esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Assumptions of the Model  
Before starting to describe the model, it is important to state the fundamental assumptions on which it is based, which are necessary to understand the work and its main objectives.  
The most important observation regards the power system representation: in primary frequency control studies, i.e., in the time frame from 1 to 100 s, it is a common practice to schematize it using the Load Frequency Control (LFC) approach. In fact, in such problems, the time constants of the electromechanical variables are much smaller than the ones representing the dynamics of the mechanical variables of synchronous generation prime movers [32]. Furthermore, the time constants of the wind turbines and photovoltaic converters are very small, justifying this approach [24]. Due to this reasoning, the equivalent inertia constant of the system is the key parameter to obtain the dynamic frequency characteristics [33], together with the load damping constant, which describes the sensitivity of load power to the frequency variations [32]. This allows for the representation of the entire system response just through a transfer function block, making the problem at the same time easier to be modeled (the system is not modeled node by node) and more general. A similar kind of reasoning is related to the generation blocks. Indeed, since the only aim of this work is to observe the inertial and the primary frequency responses, each single generating unit is not represented, all of the same type are aggregated in equivalent generating units. This is valid in particular for the variable speed wind turbines and for photovoltaics. The same principle is applied to the load, which is schematized as a unique block. The main advantages of this approach are the simplicity and the versatility of the results to a lot of practical cases, while the main drawback is the losing of detail.  
To account for the penetration of the different generating sources in the energy system, a participation factor (\(p_{i}\)) is used, which represents the fraction of the total power of the system which is generated by the \(i\)-th source.  
Other assumptions, which are quite usual in this kind of study, are related to the ambient conditions. In the simulations, the effects of variable wind speed, variable irradiance, and ambient temperature are considered. At the same time, the non-uniform distribution of the wind resources among different wind turbines is neglected, since they are considered as an aggregate; the same happens for photovoltaic modules, which work under the assumption of uniform radiation, and shading is neglected.
# Edge Federated Optimization for Heterogeneous Data
## 2 Related Works
### Microcontroller Unit  
In [24; 25; 26], the feasibility of implementing federated learning on microcontrollers (MCUs) using MCUs as client devices is demonstrated. [PERSON] et al. [24] introduce TinyFL, a federated learning approach using only MCUs for communication and aggregation, which demonstrates feasible federated learning communication and aggregation on MCUs via integrated circuits (I\({}^{2}\)C), showing speed advantages over centralized training. It validates its effectiveness with a gesture recognition case, achieving comparable accuracy to centralized training even with non-IID data. [PERSON] et al. [25] merge federated learning and transfer learning for resource-constrained IoT devices with less than 1 MB of memory. It features federated transfer learning for binary image classification on MCUs, utilizing pre-trained CNNs for feature extraction. [PERSON] et al. [26] employ federated learning on three Arduino Nano 33 BLE Sense boards to train a fully connected neural network (FCNN) for keyword spotting (KWS). The study notes communication bandwidth limitations between MCUs and the server due to serial port usage, which highlights the potential of federated learning on low-cost, low-power devices but acknowledges performance and capability limitations, particularly with complex models or large datasets, and accuracy decreases with more device participation.
## 5 Result and Discussion
### Contract of QoS Parameters with Simulation Time  
Table 7, corresponding to Figures 10 and 11 while Table 8, corresponding to Figures 12 and 13 clearly outlined the performance for considered QoS parameters on the Simulation Time and Node Speed. The observation achieved has been portrayed using the comparison graphs. The accomplished proposed OLSR is shown quite better than the existing OLSR [16] under multiple scenarios.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Average Throughput** & \\ \hline Standard OLSR & Proposed OLSR & AODV \\ \hline
58 & 67 & 51 \\ \hline \hline \end{tabular}
\end{table}
Table 6: OLSR vs. Proposed OLSR vs. AODV—Average Throughput.  
Figure 8: Comparison of Standard OLSR vs. Proposed OLSR vs. AODV Protocol: Topology Control (TC) Messages.  
Figure 10: Contrast of QoS Parameter (End to End Delay) vs. Simulation Time.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**End to End** & & & & & \\
**Delay** & & & & (bps) & \\ \hline
**Simulation** & **Standard** & **Proposed** & **Simulation** & **Standard** & **Proposed** \\
**Time** & **OLSR** & **OLSR** & **Time** & **OLSR** & **OLSR** \\ \hline
30 & 37.5 & 17 & 30 & 50 & 150 \\ \hline
80 & 32.3 & 15.5 & 80 & 45 & 132 \\ \hline
120 & 29.4 & 14.2 & 120 & 41.5 & 122 \\ \hline
180 & 26 & 13 & 180 & 36 & 111 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Simulation Time vs. QoS Parameters.  
Figure 9: Comparison of Standard OLSR vs. Proposed OLSR vs. AODV Protocol: Throughput.  
Figure 11: Contrast of QoS Parameter (Throughput) vs. Simulation Time.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Packet**} & \multicolumn{4}{c}{**TC Message**} \\
**Delivery** & & & **Sent** & \\ \hline \multirow{2}{*}{**Node Speed**} & **Standard** & **Proposed** & **Node Speed** & **Standard** & **Proposed** \\  & **OLSR** & **OLSR** & & **OLSR** & **OLSR** \\ \hline
1 & 85.2 & 94.5 & 1 & 40 & 160 \\ \hline
2 & 81.4 & 90.2 & 2 & 35 & 151 \\ \hline
3 & 75.6 & 86.8 & 3 & 32.5 & 143 \\ \hline
4 & 71.7 & 80.9 & 4 & 29 & 132 \\ \hline
5 & 70.3 & 79.4 & 5 & 26 & 121 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Node Speed vs. QoS Parameters.  
Figure 12: Contrast of QoS Parameter (Packet Delivery) vs. Node Speed.
## 3 Results and Discussion
### Spatial Distribution of Parameters Obtained by Geophysical Sensors
Nevertheless, EMI sensors are widely applied in anthropogenic [20] and natural environments [25; 26; 59] due to their effectiveness in studies of soil spatial variability and identification of HZs for purposes related to pollution remediation and precision agriculture. As a general rule, PSS surveys are carried out only after earth movements and site rearrangements, enabling researchers to collect information on the state of such places. The EMI surveys conducted pre- and post-works gave us the opportunity to (i) better identify the HZs based on both preexisting and current soil physical conditions still affecting the present soil properties, and (ii) to monitor changes in the patterns of anomalies after earth movements in the field.
# A Review of the Optimal Design of Neural Networks Based on FPGA
## 3 Application of Neural Networks Based on FPGA  
The largest proportion of FPGA applications is in the field of communication, often used in large flow data transmission, digital signal processing, and other occurrences. With the continuous development of neural networks, the application field of FPGA has expanded from the original communication to a wider range of fields, such as national defense, the military industry, the aerospace industry, industrial control, security monitoring, intelligent medical treatment, and intelligent terminals, and increasingly more intelligent products have been derived from them.  
In the academic community, the application of the combination of FPGA and neural networks has attracted increasingly more attention, and the optimal design of neural networks based on FPGA has also become a research hotspot. Scholars have also conducted much research and have obtained some results. We introduce the current research results of the combination of FPGA and neural networks in terms of three aspects: the application of FPGA-based CNN, the application of FPGA-based RNN, and the application of FPGA-based GAN, and analyzed the directions for improvement of these studies.
## 3 System Layout
### Experimental Results
\[P[kW]=\frac{Q\big{[}\frac{L}{s}\big{]}*dP[kpa]}{1000*\eta_{pump}\big{[}\% \big{]}*\eta_{motor}\big{[}\%\big{]}*\eta_{V-Bell}\big{[}\%\big{]}} \tag{1}\]  
where \(P\) denotes the consumed power in \([kW]\), \(Q\) denotes the flow rate of the raw water in [\(L\)/S], \(dP\) denotes the feed-in pressure in [\(kPa\)], \(\eta_{pump}\) denotes the efficiency of the pump, \(\eta_{motor}\) denotes the efficiency of the motor, and \(\eta_{V-Bell}\) denotes the efficiency of the V-Belt, which connects the pump to the motor, in %. Here, the readings for the flow rate and the pressure were taken from Table 2, while the  
Figure 8: Pressure versus conductivity, permeate flow rate, and power. efficiency of the pump, the motor, and the _V-Belt_ were estimated to be equal to 90%, 95%, and 88%, respectively [17; 18]. Controlling the pressure of the feed-in water enhanced the efficiency of the separation process and reduced the amount of consumed power. The idea behind this research project was to increase the pressure of the feed-in water when there is an excess of PV-generated power to reduce the conductivity of the permeate water and decrease the feed-in pressure when the PV-generated power is relatively low, to produce permeate water with a relatively high salt concentration. This decreased the required capacity of the energy storage, by using permeate water with a lower salt concentration than that required and accepted as an energy storage parameter. The total produced permeate water had an average and acceptable conductivity according to the designed rates.
# Wind Speed Measurement via Visual Recognition of Wind-Induced Waving Light Stick Target
## 6 Conclusions and Outlook  
In this work, a novel wind speed measurement methodology is proposed by visually identifying the wind-induced motion of lightweight sticks. The presented model is validated by a designed simulation set-up for a theoretical model and applied in wind tunnel tests in a stationary inner equipment cabin of a high-speed train. The conclusions derived are as follows:  
(1) Imaging models are given between the yaw angle, wind direction, and imaging length, as well as the orientation of the light stick target, when the camera is respectively placed at its bottom, side, and front, in pure and coupled imaging views. The models were validated by using a simulation set-up with multiple revolute joints to acquire different yaw angle and wind direction cases, in which the maximum error is no more than 3.4\({}^{\circ}\).  
(2) The mathematical relationship between the stick yaw angle and the wind speed was calibrated through wind tunnel tests, where a linear relationship was observed between the quadratic power of the wind speed and the compound trigonometric function of the yaw angle. The derived relationship is also explained by a mechanical formulation based on the force equilibrium of a hanging stick subjected to wind pressure and its gravity force.  
(3) In the application of wind speed measurement in the limited space of an inner equipment cabin of a high-speed train, the proposed method shows good agreement with T-tube pitot detections. The error is no bigger than \(\pm\)1.1 m/s in average RMSE at all test points in wind tunnel tests compared to the standard T-tube pitot readings, which indicates that the developed method provides an accurate and economic option for monitoring 2-D wind speed in a confined space.  
(4) Future work will focus on addressing the vertical wind speed component that will affect the stick yaw angle detection, by adding more information besides the yaw angle and projection length of the stick. We will also work on grouping different light sticks by  
Figure 26: Results comparison between detected and expected wind speeds. (**a**) Measured wind speed at different test points; (**b**) absolute error between detected and expected.  
relating their geometry and mechanical parameters with the wind speed measurement range to accommodate more complex application circumstances.  
Conceptualization, W.Z. and X.L.; methodology, W.Z., X.L. and C.J.; software, M.T.; validation, Y.W. and M.T.; formal analysis, Y.W.; investigation, A.K.; resources, C.J.; data curation, A.K.; Writing-Reviewing and Editing, A.K.; visualization, Y.W.; supervision, W.Z. and C.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China, grant number 2022 YFB4300304.  
Not applicable.  
**Data Availability Statement:** Exclude this statement.  
The authors declare no conflict of interest.
# Influence of Microbial Treatments on Vine Growth and Must Quality: Preliminary Results
## 2 Results and Discussion
### Aroma Composition
#### 2.4.3 Grape Juice
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Compounds**} & \multirow{2}{*}{**Lri. 1**} & \multirow{2}{*}{**Class**} & **Relative Abundance (\%) \(\pm\) Standard Deviation** & \multirow{2}{*}{**Significance Level**} \\ \cline{3-3} \cline{5-5}  & & & **Control Pressed Grapes** & & **Bioma Pressed Grapes** \\ \hline Hexanal & 800 & nt & 23.8 \(\pm\) 0.53 \({}^{\text{A}}\) & 15.9 \(\pm\) 0.98 \({}^{\text{B}}\) & ** \\ \hline (_E_)-2-Hexenal & 854 & nt & 27.5 \(\pm\) 0.07 \({}^{\text{B}}\) & 40.5 \(\pm\) 0.72 \({}^{\text{A}}\) & ** \\ \hline (_E_)-2-Hexen-1-ol & 862 & nt & 19.4 \(\pm\) 0.09 \({}^{\text{B}}\) & 27.4 \(\pm\) 0.73 \({}^{\text{A}}\) & ** \\ \hline
1-Hexanol & 871 & nt & 26.3 \(\pm\) 0.75 \({}^{\text{A}}\) & 14.8 \(\pm\) 1.07 \({}^{\text{B}}\) & ** \\ \hline
2-Octanone & 990 & nt & 1.1 \(\pm\) 0.02 \({}^{\text{A}}\) & 0.2 \(\pm\) 0.02 \({}^{\text{B}}\) & *** \\ \hline (_E_)-2-hexenyl acetate & 1017 & nt & 0.4 \(\pm\) 0.01 \({}^{\text{B}}\) & 0.7 \(\pm\) 0.04 \({}^{\text{A}}\) & ** \\ \hline
1-Octanol & 1069 & nt & 0.1 \(\pm\) 0.00 & -2 \({}^{\text{A}}\) & n.s. \\ \hline
