esa-sceva/satcom-chunk-collection huggingface dataset:
# Resource Management and Secure Data Exchange for Mobile Sensors Using Ethereum Blockchain
## 4 Methodology
### Validation of MS Identity
In contrast, this further verifies that the trust score \(s(tr_{j})\) is higher than the cut-off, followed by successfully generating a response message to validate the sensor \(s_{j}\). However, there remains a likelihood of intrusive activity despite completing the enrollment procedure for the mobile sensors. To alleviate this issue, communicating mobile sensors must possess individual trust scores before disseminating the contents among the users. The flowchart of the implemented validation is shown in Figure 4.  
The computed trust score must be forwarded to the AP residing near the mobile sensors. By adopting the proposed consensus mechanism, an Ethereum blockchain was formed by considering the trust score for storage. The computation of the trust score \(Tr\) is mathematically expressed by Equation (7). \[Tr_{p}=\rho_{1}.h_{6}+\rho_{2}.h_{7}+\rho_{3}.h_{8} \tag{7}\]  
Figure 4: Flowchart of adopted validation. In Equation (7), the evaluation of the primary trust score \(Tr_{p}\) is performed using two sets of attributes, \((\rho_{1},\rho_{2},\rho_{3})\) and \((h_{6},h_{7},h_{8})\). The attributes \(\rho_{1}\), \(\rho_{2}\), and \(\rho_{3}\) represent the state of the communication channel, the current state of the mobile sensor, and the penalty for outlier data, respectively. The attribute \(h_{6}\) represents an accomplished number of communications divided by the summation of accomplished and failed data transmissions. Attribute \(h_{7}\) represents original sourced data divided by an accomplished number of communications, while attribute \(h_{8}\) represents outlier data divided by an accomplished number of communications. However, as mentioned earlier, the computation of the trust score is for two mobile sensors in the direct communication range with each other. Otherwise, the mobile sensor \(s_{j}\) is required to compute the trust score values obtained by any other mobile sensor directly communicating with the target mobile sensor. The computation of the secondary trust score is mathematically expressed by Equation (8). \[Tr_{s}=h_{9}/\eta \tag{8}\]  
In Equation (8), the secondary trust score \(Tr_{s}\) is calculated by dividing attribute \(h_{9}\) by variable \(\eta\) representing the number of mobile sensors. The attribute \(h_{9}\) represents the summed-up values of all the mobile sensors (1, 2,... \(\eta\)). Hence, the final trust score is computed by considering both primary trust \(Tr_{p}\) and secondary trust \(Tr_{s}\), as shown by Equation (9). \[\Omega(p,q):h_{1}\to h_{2}+h_{3} \tag{3}\]0  
Equation (9) computes the primary trust score. The primary trust is calculated when two mobile sensors are in direct communication. In contrast, the nodes must depend on the secondary trust calculation if they are not in direct communication.
## 4 Cryptographic Analysis
### Number of Sample Change Rate  
The number of the sample change rate (NSCR) is a test that determines the quality of encryption algorithms. The test compares the corresponding original sample values and encrypted audio file and shows the difference in percentage. NSCR can be calculated as follows:  
\[NSCR=\frac{\sum_{i=1}^{N}{D_{i}}}{N}x100\% \tag{12}\]  
where:  
\[D_{i}=\left\{{\begin{array}{*{20}{c}}{1,x_{i}\
eq y_{i}}\\ {0,otherwise}\end{array}}\right. \tag{13}\]  
In Equation (12), corresponding sample values of the plain and encrypted files are \(x_{i}\) and \(y_{i}\) and the total number of samples is N. The obtained test results are shown in Table 2. The numbers are similar to the results of other audio encryption schemes [5,8,27].  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Audio File** & **File Size** & **File Length** & **Corellation Coefficient** \\ \hline usb-headset-weird & 200 kb & 2.32 s & \(-\)0.0248023 \\ cartoon-mumble-speak & 298.1 kb & 3.45 s & 0.0008942 \\ grito-wav & 385.3 kb & 4.47 s & 0.0167187 \\ a-strange-dream & 495 kb & 5.74 s & \(-\)0.0376280 \\ radio & 691 kb & 8.02 s & 0.0222683 \\ insult & 832 kb & 9.66 s & 0.0157122 \\ sharpening-blade-long-blade & 1228.8 kb & 14.48 s & \(-\)0.0211372 \\ airport-security-message & 1740.8 kb & 20.28 s & \(-\)0.0335765 \\ shakuhachi-grave-5 & 2048 kb & 23.41 s & \(-\)0.0068954 \\ Ref. [5] & - & 7 s & 0.0233000 \\ Ref. [8] & 2.33 mb & 13.85 s & 0.0004710 \\ Ref. [27] & - & - & 0.0000900 \\ AES [27] & - & - & 0.0097100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Correlation between plain and encrypted audio files.
