esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Local Energy Markets
### LEM Market Platforms and Projects
Likewise, Equigy [79] is a European TSO-based BCT initiative that promotes the active participation of households in generating flexibility from decentralized systems through blockchain technology. The Equigy project also provides congestion management services for DSOs. Among the array of flexible market platforms, Enera is a German initiative aimed at attaining supply-demand flexibility to address congestion challenges faced by DSOs [80]. The Enera project involves a series of actions, including bid collection, settlement, market clearing, aggregation activities, network impact assessments, and flexibility activation. GOPACS (Grid Operators Platform for Congestion Solutions) is a Dutch platform that offers congestion management at all voltage levels to provide flexibility for TSO/DSO coordination [81]. GOPACS is a two-way platform where DSOs and TSOs are flexibility buyers and residential, industrial, and energy companies are flexibility sellers. Picloflex is a UK-based marketplace that operates the system efficiently by offering benchmark flexibility services to DSOs and reducing grid costs [82]. Based on locational information, the DSOs declare their flexibility needs and monitor the available resources. Nodes platform is owned by Nord Pool, which aims to stabilize grid operation through achieving network operators' flexibility and improving congestion services. Danish-based EcoGrid 2.0 [83] is a local flexibility market project in which 800 household participants play their role by offering flexibility services to DSOs and TSOs through demand response (DR) programs. The capacity limitation service and baseline flexibility service are the two enablers for the DSOs. These services can be either scheduled at a predetermined time or conditional services activated manually by the DSOs. Tiko is a Switzerland-based aggregator platform [84] to aggregate flexibility in the form of primary and secondary balancing services to the Swiss TSO. This platform is restricted to only operate in low-voltage DSO grids. Interflex is a demonstration project that focuses on how distribution grid challenges can be solved by using the flexibility services of DSOs. Upon reviewing these LEM projects, a common theme is observed, with the majority focusing on enhanced digitalization, demand flexibility, load-balancing services, and congestion management services. At the community level, these services are often facilitated by DSOs, serving as intermediaries between the transmission system and end-consumers connected to the distribution grid. Additionally, efforts are made towards energy trading mechanisms, preferences in trading prices, and trading flexibility through the utilization of BCT. In the following section, a deeper exploration into LEMs is provided, examining the intricacies of energy trading mechanisms and their diversity.
# Trade-Off Analysis of Hardware Architectures for Channel-Quality Classification Models
## 4 Methods
### Decision Trees  
For the Decision Tree, the used technique was balanced weights, with the next hyperparameters: the entropy criterion, a maximum depth of 6, a minimum samples split of 2, a minimum samples leaf of 3, and a minimum weight fraction leaf of zero. Every node is formed by a comparator, which has 2 inputs of 32 bits each: \(A\) and \(B\). The outputs are called _L_OK and _R_OK,_ which activate left and right nodes, respectively. If condition \(A\leq B\) is fulfilled, _L_OK takes a value of one, and the left node is activated; if the condition is not fulfilled, _R_OK takes a value of one, and the right node is activated. This condition is repeated for every node until a leaf is reached and the class is determined. The tree architecture with a depth of five is shown in Figure 7, where it is shown every value that is stored inside every comparator.  
