters evolved using four algorithms with different numbers of fitness evaluations. Each algorithm was run 10 times. * A total of 1,000,000 function evaluations are required to do 100,000 actual fitness evaluations.  
In another, similar work [54], analytical characterization of a 2 nd order Gm-C Sallen-Key LP filter was considered, as was the corresponding symbolic expressions for their sensitivity to component value variations. When the sensitivity is used as a goal minimization function in the optimization process, parasitic capacitances are canceled and robust designs are obtained. Recently, [PERSON] [55] proposed using multi-objective optimization, by minimizing the sensitivity to variations in the parameters and the design errors, to determine the values of the passive components (resistors and capacitors) of robust active filters. In summary, these related works all focused on parameter search instead of open-ended topological exploration to evolve robust analog filters.
###### Abstract  
Since early March 2014, the NASA/JAXA Global Precipitation Measurement Core-Observatory (GPM-CO) satellite has allowed analysis of precipitation systems around the globe, thanks to the capabilities of the GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR). In this work, we demonstrate how GPM-CO measurements obtained from 4 years of observations over the Mediterranean area can be used as an extremely effective tool to study the main climatological characteristics of the most intense Mediterranean storm structures. DPR and GMI-based Precipitation Features (PFs) parameters are used as proxies of the vertical structure and microphysical properties of these events, and their statistical distribution is analyzed to identify extremes. The analysis of annual, seasonal and geographical distribution of the identified deep convective systems highlights substantial differences in their diurnal cycle and in the distribution between land-sea and summer-winter. There is a general shift of the convective systems from the south (mostly over the sea) in the cold season, to the north (mostly over land) in the warm season. The analysis shows also that the inferred convective intensity is not always related to heavy precipitation. Known DPR and GMI-based criteria were adopted to identify overshooting top events and potential hailistorms, identify extreme deep convection signatures, like those observed for tropical and subtropical systems, and the most intense occur mostly over the sea. Although the analysis is limited to four years, the results show that the GPM-CO offers unprecedented measurements to identify and characterize extreme weather events in the Mediterranean region, with unique potentials for future long-term climatology and interannual variability analysis.  
GPM-CO; deep convection; hail; mediterraean; extreme events; GMI; DPR; microwave 2021
# A Virtual Learning Architecture Enhanced by Fog Computing and Big Data Streams
## 6 Advantages of Big Stream and Fog Computing for e-Learning Environments
### Novel Benefits  
The third type of advantages may be claimed as the most interesting. This encompasses:  
* a _social ecology localization_[48] of the students during the learning process: at home, at a restaurant, walking, at the hospital, at school, etc: Thus, teachers can monitor which parts of their subject are mainly learned in particular places and adapt their didactic strategy according to the location. On the other hand, students may tune the lesson (audio, video, slide, tests, etc.) according to the specific place;
* a _temporal analysis_ of the students' habits: Performing a granular analysis closer to the students, data regarding the time of day when they make use of various learning instruments may be directly sent to the interested teachers or educational managers, with the ultimate aim of fine-tuning the didactic materials. Moreover, through opportune mining strategies, a studying pattern of different student profiles may be drawn, e.g., working students may prefer to study overnight or during weekends;
* an _accurate driving of the learning process_ of the students, providing him/her with real-time feedback from either the instructors or the tutors: Moreover, thanks to the constant real-time monitoring of the students' progresses and results, institutions may provide a personalized student model, therefore customizing learning activities for single students or for groups;* an _adaptive planning_ and scheduling of learning activities, as well as on-the-fly courseware construction;
* a _fine-grained data flow management_: Big data stream technology allows managing and addressing data streams coming from particular students directly to the interested teachers or tutors, who in turn, may provide specific and customized suggestions and explanations placing the consumer of e-learning services, the student, at the center;
* a _dynamic and adaptive deployment_ of distance learning courses: Through tailored sensors and smart objects [49], fatigue and mental weariness or, on the contrary, an almost perfect clarity of mind may be promptly detected. As a consequence, a dynamic and adaptive learning strategy when distributing the LOs can be enacted: more interactive parts, made for example by tests, sets of questions, use cases, wikis and mental maps, may interchange themselves with more intensive and complex sections.