esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Model Assessment  
For the performance analysis of three methods (MLR, SVR, RF), we apply a five-cross-validation method (\(KFolds=5\)). In order to verify the effectiveness of three methods applied in Sentinel-1 data (VH, VV, VH and VV polarizations), the retrieved rice height and biomass are compared with ground truth measurements. The model assessments are validated through correlation coefficient (\(R^{2}\)) and Root Mean Square Error (RMSE):  
\[RMSE=\sqrt{\frac{1}{n}\Sigma_{i=1}^{n}(Y_{i}-X_{i})^{2}} \tag{2}\]  
\[R^{2}=\frac{\Sigma_{i=1}^{n}[(Y_{i}-\bar{Y}_{i})(X_{i}-\bar{X}_{i})]}{\sqrt{ \Sigma_{i=1}^{n}(Y_{i}-\bar{Y}_{i})^{2}}\times\sqrt{\Sigma_{i=1}^{n}(X_{i}- \bar{X}_{i})^{2}}} \tag{3}\]  
where \(Y_{i}\) and \(\bar{Y}_{i}\) are the estimated variables and their mean values, \(X_{i}\) and \(\bar{X}_{i}\) are the ground truth measurement variables and their mean, \(n\) is the number of data. A good retrieval result contains a low RMSE value and a high correlation coefficient.  
To select the best model among different machine learning methods, we also use Akaike's Information Criterion (AIC) and Bayesian Information Criterion (BIC) [PERSON] (1965).
## 5 Link Budget for Wireless Communication  
We develop a link budget for an out-to-in body scenario to check for communication between the Tx placed in free space and the Rx placed in the human body. Such a scenario is important in a hospital where a patient with an on- or in-body implant can transmit/receive data to/from a device placed outside the body. For carrying out the link budget calculations, the Tx is placed at a distance of 1 m from the the human body, and the Rx is placed at a distance of 20 mm in the layered medium equivalent to the frontal thorax region of Duke (example taken from Section 2.3; refer to Figure 9). Our goal here is to check if communication is possible between the Tx and Rx when they are separated by a distance of 102 cm. The parameters selected for developing the link budget are listed in Table 4. The link budget is developed at a frequency of 2.45 GHz for an input power of 25 \(\mu\)W (from the European Research Council (ERC) limitation [5]). If the carrier \(C\) to noise \(N_{0}\) ratio, \(C/N_{0}\) of the link between the Tx and the Rx exceeds the required \(C/N_{0}\), then wireless communication is possible between the Tx and the Rx.  
\[Link\;C/N_{0}=P_{t}-L_{tx\;feed}+G_{t}-L_{t}-L_{rx\;feed}-N_{0} \tag{7}\] \[Required\;C/N_{0}=\left(E_{b}/N_{0}\right)+10\;\log_{10}(B_{r})- G_{c}+G_{d}\] (8) \[L_{f}=10\log_{10}\left(\frac{4\pi d}{\lambda}\right)^{2}(dB) \tag{9}\]  
\[N_{0}=10\log_{10}\left(k\right)+10\log_{10}\left(T_{i}\right)(dB/Hz) \tag{10}\]  
\[T_{i}=T_{0}\left(NF-1\right)(K) \tag{11}\]  
By the use of Equations (7) and (8), the link \(C/N_{0}\) and required \(C/N_{0}\) can be calculated, respectively (for the notations, see Table 4). \(PL_{Model}\) takes into account path loss, calculated by the simplified model, occurring in the body, and it also consists of the antenna gain; thus, the \(G_{r}\) is not added separately in  
Figure 9: Configuration to calculate the link budget for the in-to-out body scenario.  
Equation (7). \(PL_{Sim}\) is the path loss from the FDTD simulation. The link budget calculation takes the example of the scenario of the human frontal thorax discussed in Section 4.3. From the Table 4, it is calculated that the link \(C/N_{0}\) exceeds the required \(C/N_{0}\), for a distance of 102 cm (a distance of 1 m between the Tx and the medium and 2 cm between the start of the medium and the Rx) in the case of both the model, as well as the simulation; thus, wireless communication is possible.
