esa-sceva/satcom-chunk-collection huggingface dataset:
# Privacy-Preserving Electricity Trading for Connected Microgrids
## 4 Cryptographic Background
### Digital Signatures  
In public-key cryptography, a party \(\mathcal{P}\) possessing a key pair can compute digital signatures [27].  
A digital signature of message \(M\) is usually computed over its hash digest, \(\mathcal{H}(M)\), using the private key. The resulting signature, denoted as \(\text{Sign}_{\mathcal{P}}(\mathcal{H}(M))\) is transmitted alongside message \(M\) and can be validated using the corresponding public key. Digital signatures provide integrity, authentication and nonrepudiation to \(M\).
# A Sampling-Based Unfixed Orientation Search Method for Dual Manipulator Cooperative Manufacturing
## 1 Introduction
### Relative Work
The method does not exclude collision samples with removable objects, but instead classifies them as collided obstacle(s) and allows the search for accessible paths highlighting which objects must be removed from the workspace to make a valid path. While this approach removes any obstacles along the path of the working manipulator, it does not enable the manipulator to actually bypass obstacles. [PERSON] et al. [16] used the Artificial Potential Field (APF) method to plan the paths of multiple cooperative manipulators on mobile bases. However, the study focused solely on the path of the end effector (EEF) and did not consider the entire arm, thus lacking important systemic considerations. A preliminary conclusion can be drawn from the above review that when it comes to the path planning of dual or multiple industrial manipulators, the negative influence, due to the above characteristics such as model complexity, high dimensionality, and complex planning space, will become even more critical. Multi-manipulator path-planning algorithms can be instantly divided into a map search-based method, time-sampling-based method, mathematical model method, and others [17]. The map search-based method [18; 19; 20] is a global optimization algorithm that will cost a colossal amount of time in planning map construction. In comparison, the time-sampling-based method [21; 22; 23] divides the whole planning period into several orderly but isolated intervals and splices all the calculated outcomes of each interval into a complete result path or planning map in order.
# AI-Based Intelligent Monitoring System for Estrus Prediction in the Livestock Industry
## 6 IMS for Livestock Industry
### Experimental Result
5 and 6), as shown in Figure 12, confirms that the clear characteristics of change are similar. It is necessary to further examine the correlation between the two Hanwoos, No. 5 and 6, by comparing the data. It can be seen that the overall cumulative activity data of Hanwoo No. 5 is higher than that of Hanwoo No. 6, as shown in Figure 12. It can also be confirmed that the activity is much higher when compared with other Hanwoos as well. Activity change was defined as an important condition for judging estrus manifestation. Thus, predicting Hanwoos with a high possibility of estrus manifestation based on the acquired data can be performed by comparing the data of cumulative activity using the movement distance of Hanwoo objects for the total observation time such that Hanwoos No. 5 and 6 are the target Hanwoos for estrus manifestations, as shown in Figure 13.  
As shown in Figure 14, the IMS can detect mounting activity through ARM with the pre-learning of Hanwoo class. Thus, in this experiment, Hanwoo No. 6 showed continuous mounting activities to Hanwoo No. 5, which were recognized as ARM. Therefore, the ARM finally demonstrated that it could determine the Hanwoo for estrus by repeatedly detecting mounting activities and by comparing the overall increasing data of the activity. Finally, we could verify the usability of the ARM by deciding the degree of prediction that can determine the target Hanwoo object for estrus manifestation by combining the weight of 0.7 for the detection of mounting activity and the weight of 0.3 according to the cumulative activity in consideration of the prediction degree to determine the Hanwoo expected to manifest estrus. Therefore, the IMS showed the possibility of predicting the  
Figure 14: ARM’s detection of mounting activity. Figure 12: Changes in activities of estrous Hanwoo (No. 5) and non-estrous Hanwoo (No. 6) in Group B.  
Figure 13: Cumulative activity data. target cattle for estrus manifestation through quantification of the cumulative activity and mounting activity detection data during the monitoring period. It also showed the possibility of sufficiently replacing the observation job for close checking of livestock conditions by workers in a breeding environment.