The inputs \(A_{1}\), \(A_{2}\), \(A_{4}\), \(A_{6}\), \(A_{7}\), \(A_{8}\), and \(A_{10}\) correspond to the features of the dataset. We implemented two Decision Trees with depths of six and five. The Decision Tree's complexity with a depth of six is bigger since it contains more nodes to evaluate. We pruned both trees, considering that if two leaves of a certain node belong to the same class, they are eliminated, and the node turns into a leaf with the class value.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Considered Hyperparameters** & **Obtained Hyperparameters** & **Configuration** \\ \hline \multirow{3}{*}{Decision Tree} & criterion: [entropy], min\_sample\_depth: [1:15], min\_samples\_split: [2:4], min\_samples\_leaf: [1:3], min\_weight\_fraction\_leaf: [0, 0.1, 0.01, 0.001] & criterion: [entropy], max\_depth: [6], min\_samples\_split: [2:], min\_samples\_leaf: [1:3], min\_weight\_fraction\_leaf: [0, 0.1, 0.01, 0.001] & \multirow{3}{*}{Implemented Depths: five and six, Designs} \\ \cline{3-4}  & \multirow{3}{*}{MLP} & neurons: [1:0], activation: function: [softmax, \\  & softplus, softplus, refuu, hsmigmoid, hard\_sigmoid, linear), optimizer: [SGD, RGMSprop, dagrad, \\  & addadelta, batchsize: [10, 20, 40, 60, 80, 100], & optimizer: [RMSprop, batchsize: [5], \\  & learning, rate: [0.01], 0.01, 0.1, 0.2, 0.3], weight\_init: [uniform, kcun\_uniform, normal, zero, \\  & glorot\_normal, glorot, uniform, he\_normal, \\  & he\_uniform\_dropout: [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9] & \multirow{3}{*}{MLP based on three dense layers, with the next configurations of neurons [5, 5, 1]} \\ \cline{3-4}  & \multirow{3}{*}{Cogistic Regression} & penalty: [1, 12, elsistcient, none], tol: [0.0001, 0.001, 0.01, 0.1, 0.1, 0.2, 0.3] & \multirow{3}{*}{kernel: [rbf], C: [464.1588], gamma: [0.0001], C: [10000], \\  & & \(\times\) 10\({}^{-3}\), 48.3\({}^{-1}\), 12.7\({}^{-1}\), 10.3\({}^{-2}\), 10.3\({}^{-2}\), 10.2\({}^{-3}\), 10.2\({}^{-3}\), 10.2\({}^{-2}\), 10.3\({}^{-2}\), 10.2\({}^{-3}\), 10.2\({}^
## 5 Application Deployment Strategies
### Environmental Context Detection  
Natural and environmental disasters require accurate/long-term prediction, immediate notifications, and action recommendations to provide multiple options for ensuring people's safety. In the FL-based scenario, [96] discussed the deployment of environmental image datasets, namely natural disaster analysis and waste classification. The proposed methods handled the unlabeled/unannotated training subset. A collaboration learning between ED and a central server was activated in multi-environment use cases. The methodology considered the feature extraction of the dataset, active learning, and FL framework. Active learning assisted the labeling procedures (annotation/acquisition) of each data owner. The algorithm was fed by inputting unlabeled images, and the model divided the data into seeds/pools for training. The prediction output the selection processes. FL was used for constructing the final global learning model by collaborating with both the server and client sides.
## 2 Vehicle Configuration
### Lateral Vehicle Dynamics Model  
As shown in Figure 1b, the lateral vehicle dynamics are described by a four-wheel model when the vehicle is cornering. The longitudinal, lateral, and yaw vehicle dynamic equations are expressed as:  
\[(F_{xf1}+F_{xf2})\cos\delta-(F_{yf1}+F_{yf2})\sin\delta+F_{xr1}+F_{xr2}=M\bigg{[} \dot{V}_{x}-\bigg{(}\gamma+\frac{d\beta_{v}}{dt}\bigg{)}V_{y}\bigg{]} \tag{6}\]  
\[(F_{xf1}+F_{xf2})\sin\delta+(F_{yf1}+F_{yf2})\cos\delta+F_{yr1}+F_{yr2}=M\bigg{[} \dot{V}_{y}+\bigg{(}\gamma+\frac{d\beta_{v}}{dt}\bigg{)}V_{x}\bigg{]} \tag{7}\]  
Figure 2: Torque-speed-efficiency maps: (**a**) the driving mode of the 15-kW front motor, (**b**) the driving modes of the two 7-kW rear motors.  
\[L_{f}(F_{yf1}+F_{yf2})\cos\delta-L_{r}(F_{yf1}+F_{yf2})+\frac{L_{w}}{2}\Big{(}F_{ yf1}-F_{yf2}\Big{)}\sin\delta+M_{z}=I_{z}\frac{d}{dt}\Bigg{(}y+\frac{d\beta_{v}}{dt} \Bigg{)} \tag{8}\]  
\[M_{z}=L_{f}(F_{xf1}+F_{xf2})\sin\delta+\frac{L_{w}}{2}(-F_{xf1}+F_{xf2})\cos \delta+\frac{L_{w}}{2}(-F_{xr1}+F_{xr2}) \tag{9}\]  
where \(\delta\) is the steer angle; \(F_{xf1}\) and \(F_{xr2}\) are longitudinal traction forces on the left and right front tires and these forces are assumed equal for \(\delta=0\); \(F_{yf1}\) and \(F_{yf2}\) are lateral traction forces on the left and right front tires; \(F_{xr1}\) and \(F_{yr2}\) are longitudinal traction forces on the left and right rear tires; \(F_{yr1}\) and \(F_{yr2}\) are lateral traction forces on the left and right rear tires; \(\gamma\) is the yaw velocity; \(\beta_{v}\) is the vehicle sideslip angle; \(I_{z}\) is the mass moment of inertia in the yaw direction; and \(M_{z}\) is the yaw moment for cornering. These equations were used to determine torque distributions when the vehicle is cornering.  
