esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fine-Tuning Based Approach for Daily Activity Recognition between Smart Homes
## 4 Results and Evaluation
### Experiments and Results
As shown in Figure 13, at the end of the iteration, our proposed method was close to the recognition results of the comparison method. However, our method achieved more than 75% accuracy almost at the beginning of the phase, and the comparison method did not reach a similar level until the 300 th iteration. This demonstrates that fine-tuning methods on LSTMs can learn the knowledge of the target domain extremely quickly and make corrections to the network parameters, achieving strong migration effects in a shorter period of time. As shown in Figure 14, our method showed considerable superiority, improving accuracy by 10.16%, recall by 9.17% and F1-score by 8.8% over those of the comparison method. In addition, the precision remained on par with that of the comparison method. Additionally, as seen in Figure 15, our method's metrics were also consistently higher than those of the comparison method, improving accuracy by 4.87%, recall by 3.39% and F1-score by 1.5% over those of the comparison method. By analyzing the above graphs and charts, we can observe that the accuracy was improved by 0.7-10.6% and most experiments improved by 3-5%. The precision was improved by up to 12.13%, the recall was improved by up to 10.94% and the F1-score was improved by up to 10.23% compared with those of the direct training. Additionally, the learning efficiency of the fine-tuned network was fast, and most results were optimal in about 50-100 epochs. This is because the fine-tuning incorporates the parameters of the pre-trained model. Based on this, the classification weights of the network can be corrected with a small amount of training data to achieve better classification results. This shows that our proposed fine-tuning method demonstrates excellent transfer effects on the domain of daily activity recognition and exhibits strong stability and generalization. Furthermore, the training time of the fine-tuning method is less than one-third of the time required for training from scratch, which greatly saves training time. In addition, we have compared the existing deep migration method for daily activity recognition. The method mainly uses the Domain Adaptive Neural Network (DANN) for model migration. Additionally, the experimental setups are shown in Table 6. As can be seen from Table 6 and Figures 16-18, our method outperformed the DANN method on all datasets, except for Group 11. Accuracy improved by more than 10% within most groups, and even by 23% in group 10. Precision and F1-score also improved on most of the datasets. Figure 8:0 Accuracy of our method and that of DANN, based on Group 1 to Group 12. Figure 8:1 Precision of our method and that of DANN, based on Group \(1\) to Group \(12\).
# Improvement of Electro-Optical Properties of PSLC Devices by Silver Nanowire Doping
## 3 Results and Discussion
### Effect of Ag Nanowires on Frequency Response of PSLC Devices  
Figure 7a-c shows the transmittance versus driving frequency curve of PSLC device with different Ag nanowire doping concentration at 10 V, 20 V and 30 V respectively. At 10 V, the transmittance of all PSLC devices decreases first and then increase as the frequency increases. Lowest transmittance can be obtained at 10 kHz. At 20 V and 30 V, the change of transmittance from 100 Hz to 100 kHz is negligible. At 1 MHz, the PSLC devices stop responding to electric field, which may be because this frequency is higher than the crossover frequency (\(f_{\mathrm{c}}\)) of the dielectric anisotropy of the LC used in this experiment. Under high driving voltage, the difference between PSLC devices with different Ag nanowire doping concentration is very small. The difference of F-T curves under low driving voltage is mainly caused by the variation of threshold voltage. As shown in Figure 7d, from 100 Hz to 10 kHz, the threshold voltage of all PSLC devices first decreases and then increases as the frequency increases. This trend is the same for all PSLC devices. This is because the dielectric constants of LC used in this study increase first and then decrease as a function of the frequency regardless of Ag nanowire doping concentration. The influence of driving frequency on the response time of PSLC at 30 V with different Ag nanowire doping concentration is shown in Figure 7e. It shows that, with the gradual increase in frequency, from 100 Hz to 10 kHz, the device response time has a slight downward trend, but not much. When the frequency increases from 10 kHz to 100 kHz, the response time gradually increases as the frequency increases. This trend is also the same for all PSLC devices. Therefore, the PSLC devices in this study have a good frequency modulation property, but the Ag nanowire doping has very little influence on the frequency response.  