## 4 Results and Discussion
### Italian olive oil data set  
The principal component scores of the Italian olive oil data set are given in Figure 12. From this plot, it can be seen that objects in the same classes form clusters, but the confidence intervals are overlapped with each other. The prediction accuracies of different ANN models are given in Table 6. SVM and RF models have the highest prediction accuracy of 97.9%. The results calculated by the BNN and RBFCCN models are better than the results calculated by the SCRBFN and RBFN models. The PLS-DA models yield a lower average prediction accuracy of 89.8%. A two-way ANOVA with interaction at a significance level of 0.05 was performed to analyze the sources of variation and prediction accuracies. The results of ANOVA are given in Table 7. Different modeling methods, different source regions and the interaction between the classifiers show significant differences in prediction. The ANOVA results indicate that the methods evaluated have different performances. The SVM and RF perform best among the methods evaluated. The RBFCCN and BNN have statistically better performance in predicting this data set compared to PLS-DA, RBFN and SCRBFN.  
Figure 12: A principal component score plot for the olive oil data set. Each axis is labeled with the percent total variance and the absolute eigenvalue. Each observation of the data set was scaled to [0, 1]. The 95% confidence intervals appear as an ellipse around each class. The sources regions are: (A) Calabria; (B) South Apulia; (C) Inland Sardinia; (D) East Liguria; (E) West Liguria; (F) Umbria.
# High Performance Computing in Satellite SAR Interferometry: A Critical Perspective
## 4 High Performance Computing: Fundamentals Concepts and Models
### Parallel Programming Models for HPC Systems
We also stress that other parallel programming applications exist (e.g., Pthreads, OpenACC); however, they are not considered here for the sake of brevity. The reader can refer to the widely available literature [154; 155; 156; 158; 159; 160; 162; 164; 165]. A final consideration is in order. FORTRAN remains widely used for developing HPC codes, as are C and C++; thus, HPC typically relies on compiled languages. On the contrary, interpreted language (e.g., Python, and IDL), which have a significant impact on fast prototyping productivity indeed, are not recommended for heavy computation because they might be significantly slower in terms of execution time, and they do not generally offer full support for efficient parallel computing. For an exhaustive treatment of the subject, the reader is referred to available excellent literature [154; 155; 156; 158; 159; 160; 162; 164; 165; 166; 167; 168].
# Visual Ranging Based on Object Detection Bounding Box Optimization
###### Abstract
[PERSON] [29] proposed a locating method for small object pedestrians that has a minimum longitudinal error of 3% and a minimum transverse error of 4.5% within 90 m by combining yolov3 with the geometric relation approach. The combination of deep learning object detection algorithms and visual ranging enables faster and more accurate measurement of the distance to the object. However, when most researchers apply this method, they do not consider the situation that the object detection bounding box does not fit the object due to uneven illumination, fuzzy object boundaries and other reasons, and the pixel spacing between the detection bounding box and the object results in ranging errors. In order to address this issue, this study proposes an object detection bounding box optimization method to reduce pixel spacing and increase the degree of fit between the bounding box and the object, hence increasing the range accuracy. In addition, there are few practical methods for measuring objects of unknown size in monocular visual ranging. Therefore, a similar triangle ranging method based on height difference is proposed. This method can measure the distance to objects of unknown size and also represents a new method for measuring monocular visual ranging.