For simplicity, the hill climbing resistance, aerodynamic drag, and rolling resistance of the last terms in (1) are omitted, and the roll and pitch motions are neglected. However, the vehicle in CarSim for the real-time HIL simulation was modelled with self-contained yaw, roll, and pitch dynamics.
# A Digital Twin-Based State Monitoring Method of Gear Test Bench
## 6 Discussion  
In this paper, digital twin technology is applied to the field of running state monitoring in the gear experiment process, to make up for the deficiency that the traditional running state monitoring cannot meet the gear test bench dynamic requirements of the experiment process. By establishing a state monitoring system of the gear test bench based on a digital twin model, the running state of the gear test bench under normal working conditions is simulated in real time, and the integration and visualization of the various experimental monitoring data is realized, which lays a foundation for further research on the running state monitoring, fault diagnosis, and system maintenance of the gear test bench. During the test, various factors (e.g., tooth surface wear, extrusion, and insufficient lubrication) and defects (e.g., tooth defect, tooth shape errors, gear tooth offset load, and insufficient bearing preload) in the gearbox will cause regular changes in the working parameters (e.g., vibration, temperature, and noise) of the gearbox, which reflect the working state of the entire gear test bench. For example, from the beginning of the test, the temperature of the gearbox gradually increases, and the noise gradually increases. When the temperature of the box reaches about 55 \({}^{\circ}\)C, the crack of the broken tooth begins to occur. The temperature of the gearbox then rises sharply. When the temperature reaches about 75 \({}^{\circ}\)C, the noise reaches about 82 dB, the running state of the gearbox appears abnormal, the tooth broken phenomenon occurs, and the gear test bench stops running. Through observing the verification results of the whole process of the gear fatigue test, it is concluded that the proposed state monitoring system can be applied to the running state monitoring and the performance prediction of the gear test bench.  
In this paper, the data acquisition and monitoring during the normal operation process of the gear test bench only involved the temperature, noise, and vibration. In the actual operation of the equipment, more sensors need to be installed for comprehensive data collection. Only by expanding the data collection and monitoring of various influencing factors, improving the efficiency of data collection and processing, and strengthening the function of the state monitoring system based on digital twin can the running state of equipment be monitored more effectively.  
Next, researchers will continue to promote the evaluation, implementation, and application of this state monitoring system. They will verify the practicability of the evaluation method with as many types of operational status monitoring as possible, so as to enhance the persuasiveness of the theory. Furthermore, efforts are also being made to study the deep mining and analysis of the digital twin data, so as to provide a data basis for intelligent decisions (e.g., running state evaluation, fault diagnosis of equipment) and improve the service function of the state monitoring system based on a digital twin model.
## 3 The Preliminaries
### The Proposed Localized Adaptive Denoising Technique
According to the vibration characteristic of the fault signal presented in Section 2, the frequency domain of the phase-amplitude modulation signals is visualized as a set of many similar frequency segments, each of which contains a meshing frequency harmonic as a center frequency and the sideband gear frequency tones are distributed around the center frequency in the ideal condition. The principal frequency segment (PFS) is defined as a frequency segment with a meshing frequency harmonic as a center frequency and frequency wide equally to a meshing frequency (i.e., the frequency range of PFS is from \((p-0.5)*f_{m}\) to \((p+0.5)*f_{m}\) with \(p*f_{m}\), a \(p\)-th harmonic of a meshing frequency, is a center value). However, in the real world, the amplitudes of frequency tones in each PFS (PFS power distribution) of the gearbox vibration signals are uncorrelated to each other because of the influence of random noise (white noise and band noise) on the non-linear and phase-amplitude modulation signal [48]. Due to the differences of power distributions of PFS, the general optimal parameter set of Gaussian reference signals cannot be used. Therefore, this paper proposes a new denoising technique called the localized adaptive denoising technique (LADT). The localized adaptive denoising technique adopts the ANR-GRS module from [22]. To improve the denoising capability of ANR-GRS, the LADT applies ANR-GRS to each PFS. By localized adaptive optimization, the new denoising methodology tries to find the localized optimal parameter set of a noise-simulated reference signal, which is appropriate to each specific PFS. The function block diagram of LADT is demonstrated in Figure 3. To implement the ANR-GRS method on each PFS, the band-pass Chebyshev Type-I IIR filter of order 30 [49] is used to segment the frequency spectrums of a vibration signal to M sub-signals whose frequency spectrum is as a PFS. The band-pass filter had a bandwidth similar to meshing frequency, where M is computed as the quotient of the division of the frequency range and the meshing frequency. The localized optimizing process of LADT improves the noise-reducing capability in comparison with that of the ANR-GRS method; therefore, in this study it is used for denoising the vibration signal before the feature engineering process. Figure 2: Schematic diagram of the ANR-GRS module.