Figure 7: \(Cont.\)
# Part-Based Obstacle Detection Using a Multiple Output Neural Network
## 3 Solution Description
### Vanishing-Point Computation  
Vanishing-point computation methods have been presented in the literature. Most methods are based either on line intersections or feature analysis or use artificial networks to predict the 2D point coordinates of the vanishing point [33]. In this work, we make use of the results of our previously published work [34] to produce the required training images for the neural network. Our previous paper presented an algorithm for detecting the vanishing point by computing the orientation and magnitude of the gradient that is used to generate three vote-map images. The first image contains the vote map of the features from the left side of the input image, the second image has the vote-map features from the right side of the input image, and the third image is actually the multiplication result of the first two images (left and right vote maps). Figure 14 presents an example of the three vote-map images. The voting maps can be computed using classic image processing techniques (by computing the orientation of the gradients) or can be extracted directly from a fully convolutional CNN, such as the one proposed in this paper.  
The coordinates of the vanishing point can be computed using a sliding window on the third image, or by extracting the maximum from the third vote map image. Extracting the maximum takes an additional 0.09 ms on average to compute; therefore, we used this version. Extracting the VP coordinates can be achieved directly in an end-to-end manner via a CNN with a different architecture; however, in our case, we preferred to leverage the shared layers from the encoder, in order to produce the three vote-map images using semantic segmentation and then extract the coordinates from the third output layer. The other two predicted layers (the left and right vote maps) can be used as input for a lane detection system on marked roads, or as an additional step to validate the extracted vanishing-point coordinates.  
Computing the vanishing point is relevant, due to the fact that it can be used to compute the extrinsic camera parameters if we assume some geometric constraints (a flat road assumption and a small lens distortion). The pitch and yaw angles of the camera with respect to the world can be computed if the focal distance is also known (along with the image size), as we presented in [33].
# Hybrid Deep Reinforcement Learning for Pairs Trading
## 2 Background
### Reinforcement Learning  
In reinforcement learning, an agent is a learner and decision maker that autonomously learns how to maximize the total reward by interacting with its environment through sensors that observe the environment and effectors that execute the action selected by the agent and provide the reward to the agent [9; 10; 11; 12; 13; 14]. The agent develops a deterministic policy \(\mu(s)\) that maps a state to an action, or a stochastic policy \(\pi(a|s)\) that maps a state to a probability distribution over actions. At each time step \(t\), the agent observes a state \(s_{t}\), selects an action \(a_{t}\), receives a reward \(r_{t}\), and then transitions to a new state \(s_{t+1}\). This interaction is modeled as a Markov decision process (MDP) represented by \(\langle S,\ A,\ P,R,\gamma\rangle\), where \(S\) is the state space, \(A\) is the action space, \(P(s_{t},a_{t},s_{t+1})\) is the state transition probability, \(R(s,a)\) is the reward function, and \(\gamma\ \in\ [0,1]\) is the discount factor, which determines the present value of future rewards. The sum of the discounted rewards is defined as the discounted return \(G_{t}\) as follows:  
\[G_{t}=\sum_{i=0}^{\infty}\gamma^{i}r_{t+i}=r_{t}+\gamma G_{t+1} \tag{2}\]  
In algorithmic trading, states are not directly provided; rather, they must be constructed from a history of observations. We can model this by extending the MDP model with observation space \(O\) and observation probability \(Z(\,o|s,a)\). This extended model is referred to as a partially observable MDP model [15].
# Low-Power Wireless Data Transfer System for Stimulation in an Intracritical Visual Prosthesis
## 3 System Architecture
### Implanted Receiver  
At the receiver side, the entire signal is bandpass sampled to allow for digital demodulation. The bandpass sampling is essentially a 1-bit analog to digital converter (1-bit ADC) [14]. This can be implemented using a comparator. After this step, the resulting signal is then non-coherently digitally demodulated. The non-coherent digital demodulator is the central part of the receiver. These two parts will be described next.
