esa-sceva/satcom-chunk-collection huggingface dataset:
# OpticalTrust: A Sensor-to-Blockchain Framework Using Free-Space Optical Communication
###### Abstract  
In the dynamic landscape of the tech industry, the escalating requirement for swift and secure data transmission has catalyzed innovation in integrated communication systems. Free-Space Optics (FSOs) has emerged as a promising contender in optical communications. While conventional optical fiber systems can achieve bit rates of up to 40 Gbps with proper design, they are limited primarily by electronics rather than semiconductor laser capabilities. This study presents an integrated framework that combines FSOs, blockchain technology, and sensor networks to address challenges in data transmission, security, and environmental adaptation. This study analyzes FSOs system performance through the Quality (Q) Factor and Bit Error Rate (BER), comparing systems with and without Erbium-Doped Fiber Amplifiers (EDFAs) across various bit rates (8, 12, 16, and 20 Gbps) and transmission distances (5-25 km). To enhance data security and reliability, a blockchain architecture is incorporated with smart contracts and an InterPlanetary File System (IPFS) for storing and validating results generated from FSOs simulation. Additionally, this study explores the design of sensor network models for FSOs technology by investigating how distributed sensor arrays can be theoretically integrated with FSOs systems, with testing focused on FSOs performance and blockchain implementation.  
sensor network; free-space optical communication; BER; OptiSystem; EDFA; blockchain; interplanetary file system (IPFS); smart contract +
Footnote †: journal: Sensors  
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Footnote †: journal: Sensors  
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Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Materials and Methods
### Design and Manufacturing
#### 3.1.2 Central Processing Unit (CPU)  
The CPU is the subsystem that administrates the smart sensor. A commercial development board WEMOS-D1-R1 was employed because it met all the sensor requirements, including power supply, input and output pins, microprocessor capabilities, and wireless-communication characteristics.  
The Arduino UNO integrated development environment (IDE) was employed to program WEMOS-D1-R1 CPU. This open-source, cross-platform application was compatible with WEMOS-D1-R1, and supported all the necessary features for reading sensor data (programming the 10-bit resolution analog-to-digital converter (ADC), sampling frequencies up to 9600 Hz, and input-pin configuration), and for the communication with the wireless ESP8266 module for data transmission to the cloud.  
For safety reasons, the CPU and its connections were placed in protective housing designed with 3D Autodesk Inventor CAD software. The case was divided into 2 parts, base and top, which were joined and secured by screws. Moreover, fixed headers were added to the case to easily interconnect the CPU with the other modules. The connection between the CPU and the energy and harvesting subsystems, which is explained in the following subsection, was a 3-pin cable, providing \(-5\) V, electrical ground, and \(+5\) V. For the sensor connection, the case had a 6-pin interface with the previously mentioned electrical signals and 3 analog inputs (A0, A1, and A2). The CAD design and the physical circuits are shown in Figure 8.  
Figure 8: Central processing unit (CPU) subsystem and communication interfaces. (**a**) Isometric CAD view; (**b**) manufactured circuit.  
Figure 7: (**a**) Exploded isometric view of computer-aided design (CAD). (**b**) Prototype with all the signal-conditioning elements.  
The next task was to manufacture the protective case with PLA material in a 3D printer and to assemble the components. The WEMOS board was secured with 4 screws attached to the bottom face of the case that were locked with metallic nuts. The communication interfaces were set inside the case according to the array of headers established during the case-design phase.
