esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fast Calibration Method for Sensors of Atmospheric Detection System
## 4 Evaluation
### Dynamic Model Parameter Setting  
A large number of practical experiments have proved that the ethanol temperature changes every \(\Delta T_{1}=10\)\({}^{\circ}\)C, the ethanol to achieve temperature stability of the regulation time is stable in \(t_{a}=120\) s, and the sampling interval is \(\Delta t=0.1\) s. Combined with Equation (3), can be obtained from the transfer function of the ethanol temperature chamber as  
\[G_{1}(z)=\frac{3.328\times 10^{-3}}{z-0.997}. \tag{17}\]  
In this paper, the Tube-Air-ADS system is modeled and quantified by COMSOL Multiphysics. The boundary conditions are obtained and differential equations are solved by using the laminar flow and fluid heat transfer modules. The transient processes of temperature and velocity fields in the glass tube are studied. The material parameters of glass, air, and ethanol from the COMSOL internal material library are used and the thermal conductivity \(k_{foam}=0.03\) W\(/(\mathrm{m}\cdot\mathrm{K})\), density \(\rho_{foam}=60\) kg\(/\mathrm{m}^{3}\), and constant pressure heat capacity \(c_{p_{-foam}}=200\) J\(/(\mathrm{kg}\cdot\mathrm{K})\) of the foam are set according to the real system dimensions. Figure 5 shows the established FEA geometry model and the distribution of temperature field \(\mathbf{T}(z,r,t)\) and velocity field \(\mathbf{u}(z,r,t)\) at \(t=20\) s and \(t=200\) s after the beginning of the simulation. As the simulation progresses, the distribution of temperature and velocity fields gradually tends to the steady state.  
The simulation time step \(\Delta t\) is set to 0.1 s and simulation time to 200 s. As shown in Figure 6, a step temperature input is given to the Tube-Air-ADS system at 10 s of the simulation, i.e., the ethanol temperature outside the glass tube is changed by 10 \({}^{\circ}\)C from the steady state.  
Figure 5: (**a**) Temperature field distribution in the glass tube at \(t=20\) s and \(t=200\) s. (**b**) Velocity field distribution in the glass tube at \(t=20\) s and \(t=200\) s.  
The S-K iterative method mentioned in Equation (11) is used to fit the temperature dynamic response of the Tube-Air-ADS system obtained from the finite element analysis. \(G_{2}(z)\) has one zero and two poles, and the sampling time \(\Delta t=0.1\)s of the system is set in the fitting process. The accuracy of the curve fit, as shown in Figure 6, is 96.29%. The dynamic transfer function of the ADS system is as follows:  
\[G_{2}(z)=\frac{3.758\times 10^{-4}z^{-1}}{1-1.955z^{-1}+0.955z^{-2}}. \tag{18}\]
## 5 Demonstration Use-Case  
The MESS system was deployed in a pilot cyber-physical production and logistics system which was dedicated to research, education, training, and demonstrations in the academic, industrial, and public sectors [55]. In this _learning factory_, the goal was to verify and validate the MESS system and demonstrate its main services and facilities. The scenarios in the demonstration use-case were rather straightforward by design, in order to clearly exhibit the following features of the MESS: (1) interlinking of heterogeneous resources both (2) of production and of internal logistics; (3) inclusion of physical resources and their full-fledged digital twins; (4) integration of both legacy and state-of-the-art I4.0 ready, as well as (5) of fully automated and human-assisted system components; (6) and demonstration of their easy interoperability. Here, the emphasis is on the fact that some of the integrated elements--such as an assembly line [56], human-robot collaborative workcells [57], or a small fleet of AGVs [58]--were complete, complex CPSs themselves, accompanied by their specific, custom-tailored digital twins.
# A Data-Driven Deep Learning Framework for Prediction of Traffic Crashes at Road Intersections
## 5 Performance Evaluation
### Performance Evaluation of RoadInTCP
In real-world New York City traffic datasets from 2014 to the first half of 2016, the _RoadInTCP_ model has an improvement ratio, compared with the Best Baseline (marked by *), ranging from 3.634% to 5.591%.