# 5G Cross-Border Operation for Connected and Automated Mobility: Challenges and Solutions
## 4 5 GCroCo Solutions  
Considering the requirements of the use cases presented above and summarized in Table 1, 5 GCroCo has identified a set of key 5G technologies, which will become enablers for CCAM. Many of them have been thoroughly evaluated in previous and ongoing research and innovation projects. Some of them are even commercially deployed already. The motivation of 5 GCroCo is to evolve them to also fulfill their purpose and role in overall QoS fulfillment in cross-border, cross-MNO, cross-vendor, and cross-OEM deployments. Service continuity is a particular goal in this context. The key identified technologies are:  
* MEC-enabled distributed computing;* QoS Prediction;
* E2E QoS with network slicing;
* Mobile network-supported precise localization;
* Security.  
The V2X services that will be studied and trialed in 5 GCroCo for the use cases described in the previous section have unique characteristics which make the use of these technologies particularly interesting. First, a limited area of interest. Information is often only needed close to the source where it was generated. This is true for many, but not all applications. It applies in particular to the use cases of HD maps generation and ACCA. Direct communication omitting the cellular network and MEC-enabled cellular networks must, therefore, be part of the V2X architecture. The second unique property is the multi-OEM and multi-MNO challenge. This one is tightly coupled with the first one. For typical mobile radio network services like voice and data communication, it does not matter that peering points between MNOs, vehicular clouds and public data networks are located far from the \"edge\". In a MEC-enabled V2X architecture this problem must be solved, and the solution cannot be to have just one MEC provider. The third unique property is the role of the road authority as another source and sink of information. This comes with often closed, sometimes even proprietary, IT-systems needing integration in a MEC-enabled distributed computing V2X network architecture. A particular challenge arising from this is that crossing national, in some cases also regional, borders results in a new road authority with its own IT infrastructure becoming responsible. With these technologies, 5 GCroCo will address the gap of existing cellular V2X technologies (such as LTE Release 14) by enhancing a number of KPIs in the 5G network, such as latency, reliability, packet error rate, etc., even under cross-country, cross-MNO, cross-OEM and cross-vendor operations.  
In the following sub-sections, it will be further elaborated on how these solutions will find application in the use cases presented in the previous chapter.
## 2 Methodology
### Machine Learning Model Application
#### 2.5.2 Implementation of Deep Ensemble Super Learner Model
All Bayesian optimised base learners were trained on the whole training dataset using the k-fold cross-validation. The performance of these base learners is subsequently stored. 4. The meta-model dataset is used to train the super learner using linear regression for the sequential combination of all base learners. 5. All base learners and super learners' performances on the test data are evaluated. 6. The super learner predictions function using the meta-model will map new data sets into a prediction set to make predictions for new data. Due to the stochastic nature of the models, numerical variations, and the evaluation criteria used, the results may vary; hence it is encouraged to run the model multiple times to get an averaged outcome. Figure 5: Simplified workflow of the methodology used in developing the mathematical formula and machine learning model in this study. Algorithm 1 is about model architecture creating a stack of ensemble models as base learners for the Super Learner. Modified after [56]. ```
1 defget_models():
2 models=list()
3 models.append(DeepLearning_regression_model())
4 models.append(AdaBoostRegressor())
5 models.append(DecisionTreeRegressor())
6 models.append(LGBMRegressor())
7 models.append(CatBoostRegressor())
8 models.append(XGBRegressor())
9 models.append(RandomForestRegressor())
10 models.append(BaggingRegressor())
11 models.append(ExtraTreesRegressor())
12 returnmodels
```  
**Algorithm 1** Stacking an ensemble of base learners  
Algorithm 2 is about collecting out of fold predictions from K-fold cross-validation to create meta model dataset to train and test the Deep Ensemble Super Learner [56]. ```
defget_out_of_fold_predictions(X,y,models):

meta_X,meta_y=list(),list()
#Data splitting

kfold=Kfold(n_splits=10,shuffle=True)
#Data split computation

fortrain_ix,test_ix in kfold_split(X):

fold_yhats=list()
#get data

train_X,test_X=X[train_ix],X[test_ix]

train_y,test_y=y[train_ix],y[test_ix]

meta_y.extend(test_y)
#Train and predict with each sub-model

formodel in models:

model.fit(train_X,train_y)

yped=model.predict(test_X)
#storecolumns

fold_yped.append(ypred.reshape(len(ypred),1))
#storefoldyped as columns

meta_X.append(hstack(fold_ypred))

returnvstack(meta_X),asarray(meta_y)
```  
**Algorithm 2** Collecting out of fold predictions from k-fold cross-validation
## 2 Materials and Methods
### The Probe
#### 2.2.1 Capacitor Sizing  
The electrical soil model was used at the center frequency of 433 MHz. A critical step was sizing the capacitor, which was essential for enhancing the quality of information transfer and measurement accuracy. This task was particularly challenging, due to the conductive nature of the soil, which added a layer of complexity to our work.  