## 3 Modal Identification
### Signal Processing Procedure to Limit the Disturbance of Measurement Noise on Modal Identification
The filter used in the filtfilt function is a 4 th-order lowpass elliptic filter (ellip function in MATLAB(r)) with 0.5 dB of passband ripple, 40 dB of stopband attenuation, and a passband edge spatial frequency of 20 m\({}^{-1}\), which, for mode shapes sampled at nearly 592 m\({}^{-1}\), corresponds to 0.068\(\pi\) rad/sample. Figure 10b shows the mode shape of Mode 6 of the clamped-clamped beam for ten tests after applying this normalization. Finally, the relative standard deviation is calculated. Figure 11 illustrates, in log scale along the y-axis, the relative standard deviation obtained for ten tests before and after the renormalization of four mode shapes of the clamped-clamped beam. Figure 11: Comparison of relative standard deviation before (blue asterisks) and after (red circles) the renormalization of mode shapes obtained for ten tests. Figure 10: Comparison of different normalization criteria on the dispersion obtained for ten tests for mode shape of Mode 6 of the clamped–clamped beam. The number of measurement points to take into account in the calculation of the average and the relative standard deviation of modal damping ratio is also important. The frequency being a more stable modal parameter, the average frequencies are not sensitive to the number of points used to calculate them. Figure 12 shows the evolution of the average modal damping ratio for the first six modes of the clamped-clamped beam according to the number of measurement points taken into account in the calculation. As the information is better when the modal amplitude is greater, the measurement points used for the calculation of frequencies and modal damping ratios are selected based on their modal amplitude using a threshold (equal to 0.5). This analysis is performed on the two data sets obtained from full-field measurements and accelerometers.
## 2 Materials and Methods
### Barrier Properties  
#### 2.5.1 Water Vapour Permeability (WVP)  
The WVP of the films was determined using the ASTM E96-95 40 gravimetric method, using samples conditioned one week in hermetic desiccators at 25 \({}^{\circ}\)C and 53% RH considering the modification proposed by [PERSON], [PERSON], and [PERSON] [18]. The films were chosen based on the absence of physical defects. First, distilled water was incorporated into the [PERSON] permeation cups (3.5 cm diameter, Elcometer 5100/1, Argenteau, Belgium) to expose the film to 100% RH on one side. Then, each cup was placed in a cabinet of balanced relative humidity at 25 \({}^{\circ}\)C, with a fan placed in the upper part of the cup to reduce the resistance to the transport of water vapour, avoiding the stagnant layer effect in this exposure. It should be noted that the RH of the cabinets (53%) was kept constant  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Formulations** & **Starch** & **Glycerol** & **Soy Lecithin** & **LiEO** & **FeEO** & **LaEO** \\ \hline F1 & 0.8000 & 0.2000 & & & \\ F2 li & 0.7407 & 0.1852 & 0.0370 & 0.0370 & \\ F3 fe & 0.7407 & 0.1852 & 0.0370 & & 0.0370 & \\ F4 la & 0.7407 & 0.1852 & 0.0370 & & 0.0370 & \\ F5 lifela & 0.7407 & 0.1852 & 0.0370 & 0.0123 & 0.0123 & 0.0123 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Studied formulations expressed in mass fraction.  
using supersaturated solutions of magnesium nitrate-6-hydrate. The free surface of the film during film formation was exposed to the lowest relative humidity to simulate the actual application of films in high water activity products when stored at intermediate relative humidity. Subsequently, the cups were periodically weighed (0.0001 g). The water vapour transmission (\(WVTR\)) was determined from the slope obtained from the regression analysis of the weight loss data about time, once the state stationary, divided by the area of the film. Considering the \(WVTR\) data, the vapour pressure on the inner surface of the film (p 2) was obtained with Equation (1), proposed by [PERSON] et al. (2018), to correct the effect of the concentration gradients established in the stagnant air space inside the bowl.  
\[WVTR=\frac{PDL_{n}\left[\frac{P-P_{2}}{P-P_{1}}\right]}{RT\ \Delta z} \tag{1}\]  
where \(P\), total pressure (atm); \(D\), the diffusivity of water through the air at 25 \({}^{\circ}\)C (m\({}^{2}\)/s); \(R\), gas law constant (82.057 \(\times\) 10\({}^{-3}\) m\({}^{3}\) atm kmol\({}^{-1}\) K\({}^{-1}\)); \(T\), absolute temperature (K); \(\Delta z\), mean height of the stagnant air gap (m), considering the initial and final \(z\) value; \(P_{1}\), water vapour pressure on the surface of the solution (atm); and \(P_{2}\), corrected water vapour pressure at the inner surface of the film (atm). The water vapour permeability was calculated with Equation (2) as a function of \(P_{2}\) and \(P_{3}\) (pressure on the outer surface of the film in the cabinet). Permeability was achieved by multiplying the permeability by the average film thickness.  