# Benchmarking Deep Learning Methods for Aspect Level Sentiment Classification
## 3 Deep Learning Methods for Aspect Level Sentiment Classification
### Data Modelling Procedure for Deep Learning-Based ALSC
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Deep Learning Method**} & \multirow{2}{*}{**Description**} & \multirow{2}{*}{**Input to the Method**} & \multicolumn{2}{c}{**Neural**} & \multirow{2}{*}{**Attention Used**} & \multirow{2}{*}{**Syntactical Information**} \\  & & & & **Method** & & & \\  & & & & & **Architecture** & & \\ \hline \multirow{4}{*}{DL1} & \multirow{4}{*}{ContextAvg} & Utilizes the deep memory network for capturing context and then the average context vector is fed into the softmax layer for sentiment prediction. & \multirow{4}{*}{S, A} & Memory & No & No \\  & & & & & Network & & No \\ \hline \multirow{4}{*}{DL2} & \multirow{4}{*}{AEContextAvg} & Extension of ContextAvg (DL1) method in which average of aspect vector is also fed with context word vector to the softmax layer for prediction. & \multirow{4}{*}{S} & Memory & No & No \\  & & & & Network & & No \\ \hline \multirow{4}{*}{DL3} & \multirow{4}{*}{MemNet [29]} & MemNet is a variant of DL1 that utilizes a deep memory network with only context attention enabled for perception purposes. & \multirow{4}{*}{C, A, C\({}_{1}\) Network} & \multirow{4}{*}{Yes} & \multirow{4}{*}{No} \\  & & Attention-based LSTM is used to generate the hidden vector for the sentence representation which can provide attention to different parts of a sentence depending on the aspect term. & \multirow{4}{*}{S} & \multirow{4}{*}{LSTM} & \multirow{4}{*}{Yes} & \multirow{4}{*}{No} \\  & & Attention Based LSTM with aspect embedding is an extension of & & & & \\ \cline{1-1}  & & ATAE-LSTM (DL4) where aspect embeddings are also used to generate a better representation of sentences for prediction. & \multirow{4}{*}{S, A} & \multirow{4}{*}{LSTM} & \multirow{4}{*}{Yes} & \multirow{4}{*}{No} \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \hline \multirow{4}{*}{DL6} & \multirow{4}{*}{LSTM [21]} & Utilizes Long Short Term Network (LSTM) for generating hidden vector for the sentence which is fed into softmax layer for predicting the sentiment polarity of an aspect. & \multirow{4}{*}{S} & \multirow{4}{*}{LSTM} & \multirow{4}{*}{No} & \multirow{4}{*}{No} \\ \cline{1-1}  & & TD-LSTM (Target dependent-LSTM) model utilizes two LSTMs: LSTM\({}_{\text{L}}\) and LSTM\({}_{\text{R}}\) for collectively considering left and right context of target. & \multirow{4}{*}{C\({}_{1}+\)A, C\({}_{\text{r}}+\)A} & \multirow{4}{*}{LSTM} & \multirow{4}{*}{No} & \multirow{4}{*}{No} \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Deep learning methods with their description.
# Image Quality Assessment of Digital Image Capturing Devices for Melanoma Detection
###### Abstract  
The fast-growing incidence of skin cancer, especially melanoma, is the guiding principle for intense development of various digital image-capturing devices providing easier recognition of melanoma by dermatologists. Handheld and digital dermoscopy, following of mole changes with smartphones and digital analysing of mole images, is based on evaluation of the colours, shape and deep structures in the skin moles. Incorrect colour information of an image, under- or overexposed images, lack of sharpness and low resolution of the images, can lead to melanoma misdiagnosis. The purpose of our study was to determine the colour error in the image according to the given lighting conditions and different camera settings. We focused on measuring the image quality parameters of smartphones and high-resolution cameras to compare them with the results of state-of-the-art dermoscopy device systems. We applied standardised measuring methods. The spatial frequency response method was applied for measuring the sharpness and resolution of the tested camera systems. Colour images with known reference values were captured from the test target, to evaluate colour error as a CIELAB (Commission Internationale de l'Eclairage) \(\Delta E^{*}_{ab}\) colour difference as seen by a human observer. The results of our measurements yielded two significant findings. First, all tested cameras produced inaccurate colours when operating in automatic mode, and second, the amount of sharpening was too intensive. These deficiencies can be eliminated through adjusting the camera parameters manually or by image post-production. The presented two-step camera calibration procedure improves the colour accuracy of captured clinical and dermoscopy images significantly.  