There are two types of parallel plate capacitors to consider: those with circular plates and those with rectangular plates. To size the capacitor, we conducted a study of capacitance parameters, to determine the optimal values for the designed capacitor probe. The outcomes of these tests are presented in Figures 5 and 6 below.  
The simulated results in Figures 7 and 8 highlight the impact of the distance between the parallel plates. Notably, as the separation distance between the plates increased, the total capacitance of the circular parallel plates diminished. This phenomenon can be attributed to the reduction in the edge effect associated with fringe capacitance. Furthermore, it is observed that the fringe effect in the circular parallel plate capacitors was significantly reduced compared to that in the rectangular parallel plate capacitors.  
In this context, let \(r\) represent the radius of the plates, \(\epsilon_{r}\) denote the relative permittivity of the dielectric material situated between the plates, and \(d\) indicate the thickness of the dielectric material or the separation distance between the plates.  
For fixed values of \(\epsilon_{r}\) and \(r\), any alteration in the thickness \(d\) of the dielectric medium will result in changes to the capacitance of the capacitor, as illustrated in Figure 9. Specifically, increasing the separation distance \(d\) between the two plates leads to a decrease in capacitance. This is because a larger separation reduces the electric field strength between the plates, thereby diminishing the capacitor's ability to store charge.  
Conversely, reducing the separation distance \(d\) enhances the capacitance, as the electric field strength increases, allowing the capacitor to store more charge for a given voltage.  
In addition, the radius of the plates plays a crucial role in determining the capacitance. Increasing the radius \(r\) of the plates expands the area available for charge storage, resulting in an increase in capacitance. This relationship is due to the fact that capacitance is directly proportional to the plate area.  
On the other hand, reducing the area of the plates, which occurs when the radius \(r\) is decreased, results in a lower capacitance. Thus, both the thickness of the dielectric medium and the size of the plates significantly influence the performance characteristics of the capacitor.  
Figure 8: Simulated total capacitance for rectangular plate capacitors for different plate separations.  
Figure 9: Simulated total capacitance for circular plate capacitors for different plate radius.
# Deep Learning Method for Fault Detection of Wind Turbine Converter
## 1 Introduction
### Data-Driven Methods
Therefore, this paper applies the improved AOctConv structure to the ResNet50 backbone network and hence proposes the AOC-ResNet50 network to realize fault detection for the wind turbine power converter. In summary, the overall workflow of this paper is illustrated in Figure 1. The first step is to determine the fault indicator variables that provide some indications or reflection of the converter health status. Seven fault indicator variables were selected based on analyses of existing failure cases and understanding of the requirements for converter functions. Step 2 is to determine an appropriate approach for fault detection. In this paper, the data-driven approach was selected based on the literature review and discussions as well as the available data. Step 3 is to select the techniques for modelling and analysis for which CNN models were applied to feature extraction of radar charts generated using SCADA system data. Step 4 is to give an introduction to the typical CNN structures for the purpose of the development of a new improved CNN architecture. Step 5 is to propose a new improved CNN architecture. With this new development, Step 6 is to apply the AOC-ResNet50 network to converter fault detection, and Step 7 provides a brief discussion based on the research findings and conclusion of the paper. In view of the overall process as discussed above, the remainder of this paper is organized as follows: Section 2 gives a brief introduction to the generation of radar charts; Section 3 presents deep learning network principles; Section 4 describes the improved convolutional neural network; Section 5 discusses the fault detection of the wind turbine power converter using the convolutional neural network models with a comparison of the model performance; Section 6 provides a brief discussion, and Section 7 concludes the paper and indicates future research directions.
# Continuous Space Wireless Communication Tower Placement by Hybrid Simulated Annealing
## 5 Experimental Analysis
### Scalability  
To test the scalability of the hybrid SA, we check if the hybrid SA can find an optimal or near-optimal solution in a reasonable time when the problem size increases. In previous experiments, we also recorded the computation time of the hybrid SA algorithm for each of the test problems. Figure 7 shows how the computation time of the hybrid SA algorithm increases linearly as the number of potential users increases. Figure 8 shows how the computation time of the hybrid SA algorithm also increases linearly as the number of  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multirow{2}{*}{**Towers (\#)**} & \multirow{2}{*}{**Users (\#)**} & \multicolumn{6}{c}{**Conventional SA**} & \multicolumn{6}{c}{**Hybrid SA**} \\ \cline{3-11}  & & & **Ave** & & **Best** & **Worst** & **StdDev** & **Ave** & **Best** & **Worst** & **StdDev** \\ \hline
1 & 10 & 100 & 0.790 & 0.87 & 0.72 & 0.0335 & 0.997 & 1.00 & 0.97 & 0.0084  
wireless communication towers increases, although there are some skews in the curve. All experiments were conducted on a desktop computer with an Intel Core 2 Duo CPU of 3.00 GHz and 4.00 GB RAM. It can be seen from the figure that the time efficiency of the hybrid SA is bounded by \(O(n)\), where \(n\) is the total number of users. Thus, it can be concluded that the hybrid SA is scalable.
# Slip Estimation for Mars Rover Zhurong Based on Data Drive
## 2 Ground Tests and on-Orbit Validation
### Ground Tests  
The ground tests were performed in the Chinese Academy of Space Technology. The indoor test site was composed of a horizontal area (25.3 m \(\times\) 13.2 m) and an adjustable angle ramp area (8 m \(\times\) 5 m), and the simulated angle could increase up to 35\({}^{\circ}\)[21]. Figure 2 displays the indoor test site of ground tests. To simulate the situation of Zhuurong on the surface of Mars as much as possible, the test site was equipped with the following key devices or equipment.  
1. Low-gravity simulation device. For the gravitational acceleration for Mars, which is about 40% of that on Earth, the whole test site was equipped with a low-gravity simulation device to provide a simulated Martian gravity environment for the rover, so that the vertical pressure of each wheel always stayed consistent with that on Mars. The low-gravity simulator had the ability to follow the movement of the rover and the simulation effect remained stable during tests [22].
2. The validator of Zhuurong. The rover used in tests was the validator of Zhuurong, whether regarding the structural configuration, sensor configuration, or material, it was largely the same as Zhuurong.
3. Simulated soil. The soil is an important part of the surface environment of Mars, and the mechanical parameters of the soil are closely related to the wheel movement [23]. The simulated Mars soil, named JLU Mars-2, was used in this paper, and was laid out on the test site with a thickness of more than 500 mm [21]. The JLU Mars-2 used volcanic ash as the raw material, and the mechanical parameters of the soil were adjusted through a sprinkling and compaction process; Table 1 presents the mechanical parameters of JLU Mars-2 [24,25].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(k_{c}\)(kPa \(\cdot\)m\({}^{-(n+1)}\)) & \(k_{sp}\)(kPa \(\cdot\)m\({}^{-(n+2)}\)) & \(n\) & \(c\) (kPa) & \(\varphi\) (\({}^{\circ}\)) & \(k\) (m) \\ \hline
15.69 & 983.36 & 1.023 & 0.609 & 37.2 & 0.019 \\ \hline \hline \end{tabular}  
* \(k_{c}\): the cohesive modulus of the soil; \(k_{p}\): the frictional modulus; \(n\): slip sinkage exponent; \(c\): cohesion; \(\varphi\): internal friction angle; \(k\): shear deformation modulus.  
\end{table}
Table 1: The mechanical parameters of JLU Mars-2.