## 5 Discussion
In this study, the analysis of the impact of the flying altitude found that the foliage profiles derived from different flying altitudes showed little difference, and they were all close to each other. The error analyses also found that the flying altitude affected the foliage profile and total _LAI_ estimation very little, and the effects could be ignored. This phenomenon might be due to the opposite effects for the return energy profile derived from the increase of the footprint size and the decrease of the pulse density. Therefore, the flying altitude can affect the return energy, but the prediction error of the foliage profile and the total _LAI_ do not change much. This result is in agreement with the findings of [PERSON] et al. [44]  
Several studies have explored the influence of pulse density on forest parameter estimations [45; 46; 47]. These studies found that the precision of the canopy height, diameter at breast height, and stand volume estimations were all insensitive to the reduction in pulse density until the pulse density dropped below one pulse/m\({}^{2}\)[45; 46]. When the pulse density was smaller, the return energy of the whole plot was smaller. Therefore, the pulse density could significantly affect the return energy profile of the plot. In this study, the analysis of the sensitivity of the foliage profile estimation to pulse density found that the foliage profiles derived from the different pulse densities were all close to each other, and the _RMSE_s and _Diff_s were all very small. Therefore, the effect of pulse density on foliage profile retrieval was weak and could be ignored. Of course, the pulse densities used in this study were only from 0.25 pulse/m\({}^{2}\) to eight pulses/m\({}^{2}\), which might not be sufficient to draw these conclusions. A larger range of pulse densities and other forest types need to be studied in further research. Although the return energy profiles with different pulse densities were obviously different, the foliage profile was essentially constant. This was because the foliage profile was derived from the GORT model, which was based on the gap probability.
## 2 Materials And Methods
### Experimental Site
#### 2.2.1 Meteorological and Flux Data  
Atmospheric measurements were collected by the flux tower using the 3D sonic anemometer (Wind Master Pro; Gill Instruments, Hampshire, UK) measuring wind and air temperature components, a gas analyzer (LI7500, LI-COR Inc., Lincoln, NE, USA) measuring the H\({}_{2}\)O and CO\({}_{2}\) concentration sampled at a 10 Hz. Additionally measured were air temperature and relative humidity with a thermo-hygrometer (HMP155, Vaisala, Finland) and precipitation with a rainfall sensor (TR525 USW, Texas Electronics, TX, USA), and net radiation and short-wave incident radiation sensors (CNR4, Kipp & Zonen, Delft, The Netherlands). All measurements occurred at 3 m height. Soil heat flux was measured with soil heat plates (HFP01, Hukseflux Thermal Sensors B.V., Delft, The Netherlands) placed at 0.10 m depth and soil water content was measured using water content reflectometers (CS 616, Campbell Scientific Inc., Logan, UT, USA) at a depth of 0.10 m.  
The forcing data were gap filled. Missing data occurred due to power outages and equipment malfunctions. Missing _ws_, _Tair_, _Prec_, _SWin_, and \(RH\) were filled directly with data collected from the Automatic Weather Station--AWS (OMM: 86977) located approximately 3.8 km away from the experimental site. After this procedure, gaps remain in _Prec_ (0.05%) and _ws_ (0.03%) data were filled through the application of the inverse distance weighted interpolation method [48] using data of neighboring AWSs positioned within a 200 km radius of Santa Maria. For incident long wave radiation gaps (7.32%), we follow [PERSON] et al. (2020) (manuscript in preparation).  