2-Nonanone & 1093 & nt & 0.2 \(\pm\) 0.00 \({}^{\text{A}}\) & - B & ** \\ \hline Nonanal & 1105 & nt & 0.6 \(\pm\) 0.02 \({}^{\text{A}}\) & 0.3 \(\pm\) 0.02 \({}^{\text{B}}\) & *** \\ \hline Decanal & 1206 & nt & 0.5 \(\pm\) 0.01 \({}^{\text{A}}\) & 0.2 \(\pm\) 0.02 \({}^{\text{B}}\) & *** \\ \hline Other non-terpene derivatives (nt) & & 100 \(\pm\) 0.01 & 100 \(\pm\) 0.00 & n.s. \\ \hline Total identified (\%) & & 100 \(\pm\) 0.01 & 100 \(\pm\) 0.00 & \\ \hline \hline \end{tabular}  
\({}^{1}\) Linear retention index on a HP 5-MS capillary column; \({}^{2}\) not detected. Superscript uppercase letters (A–B) indicate statistically significant differences between Bioma and control samples. The asterisks in the “Significant level” column indicates **_p_ \(<\) 0.01, **_p_ \(<\) 0.0001, while n.s. means not significant.  
\end{table} 
Table 5: Complete volatile profile of the pressed grape berries.
###### Abstract  
This study presents a visual-only odometry technique of a low-profile pallet robot using image feature tracking in ground plane images generated from a fisheye camera. The fisheye camera is commonly used in many robot vision applications because it provides a larger field of view (FoV) around a robot. However, because of the large radial distortion, the fisheye image is generally converted to a pinhole image for visual feature tracking or matching. Although the radial distortion can be eliminated via image undistortion with the lens calibration parameters, it causes several side effects, such as degraded image resolution and a significant reduction in the FoV. In this paper, instead of using the pinhole model, we propose to generate a ground plane image (GPI) from the fisheye image. GPI is a virtual top-view image that only contains the ground plane at the front of the robot. First, the original fisheye image is projected to several virtual pinhole images to generate a cubemap. Second, the front and bottom faces of the cubemap are projected to a GPI. Third, the GPI is homographically transformed again to further reduce image distortion. As a result, an accurate ortho-rectified ground plane image is obtained from the virtual top-view camera. For visual odometry using the ortho-rectified GPI, a number of 2D motion vectors are obtained using feature extraction and tracking between the previous and current frames in the GPI. By calculating a scaled motion vector, which is the measurement of the virtual wheel encoder of the mobile robot, we estimate the velocity and steering angle of the virtual wheel using the motion vector. Finally, we estimate the pose of the mobile robot by applying a kinematic model to the mobile robot.  
fisheye lens; cubemap; ground plane image; pallet mobile robot +
Footnote †: journal: Applied  
1 School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Puk-gu, Daeqq 14566, Republic of Korea 1  
[PERSON], 224 Harmony-ro, Yoonsu-gu, Inchen 2011, Republic of Korea; [EMAIL_ADDRESS]  
KLA Corporation, 830 Dongtansunwhan-daero, Hwaseong-si 18468, Republic of Korea; [EMAIL_ADDRESS]
# Super-Resolution Lensless Imaging of Cells Using Brownian Motion
## 2 Materials and Methods
### Super-Resolution Reconstruction Algorithm for Cell Image in a Lensless System
#### 2.3.1 Motion Estimation Algorithm
\(M_{k}\) represents the translation and rotation caused by the inherent Brownian motion of the cell; \(B_{k}\) represents the blurring of optics, motion, and sensors; \(D\) represents the down sampling, where the pixel units of the sensor are arranged tightly, and the down-sampling matrix is determined by the pixel size; and noise \(N_{k}\) is mainly introduced by the sensor thermal noise. The solution in Equation (17) is the translation and rotation parameters estimated by Keren [32]. The Keren method is suitable for small amplitude translation and rotation with high accuracy. Therefore, it is suitable for precise estimation as a two-step estimation method. The accuracy of image registration can be improved using the first step of integer pixel image registration and then Keren estimation, which provides a good foundation for the super-resolution reconstruction of subsequent cell images.
## 3 The Proposed Methodology for Prediction of Bearing Defect Degradation Using Deep Neural Network
### DNN for Defect Degradation Prediction  
A deep learning technique was firstly applied for image classification [24], which was utilized to minimize the dimensionality of data representation and recognize targets effectively. For outstanding learning capability, the deep learning method has also been used successfully for image enhancement [25], in which a deep convolution neural network was presented to improve the contrast of low-quality images collected from under- or overexposed images. Consequently, a DNN can be trained to learn useful feature information directly from a dataset without requiring expert knowledge in the specified fields. In this paper, the proposed effective DNN-based method for predicting bearing fault degradation levels included three procedures: data acquisition of bearing defects under different crack size conditions, a learning process for DNN training, and evaluation of predicted results of bearing degradation levels, as illustrated in Figure 4. The DNN training process is thoroughly presented in the following sections.  
DNN is a stacked layer model in which the layers are connected together and there are no connections of nodes within the same layer [16]. A DNN includes an input layer, an output layer, and a few hidden layers placed between them in the model. The number of nodes of an input layer is set corresponding to the dimensionality of the input data. Similarly, the number of nodes of an output layer is defined corresponding to the dimensionality of the target data. The number of nodes of every hidden neural layer is set in accordance with the network function, for which there are no required strict regulations. Each node in the next layer is directly linked to all nodes in the previous layer. Nodes of the first layer receive the input data and transmit them to other layers, while nodes of the last layer output the targets. The nonlinear relationship between the DNN layers is indicated by the following equations:  
\[z_{j}^{l}=\sum_{i}w_{ij}^{l}x_{i}^{l-1}+b_{j}^{l} \tag{6}\]  
Figure 4: The flowchart of the proposed deep neural network (DNN) approach for predicting bearing fault degradation.  
\[\mathrm{h}_{W,b}(x)=x_{j}^{l}=f(z_{j}^{l})=f(\sum_{i}w_{ij}^{l}x_{i}^{l-1}+b_{j}^{l}), \tag{7}\]  
where \(x_{j}^{l}\) is the activation value of neuron j in layer \(l\); \(z_{j}^{l}\) is a linear activation combination of neurons in the previous layer; \(b_{j}^{l}\) is the bias value of neuron j in layer l; \(w_{ij}^{l}\) is the weight parameter between nodes i in layer \(l-1\) and j in layer \(l\); and \(f(.)\) is the activation function, which is usually chosen to be sigmoidal of the form \(f(z)=\frac{1}{1+e^{-z}}\) and mostly used in DNN. Given the fixed setting of the model parameters \((W,b)\) to our data, a nonlinear form of hypotheses \(h_{W,b}(x)\) denoted the output of the neural network, which gave us a real number. The network outputs were serially calculated layer by layer on the model's parameters, which were initialized appropriately.
###### Abstract  
Today IoT integrate thousands of inter networks and sensing devices e.g., vehicular networks, which are considered to be challenging due to its high speed and network dynamics. The goal of future vehicular networks is to improve road safety, promote commercial or infotainment products and to reduce the traffic accidents. All these applications are based on the information exchange among nodes, so not only reliable data delivery but also the authenticity and credibility of the data itself are prerequisite. To cope with the aforementioned problem, trust management come up as promising candidate to conduct node's transaction and interaction management, which requires distributed mobile nodes cooperation for achieving design goals. In this paper, we propose a trust-based routing protocol i.e., 3 VSR (Three Valued Secure Routing), which extends the widely used AODV (Ad hoc On-demand Distance Vector) routing protocol and employs the idea of Sensing Logic-based trust model to enhance the security solution of VANET (Vehicular Ad-Hoc Network). The existing routing protocol are mostly based on key or signature-based schemes, which off course increases computation overhead. In our proposed 3 VSR, trust among entities is updated frequently by means of opinion derived from sensing logic due to vehicles random topologies. In 3 VSR the theoretical capabilities are based on Dirichlet distribution by considering prior and posterior uncertainty of the said event. Also by using trust recommendation message exchange, nodes are able to reduce computation and routing overhead. The simulated results shows that the proposed scheme is secure and practical.  
trust model; sensing logic, dirichlet distribution, AODV, Vehicular Ad-Hoc Networks +
Footnote †: journal: Sensors