# Wireless Mesh Networking: An IoT-Oriented Perspective Survey on Relevant Technologies
## 7 Ongoing and Future Developments
### Smart Agriculture
Concerning the fields spread on a wide area and cultivated with different types of plants and fruit trees, one can imagine a deployment where each cultivation is monitored through different sensors connected to a processing device, in turn equipped with an IEEE 802.15.4 radio transceiver. In this way, the information collected in each cultivation can be transmitted across the IEEE 802.15.4 mesh network, while being captured by \"intermediate\" gateways placed among the rows (e.g., among the rows of vines). In detail, an intermediate gateway may also play the role of a weather station for the surrounding area, through which it is possible to retrieve weather conditions in different  
Figure 13: General view of the target farm considered in a smart agriculture use case, composed by the farmer’s gateway (blue icon), an agricultural vegetable garden (marker 1), a botany greenhouse (marker 2), a livestock stable (marker 3), various fields (markers 4), and several weather monitoring stations (weather icons). cultyated fields (via several weather sensors placed at different heights). Hence, this weather station is equipped with: (i) an IEEE 802.15.4 radio interface, being part of that mesh network; (ii) a LoRa transceiver, for transmitting the collected information through the LoRa-based mesh network to the smart gateway placed at the farmer's house; and (iii) a Wi-Fi radio interface, to enable a mobile node (e.g., an Unmanned Aerial Vehicle, UAV, a mobile smartphone, etc.) to retrieve weather information directly from the weather station. Finally, an animal can be equipped with a wearable device for monitoring its health, through different indicators and following its daily walks (e.g., through a GPS transceiver), thus making this information available to the farmer, collecting it through a long-range LoRa-based mesh network.
## 2 Problem Formulation
### Resource Allocation Model Based on Platform and User Satisfaction
The above equations are also calculated under the constraints in Equations (9)-(11). Users expect to improve the time and price effectiveness of resources, so we also propose the concept of user satisfaction. Equation (8) is extended as follows:  
\[\text{max}F_{u}\ =\ k_{t}\times\mu_{t}+k_{p}\times\mu_{p}, \tag{16}\]  
\[\mu_{t}\ =\ \left\{ \begin{array}{l}0,\ \sigma_{t}<a_{t}\\ \\ \frac{\sigma_{t}-a_{t}}{b_{ft}-a_{ft}},\ a_{t}\leq\sigma_{t}\leq b_{t}\\ \\ 1,\ \sigma_{t}>b_{t}\end{array} \right., \tag{17}\]  
subject to  
\[\mu_{p}\ =\ \left\{ \begin{array}{l}0,\ \sigma_{p}<a_{p}\\ \\ \frac{\sigma_{p}-a_{p}}{b_{p}-a_{p}},\ a_{p}\leq\sigma_{p}\leq b_{p}\\ \\ 1,\ \sigma_{p}>b_{p}\end{array} \right., \tag{18}\]  
where \(F_{u}\) denotes user satisfaction, \(a_{t}\) and \(b_{t}\) are the lowest and highest user expectations of the time benefit evaluation, respectively, and \(a_{p}\) and \(b_{p}\) are the lowest and highest user expectations of the price benefit evaluation, respectively. The above equations are also subject to the constraint conditions in Equations (9)-(11). Users want to meet their maintenance time and price requirements at the same time, ensuring that user satisfaction is as high as possible. However, the cloud manufacturing platform should also consider both maintenance time and cost, to ensure the benefits of the cloud manufacturing platform. Therefore, the comprehensive benefit evaluation function is expressed by the following formula, which is the fitness function that will be solved for in the following section:  
\[\text{max}fitness\ =\ \tau\times F_{p}+(1-\tau)\times F_{u} \tag{19}\]where \(\tau\) is the weight adjustment coefficient.
# Predicting the Gap in the Day-Ahead and Real-Time Market Prices Leveraging Exogenous Weather Data
###### Abstract
In [27], the researchers used the Generative Adversarial Network (GAN)-based video prediction approach on market data from ISO NE to predict RTM LMPs. The market data images are created from the historical data, and by the concatenation of these images, a video stream is created. Consequently, the prediction of the next frame is used to predict the next-hour RTM LMP. The proposed method achieved approximately an 11% MAPE score. However, weather data sets are not utilized to enhance the prediction model of the price spikes. The enhanced convolutional neural networks are also used in [28] to predict electricity load and prices. Here, feature selection is carried out using the Random Forest model, and the extracted features are passed to the convolutional layer, which later is filtered using the max pooling layer. The showcased work resulted in smaller error measurements than the SVR using NYISO market data. Leveraging the LSTM network to predict the gap between RTM and DAM prices using weather features brings the following question to mind: Can we improve the prediction of the price gap across the DAM and RTM by leveraging exogenous information (e.g., weather data, including solar irradiance)? The contributions of this paper are summarized as follows:  
1. [noitemsep,topsep=0 pt]
2. Syncing the exogenous information on weather data with the electricity market information, i.e., prices and demand data, to create an extensive dataset. The significance of leveraging the external dataset is illustrated, and the importance of features is also demonstrated. 3. Both the DAM and RTM are analyzed for price prediction. A realistic set of assumptions is made regarding the availability of features for both the RTM and DAM once the prices are predicted 24-36 h in advance for the following market operation day upon the clearing of the market. 4. The ensemble learning method, namely the Random Forest (RF), is used to calculate the probability distribution of the predicted market prices for the DAM and RTM, as well as the gap. 5. An LSTM architecture is deployed to enhance predictions given the complexity of predicting values for the time series dataset. The proposed model is compared with other statistical machine learning methods, which demonstrate significant improvements. The rest of the paper is organized as follows. The learning methods used and the LSTM network are discussed in Section 2: Learning Methods. The metrics deployed to evaluate the performance of learning methods are presented in Section 3. The data collection and data cleansing procedures are detailed in Section 4: Data. The performance of the proposed method is evaluated in Section 5. The paper is concluded in Section 6.
## 1 Introduction
By integrating multiple types of data during a fault, multimodal features are extracted and used for diagnosis. This approach significantly enhances the efficiency of fault diagnosis, making it easier for users to manage their devices. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Fault Type** & **Description** & **Occurrence Frequency (\%)** \\ \hline Battery Issues & Shortened battery life or inability to charge & 20–30\% \\ Screen Problems & Display malfunctions or touch sensitivity loss & 15–20\% \\ Overheating & Device overheating, leading to performance drop & 10–15\% \\ Software Crashes & Frequent crashes of system or applications & 10–12\% \\ Power Supply Issues & Malfunctions in charger or power management & 8–10\% \\ Audio Problems & Speaker or headphone jack malfunctions & 5–8\% \\ Mechanical Failures & Buttons or switches becoming unresponsive & 5–7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The occurrence frequency of the most common faults in electronic devices. Figure 1: Diagram of latent faults in consumer electronics. 2. We design a fault text generation model based on a multimodal knowledge graph specifically for consumer electronics. By integrating a semi-supervised multimodal graph convolutional network, the model improves the ability to capture correlations between fault data, ensuring the accuracy of the generated fault diagnosis text. 3. Experimental results show that our model outperforms baseline models in F1 score and accuracy. The rest of the paper is organized as follows: Section 2 reviews related work, Section 3 describes the model's architecture and methods, Section 4 details the experiments and results analysis, Section 5 provides a summary, and Section 6 outlines future work.
## 3 Methods
### Spectral Reflectance  
The hyperspectral sensor records spectral radiance which depends on several factors including illumination, atmospheric effects, the geometry of the object, and the sensor's characteristics. Controlling these factors will require a lot of effort, time, capital, and sometimes it is physically impossible [35]. To remove these effects from the acquired radiance data spectral, reflectance needs to be calculated. Spectral reflectance is defined as the ratio of reflected incident light energy [36]. The incident light is measured after reflection from a reference material of known reflectance. As the reference material is placed with samples, it is safe to assume that the reflected energy from the reference material has also experienced the same effects as the energy which is reflected from samples. For the sensor's characteristics response, frames were captured while keeping the shutter closed. Therefore, these acquired frames provided information regarding the sensitivity of the camera at each wavelength and electrical noise. Using these references and encoded image radiance, a linear equation can be derived relating radiance to reflectance for each spectral band:  
\[R=\frac{R_{r}-B}{W-B} \tag{1}\]  
where \(R\) is the reflectance of the data cube; \(R_{r}\) is the radiance captured of a given sample; and \(B\) and \(W\) are the frames captured for dark and white reference, respectively. The same process was repeated for each spectral band to obtain the complete reflectance spectra of the sample. This method of calculating reflectance from encoded radiance is referred to as the Empirical Line Method (ELM) [36]. The slope of the line gauge the multiplicative radiance while the radiance intercept represents the camera's offset. The calculated reflectance is referred to as apparent reflectance because it does not account for the topography of the sample.
# Optimization of Optical Networks Based on CDC-ROADM Technology
## 1 Introduction
Thus only a colorless, directionless architecture combined with contentionless functionality is the end goal of any network operator that has deployed or is planning to deploy ultimate level of flexibility in the optical layer [9] especially together with flexible wavelength assignment [4; 10]. Further, the advantages of CDC-ROADMs enable operators to offer a flexible service and provide significant savings in Operational Expenditure (OpEx) and Capital Expenditure (CapEx). OpEx reductions are delivered primarily by means of touchless provisioning and activation of network bandwidth. Finally, it is noted that another important problem arises from the need for manual provisioning in C-ROADM systems since it makes them vulnerable to a possibility of human errors. Obviously, with CDC-ROADMs, the risk resulting from human error is significantly reduced because of fully automated provisioning. During the last decade, the attention of the telecommunication community has been concentrated on Routing and Wavelength Assignment (RWA) and Routing and Spectrum Allocation (RSA) problems. Consequently, numerous exact and heuristic methods are now available to solve RWA and RSA problems in static [9; 10; 11] and dynamic [12; 13; 14] environment. However, so far the relevance of the inner optical node architecture in the DWDM network optimization has not been fully considered. In [15] a detailed node cost model has been presented. However, the authors did not focus on optimization. There are works dealing with single aspects of the node architecture optimization problem [16]. However, the complete picture has not yet been presented. This study concentrates on the optical node structure and its relevance in DWDM network resources optimization by taking into account various types of transponders, multiplexers (filters, which differ for various technologies)  
Figure 2: Schematic diagram of conventional CDC-ROADM. In this particular 3-degree CDC-ROADM architecture it is possible to add/drop 3 wavelengths \(\lambda_{1}\) (green color) at the same mux/demux module, scheduled for 3 different directions: West, North and East. and wavelength selective switches [3]. The problem is formulated using Mixed Integer Programming (MIP). A comparative study of C-ROADM and CDC-ROADM node network is performed. Network topologies are realistic and represent optical DWDM networks from selected countries [17]. Traffic demands are typical for DWDM networks and are represented using a traffic matrix. The rest of the paper is organized as follows. After introduction, in the second section the model details for various technologies are provided. In the third section a discussion of the results is provided, which is followed by a concise summary given in the last section.
## 3 Results  
### Image Acquisition and Deflectometry  
The following system parameters result from the geometry and measurements performed:  
* Resolution X-direction: 3.97 \(\upmu\)m per pixel, at 4096 pixel per line
* Resolution Y-direction: 9.42 \(\upmu\)m per pixel, at 3335 pixel per rotation  
To be able to test the system practically, a rolling element was specifically deformed. For this purpose, the rolling element was clamped together with a metal ball in a vice with moderate force. The result is a hemispherical impression on the rolling element surface. This rolling element was recorded by the system and the defect location was cut out by software. The size of the cutout is 1.3 mm by 1.8 mm. The following figure shows the results of the deflectometric process. First, the unprocessed image of the rolling element surface can be seen in Figure 4a. Next to this, in Figure 4b, the calculated gradient field is shown in the form of a normal map.  