# An Improved Approach to DNS Covert Channel Detection Based on DBM-ENSec
## 3 Methodology
### DNS Covert Channel Detection Module
Input Vector Transformation: Each element of the input sequence is transformed through a linear transformation into three distinct vector spaces: Query, Key, and Value. 2. Calculate Attention Score: The dot product between the query vectors and the key vectors is computed, subsequently scaled, and normalized, typically employing the softmax function, to derive the attention score. The precise formula is presented in Equation (4). \[\text{Attention}(Q,K,V)=\text{softmax}\left(\frac{QK^{T}}{\sqrt{d_{k}}}\right)V, \tag{4}\]  
where \(d_{k}\) represents the dimension of the key vector, which is used to scale the result of the dot product. 3. Weighted Summing: The attention scores are employed to weight and aggregate the value vectors, resulting in the creation of a new representation. The computational process of the self-attention mechanism is illustrated in Figure 4:0  
(5) Flatten Layer:  
The Flatten layer is specifically designed to transform a multi-dimensional feature map into a one-dimensional vector, which is commonly employed in the final stage of a convolutional neural network. The feature maps generated by convolutional or other feature extraction layers are often multidimensional; however, classifiers require one-dimensional vectors as input. The Flatten layer facilitates this transformation, thereby enabling subsequent classifiers to effectively process the extracted features. (6) ENSec:  
The classifier utilized in this study is an ensemble learning model developed by integrating four distinct machine learning algorithms through a combinatorial strategy. The algorithms employed include Random Forest [24], XGBoost (eXtreme Gradient Boosting) [25], LightGBM (Light Gradient Boosting Machine) [26], and CatBoost (Categorical Boosting). The operation of each sub-model can be summarized as follows: It initially predicts DNS traffic, yielding four prediction values (0 or 1). These values are subsequently utilized to compute a decision score, with the corresponding F1 scores of the models serving as weights. The decision score is then compared against a predetermined threshold to ascertain whether the traffic is classified as a DCC. This approach amalgamates the  
Figure 4:1 Calculation process of the self-attention mechanism. prediction outcomes of the models along with their performance weights to enhance the accuracy and reliability of the final decision. The formula for calculating the decision score is presented in Equation (5). Figure 4:2  
where \(Fi\) represents the F1 score of each model and \(di\) denotes the prediction made by each model for the given DNS data, with a prediction of 1 indicating the presence of a DCC and 0 otherwise.
## 3 Results and Discussion
### Base Pressure Coefficient  
The base pressure coefficient, defined as the pressure difference in the wake of the cylinder, is an important component of the drag [4; 6; 8]. Figure 7 shows the variation of the base pressure coefficient \(\overline{C}_{pb}\) with \(Re\) at different height of models. The stronger free end effect of M2 resulting in \(\overline{C}_{pb}\) at all heights was smaller than that of M1. The value of \(\overline{C}_{pb}\) for M1 increased at \(Re\) = 1.4 \(\times\) 10\({}^{5}\)-2.2 \(\times\) 10\({}^{5}\), but gradually decreased at the \(Re\) = 2.2 \(\times\) 10\({}^{5}\)-4.7 \(\times\) 10\({}^{5}\) due to the narrower wake flow in the critical regime. In cases where the model height was below 0.5 H, the value of \(\overline{C}_{pb}\) increased with height, while at 0.7 H and 0.91 H, there was a sharp decrease after \(Re\) = 3.4 \(\times\) 10\({}^{5}\). As for the M2 model, the value of \(\overline{C}_{pb}\) decreased at \(Re\) = 1.5 \(\times\) 10\({}^{5}\)-2.7 \(\times\) 10\({}^{5}\), then showed a slight upward trend; it also increased with a height below 0.5 H. It can be deduced that the flow around low-aspect-ratio cylinders is not uniform along the height, which is consistent with [PERSON]'s [19] research.  
Figure 8 depicts the influence of wind fields. For the M1 model, the variation of \(\overline{C}_{pb}\) in T1 was completely different from the other wind fields, and peaked at \(Re\) = 2.2 \(\times\) 10\({}^{5}\). In the T2 and T3 wind fields, the change tended to decrease before \(Re\) = 2.2 \(\times\) 10\({}^{5}\), then increased to become consistent; however, a continuous increase depending on \(Re\) was observed in the T4 wind field. For the M2 model, due to the stronger free end effect, the value of \(\overline{C}_{pb}\) was  
Figure 6: The distribution of the wind pressure coefficient at 0.5 H of models under different wind fields. (**a**) M1; (**b**) M2.  
Figure 7: The variation of the base pressure coefficient with \(Re\) at different height of models.  
smaller than M1, and the variation in the four wind fields was not as great as that of M1. In general, the value of \(\overline{C}_{pb}\) also tends to be a higher \(Re\) state with increasing turbulence.
## 3 Conclusions  
Previous studies on particle motion characteristics and causes of spool valve hysteresis provide scientific references and theoretical guidance for this paper. In this study, we established a systematical mechanical model of spool clamping force under the action of particles, and measured the pulsating curve of spool clamping under the action of particles in different materials, sizes, and volume concentrations. By fitting the test data, we worked out the empirical equation of datum clamping force and run out so as to provide a theoretical basis and experimental reference to similar studies. The mathematical model and empirical equations generated in this study can be used to estimate the sudden failure caused by the sudden increase in friction in spool valves under the action of particles, which has positive significance for the study of the predictive maintenance of hydraulic equipment. In this study, we come to several conclusions as below:  
1. The clamping force of spool triggered by solid particles can be considered as a stacking effect of each tiny particle, which is mainly correlated with the material, size, and concentration of particles. However, because of the rotation of particles and varying posture of the spool, the clamping force presents a pattern of aperiodic pulsation; the cause of spool pulsation is the rotation of the particles and changes in the spool postures.
2. After the incursion of particles into the valve clearance, the second action of spools will lead to spool clamping as the spool posture has changed. The imperfect posture of a spool in a valve hole is mainly off-centered, tilt, or off-centered + tilt. The different postures bring about clamping risk zones in the valve clearance.
3. The kinds of particles have a certain but limited effect on the spool clamping force. In general, particles with a larger elastic modulus give rise to a stronger spool clamping force, and also give rise to pulsation frequency. This accords with the physical model that we employed in this study.
4. A larger volume fraction of particles in hydraulic oil implies an increasing number of sensitive particles and a stronger spool clamping force thus engendered. Spools bear a considerably increased risk of clamping when the particle volume concentration is higher than the \"sensitive concentration\" of 5%, and the clamping force increases rapidly in the manner of a quadratic curve with concentration. When the concentration is 9%, the maximum clamping force can reach 21.5 N, which will lead to a higher risk of spool clamping.  
Figure 16: The mean value, maximum value, and pulsation comparison of clamping forces in oils containing different particle sizes.  
Within a certain size range, the probability density distribution of particle size conforms to the normal distribution function. The \"sensitive particles\" are proven to take up 0.7-1 of the valve clearances. When the ratio of particle size to clearance size is larger than 0.7, the clamping force increases rapidly in the manner of a quadratic curve. When the ratio is 0.9, the maximum clamping force can reach 15.98 N, which may lead to a higher risk of spool clamping.
Sensors_**2013**, _13_, 10191-10218; doi:10.3390/s130810191  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Digital Video Broadcast-Terrestrial (DVB-T) Single Frequency Networks Positioning in Dynamic Scenarios**  
**[PERSON] *, [PERSON] and [PERSON]**  
Politecnico di Torino, Corso Duca degli Abruzzi 24, Turin 10129, Italy;  
E-Mails: [EMAIL_ADDRESS] (L.L.P.); [EMAIL_ADDRESS] (R.G.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +39-011-090-4033; Fax: +39-011-090-4171.  
_Received: 5 July 2013; in revised form: 30 July 2013 / Accepted: 31 July 2013 / Published: 9 August 2013_  
**Abstract:** Since Global Navigation Satellite Systems (GNSS) show degraded performance in dense urban and indoor areas, a positioning sensor based on Digital Video Broadcast-Terrestrial (DVB-T) systems is presented in this paper. DVB-T signals can be considered as signals-of-opportunity for positioning, due to their good properties. One of the challenges to overcome is to distinguish the signals from different emitters. Here, we suppose that the user can first compute his position by GNSS during an initialization phase, which is used for solving all the ambiguities concerning DVB-T emitters. Starting from there, DVB-T signals can be used for aiding positioning when the user enters a GNSS-blocked area, up to a limit case, where all the GNSS satellites are not in view and only DVB-T signals are used for positioning. We tested this method by simulation, by adopting the Hata model for the emitter attenuations and the Rayleigh model for multipath. The obtained results show good performance if the receiver correctly associates the signal to the user's motion.  
**Keywords:** DVB-T; signal-of-opportunity; GNSS; positioning
# P D N: A Priori Dictionary Network for Fashion Parsing
## 3 Experiments
### CFPD and UTFPR-SBD3 Datasets
#### 3.1.2 Results  
For a fair comparison, we used the same measures (Acc and mIoU) reported by [5] to evaluate the parsing quality of the CFPD dataset. The experimental results are reported in Table 1. It can be seen that the accuracy and IoU of our model achieve 99.26% and 78.09%, respectively. Among a series of SOTA methods, EPYNET achieves the best performance, and our method further improves the accuracy by 5.8% and mIoU by 31.2% compared to EPYNET.  
Compared with the CFPD dataset, UTFR-SBD3 is a more balanced dataset, and it has higher-quality annotations. We evaluated our model on the test set of UTFR-SBD3, and the PDN achieves 99.39% accuracy and 81.00% mIoU, respectively, which are 18.1% and 15.4% higher than the EPYNET. The results show that the higher quality of annotations can help our model show a better performance. Figure 6 shows the confusion matrixes of the PDN and EPYNET on UTFR-SBD3, and we found that our model not only achieves a better overall performance but also more accurate predictions for all categories.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Method** & **Accuracy** & **mIoU** \\ \hline
[31] & Color-Fashion & - & 42.10 \\
[21] & Superpixel Parsing & 88.69 & - \\
[13] & Weak Structural & 93.06 & 53.51 \\
[37] & Locality Aware & 93.52 & 53.00 \\
[38] & Feature Pyramid & 93.82 & 54.39 \\
[5] & EPYNET & 93.83 & 59.52 \\
[32] & PaperDoll & 89.89 & - \\
[39] & ATR & 98.67 & - \\  & PDN (ours) & 99.26 & 73.99 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of experiments on CFPD dataset.  
In Figure 7, a comparative visualization of the PDN algorithm on the UTFR-SBD3 dataset is presented. The first column displays input images, the second column shows the corresponding ground truth, the third column exhibits the segmentation results obtained using the EPYNET method, and the fourth column showcases the segmentation results obtained using the PDN method.  
Figure 6: The confusion matrix of PDN results on the UTFR-SBD3 dataset, in which the left and right are the result of EPYNET and PDN, respectively.  
Figure 7: Samples of PDN results. The first column includes original images from UTFR-SBD3 dataset, the second column includes ground truth, the third column includes the results of EPYNET, and the fourth column includes the results of PDN.  
It can be observed that the PDN is more effective in recognizing correct categories and shapes. In the first set of images, the PDN successfully identifies the category of the skirt, whereas EPYNET misclassifies it. In the second and third sets of images, EPYNET segments the lower garment into two parts, namely, dress and pants, whereas the PDN closely approximates the ground truth. In the fourth set of images, EPYNET fails to completely segment the shoes, whereas the PDN achieves a complete segmentation result. These significant outcomes stem from the multi-scale architecture and attention modules designed within the PDN.
# A Methodology to Evaluate Standards and Platforms within Cyber Threat Intelligence
## 4 Standards Evaluation Results
### Standards Selected for Analysis
#### 4.1.2 Trusted Automated Exchange of Intelligence Information  
TAXII is an application layer protocol created by MITRE that defines a set of services to exchange TI information messages between organizations [50]. It was projected specifically for the transport of information formatted in the STIX language but is not limited to it. TAXII uses Hyper Text Transfer Protocol Secure (HTTPS) as the transport protocol and supports different sharing models, including _hub-and-spoke_, P2P and _publish-subscribe_.
# Satellite-Based Estimation of Carbon Dioxide Budget in Tropical Peatland Ecosystems
## Materials and Methods
### Methods
#### 2.3.2 Ecosystem Respiration  
\(R_{e}\) was calculated by using Equations (4)-(6). These equations were determined from observational data in a previous study [25] and the data distribution was visualized (Figure 3). Flux tower observations measure the total carbon budget at heights more than 40 m above the land surface. The eddy covariance method is used to determine \(R_{e}\) and the GPP by basically calculating daytime and nighttime carbon fluxes. In the flux tower sites of this study, observed half-hourly nighttime net ecosystem CO\({}_{2}\) exchange (NEE) was used as nighttime \(R_{e}\). A look-up-table (LUT) method combining environmental factors (e.g., soil moisture below 5 cm and GWL) was applied to model nighttime \(R_{e}\). For the next step, nighttime \(R_{e}\) was extrapolated to daytime \(R_{e}\) by using the established model. The estimated \(R_{e}\) was used to infer the daytime GPP by combining an LUT with PAR [25]. Finally, \(R_{e}\) in this study represents the CO\({}_{2}\) emissions of the biological activity of plants including soil respiration [25, 51]. By using Equations (4)-(6) for _UF_, _DF_, and _BF_, the \(R_{e}\) values from whole tropical peatlands in Indonesia and Borneo of Malaysia were simulated. Furthermore, we discussed the potential impacts of peatlands conditions  
Figure 2: Flowchart of this study. As CO\({}_{2}\) fluxes emit to the atmosphere, ecosystem respiration (\(R_{e}\)), and fire emissions (FE) were calculated with groundwater table (_GWT_) and moderate resolution imaging spectroradiometer (MODIS) fire products, respectively. As a CO\({}_{2}\) flux from the atmosphere into plants, the gross primary production (GPP) of the MODIS product was used. Especially, the MODIS GPP was calibrated via flux tower observation in Palangkaraya in Indonesia. Finally, the net biosphere exchange (NBE) was calculated by balancing \(R_{e}\), FE, and GPP. Dashed rectangles indicate corresponding sections in this paper.  
that had undergone drainage and fires. Finally, we used the equation of \(R_{e}\) (_UF_) (Equation (4)) when the \(R_{e}\) values of the whole peatland area were calculated, assuming that all peatlands from the GFW (reference map) were intact (natural) forest (e.g., evergreen broadleaf forest (for more land cover types information of _DF_ and _BF_, refer to Section 2.2.2)). Notably, we assumed that all peatland regions in this study were dense forest since a relevant dataset for describing the spatial distribution of disturbance levels (drainages, fires, and post-fire regrowth) was not available.  
\[R_{eUF}=-0.864(GWT)^{2}-3.5652(GWT)+10.449 \tag{4}\]  
\[R_{eDF}=0.6851(GWT)^{2}-0.5641(GWT)+10.029 \tag{5}\]  
\[R_{eBF}=-4.7917(GWT)^{3}-5.9058(GWT)^{2}-1.9878(GWT)+5.2006 \tag{6}\]
## 3 Results and Discussion
### Results of 2D CFD Modeling of the Liquid-Gas Ejector
#### 3.2.1 The Impact of Ejector's Outlet Pressure on the Removal of CO\({}_{2}\)  
The impacts of ejector outlet pressure from 0 to 2000 Pag on the removal of CO\({}_{2}\) have been depicted in Figure 14. Removal percentages were determined using Equation (33). To calculate the mass fraction of carbon dioxide (Y\({}_{\text{CO2}}\)), according to Figure 15, the ejector is divided into different parts, and the average mass fraction of each part is calculated.  
\[\%\;CO_{2}removal=\frac{Y_{i,CO_{2}}-Y_{f,\;CO_{2}}}{Y_{i,CO_{2}}}\times 100 \tag{33}\]  
where \(Y_{i,CO_{2}}\) and \(Y_{f,\;CO_{2}}\) are the initial and final mass fraction of CO\({}_{2}\) in the gas phase along the ejector, respectively. As illustrated in Figure 14, with increasing pressure from 0 to 2000 Pag, the removal of CO\({}_{2}\) enhances from 83% to 95%. According to Figure 4b in Section 3.1.1, by boosting the outlet pressure in the ejector, the gas entrainment rate decreases. Thus, less gas enters the ejector, and therefore, removal of CO\({}_{2}\) enhances. Additionally, the mass fraction contour of CO\({}_{2}\) in the gas phase along the ejector for outlet pressure from 0 to 2000 Pag is shown in Figure 16a,b, respectively, which confirms the decreasing trend of carbon dioxide along the ejector.  
Figure 13: UDF flow chart for the \(\dot{m}_{G\to L}\) maximization.  
Figure 16: The mass fraction contour of CO\({}_{2}\) in the gas phase along the ejector for (**a**) 0 Pag output pressure and (**b**) 2000 Pag output pressure.  
Figure 14: Impacts of ejector outlet pressure on removal of CO\({}_{2}\) (d\({}_{\text{nozzle}}\) = 6 mm-Q\({}_{\text{L}}\) = 2.85 \(\times\) 10\({}^{-4}\) m\({}^{3}\)/s, [MEA] = 30%Wt).  
Figure 15: Ejector segmentation to calculate the average mass fraction in each part.
# FDCNet: Frontend-Backend Fusion Dilated Network Through Channel-Attention Mechanism
## 3 Our Proposed Method--FDCNet
### The Loss Function
#### 3.4.1 The Euclidean Loss Function  
The Euclidean loss function is used to measure the difference between the output and the corresponding ground truth at pixel level. The definition is as follows:  
\[L_{E}(\Theta)=\frac{1}{N}\sum\
olimits_{i=1}^{N}\left\|F_{d}(I_{i};\Theta)-D_ {i}\right\|^{2} \tag{4}\]  
where \(\Theta\) denotes a set of the network parameters, and \(N\) is the number of training samples. \(F_{d}(I_{i};\Theta)\) represents the output estimated density map of the input image \(I_{i}\) with parameters \(\Theta\), while \(D_{i}\) is the corresponding ground truth density map.