## 2 Background
### Blockchain Technology  
Blockchain is one of the most promising, disruptive and revolutionary technologies. From the technical point of view, a blockchain is a decentralized, distributed digital ledger of economic transactions, which are managed by a cluster of computers and is a perfect example of a democratized system [10]. Blockchain consists of a chain of consecutive blocks of transactions. It is a decentralized, peer-to-peer (P2P) network, and is distributed in nature. It consists of many computers connected together, called nodes, and each node contains a copy of the digital ledger. Blockchain is characterized by consensus, distributed computation, immutability, and authentication. Validating new blocks is followed by a set of protocols and consensus from every participant of the network. The records are stored in a linear chain. Pointers and linked list data structures are used in blockchain for the block representation. Blocks are arranged in sequence and lined with each other, using a linked list. Pointers are used to point the location of the next block. A block is a collection of data that stores transaction details, such as the timestamp and link to the previous block, which is generated by a secure hash algorithm. Each block contains two parts: a block header and block body. We can identify four main categories of blockchain:
* **Public.** A public blockchain is non-restrictive and permissionless, which means that anyone with internet access can join the network. Public blockchains are considered to be \"trustless\", which means that people participating in the blockchain do not need to trust each other. The main example of a public blockchain is Bitcoin.
* **Private.** A private blockchain works like a closed network, under the control of a single entity. Those who want to join need to be accepted. Private blockchains typically are used inside a company or organization, and they are also known as permissioned blockchains. The organization can decide who can read the blockchain, submit transactions to it, and participate to the consensus process.
* **Hybrid.** As the name explains, a hybrid blockchain is a type of blockchain that combines elements of both private and public blockchains. Usually, this kind of blockchain is run by a single company that grants access to any user who satisfies pre-established criteria.
* **Consortium.** Known also as a federated blockchain, it is similar to the hybrid blockchain because it has private and public features. The main difference is that a consortium blockchain is a private blockchain with limited access to a particular group, eliminating the risk that just one entity takes control of the network. Indeed, the consensus process is controlled by a preselected group of corporations, for example. Consortium blockchains are considered permissioned blockchains, and they are suited for use in business.
## 6 Results  
In this section, the developed prediction model was tested by inputting random data to validate its performance. As shown in Table 4, three cases with arbitrary sea states were selected and fed into the trained prediction model. The input file consisted of three cross spectra, each having values at each frequency. Therefore, when a prediction was made, the result was outputted for each frequency. Since data were generated at intervals of 0.05 rad/s from 0.2 to 1.8 rad/s, a total of 34 predicted values were produced for each prediction.  
To derive a single predicted result, the 34 values needed to be processed. Figure 11 presents a comparison between the wave height and wave period predicted by the CVNN model and the actual target values. In Figure 11a, for the actual value of \(H_{s}\) = 1.5 m, the predicted results are distributed between a minimum of 1.48 m and a maximum of 1.77 m. To find the region where the data are most concentrated, the range between the minimum and maximum values was divided into five intervals. The interval with the highest concentration of predicted values was selected, and the mean of the values within that interval was adopted as the final prediction. As a result, the predicted value in Figure 11a is 1.62 m, with a 7.4% error compared to the actual value.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Case Number** & \(H_{s}\) **(m)** & \(T_{z}\) **(s)** & \(\theta\) (\({}^{\circ}\))** \\ \hline Case 1 & 1.5 & 3.5 & 30 \\ \hline Case 2 & 2.0 & 4.5 & 150 \\ \hline Case 3 & 2.5 & 5.5 & 270 \\ \hline \end{tabular}
\end{table}
Table 4: Wave conditions for the prediction model test.  
Figure 10: Prediction model performance during training.  
blue circles indicate the distribution of predicted wave directions. Using the same method applied to wave height and wave period, a single prediction value was calculated.  
The error rates for each result are summarized in Table 5. As a result, the maximum error was found to be below 10%, with most predictions having an error of less than 5%. Specifically, for wave direction, the error was consistently below 2%, demonstrating excellent prediction accuracy. The error tends to be lower when the wave height is higher and the wave period is longer, as the prediction values are more closely aligned with the range of the training data. On the other hand, when the values are closer to the edges of the training data range, the accuracy may decrease. This issue can be addressed by expanding the range of data used for training. Although the predictions were made using pseudo-measurement data obtained through numerical simulations, it is expected that high-quality results could be achieved if actual measurement data are used, provided that a high quality of data is ensured.  
Figure 12: Distribution of predicted wave directions compared to target values. (**a**) Case 1, direction. (**b**) Case 2, direction. (**c**) Case 3, direction.
# An AC/DC LED Driver with Unity Power Factor and Soft Switching
## 3 Design Equation  
### Buck-Boost Converter Equations  
It is known that the maximum value of the buck-boost inductor current happens at the time when the input-line voltage is at the peak point of the sinusoidal waveform. For ensuring DCM operation at the peak, the following inequality should be satisfied.  
\[V_{dc}>\frac{DV_{m}}{1-D}, \tag{13}\]  
where \(D\) is the duty ratio of the active switch. If Equation (13) is satisfied, the buck-boost converter can operate at DCM within the whole cycle of the input-line voltage.  
For an AC/DC buck-boost converter operating at DCM, the input-line current and the input power can be expressed as Equations (14) and (15), respectively [15].  
\[i_{in}(t)=\frac{V_{m}D^{2}}{2L_{p}f_{s}}sin(2\pi f_{L}t), \tag{14}\]  
\[P_{in}=\frac{V_{m}{}^{2}D^{2}}{4L_{p}f_{s}}, \tag{15}\]  
As shown in Equation (14), the input-line current is purely sinusoidal and in phase with the input-line voltage. It means that low THDi and high PF can be achieved by operating the buck-boost converter at DCM. From Equation (15), the output power can be expressed as  
\[P_{0}=\eta P_{in}=\frac{\eta{V_{m}}^{2}D^{2}}{4L_{p}f_{s}}, \tag{16}\]  
where \(\eta\) represents the energy-conversion efficiency of the proposed circuit.
## 4 Simulation and Experimental Results Analysis
### Simulation Analysis of the Compensation System Response
Because the purpose of the compensation system is to help the generator through the speed regulation period, the compensation system will exit the dynamic regulation process when the generator speed returns to the rated speed. After the compensation process, the inverter output active and reactive power is equal to zero when the generator returns to a steady state. This idea of treating impact load as a dynamic disturbance achieves a fast response of the load power. Figure 7a-c shows the power response waveforms of the generator, the compensation system, and the load, respectively. Comparing Figure 7a,b, the compensated system output power before 2 s is equal to zero when the system is in a steady state. The output power of the generator is equal to the line impedance and the no-load loss. When time is equal to 2 s, Figure 7a shows a smooth and slow rise in the output power of the generator, and Figure 7b shows a surge in the output power of the compensation system. This phenomenon means that the compensation system achieves high power discharge in an instant. This result also reveals the selection of super capacitor as the energy source of the compensation system in this paper. Meanwhile, combined with Figure 7a,b, it can be seen that compensation power comes from two components in this instantaneous, and Figure 7b shows that the most important is from the compensation system. When time is equal to 5 s, Figure 7a,b indicates that the compensation system is gradually withdraw  
Figure 4: Angular frequency adaptive algorithm response waveform. process. The compensation system is fully withdrawn until the engine reaches rated speed. Figure 7a-c demonstrates that the proposed NAMIMO controller effectively achieves the compensation of impact load. Figure 7d shows the power waveform of the compensation and the natural gas generation system. Figure 7d shows that the compensation system allows the impact load to achieve power balance. Note that although the impact load is active power, there is also a brief fluctuation in reactive power. Equation (7) explains active and reactive power are coupled in the compensation system. This also displays that direct power control is closer to the nature of the whole system. Figure 5: Voltage and current waveform of the load (**a**) With compensation system, (**b**) Moment of loading with compensation system, (**c**) Without compensation system, (**d**) Moment of loading without compensation system. Figure 6: Compensation system voltage and current waveform.