In the section of the defect location in Figure 4a, the image acquisition structure in the form of horizontal lines can be seen clearly. These lines are created by recording each line of the surface with three different illumination patterns. In addition, spatial brightness distribution can be seen in the form of shadows cast into the defect area. The question at this point is whether the shadowing leads to a loss of information in this area. After the deflectometric evaluation, which is shown in Figure 4b in the form of a normal map, the spherical shape of the defect can be seen easily. The areas around it have an almost flat surface. Only at the top of the defect can a bright edge be seen, which is probably due to shading, and thus to information loss at this point. Nevertheless, the deflectometric evaluation looks very promising.
# A Visual Object Tracking Algorithm Based on Improved TLD
## 3 The Improved TLD Tracking Algorithm
### HOG Descriptors  
HOG is proposed to describe visual objects robustly, based on calculating well-normalized local histograms of image gradient orientations in a dense grid, as shown in Figure 7. To obtain the HOG of an object, the visual object is primarily divided into small connected regions called cell units. Then, the gradient or edge direction of each pixel is calculated in the unit. Finally, the collected histogram information is combined to obtain the HOG descriptors [9]. The following equations relate to the calculation of the pixel gradient:  
\[G_{x}(x,y)=H(x+1,y)-H(x-1,y) \tag{2}\]  
Figure 6: The execution process of the proposed tracking algorithm.  
\[G_{y}(x,y)=H(x,y+1)-H(x,y-1) \tag{3}\] \[A(x,y)=\sqrt{G_{x}(x,y)^{2}+G_{y}(x,y)^{2}}\] (4) \[\theta(x,y)=\arctan(G_{x}(x,y)/G_{y}(x,y)) \tag{5}\]  
where \(G_{x}(x,y)\) denotes the horizontal gradient, \(G_{y}(x,y)\) denotes the vertical gradient, \(A(x,y)\) is the gradient intensity, and \(\theta(x,y)\) represents the gradient direction.  
In each frame, HOG descriptors are obtained and applied to update the filter template of KCF. Compared with scattering points, HOG is easy to calculate and not sensitive to illumination changes or motion blur. In addition, HOG is obtained by fine orientation cells and their combination from larger regions. HOG descriptors can maintain robustness to a degree if slight plastic deformation or occlusion happens [9].
## 3 Physical Layer Design
### Low-Power Transceivers  
Inter-chip wireless links require extremely low energy consumption to be competitive with high-speed serial I/O and other emerging technologies such as photonic interconnects. The power consumption in data transmission over wireless medium occurs at the transmitters and receivers. The choice of the transceiver is dependent on the choice of the physical layer and modulation technique. In the mm-wave physical layer designs, most research on wireless interconnects adopt simple modulation techniques such non-coherent On-Off-Keying (OOK) or Amplitude Shift Keying (ASK). Non-coherent modulations eliminate power-hungry, high-frequency carrier recovery circuits such as Phase Locked Loops (PLLs) resulting in low power consumption on the transceivers. A few on-chip mm-wave transceivers have been proposed for wireless interconnects in intra-chip NoCs [27; 30; 62]. Mm-wave transceivers have the advantage of being CMOS process compatible while providing high enough data rates for chip-to-chip interconnections. In [63], a 60 GHz Binary Phase Shift Keying (BPSK) transceiver is proposed. However, being a coherent modulation scheme, it requires carrier recovery circuitry resulting in relatively high-power consumption. Higher mm-wave carrier frequencies can potentially provide higher data rates. A 20 Gbps OOK transceiver operating at 260 GHz with a bit energy consumption of 58.65 pJ/bit is designed in 65 nm technology [64]. Due to the low voltage headroom in advanced technology nodes, the power output of the transmitter would be limited, therefore, most mm-wave transceivers discussed here are designed in 90 or 65 nm nodes. However, Fully Depleted Silicon on Insulator (FD-SOI) processes in advanced nodes such as 45 nm or 28 nm can provide higher power-performance trade-offs due to faster switching transistors [65]. In the next section, we present a non-coherent OOK transceiver designed at 45 nm for inter-chip wireless communication in the 60 GHz band.