\[Permeance=\frac{WVTR}{P_{2}-P_{3}} \tag{2}\]
## 2 Materials and Methods
### Existing Technologies and Materials Used  
For hardware development, we consider GPS receivers with low consumption and low cost and a PSoC (programmable system on a chip) Cypress microcontroller with very low consumption and with power-down capabilities. The optimization of wireless communication was undoubtedly a top priority, as its effectiveness directly influences the success of the project. Thus, apart from an optimized GSM system, attention will be focused on the study of low-power wide-area network (LPWAN) technology, a wireless transmission system designed to cover long distances at a modest bit rate and create a network of objects communicating with each other with low power consumption, reaching data rates between 0.3 kb/s up to a maximum of 50 kb/per channel, differing from WiFi communication, which uses high bit rates and non-negligible power consumption. Among the LPWAN available standards, we decided to use LoRa technology, a CSS (chirp spread spectrum) proprietary technology that uses free sub-gigahertz frequencies (169 Mhz, 433 Mhz, 868 Mhz, and 915 Mhz) that can reach considerable distances up to 15 km depending on the power used and territorial morphology. The rather wide diffusion of this standard among the various manufacturers allows it to have a low production cost compared to other less established standards. The current range of coverage between neighboring units within the Smart Drifter Cluster is an ongoing area of research in this project, as it is influenced by varying weather and marine conditions. Another component included is an SD flash-memory mass storage device to record data from the same drifter element and data from other drifters in the connected cluster. Possible additional hardware components consist of digital temperature sensors in the SMD package, barometers, and hydrophones.  
For the development of the software that implements the communication and control strategies of the hardware, we opted for a mesh-based architecture with methods of \"dynamic election\" of the \"contact point\" and a solid-state mass storage device for measurement history with a \"dynamic reset\" for transmission management.  
The floating frame has been designed with the aim of minimizing its environmental impact by utilizing bio-degradable materials. It has also been optimized to meet the requirements of buoyancy and drag imposed by the current, according to the specific needs of its intended use (Figure 1). Thus, part of the project is dedicated to R&D aimed at selecting the most suitable bio-degradable material for the support structure of the drifter, considering its use in the marine environment. The material needs to be strong enough to provide sufficient structural support.  
Figure 1: Smart drifter model. (**a**) Standard configuration. (**b**) Photo with two different possible configurations. (**c**) Other possible configurations by assembling the same brick units in different ways (from MDM TEAM s.r.l.—MARTA project).
## 4 Medicinal Effects of _Clitoria terntaea_
### Anti-Diabetic and Hypolipidemic Activity  
In a study by [PERSON] [22], Wistar rats that were given 400 mg of _C. ternatea_ ethanolic leaf extract orally once a day for 28 days had considerably lower blood sugar, insulin, glycosylated hemoglobin, urea, and creatinine levels than the diabetic control group. Additionally, the treated rats had lower levels of the liver enzymes (serum glutamate oxalate transaminase, serum glutamate pyruvate transaminase, lactate dehydrogenase, and alkaline phosphatase) than the diabetic control rats and were similar to the normal control rats. The effects of the _C. ternatea_ extracts on the human glycaemic response and antioxidant capability were tested in another small-scale clinical research involving 15 healthy males. In this experiment, either 1 or 2 g of _C. ternatea_ extract were consumed along with 50 g of sucrose, and the ensuing plasma glucose and insulin levels were controlled. Additionally, after ingesting the extract, the patients' postprandial plasma antioxidant capabilities were increased [1]. The earliest studies for testing the anti-diabetes properties of this flower in rats correlate to research by [PERSON] and [PERSON] in 1990. It was observed that the suppression of the \(\beta\)-galactosidase and \(\alpha\)-glucosidase activities in rats (fed ethanol extracts of flowers for 3 weeks) considerably reduced serum sugar levels in experimentally induced diabetes. The overall conclusion of these results for the above studies was attributed to the presence of rich flavonoids, anthocyanins, and alkaloid content in the extract [23].  
_C. ternatea_'s hypolipidemic activity was tested in rats that had been experimentally developed with hyperlipidemia in yet another study by [PERSON] et al. [24]. The study used rat models of poloxamer 407-induced, hyperlipidemia and diet-induced hyperlipidemia. The hydroalcoholic extracts of _C. ternatea_'s roots and seeds reduced serum total cholesterol, triglycerides, very low-density lipoprotein (VLDL), and low-density lipoprotein (LDL) cholesterol levels significantly when given orally. This correlated directly with the insulin levels in the body due to hyperglycemia. It was attributed to the polyphenol and flavonoid composition in the plant.
## 3 Results and Discussion
### Analysis of PET Degradation
#### 3.2.2 Differential Scanning Calorimetry Analysis  
The PET samples from O04 and O1 subjected to temperature treatments and storage time were used. The interpretation of the results was carried out using DSC STARe software, Version 16.30 (Build 13243). The analysis focused exclusively on the plastics of the containers subjected to A treatments for a period of 3 weeks. This is because, through DSC analysis, the thermal parameters of plastic can be identified, which are mainly altered by processes involving temperature changes. Therefore, the variations that may arise in the plastics as a result of B would be analogous to those observed in this section. Table 10 presents the obtained results after conducting this analysis on the commercial PET of O04 and O1. The data revealed notable changes that were observed among the different samples. Specifically, an increase in the glass transition temperature (Tg) was recorded in both PETs as the treatments became more severe at 40 \({}^{\circ}\)C and 60 \({}^{\circ}\)C. This increase was notable in both cases, with increments of 5.04% and 18.88% for O04, and 3.54% and 14.86% for O1, respectively. Regarding melting temperatures (Tm) and crystallization temperatures (Tc), no differences, as marked in the case of Tg, were observed. Lastly, concerning the percentage of crystallinity (Wc), variations compared to the T0a were recorded in all cases, although they were not significant. Specifically, in the case of PET O04, Wc increased at T3 and T6 treatments, while in the case of PET O1, it increased after the T3 treatment and then decreased below T0a after T6 (Table 10). The results of the Student's _t_-test reveal significant variations between groups for specific parameters, particularly \(\Delta\)Hm and \(\Delta\)Hc, indicating substantial differences in these aspects. Parameters Tg and Tm also show significant differences in certain comparisons, while Tc exhibits fewer significant differences. These variations may reflect substantial differences attributable to the applied treatments. Figure 3 presents the curves obtained during the second heating for PET O04 and PET O1. The first heating was discarded because it tends to reflect the thermal history of the material, which could introduce biases in the results. It is during the second heating that the influence of the thermomechanical history of PET is eliminated, revealing the true
# Breath Analysis: A Promising Tool for Disease Diagnosis--The Role of Sensors
## 2 The Role of Nanomaterials
### Hybrid Materials  
The combination of distinctive nanomaterials so as to form hybrid materials is extensively investigated for disease diagnosis via gas-sensing, as previously discussed. The combination of different nanomaterials improves sensing selectivity and sensitivity [7; 13; 38] due to their synergic action, rendering hybrid materials particularly promising [49]. Combinations of CNTs with MNPs, 2D materials with MOS or MNPs as well as MNPs [41; 71], MOS NPs, or CNTs with polymers are only some of the reported combinations [7]. Particularly polymers are commonly used for achieving chemical selectivity. Concerning (semi-)conducting polymers (e.g., polyaniline, polypyrrole, polythiophene), conductivity changes can be attributed to electrically-active analyte adsorption, due to redox interactions with either the backbone or the incorporated particles in the case of composites [50]. Non-conducting polymers are also used as sensing films, incorporating conductive NPs such as CNTs [50] or carbon black particles [72] for semi-selective analyte absorption; this leads to mass/conductivity changes of the polymeric nano-composites. Finally, molecularly imprinted polymers (MIPs) are a new class of sensing films in which artificial analyte-specific cavities have been created, for specific molecular recognition [73; 74].