# Effect of the Partitioning of Diffuse and Direct APAR on GPP Estimation
## 4 Discussion
### Uncertainties from LUE Simplifications  
Due to of the lack of LUE observations under both direct and diffuse radiation conditions, LUE calculated from the EC-LUE model was assumed as LUE under clear-sky conditions, and obtained LUE values under diffuse radiation conditions afterwards multiplied the enhancement factor (\(\alpha\)) [69]. This may certainly distort GPP estimations, but the point of this paper was to explore the effects of partitioning the direct and diffuse APAR in GPP estimations using big leaf models. Therefore, such simplification was reasonable here.
## 5 Results and Analysis  
### Datasets  
In order to evaluate the two-stage defect detection framework proposed in this paper, we use three datasets: VOC2007 [25], Northeastern University's public dataset NEU-DET, and its derived dataset Enriched-NEU-DET. There were 1800 pictures in total. Of these, there were 300 pictures of six kinds of defects. The six defects were marked as \"crazing,\" \"inclusion,\" \"patches,\" \"pitted_surface,\" \"rolled-in_scale,\" and \"scratches.\" The algorithm proposed in this paper firstly needs to train the Improved-YOLOv5 model and Optimized-Inception-ResnetV2 model to obtain the trained weight file for the first-stage and second-stage recognition. The datasets used in training are Enriched-NEU-DET and NEU-DET, divided into a training set, test set, and validation set in a ratio of 6:2:2.  
Since there is only one type of defect in each picture in the NEU-DET dataset, if a part of it is randomly selected to train Improved-YOLOv5, and then we complete the first and second defect recognition, step 4 (result comparison and modification in Section 2) can be removed, which diminishes the superiority and engineering practicability of the model proposed in this paper. At the same time, we wanted to further expand the dataset. Therefore, we made a further improvement to the NEU-DET dataset. First, we randomly sampled 600 images in the training set, and then used data augmentation methods such as horizontal flip, vertical flip, Mosaic technology [12], and random cropping included in YOLOv5. Finally, we expanded the dataset to 2000 sheets. In order to eliminate the limitation of the single defect type of each picture in the NEU-DET dataset, we went to a factory for field data collection. A total of 56 pictures with no fewer than two kinds of defects were collected. Data augmentation increased the sample size to 224 sheets. In the end, Enriched-NEU-DET had a total of 2224 image. Some of the images in the two datasets are shown in Figure 12.  
Since the recognition process of the first stage plays a critical role in the detection performance, we made considerable improvements to YOLOv5. In order to further verify the superiority of our improved model, in addition to the above two datasets used in the training defect recognition framework, the VOC2007 dataset was also used to test whether the improved YOLOv5 had better performance on the commonly used dataset. VOC2007 contained 9963 labeled images, with 24,640 objects labeled in 20 categories, such as people, horses, dogs, apples, etc. It was a benchmark for the evaluations of the image classification and recognition methods. In our experiments, the VOC2007 dataset was divided into training and test sets in a ratio of 9:1, in the same way as the Enriched-NEU-DET dataset.
# SINR-Based MCS Level Adaptation in CSMA/CA Wireless Networks to Embrace IoT Devices
## 5 Performance Evaluation
### The Same Number of Access Points and Users
To measure fairness among user throughputs, we use the fairness index (\(FI\)) from [34] defined as  
\[FI(T_{1},T_{2},\ldots,T_{n})=\frac{(\sum_{i=1}^{n}T_{i})^{2}}{n\sum_{i=1}^{n}T_ {i}^{2}}, \tag{4}\]  
where \(T_{i}\) is the achieved throughput by user \(i\). The results in Figure 13 show that users achieve better fairness with the proposed algorithm than with the highest MCS level.
# A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors
## 1 Introduction  
Highly automated vehicles perceive their surroundings using environmental perception sensors, such as light detection and ranging (LiDAR), radio detection and ranging (RADAR), cameras, and ultrasonic sensors. LiDAR sensors have gained significant attention over the past few years for their use in advanced driver-assistance system (ADAS) applications because they provide outstanding angular resolution and higher-ranging accuracy [1]. As a result, the automotive LiDAR sensor market is predicted to be worth around 2 billion USD in 2027, up from 26 million USD in 2020 [2]. However, it is common knowledge that the LiDAR sensor performance degrades significantly under the influence of certain environmental conditions, such as rain, fog, snow, and sunlight. The effects of these weather phenomena must therefore be taken into account when designing LiDAR sensors, and the measurement performance of the sensors must be validated before using them in highly-automated driving settings. Furthermore, billions of miles of test driving are required for automated vehicles to demonstrate that they reliably prevent fatalities and injuries, which is not feasible in the real world due to cost and time constraints [3].  
Simulation-based testing can be an alternative to tackle this challenge, but it requires that the effects of rain and fog are modeled with a high degree of realism in the LiDAR sensor models so they can exhibit the complexity and the behavior of real-life sensors.  
In this work, we have modeled the rain and fog effect using the Mie scattering theory in the virtual LiDAR sensor developed by the authors in their previous work [4]. It should be noted that the sensor model considers scan pattern modeling and the real sensor's complete signal processing. Moreover, it also considers the optical losses, inherent detector effects, effects generated by the electrical amplification, and noise produced by sunlight to generate a realistic output. The sensor model is developed using the standardized open simulation interface (OSI) and functional mock-up interfaces (FMI) to make it tool-independent [4]. We have conducted the rain measurements at the large-scale rain area of the National Research Institute for Earth Science and Disaster Prevention (NIED) in Japan [5] and the fog measurements at CARISSMA (Technische Hochschule Ingolstadt, Germany) [6]. The real measurements and the simulation results are compared to validate the modeling of the rain and fog effects on the time domain and point cloud levels. Furthermore, key performance indicators (KPIs) are defined to validate the rain and fog effects modeling at the time domain and point cloud levels.  
The paper is structured as follows. Section 2 describes the LiDAR sensor background, followed by an overview of the related work in Section 3. The modeling of rain and fog effects is described in Section 4, and the results are discussed in Section 5. Finally, Sections 6 and 7 provide the conclusion and outlook.
## 3 Preliminaries
### Bilinear Pairing  
Given a security parameter \(\kappa\in\mathbb{Z}^{+}\), a bilinear-parameter generation algorithm \(\mathcal{G}(\kappa)\) outputs a tuple \((p,g,\mathbb{G},\mathbb{G}_{T},e)\), where \(p\) is a \(\kappa\)-bit prime number, \(\mathbb{G}\) and \(\mathbb{G}_{T}\) are two multiplicative cyclic groups of order \(p\), \(g\) is a generator of \(\mathbb{G}\), and \(e:\mathbb{G}\times\mathbb{G}\rightarrow\mathbb{G}_{T}\) is a bilinear pairing with the following properties:  
* Bilinearity: for all \(u,v\in\mathbb{G}\) and \(a,b\in\mathbb{Z}_{p}^{*}\), we have \(e(u^{a},v^{b})=e(u,v)^{ab}\).
* Non-degeneracy: \(e(g,g)\
eq 1\).  
The definitions of bilinear pairing parameter generator and the related complexity assumptions are described below [25; 29].  
Definition 1: (Bilinear generator) The bilinear parameter generator \(Gen(\cdot)\) is a probabilistic algorithm that takes a security parameter \(\kappa\) as input and outputs a \(\mathbb{G}\)-tuple \((\mathbb{G},\mathbb{G}_{T},p,g,e)\).  
Definition 2: (Computational Diffie-Hellman (CDH) problem) Given \((g,g^{a},g^{b})\in\mathbb{G}\), for unknown \(a,b\in\mathbb{Z}_{p}^{*}\), compute \(g^{ab}\in\mathbb{G}\).  
Definition 3: (Discrete logarithm (DL) problem) Given \(Q\in\mathbb{G}\), compute \(a\in\mathbb{Z}_{p}^{*}\) such that \(g^{a}=Q\).