The turbulent fluxes, latent (\(LE\)) and sensible (\(H\)) heat fluxes, and CO\({}_{2}\) flux were estimated by Eddy Covariance (EC) method [49] using _EddyPro_ software version 6.1 (Li-Cor, Lincoln, NE, USA), with the same configurations as [46] (30-min average from high frequency data \(-10\) Hz). No gap filling was used in turbulent fluxes data.  
The energy balance (EB) closure (\(Rn-G=LE+H\)) was used to filter the fluxes. The energy balance ratio (\(EBR\)) given by the ratio between the cumulative sum of available energy (\(Rn-G\)) and turbulent fluxes (\(LE+H\)):  
\[EBR=\frac{\Sigma(LE+H)}{\Sigma(R_{n}-G)} \tag{34}\]  
Fluxes of lower (upper) values of the 1% (99%) percentile for the given time step were excluded from the analyses. Flux data with \"poor\" BE closure values [25; 50; 51] were removed. In this case, the inequality written is as  
\[1-\delta\leq|EBR|\leq 1+\delta \tag{35}\]  
where \(\delta=0.2\), must be satisfied. Thus, we only consider values with daily energy component sums measured by the EC method of within 80% of the energy balance measured from radiative instruments and from the heat flux of the soil. The daily averages of turbulent fluxes were calculated considering the days on which at least 19 hourly measurements were recorded (at least 80% of the measures).
## 1 Real-Time Protozoa Detection from Microscopic Imaging Using YOLOv4 Algorithm
In [24], a segmentation-driven RetinaNet system was based on region-based convolutional neural networks (RCNN). Due to the insufficient data, augmentation was made with image enhancement methods for 69 images and 117 samples for eight species. However, this caused inaccurate detections. The mAP for the average precision of segmentation hierarchical retina was 93%. They implemented random horizontal flip augmentation to prevent facing local minimums. In [25], as an object detection algorithm, single short detection (SSD) and Faster R-CNN methods were used. In the study, there were 643 images with 750 \(\times\) 750 dimensions. A faster R-CNN method consists of forming region predictions, and then CNN is applied to classify object type and the location of bounding box. The method has a region of interest to determine features and to categorize objects. The network is built on ResNet50 and ResNet101. As a performance evaluation metric, mAP is 94.48% for ResNet101, with a 42.8 ms inference time. And single shot detector (SSD) is implemented for multiple object detection. SSD is selected due to quick extraction and mobile use. The network consolidates predictions from multiple feature maps with varying resolutions to naturally determine objects of different sizes. In contrast to methods demanding object proposals, SSD eliminates the need for generating proposals and subsequent pixel or feature recalibrations because it handles all calculations in a single network. As a performance evaluation metric, mAP is 83.97% for InceptionV2, with a 2.24 ms inference time. Apart from these methods, in [26], a hybrid method was applied to detect and classify intestinal parasites. In their study, there were 15 different types of parasites located in images from different parasite database groups. The dataset contains 12.225 images separated into 40% for training, 30% for validation, and 30% for testing. The study consisted of two sections. One of them was manual feature extraction (DS1) and support vector classification. In the second part, VGG-16 based on deep neural network was used for image feature extraction and classification (DS2). The hybrid model consisted of DS1 and DS2. The efficiency was not at the desired level and the complexity needed to be simplified. In [27], the authors applied U-Net, a fully convolutional network model for leishmaniasisisparasite segmentation. A total of 37 images belong to the leishmaniasis dataset, with five classes used in this work. As a performance evaluation metric, the average precision value for five classes is 75.32%. In [24], CNN-based UNet and Unet++ were implemented with 954 microscopic images with 1536 \(\times\) 1536 resolution for backillus anthracis bacteria. They reached a recall of 98% and a precision of 87% for the whole test images. Table 1 shows a summary of some articles published in the last decade regarding the detection of protozoa, cholera pathogen, malaria parasite, intestinal protozoa.
# A Comprehensive Review of Sensor-Based Smart Building Monitoring and Data Gathering Techniques
## 3 Smart Building Monitoring Applications
### Energy Management
Additionally, monitoring the behaviors and habits of building occupants is imperative, as they constitute the primary energy consumers. By discerning occupant energy utilization patterns, it becomes feasible to adapt energy consumption to meet the preferences of occupants while concurrently curbing overall energy usage. Energy usage adjustments can be made based on physical environmental parameters (temperature, lighting, and humidity) and occupant preferences by creating their personalized comfort profile (e.g., by monitoring it with sensors or surveys) [35].
# Marine Diesel Engine Fault Detection Based on Xilinx ZYNQ SoC
## 3 Results
### Convolutional Neural Network in the PL of ZYNQ
#### 3.2.1 Data Processing and Model Building  
We simulated a dataset consisting of 2200 data points to identify five types of fault states. The data were converted into a three-dimensional matrix of size (11 \(\times\) 23 \(\times\) 200) and then input into the convolutional neural network (CNN) model. The model was trained using 70% randomly selected data as the training set and 30% as the validation set.  
In this program, we employed three convolutional layers, each followed by normalization, activation, and pooling operations. The three convolutional layers used 16, 32, and 64 filters of size 22, respectively. The choice of filter size affects computational complexity, and using 22 filters instead of 3 \(\times\) 3 helps simplify computations, thus reducing hardware stress. Batch normalization, a technique to accelerate neural network training and improve performance, was utilized to address gradient vanishing or exploding issues and also acts as a regularization method, reducing model complexity and enhancing generalization ability.  
After the convolutional layers, the data flow into fully connected layers. The first fully connected layer in this program consists of 50 neurons with ReLU activation function, and the final layer comprises one neuron to produce the ultimate result.  
The diagram in Figure 10 illustrates the convolutional neural network model employed in this program.  
Figure 10: Convolutional neural network model.  
Figure 9: Data processing flowchart.
## Methods
### Data Processing
#### 3.1.1 Extraterrestrial Solar Radiation (ESR)  
Solar radiation carried from space to the top of the atmosphere is called ESR. ESR plays an important role for meteorological parameters and can be estimated using the coordinates of the area, Julian day, and local standard time, as follows [27; 28]:  
\[R_{a}=\frac{12\times 60}{\pi}G_{SC}d_{r}((\omega_{2}-\omega_{1})\sin\varphi\sin \delta+\cos\varphi\cos\delta(\sin\omega_{2}-\sin\omega_{1})), \tag{1}\]  
\[\omega_{1}=\omega-\frac{\pi t_{1}}{24}, \tag{2}\]  
\[\omega_{2}=\omega+\frac{\pi t_{1}}{24}, \tag{3}\]  
\[d_{r}=1+0.033\cos\frac{2\pi}{365}J, \tag{4}\]  
\[\delta=0.409\sin\left(\frac{2\pi}{365}J-1.39\right), \tag{5}\]  
\[\omega=\frac{\pi}{12}[(t+0.06667(L_{z}-L_{m})+S_{c})-12], \tag{6}\]  
\[S_{c}=0.1645\sin 2b-0.1255\cos b-0.025\sin b, \tag{7}\]  
\[b=\frac{2\pi(J-81)}{364}, \tag{8}\]  
where \(R_{a}\) and \(G_{SC}\) denote ESR (in MJ M\({}^{-2}\)) and the total solar irradiance, respectively; \(\omega_{1}\), \(\omega_{2}\), and \(\omega\) indicate the solar time angle, an angular measure derived from the Earth's rotation on the polar axis, at beginning, end, and midpoint of the period (in rad), respectively; \(d_{r}\) and \(J\) represent the inverse relative Earth-Sun distance and Julian day, respectively; \(t\) and \(t_{1}\) refer to the standard time at the midpoint of the period and the length of the period, respectively; \(\varphi\) and \(\delta\) are latitude and solar declination (in rad), respectively; \(L_{z}\), \(L_{m}\), and \(S_{c}\) refer to the longitude of the local time zone, the latitude of the measurement site, and the seasonal correction for solar time, respectively; \(b\) indicates the parameter for seasonal variation of solar time. Solar time angle is related to the midpoint of the period corrected by the difference in longitude between the measurement site and local time zone; the longitude of the local time zone indicates the location of the sun at the zenith based on the local standard time. Because ESR indicates the solar radiation incident on the top of the atmosphere, it should be physically greater than or equal to 0. The calculation of and information regarding each parameter are detailed in [PERSON] et al. [27].
# Unravelling the Molecular Regulation Mechanisms of Slow Ripening Trait in _Prunus persica_
## 3 Discussion
### Abscisic Acid and Jasmotic Acid Production Results  
The phytohormone abscisic acid (ABA) is an isoprenoid [43] with reported roles in embryogenesis and seed maturation [44], seed dormancy and germination [45], and adaptation to abiotic stress [46; 47]. Previous studies have reported a correlation between the ABA and GA hormonal pathways during seed development. During late embryogenesis, ABA promoted seed germination, blocking the embryo growth by counteracting the function of GA [48]. Transcript accumulation differences were observed in all genes related to the ABA biosynthetic pathway at the ripening stage (T7). These results suggest less ABA production in SR individuals at T7. In addition, since \(\beta\)-carotene is an intermediate substrate in the ABA biosynthetic pathway, our results suggest that there may also be a reduction of the accumulation of \(\beta\)-carotene in SR individuals. The molecular mechanism by which the absence of \(NAC072\) at T2 affects ABA production at T7 remains unclear.  
On the other hand, jasmotic acid (JA) is an endogenous plant hormone responsible for the plant response to biotic and abiotic stress [49]. JA is also involved in fruit ripening, pollen survival, root growth, and plant response to injury [50; 51]. JA is derived from the octadecanoid pathway by forming a 12-oxophyllotonic acid (OPTDA), a precursor of JA [52; 53]. Previous research reported that OPDA and JA act independently to promote different plant responses [54]. The enzymes described as 12-oxophyllotonicoate reductase (OPR) have a key role in regulating OPDA/JA accumulation by using OPDA to promote JA biosynthesis [55]. In this study, the expression differences in several OPRs at ripening were identified between the NR and SR phenotypes. These results suggest a differential OPDA or JA accumulation and differentially signaling responses between the NR and SR phenotypes, but experiments such as exogenous applications of OPDA or JA in slow ripening individuals are necessary to confirm this hypothesis.  
The results obtained in this research suggest lower ABA and JA production in SR individuals only in late stages of fruit development (T7; ripening) supported by the lesser transcript accumulation of genes related to these hormone biosynthetic pathways (Figure 3). Therefore, it is possible that the differences related to ethylene, ABA, and JA in T7 do not have a direct correlation with the candidate gene _NAC072_. This observation was derived; since _NAC072_ showed a peak of expression in T2, we believe that it causes an arrest of the normal development of the peach fruit. Considering that ethylene, ABA and JA are hormones with an essential function in normal fruit ripening, and that they have been widely studied in the later stages of its development, it is possible that the observed hormonal differences were a consequence of the effect of _NAC072_ in peach development, and not a direct regulation of _NAC072_.
## 3 Results and Discussion  
### Moisture Content  
In the first step, the study was conducted to measure the moisture content of black-currant powder samples, which differed in the proportion and type of carrier (Figure 2). The purpose of the analysis was to compare the carriers between the samples used in this research action. In relation to previous research associated with raspberry powders [23; 26], it was confirmed that carriers with a lower proportion in the fruit solution resulted in the lowest moisture content in the fruit powders. It was observed that raspberry powders enriched with inulin (IN) achieved equally low moisture levels in the current study of currant powders. As a result, blackcurrant powders as well as raspberry powders [26] enriched with inulin (IN) have one of the lowest water contents compared to other blackcurrant powder samples containing other carriers. The current studies also noted that microcrystalline cellulose (C) and fiber (F) achieved low moisture content in the samples. With the perspective of preserving these products for a longer shelf life, designing fruit powders with an inulin carrier for blackcurrant as well as raspberry seems to be a more favorable endeavor. The current research confirms unequivocally that blackcurrant powders with gum arabic (GA) achieve the highest moisture content in the samples, regardless of the carrier content in the fruit solution. However, within the framework of food preservation and the method of storage for a longer period of time, the drying of fruit powders with gum arabic seems to be a less effective operation [27; 28; 29]. The reason for the high moisture  
Figure 1: MLPN generation procedure based on machine learning.  
content of gum arabic was determined by its properties. In the current study of blackcurrant powders with a maltodextrin carrier, comparable moisture content results were obtained to those for raspberry powders [23; 26]. It has been confirmed that the difference in moisture content change for maltodextrin (MD) of between 50% and 30% with the participation of this carrier is the largest.