d
# Numerical Test of Several Controllers for Underactuated Underwater Vehicles
## 4 Comparative Simulation Test
### Performance Test Assumptions  
The numerical simulations were done in order to show performance of each control algorithm.  
For tracking, the following desired trajectory position profiles were assumed for _Test 1_ (linear):  
\[p_{d1}=\left[\begin{array}{c}0.5t\\ 0.4t\end{array}\right], \tag{33}\]  
and different trajectory profile for _Test 2_ (sine):  
\[p_{d2}=\left[\begin{array}{c}0.5t,\\ 4\sin(0.1t)\end{array}\right]. \tag{34}\]  
Motion of the vehicle in a straight line is substantially different from motion along a sine trajectory. In preliminary tests, assuming of these two types of trajectory makes sense. However, in more detailed studies, changes in the shape of the trajectory should be taken into account. All algorithms used for control were investigated on original vehicle described in the source references. Therefore, it can be concluded that they are also useful for vehicles with different dynamics.  
For underactuated underwater vehicles in the practical engineering, it is also assumed that the control inputs and velocities are bounded. The problem of taking into account the constraints of forces and the assumption about the limitation of the input control signals for underactuated vehicles has been considered in a number of works, e.g., in [20,40,41,42]. Conditions of the tests conducted in Matlab/Simulink were as follows: the forces and torques values were limited due to the vehicle construction and the used thrusters, i.e., \(|\tau_{u}|\leq 160\) N, \(|\tau_{r}|\leq 160\) Nm for Kambara and \(|\tau_{u}|\leq 1800\) N, \(|\tau_{r}|\leq 1800\) Nm for ROPOS. The time of motion for the linear trajectory was assumed as \(t=30\) s, whereas for sine trajectory \(t=60\) s with \(\Delta t=0.01\). The integration method used was code 3 Bogacki-Shampine (in spite of that other solvers were also taken into account). The desired  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Kambara**} & \multicolumn{2}{c}{**ROPOS**} \\
**Symbol** & **Value** & **Value** & **Unit** \\ \hline \(L\) & \(1.2\) & \(1.75\) & m \\ \(b\) & \(1.5\) & \(2.6\) & m \\ \(m\) & \(117\) & \(2268\) & kg \\ \(J\) & \(13.4\) & \(2457\) & kgm\({}^{2}\) \\ \(X_{ii}\) & \(-58.4\) & \(-4380\) & kg \\ \(Y_{\oplus}\) & \(-23.8\) & \(-9518\) & kg \\ \(N_{r}\) & \(-2.67\) & \(-5000\) & kgm\({}^{2}\) \\ \(X_{u}\) & \(-120\) & \(-725\) & kg/s \\ \(Y_{\oplus}\) & \(-90\) & \(-1240\) & kg/s \\ \(N_{r}\) & \(-18\) & \(-1804\) & kgm\({}^{2}\)/(rad s) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of Kambara and ROPOS autonomous underwater vehicle (AUV).  
position trajectory was described by (33) and (34), respectively. The start point was assumed as \(p_{0}=[-0.5\;\;0.5]^{T}\;\;\text{m}\).
## 4 Experiment and Results
### Performance Measures
#### 4.3.4 Precision  
The precision score calculates the correctly classified positive values. It is computed by Equation (44):  
\[Precision=\frac{TP}{(TP+FP)}. \tag{44}\]  
Unlike food categorization, in which classification can be either correct or wrong. For food ingredient detection, PKELM may correctly classify several food ingredients and misclassifies other food ingredients, resulting in partial correctness of the classification. Therefore, we cannot directly employ evaluation metrics used to measure the classification performance of food categorization. This study uses the metrics based on the existing literature for multi-label classification. It uses _Precision_, _Recall_, and \(F1\)-_score_ to measure performance. Consider \(x_{i},Y_{i}\) with \(L\) number of labels as training datasets. Let assume MLC is the training method and \(Z_{i}=MLC(x_{i})\) is the output labels predicted by the classification method. Then these metrics are defined as follows: